Ergonomic design visualization mapping- developing an assistive model for design activities

International Journal of Industrial Ergonomics 74 (2019) 102859

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International Journal of Industrial Ergonomics journal homepage: www.elsevier.com/locate/ergon

Ergonomic design visualization mapping- developing an assistive model for design activities

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Ranit Eldar∗, Dafna Fisher-Gewirtzman Faculty of Architecture and Town Planning, Technion - Israel Institute of Technology, Technion City, Haifa, 3200003, Israel

ARTICLE INFO

ABSTRACT

Keywords: Ergonomic design interface Visualization mapping Postural comfort ROM WMSDs

Ergonomic models and techniques are a fundamental issue in the design of comfortable and safe products and spaces. User studies, related to visualization tools are current issues in the ergonomics and design visualization literature. But researchers have begun to discover that user study is rarely straightforward, especially when drawing visualization data from interdisciplinary sources. The availability of a plethora of visualization techniques can make it difficult to determine the most appropriate technique to convey maximum possible understanding. The RT-MHV (“Real-time”– “Motion history volumes”) 3D computerized assessment model, developed by the authors, demonstrates a local risk evaluation of work-related musculoskeletal disorders (WMSDs), based on realtime and on motion history volumes. With the model, the visual display of the WMSD risk level for each body segment is defined by color-coding at points surrounding an avatar's segment, representing an actual user. The values associated with areas with an increased risk of WMSDs can be identified and iterated quickly, so as to determine the “optimal posture”. Designers can share this knowledge by recording the user's postural interactions, defined through the mapping of geometric comfort data and WMSD risk level categories. The challenge in the development process was to overcome existing “gaps” between ergonomics data and designer requirements. Further research on the RT-MHV model is recommended, principally for developing stand-alone CAD software. An aggregated statistical information database and complete body joints visualizations will be computerized in due course. 2D tabulation and statistical information relating to body joints will be made available on demand.

1. Introduction Ergonomic models and techniques are a fundamental issue in the design of comfortable and functional products. The benefits of the use of such models in the early stages of the design process are widely recognized. In this context, human motion properties are recognized as representing an important criterion in the evaluation of designed artifacts and spaces.Whilst a wide range of assessment methods for determining human comfort are available, researchers have noted that ergonomic data often has very little impact on the design process (Zitkus et al., 2013, 2012; Zhang, 2005; Vicente et al., 1993), or that ergonomics data is not deployed effectively by designers. Other researchers (Nickpour and Dong, 2011) have shown that the use of existing ergonomic data tools (i.e. books, guidelines, software packages, online sources) in the design process by experienced designers is very limited. An assessment of the gap between the ergonomics domain and the

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design domain led the authors to the possible cause of this gap, and provided contextual understanding of the preferences and needs of designers. Because ergonomists tend to have a background in science (i.e., psychology, biomechanics, or mechanical engineering), they are often familiar with the use of numbers and structured diagrams; designers, on the other hand, tend to be “visual thinkers”, more attuned to the use of images, pictorial information and three-dimensional presentation. With this in mind, the authors determined to provide designers with better access to ergonomics data, principally by presenting such information in a more effective way. The RT-MHV (“Real-time”– “Motion history volumes”) visual model, developed by the authors, seeks to combine the preferences of designers, with regard to assessing the comfort characteristics of the interaction between a user and the product and space. Hence, the objective of this paper is to evaluate the dynamic representation (DR) of RT-MHV visual information related to human motion, for application in the design process.

Corresponding author. E-mail addresses: [email protected] (R. Eldar), [email protected] (D. Fisher-Gewirtzman).

https://doi.org/10.1016/j.ergon.2019.102859 Received 26 May 2019; Received in revised form 12 September 2019; Accepted 16 September 2019 0169-8141/ © 2019 Elsevier B.V. All rights reserved.

International Journal of Industrial Ergonomics 74 (2019) 102859

R. Eldar and D. Fisher-Gewirtzman

Abbreviations

(MoCap) (MSDs) (PIG) (RGB) (ROM) (RT) (RULA) (TS) (WMSDs) (1D) (2D) (3D)

(CAD) Computer-aided design (CCS) Cartesian coordinates system (E-worker) Electronic worker (DHM) Digital Human Modeling (DMU) Digital Mockup (DR) Dynamic representation (GUI) Graphical user interface (ISB) International Society of Biomechanics (MHV) Motion history volume (MIL-STD) Military standard 2. Towards an assistive model for design activities

Motion-capture Musculoskeletal disorders Plug-in-Gait Red, Green, Blue Range of motion Real-time Rapid Upper Limb Assessment Task support Work-related musculoskeletal disorders One-dimensional Two-dimensional Three-dimensional

d. Considerable, and confusing, variations in methods and guidelines (Schmidt et al., 2014; Stubbs et al., 1993). e. The lack of unpredicted and dynamic postural-behavior information (Schmidt et al., 2014; Zhang, 2005), such as case studies of the electronic worker (i.e., e-worker) using the third-workplace.

2.1. Definition of the problem Body posture is an important consideration in the design of safe and comfortable products (Bridger, 1991). However, generating the human body into various postures, e.g., by means of digital human modeling (DHM) simulation, can be a “complicated task” (Schall et al., 2018; Rosenthal et al., 2013). This underlines the importance of improving ergonomic design, mainly through taking into account comfort and health factors, to support the application of existing knowledge and the design process more generally (Meister, 2018; Young et al., 2012). The challenge here is how, on the one hand, to build a database resource of physical ergonomic knowledge related to the human body across a range of dynamic postures; and on the other hand, how to convey this information to designers, who are often unfamiliar with this crucial knowledge. A number of studies focusing on postural assessment methods and human comfort have been conducted. The available postural behavior assessment methods and guidelines are, in the main:

