- Email: [email protected]
egress discomfort based on human motion and biomechanical analysis
Applied Ergonomics 75 (2019) 263–271
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Applied Ergonomics journal homepage: www.elsevier.com/locate/apergo
A novel approach to predict ingress/egress discomfort based on human motion and biomechanical analysis
T
Younguk Kim, Kunwoo Lee∗ School of Mechanical and Aerospace Engineering, Seoul National University, Seoul, Republic of Korea
A R T I C LE I N FO
A B S T R A C T
Keywords: Ingress/egress Biomechanics Discomfort Motion analysis Feature selection
This study proposes an ingress/egress discomfort prediction algorithm using an in-depth biomechanical method and motion capture database. The ingress/egress motion of the subject was captured using an optical motion capture system and physically adjustable vehicle mock-up. The subjective discomfort evaluation data were also recorded at the same time. The inverse kinematics and inverse dynamics were performed to analyze captured ingress/egress motion. These procedure provide motion and joint torque information on each subject. Based on the analysis results, this study proposes the following novel features: accumulated movement of joint and sum of rectified joint torque. This study conducted a feature selection procedure to identify a relevant feature subset. Recursive feature selection and optimal feature selection methods found the most relevant feature subset with collected subjective responses. Finally, we constructed the prediction model using support vector machine. The prediction model was evaluated through prediction accuracy and statistical analysis. For comparison with the previous study, this study implemented two representative models and compare the result with those of the previous studies using the identical dataset. The effectiveness of proposed algorithm was demonstrated in comparison with previous studies.
1. Introduction Manufacturers have increased their consideration of human comfort in the development of products to improve their quality. In particular, the field of automotive design, which involves many interactions between humans and products, has shown great interest in human-centered design. Hence, a number of vehicle interior studies have considered ergonomic results (Chaffin, 2005; Jung et al., 2009). Discomfort of ingress/egress is one of the main considerations in designing an ergonomic vehicle. The constraints of vehicle door size, opening angle, and seat arrangement require complex full-body movements to enter and exit the vehicle (Robert et al., 2014). Furthermore, ingress/egress involves a sit-to-stand motion, which is a highly demanding task. The sit-to-stand motion needs many biomechanical abilities, such as high knee torque, balance, choice of seat rise strategy, and joint strength (Hughes et al., 1994; Schenkman et al., 1996; Lord et al., 2002). Because these demands make the discomfort of ingress/egress motion an important vehicle design factor, many researchers have investigated ingress/egress motion to improve the ergonomic design of their vehicle (Shippen and May 2016; Masoud et al., 2017). Numerous studies have investigated the statistical relationship
between vehicle design parameters and discomfort of ingress/egress motion (Giacomin and Quattrocolo, 1997; Causse et al., 2012; Herriotts, 2005). Researchers have used actual vehicles or physically adjustable mock-ups of a vehicle to collect subjective responses in variable environments. Giacomin and Quattrocolo. (1997) found a relationship between the rear seat parameter of the vehicle and subjective responses. Causse et al. (2012) analyzed discomfort of ingress/egress motion in variable roof height conditions. Herriotts (2005) identified discomfort parameters using questionnaire results from old drivers. These research methods were effective, but they had the drawback of depending on the subjective views of the research participants. Therefore, it is necessary to find an objective and quantitative discomfort assessment method for passengers. Previous ergonomics studies used a postural-based discomfort evaluation method. A representative example is rapid upper limb assessment (RULA), which considers the posture and load requirements of a job task on the upper extremities. Postural-based discomfort evaluation has also been used in passenger discomfort studies in ingress/egress motion. Dufour and Wang (2005) proposed the concept of “neutral movements” using joint angle of ingress/egress motion. These researchers collected joint angle data from motion capture system and
∗ Corresponding author. School of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Daehak-dong, Gwanak-gu, Seoul, 151-744, Republic of Korea. E-mail address: [email protected] (K. Lee).
https://doi.org/10.1016/j.apergo.2018.11.003 Received 31 January 2018; Received in revised form 1 October 2018; Accepted 11 November 2018 0003-6870/ © 2018 Elsevier Ltd. All rights reserved.
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evaluated passenger comfort/discomfort. Masoud et al. (2016) constructed a statistical model between captured ingress motion and subjective responses. These researchers only analyzed human motion and did not consider the biomechanical load of joints and muscles. To find the relationship between in-depth biomechanical parameters and discomfort, Kim and Lee (2009) proposed a method using virtual muscle force and fuzzy logic. Choi and Lee (2015) analyzed ingress/egress discomfort using the maximum voluntary contraction (MVC) and linear regression method. Recently, there have been many papers that have dealt with posture and muscular activity. Anderson et al. (2010) proposed an efficient optimization method in the overdetermined biomechanical system. Trapanese et al. (2016) analyzed the relationship between passenger comfort/discomfort in vehicle cabin and muscular activity using the AnyBody Modeling System. Groenesteijn et al. (2012) investigated the relationship between posture characteristics and discomfort in office tasks. These studies achieved muscular activity using musculoskeletal simulation. However, most of the results were inaccurate or costly because it was difficult to construct a muscle model that reflected individual characteristics. On the other hand, joint-based force prediction research has shown highly accurate results in recent research. Jung et al. estimate knee joint and reaction force with musculoskeletal model. (2016). Forner-Cordero et al. calculate inverse dynamics during gait with restricted ground reaction force information. (2006) Therefore, a prediction model with joint motion and torque was constructed for this study. In this paper, we propose an ingress/egress discomfort prediction algorithm using an in-depth biomechanical method and motion capture database. Although there may be various factors affecting passenger discomfort, this study concentrated on two factors: accumulated movement of joint and sum of joint torque. We collected captured ingress/egress motion and subjective responses data in physically adjustable mock-ups of the vehicle. Obtained data were converted to joint angle and torque data through biomechanical analysis. We generated the feature set from biomechanical analysis results. The challenge in determining a relationship between analysis results and subjective response is the high dimensionality of feature sets. Generally, the statistical model assumes that the number of samples is higher than the number of feature dimensions. A large number of feature vectors is able to reduce the performance of a classification model in limited database (Fan and Li, 2006). Furthermore, this may lead to overfitting in generalization (Saeys et al., 2007). Many previous studies claimed that a proper feature selection method is expected to avoid the overfitting problem and improve performance by eliminating redundant or irrelevant features. For this study, we used recursive feature elimination and optimal feature subset selection to select highly relevant features for the output and compared the prediction performance of both selection methods. We also compared performance when all features were used. In this study, we implemented two reference models for comparison with previous studies. The first reference was the statistical model between vehicle/passenger parameters and subjective discomfort. Many studies have statistically analyzed vehicle parameters and discomfort (Giacomin and Quattrocolo, 1997; Causse et al., 2012). This study constructed a reference statistical model between vehicle/passenger parameters and subjective discomfort for comparison. The second reference was the statistical model between motion stream data and subjective discomfort. Recently, Masoud et al. (2016) constructed a discomfort prediction model with motion stream data and stepwise group selection. We implemented their method and compared the result using the identical dataset. This paper is organized as follows. Section 2 presents the experiment data and proposed algorithm to predict subjective responses. Section 3 provides the results of the proposed algorithm and comparison with reference models. Finally, Section 4 contains the discussion of our research and concluding remarks.
