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Human body description format
Accepted Manuscript
Human Body Description Format Ondrej Kainz , Frantiˇsek Jakab , Roman Vapen´ ık , ´ Miroslav Michalko PII: DOI: Reference:
S0920-5489(17)30298-2 10.1016/j.csi.2018.01.001 CSI 3261
To appear in:
Computer Standards & Interfaces
Received date: Revised date: Accepted date:
27 July 2017 7 December 2017 5 January 2018
Please cite this article as: Ondrej Kainz , Frantiˇsek Jakab , Roman Vapen´ ık , Miroslav Michalko , Hu´ man Body Description Format, Computer Standards & Interfaces (2018), doi: 10.1016/j.csi.2018.01.001
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Highlights A Human body description format is proposed. The mechanism for processing format parameters is shown and explained. A set of sample human body parameters is selected to prove the usability of the format. Experimental implementation is carried out using the format.
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Human Body Description Format a
Ondrej Kainz , František Jakaba, Roman Vápeníka, Miroslav Michalkoa a
DCI, TUKE University, Letna 9, 042 00 Kosice, Slovakia
[email protected] [email protected] [email protected] [email protected]
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Abstract In this paper, the format for the visual description of the human body is presented. The structural data entry of body parameters is considered as relevant due to gaps in this area, as confirmed by several standardization authorities. Research introduced in this paper fills this void through the proposal of the Human Body Description Format, or for short, HBDF. The principal goal is to present a universal technique for parameters entry, processing and storage. Two principal methods for extraction are considered, namely, ISO-based measurements and the traditional tailoring approach. The latter is covered mainly for long term tradition, while the main focus is put on the first type of measurements. The sample set of parameters was selected based on the analysis of both approaches for extraction. This format is designed to utilize XML dialect to store the extracted data. A weighted pseudograph is used for the representation of ISO-based measurements, allowing for further development of the HBDF matrix, being the adjacency matrix of all the parameters. Several other methods for processing the data are proposed such as compressing the matrix, creating a distance matrix, calculating distances, etc. The format was experimentally verified by a set of tests using the prototype measurement device and proves to be valid in terms of research focus.
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Keywords HBDF: Human body; Human body description format; ISO standard; Visual description; XML dialect
1. Introduction
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Virtually anything that surrounds us can be processed to some discrete form. Many shapes of such data exist, e.g. taking digital images, recording audio tracks, or measuring lengths using a digital meter. Different mathematical apparatuses may be applied once the continuous real-world data is converted to discrete form. One common thing that all discrete data share is having a specific kind of structure that can be decoded by humans or machines. The human body is not an exception and can be described by discrete values. This may include information of its parameters (lengths or distances) or internal structures. Such data can then be utilized in healthcare research projects (e.g. Granberry et al. or Hofmann et al. [1,2]), and in many other research projects utilizing information on the human body (e.g. Griffin et al. or Langmead et al. [3,4]). Standardization of the extracted body data may be of great use providing the uniform techniques of data extraction is followed. The main topic of this paper is the proposal of formal apparatuses used for recording various human body parameters (e.g. distance from shoulder to hip). Multiple physical landmarks (Han and Nam [5]) are located on the human body and could serve in the identification of specific distances or circumferences. Standardized regulations for finding the landmarks exist, e.g. defined by ISO-7250 [6] or its most recent version [7]. However, no standardized way that would enable structural recording is available. Several things must be considered when proposing the way of manipulating the extracted data, e.g. the possibility to perform mathematical calculations.
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In this research, we present an approach to record human body data in a structural format. Selected parameters defined by ISO standards, specifically ISO-7250, are used in the format to show the method of data entry. Note that we utilize the landmarks as defined by ISO standards from 1996, however utilization of the most recent version is possible. The proposed format for processing human body parameters described in this paper serves as a template and may be adapted to various standards that utilize human body landmarks as point-to-point connections. Processing includes recording of data in the form of XML dialect that may be further transformed to a pseudograph or adjacency matrix. Such structures then allow finding a distance from one parameter (human body part) to another (human body part). Processing and examples of the extracted data is also presented and discussed. 1.1 Need of Having the Format
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The development of a format for the description of human body parameters was a natural consequence of the need to have standardized input and output data for research projects that have been carried out in the last few years. Different formats have already been successfully deployed in several research projects carried out at the Technical university of Kosice, e.g. anthropometric device by Kainz et al. [8,9,10], and software solution for visual extraction of human body parameters (Kainz et al. [11]). The described format is not a fully standardized language, but rather, it is a concept of a description format that may be further used in a variety of scientific areas and real-world applications. Other possible deployments of format are presented next. HBDF can be used in conjunction with the kinematics of joints and recognition of its patterns in terms of gait (Mun et al. [12,13]). Analogously, it might be deployed in stereo-photogrammetric systems composed by IR cameras (Altilio et al. [14]). In Han et al. [15] the description format could aid in the analysis of gait of Parkinson's disease patients. A standardized way of data entry should provide more transparency to the research processed and thus enhance the overall comparison. The authors of another research study presented in Quental et al. [16] developed a computational model for evaluation of the wear resistance of anatomical and reverse prostheses. Additional data on human body parameters acquired in the later studies might provide complementary feedback under the real conditions. Except for body mass index, the additional information could be utilization of HBDF in the research on gender differences and the response of the autonomic nervous system considering the passive lower limb movement, as presented in Shi et al. [17]. This is to provide additional and useful data on participants. The possible utilization of HBDF has the potential to be useful in describing the patients undergoing neurorehabilitation [18]. The next case is in quick diagnosis of idiopathic scoliosis, in which extended HBDF may be used as an assistive tool (Newell et al. [19]). Finally, the outputs of the description format proposed in this paper could aid in the construction of a 3D model, such as the one presented in Valenti et al. [20], in the statistical comparison of a healthy and a diseased subject. Before we provide more information on the format, the parameters must be defined. The principal focus is ISO measurements. These parameters are the main focus of this format due to the nature of the measurement, the standardized procedures of measurement and the definition of landmarks. 1.2 Defining the Parameters in Anthropometry Several parameters have to be defined to allow for the construction of a human body model. Below we present a brief analysis that was carried out when selecting the relevant data. Two types of measurements are considered, anthropometric and tailoring. The authors of [21], in their report on anthropometric measurements, extracted up to 100 measurements, though these do not follow the above-mentioned ISO standard. Another document [22], which is following ISO 7250, carried out extraction of over 70 measurements. Many of the measurements in the case of ISO 7250 are collected from the floor (being the
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reference point) to specific parameters, e.g. height, cervical, shoulder etc. Note that these types of measurements do not follow the contour of the human body but are point-to-point distances collected with anthropometric devices. Other types, also included in this format, are length measurements that follow the contour of the human body – these are similar to the lengths in the tailoring industry, i.e. arm length, upper arm length, forearm length or chest and waist circumference. Both of these were considered in the selection of specific human body parameters, while the focus is on the ISO standard, i.e. ISO-based measurements.
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We consider landmarking to be an important factor when carrying out the anthropometric measurements, and for this reason, the proper landmarks have to be selected. The landmark should be manually marked with a non-permanent pencil. More information about landmarks is discussed by Kouchi [23] and Norton and Olds [24]. In the case of estimation of parameters from the digital image, we advise for the utilization of the abovementioned markings that will aid in the identification processes. Manual measurement using third party devices is to follow the measurements as discussed above.
2. The Structure of HBDF Parameters
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The HBDF is designed to allow for the processing of human body parameters as extracted using real hardware devices or other means (e.g. digital camera images). There have been attempts to partially formalize the human body parameters, e.g. Humanoid Animation Working Group [25], however, the proposal introduces the complex entry of input data since it focuses on the implementation of humanoids in motion, not static characteristics and related dimensional lengths. The recent communication with the International Organization for Standardization, World Wide Web Consortium (W3C) and the Institute of Electrical and Electronics Engineers (IEEE), realized as part of the presented research, confirmed that no specific format for the description of human body parameters exists. Bearing in mind this fact, the goal is to introduce some structure to the recording and subsequent processing of these parameters. Several human body lengths, circumferences and also breadths are to be considered. Further, we principally consider measurements based on the ISO 7250 standard and subsequent measurements from the tailoring industry. The format is aimed on the extraction of static dimensional units, presuming most of these parameters in coronal plane. This approach is to allow universal entry of human data in a simple and readable form. The selection of human body parameters follows the prior analysis of parameters in tailoring and anthropometric measurements. Measurements were broken down based on the following human body parts: head, torso, arm and leg. Additional data (see Figure 1) on the extraction process are expected to define the measurement process more closely; personal information being one of these. Specific implementation should handle mapping of the created file to the specific individual, and it can be solved by the name of the participant or the internal ID number assigned to the person. Other information that might be relevant for statistical purposes is information on the age of the participant and the date, i.e. when the measurement was taken. Weight and height are also considered as a relevant information on the very measurement.
