This paper presents a bioinspired lower extremity exoskeleton robot. The proposed exoskeleton robot can be adjusted in structure to meet the wearer’s height of 150–185 cm and has a good gait stability. In the gait control part, a method of identifying different locomotion modes is proposed; five common locomotion modes are considered in this paper, including sitting down, standing up, level-ground walking, ascending stairs, and descending stairs. The identification is depended on angle information of the hip, knee, and ankle joints. A deep locomotion mode identification model (DLMIM) based on long-short term memory (LSTM) architecture is proposed in this paper for exploiting the angle data. We conducted two experiments to verify the effectiveness of the proposed method. Experimental results show that the DLMIM is capable of learning inherent characteristics of joint angles and achieves more accurate identification than the other models. The last experiment demonstrates that the DLMIM can recognize transitions between different locomotion modes in time and the real-time performance varies with each individual.
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