Table 1 Subject information. Subject Number
Age
Height(m)
Weight(kg)
1 2 3 4 5 6 7 8 9 10
27 30 23 20 22 22 18 25 25 24
1.84 1.82 1.77 1.85 1.75 1.78 1.78 1.78 1.77 1.86
78 99 65 77 65 72 68 84 70 75
2. Methods 2.1. Data collection A group of 10 healthy males (age 20.5 ± 5 yrs, height 1.8 ± 0.05 m) who were familiar with driving volunteered for this study. All subjects were injury-free before and during the experiment. Detailed information about the subjects are in Table 1. Before the experiment, all subjects were informed regarding the test procedures, protocols, and potential risks of participation. Written informed consent from the participant subjects was obtained. The experiment was approved by the Ethics Committee of Seoul National University (IRB). We captured all ingress/egress motions of subjects through an optical motion capture system (12 cameras, MX-T160, Vicon, Oxford, UK). All cameras were arranged to capture whole body movements. The subjects were marked with reflective optical markers following a plugin-gait marker set. To avoid foot segment occlusion, we attached three more markers in the anatomical landmark of foot segments. The captured data were resampled at 100 Hz. Fig. 1 shows a physically adjustable mock-up for providing variable ingress/egress environments. The adjustable design parameters are horizontal seat location, side width, angle of an open door, and door trim extrusion, which are shown in Fig. 1. The experiment was performed on 2 types of vehicles and 25 combinations of design parameters. The adjustable design parameters are shown in Fig. 1. All measured marker data were labeled manually. All measured marker location unit followed SI base unit. This study measured ground reaction force for dynamics analysis. The ground reaction force was measured from two force plates at 1000 Hz (AMTI, Boston, MA, USA). The subjective response of each participant was recorded after each ingress/egress trial. All measured force unit followed SI base unit. All participates evaluated ingress/ egress motion from 1 to 3 (discomfort), 4–6 (normal), and 7–9 (comfort). A previous study converted this result to 0/1 response through the cut point (Masoud et al., 2016). Our study selected the cut point as 5 and converted the subjective response to 0/1 response data. The experiment was conducted after all subjects had become fully familiar with ingress/egress environment. To minimize the fatigue effect, the subjects were allowed to take sufficient breaks between each experiment. The total number of motions and subjective data pairs was 466. All subjects were free to select their ingress/egress motion strategy, but all selected the right-leg-first strategy.
2.2. Biomechanical analysis This study calculated joint movement and torque with measured data and OpenSim 3.3 (Delp et al., 2007). Before the calculation, all marker data and ground reaction forces were filtered with a low-pass filter at the cutoff frequency of 6 Hz. Our study used the Hamner et al., 2010 running model (Hamner et al., 2010). It has 37 degrees of freedom, but our research locked the metatarsophalangeal joint in both calcaneus segments. Fig. 2 includes detailed information about the 264
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Fig. 1. Physical adjustable mock-up and design parameters.
sum of torque is the summation value of each rectified joint torque. Our study used sum of rectified joint torque as a feature vector. Additionally, the difference of each subject anthropometry information led to different subjective response results. To reflect this, this study used height, weight, and age as feature vector. Our study generated 73 feature vectors representing ingress/egress discomfort, which are composed of 35 accumulated movement of joint angles, 35 Sum of rectified joint torques, height, weight and age.
biomechanical calculation model. All models were scaled prior to kinematic and dynamics calculation. Models were scaled with virtual and optical marker data that were located in anatomical landmarks. To synchronize data, all data were resampled at 100 Hz. Our study performed inverse kinematics with measured ingress/egress marker data. Inverse kinematics calculates model-generalized coordinates (joint angle, pelvis rotation, and translation) that best reproduce the raw marker data measured from experimental data (Delp et al., 2007). The inverse kinematics perform optimization to minimize squared error between measured and virtual marker data. All measured marker data were converted to joint angle data with inverse kinematics procedure. The joint angle represented with degree. By using joint angle and force data, we were able to calculate inverse dynamics procedure. Inverse dynamics solves the multibody dynamics problem to obtain the torque of each joint. This allows achieving joint movement and torque from experimental data (Delp et al., 2007). In previous research, curve normalization and b-spline basis functions were utilized for feature modeling. The b-spline is a curve representation method that can express a smooth curve by a small number of basis coefficients. This approach is quite efficient, but it produces a large number of features. For example, 9 control points in 37 joints produce 333 features. Such a large number of features may reduce the performance of the prediction model in a small dataset. Furthermore, curve normalization excludes the relationship between elapsed time and ingress/egress discomfort. The selected feature vector that includes original feature characteristics improves the performance of classification. Therefore, it is necessary to select novel features such as consideration of time. This study proposed the following novel features: accumulated movement of joint and sum of rectified joint torque. Clearly, a large amount of joint movement or torque causes high levels of discomfort (Boussenna et al., 1982; Kee et al., 2013; Chihara et al., 2014). In this study, we used chordal distance to find the accumulated distance of each joint. A joint angle curve can be approximated to the sum of small line segments, so chord distance calculates accumulated movement by summing the length of line segments. This allowed us to extract accumulated movement feature of each joint from whole body movement data. For the sum of ratified joint torque feature, this study performed numerical integration of each joint torque. This is shown as (1): tingress, end
Sum of torquei =
∫ tingress, start
2.3. Feature selection process and performance evaluation In previous studies, irrelevant features reduced prediction accuracy and training performance. The term irrelevant feature means a feature that did not influence the output value. To avoid this problem, it was necessary to select the subset of the relevant feature from the original feature vectors. As mentioned above, the feature selection procedure assisted in the simplification of the model, reduced training time, and enhanced generalization by reducing overfitting. Therefore, this study selected relevant feature subsets through a proper feature selection method. For the selection process, this study defines an irrelevant feature as the feature that does not influence the passenger comfort/discomfort rate in ingress/egress motion. Given N number of features, the feature selection is to find the best performance subset among 2N possible subsets. This requires an exponential amount of computation load as the number of N increases (Anbarasi et al., 2010; Gheyas and Smith, 2010). Many previous studies used various algorithms to solve this problem(Shah and Kusiak, 2004; Zhang et al., 2014; Üstünkar et al., 2012). This study used recursive feature selection method, which is a wrapper method to find the best performance feature subset. Furthermore, we proposed another solution to solve the selection problem using covariance matrix adaptation evolution strategy (CMA-ES) optimization. Through comparison of two methods, this study selected an optimal feature subset representing subject response. A support vector machine (SVM) with a cubic kernel was used for performance evaluation (Cristianini and Shawe-Taylor, 2000; Fan et al., 2005). The recursive feature selection sequentially eliminates the lowest relevant features from the whole feature vector. The algorithm for recursive feature selection and the calculation of feature importance are shown in Fig. 3 and Fig. 4. This procedure reduces 2N subset search space to N (N+1)/2. One of the important issues of recursive feature selection is defining feature importance. This study defined feature importance as the difference in classifier performance. It calculated the difference between the performance of a whole feature set and performance of a feature set with specific features excluded. This evaluation and elimination process was repeatedly conducted until all features were evaluated and we could find the best performance subset through
tegress, end
T (t )i dt +
∫
T (t )i dt
tegress, start
where tingress, start, tingres,end, tegress, start and tegress,end are the start and end time of ingress/egress movement, respectively; i is index of each joint angle; T is joint torque . All the joint torque value is converted to constant polarity (positive) at its output value for rectification, and the 265
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Fig. 2. Detailed information of biomechanical calculation model.
combinatorial optimization. The design variable of optimization was variable sets of the true/false label. Only true label variable were used in the training procedure and evaluation. The evaluation procedure was performed through the SVM classifier and 5-fold cross-validation. The objective function of the optimization was maximizing classification performance. After optimization, we were able to find a feature subset that maximized performance. The objective of the previous stage was to reduce dimensions and find the best feature subset to express subjective response. The next procedure was the construction of the classification model with a selected feature subset. This study used SVM for classification. SVM defines an optimal hyperplane that separates response class. The optimal hyperplane maximizes the distance between classes. In many cases, classes are not able to separate linearly, so researchers have used the kernel function. Through the kernel function, original data map to other dimensions that are able to separate class linearly. This study selected a
this procedure. Finding the best performance subset is considered the combinatorial optimization problem. The combinatorial optimization problem involves finding an optimal solution from a finite set of objects (e.g., the knapsack problem, traveling salesman problem, and minimum spanning tree problem) (Korte and Vygen, 2012). There are many approaches to solve this problem. The representative method uses a genetic algorithm (Shah and Kusiak, 2004), particle swarm (Zhang et al., 2014), and simulated annealing (Üstünkar et al., 2012). This study used a CMA-ES algorithm to solve the selection problem and find an optimal feature subset. The CMA-ES is a state-of-the-art derivative-free optimization method that uses evaluation strategies (Hansen, 2006). It uses various optimization problems such as hyperparameter optimization in machine learning theory. We performed optimization based on CMAES. Fig. 5 shows the details of the procedure. As Fig. 5 shows, we found an optimal feature subset using 266
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Fig. 3. Algorithm for recursive feature selection.