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Figure 1 HBDF: Basic structure of data input
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As stated above, two types of measurements of human body parameters are considered, i.e. ISO 7250 and tailoring. We consider it important to define the device used for measurements. Three types of devices were taken into account in the current state of research: tape meter, anthropometer and other device (e.g. specific prototyping device).
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In total, thirteen ISO-based parameters related to anthropometry were selected for the proposal of format structure. Eight of these include either circumference or breadth and circumference. The other five parameters do not contain any additional data except for height from the reference point. For every parameter, the information on height is presumed to be extracted, using the ground as a reference point. Using the ground as a reference point is defined by ISO standard [6] and most of the utilized measurements also use the ground as the reference point. Note that the only selected parameters of ISO standard were covered in the research, principally those that we considered as the most relevant. Other parameters may be added in future research. The naming of parameters is designed to follow the ISO standard, having the specific names of ISO standards replaced by the block of specific parameters, e.g. face length is represented as a length between sellion and gnathion. This was done to make the measurement process as effective as possible, thus unnecessary parameters or repeating parameters were eliminated. Some extracted data are also nested as a part of other parameters. An example is acromion, which covers the measurement of height, breadth, and circumference. Breadth is the length of the left acromion to the right acromion (or vice versa). A list of parameters along with information on nested parameters is shown in Figure 2, coloring and abbreviations of parameters are the same for Figures 4 and 5.
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Figure 2 HBDF: Human body parameters in ISO
3. HBDF as XML Dialect
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Parameters and additional data (on the measurement process and the person) are combined in the proposed structure of the XML dialect. Human Body Description Format (HBDF) is the proposed format for the structured description of the human body by the authors. Note that this format is in this stage merely a proposal that might eventually evolve to Human body description language, or for short, HBDL. As noted, HBDF in this stage of research is designed as an XML dialect. Hierarchical XML tree structure has HBDF as its root node. Three descendant nodes cover the information on subject of measurement, type of device used and type of the measurement itself. First, two included child nodes are not further branched. However, based on the type measurement two cases are possible tailoring or ISO 7250. For each measurement, the new file is presumed to be created. It is also expected that not all the entries will be available on the input at once. Additional information about the measurement can be provided later or prior to the measurement of human body parameters. Also, one type of measurement can be fully ignored depending on the measurement approach. Even though some parameters are the same in both measurements, it is decided that they will present sole nodes due to different approaches to measurement. In the measurement of parameters, the breadth and circumference are child nodes of the specific parameters, providing additional information on parameters. However, each parameter in the case of ISO contains the information on its height from the ground. Differentiation of individual measurements is not covered in the XML dialect proposal, e.g. filename may be used for such purposes. Note, that this dialect serves only for storing data that are used as an input for a pseudograph. Presented next is the structure of ISO-based parameters in the form of a pseudograph and an HBDF matrix. These structures allow processing of the extracted data from the XML dialect. Only collected ISO-based data (from the specific device or that which is collected manually) are the input to these structures, i.e. mostly distances from the reference point, while the other point-to-point distances are calculated based on the available data. Parameters in the XML dialect related to tailoring measurements do not use a pseudograph or matrix structures.
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4. HBDF as Pseudograph
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Collected ISO-based data stored in HBDF (XML file) can be expressed as a weighted pseudograph structure. In the proposal of the pseudograph, we follow a sample set of articulations as depicted in Figure 2. Vertices of pseudographs are in the most cases represented by the articulations of the human body (e.g. acromion), with the topmost being the vertex of head. The design follows the ISO standard [6] and its approach to extraction, however, other standards may be used with HBDF as well. Formation of pseudograph presumes specific features of the measurement process, mainly the presence of a reference point and point-to-point measurements. Proposed weighted connected pseudograph G consists of finite set V of vertices v1,v2,v3,...,v16 and a set of pairs of vertices (u,v), denoted as E. The edge (u,v) is from u to v and is incident from u to v. Each edge that connects the set of two vertices has specific weight w(e). Weighted pseudograph G has only positive-weights. In Figure 3, the simple weighted pseudograph with two nodes is shown, where N represents the first and M the latter human body parameter. Weight mx,y is the real distance between these two parameters. Self-loop is an edge (v,v) between vertex and itself. In our case, the self-loop is circumference of specific parameter. In Figure 3 the self-loop is denoted as cx,y.
Figure 3 Weighted pseudograph in HBDF
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The specific parameters of ISO standard can be interpreted visually and enable the formation of the pseudograph based on these parameters (see Figure 4).
Figure 4 Visualization of ISO Parameters
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The location and connection of one node to another in Figure 4 resembles the connection of individual vertices of the weighted pseudograph. This connection between nodes (i.e. the real distance) is of certain lengths that is represented by the weight of the edge. Further, circumferences marked by a half circle are analogous to the self-loop of a specific vertex. Color (based on Figure 2) distinguishes individual parameters. Ground, marked by gd or white circle in Figure 4, is actually the reference point of most measurements (except for circumferences, foot, buttock, acromion and iliac spine). Measured height of the vertex from the reference point or distance from the other vertex serves to calculate the weight of the edge. The nature of measurement and values that are being extracted allows for the creation of a pseudograph of parameters. The relation of measurements is realized by edges, where one measurement follows another, until all are carried out. Further, each measurement has the output value in the form of millimeters or inches. The name of the measurement is hence associated with the specific extracted value. These values are then used to calculate the distances between the parameters following the information from the pseudograph. These distances are depicted in the pseudograph as weights of edges. Parameters in the form of a weighted pseudograph are depicted in Figure 5, while following the naming in Figure 2.
Figure 5 Parameters of weighted pseudograph
The numbering of distances is dependent on the human body part that is being measured, m1,y for head, m2,y for torso, m3,y for arm and m4,y for leg. Note that the vertex denoted as gd stands for ground. Measurement of buttock along with the measurement of foot, acromion breadth and iliac spine breadth represent four values that do not have ground as a reference point. Further, buttock and foot demonstrate measurement of depth, while the other length measurements are presumed to be in one vertical (coronal) plane.
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Circumferences were merged with vertices in the form of self-loops. Circumference of the head is part of sellion vertex, circumference of chest is part of acromion, tight and calf are part of knee and tibia, respectively. This approach led to a decrease of vertices and simplified the whole structure. Another type of measurement is breadth. This measurement is valid for two vertices, i.e. acromion and iliac spine. The measurement of the breadth is a special case: the vertex is broken down to two separate vertices making the breadth a form of the length. Most of the measurements are straightforward, however the measurement of the buttock is measured in the sitting position (based on ISO [6]), i.e. ground is not the reference point for measurement. Measurements may be divided to: Directly measured: Cover the heights of vertices and all of the circumferences cx,y. Special case are edges m2,3, m2,5, m4,4 and m4,5, note that these do not have ground as reference point. Calculated: Includes the distances (edges) between the vertices mx,y. 4.1 Calculation of Weights
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Transition from one vertex (u) to another forms an edge e with some weight m. Each weight has an individual value of x and y that uniquely identify the parameters. The value of x is from 1 to 4, identifying measurements of head, torso, arm, leg, respectively. Another value y defines the specific measurement and ranges from 1 to 6. The weights are more complex to acquire, while the direct measurement of lengths or circumferences is rather clear. The value of specific weight is dependent on the extracted (measured) value of the vertex. In this approach, we utilize the knowledge of the measured distance from the ground. We have available two separate parameters of various heights with the same reference starting point. This property allows to subtract one value from another and hence provide the actual distance between values. An example may be sellion, which height is measured from the ground and another height of gnathion. The same applies for sellion, as this value is measured from the ground. The formula based on the pseudograph is subtraction of gn from sn that is equal to m1,1, where the output (m1,1) is the face length. In general form: | | (1.1) where the u is the value of vertex and v is the value of the neighboring vertex connected through the edge. Special case of distance is (m2,4) and (m2,6). Calculation in this case has to be done in a different manner. Distances between vertices, an1, an2, and nk or is1, is2 and ch, form the isosceles triangles. Thus, the triangle height and base is known, hence the unknown distance may be calculated: √
(1.2)
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where the (mx,y) is the distance (m2,4) or (m2,6), h is calculated from Eq. 1.1 as an - nk or is - ch, respectively. And (mm,n) is the actual distance between an1 and an2, or is1 and is2. The matrix of parameters can be formed once the weights of parameters are calculated. HBDF matrix is presented in the next part and contains just a sample number of parameters. 4.2 Matrix of Parameters The nature of parameters and also its representation in form of a weighted pseudograph allows representation of extracted parameters in the form of a symmetric matrix (referred to as HBDF matrix).
