Accurate detection of locomotion transitions, such as walk to sit, walk to stair ascent, and descent, is crucial to effectively control robotic assistive devices, such as lower-limb exoskeletons, as each locomotion mode requires specific assistance. Variability in collected sensor data introduced by user- or system-specific characteristics makes it challenging to maintain high transition detection accuracy while avoiding latency using non-adaptive classification models. In this study, we identified key factors influencing transition detection performance, including variations in user behavior, and different mechanical designs of the exoskeletons. To boost the transition detection accuracy, we introduced two methods for adapting a finite-state machine classifier to system- and user-specific variability: a Statistics-Based approach and Bayesian Optimization. Our experimental results demonstrate that both methods remarkably improve transition detection accuracy across diverse users, achieving up to an 80% increase in certain scenarios compared to the non-personalized threshold method. These findings emphasize the importance of personalization in adaptive control systems, underscoring the potential for enhanced user experience and effectiveness in assistive devices. By incorporating subject- and system-specific data into the model training process, our approach offers a precise and reliable solution for detecting locomotion transitions, catering to individual user needs, and ultimately improving the performance of assistive devices.

翻译：准确检测运动模式转换（如行走至坐下、行走至楼梯上行与下行）对于有效控制机器人辅助设备（如下肢外骨骼）至关重要，因为每种运动模式均需特定的辅助策略。由用户或系统特异性特征引起的传感器数据变异，使得在使用非自适应分类模型时，在避免延迟的同时保持高转换检测精度面临挑战。本研究识别了影响转换检测性能的关键因素，包括用户行为差异以及外骨骼机械设计的多样性。为提升转换检测精度，我们提出了两种使有限状态机分类器适应系统与用户特异性变异的方法：基于统计的方法与贝叶斯优化。实验结果表明，这两种方法均能显著提升不同用户间的转换检测准确率，在特定场景下较非个性化阈值方法最高提升80%。这些发现强调了自适应控制系统中个性化的重要性，凸显了提升辅助设备用户体验与效能的潜力。通过将主体与系统特异性数据纳入模型训练过程，我们的方法为运动模式转换检测提供了精确可靠的解决方案，能够满足个体用户需求，并最终提升辅助设备的整体性能。