Consequently, the application of postural assessments in the design process can be a challenging and complicated task. Conventional technologies and methods do not support the generation of relevant postures and all the current postural features, which include: a. Work-related musculoskeletal disorders (WMSDs), which are known to influence well-being parameters (e.g., health and comfort) and optimal performance (Dul et al., 2012); b. Three-dimensional real-time representation of postures, given that movement, generally, is dynamic in nature; c. A full range of postures, of the whole body and across multiple tasks. Several researchers have agreed (Bogović et al., 2018; Zhang, 2005) that the most effective solution to these challenges would most probably derive from a combination of advanced dynamic anthropometric methods and high-end CAD technologies. Based on the above, it is clear that conventional DHM or empirical methods, based on 1D/2D anthropometric data (dimensions and angles), cannot provide sufficient or effective posture assessment. In addition to this, conventional assessment methods tend to be cumbersome, error-prone, time-consuming, and are not cost-effective. Adaptive computational methods, based on numerical algorithms and motion capture (MoCap) processes, present as more appropriate tools for ergonomic design assessment applications.

a. Related to static postural-behavior (i.e. LUBA, an assessment technique for postural loading on the upper body, by Kee and Karwowski, 2001); b. Focused on predictive postures (i.e. DHM, HumanCAD software; http://www.nexgenergo.com; Zhang, 2005); c. Linked to popular or common locations, such as leisure locations (Panero and Zelnik, 1979), offices (Pheasant and Haslegrave, 2005; Eldar, 1998), and industrial environments (Schall et al., 2018); d. Lacking with regard to specific workplaces, such as the public workplace (also known as the third-workplace), which to a certain degree combines characteristics of both the office and leisure locations (Hardy et al., 2008). As such, such locations lack ergonomic design guidelines similar to those developed for office work (i.e. ISO-9241, ANSI/HFES 100 (USA), and CSA-Z412-M89 (Canada), Military Standard (i.e. MIL-STD 1472-G; USA; http://everyspec. com/library.php).

2.2. Research objective and questions The objective of this research was to develop an assessment model (the RT-MHV model) for visualizing dynamic postural comfort level, operating in real time (“RT”) and based on motion history volumes (“MHV”) mapping, for use in ergonomic design. Our goal was to combine a range of disciplines, namely ergonomics, design, and computer technology, to create an assistive model for design applications. The focus was on ergonomics as design-driven; consequently, our principal research questions centered on the issues related to this orientation. Consequently, the somewhat limited set of questions should be seen in the context of the direct focus on the research objective. The research questions were:

Nevertheless, some researchers (Zitkus et al., 2012; Burns and Vicente, 1996) have concluded that these methods are not widely used by designers or the industry, for a variety of reasons. These include: a. The complexity of the methods related to the integration of the information into the design process, mainly because the limited knowledge of ergonomics on the part of product designers has not been taken into account (Zitkus et al., 2012). b. The lack of contextual or specific information related to design and time consumption (Burns and Vicente, 1996). c. The fact that such information may be contingent, to some extent, on subjective estimations by health sector professionals (Schmidt et al., 2014; Zhang, 2005).

a. How can one raise awareness of the potential role that ergonomic knowledge can play in the design process? b. How can the Digital Mockup (DMU) process be utilized to simplify the visualization deliverables for ergonomic design application? c. What is the most effective means of representing the large volume of 2

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data relating to the human body's range of motion (ROM), as gathered by the motion capture (MoCap) technique? d. What types of visualization will be the most effective for posture transformation? e. What are the most intuitive and straightforward ergonomic assessment methods that can be utilized in a design application?

Vicente, 1996). 2.4. Research hypothesis We hypothesized that conventional methods of assessing human posture can be replaced by a new methodology, based on a MoCap visualization of ROM mapping. Having recognized the opportunities offered by developments in fast computation, and the need for an ergonomic assessment model more suited to the design process and adaptable to the needs of designers, we concluded that efficient posture assessment can be achieved by integrating these options. Conventional posture assessments (e.g., RULA; the Rapid Upper Limb Assessment method by McAtamney and Corlett, 1993) are based on 1D/2D static measurements of the posture generation process. The proposed new posture assessment, informed by the relevant literature on WMSDs risk level recommendations, evaluates three-dimensional dynamic postures in real time, directly from the MoCap positional data. Our main hypothesis was that using the RT-MHV model would contribute to more accurate postural comfort and WMSDS risk level assessments. We assumed that the RT-MHV visualization mapping assessment might be more accurate due to the methods and techniques combined for the development of the proposed model:

Our ultimate ambition is to integrate the RT-MHV ergonomic design assessment model into industrial design and workplace design practices. Hence, we hope to assist designers in the evaluation of user's comfort/discomfort levels, highlighting the correlation between the product design and workplace. This will give designers the opportunity to introduce insights from ergonomic research into product design and workplace practice, which in turn will ultimately contribute to healthier and efficient user. 2.3. The user profile: designer requirements Our aim was to define two types of “end-users” of the RT-MHV model: a. Main-users: designers (mainly industrial designers). b. Secondary-users: ergonomists who are also designers (“ergonomic designers” or “expert users”: Luximon et al., 2018; Harvey et al., 2014). Ergonomic designers usually have advanced academic knowledge and experience in the design discipline (e.g., product design, interior design or architecture), as well as in the human factors/ergonomics (HF/E) discipline (e.g., psychology, biomechanical engineering, physiotherapy, and industrial engineering).

a. Validated MoCap process (Duffell et al., 2014). b. A digitalization process algorithm, proposed for the RT-MHV model visualization c. Reliable literature recommendations (mainly the RULA method). d. Computation process: it is known that conventional posture assessments are prone to error, due to the manual measurement of 1D/2D motion data and the manual identification of anatomical landmarks, both at times based on subjective opinion. A more accurate assessment of postural comfort assessment can be achieved through the MoCap system, a digitalization process and literature recommendations, because the computer will perform the necessary calculations, thus reducing the need for manual measurement and processing.