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Figure 6 Adjacency matrix of human body parameters
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A matrix of size 16x16 (see Figure 6) is created, where each column and row vector resembles the specific parameter. The matrix is constructed following the weighted pseudograph depicted in Figure 5, also the naming of vertices, edges and loops is based on this pseudograph, and it is created as: if ( ) { (1.3) otherwise Providing the edge between the vertices exists (is equal to one), the two vertices have adjacency and some specific weight (distance). The order in which the vertices are extracted is not relevant. However, in order to acquire the whole skeletal model, the vertices sn, an2 and is2 can be considered as initial nodes. In this way, the directed pseudograph can be constructed having as the main walks in pseudograph: head and torso: sn, gn, nk, us, ch, arm: an2, an1, ew, hd, leg: is2, is1, ke, ta, gd, ft. The main diagonal of the matrix (Figure 6) represents the parameters (ci,j), i.e. circumference. Circumference is stated only where applicable (designed). The rest of the parameters in the matrix are directly related to the calculated or measured distances mx,y. The proposed pseudograph solution may be expanded in later research, however this shows the approach or template for the structural form of the entry of human body parameters. Utilization of a matrix to describe the extracted parameters and their relation to other parameters is considered as one of the possible approaches of data entry. Distances, heights, circumferences, and their mutual relations are easily readable and extractable. Further, having data in this form may be useful in further data processing or other mathematical operations. 4.3 Compressing the Matrix of Parameters The created matrix is 16 by 16, i.e. symmetric. However, most of the space is occupied by zero values. To express the relations of parameters only one side of matrix is actually required, hence the matrix can be considered as upper or lower triangular. Having an upper triangular matrix, the number of non-zero values is
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21, which sparsity of 91.8 % and density of 8.2 %. Bearing this in mind, one of the options to compress the matrix is to express it as a sparse matrix. We present multiple forms of utilization of sparse matrix storage formats. The first being the simplest, utilizing the coordinates - coordinate list (COO), where the column and row number identify the value, see Figure 7.
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Figure 7 Parameters representation as coordinate list
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Another storage format that may be utilized to represent the matrix is compressed sparse row (CSR). This format has a long tradition and stores the matrix in three arrays (A, IA and JA). The first contains the values of the matrix that are non-zero, IA is the row offset m+1 if matrix M(m x n) and is incremented by the number of elements in each row. Third JA, is for column indices. Implemented on the parameters in the matrix, its output can be seen in Figure 8.
Figure 8 Parameters representation as compressed sparse row
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The last type of storage format we present is using diagonal, as is shown in the Figure 6. seven diagonals contain non-zero values. However, if the values of the upper triangular matrix are considered, then only four diagonals are relevant. These include the main diagonal containing the c values, the diagonal containing m1,1 up to m4,4 and finally the diagonal with values m2,4 to m4,5. The representation as a banded matrix is shown in Figure 9. The letter d in the Figure 9 maps the diagonal, where the main diagonal of the original matrix (M) is assigned with a number of 0 and subsequently neighboring diagonals of the upper diagonal matrix are assigned numbers {1,2,3...}. Presented methods for reduction of matrix representation are implemented in most software solutions available on the market, and should these not be implemented, there is an algorithm for each of these. The main point presented here is that using few input data, we can extract the whole skeletal model for an ISObased measurement.
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Figure 9 Parameters representation as banded matrix 4.4 Distance Matrix
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Distance from one vertex to any other vertex is extractable since we have a pseudograph that is a tree, while not considering loops. Knowing the exact distance of one parameter to another may be useful in various types of use cases or research studies. A distance matrix, same as the adjacency matrix, is also a symmetric matrix D(G). Distance d(i,j) between u to v vertices of pseudograph G is the shortest path. If we do not consider circumferences, i.e. self-loops of specific vertices, the pseudograph can be considered as a tree, where the shortest path is actually also the only path (in presented sample of parameters). Distance matrix of tree G (see Figure 5) is then constructed as shown in Figure 10.
Figure 10 Distance matrix of human body parameters
Each distance dx,y is defined by the edge weight mx,y or its sums. Series of edges mx,y, where x is {1,2,3,4}, based on the type of body part, and y is {1,2,...,5,6}, based on the specific parameter of body part, define the other distances. The distance of the body parameter sn to ft is the distance marked as d1,15 and calculated as:
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(1.4)
The summation of all distances can be calculated as: ∑ ∑ ∑ ∑ (1.5) Specific distance dx,y is the selection of distances based on the weight w(e) and walk in graph (tree). It may be troublesome to calculate the distances, bearing in mind that more parameters may be added. The Dijkstra algorithm for calculation of the distances may be deployed should the matrix reach a specific size. Deployment of Dijkstra was not covered in the research.
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4.5 Traversal of Parameters It is possible that some vertices of the pseudograph will not be available, hence the systematic search has to be done. The proposal is to utilize the breadth-first search algorithm, described by Koffman and Wolfgang [26], where the arbitrary vertex is selected and labeled with 0, then adjacent vertices are visited, until all the nodes of pseudograph are visited.
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5. Visualization of Parameters
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Visualization of extracted data (collected from ISO-based measurement) is shown in Figure 11. Parameters of the pseudograph are represented by vertices, however, circumferences depend on the location having their actual size is reflected. Heights of individual parameters are shown as well.
Figure 11 Visual illustration of HBDF using HBDF This form of visualization may be useful in the visual comparison of specific parameters, since the actual distances and circumferences of the human body are depicted.
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Software solution [27] as part of the experimental implementation was designed and implemented, based on the input data from the XML dialect.
6. Experimental Testing: Extraction Using the Anthropometric Module
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A prototype of a computational anthropometric device [8] was created as part of the related research. This prototype device allows the extraction of human body parameters. The HBDF skeletal representation, as proposed above, is utilized. ISO-based measurements are deployed in the presented pilot experiments. Extraction using the proposed electronic device allows for the acquisition of all the parameters depicted in Figure 5. The presence of flexible tape meter allows for the measurement of both circumferences and lengths. The measurement process collected data to the proposed XML dialect and the full HBDF matrix is created. The accuracy of the measurement was estimated to be up to 2 mm. The basis of the measurement for almost all parameters is the ground. This presumes the fixation of a tape meter to the ground, where the device is in the horizontal level to the reference ground. The beginning of the tape meter was attached to the ground, which enabled full expansion in the y direction. In order to achieve that only the y coordinate is to be changed, the bubble level is attached to the anthropometric module. One more obstacle was still apparent – the nature of the measurements or utilization of a traditional anthropometer presumes having some form of claws or arm for exact marking of the landmark. This is solved by a laser pointer attached onto the module case. Note that the distance between the pointer device and tape meter was measured and considered in the measurement process. The measurement consists also of five testing subjects. The principal analogous requirement in the measurement was the proper posture of the person and minimum clothes, i.e. being mostly half-naked. A real sample from the measurement in form of HBDF matrix (Figure 12) is presented next. Data was extracted from the male subject having height of 1790 mm. Each measurement produced an XML file that was validated in the post-process, also the information on the person was added after the measurement. Measurements include information on the node heights, circumferences, and breadths; two depth measurements include foot and buttock.
Figure 12 HBDF Pilot experiments: Extraction using pilot experimental hardware device [in mm]
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Visualization of extracted data is possible through the construction of the proposed skeleton model based on the HBDF matrix, and is analogous to the model in Figure 11. All the parameters of the matrix are known; hence the visualization of circumferences is possible. The visual output of the HBDF matrix in Figure 12 is depicted in Figure 13. However, note that circumferences are in ratio 1:4 in terms of other dimensions to fit the model. Visual representation in the form of the graph is possible as well. Utilization of this approach enables visualization of the parameters of the HBDF matrix and also allows for the comparison of multiple persons.
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Figure 13 Visualization of HBDF in the extraction using anthropometric hardware
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The presented pilot experiments presumed development the interface [10] for data visualization and processing, it has as the input the proposed XML dialect. The solution allows manual input of data and export of the XML file. The preview and edit of the collected data and on the subject of measurement is possible. HBDF, as a sole solution, is also being deployed in other research related to measurements of human body parameters, e.g. construction of human skeletal model using smartphone device. This approach presumes the utilization of visual markers as a reference point placed on the ground (i.e. analogous reference point to ISObased measurement). Another project is focused on the estimation of a skeletal model from the image based on the AruCo markers that are located directly on a person. The proposed XML dialect is to be deployed in the just stated research projects.
