Assistive devices, such as exoskeletons and prostheses, have revolutionized the field of rehabilitation and mobility assistance. Efficiently detecting transitions between different activities, such as walking, stair ascending and descending, and sitting, is crucial for ensuring adaptive control and enhancing user experience. We here present an approach for real-time transition detection, aimed at optimizing the processing-time performance. By establishing activity-specific threshold values through trained machine learning models, we effectively distinguish motion patterns and we identify transition moments between locomotion modes. This threshold-based method improves real-time embedded processing time performance by up to 11 times compared to machine learning approaches. The efficacy of the developed finite-state machine is validated using data collected from three different measurement systems. Moreover, experiments with healthy participants were conducted on an active pelvis orthosis to validate the robustness and reliability of our approach. The proposed algorithm achieved high accuracy in detecting transitions between activities. These promising results show the robustness and reliability of the method, reinforcing its potential for integration into practical applications.

翻译：辅助设备，如外骨骼和假肢，已彻底改变了康复和行动辅助领域。高效检测不同活动之间的过渡（例如行走、上楼梯、下楼梯和坐姿）对于确保自适应控制和提升用户体验至关重要。本文提出了一种针对实时过渡检测的方法，旨在优化处理时间性能。通过训练机器学习模型建立活动特定的阈值，我们有效区分运动模式并识别运动模式之间的过渡时刻。与机器学习方法相比，这种基于阈值的方法将实时嵌入式处理时间性能提升了高达11倍。使用三个不同测量系统采集的数据验证了所开发有限状态机的有效性。此外，在健康参与者身上进行了主动骨盆矫形器的实验，以验证我们方法的鲁棒性和可靠性。所提出的算法在检测活动间过渡时实现了高精度。这些令人鼓舞的结果展示了该方法的鲁棒性和可靠性，强化了其在实际应用中的集成潜力。