Accessible Remote Rehabilitation System for Real-Time Biomechanical Monitoring

Официальное название

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

Обзор

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.

Подробное описание

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.

Вмешательства

• Устройство 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.
• Поведенческое 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.

Первичные конечные точки

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

Вторичные конечные точки (4)

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

Критерии участия

Критерии включения

• 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.

Критерии исключения

• 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.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Распределение

Рандомизированное

Модель

Параллельные группы

Маскирование

Открытое

Основная цель

Лечение

Центры проведения

США · 2 центра

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

Идентификаторы

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

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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