7. Conclusion
The research presented in this paper was focused on the proposal of the format for structural entry and processing of anthropometric parameters. The format is designed to be usable with various anthropometric standards that follow point-to-point measurements, thus it can be easily adjusted to more recent versions of ISO 7250 [7]. Furthermore, it allows recording of data from traditional tailoring measurements. Both approaches are included in XML format, while point-to-point measurements can be further transformed to the proposed pseudograph and HBDF matrix.
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Acknowledgment
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The main focus is point-to-point measurements that utilize specific reference points, e.g. the ground. Such measurements are introduced in ISO 7250 [6]. A sample of relevant parameters from this standard was used in the format to prove usability of the proposed solution. Stimuli that led to the proposal of the format for description of human body parameters is void in the research area, as was also confirmed by the most acknowledged standardization organizations. A novel way for representing human body parameters in the form of a weighted pseudograph with loops was proposed. This allows for multiple actions to be carried out on data. Overall, 13 human body parameters were selected as the most relevant. Some of these further encompass other parameters such as circumference or breadth. Heights of these 13 main body parameters and the additional data are the input to the pseudograph, and these are the directly measured data. The distances between specific parameters are considered as calculated data. Weights of edges are used to represent the distances in the pseudograph. The approach for their calculation was introduced and described. Circumferences, on the other hand, are represented as loops of specific parameters. The parameters of the pseudograph, having each parameter uniquely defined, are also represented as an adjacency matrix. This enables the processing of data or even the storing of data. Methods for compressing the human body matrix parameters were presented. Thus, the description of visual human body parameters is possible using a few numbers. The distance matrix can be further created from the adjacency matrix. Specific distances of one parameter to another are easily extractable. Considerations of the utilization of ISO-based measurement data in tailoring practice were made and in the current state of research it was evaluated as redundant to merge these methods due to a different approach in the measurement process. XML dialect is the format used for the data entry. Its structure was described and presented. This format was verified on a sample of the real data. A possible drawback of the format may occur in the case of abundant parameters in the sagittal plane, however this is less likely. Part of the research included pilot experiments, which proved the proposed HBDF to be useful in measurement using the prototype anthropometric module. The non-visual extraction technique followed the ISO-based measurement process and collected data allowed for the creation of a full pseudograph and related HBDF matrix. The form of visual representation is the construction of the skeletal model from the XML dialect that was proposed as part of the description format. Errors estimated throughout the testing were analogous to errors introduced by the tape meter. Pilot experiments are considered successful in terms of the given requirements and assumptions presented in the research.
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This publication is the result of the Project implementation: University Science Park TECHNICOM for Innovation Applications Supported by Knowledge Technology, Phase II., ITMS: 313011D232, supported by the Research & Inovation Operational Programme funded by the ERDF. We support research activities in Slovakia/This project is being co-financed by the European Union.
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References
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1. R. Granberry, J. Duvall, L. E. Dunne and B. Holschuh, “An analysis of anthropometric geometric variability of the lower leg for the fit & function of advanced functional garments,” in ACM International Symposium on Wearable Computers, Maui, 2017. 2. M. Hofmann, J. Harris, S. E. Hudson and J. Mankoff, “Helping Hands: Requirements for a Prototyping Methodology for Upper-limb Prosthetics Users,” in CHI Conference on Human Factors in Computing Systems, San Jose, 2016. 3. L. Griffin, C. Compton and L. E. Dunne, “An analysis of the variability of anatomical body references within ready-to-wear garment sizes,” in International Symposium on Wearable Computers, Heidelberg, 2016. 4. A. Langmead, P. Rodriguez, S. P. Satheesan and A. Craig, “Extracting Meaningful Data from Decomposing Bodies,” in Practice and Experience in Advanced Research Computing on Sustainability, Success and Impact, Los Angeles, 2017. 5. H. Han and Y. Nam, “Automatic body landmark identification for various body figures,” International Journal of Industrial Ergonomics, vol. 41, pp. 592-606, 2011. 6. ISO. ISO 7250:1996 Basic human body measurements for technological design, 1996. 7. ISO. ISO 7250-1:2017 Basic human body measurements for technological design -- Part 1: Body measurement definitions and landmarks, 2017. 8. O. Kainz, D. Cymbalak, F. Jakab and M. Michalko, “Personal mobile meter for development of human body skeletal model,” in 7th Annual Information Technology, Electronics and Mobile Communication Conference, Vancouver, 2016. 9. O. Kainz, M. Michalko, F. Jakab, “On the Extraction of Anthropometric Parameters by Visual and Non-Visual Means,” EAI Endorsed Transactions on Pervasive Health and Technology, vol. 17, no. 12, pp. 1-6, 2017. 10. O. Kainz, F. Jakab, M. Michalko, „Proposal of Human Body Description Format XML Schema and Validation of Anthropometric Parameters,” in 14th International Scientific Conference on Informatics, Poprad, 2017. 11. O. Kainz, F. Jakab, M. Michalko, R. Vapenik and D. Cymbalak, “Kinect as a tool in estimation of selected human body dimensions,” in International Conference on Emerging eLearning Technologies and Applications, Vysoké Tatry, 2016. 12. K.-R. Mun, Z. Guo and H. Yu, “Restriction of pelvic lateral and rotational motions alters lower limb kinematics and muscle activation pattern during over-ground walking,” Medical & Biological Engineering & Computing, vol. 54, pp. 1621–1629, 2016. 13. K.-R. Mun, S.B. Lim, Z. Guo and H. Yu, “Biomechanical effects of body weight support with a novel robotic walker for over-ground gait rehabilitation,” Medical & Biological Engineering & Computing, vol. 55, pp. 315–326, 2016. 14. R. Altilio, M. Paoloni and M. Panella, “Selection of clinical features for pattern recognition applied to gait analysis,” Medical & Biological Engineering & Computing, vol. 55, pp. 685–695, 2016. 15. J. Han, H.S. Jeon, W.J. Yi, B.S. Jeon and K.S. Park, “Adaptive windowing for gait phase discrimination in Parkinsonian gait using 3-axis acceleration signals,” Medical & Biological Engineering & Computing, vol. 47, pp. 1155–1164, 2009. 16. C. Quental, J. Folgado, P.R. Fernandes and J. Monteiro, “Computational analysis of polyethylene wear in anatomical and reverse shoulder prostheses,” Medical & Biological Engineering & Computing, vol. 53, pp. 111–122, 2014.
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17. P. Shi, S. Hu and H. Yu, “The response of the autonomic nervous system to passive lower limb movement and gender differences,” Medical & Biological Engineering & Computing, vol. 54, pp. 1159–1167, 2015. 18. M. Jochumsen, I.K. Niazi, K. Dremstrup and E.N. Kamavuako, “Detecting and classifying three different hand movement types through electroencephalography recordings for neurorehabilitation,” Medical & Biological Engineering & Computing, vol. 54, pp. 1491–1501, 2015. 19. N. Newell, C.A. Grant, B.E. Keenan, M.T. Izatt, M.J. Pearcy and C.J. Adam, “A comparison of four techniques to measure anterior and posterior vertebral body heights and sagittal plane wedge angles in adolescent idiopathic scoliosis,” Medical & Biological Engineering & Computing, vol. 55, pp. 561–572, 2016. 20. M. Valenti, G. Ferrigno, D. Martina, W. Yu, G. Zheng, M.A. Shandiz, C. Anglin and E.D. Momi, “Gaussian mixture models based 2D–3D registration of bone shapes for orthopedic surgery planning,” Medical & Biological Engineering & Computing, vol. 54, pp. 1727–1740, 2016. 21. K.M. Robinette, S. Blackwell and H. Daanen, Civilian American and European Surface Anthropometry Resource (CAESAR). SAE International, Dayton, OH. 22. Ergonomics-Basic human body measurements for technological design, JIS Z 8500:2002. 23. M. Kouchi, “Anthropometric methods for apparel design: body measurement devices and techniques”. Anthropometry, Apparel Sizing and Design, pp. 67–94, 2014. 24. K. Norton, T. Olds, Anthropometrica: a textbook of body measurement for sports and health courses. New Delhi: CBS Publishers & Distributors PVT. Ltd., 2009. 25. VRML Humanoid Animation Working Group, “Specification for a Standard Humanoid”. Retrieved November 15, 2017, from: http://h-anim.org/Specifications/H-Anim1.1/ 26. E.B. Koffman and P.A.T. Wolfgang, Data structures: abstraction and design using Java. New York: John Wiley et Sons Inc, 2016. 27. J. Foltín, O. Kainz, M. Michalko, R. Vápeník and F. Jakab, “Solution for collection of anthropometric parameters,” Electrical Engineering and Informatics 8 : proceedings of the Faculty of Electrical Engineering and Informatics of the Technical University of Košice, pp. 223-228, 2017.