joint torque, which express discomfort of ingress/egress, were calculated for generating training features. To obtain the best subset features, the feature selection process was performed. Table 2 shows the selected feature results from recursive feature selection and optimal feature selection. The recursive feature selection results achieved the best performance in 29 features. The optimal feature selection results achieved the best performance in 34 features. The 26 features matched for two selected feature subsets. Through this procedure, our research selected relevant feature subsets from 73 feature vectors, which are composed of 35 accumulated movement of joint angles, 35 Sum of rectified joint torques, height, weight and age. Based on the selected feature subset, this study calculated prediction accuracy. Table 3 shows the prediction accuracy of the proposed algorithm. Next, 5-fold cross-validation was used for calculating prediction accuracy. The proposed algorithm showed a high prediction accuracy—89.06% in recursive feature selection and 90.13% in optimal feature selection. The accuracy of the whole feature vector was 84.98%, which was a worse result than the selected feature results. These results show that selection procedure improves prediction accuracy. Additionally, this study calculated the AUC value of the ROC curve for evaluating results. The ROC curve consists of the true positive rate and false positive rate. When AUC values are between 0.9 and 1, the classifier performance is evaluated as “highly accurate” statistically
third-order polynomial kernel for classification. In the development process, we investigated the performance of k-nearest neighbors and tree-based classifiers. SVM outperformed these methods. However, as previous studies mentioned, this method cannot be the guarantee for all motion data (Masoud et al., 2016). We performed 5-fold cross-validation to evaluate and compare performance. For comparison with the reference model, we calculated prediction accuracy and the area under curve (AUC) value of the receiver operating characteristic (ROC) curve. 3. Results This section provides the results of the application of the proposed algorithm to ingress/egress experiment data. The results include the best performance feature subset from the selection procedure (recursive feature selection, optimal feature selection) and prediction performance (accuracy, AUC value of ROC curve). Furthermore, this study evaluates the proposed algorithm through a comparison between previous researches. As noted above, we measured ingress/egress movement using an optical motion capture system, force plates, and a physically adjustable mock-up of vehicle. This allowed us to calculate the movement and torque of each joint using inverse kinematics and inverse dynamics. Based on data, accumulated movement of joint and sum of rectified
Fig. 4. Calculation of feature importance. 267
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Fig. 5. Schematic diagram of optimal feature selection.
Table 2 Feature selection results.
Table 3 Prediction accuracy of Unselected, RFE and Optimal Feature Selection.
No.
Recursive Feature Selection Results
Optimal Feature Selection Results
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
SRJT of pelvis rotation SRJT of hip flexion(right) SRJT of hip adduction(right) SRJT of knee flexion(right) SRJT of ankle eversion(right) SRJT of hip adduction(left) SRJT of hip rotation(left) SRJT of shoulder flexion(right) SRJT of shoulder adduction(right) SRJT of elbow flexion(left) AMJA of pelvis translation(x-axis) AMJA of pelvis translation(y-axis) AMJA of pelvis translation(z-axis) AMJA of hip rotation(right) AMJA of knee flexion(right) AMJA of ankle dorsiflexion(right) AMJA of ankle eversion(right) AMJA of hip adduction(left) AMJA of ankle dorsiflexion(left) AMJA of shoulder flexion(right) AMJA of shoulder rotation(right) AMJA of wrist flexion(right) AMJA of shoulder adduction(left) AMJA of shoulder rotation(left) AMJA of elbow flexion(left) AMJA of elbow pronation(left) AMJA of wrist flexion(left) AMJA of wrist deviation(left) Height
SRJT of pelvis rotation SRJT of pelvis translation(z-axis) SRJT of hip flexion(right) SRJT of hip adduction(right) SRJT of knee flexion(right) SRJT of ankle dorsiflexion(right) SRJT of ankle eversion(right) SRJT of hip flexion(left) SRJT of hip adduction(left) SRJT of hip rotation(left) SRJT of lumbar bending SRJT of shoulder flexion(right) SRJT of shoulder adduction(left) SRJT of elbow flexion(left) AMJA of pelvis translation(x-axis) AMJA of pelvis translation(y-axis) AMJA of pelvis translation(z-axis) AMJA of hip rotation(right) AMJA of knee flexion(right) AMJA of ankle dorsiflexion(right) AMJA of ankle eversion(right) AMJA of hip adduction(left) AMJA of ankle eversion(left) AMJA of shoulder flexion(right) AMJA of shoulder rotation(right) AMJA of elbow pronation(right) AMJA of shoulder adduction(left) AMJA of shoulder rotation(left) AMJA of elbow flexion(left) AMJA of elbow pronation(left) AMJA of wrist flexion(left) AMJA of wrist deviation(left) Age Height
Selection Method
Unselected
Recursive Feature selection
Optimal Feature Selection
Accuracy(%)
84.98
89.06
90.13
(Greiner et al., 2000). Fig. 6 shows the ROC curve and AUC value of the prediction model. The AUC value of the proposed algorithm was 0.94 in recursive feature selection and 0.95 in optimal feature selection, which was in the highly accurate range. In the total results, the feature subset from optimal feature selection achieved better performance than the feature subset from recursive feature selection. This indicated that optimal feature selection extracts more relevant feature subsets than recursive feature selection. This study performed a comparison between the proposed algorithm and previous research. For comparison, we implemented and evaluated two representative prediction models. All evaluation procedures used identical data sets. The results are shown in Fig. 7. In identical conditions, the prediction accuracy of the vehicle and passenger parameters model is 73.61% accuracy, whereas the prediction accuracy of Masoud et al.’s (2016) model was 79.83% accuracy. The proposed algorithm showed 89.06% (recursive feature selection) and 90.13% (optimal feature selection) accuracy, which indicates the superiority of the proposed algorithm. This result could also be seen in the AUC value results. Fig. 6 shows the AUC value of the reference model. The AUC value of the vehicle and passenger parameters model was 0.80, whereas the AUC value of Masoud et al.’s (2016) model was 0.85. The proposed algorithm showed 0.95 and 0.94, which is a higher value than that of the reference model. Therefore, the proposed algorithm demonstrated better prediction performance than the reference models.
* AMJA: Accumulated movement of joint angle. * SRJT: Sum of rectified joint torque.
4. Discussion and conclusion This study proposed an ingress/egress discomfort prediction 268
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Fig. 6. ROC curve result of classifier.
parameters or motion capture data. This proved that the selected features from the proposed algorithm had a better representation of discomfort compared than previous research. Our study used two types of feature selection methods. We used the recursive feature selection method and optimal feature selection method and compared the results. The optimal feature selection method
algorithm using an in-depth biomechanical method and motion capture database. The biomechanical simulation allowed us to conduct an indepth investigation of ingress/egress motion. In particular, calculation of joint angle and torque based on digital human model provided key information for discomfort analysis. This demonstrated better performance than previous research based on vehicle and passenger
Fig. 7. Comparison results of reference model. 269
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manuscript. This paper has been neither published nor submitted elsewhere for publication, in whole or in part, either in a serial, professional journal or as a part in a book that is formally published and made available to the public.
