Human-gait-phase-recognition is an important technology in the field of exoskeleton robot control and medical rehabilitation. Inertial sensors with accelerometers and gyroscopes are easy to wear, inexpensive and have great potential for analyzing gait dynamics. However, current deep-learning methods extract spatial and temporal features in isolation—while ignoring the inherent correlation in high-dimensional spaces—which limits the accuracy of a single model. This paper proposes an effective hybrid deep-learning framework based on the fusion of multiple spatiotemporal networks (FMS-Net), which is used to detect asynchronous phases from IMU signals. More specifically, it first uses a gait-information acquisition system to collect IMU sensor data fixed on the lower leg. Through data preprocessing, the framework constructs a spatial feature extractor with CNN module and a temporal feature extractor, combined with LSTM module. Finally, a skip-connection structure and the two-layer fully connected layer fusion module are used to achieve the final gait recognition. Experimental results show that this method has better identification accuracy than other comparative methods with the macro-F1 reaching 96.7%.

中文翻译：

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用于步态相位检测的多个时空网络的基于加速的融合。


人体步态相位识别是外骨骼机器人控制和医疗康复领域的一项重要技术。带有加速度计和陀螺仪的惯性传感器易于佩戴、价格低廉，并且在分析步态动力学方面具有巨大潜力。然而，当前的深度学习方法孤立地提取空间和时间特征，而忽略了高维空间中的固有相关性，这限制了单个模型的准确性。本文提出了一种基于多个时空网络（FMS-Net）融合的有效混合深度学习框架，用于检测 IMU 信号的异步相位。更具体地说，它首先使用步态信息采集系统来收集固定在小腿上的 IMU 传感器数据。通过数据预处理，该框架结合 LSTM 模块构建了带有 CNN 模块的空间特征提取器和时间特征提取器。最后采用跳跃连接结构和两层全连接层融合模块实现最终的步态识别。实验结果表明，该方法比其他对比方法具有更好的识别准确率，宏F1达到96.7%。