Human Body Description Format Ondrej Kainz , Frantiˇsek Jakab , Roman Vapen´ ık , ´ Miroslav Michalko PII: DOI: Reference:
S0920-5489(17)30298-2 10.1016/j.csi.2018.01.001 CSI 3261
To appear in:
Computer Standards & Interfaces
Received date: Revised date: Accepted date:
27 July 2017 7 December 2017 5 January 2018
Please cite this article as: Ondrej Kainz , Frantiˇsek Jakab , Roman Vapen´ ık , Miroslav Michalko , Hu´ man Body Description Format, Computer Standards & Interfaces (2018), doi: 10.1016/j.csi.2018.01.001
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Highlights A Human body description format is proposed. The mechanism for processing format parameters is shown and explained. A set of sample human body parameters is selected to prove the usability of the format. Experimental implementation is carried out using the format.
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Human Body Description Format a
Ondrej Kainz , František Jakaba, Roman Vápeníka, Miroslav Michalkoa a
DCI, TUKE University, Letna 9, 042 00 Kosice, Slovakia
[email protected] [email protected] [email protected] [email protected]
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Abstract In this paper, the format for the visual description of the human body is presented. The structural data entry of body parameters is considered as relevant due to gaps in this area, as confirmed by several standardization authorities. Research introduced in this paper fills this void through the proposal of the Human Body Description Format, or for short, HBDF. The principal goal is to present a universal technique for parameters entry, processing and storage. Two principal methods for extraction are considered, namely, ISO-based measurements and the traditional tailoring approach. The latter is covered mainly for long term tradition, while the main focus is put on the first type of measurements. The sample set of parameters was selected based on the analysis of both approaches for extraction. This format is designed to utilize XML dialect to store the extracted data. A weighted pseudograph is used for the representation of ISO-based measurements, allowing for further development of the HBDF matrix, being the adjacency matrix of all the parameters. Several other methods for processing the data are proposed such as compressing the matrix, creating a distance matrix, calculating distances, etc. The format was experimentally verified by a set of tests using the prototype measurement device and proves to be valid in terms of research focus.
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Keywords HBDF: Human body; Human body description format; ISO standard; Visual description; XML dialect
1. Introduction
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Virtually anything that surrounds us can be processed to some discrete form. Many shapes of such data exist, e.g. taking digital images, recording audio tracks, or measuring lengths using a digital meter. Different mathematical apparatuses may be applied once the continuous real-world data is converted to discrete form. One common thing that all discrete data share is having a specific kind of structure that can be decoded by humans or machines. The human body is not an exception and can be described by discrete values. This may include information of its parameters (lengths or distances) or internal structures. Such data can then be utilized in healthcare research projects (e.g. Granberry et al. or Hofmann et al. [1,2]), and in many other research projects utilizing information on the human body (e.g. Griffin et al. or Langmead et al. [3,4]). Standardization of the extracted body data may be of great use providing the uniform techniques of data extraction is followed. The main topic of this paper is the proposal of formal apparatuses used for recording various human body parameters (e.g. distance from shoulder to hip). Multiple physical landmarks (Han and Nam [5]) are located on the human body and could serve in the identification of specific distances or circumferences. Standardized regulations for finding the landmarks exist, e.g. defined by ISO-7250 [6] or its most recent version [7]. However, no standardized way that would enable structural recording is available. Several things must be considered when proposing the way of manipulating the extracted data, e.g. the possibility to perform mathematical calculations.
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In this research, we present an approach to record human body data in a structural format. Selected parameters defined by ISO standards, specifically ISO-7250, are used in the format to show the method of data entry. Note that we utilize the landmarks as defined by ISO standards from 1996, however utilization of the most recent version is possible. The proposed format for processing human body parameters described in this paper serves as a template and may be adapted to various standards that utilize human body landmarks as point-to-point connections. Processing includes recording of data in the form of XML dialect that may be further transformed to a pseudograph or adjacency matrix. Such structures then allow finding a distance from one parameter (human body part) to another (human body part). Processing and examples of the extracted data is also presented and discussed. 1.1 Need of Having the Format
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The development of a format for the description of human body parameters was a natural consequence of the need to have standardized input and output data for research projects that have been carried out in the last few years. Different formats have already been successfully deployed in several research projects carried out at the Technical university of Kosice, e.g. anthropometric device by Kainz et al. [8,9,10], and software solution for visual extraction of human body parameters (Kainz et al. [11]). The described format is not a fully standardized language, but rather, it is a concept of a description format that may be further used in a variety of scientific areas and real-world applications. Other possible deployments of format are presented next. HBDF can be used in conjunction with the kinematics of joints and recognition of its patterns in terms of gait (Mun et al. [12,13]). Analogously, it might be deployed in stereo-photogrammetric systems composed by IR cameras (Altilio et al. [14]). In Han et al. [15] the description format could aid in the analysis of gait of Parkinson's disease patients. A standardized way of data entry should provide more transparency to the research processed and thus enhance the overall comparison. The authors of another research study presented in Quental et al. [16] developed a computational model for evaluation of the wear resistance of anatomical and reverse prostheses. Additional data on human body parameters acquired in the later studies might provide complementary feedback under the real conditions. Except for body mass index, the additional information could be utilization of HBDF in the research on gender differences and the response of the autonomic nervous system considering the passive lower limb movement, as presented in Shi et al. [17]. This is to provide additional and useful data on participants. The possible utilization of HBDF has the potential to be useful in describing the patients undergoing neurorehabilitation [18]. The next case is in quick diagnosis of idiopathic scoliosis, in which extended HBDF may be used as an assistive tool (Newell et al. [19]). Finally, the outputs of the description format proposed in this paper could aid in the construction of a 3D model, such as the one presented in Valenti et al. [20], in the statistical comparison of a healthy and a diseased subject. Before we provide more information on the format, the parameters must be defined. The principal focus is ISO measurements. These parameters are the main focus of this format due to the nature of the measurement, the standardized procedures of measurement and the definition of landmarks. 1.2 Defining the Parameters in Anthropometry Several parameters have to be defined to allow for the construction of a human body model. Below we present a brief analysis that was carried out when selecting the relevant data. Two types of measurements are considered, anthropometric and tailoring. The authors of [21], in their report on anthropometric measurements, extracted up to 100 measurements, though these do not follow the above-mentioned ISO standard. Another document [22], which is following ISO 7250, carried out extraction of over 70 measurements. Many of the measurements in the case of ISO 7250 are collected from the floor (being the
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reference point) to specific parameters, e.g. height, cervical, shoulder etc. Note that these types of measurements do not follow the contour of the human body but are point-to-point distances collected with anthropometric devices. Other types, also included in this format, are length measurements that follow the contour of the human body – these are similar to the lengths in the tailoring industry, i.e. arm length, upper arm length, forearm length or chest and waist circumference. Both of these were considered in the selection of specific human body parameters, while the focus is on the ISO standard, i.e. ISO-based measurements.
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We consider landmarking to be an important factor when carrying out the anthropometric measurements, and for this reason, the proper landmarks have to be selected. The landmark should be manually marked with a non-permanent pencil. More information about landmarks is discussed by Kouchi [23] and Norton and Olds [24]. In the case of estimation of parameters from the digital image, we advise for the utilization of the abovementioned markings that will aid in the identification processes. Manual measurement using third party devices is to follow the measurements as discussed above.
2. The Structure of HBDF Parameters
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The HBDF is designed to allow for the processing of human body parameters as extracted using real hardware devices or other means (e.g. digital camera images). There have been attempts to partially formalize the human body parameters, e.g. Humanoid Animation Working Group [25], however, the proposal introduces the complex entry of input data since it focuses on the implementation of humanoids in motion, not static characteristics and related dimensional lengths. The recent communication with the International Organization for Standardization, World Wide Web Consortium (W3C) and the Institute of Electrical and Electronics Engineers (IEEE), realized as part of the presented research, confirmed that no specific format for the description of human body parameters exists. Bearing in mind this fact, the goal is to introduce some structure to the recording and subsequent processing of these parameters. Several human body lengths, circumferences and also breadths are to be considered. Further, we principally consider measurements based on the ISO 7250 standard and subsequent measurements from the tailoring industry. The format is aimed on the extraction of static dimensional units, presuming most of these parameters in coronal plane. This approach is to allow universal entry of human data in a simple and readable form. The selection of human body parameters follows the prior analysis of parameters in tailoring and anthropometric measurements. Measurements were broken down based on the following human body parts: head, torso, arm and leg. Additional data (see Figure 1) on the extraction process are expected to define the measurement process more closely; personal information being one of these. Specific implementation should handle mapping of the created file to the specific individual, and it can be solved by the name of the participant or the internal ID number assigned to the person. Other information that might be relevant for statistical purposes is information on the age of the participant and the date, i.e. when the measurement was taken. Weight and height are also considered as a relevant information on the very measurement.