gave slightly better results than the recursive feature selection. This may have caused the inability to re-evaluate the usefulness of a feature after it had been removed (Ladha and Deepa, 2011). In recursive feature selection, if a particular useful feature is discarded as an accidental factor, it will not be reevaluated. Consequently, the recursive feature selection algorithm may have eliminated the relevant feature in the feature selection process. At the same time, optimal feature selection based on CMA-ES found a global optimal subset, solving this problem. Therefore, optimal feature selection achieved better results. In addition, both feature selection methods had 26 common features and 11 different features (three features in the recursive subset that the optimal subset does not have, and eight features in the optimal subset that the recursive subset does not have). However, our selection results improved only 1.07% in the optimal feature selection process. This result reveals that 11 different features contributed little to the prediction results. Our study result provide how body segments determine ingress/ egress discomfort. Following the selected feature subsets, the ratios of upper body joint was 41.38% (recursive feature selection) and 35.29% (optimal feature selection). This was similar to the ratios of the lower body joint, which were 41.38% (recursive feature selection) and 41.18% (optimal feature selection). Further, the selected feature subset showed that that the ratios of the right side joint was 44.83% (recursive feature selection) and 38.24% (optimal feature selection). This was similar to the ratios of the left side joint, which were 37.93% (recursive feature selection) and 38.23% (optimal feature selection). Although all subjects selected the right-leg-first strategy, the selected feature subset ratios of the right and left sides had similar values. This result revealed that ingress/egress discomfort is affected by overall joint discomfort rather than the effect of independent joints, as in previous studies (Shippen & May 2016). The main contribution of this study is that it proposed a novel method to evaluate passenger discomfort through digital human models and motion capture. It provided more in-depth ingress/egress analysis results than previous studies. Additionally, the proposed method reduced a large number of feature candidates efficiently. Accumulated movement of joint and sum of rectified joint torque were found to be successful ways to represent the analyzed data. These features allowed us to represent features from temporal data successfully, and they contributed to constructing a high-performance prediction model. This study also proposed CMA-ES-based joint feature selection methods. This method achieved better performance compare to unselected and recursive feature selection results. The proposed prediction method achieved better performance compared to previous studies. This study proposed the novel prediction method of passenger subjective response in ingress/egress movement. Constructing the discomfort prediction model through in-depth biomechanical analysis will provide helpful perspectives to vehicle interior researchers. Further work may necessary to improve our prediction model. Consideration of joint tolerance and capacity of each joint in the feature selection process can help to develop the improved prediction model. In addition, this study collects experimental data from 10 subjects, 500 conditions. The larger database with the various subject may allow us to find more precise classification results and the relationship between discomfort and subject anthropometry. This research concentrated on only joint movement and torque in ingress/egress motion. Considering other biomechanical features such as metabolic cost, electromyography signal may improve the prediction model of ingress/egress discomfort. Furthermore, this study considered only physical load features and movement features of the joint in constructing the prediction model; therefore, it may have limited application for dealing with discomfort which is unrelated to physical load or movements.
Acknowledgements The Institute of Engineering Research at Seoul National University provided research facilities for this work. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.apergo.2018.11.003. References Anbarasi, M., Anupriya, E., Iyengar, N., 2010. Enhanced prediction of heart disease with feature subset selection using genetic algorithm. Int. J. Eng. Sci. Technol. 2, 5370–5376. Andersen, M.S., Damsgaard, M., MacWilliams, B., Rasmussen, J., 2010. A computationally efficient optimisation-based method for parameter identification of kinematically determinate and over-determinate biomechanical systems. Comput. Methods Biomech. Biomed. Eng. 13, 171–183. Boussenna, M., Corlett, E., Pheasant, S., 1982. The relation between discomfort and postural loading at the joints. Ergonomics 25, 315–322. Causse, J., Wang, X., Denninger, L., 2012. An experimental investigation on the requirement of roof height and sill width for car ingress and egress. Ergonomics 55, 1596–1611. Chaffin, D.B., 2005. Improving digital human modelling for proactive ergonomics in design. Ergonomics 48, 478–491. Chihara, T., Izumi, T., Seo, A., 2014. Perceived discomfort functions based on joint moment for various joint motion directions of the upper limb. Appl. Ergon. 45, 308–317. Choi, N.-C., Lee, S.H., 2015. Discomfort evaluation of truck ingress/egress motions based on biomechanical analysis. Sensors 15, 13568–13590. Cristianini, N., Shawe-Taylor, J., 2000. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge university press. Delp, S.L., Anderson, F.C., Arnold, A.S., Loan, P., Habib, A., John, C.T., Guendelman, E., Thelen, D.G., 2007. OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans. Biomed. Eng. 54, 1940–1950. Dufour, F., Wang, X., 2005. Discomfort Assessment of Car Ingress/egress Motions Using the Concept of Neutral Movement. (SAE Technical Paper). Jung, Y., Phan, C.-B., Koo, S., 2016. Intra-articular knee contact force estimation during walking using force-reaction elements and subject-specific joint model. J. Biomech. Eng. 138, 021016. Fan, J., Li, R., 2006. Statistical Challenges with High Dimensionality: Feature Selection in Knowledge Discovery. arXiv preprint math/0602133. Fan, R.-E., Chen, P.-H., Lin, C.-J., 2005. Working set selection using second order information for training support vector machines. J. Mach. Learn. Res. 6, 1889–1918. Forner-Cordero, A., Koopman, H., Van der Helm, F., 2006. Inverse dynamics calculations during gait with restricted ground reaction force information from pressure insoles. Gait Posture 23, 189–199. Gheyas, I.A., Smith, L.S., 2010. Feature subset selection in large dimensionality domains. Pattern Recogn. 43, 5–13. Giacomin, J., Quattrocolo, S., 1997. An analysis of human comfort when entering and exiting the rear seat of an automobile. Appl. Ergon. 28, 397–406. Greiner, M., Pfeiffer, D., Smith, R., 2000. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev. Vet. Med. 45, 23–41. Groenesteijn, L., Ellegast, R.P., Keller, K., Krause, F., Berger, H., de Looze, M.P., 2012. Office task effects on comfort and body dynamics in five dynamic office chairs. Appl. Ergon. 43, 320–328. Hamner, S.R., Seth, A., Delp, S.L., 2010. Muscle contributions to propulsion and support during running. J. Biomech. 43, 2709–2716. Hansen, N., 2006. The CMA evolution strategy: a comparing review. Towards a new evolutionary computation 75–102. Herriotts, P., 2005. Identification of vehicle design requirements for older drivers. Appl. Ergon. 36, 255–262. Hughes, M., Weiner, D., Schenkman, M., Long, R., Studenski, S., 1994. Chair rise strategies in the elderly. Clin. BioMech. 9, 187–192. Jung, M., Cho, H., Roh, T., Lee, K., 2009. Integrated framework for vehicle interior design using digital human model. J. Comput. Sci. Technol. 24, 1149–1161. Kee, D., Na, S., Chung, M.K., 2013. Effect of external load at varying hand positions on perceived discomfort. Int. J. Occup. Saf. Ergon. 19, 397–408. Kim, S., Lee, K., 2009. Development of discomfort evaluation method for car ingress motion. Int. J. Automot. Technol. 10, 619–627. Korte, B., Vygen, J., 2012. Combinatorial Optimization. Springer. Ladha, L., Deepa, T., 2011. Feature selection methods and algorithms. Int. J. Comput. Sci. Eng. 3, 1787–1797. Lord, S.R., Murray, S.M., Chapman, K., Munro, B., Tiedemann, A., 2002. Sit-to-stand
Conflicts of interest As far as the authors know there is no conflict of interest in this 270
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Shah, S.C., Kusiak, A., 2004. Data mining and genetic algorithm based gene/SNP selection. Artif. Intell. Med. 31, 183–196. Shippen, J., May, B., 2016. Constitutive kinematic modes and shapes during vehicle ingress/egress. Appl. Ergon. 56, 127–135. Trapanese, S., Naddeo, A., Cappetti, N., 2016. A Preventive Evaluation of Perceived Postural Comfort in Car-cockpit Design: Differences between the Postural Approach and the Accurate Muscular Simulation under Different Load Conditions in the Case of Steering-wheel Usage. SAE Technical Paper. Üstünkar, G., Özöğür-Akyüz, S., Weber, G.W., Friedrich, C.M., Son, Y.A., 2012. Selection of representative SNP sets for genome-wide association studies: a metaheuristic approach. Optimization Letters 6, 1207–1218. Zhang, Y., Wang, S., Phillips, P., Ji, G., 2014. Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowl. Base Syst. 64, 22–31.
performance depends on sensation, speed, balance, and psychological status in addition to strength in older people. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 57, M539–M543. Masoud, H.I., Reed, M.P., Paynabar, K., Wang, N., Jin, J.J., Wan, J., Kozak, K.K., GomezLevi, G., 2016. Predicting subjective responses from human motion: application to vehicle ingress assessment. J. Manuf. Sci. Eng. 138, 061001. Masoud, H.I., Zerehsaz, Y., Jin, J.J., 2017. Analysis of human motion variation patterns using UMPCA. Appl. Ergon. 59, 401–409. Robert, T., Causse, J., Denninger, L., Wang, X., 2014. A 3D analysis of the joint torques developed during driver's ingress–egress motion. Ergonomics 57, 1008–1020. Saeys, Y., Inza, I., Larrañaga, P., 2007. A review of feature selection techniques in bioinformatics. Bioinformatics 23, 2507–2517. Schenkman, M., Hughes, M.A., Samsa, G., Studenski, S., 1996. The relative importance of strength and balance in chair rise by functionally impaired older individuals. J. Am. Geriatr. Soc. 44, 1441–1446.
