Open AccessComparative accuracy of a shoulder range motion measurement sensor and Vicon 3D motion capture for shoulder abduction in frozen shoulder
Author(s) -
Chanhee Park,
Yeongsang An,
Hyunsik Yoon,
Ilbong Park,
Dong Kyu Kim,
Chungyoo Kim,
Youngjoo Cha
Publication year - 2022
Publication title -
technology and health care
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.281
H-Index - 44
eISSN - 1878-7401
pISSN - 0928-7329
DOI - 10.3233/thc-228024
Subject(s) - shoulders , range of motion , motion capture , shoulder joint , kinematics , frozen shoulder , physical medicine and rehabilitation , artificial intelligence , computer science , linear regression , motion (physics) , motion analysis , medicine , physical therapy , computer vision , surgery , machine learning , physics , classical mechanics
BACKGROUND: Although patients with frozen shoulders have the range of motion (ROM) of their shoulder’s abduction movements measured at hospital and the physical therapy visits, multiple visits to check for progress is often difficult. Thus, we developed an artificial intelligence-based image recognition detectable sensor (AIRDS) intended for easy use at home. OBJECTIVE: The purpose of this study was to determine the accuracy of a sensor (AIRDS) measuring shoulder abduction angle, thus offering a valid and feasible system for monitoring patients with frozen shoulder. METHODS: Ten patients with frozen shoulder (5 males, 5 females) performed shoulder joint movements while being measured with the AIRDS system and the 3-dimensional Vicon system. The measure of the outcome included the linear regression of the shoulder abduction joint kinematics. RESULTS: Linear regression analysis of the AIRDS system and the Vicon system demonstrated a significant correlation coefficient of R2= 0.9979 (P< 0.05). CONCLUSIONS: Our results provide novel, promising evidence that AIRDS can accurately measure the timing and total spatial characteristics of clinical movements. AIRDS is designed to provide real-time ROM measurements for joint mobility using artificial intelligence instead of the judgement of the physical therapist.