Hand Injury RehabilitationNCT07492797
Accessible Remote Rehabilitation System for Real-Time Biomechanical Monitoring
A study testing AI-Based Camera Tele-Rehabilitation Monitoring System and Standard Telehealth Rehabilitation in people with Hand Injury Rehabilitation and Postoperative Rehabilitation, run by Mississippi State University at 2 sites in United States.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Hand Injury Rehabilitation, Postoperative Rehabilitation
- What is being tested?
- AI-Based Camera Tele-Rehabilitation Monitoring System and Standard Telehealth Rehabilitation
- Type of treatment
- Medical device, Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 40 participants
- Where
- 2 sitesUnited States
- Study dates
- 1 Dec 2026to 14 Sep 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
United States2 sites
University of Mississippi Medical Center
Jackson, Mississippi, 39216
Marc Walker, MD, mwalker6@umc.edu
Mississippi State University
Starkville, Mississippi, 39759
Soroush Korivand, PhD, 662-325-9154, skorivand@me.msstate.edu
Soroush Korivand, PhD
About this study
Official title: Development and Clinical Validation of an AI-Based Camera System for Real-Time Biomechanical Monitoring in Upper-Limb Rehabilitation
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.
Read the 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.
- Being tested
- AI-Based Camera Tele-Rehabilitation Monitoring System, Standard Telehealth Rehabilitation
- Keywords
- Tele-rehabilitation, Computer Vision, Biomechanics, Hand Motion Analysis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Based Camera Tele-Rehabilitation Monitoring SystemDevice
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.
Standard Telehealth RehabilitationBehavioural
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.
| Study group | Role | What they receive |
|---|---|---|
| Camera-Based Biomechanical Monitoring (Intervention) | Experimental | Participants perform standardized hand/upper-limb rehabilitation exercises while an AI-based camera system estimates joint torque, muscle force, and range of motion in real time. Clinicians may use the biomechanical feedback to guide rehabilitation adjustments over the 6-week study period. Receives: Device: AI-Based Camera Tele-Rehabilitation Monitoring System |
| Standard Telehealth Rehabilitation (Control) | Active comparator | Participants receive standard telehealth rehabilitation with periodic/weekly check-ins and usual care guidance. No real-time camera-based biomechanical monitoring feedback is provided. Receives: Behavioral: Standard Telehealth Rehabilitation |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of Camera-Based Joint Torque Estimation
Accuracy of the AI-based camera system in estimating joint torque during rehabilitation exercises compared with gold-standard dynamometer measurements. Accuracy will be evaluated using mean absolute percentage error (MAPE) between estimated torque values and reference dynamometer readings.
Measured: Baseline assessment session
Correlation Between Camera-Based and Clinical Biomechanical Measurements
Agreement between biomechanical parameters estimated by the camera-based system and reference clinical measurements. Pearson correlation coefficients and Bland-Altman analysis will be used to evaluate agreement between estimated joint torque and gold-standard measurements.
Measured: Baseline assessment session
Secondary outcomes (4)
Grip Strength Improvement
Change in hand grip strength measured using a clinical dynamometer during the rehabilitation program.
Measured: Baseline, 3 weeks, and 6 weeks
Range of Motion Improvement
Change in hand and finger joint range of motion measured using standard clinical goniometry during the rehabilitation period.
Measured: Baseline, 3 weeks, and 6 weeks
Functional Recovery Time
Time required for participants to regain at least 80% of their pre-injury hand function based on clinical functional assessments.
Measured: Up to 6 weeks
Patient Adherence to Rehabilitation Exercises
Participant adherence to prescribed rehabilitation exercises measured by completion rate of assigned therapy sessions during the study period.
Measured: Up to 6 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Mar 2026
- Study start1 Dec 2026 (estimated)
- Primary completion14 Sep 2027 (estimated)
- Study completion14 Sep 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Hand Injury RehabilitationStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- Participants will be randomly assigned to one of two parallel groups. The intervention group will receive daily biomechanical monitoring using a camera-based tele-rehabilitation system that estimates joint torque, muscle force, and range of motion during rehabilitation exercises. Clinicians may use these data to adjust therapy recommendations. The control group will receive standard telehealth rehabilitation with periodic check-ins but without real-time biomechanical monitoring. Outcomes will be compared over a six-week rehabilitation period.
