Zheng Yang, He Dimao, He Yuan, Kong Xiangrui, Fan Xiaochen, Li Min, Xu Guanghua, Yin Jichao
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
State Industry-Education Integration Center for Medical Innovations, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel). 2025 Aug 19;25(16):5142. doi: 10.3390/s25165142.
Knee joint stiffness occurs and severely limits its range of motion (ROM) after facture around the knee. During mobility training, knee joints need to be flexed to the maximum angle position (maxAP) that can induce pain at an appropriate level in order to pull apart intra-articular adhesive structures while avoiding secondary injuries. However, the maxAP varies with training and is mostly determined by the pain level of patients. In this study, the feasibility of utilizing electromyogram (EMG) activities to detect maxAP was investigated. Specifically, the maxAP detection was converted into a binary classification between pain level three of the numerical rating scales (pain) and below (painless) according to clinical requirements. Firstly, 12 post-fracture patients with knee joint stiffness participated in Experiment I, with a therapist performing routine mobility training and EMG signals being recorded from knee flexors and extensors. The results showed that the extracted EMG features were significantly different between the pain and painless states. Then, the maxAP estimation performance was tested on a knee rehabilitation robot in Experiment II, with another seven patients being involved. The support vector machine and random forest models were used to classify between pain and painless states and obtained a mean accuracy of 87.90% ± 4.55% and 89.10% ± 4.39%, respectively, leading to an average estimation bias of 6.5° ± 5.1° and 4.5° ± 3.5°. These results indicated that the pain-induced EMG can be used to accurately classify pain states for the maxAP estimation in post-fracture mobility training, which can potentially facilitate the application of robotic techniques in fracture rehabilitation.
膝关节周围骨折后会出现膝关节僵硬，严重限制其活动范围（ROM）。在活动训练过程中，膝关节需要屈曲到能引起适当程度疼痛的最大角度位置（最大角度位置），以便拉开关节内粘连结构，同时避免二次损伤。然而，最大角度位置会因训练而有所不同，且主要由患者的疼痛程度决定。在本研究中，探讨了利用肌电图（EMG）活动检测最大角度位置的可行性。具体而言，根据临床需求，将最大角度位置检测转换为数字评分量表中疼痛程度三级及以下（无痛）之间的二分类。首先，12名膝关节僵硬的骨折后患者参与了实验I，由治疗师进行常规活动训练，并记录膝关节屈伸肌的EMG信号。结果表明，提取的EMG特征在疼痛和无痛状态之间存在显著差异。然后，在实验II中，在膝关节康复机器人上测试了最大角度位置估计性能，另外7名患者参与其中。使用支持向量机和随机森林模型对疼痛和无痛状态进行分类，平均准确率分别为87.90%±4.55%和89.10%±4.39%，平均估计偏差分别为6.5°±5.1°和4.5°±3.5°。这些结果表明，疼痛诱发的EMG可用于准确分类疼痛状态，以估计骨折后活动训练中的最大角度位置，这可能有助于机器人技术在骨折康复中的应用。
