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A Sensing based Approach to Assessing and Predicting Musculoskeletal Symptoms Work-From-Home Employees
SessionPoster Session 2
DescriptionThis project examines musculoskeletal pain levels among office workers before (in office settings) and after (in Work From Home settings) the COVID-19 pandemic. Data were collected from participants across 4 time intervals, measuring self-reported pain levels in different body regions and objective physical activity indicators, including heart rate (HR) and metabolic equivalent of task (MET). Firstly, statistical Analysis methods were used to evaluate pain progression and physiological changes over time. Secondly, machine learning techniques, particularly Temporal Convolutional Networks (TCN), were applied to model the patterns of physical activity and predict pain levels. The primary goal of the study is to understand how WFH affects musculoskeletal health, identify key risk factors associated with increased pain. The findings highlight the critical need for proactive strategies to mitigate the physical health impacts of remote work environments.
Contributors
Bowang Yan
The State University of New York at Buffalo
Xin Zhou
State University of New York at Binghamton
The State University of New York at Buffalo
Jia-hua Lin
SHARP, Washington Department of Labor and Industries
University of Washington
Jing Yang
The State University of New York at Buffalo
Event Type
Poster
TimeWednesday, October 15th5:30pm - 6:30pm CDT
LocationRiverside East
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