- Masking
- None (open label)
- Enrolment
- 40 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Mississippi State University
- Sponsor type
- University, hospital or other
- Collaborators
- University of Mississippi Medical Center
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07492797
- Sponsor's protocol ID
- MSU-UMMC-TELE-REHAB-001
- Other IDs
- U54GM115428
- Status last verified
- Nov 2026
- First submitted
- 5 Mar 2026
- Last updated
- 6 Nov 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: De-identified individual participant data (IPD) from this study may be shared upon reasonable request following study completion. Data to be shared include derived biomechanical parameters (e.g., joint torque, range of motion), summary performance metrics, and associated metadata necessary for interpretation. Raw video data will not be shared due to potential identifiability concerns. All shared data will be de-identified in accordance with institutional policies and IRB requirements. Access will be provided to qualified researchers upon approval of a data use agreement and for purposes consistent with the original study objectives.
Study at a glance
From the registry record, last updated by the sponsor on 6 Nov 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Clinical Validation of an AI-Based Camera System for Real-Time Biomechanical Monitoring in Upper-Limb Rehabilitation
- ClinicalTrials.gov ID
- NCT07492797
- Acronym / sponsor ID
- MSU-UMMC-TELE-REHAB-001
- Keywords
- Tele-rehabilitation, Computer Vision, Biomechanics, Hand Motion Analysis
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 40 participants (estimated)
- Lead sponsor
- Mississippi State UniversityUniversity, hospital or other
- Collaborators
- University of Mississippi Medical Center
- Study start
- 1 Dec 2026 (estimated)
- Primary completion
- 14 Sep 2027 (estimated)
- Study completion
- 14 Sep 2027 (estimated)
- First posted
- 25 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- Participants will be randomly assigned to one of two parallel groups. The intervention group will receive daily biomechanical monitoring using a camera-based tele-rehabilitation system that estimates joint torque, muscle force, and range of motion during rehabilitation exercises. Clinicians may use these data to adjust therapy recommendations. The control group will receive standard telehealth rehabilitation with periodic check-ins but without real-time biomechanical monitoring. Outcomes will be compared over a six-week rehabilitation period.
| Arm | Type | Receives |
|---|---|---|
| Camera-Based Biomechanical Monitoring (Intervention) | Experimental | Device: AI-Based Camera Tele-Rehabilitation Monitoring SystemParticipants perform standardized hand/upper-limb rehabilitation exercises while an AI-based camera system estimates joint torque, muscle force, and range of motion in real time. Clinicians may use the biomechanical feedback to guide rehabilitation adjustments over the 6-week study period. |
| Standard Telehealth Rehabilitation (Control) | Active comparator | Behavioral: Standard Telehealth RehabilitationParticipants receive standard telehealth rehabilitation with periodic/weekly check-ins and usual care guidance. No real-time camera-based biomechanical monitoring feedback is provided. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-Based Camera Tele-Rehabilitation Monitoring SystemA 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. | Device | Camera-Based Biomechanical Monitoring (Intervention) |
| Standard Telehealth RehabilitationParticipants 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. | Behavioural | Standard Telehealth Rehabilitation (Control) |
Primary outcome
Accuracy of Camera-Based Joint Torque Estimation
Time frame: Baseline assessment session
Accuracy of the AI-based camera system in estimating joint torque during rehabilitation exercises compared with gold-standard dynamometer measurements. Accuracy will be evaluated using mean absolute percentage error (MAPE) between estimated torque values and reference dynamometer readings.
Correlation Between Camera-Based and Clinical Biomechanical Measurements
Time frame: Baseline assessment session
Agreement between biomechanical parameters estimated by the camera-based system and reference clinical measurements. Pearson correlation coefficients and Bland-Altman analysis will be used to evaluate agreement between estimated joint torque and gold-standard measurements.
4 secondary outcomes
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Nov 2026.
- Individual participant dataSharing planned: Undecided. De-identified individual participant data (IPD) from this study may be shared upon reasonable request following study completion. Data to be shared include derived biomechanical parameters (e.g., joint torque, range of motion), summary performance metrics, and associated metadata necessary for interpretation. Raw video data will not be shared due to potential identifiability concerns. All shared data will be de-identified in accordance with institutional policies and IRB requirements. Access will be provided to qualified researchers upon approval of a data use agreement and for purposes consistent with the original study objectives.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-Based Camera Tele-Rehabilitation Monitoring System and Standard Telehealth Rehabilitation in people with Hand Injury Rehabilitation and Postoperative Rehabilitation, run by Mississippi State University at 2 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 6 Nov 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Soroush Korivand, PhD (662-325-9154, skorivand@me.msstate.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of Camera-Based Joint Torque Estimation (time frame: Baseline assessment session). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Nov 2026.