TY - GEN
T1 - E-health of Construction Works
T2 - 14th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2018
AU - Zhao, Junqi
AU - Obonyo, Esther Adhiambo
PY - 2018/12/26
Y1 - 2018/12/26
N2 - This research is direct at developing an e-health approach for preventing the Musculoskeletal Disorders in construction through monitoring injury risk. The proposed approach leverages activities recognition through motion capturing techniques to monitor, assess, and reduce injury risk. More specifically, the authors used the Inertia Measurement Units, a motion-sensing tool to develop a concept for a wearable motion-data capturing prototype. The captured motion data was analyzed using data-driven, Machine Learning techniques to identify injury-prone activities. Instead of adopting a generic recognition model, a novel rapid model training process was investigated to configure a user-specific activity recognition model, aiming at improving the recognition accuracy with reduced computational effort. The customized model was based on the optimal configuration of data segmentation window size, feature sets, and classification algorithms for a specific user's activity data. The feasibility study of the proposed approach has shown the personalized model achieved an average overall recognition accuracy of 0.81 and 0.74 for two sets of activities. The recognition model's operation time was also reduced to under 0.01 seconds. The proposed approach can help to address the scalability challenge for data-driven activity recognition, and further, improve the effectiveness and practicality for proactive injury prevention on construction job site.
AB - This research is direct at developing an e-health approach for preventing the Musculoskeletal Disorders in construction through monitoring injury risk. The proposed approach leverages activities recognition through motion capturing techniques to monitor, assess, and reduce injury risk. More specifically, the authors used the Inertia Measurement Units, a motion-sensing tool to develop a concept for a wearable motion-data capturing prototype. The captured motion data was analyzed using data-driven, Machine Learning techniques to identify injury-prone activities. Instead of adopting a generic recognition model, a novel rapid model training process was investigated to configure a user-specific activity recognition model, aiming at improving the recognition accuracy with reduced computational effort. The customized model was based on the optimal configuration of data segmentation window size, feature sets, and classification algorithms for a specific user's activity data. The feasibility study of the proposed approach has shown the personalized model achieved an average overall recognition accuracy of 0.81 and 0.74 for two sets of activities. The recognition model's operation time was also reduced to under 0.01 seconds. The proposed approach can help to address the scalability challenge for data-driven activity recognition, and further, improve the effectiveness and practicality for proactive injury prevention on construction job site.
UR - http://www.scopus.com/inward/record.url?scp=85060784461&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060784461&partnerID=8YFLogxK
U2 - 10.1109/WiMOB.2018.8589167
DO - 10.1109/WiMOB.2018.8589167
M3 - Conference contribution
AN - SCOPUS:85060784461
T3 - International Conference on Wireless and Mobile Computing, Networking and Communications
SP - 145
EP - 152
BT - 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2018
PB - IEEE Computer Society
Y2 - 15 October 2018 through 17 October 2018
ER -