TY - JOUR
T1 - Wearable insole pressure system for automated detection and classification of awkward working postures in construction workers
AU - Antwi-Afari, Maxwell Fordjour
AU - Li, Heng
AU - Yu, Yantao
AU - Kong, Liulin
PY - 2018/12
Y1 - 2018/12
N2 - 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.
AB - 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.
KW - Awkward working postures
KW - Construction workers
KW - Foot plantar pressure distribution
KW - Supervised machine learning classifiers
KW - Wearable insole pressure system
KW - Work-related musculoskeletal disorders
UR - http://www.scopus.com/inward/record.url?scp=85054799691&partnerID=8YFLogxK
UR - https://www.sciencedirect.com/science/article/pii/S0926580518303819?via%3Dihub
U2 - 10.1016/j.autcon.2018.10.004
DO - 10.1016/j.autcon.2018.10.004
M3 - Article
AN - SCOPUS:85054799691
SN - 0926-5805
VL - 96
SP - 433
EP - 441
JO - Automation in Construction
JF - Automation in Construction
ER -