Research on the Relationship Between Scoliosis, Pain, Quality of Life, and Trunk Muscle Compensation Patterns During Functional Upper Extremity Movements Among Patients with Duchenne Muscular Dystrophy Using Surface Electromyography and Computer Vision Analysis.
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Duchenne Muscular Dystrophy (DMD)
- Sponsor
- Seoul National University Hospital
- Enrollment
- 30
- Locations
- 1
- Primary Endpoint
- Surface Electromyography (sEMG)
- Status
- Recruiting
- Last Updated
- last year
Overview
Brief Summary
- Objective:
The objective of this observational study is to evaluate and quantify trunk muscle compensatory movement patterns in patients with Duchenne Muscular Dystrophy (DMD) using computer vision technology. Additionally, the study seeks to explore the relationship between these compensatory patterns and scoliosis, upper limb function, pain levels, and quality of life during functional upper limb movements.
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Key Research Questions:
- Can trunk compensatory movement patterns be accurately measured using computer vision analysis? 2) Are these compensatory patterns correlated with scoliosis, upper limb function levels, pain, and quality of life? 3) Do these patterns and their correlations change over time?
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Methodology:
- Participants: Patients diagnosed with Duchenne Muscular Dystrophy will be recruited for this study.
- Assessments:
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Scoliosis Evaluation:
- Cobb angle measurement via X-ray imaging.
- Upper Limb Function Assessment:
- Performance of the Upper Limb Module 2.0 (PUL 2.0).
- Brooke Upper Extremity Functional Classification Score.
- Korean version of the Duchenne Muscular Dystrophy Functional Ability Self-Assessment Tool (K-DMDSAT).
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Pain Measurement:
- Korean version of the PainDETECT Questionnaire (KPD-Q).
- Short Form McGill Pain Questionnaire.
- Quality of Life Assessment:
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Duchenne Muscular Dystrophy Quality of Life Questionnaire (DMD-QoL).
- Trunk Compensation Analysis:
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Surface electromyography (sEMG) to measure muscle activation.
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Video analysis using computer vision to quantify trunk compensatory movement patterns.
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The following tasks will be evaluated using the dominant arm for sEMG and video analysis:
i. Pouring water into a cup. ii. Lifting a cup to drink water. iii. Grooming the front of the hair. iv. Moving small blocks within one minute (Box and Block Test). v. Reaching toward nearby objects in the front, left, and right directions.
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Front: Directly in front of the participant's line of sight.
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Left and right: Approximately 45 degrees to the left and right from the participant's front.
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Nearby objects: A water bottle or cup weighing approximately 250g, placed at arm's length.
vi. Reaching toward distant objects in the front, left, and right directions.
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Distant objects: A water bottle or cup weighing approximately 250g, placed at 1.5 times the participant's arm length.
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The sEMG attachment sites are as follows:
i. Muscles for assessing upper limb functional movements:
- Deltoid
- Pectoralis major
- Trapezius
- Biceps brachii ii. Muscles for assessing trunk compensatory actions:
- Sternocleidomastoid
- Longissimus muscle
- External oblique abdominal muscle
Investigators
Woo Hyung Lee
Associate professor
Seoul National University Hospital
Eligibility Criteria
Inclusion Criteria
- Not provided
Exclusion Criteria
- Not provided
Outcomes
Primary Outcomes
Surface Electromyography (sEMG)
Time Frame: enrollment, 6 months after, and 12 months after since the enrollment
The purpose of using surface electromyography (sEMG) in this study is to measure and analyze the activation levels and patterns of trunk compensatory muscles during the performance of functional upper limb movements. This assessment aims to understand how trunk muscles compensate for upper limb movements, particularly in relation to task performance efficiency.
Computer Vision-Based Video Analysis
Time Frame: enrollment, 6 months after, and 12 months after since the enrollment
Videos are recorded simultaneously with surface electromyography (sEMG) while participants perform functional upper limb movements. Recordings are taken from two perspectives: the front view and the dominant arm side view, with synchronized matching of the videos. Video recording is conducted using a video camera mounted on a fixed tripod. The recorded videos are analyzed using a convolutional neural network (CNN)-based body part detection model, producing skeleton-based outputs for movement analysis. The relative trunk motion of the participant is extracted as positional coordinates over time, which are further processed to calculate velocity, acceleration, and jerk. These time-series signals are analyzed for smoothness and sample entropy. By matching the movement data with corresponding sEMG signals, biomechanical compensatory parameters are identified and key compensatory features are derived. Comparative analyses with healthy controls are performed to validate these parameters.
Secondary Outcomes
- Brooke Score(enrollment, 6 months after, and 12 months after since the enrollment)
- Performance of the Upper Limb Module 2.0 (PUL 2.0)(enrollment, 6 months after, and 12 months after since the enrollment)
- Korean version of the Duchenne Muscular Dystrophy Functional Ability Self-Assessment Tool (K-DMDSAT)(enrollment, 6 months after, and 12 months after since the enrollment)
- Korean version of the PainDETECT Questionnaire (KPD-Q)(enrollment, 6 months after, and 12 months after since the enrollment)
- Short Form McGill Pain Questionnaire (SF-MPQ) Korean version(enrollment, 6 months after, and 12 months after since the enrollment)
- Duchenne Muscular Dystrophy Quality of Life Questionnaire (DMD-QoL)(enrollment, 6 months after, and 12 months after since the enrollment)