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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2023, Vol. 57 Issue (10): 2077-2085    DOI: 10.3785/j.issn.1008-973X.2023.10.016
    
Evaluation of exoskeleton wearing fatigue based on surface electromyography and gait
Kai-lun HE1(),Jian LV1,*(),Lin LI2,Zhao XU1,Wei-jie PAN1
1. Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China
2. Guizhou Aerospace Control Technology Co. Ltd, Guiyang 550009, China
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Abstract  

A comprehensive efficiency evaluation method on exoskeleton was proposed combined with the electromyogram fatigue threshold (EMGFT), biomechanical analysis and session rating of perceived exertion (sRPE), aiming at the one-sided problem of evaluating the fatigue status of the exoskeleton of the wearable passive lower extremities. Different from the single measurement method including the traditional surface electromyography (sEMG) or blood oxygen saturation, the proposed method could effectively improve the measurement accuracy of passive loading lower extremity exoskeleton performance. The gaits of the subjects were compared and analyzed, the spatial position information and muscle force generation were obtained, and the stability of lower limb knee joint was calculated. The sEMG of the subjects was collected and preprocessed, and the muscle fatigue threshold was calculated. The evaluation method is effective for evaluating the comprehensive efficiency evaluation method on exoskeleton. The extremities delayed the arrival time of EMGFT by an average of 42.9%, improved the stability of the lower limb by 75.8%, and relieved the subjective fatigue by 30.3%.

Key words： passive lower extremity exoskeleton      muscle fatigue threshold      motion capture      gait analysis      subjective fatigue scale     
Received: 25 October 2022      Published: 18 October 2023
CLC:  TH 122  
Fund:  国家自然科学基金资助项目（52065010）；贵州省科技支撑计划（黔科合支撑[2022]一般197）资助项目；贵州省基础研究计划项目（黔科合基础-ZK[2021]一般341）
Corresponding Authors: Jian LV     E-mail: 1306798570@qq.com;jlv@gzu.edu.cn
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Kai-lun HE
Jian LV
Lin LI
Zhao XU
Wei-jie PAN
Cite this article:

Kai-lun HE,Jian LV,Lin LI,Zhao XU,Wei-jie PAN. Evaluation of exoskeleton wearing fatigue based on surface electromyography and gait. Journal of ZheJiang University (Engineering Science), 2023, 57(10): 2077-2085.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2023.10.016     OR     https://www.zjujournals.com/eng/Y2023/V57/I10/2077


基于表面肌电与步态的外骨骼穿戴疲劳评测

针对穿戴无源下肢外骨骼疲劳状态评价片面的问题，提出一种结合肌肉疲劳阈值（EMGFT）、生物力学分析和主观疲劳自觉量表（sRPE）的外骨骼综合效能评价方法. 区别于传统表面肌电信号（sEMG）或血氧饱和度的单一测定方法，所提方法可以有效提高无源负载下肢外骨骼效能评测精度. 通过动作捕捉对受试者进行步态对比分析，获取空间位置信息与肌肉发力情况，并计算下肢膝关节稳定性；采集受试者的sEMG进行预处理，并计算肌肉疲劳阈值；结合sRPE评分与膝关节偏移量方差对EMGFT进行主客观验证. 结果表明所提方法可以有效评价无源下肢外骨骼，外骨骼使受试者EMGFT到达时间平均推迟了42.9%，下肢稳定性提升了75.8%，主观疲劳感受缓解了30. 3%.


关键词： 无源下肢外骨骼,  肌肉疲劳阈值,  动作捕捉,  步态分析,  主观疲劳量表 
Fig.1 Comparative experimental process for gait analysis
状态 负载质量/kg 分组情况 成员

配戴外骨骼（A组）
20 		普通组A1 1~6号
熟练组A2 7~12号
强壮组A3 13~18号

未佩戴外骨骼（B组）
20 		普通组B1 1~6号
熟练组B2 7~12号
强壮组B3 13~18号
Tab.1 Grouping of exoskeleton load experiments
Fig.2 Diagram of human gait cycle division
Fig.3 sEMG and IMU sensor distribution
Fig.4 Scenario of sEMG and IMU sensor settings
Fig.5 Subject experimental status and environment
Fig.6 Calculation schematic of EMGFT
Fig.7 Schematic diagram of knee abduction angle
RPE评分 英文表述 中文表述
0 Rest 十分放松，休息状态
1 Really easy 相当轻松
2 Easy 轻松
3 Moderate 一般
4 Sort of hard 有些费力
5 Hard 费力
6
7 Really hard 非常费力
8
9 Really really hard 非常非常费力
10 Just like my hardest race 到达极限
Tab.2 CR-10 scale Comparison table of fatigue degree
Fig.8 Time and frequency domain diagrams of sEMG signal preprocessing
Fig.9 RMS fitting of line intersections and trend of arrival time of EMGFT
对象组 EMGFT平均到达时间/s 变化时长/s
A组 B组
1 23.4 24.7 1.3
2 83.5 31.8 51.7
3 116.2 71.6 44.6
Tab.3 EMGFT average arrival time and duration of change in group A and B
Fig.10 Fitting line and slope of fitting line for variance of knee joint offset
测试对象 0 min 2 min 4 min 6 min F
1号 1 4 5 6 4.21
2号 1 5 6 6
3号 0 5 6 7
4号 1 3 5 7
5号 0 5 5 6
6号 0 4 6 7
7号 0 3 3 4 2.38
8号 0 2 3 3
9号 0 3 3 4
10号 0 2 3 4
11号 1 3 3 4
12号 0 3 3 3
13号 0 2 2 3 2.08
14号 0 2 2 3
15号 0 3 3 3
16号 0 3 3 3
17号 0 2 3 4
18号 0 3 3 3
Tab.4 Group wears exoskeleton and bears weight sRPE score
测试对象 0 min 2 min 4 min 6 min F
1号 4 5 6 7 5.5
2号 3 5 5 7
3号 4 5 7 7
4号 3 4 6 7
5号 4 5 7 8
6号 4 6 6 7
7号 3 4 5 7 4.67
8号 3 3 5 6
9号 2 3 6 7
10号 3 4 5 7
11号 2 3 6 8
12号 3 4 6 7
13号 1 3 3 4 2.58
14号 1 2 2 4
15号 2 3 3 3
16号 1 2 3 4
17号 2 3 4 4
18号 1 2 2 3
Tab.5 Group B wears exoskeleton and weight-bearing sRPE score
特征组 t/s P F
A1组 28 0.28 4.21
A2组 83 0.21 2.38
A3组 117 0.11 2.08
B1组 27 0.87 5.5
B2组 31 0.92 4.67
B3组 72 0.69 2.58
提升率 42.9% 75.8% 30.3%
Tab.6 Individual group features and overall enhancement rate
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