SPECIAL SYMPOSIUM: Ai-powered Movement Analysis and a Causal Framework for Precision Rehabilitation SS1

Tuesday, October 28, 2025

8:00 AM - 9:00 AM

Location: ROOM: Buckingham Room >>> DIRECTIONS: Lobby Level >>> From the Main Elevator Bank, proceed right toward the FedEx Office. Turn right just before the FedEx Office and continue down the corridor. Turn right before the Continental Foyer. The Buckingham Room is located to your right, just before the Men's Room.

Presenter(s)

R. James Cotton, MD, PhD

Physician-Scientist
Shirley Ryan AbilityLab / Northwestern University
Chicago, IL, United States

This proposal outlines a scientific approach for advancing precision rehabilitation through a novel Causal Framework. The framework aims to optimize therapeutic interventions by integrating data science, artificial intelligence (AI), and causal inference. It synthesizes concepts from Computational Neurorehabilitation, the International Classification of Functioning and Disability (ICF), and the Rehabilitation Treatment Specification System (RTSS).

Central to this approach is the development of formal causal models, referred to as "digital twins," which simulate individual patient recovery trajectories. These models leverage heterogeneous, longitudinal data, encompassing clinical assessments and high-resolution measurements obtained via AI-powered movement analysis, such as computer vision-based gait analysis. The RTSS plays a critical role by enabling detailed documentation of treatment targets, active ingredients (viewed as causal influences), and mechanisms, which directly inform the construction of these causal models.

The resulting models allow for counterfactual analysis—predicting how a patient might respond to different interventions. This informs an Optimal Dynamic Treatment Regimen (ODTR), guiding the selection of therapies to maximize long-term function across multiple ICF levels (body function, activity, participation), focusing on patient-valued outcomes.

Illustrative case studies include EMG biofeedback for spinal cord injury, modeling its effects on motor function via mechanisms like activity-dependent plasticity, and post-stroke gait rehabilitation, integrating neurophysiology and kinematics to differentiate recovery mechanisms and predict outcomes from various interventions (e.g., high-intensity training, FES, AFOs).

The framework acknowledges challenges like data integration and model complexity, emphasizing the need for transdisciplinary efforts and advanced methods like causal representation learning. Its goal is to provide a robust platform for improving rehabilitation outcomes through mechanistically informed, personalized treatment strategies.

Learning Objectives:

Identify how causal inference can be applied to the mechanics of recovery and the influence of therapeutic interventions.
Discuss opportunities for advances in AI to enable analysis of gait in the clinic using both multiview and smartphone view.
List the three elements documented in the rehabilitation treatment specification system and how they support building causal models of rehabilitation.