Yan Jianjun, Xiong Weixiang, Jin Li, Jiang Jinlin, Yang Zhihao, Hu Shuai, Zhang Qinghong
Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, East China University of Science and Technology, Shanghai 200237, China.
Shanghai Aerospace Control Technology Research Institute, Shanghai 201108, China.
iScience. 2024 Aug 2;27(9):110646. doi: 10.1016/j.isci.2024.110646. eCollection 2024 Sep 20.
Gait recognition is one of the key technologies for exoskeleton robot control, while the current IMU-based gait recognition methods only use inertial data and do not fully consider the interconnections of human spatial structure and human joints. In this regard, a skeleton-based gait recognition approach with inertial measurement units using spatial temporal graph convolutional networks with spatial and temporal attention is proposed. A human forward kinematics solver module was used for constructing different human skeleton models and a temporal attention module was added for capturing the more important time frames in the gait cycle. Moreover, the two-stream structure was used to construct spatial temporal graph convolutional networks with spatial and temporal attention for gait recognition, and an average accuracy of about 99% was obtained in user experiments, which is the best performance compared to other algorithms, provides certain reference for gait recognition and real-time control of exoskeleton robots.
步态识别是外骨骼机器人控制的关键技术之一，而当前基于惯性测量单元（IMU）的步态识别方法仅使用惯性数据，没有充分考虑人体空间结构和人体关节的相互联系。对此，提出了一种基于骨架的步态识别方法，该方法使用带有空间和时间注意力的时空图卷积网络以及惯性测量单元。使用人体正向运动学求解器模块构建不同的人体骨架模型，并添加时间注意力模块以捕捉步态周期中更重要的时间帧。此外，采用双流结构构建带有空间和时间注意力的时空图卷积网络用于步态识别，在用户实验中获得了约99%的平均准确率，与其他算法相比性能最佳，为外骨骼机器人的步态识别和实时控制提供了一定参考。
