Classification of Whole-Body Postural Discomfort Using Cluster Analysis

Individual-specific postural discomfort prediction using decision tree models

The objective of the current study was to explore the utilization of the decision tree (DT) algorithm to model posture-discomfort relationships at the individual level. The DT algorithm has the advantage that it makes no assumptions about the distribution of data, is robust in representing non-linear data with noise, and produces white-box models that are interpretable. Individual-level modelling is essential for examining individual-specific postural discomfort perception processes and understanding the inter-individual variability. It also has practical applications, including the development of individual-specific digital human models and more precise and informative population accommodation analysis. Individual-specific DT models were generated using postural discomfort rating data for various seated upper body postures to predict discomfort based on postural and task variables. The individual-specific DT models accurately predicted postural discomfort and revealed large inter-individual variability in the modelling results. DT modelling is expected to greatly facilitate investigating the human discomfort perception process.

Sex differences in perceived discomfort during seated static posture holding

This study investigated how sex modifies postural discomfort perception during a sagittally-symmetric, seated static posture holding (SPH) task. Ten male and 10 female participants performed SPH and conducted subjective discomfort ratings in a total of 108 task conditions. A regression analysis found that the impacts of the body joint reactive moments on perceived discomfort were larger for the female group than the male whereas that of the shoulder joint angle was more pronounced for the male than the female. Also, some of the 108 task conditions were found to be more uncomfortable for the male group, while some others, for the female. The observed sex impacts are thought to be due to the sex differences in physical work capacities (muscular strength and joint flexibility). The results suggest that new posture analysis tools allowing sex-specific analyses are needed as they would improve the accuracy and precision of ergonomics posture analyses. Practitioner summary: This study empirically investigated how sex modifies postural discomfort perception during a seated posture holding (SPH) task. Sex was found to modify the impacts of joint reactive moments and the shoulder joint angle. The study results seem to reflect the sex differences in muscular strength and joint flexibility.

Prediction and comparison of postural discomfort based on MLP and quadratic regression

OBJECTIVE

The objective of this study was to predict postural discomfort based on the deep learning-based regression (multilayer perceptron [MLP] model).

METHODS

A total of 95 participants performed 45 different static postures as a combination of 3 neck angles, 5 trunk angles, and 3 knee angles and rated the whole-body discomfort. Two different combinations of variables including model 1 (all variables: gender, height, weight, exercise, body segment angles) and model 2 (gender, body segment angles) were tested. The MLP regression and a conventional regression (quadratic regression) were both conducted, and the performance was compared.

RESULTS

In the overall regression analysis, the quadratic regression showed better performance than the MLP regression. For the postural discomfort group-specific analysis, MLP regression showed greater performance than the quadratic regression especially in the high postural discomfort group. The MLP regression also showed better performance in predicting postural discomfort among individuals who had a variability of subjective rating among different postures compared to the quadratic regression. The deep learning for postural discomfort prediction would be useful for the efficient job risk assessment for various industries that involve prolonged static postures.

CONCLUSIONS

The deep learning for postural discomfort prediction would be useful for the efficient job risk assessment for various industries that involve prolonged static postures. This information would be meaningful as basic research data to study in predicting psychophysical data in ergonomics.