A survey carried out by Mühlstedt (2012) indicated the importance of ergonomic visual software tools across all areas of product planning, manufacturing, and the evaluation of human posture interfaces with products and workspaces. Some methods do support, to some extent, the analysis of postures, physical stress, and possible improvements. However, they do not emphasize the display of intuitive and reliable information for the designer. Ergonomists continue to use tables and graphs for analysis and as assessment tools, e.g., time line or statistical tabulation, presenting changes in body joint ROM over time. The interpretation of visual information requires training in ergonomics and biomechanics and a certain degree of skill, and consequently is not suitable designers lacking this multidisciplinary knowledge. In addition, the information is not presented or viewed in its dynamic or relative context, e.g., the worker's exact joint ROM in specific postures. As a result, interpreting the movement sequence becomes particularly difficult or impossible, even for ergonomics and biomechanics professionals. A dynamic contextual presentation during the design process, taking into account ergonomic problems related to harmful posture, will inform and improve the design process, and will help avoid prevent the knock-on expense of redesign and product changes. Mackinlay (1986) discussed graphical design issues on the basis of two criteria: expressiveness and effectiveness. Expressiveness determines whether a graphical language can express the desired information. Effectiveness determines whether a graphical language exploits the capabilities of the output medium and the human visual system. These two criteria are the basis for our proposal for the effective visualization of design issues. To be effective and meaningful, any visualization system should take these two key criteria into account. Informed by a review of the relevant literature, we gathered a number of common specifications relating to the practice requirements of designers, which guided our initial model development. These were seamless integration with other tools typically used by designers (Zitkus et al., 2012); a preference for simple and coherent ergonomic measurement methods (Schmidt et al., 2014; Stubbs et al., 1993); contextual or specific ergonomic information (Burns et al., 1997; Burns and

Based on the research objective, questions, and main hypothesis, the sub-hypotheses were: a. Less visual information - more assessment accuracy. It can be hypothesized that a visual model orientated to a limited set of joints will achieve better assessment accuracy than a model oriented to the simultaneous processing of all or most of the body joints. A few slow and simple motions, rather than a large number of rapid and complicated motions, will lead to better assessment accuracy. b. The combination of geometric data and selective joint ROM data may provide sufficient visual information for posture assessment. For every assessment, a design solution should focus on the ROM of a specific joint ROM, and not on the ROM of the joints of the body as a whole. For example, the rotation or lateral tilt of the subject's neck and trunk is assumed to be less significant than the subject's flexion and extension motions, when the target artifact (such as a tablet) is directly centered in front of the user's body. Hence, after visual feedback by the RT-MHV model, such rotations can be excluded, thus simplifying the entire visualization. c. By using the RT-MHV model, the postural comfort and WMSDS risk level assessment can be simplified. 3. The postural-comfort levels experiment The RT-MHV model development was based on data collection from 3

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a laboratory experiment, which executed a case study of the posturalcomfort levels of a mobile electronic worker (e-worker) working on a tablet in the public third-workplace. Body postures were measured using a motion capture system, which determined the angles between adjacent limbs, head, neck and trunk. Experiment Protocol: The main experiment (n = 3), based on conclusions drawn from a literature review and pilot study (n = 1) was carried out at the Biomechanics Laboratory, (http://brml.technion.ac. il/), Technion - Israel Institute of Technology. Three healthy, righthanded males, identified as e-workers, were recruited through electronic advertisements in the Technion-Israel Institute of Technology and through the laboratory administration. Each subject signed an informed consent prior to beginning the experiment, and paid basis. Eight-infrared cameras, using the MoCap system (Vicon UK system; http://www.vicon.com/) and with a sampling rate of 100 Hz per minute, tracked and recorded 42 passive reflective markers. A valid Oxford “Plug in gait” (PIG) full-body protocol associated with segment ROM (https://www.vicon.com/downloads/documentation/plug-ingait-product-guide) and WMSD risk levels was used. Two video cameras (Sanyo digital camera CA100), to the front and side of the subject, determined the interfaces between the body segments and workplace settings. A digital video image was simultaneously photographed, to provide quality control of the data. Subjects were not randomly chosen, but were selected according to the anthropometric dimensions of an average male population (height 175 ± 3 cm, weight 76 ± 3 kg and elbow height from seat 25 ± 1 cm; Panero and Zelnik, 1979); and age, between 25 and 45 years old, which ensured skeletal maturity and limited degenerative changes. The average profile was 27.5 ± 0.5 years, height 177.5 ± 2.5 cm, weight 76.5 ± 3.5 kg, elbow height from seat 25 ± 1 cm. The whole body of the subjects was represented by a biomechanical model, composed of joints and rigid segments connected by anatomically restricted articulations, and based on the valid Oxford PIG full-body protocol (Wu et al., 2005; Vaughan et al., 1992). Working on the assumption that a seated e-worker could be accurately represented by the system of links, a valid Oxford PIG full-body protocol (http://www.vicon.com/) consisting of 42 passive reflective markers was tracked and recorded. An ergonomics professional and a

laboratory engineer placed 42 markers on the subject's body and captured a T-pose position (Fig. 1, Picture a). Each marker was labeled, and the relevant body segments were reconstructed. The vertices of each segment were defined by the relative positions of the reflective markers. Using a “Cartesian coordinates system,” locations and orientations of segments were determined for three workplace configurations (Fig. 1). Hence, the experiment study centered on 37 joints rotations variables, spread across 89 work-related musculoskeletal disorder (WMRD) risk levels for each worker. Based on a preliminary pilot of three e-workers, this study focused on three e-worker experiments, completing a set of simulated tablet tasks in three sedentary third-workplaces configurations: restaurant (tablet held on a high table), lounge (held on a lower table), and anywhere (held with the user's hands or supported on lap). The findings of 801 variables were compared and analyzed, determining the relative time in which the e-worker's joints and segment distributed across the three WMSDs risk categories based on published data. These were then compared with subjective self-reported questionnaires. The full description of the experiment and results is described in Eldar and Fisher-Gewirtzman (2019). 4. The development of the RT-MHV visualization model In order to gain an understanding of the large data sets from the experiment case study, efficient algorithm and intuitive 3D graphical user interfaces (GUI), were developed. In the current research, we based the Digital Mockup on the motion-capture (MoCap) process. This was done by manipulating a skeleton and simulating a virtual character whose trajectories were adapted to the character skeleton and morphology. In the experiment laboratory, the motion tracking system digitalized the worker's movements into motion data, thereby replicating the coordinates of the worker's joints. The data was further mapped into the form of a virtual human, manipulating the same operation in the virtual environment. Lastly, all motion data and interaction information were processed by CAD software (Motionbuilder software, https://www. autodesk.com/products/motionbuilder/overview; Softimage software, https://www.autodesk.com/products/softimage/overview), to assess the performance of the manual operation.