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Applied Ergonomics journal homepage: www.elsevier.com/locate/apergo
A novel approach to predict ingress/egress discomfort based on human motion and biomechanical analysis
T
Younguk Kim, Kunwoo Lee∗ School of Mechanical and Aerospace Engineering, Seoul National University, Seoul, Republic of Korea
A R T I C LE I N FO
A B S T R A C T
Keywords: Ingress/egress Biomechanics Discomfort Motion analysis Feature selection
This study proposes an ingress/egress discomfort prediction algorithm using an in-depth biomechanical method and motion capture database. The ingress/egress motion of the subject was captured using an optical motion capture system and physically adjustable vehicle mock-up. The subjective discomfort evaluation data were also recorded at the same time. The inverse kinematics and inverse dynamics were performed to analyze captured ingress/egress motion. These procedure provide motion and joint torque information on each subject. Based on the analysis results, this study proposes the following novel features: accumulated movement of joint and sum of rectified joint torque. This study conducted a feature selection procedure to identify a relevant feature subset. Recursive feature selection and optimal feature selection methods found the most relevant feature subset with collected subjective responses. Finally, we constructed the prediction model using support vector machine. The prediction model was evaluated through prediction accuracy and statistical analysis. For comparison with the previous study, this study implemented two representative models and compare the result with those of the previous studies using the identical dataset. The effectiveness of proposed algorithm was demonstrated in comparison with previous studies.
1. Introduction Manufacturers have increased their consideration of human comfort in the development of products to improve their quality. In particular, the field of automotive design, which involves many interactions between humans and products, has shown great interest in human-centered design. Hence, a number of vehicle interior studies have considered ergonomic results (Chaffin, 2005; Jung et al., 2009). Discomfort of ingress/egress is one of the main considerations in designing an ergonomic vehicle. The constraints of vehicle door size, opening angle, and seat arrangement require complex full-body movements to enter and exit the vehicle (Robert et al., 2014). Furthermore, ingress/egress involves a sit-to-stand motion, which is a highly demanding task. The sit-to-stand motion needs many biomechanical abilities, such as high knee torque, balance, choice of seat rise strategy, and joint strength (Hughes et al., 1994; Schenkman et al., 1996; Lord et al., 2002). Because these demands make the discomfort of ingress/egress motion an important vehicle design factor, many researchers have investigated ingress/egress motion to improve the ergonomic design of their vehicle (Shippen and May 2016; Masoud et al., 2017). Numerous studies have investigated the statistical relationship
between vehicle design parameters and discomfort of ingress/egress motion (Giacomin and Quattrocolo, 1997; Causse et al., 2012; Herriotts, 2005). Researchers have used actual vehicles or physically adjustable mock-ups of a vehicle to collect subjective responses in variable environments. Giacomin and Quattrocolo. (1997) found a relationship between the rear seat parameter of the vehicle and subjective responses. Causse et al. (2012) analyzed discomfort of ingress/egress motion in variable roof height conditions. Herriotts (2005) identified discomfort parameters using questionnaire results from old drivers. These research methods were effective, but they had the drawback of depending on the subjective views of the research participants. Therefore, it is necessary to find an objective and quantitative discomfort assessment method for passengers. Previous ergonomics studies used a postural-based discomfort evaluation method. A representative example is rapid upper limb assessment (RULA), which considers the posture and load requirements of a job task on the upper extremities. Postural-based discomfort evaluation has also been used in passenger discomfort studies in ingress/egress motion. Dufour and Wang (2005) proposed the concept of “neutral movements” using joint angle of ingress/egress motion. These researchers collected joint angle data from motion capture system and
∗ Corresponding author. School of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Daehak-dong, Gwanak-gu, Seoul, 151-744, Republic of Korea. E-mail address: [email protected] (K. Lee).
https://doi.org/10.1016/j.apergo.2018.11.003 Received 31 January 2018; Received in revised form 1 October 2018; Accepted 11 November 2018 0003-6870/ © 2018 Elsevier Ltd. All rights reserved.
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evaluated passenger comfort/discomfort. Masoud et al. (2016) constructed a statistical model between captured ingress motion and subjective responses. These researchers only analyzed human motion and did not consider the biomechanical load of joints and muscles. To find the relationship between in-depth biomechanical parameters and discomfort, Kim and Lee (2009) proposed a method using virtual muscle force and fuzzy logic. Choi and Lee (2015) analyzed ingress/egress discomfort using the maximum voluntary contraction (MVC) and linear regression method. Recently, there have been many papers that have dealt with posture and muscular activity. Anderson et al. (2010) proposed an efficient optimization method in the overdetermined biomechanical system. Trapanese et al. (2016) analyzed the relationship between passenger comfort/discomfort in vehicle cabin and muscular activity using the AnyBody Modeling System. Groenesteijn et al. (2012) investigated the relationship between posture characteristics and discomfort in office tasks. These studies achieved muscular activity using musculoskeletal simulation. However, most of the results were inaccurate or costly because it was difficult to construct a muscle model that reflected individual characteristics. On the other hand, joint-based force prediction research has shown highly accurate results in recent research. Jung et al. estimate knee joint and reaction force with musculoskeletal model. (2016). Forner-Cordero et al. calculate inverse dynamics during gait with restricted ground reaction force information. (2006) Therefore, a prediction model with joint motion and torque was constructed for this study. In this paper, we propose an ingress/egress discomfort prediction algorithm using an in-depth biomechanical method and motion capture database. Although there may be various factors affecting passenger discomfort, this study concentrated on two factors: accumulated movement of joint and sum of joint torque. We collected captured ingress/egress motion and subjective responses data in physically adjustable mock-ups of the vehicle. Obtained data were converted to joint angle and torque data through biomechanical analysis. We generated the feature set from biomechanical analysis results. The challenge in determining a relationship between analysis results and subjective response is the high dimensionality of feature sets. Generally, the statistical model assumes that the number of samples is higher than the number of feature dimensions. A large number of feature vectors is able to reduce the performance of a classification model in limited database (Fan and Li, 2006). Furthermore, this may lead to overfitting in generalization (Saeys et al., 2007). Many previous studies claimed that a proper feature selection method is expected to avoid the overfitting problem and improve performance by eliminating redundant or irrelevant features. For this study, we used recursive feature elimination and optimal feature subset selection to select highly relevant features for the output and compared the prediction performance of both selection methods. We also compared performance when all features were used. In this study, we implemented two reference models for comparison with previous studies. The first reference was the statistical model between vehicle/passenger parameters and subjective discomfort. Many studies have statistically analyzed vehicle parameters and discomfort (Giacomin and Quattrocolo, 1997; Causse et al., 2012). This study constructed a reference statistical model between vehicle/passenger parameters and subjective discomfort for comparison. The second reference was the statistical model between motion stream data and subjective discomfort. Recently, Masoud et al. (2016) constructed a discomfort prediction model with motion stream data and stepwise group selection. We implemented their method and compared the result using the identical dataset. This paper is organized as follows. Section 2 presents the experiment data and proposed algorithm to predict subjective responses. Section 3 provides the results of the proposed algorithm and comparison with reference models. Finally, Section 4 contains the discussion of our research and concluding remarks.
