基于多传感器信息融合的外骨骼运动意图辨识

Exoskeleton Motion Intention Recognition Based on Multi-sensor Information Fusion

为了实现外骨骼机器人的柔顺运动控制,需要对穿戴者的运动意图进行实时准确地辨识与预测。本研究利用多传感器信息融合的方法完成对穿戴者运动意图的识别。通过对多种机器学习算法在识别准确性、资源消耗和处理实时性进行比较、最终确定利用支持向量机(SVM)实现对日常8个运动模式(静坐、双腿站立、步行、跑步、上下斜坡和上下楼梯)完成动作模式的识别,识别平均准确率达到95%。对于运动相位和运动切换事件的预测,利用神经-模糊推理理论完成运动相位识别与状态切换事件的预测。在给定的测试集上相位识别准确率为99%,且预测的状态切换时刻与真实时间的偏移绝对值的均值为61.6 ms,满足外骨骼柔顺控制对预测时间的要求。

Accurate identification and prediction of a wearer's motion intention in real time are necessary to realize the compliant motion control of exoskeleton robots.Thus,we use the multi-sensor information fusion method to recognize the wearer's motion intention.The comparison of various machine learning algorithms with respect to recognition accuracy,resource consumption,and real-time processing revealed that support vector machine can recognize eight daily motion patterns(sitting,standing,walking,running,ramp ascent,ramp descent,stairs ascent and stairs descent),at an average recognition accuracy rate of 95%.The neuro-fuzzy inference theory is adopted to predict motion phase and motion switching events.On the given test set,the phase recognition accuracy rate is 99%,and the average absolute value of the deviation between the predicted and real-time state switching moments is 61.5 ms.This observation meets the requirements of exoskeleton compliance control for predicting time.