リアルタイム生体力学モニタリングのためのアクセシブル遠隔リハビリテーションシステム
AIベースのカメラシステムによる上肢リハビリテーションのためのリアルタイム生体力学モニタリングの開発と臨床的検証
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.
調査の概要
状態
状態
条件
条件
介入・治療
介入・治療
詳細な説明
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.
研究の種類
研究の種類
入学 (推定)
入学
段階
段階
- 適用できない
連絡先と場所
研究連絡先
研究連絡先
- 名前：Soroush Korivand, PhD
- 電話番号：662-325-9154
- メール：skorivand@me.msstate.edu
研究場所
-
-
Mississippi
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Jackson、Mississippi、アメリカ、39216
- University of Mississippi Medical Center
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コンタクト：
- Marc Walker, MD
- メール：mwalker6@umc.edu
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Starkville、Mississippi、アメリカ、39759
- Mississippi State University
-
コンタクト：
- Soroush Korivand, PhD
- 電話番号：662-325-9154
- メール：skorivand@me.msstate.edu
-
主任研究者：
- Soroush Korivand, PhD
-
-
参加基準
適格基準
適格基準
就学可能な年齢
健康ボランティアの受け入れ
受講資格のある性別
説明
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.
研究計画
研究はどのように設計されていますか？
デザインの詳細
- 主な目的：処理
- 割り当て：ランダム化
- 介入モデル：並列代入
- マスキング：なし（オープンラベル）
アーム数
武器と介入
参加者グループ / アーム参加者グループ / アーム |
介入・治療介入・治療 |
|---|---|
|
実験的：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. |
|
アクティブコンパレータ：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. |
この研究は何を測定していますか？
主要な結果の測定
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Accuracy of Camera-Based Joint Torque Estimation
時間枠：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
時間枠：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
|
二次結果の測定
二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Grip Strength Improvement
時間枠：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
時間枠：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
時間枠：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
時間枠：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
|
協力者と研究者
協力者
協力者
研究記録日
主要日程の研究
研究開始 (推定)
研究開始
一次修了 (推定)
一次修了
研究の完了 (推定)
研究の完了
試験登録日
最初に提出
最初に提出
QC基準を満たした最初の提出物
QC基準を満たした最初の提出物
最初の投稿 (実際)
最初の投稿
学習記録の更新
投稿された最後の更新 (実際)
投稿された最後の更新
QC基準を満たした最後の更新が送信されました
QC基準を満たした最後の更新が送信されました
最終確認日
最終確認日
詳しくは
本研究に関する用語
キーワード
その他の研究ID番号
その他の研究ID番号
- MSU-UMMC-TELE-REHAB-001
- U54GM115428 (米国 NIH グラント/契約)
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
IPD プランの説明
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.
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
米国FDA規制機器製品の研究
この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。