Automated ergonomic risk monitoring using body-mounted sensors and machine learning. (October 2018)

• Record Type: Journal Article
• Authors: Nath, Nipun D.; Chaspari, Theodora; Behzadan, Amir H.
• Is Part Of: Advanced engineering informatics. Volume 38(2018)
• Page Start: 514
• Page End: 526
• Publication Date: 2018-10
• Subjects: Construction health -- Wearable sensors -- Ergonomics -- Overexertion -- Human activity recognition -- Machine learning; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2018.08.020
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: Overexertion can cause bodily injury in lift/lower/carry, and push/pull activities. Machine learning helps recognize activities from body-mounted smartphone data. In this research, activity duration and frequency are linked to overexertion risk. Best results are achieved when smartphone is placed on the subject's upper-arm. The designed algorithm is validated using leave-one-subject-out cross-validation. Abstract: Workers in various industries are often subject to challenging physical motions that may lead to work-related musculoskeletal disorders (WMSDs). To prevent WMSDs, health and safety organizations have established rules and guidelines that regulate duration and frequency of labor-intensive activities. In this paper, a methodology is introduced to unobtrusively evaluate the ergonomic risk levels caused by overexertion. This is achieved by collecting time-stamped motion data from body-mounted smartphones (i.e., accelerometer, linear accelerometer, and gyroscope signals), automatically detecting workers' activities through a classification framework, and estimating activity duration and frequency information. This study also investigates various data acquisition and processing settings (e.g., smartphone's position, calibration, window size, and feature types) through a leave-one-subject-out cross-validation framework. Results indicate that signals collected from arm-mounted smartphone device, when calibrated, can yield accuracy up to 90.2% in the considered 3-class classification task. Further post-processing the output of activity classification yields very accurate estimation of the corresponding ergonomic risk levels. This work contributes to the body of knowledge by expanding the current state in workplace health assessment by designing and testing ubiquitous wearable technology to improve the timeliness and quality of ergonomic-related data collection and analysis.