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Figure 1 HBDF: Basic structure of data input
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As stated above, two types of measurements of human body parameters are considered, i.e. ISO 7250 and tailoring. We consider it important to define the device used for measurements. Three types of devices were taken into account in the current state of research: tape meter, anthropometer and other device (e.g. specific prototyping device).
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In total, thirteen ISO-based parameters related to anthropometry were selected for the proposal of format structure. Eight of these include either circumference or breadth and circumference. The other five parameters do not contain any additional data except for height from the reference point. For every parameter, the information on height is presumed to be extracted, using the ground as a reference point. Using the ground as a reference point is defined by ISO standard [6] and most of the utilized measurements also use the ground as the reference point. Note that the only selected parameters of ISO standard were covered in the research, principally those that we considered as the most relevant. Other parameters may be added in future research. The naming of parameters is designed to follow the ISO standard, having the specific names of ISO standards replaced by the block of specific parameters, e.g. face length is represented as a length between sellion and gnathion. This was done to make the measurement process as effective as possible, thus unnecessary parameters or repeating parameters were eliminated. Some extracted data are also nested as a part of other parameters. An example is acromion, which covers the measurement of height, breadth, and circumference. Breadth is the length of the left acromion to the right acromion (or vice versa). A list of parameters along with information on nested parameters is shown in Figure 2, coloring and abbreviations of parameters are the same for Figures 4 and 5.
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Figure 2 HBDF: Human body parameters in ISO
3. HBDF as XML Dialect
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Parameters and additional data (on the measurement process and the person) are combined in the proposed structure of the XML dialect. Human Body Description Format (HBDF) is the proposed format for the structured description of the human body by the authors. Note that this format is in this stage merely a proposal that might eventually evolve to Human body description language, or for short, HBDL. As noted, HBDF in this stage of research is designed as an XML dialect. Hierarchical XML tree structure has HBDF as its root node. Three descendant nodes cover the information on subject of measurement, type of device used and type of the measurement itself. First, two included child nodes are not further branched. However, based on the type measurement two cases are possible tailoring or ISO 7250. For each measurement, the new file is presumed to be created. It is also expected that not all the entries will be available on the input at once. Additional information about the measurement can be provided later or prior to the measurement of human body parameters. Also, one type of measurement can be fully ignored depending on the measurement approach. Even though some parameters are the same in both measurements, it is decided that they will present sole nodes due to different approaches to measurement. In the measurement of parameters, the breadth and circumference are child nodes of the specific parameters, providing additional information on parameters. However, each parameter in the case of ISO contains the information on its height from the ground. Differentiation of individual measurements is not covered in the XML dialect proposal, e.g. filename may be used for such purposes. Note, that this dialect serves only for storing data that are used as an input for a pseudograph. Presented next is the structure of ISO-based parameters in the form of a pseudograph and an HBDF matrix. These structures allow processing of the extracted data from the XML dialect. Only collected ISO-based data (from the specific device or that which is collected manually) are the input to these structures, i.e. mostly distances from the reference point, while the other point-to-point distances are calculated based on the available data. Parameters in the XML dialect related to tailoring measurements do not use a pseudograph or matrix structures.
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4. HBDF as Pseudograph
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Collected ISO-based data stored in HBDF (XML file) can be expressed as a weighted pseudograph structure. In the proposal of the pseudograph, we follow a sample set of articulations as depicted in Figure 2. Vertices of pseudographs are in the most cases represented by the articulations of the human body (e.g. acromion), with the topmost being the vertex of head. The design follows the ISO standard [6] and its approach to extraction, however, other standards may be used with HBDF as well. Formation of pseudograph presumes specific features of the measurement process, mainly the presence of a reference point and point-to-point measurements. Proposed weighted connected pseudograph G consists of finite set V of vertices v1,v2,v3,...,v16 and a set of pairs of vertices (u,v), denoted as E. The edge (u,v) is from u to v and is incident from u to v. Each edge that connects the set of two vertices has specific weight w(e). Weighted pseudograph G has only positive-weights. In Figure 3, the simple weighted pseudograph with two nodes is shown, where N represents the first and M the latter human body parameter. Weight mx,y is the real distance between these two parameters. Self-loop is an edge (v,v) between vertex and itself. In our case, the self-loop is circumference of specific parameter. In Figure 3 the self-loop is denoted as cx,y.
Figure 3 Weighted pseudograph in HBDF
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The specific parameters of ISO standard can be interpreted visually and enable the formation of the pseudograph based on these parameters (see Figure 4).
Figure 4 Visualization of ISO Parameters
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The location and connection of one node to another in Figure 4 resembles the connection of individual vertices of the weighted pseudograph. This connection between nodes (i.e. the real distance) is of certain lengths that is represented by the weight of the edge. Further, circumferences marked by a half circle are analogous to the self-loop of a specific vertex. Color (based on Figure 2) distinguishes individual parameters. Ground, marked by gd or white circle in Figure 4, is actually the reference point of most measurements (except for circumferences, foot, buttock, acromion and iliac spine). Measured height of the vertex from the reference point or distance from the other vertex serves to calculate the weight of the edge. The nature of measurement and values that are being extracted allows for the creation of a pseudograph of parameters. The relation of measurements is realized by edges, where one measurement follows another, until all are carried out. Further, each measurement has the output value in the form of millimeters or inches. The name of the measurement is hence associated with the specific extracted value. These values are then used to calculate the distances between the parameters following the information from the pseudograph. These distances are depicted in the pseudograph as weights of edges. Parameters in the form of a weighted pseudograph are depicted in Figure 5, while following the naming in Figure 2.
Figure 5 Parameters of weighted pseudograph
The numbering of distances is dependent on the human body part that is being measured, m1,y for head, m2,y for torso, m3,y for arm and m4,y for leg. Note that the vertex denoted as gd stands for ground. Measurement of buttock along with the measurement of foot, acromion breadth and iliac spine breadth represent four values that do not have ground as a reference point. Further, buttock and foot demonstrate measurement of depth, while the other length measurements are presumed to be in one vertical (coronal) plane.
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Circumferences were merged with vertices in the form of self-loops. Circumference of the head is part of sellion vertex, circumference of chest is part of acromion, tight and calf are part of knee and tibia, respectively. This approach led to a decrease of vertices and simplified the whole structure. Another type of measurement is breadth. This measurement is valid for two vertices, i.e. acromion and iliac spine. The measurement of the breadth is a special case: the vertex is broken down to two separate vertices making the breadth a form of the length. Most of the measurements are straightforward, however the measurement of the buttock is measured in the sitting position (based on ISO [6]), i.e. ground is not the reference point for measurement. Measurements may be divided to: Directly measured: Cover the heights of vertices and all of the circumferences cx,y. Special case are edges m2,3, m2,5, m4,4 and m4,5, note that these do not have ground as reference point. Calculated: Includes the distances (edges) between the vertices mx,y. 4.1 Calculation of Weights
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Transition from one vertex (u) to another forms an edge e with some weight m. Each weight has an individual value of x and y that uniquely identify the parameters. The value of x is from 1 to 4, identifying measurements of head, torso, arm, leg, respectively. Another value y defines the specific measurement and ranges from 1 to 6. The weights are more complex to acquire, while the direct measurement of lengths or circumferences is rather clear. The value of specific weight is dependent on the extracted (measured) value of the vertex. In this approach, we utilize the knowledge of the measured distance from the ground. We have available two separate parameters of various heights with the same reference starting point. This property allows to subtract one value from another and hence provide the actual distance between values. An example may be sellion, which height is measured from the ground and another height of gnathion. The same applies for sellion, as this value is measured from the ground. The formula based on the pseudograph is subtraction of gn from sn that is equal to m1,1, where the output (m1,1) is the face length. In general form: | | (1.1) where the u is the value of vertex and v is the value of the neighboring vertex connected through the edge. Special case of distance is (m2,4) and (m2,6). Calculation in this case has to be done in a different manner. Distances between vertices, an1, an2, and nk or is1, is2 and ch, form the isosceles triangles. Thus, the triangle height and base is known, hence the unknown distance may be calculated: √
(1.2)
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where the (mx,y) is the distance (m2,4) or (m2,6), h is calculated from Eq. 1.1 as an - nk or is - ch, respectively. And (mm,n) is the actual distance between an1 and an2, or is1 and is2. The matrix of parameters can be formed once the weights of parameters are calculated. HBDF matrix is presented in the next part and contains just a sample number of parameters. 4.2 Matrix of Parameters The nature of parameters and also its representation in form of a weighted pseudograph allows representation of extracted parameters in the form of a symmetric matrix (referred to as HBDF matrix).