Table 1 Subject information. Subject Number
Age
Height(m)
Weight(kg)
1 2 3 4 5 6 7 8 9 10
27 30 23 20 22 22 18 25 25 24
1.84 1.82 1.77 1.85 1.75 1.78 1.78 1.78 1.77 1.86
78 99 65 77 65 72 68 84 70 75
2. Methods 2.1. Data collection A group of 10 healthy males (age 20.5 ± 5 yrs, height 1.8 ± 0.05 m) who were familiar with driving volunteered for this study. All subjects were injury-free before and during the experiment. Detailed information about the subjects are in Table 1. Before the experiment, all subjects were informed regarding the test procedures, protocols, and potential risks of participation. Written informed consent from the participant subjects was obtained. The experiment was approved by the Ethics Committee of Seoul National University (IRB). We captured all ingress/egress motions of subjects through an optical motion capture system (12 cameras, MX-T160, Vicon, Oxford, UK). All cameras were arranged to capture whole body movements. The subjects were marked with reflective optical markers following a plugin-gait marker set. To avoid foot segment occlusion, we attached three more markers in the anatomical landmark of foot segments. The captured data were resampled at 100 Hz. Fig. 1 shows a physically adjustable mock-up for providing variable ingress/egress environments. The adjustable design parameters are horizontal seat location, side width, angle of an open door, and door trim extrusion, which are shown in Fig. 1. The experiment was performed on 2 types of vehicles and 25 combinations of design parameters. The adjustable design parameters are shown in Fig. 1. All measured marker data were labeled manually. All measured marker location unit followed SI base unit. This study measured ground reaction force for dynamics analysis. The ground reaction force was measured from two force plates at 1000 Hz (AMTI, Boston, MA, USA). The subjective response of each participant was recorded after each ingress/egress trial. All measured force unit followed SI base unit. All participates evaluated ingress/ egress motion from 1 to 3 (discomfort), 4–6 (normal), and 7–9 (comfort). A previous study converted this result to 0/1 response through the cut point (Masoud et al., 2016). Our study selected the cut point as 5 and converted the subjective response to 0/1 response data. The experiment was conducted after all subjects had become fully familiar with ingress/egress environment. To minimize the fatigue effect, the subjects were allowed to take sufficient breaks between each experiment. The total number of motions and subjective data pairs was 466. All subjects were free to select their ingress/egress motion strategy, but all selected the right-leg-first strategy.
2.2. Biomechanical analysis This study calculated joint movement and torque with measured data and OpenSim 3.3 (Delp et al., 2007). Before the calculation, all marker data and ground reaction forces were filtered with a low-pass filter at the cutoff frequency of 6 Hz. Our study used the Hamner et al., 2010 running model (Hamner et al., 2010). It has 37 degrees of freedom, but our research locked the metatarsophalangeal joint in both calcaneus segments. Fig. 2 includes detailed information about the 264
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Fig. 1. Physical adjustable mock-up and design parameters.
sum of torque is the summation value of each rectified joint torque. Our study used sum of rectified joint torque as a feature vector. Additionally, the difference of each subject anthropometry information led to different subjective response results. To reflect this, this study used height, weight, and age as feature vector. Our study generated 73 feature vectors representing ingress/egress discomfort, which are composed of 35 accumulated movement of joint angles, 35 Sum of rectified joint torques, height, weight and age.
biomechanical calculation model. All models were scaled prior to kinematic and dynamics calculation. Models were scaled with virtual and optical marker data that were located in anatomical landmarks. To synchronize data, all data were resampled at 100 Hz. Our study performed inverse kinematics with measured ingress/egress marker data. Inverse kinematics calculates model-generalized coordinates (joint angle, pelvis rotation, and translation) that best reproduce the raw marker data measured from experimental data (Delp et al., 2007). The inverse kinematics perform optimization to minimize squared error between measured and virtual marker data. All measured marker data were converted to joint angle data with inverse kinematics procedure. The joint angle represented with degree. By using joint angle and force data, we were able to calculate inverse dynamics procedure. Inverse dynamics solves the multibody dynamics problem to obtain the torque of each joint. This allows achieving joint movement and torque from experimental data (Delp et al., 2007). In previous research, curve normalization and b-spline basis functions were utilized for feature modeling. The b-spline is a curve representation method that can express a smooth curve by a small number of basis coefficients. This approach is quite efficient, but it produces a large number of features. For example, 9 control points in 37 joints produce 333 features. Such a large number of features may reduce the performance of the prediction model in a small dataset. Furthermore, curve normalization excludes the relationship between elapsed time and ingress/egress discomfort. The selected feature vector that includes original feature characteristics improves the performance of classification. Therefore, it is necessary to select novel features such as consideration of time. This study proposed the following novel features: accumulated movement of joint and sum of rectified joint torque. Clearly, a large amount of joint movement or torque causes high levels of discomfort (Boussenna et al., 1982; Kee et al., 2013; Chihara et al., 2014). In this study, we used chordal distance to find the accumulated distance of each joint. A joint angle curve can be approximated to the sum of small line segments, so chord distance calculates accumulated movement by summing the length of line segments. This allowed us to extract accumulated movement feature of each joint from whole body movement data. For the sum of ratified joint torque feature, this study performed numerical integration of each joint torque. This is shown as (1): tingress, end
Sum of torquei =
∫ tingress, start
2.3. Feature selection process and performance evaluation In previous studies, irrelevant features reduced prediction accuracy and training performance. The term irrelevant feature means a feature that did not influence the output value. To avoid this problem, it was necessary to select the subset of the relevant feature from the original feature vectors. As mentioned above, the feature selection procedure assisted in the simplification of the model, reduced training time, and enhanced generalization by reducing overfitting. Therefore, this study selected relevant feature subsets through a proper feature selection method. For the selection process, this study defines an irrelevant feature as the feature that does not influence the passenger comfort/discomfort rate in ingress/egress motion. Given N number of features, the feature selection is to find the best performance subset among 2N possible subsets. This requires an exponential amount of computation load as the number of N increases (Anbarasi et al., 2010; Gheyas and Smith, 2010). Many previous studies used various algorithms to solve this problem(Shah and Kusiak, 2004; Zhang et al., 2014; Üstünkar et al., 2012). This study used recursive feature selection method, which is a wrapper method to find the best performance feature subset. Furthermore, we proposed another solution to solve the selection problem using covariance matrix adaptation evolution strategy (CMA-ES) optimization. Through comparison of two methods, this study selected an optimal feature subset representing subject response. A support vector machine (SVM) with a cubic kernel was used for performance evaluation (Cristianini and Shawe-Taylor, 2000; Fan et al., 2005). The recursive feature selection sequentially eliminates the lowest relevant features from the whole feature vector. The algorithm for recursive feature selection and the calculation of feature importance are shown in Fig. 3 and Fig. 4. This procedure reduces 2N subset search space to N (N+1)/2. One of the important issues of recursive feature selection is defining feature importance. This study defined feature importance as the difference in classifier performance. It calculated the difference between the performance of a whole feature set and performance of a feature set with specific features excluded. This evaluation and elimination process was repeatedly conducted until all features were evaluated and we could find the best performance subset through
tegress, end
T (t )i dt +
∫
T (t )i dt
tegress, start
where tingress, start, tingres,end, tegress, start and tegress,end are the start and end time of ingress/egress movement, respectively; i is index of each joint angle; T is joint torque . All the joint torque value is converted to constant polarity (positive) at its output value for rectification, and the 265
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Fig. 2. Detailed information of biomechanical calculation model.
combinatorial optimization. The design variable of optimization was variable sets of the true/false label. Only true label variable were used in the training procedure and evaluation. The evaluation procedure was performed through the SVM classifier and 5-fold cross-validation. The objective function of the optimization was maximizing classification performance. After optimization, we were able to find a feature subset that maximized performance. The objective of the previous stage was to reduce dimensions and find the best feature subset to express subjective response. The next procedure was the construction of the classification model with a selected feature subset. This study used SVM for classification. SVM defines an optimal hyperplane that separates response class. The optimal hyperplane maximizes the distance between classes. In many cases, classes are not able to separate linearly, so researchers have used the kernel function. Through the kernel function, original data map to other dimensions that are able to separate class linearly. This study selected a
this procedure. Finding the best performance subset is considered the combinatorial optimization problem. The combinatorial optimization problem involves finding an optimal solution from a finite set of objects (e.g., the knapsack problem, traveling salesman problem, and minimum spanning tree problem) (Korte and Vygen, 2012). There are many approaches to solve this problem. The representative method uses a genetic algorithm (Shah and Kusiak, 2004), particle swarm (Zhang et al., 2014), and simulated annealing (Üstünkar et al., 2012). This study used a CMA-ES algorithm to solve the selection problem and find an optimal feature subset. The CMA-ES is a state-of-the-art derivative-free optimization method that uses evaluation strategies (Hansen, 2006). It uses various optimization problems such as hyperparameter optimization in machine learning theory. We performed optimization based on CMAES. Fig. 5 shows the details of the procedure. As Fig. 5 shows, we found an optimal feature subset using 266
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Fig. 3. Algorithm for recursive feature selection.
