Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan 430205, China.
Key Laboratory of Image Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
Sensors (Basel). 2018 Aug 22;18(9):2761. doi: 10.3390/s18092761.
Human locomotion is a coordinated motion between the upper and lower limbs, which should be considered in terms of both the user's normal walking state and abnormal walking state for a walking-aid robot system. Therefore, a novel coordinated motion fusion-based walking-aid robot system was proposed. To develop the accurate human motion intention (HMI) of such robots when the user is in normal walking state, force-sensing resistor (FSR) sensors and a laser range finder (LRF) are used to detect the two HMIs expressed by the user's upper and lower limbs. Then, a fuzzy logic control (FLC)-Kalman filter (LF)-based coordinated motion fusion algorithm is proposed to synthesize these two segmental HMIs to obtain an accurate HMI. A support vector machine (SVM)-based fall detection algorithm is used to detect whether the user is going to fall and to distinguish the user's falling mode when he/she is in an abnormal walking state. The experimental results verify the effectiveness of the proposed algorithms.
人类运动是上下肢协调运动,对于助行机器人系统,应同时考虑用户正常行走状态和异常行走状态。因此,提出了一种新的基于协调运动融合的助行机器人系统。为了开发当用户处于正常行走状态时机器人的精确人体运动意图(HMI),使用力敏电阻(FSR)传感器和激光测距仪(LRF)来检测用户上下肢表达的两个 HMI。然后,提出了一种基于模糊逻辑控制(FLC)-卡尔曼滤波器(LF)的协调运动融合算法,以综合这两个分段 HMI,从而获得精确的 HMI。基于支持向量机(SVM)的跌倒检测算法用于检测用户是否将要跌倒,并在异常行走状态下区分用户的跌倒模式。实验结果验证了所提出算法的有效性。