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Figure 6 Adjacency matrix of human body parameters
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A matrix of size 16x16 (see Figure 6) is created, where each column and row vector resembles the specific parameter. The matrix is constructed following the weighted pseudograph depicted in Figure 5, also the naming of vertices, edges and loops is based on this pseudograph, and it is created as: if ( ) { (1.3) otherwise Providing the edge between the vertices exists (is equal to one), the two vertices have adjacency and some specific weight (distance). The order in which the vertices are extracted is not relevant. However, in order to acquire the whole skeletal model, the vertices sn, an2 and is2 can be considered as initial nodes. In this way, the directed pseudograph can be constructed having as the main walks in pseudograph: head and torso: sn, gn, nk, us, ch, arm: an2, an1, ew, hd, leg: is2, is1, ke, ta, gd, ft. The main diagonal of the matrix (Figure 6) represents the parameters (ci,j), i.e. circumference. Circumference is stated only where applicable (designed). The rest of the parameters in the matrix are directly related to the calculated or measured distances mx,y. The proposed pseudograph solution may be expanded in later research, however this shows the approach or template for the structural form of the entry of human body parameters. Utilization of a matrix to describe the extracted parameters and their relation to other parameters is considered as one of the possible approaches of data entry. Distances, heights, circumferences, and their mutual relations are easily readable and extractable. Further, having data in this form may be useful in further data processing or other mathematical operations. 4.3 Compressing the Matrix of Parameters The created matrix is 16 by 16, i.e. symmetric. However, most of the space is occupied by zero values. To express the relations of parameters only one side of matrix is actually required, hence the matrix can be considered as upper or lower triangular. Having an upper triangular matrix, the number of non-zero values is
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21, which sparsity of 91.8 % and density of 8.2 %. Bearing this in mind, one of the options to compress the matrix is to express it as a sparse matrix. We present multiple forms of utilization of sparse matrix storage formats. The first being the simplest, utilizing the coordinates - coordinate list (COO), where the column and row number identify the value, see Figure 7.
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Figure 7 Parameters representation as coordinate list
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Another storage format that may be utilized to represent the matrix is compressed sparse row (CSR). This format has a long tradition and stores the matrix in three arrays (A, IA and JA). The first contains the values of the matrix that are non-zero, IA is the row offset m+1 if matrix M(m x n) and is incremented by the number of elements in each row. Third JA, is for column indices. Implemented on the parameters in the matrix, its output can be seen in Figure 8.
Figure 8 Parameters representation as compressed sparse row
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The last type of storage format we present is using diagonal, as is shown in the Figure 6. seven diagonals contain non-zero values. However, if the values of the upper triangular matrix are considered, then only four diagonals are relevant. These include the main diagonal containing the c values, the diagonal containing m1,1 up to m4,4 and finally the diagonal with values m2,4 to m4,5. The representation as a banded matrix is shown in Figure 9. The letter d in the Figure 9 maps the diagonal, where the main diagonal of the original matrix (M) is assigned with a number of 0 and subsequently neighboring diagonals of the upper diagonal matrix are assigned numbers {1,2,3...}. Presented methods for reduction of matrix representation are implemented in most software solutions available on the market, and should these not be implemented, there is an algorithm for each of these. The main point presented here is that using few input data, we can extract the whole skeletal model for an ISObased measurement.
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Figure 9 Parameters representation as banded matrix 4.4 Distance Matrix
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Distance from one vertex to any other vertex is extractable since we have a pseudograph that is a tree, while not considering loops. Knowing the exact distance of one parameter to another may be useful in various types of use cases or research studies. A distance matrix, same as the adjacency matrix, is also a symmetric matrix D(G). Distance d(i,j) between u to v vertices of pseudograph G is the shortest path. If we do not consider circumferences, i.e. self-loops of specific vertices, the pseudograph can be considered as a tree, where the shortest path is actually also the only path (in presented sample of parameters). Distance matrix of tree G (see Figure 5) is then constructed as shown in Figure 10.
Figure 10 Distance matrix of human body parameters
Each distance dx,y is defined by the edge weight mx,y or its sums. Series of edges mx,y, where x is {1,2,3,4}, based on the type of body part, and y is {1,2,...,5,6}, based on the specific parameter of body part, define the other distances. The distance of the body parameter sn to ft is the distance marked as d1,15 and calculated as:
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(1.4)
The summation of all distances can be calculated as: ∑ ∑ ∑ ∑ (1.5) Specific distance dx,y is the selection of distances based on the weight w(e) and walk in graph (tree). It may be troublesome to calculate the distances, bearing in mind that more parameters may be added. The Dijkstra algorithm for calculation of the distances may be deployed should the matrix reach a specific size. Deployment of Dijkstra was not covered in the research.
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4.5 Traversal of Parameters It is possible that some vertices of the pseudograph will not be available, hence the systematic search has to be done. The proposal is to utilize the breadth-first search algorithm, described by Koffman and Wolfgang [26], where the arbitrary vertex is selected and labeled with 0, then adjacent vertices are visited, until all the nodes of pseudograph are visited.
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5. Visualization of Parameters
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Visualization of extracted data (collected from ISO-based measurement) is shown in Figure 11. Parameters of the pseudograph are represented by vertices, however, circumferences depend on the location having their actual size is reflected. Heights of individual parameters are shown as well.
Figure 11 Visual illustration of HBDF using HBDF This form of visualization may be useful in the visual comparison of specific parameters, since the actual distances and circumferences of the human body are depicted.
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Software solution [27] as part of the experimental implementation was designed and implemented, based on the input data from the XML dialect.
6. Experimental Testing: Extraction Using the Anthropometric Module
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A prototype of a computational anthropometric device [8] was created as part of the related research. This prototype device allows the extraction of human body parameters. The HBDF skeletal representation, as proposed above, is utilized. ISO-based measurements are deployed in the presented pilot experiments. Extraction using the proposed electronic device allows for the acquisition of all the parameters depicted in Figure 5. The presence of flexible tape meter allows for the measurement of both circumferences and lengths. The measurement process collected data to the proposed XML dialect and the full HBDF matrix is created. The accuracy of the measurement was estimated to be up to 2 mm. The basis of the measurement for almost all parameters is the ground. This presumes the fixation of a tape meter to the ground, where the device is in the horizontal level to the reference ground. The beginning of the tape meter was attached to the ground, which enabled full expansion in the y direction. In order to achieve that only the y coordinate is to be changed, the bubble level is attached to the anthropometric module. One more obstacle was still apparent – the nature of the measurements or utilization of a traditional anthropometer presumes having some form of claws or arm for exact marking of the landmark. This is solved by a laser pointer attached onto the module case. Note that the distance between the pointer device and tape meter was measured and considered in the measurement process. The measurement consists also of five testing subjects. The principal analogous requirement in the measurement was the proper posture of the person and minimum clothes, i.e. being mostly half-naked. A real sample from the measurement in form of HBDF matrix (Figure 12) is presented next. Data was extracted from the male subject having height of 1790 mm. Each measurement produced an XML file that was validated in the post-process, also the information on the person was added after the measurement. Measurements include information on the node heights, circumferences, and breadths; two depth measurements include foot and buttock.
Figure 12 HBDF Pilot experiments: Extraction using pilot experimental hardware device [in mm]
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Visualization of extracted data is possible through the construction of the proposed skeleton model based on the HBDF matrix, and is analogous to the model in Figure 11. All the parameters of the matrix are known; hence the visualization of circumferences is possible. The visual output of the HBDF matrix in Figure 12 is depicted in Figure 13. However, note that circumferences are in ratio 1:4 in terms of other dimensions to fit the model. Visual representation in the form of the graph is possible as well. Utilization of this approach enables visualization of the parameters of the HBDF matrix and also allows for the comparison of multiple persons.
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Figure 13 Visualization of HBDF in the extraction using anthropometric hardware
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The presented pilot experiments presumed development the interface [10] for data visualization and processing, it has as the input the proposed XML dialect. The solution allows manual input of data and export of the XML file. The preview and edit of the collected data and on the subject of measurement is possible. HBDF, as a sole solution, is also being deployed in other research related to measurements of human body parameters, e.g. construction of human skeletal model using smartphone device. This approach presumes the utilization of visual markers as a reference point placed on the ground (i.e. analogous reference point to ISObased measurement). Another project is focused on the estimation of a skeletal model from the image based on the AruCo markers that are located directly on a person. The proposed XML dialect is to be deployed in the just stated research projects.
