Walking-assistive devices require adaptive control methods to ensure smooth transitions between various modes of locomotion. For this purpose, detecting human locomotion modes (e.g., level walking or stair ascent) in advance is crucial for improving the intelligence and transparency of such robotic systems. This study proposes Deep-STF, a unified end-to-end deep learning model designed for integrated feature extraction in spatial, temporal, and frequency dimensions from surface electromyography (sEMG) signals. Our model enables accurate and robust continuous prediction of nine locomotion modes and 15 transitions at varying prediction time intervals, ranging from 100 to 500 ms. In addition, we introduced the concept of 'stable prediction time' as a distinct metric to quantify prediction efficiency. This term refers to the duration during which consistent and accurate predictions of mode transitions are made, measured from the time of the fifth correct prediction to the occurrence of the critical event leading to the task transition. This distinction between stable prediction time and prediction time is vital as it underscores our focus on the precision and reliability of mode transition predictions. Experimental results showcased Deep-STP's cutting-edge prediction performance across diverse locomotion modes and transitions, relying solely on sEMG data. When forecasting 100 ms ahead, Deep-STF surpassed CNN and other machine learning techniques, achieving an outstanding average prediction accuracy of 96.48%. Even with an extended 500 ms prediction horizon, accuracy only marginally decreased to 93.00%. The averaged stable prediction times for detecting next upcoming transitions spanned from 28.15 to 372.21 ms across the 100-500 ms time advances.

翻译：步行辅助设备需要自适应控制方法以确保不同运动模式之间的平稳切换。为此，提前识别人体运动模式（如平地行走或上楼梯）对于提升此类机器人系统的智能性和透明度至关重要。本研究提出Deep-STF——一种统一的端到端深度学习模型，专门用于从表面肌电信号中提取空间、时间和频率维度的集成特征。该模型能够在100至500毫秒的不同预测时间间隔内，准确且稳健地连续预测九种运动模式及15种模式转换。此外，我们引入"稳定预测时间"这一独特指标来量化预测效率。该术语指从第五次正确预测发生到导致任务转换的关键事件出现期间，对模式转换进行一致且准确预测的持续时间。稳定预测时间与预测时间的区分至关重要，因为这突显了我们对模式转换预测精确性与可靠性的关注。实验结果表明，仅依赖表面肌电数据，Deep-STF在多种运动模式及转换中展现了卓越的预测性能。在提前100毫秒预测时，Deep-STF超越CNN及其他机器学习技术，实现了96.48%的卓越平均预测准确率。即便将预测时间延长至500毫秒，准确率仅小幅下降至93.00%。在100-500毫秒预测时间提前量下，检测即将发生的模式转换的平均稳定预测时间范围为28.15至372.21毫秒。