Real-time human intent recognition is important for controlling low-limb wearable robots. In this paper, to achieve continuous and precise recognition results on different terrains, we propose a real-time training and recognition method for six locomotion modes including standing, level ground walking, ramp ascending, ramp descending, stair ascending and stair descending. A locomotion recognition system is designed for the real-time recognition purpose with an embedded BPNN-based algorithm. A wearable powered orthosis integrated with this system and two inertial measurement units is used as the experimental setup to evaluate the performance of the designed method while providing hip assistance. Experiments including on-board training and real-time recognition parts are carried out on three able-bodied subjects. The overall recognition accuracies of six locomotion modes based on subject-dependent models are 98.43% and 98.03% respectively, with the wearable orthosis in two different assistance strategies. The cost time of recognition decision delivered to the orthosis is about 0.9[Formula: see text]ms. Experimental results show an effective and promising performance of the proposed method to realize real-time training and recognition for future control of low-limb wearable robots assisting users on different terrains.

中文翻译：

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基于 BPNN 的具有不同辅助策略的主动骨盆矫形器的运动模式实时识别

实时人类意图识别对于控制下肢可穿戴机器人很重要。在本文中，为了在不同地形上实现连续和精确的识别结果，我们提出了一种针对站立、平地行走、坡道上升、坡道下降、楼梯上升和楼梯下降六种运动模式的实时训练和识别方法。运动识别系统是为实时识别目的而设计的，具有嵌入式基于 BPNN 的算法。将与该系统和两个惯性测量单元集成的可穿戴动力矫形器用作实验装置，以评估设计方法的性能，同时提供髋关节辅助。对三个身体健全的受试者进行了包括车载训练和实时识别部分的实验。基于主体依赖模型的六种运动模式的整体识别准确率分别为98.43%和98.03%，可穿戴矫形器采用两种不同的辅助策略。传送到矫形器的识别决策的成本时间约为0.9[公式：见正文]ms。实验结果表明，所提出的方法在实现实时训练和识别未来控制下肢可穿戴机器人在不同地形上辅助用户方面具有有效且有前景的性能。