Accessible Remote Rehabilitation System for Real-Time Biomechanical Monitoring

Official title

Development and Clinical Validation of an AI-Based Camera System for Real-Time Biomechanical Monitoring in Upper-Limb Rehabilitation

Overview

This study evaluates a novel camera-based system designed to support remote rehabilitation by measuring hand and upper-limb biomechanics in real time. Many patients recovering from musculoskeletal or neurological conditions require frequent monitoring during rehabilitation, but regular clinic visits may be difficult due to distance, cost, or limited access to specialized care. Current telehealth approaches typically rely on qualitative assessments or self-reported feedback rather than objective biomechanical measurements. The purpose of this study is to determine whether a computer vision-based system can accurately estimate biomechanical parameters such as joint angles, range of motion, muscle force, and joint torque using only a standard camera. The system analyzes hand movement using artificial intelligence and biomechanical modeling to provide real-time measurements during rehabilitation exercises. Participants will perform guided hand-movement tasks while the system records video and extracts anatomical landmarks. These data will be used to compute biomechanical parameters and assess whether the system can reliably monitor rehabilitation progress remotely. The results will help determine whether this technology can provide clinicians with objective, continuous data to support personalized rehabilitation and improve patient outcomes.

Detailed description

This study aims to develop and validate a camera-based tele-rehabilitation platform capable of estimating biomechanical parameters of the human hand and upper limb in real time. Musculoskeletal and neurological conditions often require continuous monitoring during rehabilitation, yet many patients-particularly those in rural or underserved regions-have limited access to frequent in-person therapy sessions. Existing telehealth systems primarily rely on subjective reporting or periodic video consultations and often lack quantitative biomechanical measurements necessary for precise monitoring of recovery.

The objective of this research is to evaluate whether computer vision and biomechanical modeling can provide accurate, quantitative measurements of joint motion and force using a single camera. The central hypothesis is that artificial intelligence algorithms can detect anatomical landmarks of the hand from video data and combine them with mechanical modeling techniques to estimate joint angles, torques, and muscle forces in real time. Continuous biomechanical tracking may allow clinicians to better monitor rehabilitation progress and make timely adjustments to therapy protocols.

Participants will perform standardized hand-movement exercises while video data are captured using a consumer-grade camera such as a smartphone or laptop camera. Computer vision algorithms will identify hand landmarks and calculate joint kinematics. These measurements will then be integrated with inverse dynamics modeling to estimate biomechanical parameters including joint torque, range of motion, and force generation.

The study will evaluate the reliability and validity of the proposed system by comparing the computed biomechanical measurements with established biomechanical models and reference datasets. Key outcomes include the accuracy of landmark detection, reliability of biomechanical parameter estimation, and feasibility of remote monitoring during rehabilitation exercises.

Successful completion of this study will demonstrate the feasibility of a low-cost, accessible tele-rehabilitation platform capable of delivering objective biomechanical feedback to clinicians and patients. This approach has the potential to improve access to rehabilitation services, enhance patient engagement, and support data-driven clinical decision-making in remote healthcare settings.

Interventions

• Device AI-Based Camera Tele-Rehabilitation Monitoring System
  A single-camera, computer vision and inverse-dynamics modeling system that estimates biomechanical parameters (joint torque, muscle force, and range of motion) from video-based hand landmark tracking during rehabilitation exercises.
• Behavioral Standard Telehealth Rehabilitation
  Participants perform standard rehabilitation exercises and receive routine telehealth follow-up with clinicians according to usual care practices. No camera-based biomechanical monitoring system is used during the rehabilitation process.

Primary outcome measures

• Accuracy of Camera-Based Joint Torque Estimation [Time frame: Baseline assessment session]
• Correlation Between Camera-Based and Clinical Biomechanical Measurements [Time frame: Baseline assessment session]

Secondary outcome measures (4)

• Grip Strength Improvement [Time frame: Baseline, 3 weeks, and 6 weeks]
• Range of Motion Improvement [Time frame: Baseline, 3 weeks, and 6 weeks]
• Functional Recovery Time [Time frame: Up to 6 weeks]
• Patient Adherence to Rehabilitation Exercises [Time frame: Up to 6 weeks]

Eligibility criteria

Inclusion criteria

• Adults aged 18 years or older.
• Individuals undergoing or recovering from upper-limb or hand rehabilitation following musculoskeletal or neurological injury or surgery.
• Ability to perform basic hand or upper-limb movement tasks required for the rehabilitation exercises.
• Ability to understand study instructions and provide informed consent.

Exclusion criteria

• Severe cognitive impairment preventing understanding of study procedures.
• Medical conditions that prevent safe participation in hand or upper-limb rehabilitation exercises.
• Severe visual impairment preventing interaction with the camera-based monitoring system.
• Participation in another interventional study that could affect rehabilitation outcomes.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Allocation

Randomized

Model

Parallel assignment

Masking

Open label

Primary purpose

Treatment

Study locations

United States · 2 centers

• University of Mississippi Medical Center — Jackson
• Mississippi State University — Starkville

Identifiers

NCT: NCT07492797 · MSU-UMMC-TELE-REHAB-001 · U54GM115428

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