joint torque, which express discomfort of ingress/egress, were calculated for generating training features. To obtain the best subset features, the feature selection process was performed. Table 2 shows the selected feature results from recursive feature selection and optimal feature selection. The recursive feature selection results achieved the best performance in 29 features. The optimal feature selection results achieved the best performance in 34 features. The 26 features matched for two selected feature subsets. Through this procedure, our research selected relevant feature subsets from 73 feature vectors, which are composed of 35 accumulated movement of joint angles, 35 Sum of rectified joint torques, height, weight and age. Based on the selected feature subset, this study calculated prediction accuracy. Table 3 shows the prediction accuracy of the proposed algorithm. Next, 5-fold cross-validation was used for calculating prediction accuracy. The proposed algorithm showed a high prediction accuracy—89.06% in recursive feature selection and 90.13% in optimal feature selection. The accuracy of the whole feature vector was 84.98%, which was a worse result than the selected feature results. These results show that selection procedure improves prediction accuracy. Additionally, this study calculated the AUC value of the ROC curve for evaluating results. The ROC curve consists of the true positive rate and false positive rate. When AUC values are between 0.9 and 1, the classifier performance is evaluated as “highly accurate” statistically
third-order polynomial kernel for classification. In the development process, we investigated the performance of k-nearest neighbors and tree-based classifiers. SVM outperformed these methods. However, as previous studies mentioned, this method cannot be the guarantee for all motion data (Masoud et al., 2016). We performed 5-fold cross-validation to evaluate and compare performance. For comparison with the reference model, we calculated prediction accuracy and the area under curve (AUC) value of the receiver operating characteristic (ROC) curve. 3. Results This section provides the results of the application of the proposed algorithm to ingress/egress experiment data. The results include the best performance feature subset from the selection procedure (recursive feature selection, optimal feature selection) and prediction performance (accuracy, AUC value of ROC curve). Furthermore, this study evaluates the proposed algorithm through a comparison between previous researches. As noted above, we measured ingress/egress movement using an optical motion capture system, force plates, and a physically adjustable mock-up of vehicle. This allowed us to calculate the movement and torque of each joint using inverse kinematics and inverse dynamics. Based on data, accumulated movement of joint and sum of rectified
Fig. 4. Calculation of feature importance. 267
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Fig. 5. Schematic diagram of optimal feature selection.
Table 2 Feature selection results.
Table 3 Prediction accuracy of Unselected, RFE and Optimal Feature Selection.
No.
Recursive Feature Selection Results
Optimal Feature Selection Results
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
SRJT of pelvis rotation SRJT of hip flexion(right) SRJT of hip adduction(right) SRJT of knee flexion(right) SRJT of ankle eversion(right) SRJT of hip adduction(left) SRJT of hip rotation(left) SRJT of shoulder flexion(right) SRJT of shoulder adduction(right) SRJT of elbow flexion(left) AMJA of pelvis translation(x-axis) AMJA of pelvis translation(y-axis) AMJA of pelvis translation(z-axis) AMJA of hip rotation(right) AMJA of knee flexion(right) AMJA of ankle dorsiflexion(right) AMJA of ankle eversion(right) AMJA of hip adduction(left) AMJA of ankle dorsiflexion(left) AMJA of shoulder flexion(right) AMJA of shoulder rotation(right) AMJA of wrist flexion(right) AMJA of shoulder adduction(left) AMJA of shoulder rotation(left) AMJA of elbow flexion(left) AMJA of elbow pronation(left) AMJA of wrist flexion(left) AMJA of wrist deviation(left) Height
SRJT of pelvis rotation SRJT of pelvis translation(z-axis) SRJT of hip flexion(right) SRJT of hip adduction(right) SRJT of knee flexion(right) SRJT of ankle dorsiflexion(right) SRJT of ankle eversion(right) SRJT of hip flexion(left) SRJT of hip adduction(left) SRJT of hip rotation(left) SRJT of lumbar bending SRJT of shoulder flexion(right) SRJT of shoulder adduction(left) SRJT of elbow flexion(left) AMJA of pelvis translation(x-axis) AMJA of pelvis translation(y-axis) AMJA of pelvis translation(z-axis) AMJA of hip rotation(right) AMJA of knee flexion(right) AMJA of ankle dorsiflexion(right) AMJA of ankle eversion(right) AMJA of hip adduction(left) AMJA of ankle eversion(left) AMJA of shoulder flexion(right) AMJA of shoulder rotation(right) AMJA of elbow pronation(right) AMJA of shoulder adduction(left) AMJA of shoulder rotation(left) AMJA of elbow flexion(left) AMJA of elbow pronation(left) AMJA of wrist flexion(left) AMJA of wrist deviation(left) Age Height
Selection Method
Unselected
Recursive Feature selection
Optimal Feature Selection
Accuracy(%)
84.98
89.06
90.13
(Greiner et al., 2000). Fig. 6 shows the ROC curve and AUC value of the prediction model. The AUC value of the proposed algorithm was 0.94 in recursive feature selection and 0.95 in optimal feature selection, which was in the highly accurate range. In the total results, the feature subset from optimal feature selection achieved better performance than the feature subset from recursive feature selection. This indicated that optimal feature selection extracts more relevant feature subsets than recursive feature selection. This study performed a comparison between the proposed algorithm and previous research. For comparison, we implemented and evaluated two representative prediction models. All evaluation procedures used identical data sets. The results are shown in Fig. 7. In identical conditions, the prediction accuracy of the vehicle and passenger parameters model is 73.61% accuracy, whereas the prediction accuracy of Masoud et al.’s (2016) model was 79.83% accuracy. The proposed algorithm showed 89.06% (recursive feature selection) and 90.13% (optimal feature selection) accuracy, which indicates the superiority of the proposed algorithm. This result could also be seen in the AUC value results. Fig. 6 shows the AUC value of the reference model. The AUC value of the vehicle and passenger parameters model was 0.80, whereas the AUC value of Masoud et al.’s (2016) model was 0.85. The proposed algorithm showed 0.95 and 0.94, which is a higher value than that of the reference model. Therefore, the proposed algorithm demonstrated better prediction performance than the reference models.
* AMJA: Accumulated movement of joint angle. * SRJT: Sum of rectified joint torque.
4. Discussion and conclusion This study proposed an ingress/egress discomfort prediction 268
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Fig. 6. ROC curve result of classifier.
parameters or motion capture data. This proved that the selected features from the proposed algorithm had a better representation of discomfort compared than previous research. Our study used two types of feature selection methods. We used the recursive feature selection method and optimal feature selection method and compared the results. The optimal feature selection method
algorithm using an in-depth biomechanical method and motion capture database. The biomechanical simulation allowed us to conduct an indepth investigation of ingress/egress motion. In particular, calculation of joint angle and torque based on digital human model provided key information for discomfort analysis. This demonstrated better performance than previous research based on vehicle and passenger
Fig. 7. Comparison results of reference model. 269
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manuscript. This paper has been neither published nor submitted elsewhere for publication, in whole or in part, either in a serial, professional journal or as a part in a book that is formally published and made available to the public.