Fig. 1. DMU kinematic visualization steps for the RT-MHV model development (numbers below the pictures explained in the text). 4

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The principles and steps of kinematic visualization for the RT-MHV model (Fig. 1):

Gewirtzman, 2019: “The e-workers’ postural comfort at the ‘thirdplace’: Ergonomic design assessment”, paper under review). Fig. 2 represents the preliminary visualization concept, outlined as follows (letters are presented in the center picture):

a. Calibrating process: Static recording of the subject in the T-pose position, executed in a MoCap laboratory before the trial. This step consisted of capturing the motion trajectories of an human being (i.e., the actual e-worker, not an avatar), using emitters and receptors positioned on the subject's body (i.e., a 42 direct motioncapture marker system) as a plug in gate biomechanical model (PIG, Wu et al., 2005); b. Executing the MoCap laboratory trial c. Importing the T-pose raw data: To animate human movement, it was necessary to transform the external data into internal joint centers, using algorithms to correct errors (noise correction, simplification of the virtual skeleton, adapting the motion to a new skeleton; Dutta, 2012; Hoyet, 2010); d. Assigning and fitting an inverse angular kinematics avatar to the Tpose marker cluster: Assignment of the markers to the avatar was done by defining marker clusters in relation to body segments. The vertices of each segment were defined by the relative positions of the reflective markers. Using the Cartesian coordinates system (CCS; X/Y/Z axes), the locations and orientations of each segment were determined; e. Modifying the avatar to match the subject's anthropometric data; f. Assigning the marker cluster: Once the marker cluster had been fully assigned to the avatar, the MoCap data (e.g., C3D file format) was transferred, including all of the trials (body segment movement data); g. Building the workplace settings (e.g., table, chair, leg support, tablet) into the CAD software (Solidworks software; http://www. solidworks.com/); h. Inserting the 3D workplace setting models into the scene: Marker motion was imported into a FBX file format for visualization purposes. The process did not change the data, only the format. Importing the FBX (Filmbox) files into the CAD software allowed for the visualization of the e-worker in the workplace.

a. Total motion envelope (purple sphere); all head/neck motions, over a time sequence; b. Literature recommendations of a “low risk level/neutral ROM” motion envelope (green sphere). A “motion path” was created, to visualize the actual path of the marker over time, combined with the mapping of the ROM orientation. Expression values were attached to a CCS system by “RGB schemes” (e.g., red, green and blue, referring to the X, Y and Z axis). The right middle figure shows an example of the 3D mapping outer of the neutral range; c. Median motion envelope (gray sphere); calculating the radius and distance from the temporal center of the neck marker over a time sequence. The joint marker moves in space during the time, creating a 3D trajectory in space (a “motion path”, gold in color). Joint marker location in specific positions over time was calculated, as a percentage, and a 3D sphere was created to represent the volume within which the marker dwelled for 50% of the time (the actual percentage to be used can be determined by the designer or other user). Conclusions, based on the preliminary visual mapping in a sphere, indicated that this exercise was very simplistic and did not take into consideration the time dimension, only the global space. The visualization was too cluttered, and numerical data in this form was somewhat redundant. Furthermore, the visualization of a simple sphere also included large areas with no marker. The goal of the second “numerical data concept,” informed by the lessons learned in the first attempt, was to provide simple and minimal dynamic visual information regarding the change of ROM angles over time (Fig. 3, left). CCS system dialogue panels use color-coding techniques to provide postural ROM information for all of the e-worker's segments and body joints; this was done by embedding a “local coordinate system” (LCS; X, Y, and Z) into each joint, to define a numerical angle (flexion-extension by X, side bending by Y, and rotation by Z). Each axis provided the numerical angle expression and color on a time line: green (low risk level, neutral range); orange (moderate risk level); and red (high-risk level). Hence, we named this the “traffic light

Deployment of visual mapping concepts: Our preliminary task utilizing the visual mapping model was to envelop the 3D motion curve into spheres, representing recommendations from the empirical literature and the experiment's findings (to be published in Eldar and Fisher-

Fig. 2. Geometric visualization and numerical ROM expression (angles) of the e-worker's neck at the MoCap laboratory. 5

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concept.” The neutral range is provided in parenthesis, near the numerical expression. Two shortcomings were revealed in the second attempt. Firstly, due to the size of the data set, the visual implementation of more than one joint proved to be confusing. Secondly, the history of the motion path, correlated to the change of ROM, was unclear. An enhanced concept was then developed (named “CCS scheme”; Fig. 5). The third “color-coding concept” was then developed. This provided dynamic data concerning WMSD risk levels over time through three rings at each joint, representing the three rotations (Fig. 3, right figures). Each axis provided the numerical angle expression using the “traffic light concept.” The change in the tablet color provided complementary feedback. This is represented by the tablet color as the highest risk at a given moment. Conclusions based on this iteration demonstrated a simple and intuitive feedback to some extent, even though the motion path and the motion history volume over time was missing. The change of th tablet color provided good feedback, as long as the model was focused on one joint only.

high) and their motion history envelopes (e.g., MHV), in real-time. The aim was to address large volumes of information in a simple and natural dialogue. In order to adapt the ROM visualization mapping to the requirements of designers, we introduced a few visual options (Fig. 5) as part of the model development process. Firstly, the RT-MHV model visualizations scheme concept was developed. Then, based on the RT-MHV model concept, we then introduced the dynamic schemes and visual options and the MHV feedback. A complementary visual information database option was then introduced, divided into a screen layout concept and a 2D complementary tabulation visualization. The goal of the RT-MHV model visualizations scheme concept was to provide simple and minimal dynamic representations of the change in ROM, over time. A digitalization process algorithm proposed for the RT-MHV model visualization provided postural ROM information for all of the avatar's joints (representing the motion of a real user).

