Automation in Construction

Wearable insole pressure system for automated detection and classification of awkward working postures in construction workers

Maxwell Fordjour Antwi-Afari, Heng Li, Yantao Yu, Liulin Kong

Department of Building and Real Estate, Faculty of Construction and Environment, Hong Kong Polytechnic University, Hong Kong


Awkward working postures, Construction workers, Foot plantar pressure distribution, Supervised machine learning classifiers, Wearable insole pressure system, Work-related musculoskeletal disorders


Awkward working postures are the main risk factor for work-related musculoskeletal disorders (WMSDs) causing non-fatal occupational injuries among construction workers. However, it remains a challenge to use existing risk assessment methods for detecting and classifying awkward working postures because these methods are either intrusive or rely on subjective judgment. Therefore, this study developed a novel and non-invasive method to automatically detect and classify awkward working postures based on foot plantar pressure distribution data measured by a wearable insole pressure system. Ten asymptomatic participants performed five different types of awkward working postures (i.e., overhead working, squatting, stooping, semi-squatting, and one-legged kneeling) in a laboratory setting. Four supervised machine learning classifiers (i.e., artificial neural network (ANN), decision tree (DT), K-nearest neighbor (KNN), and support vector machine (SVM)) were used for classification performance using a 0.32 s window size. Cross-validation results showed that the SVM classifier (i.e., the best classifier) obtained a classification performance with an accuracy of 99.70% and a sensitivity of each awkward working posture was above 99.00% at 0.32 s window size. The findings substantiated that it is feasible to use a wearable insole pressure system to identify risk factors for developing WMSDs, and could help safety managers to minimize workers' exposure to awkward working postures.

Moticon's Summary

Work-related musculoskeletal disorders (WMSDs) are a significant issue in the construction industry. Current ergonomic risk assessment methods for WMSDs, such as self-reports and observational methods, are often intrusive and subjective. This study proposes a novel non-invasive approach using a Moticon sensor insoles to automatically detect and classify awkward working postures based on foot plantar pressure distribution data. A simulated laboratory experiment was conducted with ten participants performing various awkward postures. Four supervised machine learning classifiers (ANN, DT, KNN, and SVM) were evaluated for their effectiveness in detecting and classifying these postures. Results showed that the SVM classifier achieved the highest accuracy at 99.70%. This study highlights the potential of using sensor insoles for continuous ergonomic risk assessment, providing a practical solution for reducing WMSDs among construction workers. Future research should focus on integrating additional sensors and testing in real-world construction settings.

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