gave slightly better results than the recursive feature selection. This may have caused the inability to re-evaluate the usefulness of a feature after it had been removed (Ladha and Deepa, 2011). In recursive feature selection, if a particular useful feature is discarded as an accidental factor, it will not be reevaluated. Consequently, the recursive feature selection algorithm may have eliminated the relevant feature in the feature selection process. At the same time, optimal feature selection based on CMA-ES found a global optimal subset, solving this problem. Therefore, optimal feature selection achieved better results. In addition, both feature selection methods had 26 common features and 11 different features (three features in the recursive subset that the optimal subset does not have, and eight features in the optimal subset that the recursive subset does not have). However, our selection results improved only 1.07% in the optimal feature selection process. This result reveals that 11 different features contributed little to the prediction results. Our study result provide how body segments determine ingress/ egress discomfort. Following the selected feature subsets, the ratios of upper body joint was 41.38% (recursive feature selection) and 35.29% (optimal feature selection). This was similar to the ratios of the lower body joint, which were 41.38% (recursive feature selection) and 41.18% (optimal feature selection). Further, the selected feature subset showed that that the ratios of the right side joint was 44.83% (recursive feature selection) and 38.24% (optimal feature selection). This was similar to the ratios of the left side joint, which were 37.93% (recursive feature selection) and 38.23% (optimal feature selection). Although all subjects selected the right-leg-first strategy, the selected feature subset ratios of the right and left sides had similar values. This result revealed that ingress/egress discomfort is affected by overall joint discomfort rather than the effect of independent joints, as in previous studies (Shippen & May 2016). The main contribution of this study is that it proposed a novel method to evaluate passenger discomfort through digital human models and motion capture. It provided more in-depth ingress/egress analysis results than previous studies. Additionally, the proposed method reduced a large number of feature candidates efficiently. Accumulated movement of joint and sum of rectified joint torque were found to be successful ways to represent the analyzed data. These features allowed us to represent features from temporal data successfully, and they contributed to constructing a high-performance prediction model. This study also proposed CMA-ES-based joint feature selection methods. This method achieved better performance compare to unselected and recursive feature selection results. The proposed prediction method achieved better performance compared to previous studies. This study proposed the novel prediction method of passenger subjective response in ingress/egress movement. Constructing the discomfort prediction model through in-depth biomechanical analysis will provide helpful perspectives to vehicle interior researchers. Further work may necessary to improve our prediction model. Consideration of joint tolerance and capacity of each joint in the feature selection process can help to develop the improved prediction model. In addition, this study collects experimental data from 10 subjects, 500 conditions. The larger database with the various subject may allow us to find more precise classification results and the relationship between discomfort and subject anthropometry. This research concentrated on only joint movement and torque in ingress/egress motion. Considering other biomechanical features such as metabolic cost, electromyography signal may improve the prediction model of ingress/egress discomfort. Furthermore, this study considered only physical load features and movement features of the joint in constructing the prediction model; therefore, it may have limited application for dealing with discomfort which is unrelated to physical load or movements.
Acknowledgements The Institute of Engineering Research at Seoul National University provided research facilities for this work. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.apergo.2018.11.003. References Anbarasi, M., Anupriya, E., Iyengar, N., 2010. Enhanced prediction of heart disease with feature subset selection using genetic algorithm. Int. J. Eng. Sci. Technol. 2, 5370–5376. Andersen, M.S., Damsgaard, M., MacWilliams, B., Rasmussen, J., 2010. A computationally efficient optimisation-based method for parameter identification of kinematically determinate and over-determinate biomechanical systems. Comput. Methods Biomech. Biomed. Eng. 13, 171–183. Boussenna, M., Corlett, E., Pheasant, S., 1982. The relation between discomfort and postural loading at the joints. Ergonomics 25, 315–322. Causse, J., Wang, X., Denninger, L., 2012. An experimental investigation on the requirement of roof height and sill width for car ingress and egress. Ergonomics 55, 1596–1611. Chaffin, D.B., 2005. Improving digital human modelling for proactive ergonomics in design. Ergonomics 48, 478–491. Chihara, T., Izumi, T., Seo, A., 2014. Perceived discomfort functions based on joint moment for various joint motion directions of the upper limb. Appl. Ergon. 45, 308–317. Choi, N.-C., Lee, S.H., 2015. Discomfort evaluation of truck ingress/egress motions based on biomechanical analysis. Sensors 15, 13568–13590. Cristianini, N., Shawe-Taylor, J., 2000. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge university press. Delp, S.L., Anderson, F.C., Arnold, A.S., Loan, P., Habib, A., John, C.T., Guendelman, E., Thelen, D.G., 2007. OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans. Biomed. Eng. 54, 1940–1950. Dufour, F., Wang, X., 2005. Discomfort Assessment of Car Ingress/egress Motions Using the Concept of Neutral Movement. (SAE Technical Paper). Jung, Y., Phan, C.-B., Koo, S., 2016. Intra-articular knee contact force estimation during walking using force-reaction elements and subject-specific joint model. J. Biomech. Eng. 138, 021016. Fan, J., Li, R., 2006. Statistical Challenges with High Dimensionality: Feature Selection in Knowledge Discovery. arXiv preprint math/0602133. Fan, R.-E., Chen, P.-H., Lin, C.-J., 2005. Working set selection using second order information for training support vector machines. J. Mach. Learn. Res. 6, 1889–1918. Forner-Cordero, A., Koopman, H., Van der Helm, F., 2006. Inverse dynamics calculations during gait with restricted ground reaction force information from pressure insoles. Gait Posture 23, 189–199. Gheyas, I.A., Smith, L.S., 2010. Feature subset selection in large dimensionality domains. Pattern Recogn. 43, 5–13. Giacomin, J., Quattrocolo, S., 1997. An analysis of human comfort when entering and exiting the rear seat of an automobile. Appl. Ergon. 28, 397–406. Greiner, M., Pfeiffer, D., Smith, R., 2000. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev. Vet. Med. 45, 23–41. Groenesteijn, L., Ellegast, R.P., Keller, K., Krause, F., Berger, H., de Looze, M.P., 2012. Office task effects on comfort and body dynamics in five dynamic office chairs. Appl. Ergon. 43, 320–328. Hamner, S.R., Seth, A., Delp, S.L., 2010. Muscle contributions to propulsion and support during running. J. Biomech. 43, 2709–2716. Hansen, N., 2006. The CMA evolution strategy: a comparing review. Towards a new evolutionary computation 75–102. Herriotts, P., 2005. Identification of vehicle design requirements for older drivers. Appl. Ergon. 36, 255–262. Hughes, M., Weiner, D., Schenkman, M., Long, R., Studenski, S., 1994. Chair rise strategies in the elderly. Clin. BioMech. 9, 187–192. Jung, M., Cho, H., Roh, T., Lee, K., 2009. Integrated framework for vehicle interior design using digital human model. J. Comput. Sci. Technol. 24, 1149–1161. Kee, D., Na, S., Chung, M.K., 2013. Effect of external load at varying hand positions on perceived discomfort. Int. J. Occup. Saf. Ergon. 19, 397–408. Kim, S., Lee, K., 2009. Development of discomfort evaluation method for car ingress motion. Int. J. Automot. Technol. 10, 619–627. Korte, B., Vygen, J., 2012. Combinatorial Optimization. Springer. Ladha, L., Deepa, T., 2011. Feature selection methods and algorithms. Int. J. Comput. Sci. Eng. 3, 1787–1797. Lord, S.R., Murray, S.M., Chapman, K., Munro, B., Tiedemann, A., 2002. Sit-to-stand
Conflicts of interest As far as the authors know there is no conflict of interest in this 270
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Y. Kim, K. Lee
Shah, S.C., Kusiak, A., 2004. Data mining and genetic algorithm based gene/SNP selection. Artif. Intell. Med. 31, 183–196. Shippen, J., May, B., 2016. Constitutive kinematic modes and shapes during vehicle ingress/egress. Appl. Ergon. 56, 127–135. Trapanese, S., Naddeo, A., Cappetti, N., 2016. A Preventive Evaluation of Perceived Postural Comfort in Car-cockpit Design: Differences between the Postural Approach and the Accurate Muscular Simulation under Different Load Conditions in the Case of Steering-wheel Usage. SAE Technical Paper. Üstünkar, G., Özöğür-Akyüz, S., Weber, G.W., Friedrich, C.M., Son, Y.A., 2012. Selection of representative SNP sets for genome-wide association studies: a metaheuristic approach. Optimization Letters 6, 1207–1218. Zhang, Y., Wang, S., Phillips, P., Ji, G., 2014. Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowl. Base Syst. 64, 22–31.
performance depends on sensation, speed, balance, and psychological status in addition to strength in older people. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 57, M539–M543. Masoud, H.I., Reed, M.P., Paynabar, K., Wang, N., Jin, J.J., Wan, J., Kozak, K.K., GomezLevi, G., 2016. Predicting subjective responses from human motion: application to vehicle ingress assessment. J. Manuf. Sci. Eng. 138, 061001. Masoud, H.I., Zerehsaz, Y., Jin, J.J., 2017. Analysis of human motion variation patterns using UMPCA. Appl. Ergon. 59, 401–409. Robert, T., Causse, J., Denninger, L., Wang, X., 2014. A 3D analysis of the joint torques developed during driver's ingress–egress motion. Ergonomics 57, 1008–1020. Saeys, Y., Inza, I., Larrañaga, P., 2007. A review of feature selection techniques in bioinformatics. Bioinformatics 23, 2507–2517. Schenkman, M., Hughes, M.A., Samsa, G., Studenski, S., 1996. The relative importance of strength and balance in chair rise by functionally impaired older individuals. J. Am. Geriatr. Soc. 44, 1441–1446.
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