Fig. 3. Mapping ROM orientation concepts: The ‘numerical data concept’ (left; Motionbuilder software, https://www.autodesk.com/products/motionbuilder/ overview) and the ‘color-coding concept’ (right; Softimage software https://www.autodesk.com/products/softimage/overview). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Further analysis was then initiated, in order to develop the mapping of the spatial location volume and the ROM orientation of the eworker's body joints and segments. The “isosurfaces” envelope (e.g., the envelope surface represents points of a constant value of time within a volume of space), employed in the RT-MHV model (Fig. 6, example d) took both into consideration, by encompassing the total trajectory of the marker over time and space, eliminating the statistical extremes and clearly showed the center path. Critical and significant volumetric data regarding the e-worker population and ROM can provide some insight into workspace design settings, through simply plotting the defining characteristics of these features into the 3D space. This can give a designer sufficient information regarding the distribution of setting preference, a neutral (and thus recommended) position, and human interfaces regarding the locations and occupied volume and angles.

The visualization was based on three components (Fig. 4): a. Three joint rotations in real-time motion, projected on to three “joint plane rings”. The scheme is based on the standardization scheme of the International Society of Biomechanics (ISB, https://isbweb.org/) of the Joint Coordinate System (JCS). Each “joint ring” can be presented in a different view, which allows for a focus on the relevant rotation in the related plane. Fig. 4, left figure, introduces the three joint plane rings: I. “Side bending ring” projected on the frontal plane; II. “Flexion-extension ring” projected on the right plane; III. “Rotation ring” projected on the top plane. b. The “traffic light” color-coding visualization (green, orange and red, relating to the three risk level categories at each joint: low, moderate and high, respectively), projected on the three “joint plane rings.” c. The isosurface envelope employed in the RT-MHV model utilized the three joint planes, by encompassing the total trajectory of the colors over time.

4.1. The RT-MHV model visualization challenges and the scheme concepts Based on the three iterations mentioned above, we were able to develop the RT-MHV scheme. Our principal objective was to represent a large MoCap database of the postures of e-workers, determined by three joint rotations (flexion-extension, side bending, and rotation) of ten joints and segments, according to three risk levels (low, moderate, and

The proposed visualization development was based on a combination of the previous concepts mentioned above:

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Fig. 4. RT-MHV model visualizations based on the neck joint.

I. The “joint ring planes” (Fig. 4, Component a) were based on the third concept of the “three rings” at each joint, representing the three rotations (Fig. 3, right figure). II. The “traffic light” (Fig. 4, Component b) risk level colors were based on the “color-coding” scheme (Fig. 3, left Figure). III. The MHV component (Fig. 4, Component c) was a neutral development, based on the first concept (Fig. 3) of an isosurface envelope.

WMSD risk level expression color on a time line, using the “traffic light” visualization. The green color represented a low WMSDs risk level/neutral range; the orange color, moderate risk level; and the red color, high-risk level. For example, when the subject flexed his neck from the neutral range to the moderate range, the “flexionextension ring” changed color from green to orange. The “traffic light” color-coding scheme was based on the RULA (McAtamney and Corlett, 1993) and the NERPA (Sanchez-Lite et al., 2013) assessment tools. We assumed that designers would be acquainted with “trafficlight” coding; c. The MHV rationale (Fig. 4c).

The rationale of the RT-MHV model visualizations (Fig. 4): a. The “joint rings” rationale (Fig. 4.a, right view): The user (the designer) could employ the toolbar planes used in the design process, focusing on each rotation separately. The flexion-extension “joint rings” ROM rotations can be viewed in the “right plane” (represented by the sagittal plane); the side bending ROM in the “frontal plane” (represented by the coronal plane); and the rotation ROM in the “top plane” (represented by the transverse plane). Hence, several “joint rings,” representing each body joint (e.g., the neck joint) were represented by three “joint rings,” and the elbow and knee by one (flexion-extension ROM) “joint ring”; b. The “traffic light” rationale (Fig. 4b): Each plane displayed the

4.2. The dynamic schemes and visual options Two dynamic scheme concepts are discussed in the current section; the CCS (Fig. 5, option I), and the “RT-MHV model scheme” (Fig. 5, option II). There were four visual options for each scheme (Fig. 5): a. “RGB” colors; b. “traffic light”; c. “angles and traffic light”; and d. MHV. Each option is introduced separately. Two dynamic scheme options (Fig. 5, scheme option I and scheme option II):

Fig. 5. Two dynamic schemes and four visual options. 7

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I. The CCS scheme: this option followed guidelines of the International Society of Biomechanics (ISB, https://isbweb.org/). CCS recommendations: flexion-extension motions defined by X-axis, sidebending motions by Y-axis, and rotation motions by Z-axis. II. The “RT-MHV model scheme”: this option was introduced in (Fig. 4).

4.3. Motion history volume (MHV) feedback In the current section, we introduce the novel development of workrelated musculoskeletal disorder (WMSD (risk levels visualization. The motion of each joint was presented as a projection on an isosurface of its real volume plane combinations (Fig. 4), not just as an expression of one determined plane (sagittal, coronal or transverse). Moreover, the visualization showed 1 min (i.e., 60 s) of the motion envelope and its risk level coding. By using MHV, we referred to the geometric data examined by the isosurface analysis (Hansen and Johnson, 2005). The MHV represented an accumulative volume created through capture time. The MHV visualized all positions for a given joint during the recorded time. Joint rotations were visualized through color space textures on the volume. MHV generation (Fig. 6) created a particle cloud, constructed per frame from the marker data. The total volume of the particle cloud during motion time was calculated on the basis of a per-frame creation of a high-density polygonised dynamic envelope. The joint marker moved in space during the time (Fig. 6, Picture a), creating a 3D trajectory in space (named “motion path,” Fig. 6, Picture b). Due to the overload of time-based information during the take, we created a “particle age” slider which caused the “older particles” to disappear, avoiding visual clutter. The marker geometry node was plugged into an emitter module, which generated particles per frame. In the current setting, there were 100 particles per frame (Fig. 6, Picture d). Particle size is important for defining the density of the cloud. Density varies functionally in relation to the speed of the motion. Thus, if the density is too low in sections where the subject is moving quickly, not enough data will be generated for the visualization (e.g., Fig. 6, Picture c). Inside the emitter node, we created a function that assessed the 3D range of motion categories, and an output of a reference texture map to the particle cloud (Fig. 6, Picture d). This was based on the MoCap laboratory database and WMSD risk levels recommendations in the empirical literature. A simulation node was run per frame, to output the resulting colored particle cloud into a 3D viewer. An isosurface envelope was created around the particle cloud, to visualize the MHV.

