Title: Wearable Sensing and Intervention Strategies for Improving the Biomechanical Safety of Workers
Date: Monday, July 6th, 2026
Time: 2:30PM to 4:00PM ET
Location: MRDC 4211 or via zoom (meeting id: 966 6470 1064)
Ryan Casey
Robotics Ph.D. Student
Woodruff School of Mechanical Engineering
Georgia Institute of Technology
Committee:
Dr. Anirban Mazumdar (advisor) -- Woodruff School of Mechanical Engineering
Dr. Aaron Young (advisor) -- Woodruff School of Mechanical Engineering
Dr. Gregory Sawicki -- Woodruff School of Mechanical Engineering
Dr. Mohsen Moghaddam -- H. Milton Stewart School of Industrial and Systems Engineering
Dr. Jason Wheeler -- Sandia National Labs
Abstract:
Manual laborers face an elevated incidence of chronic overuse injury, yet existing protocols for assessing and mitigating this risk remain limited by an inability to characterize injury risk practically and a lack of validated intervention strategies. This thesis proposal examines wearable sensing and intervention systems for reducing joint contact forces (JCFs), the internal mechanical loads most closely tied to musculoskeletal injury. In aim 1, we develop a wearable sensing system combining IMUs and pressure insoles with deep learning models to estimate lower-body biomechanics in real time. We demonstrate that this system outperforms analytical methods across joint angles and moments, and report on ongoing work to refine model training specifically for knee joint loading estimation. In aim 2, we evaluate the effects of four exosuits on knee and lumbar JCFs during simulated occupational lifting tasks, using EMG-informed musculoskeletal modeling that explicitly accounts for exosuit assistance as external forces. We find that both passive and active exosuits can reduce loading at their targeted joints, and that the design and assistance profile of devices heavily impact their performance. We also find that exosuits produce measurable but inconsistent effects at non-targeted joints, motivating the importance of multi-joint analysis. In aim 3, we propose a real-time, task-agnostic biofeedback system that conveys estimated knee joint loads directly to users via augmented reality, and describe a training paradigm designed to guide users toward lifting strategies that reduce knee loading without increasing lumbar loading. Together, these studies advance the development of practical, wearable technologies for characterizing and reducing injury risk in physically demanding occupations.