膝关节置换后康复远程监测系统的应用研究  

Remote monitoring system of rehabilitation after total knee arthroplasty

作　　者：黄鹏 张昊华[3] 刘艳成[1,4] 刘庆凯[1] 全海英[1,2] 闫松华[1,2] 张宽[1,2] 

机构地区：[1]首都医科大学生物医学工程学院,北京100069 [2]首都医科大学临床生物力学应用基础研究北京市重点实验室,北京100069 [3]北京积水潭医院矫形外科,北京100035 [4]天津医院脊柱外科,天津300211

出　　处：《北京生物医学工程》2018年第1期66-72,102,共8页Beijing Biomedical Engineering

基　　金：北京市自然科学基金(7152018);首都医科大学临床生物力学应用基础研究北京市重点实验室开放研究课题资助

摘　　要：目的全膝关节置换术(total knee arthroplasty,TKA)后的居家康复训练非常重要,但是目前缺乏对患者居家康复训练的监控和指导手段,因此设计一套远程监测系统指导TKA患者在家进行屈膝和行走训练。方法系统分为数据采集模块(包括加速度计、陀螺仪、磁传感器)、患者端手机应用程序(App)、云端服务器、医生端App,患者端App可以识别屈膝训练和行走训练两种运动。采用有限状态机模型,实现坐位屈膝角度和次数的计算;采用自适应零交叉法,实现行走步数和距离的计算。选取12例健康受试者(年龄26.4岁±3.8岁、身高1.71 m±0.05 m、体重63.6 kg±8.5 kg)和12例TKA患者(年龄62.1岁±6.6岁、身高1.65 m±0.07 m、体重66.8 kg±3.5 kg)进行系统测量准确性验证。结果系统能够获取康复数据并通过患者手机端App传输到云端,医生能通过医生端App从云端读取患者的康复数据。健康受试者组和TKA患者组屈膝次数的计算准确率均为100%,步数的计算误差分别为1.6%±1.3%和3.0%±2.2%,距离误差分别为5.3%±2.9%和6.5%±4.9%。结论监测系统实现了TKA术后屈膝和行走训练的准确识别,为术后居家康复训练的远程监控和指导提供了有效方法。Objective After total knee arthroplasty（ TKA）,appropriate rehabilitation and physical activity at home is important for patients ＇recovery. We designed a system for remote monitoring the knee bending training and walking steps and distances for patients after TKA. Methods The system consisted of data acquisition module with accelerometer, gyroscope,geomagnetic sensor,patient-side App that calculates knee bending angles and times,and walking steps and distances,cloud server,and doctor-side App that reads data from cloud server. The finite state machine method was used to calculate knee bending angle and times during sitting; adaptive zero crossing method was used to calculate walking steps and distances. Twelve healthy subjects（ age 26. 4±3. 8 years,height 1. 71 m±0. 05 m,weight 63. 6 kg±8. 5 kg） and 12 TKA patients（ age 62. 1±6. 6 years,height 1. 65 m ± 0. 07 m,weight 66. 8 kg ± 3. 5 kg） were recruited to verify the accuracy of the system measurement. Results The hardware and software were successfully developed for recording and transmitting knee bending and walking data to the cloud server through patient-side App,and doctors can read the data from the cloud server through the doctor-side App. The predictive accuracy of knee flexion times is 100%. The calculation errors of walking steps are 1. 6% ± 1. 3% for healthy subjects and 3. 0% ± 2. 2% for TKA patients,and the calculation error of the walking distance are 5. 3% ±2. 9% and 6. 5% ±4. 9% respectively. Conclusions The system developed in study can effectively record and transmit knee bending and walking data of patients,and provides an effective method for remote monitoring rehabilitation and physical activity at home for patients after TKA.

关 键 词：关节置换 康复 远程监测 屈膝 行走 

分 类 号：R318.5[医药卫生—生物医学工程]