Four visual options (Fig. 5, columns a-d) for the dynamic schemes above: a. The “RGB” color option (Fig. 5, option a) follows the color recommendations of the International Society of Biomechanics (ISB, https://isbweb.org/) joints reference system (JRS): flexion-extension motions were defined by the color red (X-axis), side bending motions by the color green (Y-axis); and rotation motions by the color blue (Z-axis). Hence, the abbreviation “RGB”-red, green and blue. This was not applicable to the RT-MHV model scheme. The “RGB” option relies on standard color-coding correlating with the time-line color representation (Fig. 6), and represents the real-time joint angles. The disadvantage refers to the fact that RGB color-coding provided just the CCS identification. For the WMSD risk levels, numerical representation should have been added. But this may have been confusing, leading to an unclear and overloaded visualization. The solution was thereby provided by the “traffic light” option, (option b). b. The “traffic-light” color-coding option (red-orange-green; Fig. 5, option b) was discussed in detail in option b: “The ‘traffic light’ rationale.” c. The “angles & traffic light” option (Fig. 5, option c) provided the risk level used by the CCS scheme, with the actual motion angle expression. This was not applicable to the RT-MHV model scheme. d. The MHV option (Fig. 5, option d) provided a new option The MHV did not provide any risk level indications (low, moderate or high) for the CCS scheme. Rather, it encompassed the total trajectory of the marker over time, showing the center path (Fig. 5, option I.d). Hence, the MHV is colored in gray.

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Fig. 6. MHV generation of the shoulder joint.

4.4. Implementation of the RT-MHV for design applications

These visualizations can be highlighted on the subject's joint as well as on the related segment exposed to a musculoskeletal risk level (e.g., shoulder joint and upper hand, Fig. 7, option b). Hence, the viewer has the opportunity to understand and assess more effectively the sequence of motion, its risk level, and its motion path (Fig. 6).

Three RT-MHV visualization references for the 3D programming and GUI effort represent our ongoing work. Fig. 7, option a, relates to three sequential shoulder motions associated with the experiment tablet tasks. Visual feedback (Fig. 7, option b) was in real time and within the MHV of shoulder volumes and upper arm risk levels. The concept of the creation of a real-time “thermal” mapping of a volumetric, dynamic volume envelope was formed.

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Fig. 7. Conceptual visualizations of three-shoulder movement sequences: a. at the MoCap laboratory; b. visual feedback representation by a RT-MHV mapping model.

4.5. Complementary visual information database

an effective posture assessment.

The RT-MHV visualization development model described above presents functional proof of a concept for the future creation of a standalone software product. In order to optimize the design in respect to ergonomics, the RT-MHV assessment model is intended to help designers assess the comfort characteristics of the interaction between user, product and space. The introduction of an optional complementary screen layout and a 2D tabulation visualization (Fig. 8) were informed by our first sub-research hypothesis, i.e., to reduce the visual information load; and on the second sub-hypothesis, i.e., to provide sufficient visual information for

a. Screen layout concept The screen layout concept of the RT-MHV model is divided into four working areas (Fig. 8): the menu bar (file, edit, view, display, window and help); four optional selection libraries (scenario, population, joints and rotations); views display (can be divided into several option displays); and 2D tabulation. The screen layout concept is an extrapolation from standard software (e.g., Softimage; http://www.autodesk.com/products/softimage/ overview), to the future work of the RT-MHV tool.

Fig. 8. Screen layout concept: Current MoCap visualization (light blue frame), future development concepts of software options layout (gray dashed frame layout) and complementary tabulation (black frame). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.) 10

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b. Scenario-based options, joint rotations and the users' profile

design process often operates as a trade-off activity, one which commonly does not prioritize human motion—which is described as a “complicated task” (Rosenthal et al., 2013). Based upon a designer's knowledge of CAD, utilization of the DMU process facilitated the procedure from the perspective of health-care professional knowledge regarding the complexity and predictability of human motion. The model assists in capturing 3D motion in real space; it then transforms it into the CAD system, with which designers are familiar, thus allowing for the examination of issues that can affect a wide range of designed products. This potential applies not only to devices that can improve the postural comfort of e-worker tablet tasks in third-workplaces, but hopefully can be extended to other workplaces (such as various industries, different modes of transportation, hospitals). The ultimate goal is to improve the comfort and health of workers through estimating more precisely human motion and posture in relation to the use of artifacts designed with the help of the model. Although the RT-MHV assessment model was originally intended as a model for designers, we believe that this model can serve as a valuable tool for professionals across a wide range of disciplines, including health professionals, biomechanical engineers, industrial engineers, ergonomics experts, and design-project customers. It may also be useful in informing product assessment and improvement through user discussions (with workers, people with different abilities and capabilities, and professionals who rely on effective physical movements like athletes, for example). Although the current data set was used to analyze sedentary tasks by homogeneous e-workers, we anticipate that with relatively minimal effort the tool can be adapted to accommodate any task that requires the consideration of ergonomic strategies at the product design stage. Further research will allow for the precise means by which this this evaluative tool can be used to facilitate such discussions.

On the basis of a review of the relevant literature (Darses and Wolff, 2006; Abdel-Malek et al., 2009), we presumed that some of the designers would not have any functional knowledge or understanding of the relative importance of the various joints. However, a tool that merely allows for the selection of joints may seem unacceptable, and additionally could create an undesired level of complexity and visual overload. Such outcomes may lead to a lack of confidence in the tool, or could reduce the level of effectiveness of the other displays (Fig. 8, gray dashed frame layout). Keeping this in mind, we hypothesized that it would be much more effective to provide a selection of real-life motion scenarios for the user to select from (e.g., industrial bench assembly work, with a particular machine or product part, office work with a desktop computer, or the current test scenario of tablet usage). Such kinematic chain configurations can also be offered on the basis of other relevant scenarios (Stanton et al., 2014), and in a language familiar to designers (Gallagher et al., 2008; Lofthouse, 2006) rather than terminology from the world of ergonomics. Population selection must also be provided in the standard way in order to determine a relevant user profile suited to heterogeneous populations, i.e., it should be capable of determining specific anthropometric dimensions (such as the People size software; http://openerg. com/psz/ and the Human-CAD software; http://www.nexgenergo. com/ergonomics/humancad.html); or more detailed features, such as origin, gender, age, occupation, differing capabilities etc.). The CAD software utilized with the RT-MHV model (e.g., Softimage; http:// www.autodesk.com/products/softimage/overview; Motionbuilder; http://www.autodesk.com/products/motionbuilder/overview) gives the designer the ability to control the avatar scaling. This feature may not be sufficient or effective for the design assessment process in relation to a heterogeneous population. The joint and rotation selections can be automated according to the design scenarios, or operated manually by the designer if preferred.

