Real-Time Hybrid Locomotion Mode Recognition for Lower Limb Wearable Robots

Real-time recognition of locomotion-related activities is a fundamental skill that a controller of lower limb wearable robots should possess. Subject-specific training and reliance on electromyographic interfaces are the main limitations of existing approaches. This study presents a novel methodology for real-time locomotion mode recognition of locomotion-related activities in lower limb wearable robotics. A hybrid classifier can distinguish among seven locomotion-related activities. First, a time-based approach classifies between static and dynamical states based on gait kinematics data. Second, an event-based fuzzy-logic method triggered by foot pressure sensors operates in a subject-independent fashion on a minimal set of relevant biomechanical features to classify among dynamical modes. The locomotion mode recognition algorithm is implemented on the controller of a portable powered orthosis for hip assistance. An experimental protocol is designed to evaluate the controller performance in an out-of-lab scenario without the need for subject-specific training. Experiments are conducted on six healthy volunteers performing locomotion-related activities at slow, normal, and fast speeds under the zero-torque and assistive mode of the orthosis. The overall accuracy rate of the controller is 99.4% over more than 10 000 steps, including seamless transitions between different modes. The experimental results show a successful subject-independent performance of the controller for wearable robots assisting locomotion-related activities.

实时识别与运动相关的活动是下肢可穿戴机器人的控制器应具备的一项基本技能。针对特定个体的训练以及对肌电接口的依赖是现有方法的主要局限。本研究提出了一种用于下肢可穿戴机器人中与运动相关活动的实时运动模式识别的新方法。一种混合分类器能够区分七种与运动相关的活动。首先，一种基于时间的方法根据步态运动学数据在静态和动态状态之间进行分类。其次，一种由足部压力传感器触发的基于事件的模糊逻辑方法，以一种与个体无关的方式对一组最少的相关生物力学特征进行操作，从而在动态模式之间进行分类。运动模式识别算法在用于髋关节辅助的便携式动力矫形器的控制器上得以实现。设计了一个实验方案，用于在实验室外的场景中评估控制器的性能，且无需针对特定个体进行训练。对六名健康志愿者进行了实验，这些志愿者在矫形器的零扭矩和辅助模式下，以慢速、正常速度和快速进行与运动相关的活动。在超过10000步的过程中，包括不同模式之间的无缝转换，控制器的总体准确率为99.4%。实验结果表明，该控制器在用于辅助与运动相关活动的可穿戴机器人中实现了与个体无关的成功性能。