Vision-based approach for long-term mobility monitoring: Single case study following total hip replacement

被引:19
|
作者
Dolatabadi, Elham [1 ,2 ,3 ]
Taati, Babak [3 ,4 ,5 ]
Mihailidis, Alex [1 ,2 ,3 ,4 ,6 ]
机构
[1] Univ Toronto, Intelligent Assist Technol & Syst Lab, Toronto, ON, Canada
[2] Univ Toronto, Inst Biomat & Biomed Engn, Toronto, ON, Canada
[3] Toronto Rehabil Inst, iDAPT Ctr Rehabil Res, Toronto, ON M5G 2A2, Canada
[4] Toronto Rehabil Inst, Toronto, ON M5G 2A2, Canada
[5] Univ Toronto, Dept Comp Sci, Toronto, ON, Canada
[6] Univ Toronto, Dept Occupat Sci & Occupat Therapy, Toronto, ON, Canada
来源
基金
加拿大自然科学与工程研究理事会;
关键词
balance; feasibility study; long-term monitoring; markerless vision-based system; Microsoft Kinect sensor; mobility; naturalistic follow-up; rehabilitation; sit-to-stand; spatiotemporal kinematics; total hip replacement; walking; MICROSOFT KINECT; BODY MOVEMENTS; GAIT; PERFORMANCE; VALIDITY; DESIGN; SENSOR;
D O I
10.1682/JRRD.2013.12.0263
中图分类号
R49 [康复医学];
学科分类号
100215 ;
摘要
This article presents a single case study on the feasibility of using a low-cost and portable vision-based system (a Microsoft Kinect sensor) to monitor changes in movement patterns before and after a total hip replacement surgery. The primary subject was an older male adult with total hip replacement who performed two different functional tasks: walking and sit-to-stand. The tasks were recorded with a Kinect multiple times, starting from 1 d before the surgery until 9 wk after the surgery. An automated algorithm has been developed to extract the important spatiotemporal characteristics from the video recorded functional tasks (walking and sit-to-stand). Statistical analysis was then performed by Tryon C statistic to study changes in spatiotemporal characteristics between different stages before and after the surgery. The statistical analysis indicated significant difference and slight improvement between all measures from the presurgery to each postsurgery date. The study confirmed that the Kinect sensor and an automated algorithm have the potential to be integrated into a patient's home to monitor changes in mobility during the recovery period.
引用
收藏
页码:1165 / 1176
页数:12
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