05/05/2026
By Danielle Fretwell

The Francis College of Engineering, Department of Electrical and Computer Engineering, invites you to attend a Doctoral Dissertation Proposal defense by Emi Aoki titled: "Biomechanical Digital Twins for Hand Gesture Analysis in Therapy and Occupational Tasks."

Candidate Name: Emi Aoki
Degree: Doctoral
Defense Date: Tuesday, May 5, 2026
Time: 11 a.m. - noon
Location: Falmouth 203

Committee:

  • Advisor: Kavitha Chandra, Eng.D, Professor, Electrical and Computer Engineering, UMass Lowell
  • Charles Thompson, Ph.D., Professor, Electrical and Computer Engineering, UMass Lowell
  • Lara Thompson, Ph.D., Professor, Biomedical Engineering, UMass Lowell
  • Ola Batarseh, Ph.D., Solution Architect Director, Dassault Systèmes and Adjunct Professor, UMass Lowell

Brief Abstract:
Hand mobility is essential to daily activities, communication, and work. However, hand and finger impairments from stroke, musculoskeletal disorders, and overuse in occupational tasks can reduce functional performance and quality of life. The time to recovery from such impairments is often slow when exercise regimens are not followed at therapist prescribed frequency outside the clinic. In this situation, tools that support adherence to therapeutic intervention and enable prevention of functional decline are of primary importance for enhancing well-being and productivity of humans. To address this problem, this research will investigate the design of biomechanical digital twins of the hand for gesture analysis. A systems approach is taken to understand the context and decisions the digital twin must serve. The sensing and modeling components needed to support therapy and occupational health assessments are then characterized by quantifying the gap between computational models and measurements across a range of hand gestures, including finger flexion and extension for range of motion analysis and functional tasks, such as grip and pinch. A suite of sensors, primarily including a neuromorphic vision sensor, augmented reality headset, surface electromyography, and grip and pinch force sensors, is used to record and analyze a set of hand gestures. These sensor data are used to drive three candidate biomechanical models, ranging from a single-joint system to multi-body finger and hand models, to validate their potential to capture specific features of hand motion. Deviations between model outputs and sensor measurements will be investigated, and model updates will be proposed. The transformation of these tools from a research setting into clinical and occupational health practice will be structured using model-based systems engineering and a unified architecture framework (UAF). The expected outcomes are a quantitative characterization of the gap between computational models and sensor measurements, methods for closing the gap, and a UAF model that supports adoption of the digital twin across the healthcare enterprise and occupational health system.