Seo Jeong-Woo, Kang Geon-Hui, Kim Cheol-Hyun, Jung Jeeyoun, Kim Junggil, Kang Hyeon, Lee Sangkwan
Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, Korea.
Stroke Korean Medicine Research Center, Wonkwang University, Iksan, Korea.
Arch Rehabil Res Clin Transl. 2023 Aug 18;5(4):100274. doi: 10.1016/j.arrct.2023.100274. eCollection 2023 Dec.
To confirm the characteristics of gait events and muscle activity in the lower limbs of the affected and unaffected sides in patients with hemiplegia.
Cross-sectional study.
Motion analysis laboratory of the Wonkwang University Gwangju Hospital.
Outpatients, diagnosed with ischemic stroke more than 3 months and less than 9 months before participating in the study (N=29; 11 men, 18 women).
Not applicable.
The gait event parameters and time- and frequency-domain electromyogram (EMG) parameters of the lower limbs of the affected and unaffected sides was determined using BTS motion capture with the Delsys Trigno Avanti EMG wireless system.
The swing time, stance phase, swing phase, single support phase, and median power frequency of the gastrocnemius muscle showed a significant difference between the affected and unaffected sides. Using a logistic regression model, the swing phase, single support phase, and median frequency of the gastrocnemius muscle were selected to classify the affected side.
The single support phase of the affected side is shortened to reduce load bearing, which causes a reduction in the stance phase ratio. Unlike gait-event parameters, EMG data of hemiplegic stroke patients are difficult to generalize. Among them, the logistic regression model with some affected side parameters expected to be set as the severity and improvement baseline of the affected side. Additional data collection and generalization of muscle activity is required to improve the classification model.
确认偏瘫患者患侧和健侧下肢的步态事件及肌肉活动特征。
横断面研究。
光州圆光大学医院运动分析实验室。
门诊患者,在参与研究前3个月以上且9个月以内被诊断为缺血性中风(N = 29;男性11名,女性18名)。
不适用。
使用带有Delsys Trigno Avanti肌电图无线系统的BTS运动捕捉技术,测定患侧和健侧下肢的步态事件参数以及时域和频域肌电图(EMG)参数。
患侧和健侧在摆动时间、站立期、摆动期、单支撑期以及腓肠肌的中位功率频率方面存在显著差异。使用逻辑回归模型,选择摆动期、单支撑期以及腓肠肌的中位频率来对患侧进行分类。
患侧的单支撑期缩短以减轻负重,这导致站立期比例降低。与步态事件参数不同,偏瘫中风患者的肌电图数据难以一概而论。其中,一些患侧参数的逻辑回归模型有望被设定为患侧严重程度和改善的基线。需要额外收集数据并对肌肉活动进行归纳,以改进分类模型。