Raza Ahmed, Sekiguchi Yusuke, Yaguchi Haruki, Honda Keita, Fukushi Kenichiro, Huang Chenhui, Ihara Kazuki, Nozaki Yoshitaka, Nakahara Kentaro, Izumi Shin-Ichi, Ebihara Satoru
Department of Physical Medicine and Rehabilitation, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8575, Japan.
Department of Physical Medicine and Rehabilitation, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8575, Japan.
Clin Biomech (Bristol). 2024 Jul;117:106285. doi: 10.1016/j.clinbiomech.2024.106285. Epub 2024 Jun 4.
Knee osteoarthritis negatively affects the gait of patients, especially that of elderly people. However, the assessment of wearable sensors in knee osteoarthritis patients has been under-researched. During clinical assessments, patients may change their gait patterns under the placebo effect, whereas wearable sensors can be used in any environment.
Sixty patients with knee osteoarthritis and 20 control subjects were included in the study. Wearing shoes with an IMU sensor embedded in the insoles, the participants were required to walk along a walkway. The sensor data were collected during the gait. To discriminate between healthy and knee osteoarthritis patients and to classify different subgroups of knee osteoarthritis patients (patients scheduled for surgery vs. patients not scheduled for surgery; bilateral knee osteoarthritis diagnosis vs. unilateral knee osteoarthritis diagnosis), we used a machine learning approach called the support vector machine. A total of 88 features were extracted and used for classification.
The patients vs. healthy participants were classified with 71% accuracy, 85% sensitivity, and 56% specificity. The "patients scheduled for surgery" vs. "patients not scheduled for surgery" were classified with 83% accuracy, 83% sensitivity, and 81% specificity. The bilateral knee osteoarthritis diagnosis vs. unilateral knee osteoarthritis diagnosis was classified with 81% accuracy, 75% sensitivity, and 79% specificity.
Gait analysis using wearable sensors and machine learning can discriminate between healthy and knee osteoarthritis patients and classify different subgroups with reasonable accuracy, sensitivity, and specificity. The proposed approach requires no complex gait factors and is not limited to controlled laboratory settings.
膝关节骨关节炎对患者的步态有负面影响，尤其是对老年人。然而，针对膝关节骨关节炎患者可穿戴传感器的评估研究不足。在临床评估中，患者可能会在安慰剂效应下改变其步态模式，而可穿戴传感器可在任何环境中使用。
本研究纳入了60例膝关节骨关节炎患者和20例对照受试者。参与者需穿着鞋垫中嵌入惯性测量单元（IMU）传感器的鞋子沿通道行走。在步态过程中收集传感器数据。为了区分健康人与膝关节骨关节炎患者，并对膝关节骨关节炎患者的不同亚组进行分类（计划手术的患者与未计划手术的患者；双侧膝关节骨关节炎诊断与单侧膝关节骨关节炎诊断），我们使用了一种名为支持向量机的机器学习方法。共提取了88个特征并用于分类。
患者与健康参与者的分类准确率为71%，灵敏度为85%，特异度为56%。“计划手术的患者”与“未计划手术的患者”的分类准确率为83%，灵敏度为83%，特异度为81%。双侧膝关节骨关节炎诊断与单侧膝关节骨关节炎诊断的分类准确率为81%，灵敏度为75%，特异度为79%。
使用可穿戴传感器和机器学习进行步态分析能够区分健康人与膝关节骨关节炎患者，并以合理的准确率、灵敏度和特异度对不同亚组进行分类。所提出的方法不需要复杂的步态因素，且不限于受控的实验室环境。
