Forsyth L, Ligeti A, Blyth M, Clarke J V, Riches P E
Faculty of Biomedical Engineering, University of Strathclyde, Glasgow, United Kingdom.
Faculty of Biomedical Engineering, University of Strathclyde, Glasgow, United Kingdom.
Knee. 2024 Dec;51:292-302. doi: 10.1016/j.knee.2024.10.006. Epub 2024 Oct 24.
With 100,000 total knee arthroplasty (TKA) procedures taking place in the United Kingdom annually, the demand on rehabilitation services is high. Most regimes are home-based. Without clinician-patient interaction, detection of rehabilitation concerns can be delayed, reducing the chance of successful early intervention. Wearable technologies, such as MotionSense (Stryker, US), may offer a solution to this problem by remotely supporting post-operative TKA rehabilitation through the provision of personalised rehabilitation and tracking of home exercises, enabling healthcare professionals to continuously monitor rehabilitation progress remotely. Validation of such devices against a known kinematic model in activities of daily living is important for confident interpretation of resulting clinical data. The aim of this study therefore was to validate the accuracy of MotionSense against a clinical motion capture standard.
Twenty younger and 14 older healthy, able-bodied adults attended one testing session (Younger: 24 ± 4 years old; Older: 71 ± 5 years old). Movement was tracked using Vicon motion analysis and a Plug-In-Gait lower body model was applied to all participants. Three activities were performed - walking, stair ascent, stair descent. The knee flexion angle root mean square error (RMSE) between the technologies was determined.
For both groups the knee flexion RMSE remained below 3° for all activities. The combined RMSE for all adults was 2.4° for walking, 2.7° for stair ascent, and 2.6° for stair descent. The signed error increased during the swing phase of gait.
MotionSense was found to accurately estimate knee flexion angles during several common activities compared to Vicon motion capture.
在英国,每年进行100,000例全膝关节置换术(TKA),对康复服务的需求很高。大多数康复方案以家庭为基础。由于缺乏临床医生与患者的互动,康复问题的发现可能会延迟,从而降低早期成功干预的机会。可穿戴技术,如MotionSense(美国史赛克公司),可能通过提供个性化康复方案并跟踪家庭锻炼情况,远程支持TKA术后康复,使医疗保健专业人员能够远程持续监测康复进展,从而解决这一问题。在日常生活活动中,将此类设备与已知运动学模型进行验证,对于可靠解读所得临床数据非常重要。因此,本研究的目的是对照临床运动捕捉标准验证MotionSense的准确性。
20名年轻和14名年长的健康、身体健全的成年人参加了一次测试(年轻组:24±4岁;年长组:71±5岁)。使用Vicon运动分析跟踪运动,并将插件式步态下半身模型应用于所有参与者。进行了三项活动——行走、上楼梯、下楼梯。确定了两种技术之间的膝关节屈曲角度均方根误差(RMSE)。
两组在所有活动中膝关节屈曲RMSE均保持在3°以下。所有成年人的综合RMSE在行走时为2.4°,上楼梯时为2.7°,下楼梯时为2.6°。在步态摆动期,符号误差增加。
与Vicon运动捕捉相比,发现MotionSense在几种常见活动中能准确估计膝关节屈曲角度。