Diese Seite wurde automatisch übersetzt und die Genauigkeit der Übersetzung wird nicht garantiert. Bitte wende dich an die englische Version für einen Quelltext.

Zugängliches Fernrehabilitationssystem für Echtzeit-Biomechanik-Überwachung

3. November 2026 aktualisiert von: Mississippi State University

Entwicklung und klinische Validierung eines KI-basierten Kamerasystems zur Echtzeit-Biomechaniküberwachung in der Oberkörperrehabilitation

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.

Studienübersicht

Status

Noch keine Rekrutierung

Bedingungen

Intervention / Behandlung

Detaillierte Beschreibung

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.

Studientyp

Interventionell

Einschreibung (Geschätzt)

40

Phase

  • Unzutreffend

Kontakte und Standorte

Dieser Abschnitt enthält die Kontaktdaten derjenigen, die die Studie durchführen, und Informationen darüber, wo diese Studie durchgeführt wird.

Studienkontakt

Studienorte

    • Mississippi
      • Jackson, Mississippi, Vereinigte Staaten, 39216
        • University of Mississippi Medical Center
        • Kontakt:
      • Starkville, Mississippi, Vereinigte Staaten, 39759
        • Mississippi State University
        • Kontakt:
        • Hauptermittler:
          • Soroush Korivand, PhD

Teilnahmekriterien

Forscher suchen nach Personen, die einer bestimmten Beschreibung entsprechen, die als Auswahlkriterien bezeichnet werden. Einige Beispiele für diese Kriterien sind der allgemeine Gesundheitszustand einer Person oder frühere Behandlungen.

Zulassungskriterien

Studienberechtigtes Alter

18 Jahre und älter (Erwachsene, Älterer Erwachsener)

Akzeptiert gesunde Freiwillige

Ja

Studienberechtigte Geschlechter

Alle

Beschreibung

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.

Studienplan

Dieser Abschnitt enthält Einzelheiten zum Studienplan, einschließlich des Studiendesigns und der Messung der Studieninhalte.

Wie ist die Studie aufgebaut?

Designdetails

  • Hauptzweck: Behandlung
  • Zuteilung: Zufällig
  • Interventionsmodell: Parallele Zuordnung
  • Maskierung: Keine (Offenes Etikett)

Waffen und Interventionen

Teilnehmergruppe / Arm
Intervention / Behandlung
Experimental: Camera-Based Biomechanical Monitoring (Intervention)

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.

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.

Aktiver Komparator: Standard Telehealth Rehabilitation (Control)

Participants receive standard telehealth rehabilitation with periodic/weekly check-ins and usual care guidance. No real-time camera-based biomechanical monitoring feedback is provided.

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.

Was misst die Studie?

Primäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
Accuracy of Camera-Based Joint Torque Estimation
Zeitfenster: 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.

Baseline assessment session
Correlation Between Camera-Based and Clinical Biomechanical Measurements
Zeitfenster: 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.

Baseline assessment session

Sekundäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
Grip Strength Improvement
Zeitfenster: Baseline, 3 weeks, and 6 weeks

Change in hand grip strength measured using a clinical dynamometer during the rehabilitation program.

Baseline, 3 weeks, and 6 weeks
Range of Motion Improvement
Zeitfenster: Baseline, 3 weeks, and 6 weeks

Change in hand and finger joint range of motion measured using standard clinical goniometry during the rehabilitation period.

Baseline, 3 weeks, and 6 weeks
Functional Recovery Time
Zeitfenster: Up to 6 weeks

Time required for participants to regain at least 80% of their pre-injury hand function based on clinical functional assessments.

Up to 6 weeks
Patient Adherence to Rehabilitation Exercises
Zeitfenster: Up to 6 weeks

Participant adherence to prescribed rehabilitation exercises measured by completion rate of assigned therapy sessions during the study period.

Up to 6 weeks

Mitarbeiter und Ermittler

Hier finden Sie Personen und Organisationen, die an dieser Studie beteiligt sind.

Sponsor

Mitarbeiter

Studienaufzeichnungsdaten

Diese Daten verfolgen den Fortschritt der Übermittlung von Studienaufzeichnungen und zusammenfassenden Ergebnissen an ClinicalTrials.gov. Studienaufzeichnungen und gemeldete Ergebnisse werden von der National Library of Medicine (NLM) überprüft, um sicherzustellen, dass sie bestimmten Qualitätskontrollstandards entsprechen, bevor sie auf der öffentlichen Website veröffentlicht werden.

Haupttermine studieren

Studienbeginn (Geschätzt)

1. Dezember 2026

Primärer Abschluss (Geschätzt)

14. September 2027

Studienabschluss (Geschätzt)

14. September 2027

Studienanmeldedaten

Zuerst eingereicht

5. März 2026

Zuerst eingereicht, das die QC-Kriterien erfüllt hat

19. März 2026

Zuerst gepostet (Tatsächlich)

25. März 2026

Studienaufzeichnungsaktualisierungen

Letztes Update gepostet (Tatsächlich)

6. November 2026

Letztes eingereichtes Update, das die QC-Kriterien erfüllt

3. November 2026

Zuletzt verifiziert

1. November 2026

Mehr Informationen

Begriffe im Zusammenhang mit dieser Studie

Andere Studien-ID-Nummern

  • MSU-UMMC-TELE-REHAB-001
  • U54GM115428 (US NIH Stipendium/Vertrag)

Plan für individuelle Teilnehmerdaten (IPD)

Planen Sie, individuelle Teilnehmerdaten (IPD) zu teilen?

UNENTSCHIEDEN

Beschreibung des IPD-Plans

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.

Arzneimittel- und Geräteinformationen, Studienunterlagen

Studiert ein von der US-amerikanischen FDA reguliertes Arzneimittelprodukt

Nein

Studiert ein von der US-amerikanischen FDA reguliertes Geräteprodukt

Nein

Diese Informationen wurden ohne Änderungen direkt von der Website clinicaltrials.gov abgerufen. Wenn Sie Ihre Studiendaten ändern, entfernen oder aktualisieren möchten, wenden Sie sich bitte an register@clinicaltrials.gov. Sobald eine Änderung auf clinicaltrials.gov implementiert wird, wird diese automatisch auch auf unserer Website aktualisiert .