摘要
为了准确识别下肢功能障碍患者辅助行走中的跌倒、拖拽式异常步态,从多种用户群体的普适性与便捷性出发,提出了一种基于节点迭代型模糊Petri网的非接触式异常步态识别方法。首先,论述了康复训练机器人结构及辅助行走过程中跌倒与拖拽式异常步态的行为特征;然后研发了一种多通道近距离传感器阵列实时检测步态信息,并融合了步行方向意图向量提出采用步态偏离度、频率和躯干倾斜角度作为检测系统输入参数;基于模糊隶属度函数生成网络点燃机制,并建立节点迭代型模糊Petri网系统识别异常步态;最后进行了异常步态算子推理与多模态行走跌倒检测实验,表明该算法对使用步行康复机器人过程中异常步态识别率达到91. 2%并提高了辅助行走的安全性与舒适性。所提方法可以应用于下肢行动不便人群使用类似助行器的日常起居与康复训练场景。
Aiming to accurately identify the fall and drag-and-drop abnormal gait during assisted walking of the patients with lower limb dysfunction,a non-contact abnormal gait recognition method based on node-iteration type fuzzy Petri Net is proposed from the universality and convenience of various user groups. Firstly,the structure of the rehabilitation training robot is discussed,and the behavior characteristics of the abnormal gait of fall and drag-and-drop often occurring in the course of assistant walking are described. A multichannel proximity sensor array is developed to detect gait information in real time. Integrating the intention vector of walking direction,the gait deviation,frequency and body incline angle are taken as the input parameters of the detection system. Based on the fuzzy membership function,the network ignition mechanism is generated,and the node-iteration type fuzzy Petri Net is established to recognize the abnormal gait. Finally,an abnormal gait recognition method based on node-iteration fuzzy type Petri net is proposed,and abnormal gait operator reasoning experiment and multi-mode walking fall detection experiment of walking rehabilitation training robot are carried out. The experiment results show that the algorithm can accurately recognize the abnormal gait of different walking habit groups using walking rehabilitation robot,improve the safety and comfort of the assisted walking of the users,and the recognition rate of abnormal gait reaches to 91. 2%. This proposed method can be used in the daily living and rehabilitation training of the users with lower limb dysfunction using similar walking aids.
作者
赵东辉
杨俊友
白殿春
姜银来
Zhao Donghui;Yang Junyou;Bai Dianchun;Jiang Yinlai(School of Electrical Engineering,Shenyang University of Technology,Shenyang 110870,China;Department of Mechanical Engineering and Intelligent Systems,University of Electro-Communications,Tokyo 182-8585,Japan)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2019年第4期255-264,共10页
Chinese Journal of Scientific Instrument
基金
辽宁省自然科学基金(20162536)
沈阳市双百工程重大科技成果转化(Z17-5-068)项目资助
