失重条件下舱外手套外骨骼助力效果的评估方法

Evaluation method of assistance effect of extravehicular activity glove exoskeleton under weightlessness

目的 建立多指标融合的手部抓握疲劳度预测模型,评估舱外手套外骨骼样机的助力效果。方法 采用BP神经网络算法建立手部疲劳度预测模型。通过等距抓握疲劳实验确定手部疲劳度的影响因素,确定BP神经网络的输入变量分别为圆柱直径、抓握力、抓握持续时间和肌电均方根值;通过实验和主观疲劳度量表获得每组变量对应的疲劳度数据,建立基于BP神经网络算法多源融合的疲劳度评估模型;建立疲劳度和助力效果关系模型,通过对疲劳度缓解程度评估外骨骼样机的助力效果。结果 模型预测值与目标值的相关性r=0.974,并有效预测了不同样机的助力效果。结论 结合抓握强度、抓握对象参数和人体肌电建立预测手部疲劳度的BP神经网络模型,可用来评估舱外手套外骨骼和其他手部助力装置的助力效果。

Objective To establish a multi index fusion hand grip fatigue prediction model to evaluate the power-assisted effect of the glove exoskeleton prototype for extravehicular clothing.Methods BP neural network algorithm was used to establish a hand fatigue prediction model.The related factors of hand fatigue were determined with isometric grasping fatigue experiment,and the input variables of BP neural network were determined as cylinder diameter,grasping force,grasping duration and root mean square of electromyography.The fatigue data corresponding to variables of each group were obtained through experiments and subjective fatigue measurement scales,and a fatigue evaluation model based on multi-source fusion of BP neural network algorithm was established.The relationship model between fatigue and assistance effect was established,and the assistance effect of the exoskeleton prototype was evaluated through the degree of fatigue relief.Results The correlation coefficient was 0.974 between the predicted results of the model and the target value.Moreover,it effectively predicted the assistance effect of different prototypes.Conclusion The BP neural network model established by combining the grasping strength,grasping object parameters and human electromyography can predict hand fatigue,which can be used to evaluate the assistance effect of glove exoskeleton and other hand aids.