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Human Motion Analysis with Wearable Sensing and Predictive Modeling for Improved Health and Well-being

Modeling, Computation, Nonlinearity, Randomness and Waves Seminar

Human Motion Analysis with Wearable Sensing and Predictive Modeling for Improved Health and Well-being
Series: Modeling, Computation, Nonlinearity, Randomness and Waves Seminar
Location: Online
Presenter: Sol Lim, Systems and Industrial Engineering, University of Arizona

Wearable sensing technologies that gained popularity with health and fitness tracking present many new opportunities for human factors & ergonomics research. Obtaining interpretable and actionable information from the vast amounts of data generated by these sensors will require merging traditional ergonomics theory and first principles with statistical techniques to handle extensive data. My research presents a framework for combining wearable inertial sensing, biomechanical modeling, and predictive modeling techniques for ergonomics assessment. Examples include estimating exposures to manual material handling tasks of different intensity and duration, with insights on the body’s biomechanical response to external loads in dynamic tasks. This study was motivated by the high prevalence of overexertion injuries from high force exertions and awkward postures during manual material handling, accounting for one-third of all work-related injuries costing the U.S. economy $13.7 billion annually. The developed approach aims to overcome some of the conventional challenges of manually measuring workers’ exposures to force exertions and work postures in non-routinized work such as in variable material handling (e.g., UPS/Amazon fulfillment centers), construction work, and patient care (e.g., nurses, patient transporters). I will conclude with an overview of other on-going studies to illustrate the broad potential of low-cost wearable sensing and predictive modeling to improve human health and well-being.


Bio: Sol Lim is an Assistant Professor in the Department of Systems and Industrial Engineering at the University of Arizona. She directs the Smart Life In Motion (SLIM) Lab leading research in human factors & ergonomics. She received her Ph.D. degree from the Industrial and Operations Engineering Department at the University of Michigan, Ann Arbor. She holds Master’s degrees in Biomechanical Engineering from the University of Michigan and in Industrial Engineering from Seoul National University, and B.S. degree in Clothing and Textiles from Yonsei University. Her research focuses on assessing human movement and performance by combining functional biomechanics, wearable sensing technologies, and data analytics in various environmental contexts. Examples include evaluating physical exposures in manual material handling work and field studies to assess public transportation usability involving individuals with mobility impairments. She is a recipient of over 15 awards and grants, including the best student paper award at the Occupational Ergonomics Technical Group in Human Factors and Ergonomics Society (HFES) and Research Training Grant awards from the National Institute of Occupational Safety and Health (NIOSH).

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