5.2. Contributions The introduction of an assessment model for the postural comfort of real users (in the present case, e-workers) can help reduce total product design time, and also enhance the number and quality of design options, through the enhanced evaluation from a multidisciplinary perspective. The target of improving postural assessment was chosen in order to optimize both ergonomic design and health, to predict the risks of potential WMSDs, and ultimately to promote design solutions centered on neutral user posture. Simplifying human motion knowledge is key in the design process. Introducing ergonomic features (WMSD risk levels) to the design assessment process, along with objectively transforming real user motions into a DMU CAD environment in real time, underscores the accessibility and utility of the tool. The current need is for the development of an updated unique model—simpler, reliable and in line with the anticipated purposes and knowledge of a designer—for better assessing comfort-health levels as a part of the design process (Young et al., 2012). The implementation of such a model must be in line with the knowledge base and tools of designers (e.g., CAD; Moffet et al., 2002); this will eliminate dependency on evaluations by health professionals, and will allow for the introduction of newer, more robust motion situations into the design process (e.g., the tablet-designed space).

c. A two-dimensional complementary tabulation visualization Simultaneously with the RT-MHV visual mapping feedback model, a complementary visualization of a 2D tabulation can be applied to each body joint or segment (Fig. 8, upper left black frame). The database includes a table of WMSD risk level recommendations taken from the literature, 2D time-line graphs, and statistical analysis. As stated above, this aggregated information can be computerized on demand. Each point on the graph can be selected, and the exact angle on a time-line will be available. The 2D information is inserted into a separate table, which allows for the speeding up of the analysis process with regard to frequently requested statistical information. This simplifies the visualization by reducing redundant information. Fig. 8 presents an example of a visualization of the neck joint on 2D graphs, demonstrating the three rotations on a time line: flexion-extension (X axis, red curve), adduction-abduction (Y axis, green curve) and external-internal rotation (Z axis, blue curve). The low risk level (neutral position) literature ROM recommendations are printed in bold green, and the zero-angle curve is represented by a white dashed line. This produces a better GUI. The RT-MHV model, in accordance with published comfort databases, time line and statistical analysis, consequently introduces the possibility of more precise postural behavior assessments.

5.3. Limitations

5. Conclusions and further research

Although perceptions of pain and discomfort do need to be addressed immediately through corrective postures, it is the risk associated with WMSD through habitual poor posture that are more likely to have a longer lasting impact on physical health and general wellbeing. MoCap systems, such as Vicon (http://www.vicon.com/), require an initial investment for the purchase of equipment, as well as the

5.1. Feedback and opportunities In this paper, we presented a novel ergonomic design assessment model for the purpose of visualizing postural comfort. Simplifying postural assessment knowledge is important, because the product 11

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resources necessary to cover maintenance costs. Staff with specific skills and training are also necessary to ensure the effective use of such tools. This aside, practitioners may consider the enhanced data generation capacity of the MoCap system as impractical, given the time required for analysis and interpretation of the data. The RT-MHV model, based mainly on the general RULA (McAtamney and Corlett, 1993) and the NERPA (Sanchez-Lite et al., 2013) ergonomic WMSD categories, is limited to specific joints and segments (e.g., no fingers ROM risk level). The angle categories available are too general for specific joints, which renders the model inadequate for the analysis of all types of actions and tasks. In order to resolve the issue of the lack of the postural influencing factors, several approaches have been proposed. Waser et al. (2011) recommended an intervention in the simulation process. Wongsuphasawat et al. (2011) suggested a breakdown in event sequences. These analyses, in our opinion, may impose a cumbersome burden onto the design process, because large amounts of data (and the use of multiple windows) will be needed to visualize the results—distracting the user (the designer) from the actual data (e.g., postural visualization assessment). Our opinion is based on Plumlee's and Ware's research (2006), which advocates for only a single graphic object to be held in the visual working memory for comparisons mediated by eye movements, which minimizes the likelihood of error by reducing the load on visual working memory. Therefore, we recommend simplifying postural assessment visualizations through CAD integration and computational processing.

coordinates, and will provide relatively quick, cheap (compared to the Vicon system, http://www.vicon.com/) and accurate visualizations. Global trends influence ergonomics as a field of knowledge. As discussed (Norros, 2014), these developments, and their broader significance for the field of ergonomics, need to be identified in order to set out a strategy and measurement tools for the future of product design. In the current research and experiments, we focused on three significant global trend changes with the potential to affect ergonomics. Further ergonomic design research, based on the RT-MHV model, can effect change by ensuring that work systems and products or services fit other global trends, such as: a. The older population, taking into account age-related changes in physical, cognitive, visual and other capabilities, and different aspirations; b. People from different age groups, or otherwise different capabilities and aspirations (e.g., “design for all,” Zitkus et al., 2012); c. People from different cultures with inherently different physical expectations, capabilities, and aspirations, but also differing postural behavior habits, which may affect system design (Moray, 2000; Hewes, 1955); d. Sustainability and corporate social responsibility, increasingly relevant as corporations around the world orient themselves to new roles and social positioning, given that the neglect of, or minimum engagement with health or safety parameters can inflict reputational damage on a company's image, efforts, and valuable human resources. For these reasons, more comprehensive ergonomic research should be a priority, focusing specifically on understanding the human element as a part of a complex social and cultural environment. Moreover, postural evaluation methods are also relevant for sedentary work activities. Aside from the current case study of the eworker in any kind of information and communication technology (ITC) interactions, other examples include performing surgery, industrial bench assembly, etc. Heterogeneous user profiles encompass males, females, children, the elderly, people from different origins and cultures, as well as the unique usage needs and activities of people with otherwise different capabilities. The quantitative assessment method of posture behavior patterns described here can be used to develop individualized or context specific guidelines for workstation layout, furniture, and devices that can help minimize postural strain while performing various tasks. An interactive platform: We suggest adjusting the RT-MHV model to real-time worker feedback, by allowing visual information related to local scores (e.g., WMSD risk levels). These can include general scores (e.g., muscle use, loading weight, task duration, and repetitiveness), provided through anticipated and emerging future technologies such as augmented reality displays. In order to communicate this recommendation, the warning (or feedback) should appear in real time, whenever the assessment score exceeds a predefined threshold. After the task, a summary report will be generated. This challenging futuristic development will allow the user to correct his motions, and to better understand postural behavior improvements. Designers will similarly be able to develop bespoke adjustable and responsive dynamic artifacts, based on users’ on-line scores.

