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Shoulder pain is one of the most prevalent musculoskeletal conditions. Evidence-based medicine has identified physical therapy as the most effective intervention for managing shoulder disorders. To ensure accurate diagnosis and effective treatment planning, a comprehensive evaluation that integrates various clinical findings is essential. Without timely and accurate diagnosis and intervention, shoulder pain may recur and fail to improve, limiting therapeutic outcomes.
With technological advancements, the application of mobile devices and artificial intelligence (AI) in clinical settings has become increasingly widespread. Motion capture technologies integrated into mobile platforms offer emerging solutions for clinical challenges. If clinicians are equipped with an intelligent system for shoulder assessment and intervention-one that includes image-based quantitative assessment tools, evidence-based clinical guidelines and data repositories, and home-based rehabilitation support-it may enhance diagnostic precision, increase clinical efficiency, and improve patient adherence to home exercise programs.
The aim of this study is to develop a smart technology-assisted assessment system for orthopedic physical therapy of the shoulder joint and to validate its reliability and validity. This system will provide clinicians with objective, data-driven evaluation results. In future development, it will also offer support in treatment goal setting, intervention planning, and home-based exercise guidance. The proposed intelligent system is expected to serve as an evidence-based clinical aid, enhancing both the precision and efficiency of physical therapy interventions.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| healthy | healthy subjects with no any shoulder impairment | ||
| shoulder impairment | diagnosed by Doctors or Physical Therapists with any type of non-acute shoulder impairment |
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| Measure | Description | Time Frame |
|---|---|---|
| Range of Motion | Shoulder Range of Motion | Total 3 times. First trail at day1 (after enrollment) Second trail at 1hour after first trial Third trail is at day2 |
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Inclusion Criteria for group 1:
Inclusion Criteria for group 2:
Exclusion Criteria:
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Healthy population and Shoulder impaired population.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| National Cheng Kung University | Tainan | TNN | 704 | Taiwan |
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