7. Conclusion
The research presented in this paper was focused on the proposal of the format for structural entry and processing of anthropometric parameters. The format is designed to be usable with various anthropometric standards that follow point-to-point measurements, thus it can be easily adjusted to more recent versions of ISO 7250 [7]. Furthermore, it allows recording of data from traditional tailoring measurements. Both approaches are included in XML format, while point-to-point measurements can be further transformed to the proposed pseudograph and HBDF matrix.
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Acknowledgment
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The main focus is point-to-point measurements that utilize specific reference points, e.g. the ground. Such measurements are introduced in ISO 7250 [6]. A sample of relevant parameters from this standard was used in the format to prove usability of the proposed solution. Stimuli that led to the proposal of the format for description of human body parameters is void in the research area, as was also confirmed by the most acknowledged standardization organizations. A novel way for representing human body parameters in the form of a weighted pseudograph with loops was proposed. This allows for multiple actions to be carried out on data. Overall, 13 human body parameters were selected as the most relevant. Some of these further encompass other parameters such as circumference or breadth. Heights of these 13 main body parameters and the additional data are the input to the pseudograph, and these are the directly measured data. The distances between specific parameters are considered as calculated data. Weights of edges are used to represent the distances in the pseudograph. The approach for their calculation was introduced and described. Circumferences, on the other hand, are represented as loops of specific parameters. The parameters of the pseudograph, having each parameter uniquely defined, are also represented as an adjacency matrix. This enables the processing of data or even the storing of data. Methods for compressing the human body matrix parameters were presented. Thus, the description of visual human body parameters is possible using a few numbers. The distance matrix can be further created from the adjacency matrix. Specific distances of one parameter to another are easily extractable. Considerations of the utilization of ISO-based measurement data in tailoring practice were made and in the current state of research it was evaluated as redundant to merge these methods due to a different approach in the measurement process. XML dialect is the format used for the data entry. Its structure was described and presented. This format was verified on a sample of the real data. A possible drawback of the format may occur in the case of abundant parameters in the sagittal plane, however this is less likely. Part of the research included pilot experiments, which proved the proposed HBDF to be useful in measurement using the prototype anthropometric module. The non-visual extraction technique followed the ISO-based measurement process and collected data allowed for the creation of a full pseudograph and related HBDF matrix. The form of visual representation is the construction of the skeletal model from the XML dialect that was proposed as part of the description format. Errors estimated throughout the testing were analogous to errors introduced by the tape meter. Pilot experiments are considered successful in terms of the given requirements and assumptions presented in the research.
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This publication is the result of the Project implementation: University Science Park TECHNICOM for Innovation Applications Supported by Knowledge Technology, Phase II., ITMS: 313011D232, supported by the Research & Inovation Operational Programme funded by the ERDF. We support research activities in Slovakia/This project is being co-financed by the European Union.
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References
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1. R. Granberry, J. Duvall, L. E. Dunne and B. Holschuh, “An analysis of anthropometric geometric variability of the lower leg for the fit & function of advanced functional garments,” in ACM International Symposium on Wearable Computers, Maui, 2017. 2. M. Hofmann, J. Harris, S. E. Hudson and J. Mankoff, “Helping Hands: Requirements for a Prototyping Methodology for Upper-limb Prosthetics Users,” in CHI Conference on Human Factors in Computing Systems, San Jose, 2016. 3. L. Griffin, C. Compton and L. E. Dunne, “An analysis of the variability of anatomical body references within ready-to-wear garment sizes,” in International Symposium on Wearable Computers, Heidelberg, 2016. 4. A. Langmead, P. Rodriguez, S. P. Satheesan and A. Craig, “Extracting Meaningful Data from Decomposing Bodies,” in Practice and Experience in Advanced Research Computing on Sustainability, Success and Impact, Los Angeles, 2017. 5. H. Han and Y. Nam, “Automatic body landmark identification for various body figures,” International Journal of Industrial Ergonomics, vol. 41, pp. 592-606, 2011. 6. ISO. ISO 7250:1996 Basic human body measurements for technological design, 1996. 7. ISO. ISO 7250-1:2017 Basic human body measurements for technological design -- Part 1: Body measurement definitions and landmarks, 2017. 8. O. Kainz, D. Cymbalak, F. Jakab and M. Michalko, “Personal mobile meter for development of human body skeletal model,” in 7th Annual Information Technology, Electronics and Mobile Communication Conference, Vancouver, 2016. 9. O. Kainz, M. Michalko, F. Jakab, “On the Extraction of Anthropometric Parameters by Visual and Non-Visual Means,” EAI Endorsed Transactions on Pervasive Health and Technology, vol. 17, no. 12, pp. 1-6, 2017. 10. O. Kainz, F. Jakab, M. Michalko, „Proposal of Human Body Description Format XML Schema and Validation of Anthropometric Parameters,” in 14th International Scientific Conference on Informatics, Poprad, 2017. 11. O. Kainz, F. Jakab, M. Michalko, R. Vapenik and D. Cymbalak, “Kinect as a tool in estimation of selected human body dimensions,” in International Conference on Emerging eLearning Technologies and Applications, Vysoké Tatry, 2016. 12. K.-R. Mun, Z. Guo and H. Yu, “Restriction of pelvic lateral and rotational motions alters lower limb kinematics and muscle activation pattern during over-ground walking,” Medical & Biological Engineering & Computing, vol. 54, pp. 1621–1629, 2016. 13. K.-R. Mun, S.B. Lim, Z. Guo and H. Yu, “Biomechanical effects of body weight support with a novel robotic walker for over-ground gait rehabilitation,” Medical & Biological Engineering & Computing, vol. 55, pp. 315–326, 2016. 14. R. Altilio, M. Paoloni and M. Panella, “Selection of clinical features for pattern recognition applied to gait analysis,” Medical & Biological Engineering & Computing, vol. 55, pp. 685–695, 2016. 15. J. Han, H.S. Jeon, W.J. Yi, B.S. Jeon and K.S. Park, “Adaptive windowing for gait phase discrimination in Parkinsonian gait using 3-axis acceleration signals,” Medical & Biological Engineering & Computing, vol. 47, pp. 1155–1164, 2009. 16. C. Quental, J. Folgado, P.R. Fernandes and J. Monteiro, “Computational analysis of polyethylene wear in anatomical and reverse shoulder prostheses,” Medical & Biological Engineering & Computing, vol. 53, pp. 111–122, 2014.
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AC
CE
PT
ED
M
AN US
CR IP T
17. P. Shi, S. Hu and H. Yu, “The response of the autonomic nervous system to passive lower limb movement and gender differences,” Medical & Biological Engineering & Computing, vol. 54, pp. 1159–1167, 2015. 18. M. Jochumsen, I.K. Niazi, K. Dremstrup and E.N. Kamavuako, “Detecting and classifying three different hand movement types through electroencephalography recordings for neurorehabilitation,” Medical & Biological Engineering & Computing, vol. 54, pp. 1491–1501, 2015. 19. N. Newell, C.A. Grant, B.E. Keenan, M.T. Izatt, M.J. Pearcy and C.J. Adam, “A comparison of four techniques to measure anterior and posterior vertebral body heights and sagittal plane wedge angles in adolescent idiopathic scoliosis,” Medical & Biological Engineering & Computing, vol. 55, pp. 561–572, 2016. 20. M. Valenti, G. Ferrigno, D. Martina, W. Yu, G. Zheng, M.A. Shandiz, C. Anglin and E.D. Momi, “Gaussian mixture models based 2D–3D registration of bone shapes for orthopedic surgery planning,” Medical & Biological Engineering & Computing, vol. 54, pp. 1727–1740, 2016. 21. K.M. Robinette, S. Blackwell and H. Daanen, Civilian American and European Surface Anthropometry Resource (CAESAR). SAE International, Dayton, OH. 22. Ergonomics-Basic human body measurements for technological design, JIS Z 8500:2002. 23. M. Kouchi, “Anthropometric methods for apparel design: body measurement devices and techniques”. Anthropometry, Apparel Sizing and Design, pp. 67–94, 2014. 24. K. Norton, T. Olds, Anthropometrica: a textbook of body measurement for sports and health courses. New Delhi: CBS Publishers & Distributors PVT. Ltd., 2009. 25. VRML Humanoid Animation Working Group, “Specification for a Standard Humanoid”. Retrieved November 15, 2017, from: http://h-anim.org/Specifications/H-Anim1.1/ 26. E.B. Koffman and P.A.T. Wolfgang, Data structures: abstraction and design using Java. New York: John Wiley et Sons Inc, 2016. 27. J. Foltín, O. Kainz, M. Michalko, R. Vápeník and F. Jakab, “Solution for collection of anthropometric parameters,” Electrical Engineering and Informatics 8 : proceedings of the Faculty of Electrical Engineering and Informatics of the Technical University of Košice, pp. 223-228, 2017.