5.4. Further research Given rapid developments in computer technology, digital visualizations can benefit from human motion applications. Until recently, the field of computer graphics field had been attracted to personalized human models. However, over the past few years, there has been a growing tendency, in line with developments in digital technology, toward a focus on real-time visual realism, and specifically the accuracy afforded by the use of human-spatial interaction models. Further research is required in this area to resolve the current limitations of the RT-MHV model. This may be achieved through the combination of all the relevant comfort factors (physical, physiological, cognitive) recognized as exercising some influence over human perceptions of comfort and well-being. Moreover, future developments can also take into account the requirements of a wider range of workplaces, product interfaces and subjects, with the common objective of quantifying and evaluating the applicable comfort parameters effectively. Such assessment simulations can draw from a range of assessment methods engendered by new technology, such as virtual reality (VR) and augmented reality (AR), both of which are attracting increasing research attention and interest. Future RT-MHV model development could also consider the integration of an additional objective scoring system in real time and with MHV, adaptable across a range of relevant scenarios. It has been suggested that a number of related factors should be considered with respect to human motion. The factors include robustness, accuracy, and speed (Moeslund and Granum, 2001). Scenario-based and relevant detailed factors, such as a visual system, action frequency, sequence of different interactions (such as utilizing different supports, e.g., back support and armrests), the weight of an object (to carry or handle), all can have an influence on the body's motor system, and thus issues of comfort, physical health, and general well-being. Hopefully, in the near future and with the rapid evolution of MoCap systems, the expense, time setup, and time required for CAD drawings will be reduced significantly. The latest generation of optical depth sensing cameras enhance the capability for capturing and tracking 3D

Summary The combination of the MoCap laboratory simulation, along with empirical WMSDs risk-level recommendations and a scenario-based design problem case study, has facilitated the development of a physical ergonomic design-oriented assessment model, one capable of forming a description of the user's movement with regards to a single joint or segment angle, preferred kinematic chain, or whole body posture, and in connection with artifact interface and well-being parameters. 12

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Biomechanics Laboratory, Zinman College of Physical Education & Sport Sciences, Wingate Institute, Netanya, Israel, for their professional support. Thank also to Prof. Alon Wolf and Oded Solomon, of the Biomechanics laboratory, Technicon - Israel Institute of Technology, for their assistance in the motion- capture laboratory and experiment. The authors would also like to thank Oshri Even-Zohar, for creating the custom software module in the Autodesk's XSI 3D package. This module was used for the real-time cloud visualizations described in this paper.

The experiment case study provide a full review and analysis of the DMU process and the visualizations options. It is important to develop further this rich source of information for ergonomic design-oriented purposes. As a first step, we studied the feasibility and validity of this approach using the RT-MHV model, using e-worker tablet tasks in three different third workplaces as case studies. The integration of the RT-MHV model within the ergonomic designoriented process will hopefully support the evolution of new, contextually relevant design processes. Future research might benefit from the RT-MHV tool and contribute to the entire design process: before the generation of product definition (as was the focus of the current research case study); during the conceptual phase; and during the design process itself, by involving users in the design iteration and testing of prototypes or the final product (Stappers et al., 2009). In conclusion, the field of ergonomics has the potential to contribute to the design process, by closely aligning its fundamental principles to the design of artifacts, with a view to optimizing well-being and performance. The applications of the human MoCap are numerous, and we expect that we will see continuous growth in the resources devoted to this topic in the near future, leading to new and interesting research results. We anticipate that academic and commercial interest, in the digital field and in the ergonomics and design disciplines, will accelerate the development of human assessment and modeling, generating common methods.

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Declarations of interest none. Grants and findings This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors information Ranit Eldar (PhD) is a researcher, ergonomics expert, industrial designer and university lecturer. She received her Ph.D. degree from the Faculty of Architecture and Town Planning at the Technion-Israel Institute of Technology, in collaboration with the Faculty of Medicine at Tel-Aviv University, and the Faculty of Biomechanics at the Wingate institute, Israel. Running her own company, ERGOTECH-Industrial Design and Ergonomics (https://ergotech.co.il), Ranit combines theoretical and experimental ergonomics studies with design project for numerous global organization. She has won international prices, and has worked in collaboration with R&D institutes in Europe, and with the chief scientist of the United States army; her research and products are utilized around the globe. Dafna Fisher-Gewirtzman (PhD) is an assistant Professor in the Faculty of Architecture and Town Planning at the Technion-Israel Institute of Technology, and a visiting scholar at CUSP-NYU. Her research focus is on the field of visual analysis and the simulation of urban and architectural space, directed toward the development of sustainable built environments. Her research is financially supported by the Israel Science Foundation. She is a UNESCO Fellowship recipient, and a laureate of the prestigious Yanai Prize for Excellence in Academic Education. Her work has been published in leading professional journals, and has been presented in international conferences and universities around the world. Acknowledgments The authors would like to thank Dr. Deborah Alperovitch-Najenson of the Department of Environmental and Occupational Health, Sackler Faculty of Medicine, Tel-Aviv University; and Prof. Moshe Ayalon of the 13

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