Wearable Sensing and Knee Exoskeleton Control for Awkward Gaits Assistance
Wearable Sensing and Knee Exoskeleton Control for Awkward Gaits Assistance
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DOI：
10.1109/case49997.2022.9926655
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Chunchu Zhu;Feng Han;J. Yi
中科院分区：
文献类型：
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作者：
Chunchu Zhu;Feng Han;J. Yi
产业工人在进行技术性工作时，经常长时间地执行笨拙的步态，例如蹲下、跪着等。我们提出了一种实时可穿戴传感和外骨骼控制设计，为工业工人在笨拙的步态下提供帮助。设计了一种基于可穿戴传感器的步态活动检测和位姿估计方案，用于在步行、站立、下蹲和跪姿序列中真实的预测人体运动和下肢关节角度。可穿戴双侧外骨骼在多级控制器下在各种步态下提供辅助扭矩。人体实验演示了步态检测和外骨骼控制性能。结果表明，人体步态识别的整体准确率高达95%，平均检测延迟约为50 ms。外骨骼控制策略减少了肌肉激活在膝关节伸展/屈曲高达25%，在各种固定的步态和姿势转换。
Industrial workers often perform awkward gaits such as squatting, kneeling, etc. for a prolonged time when conducting skilled tasks. We present a real-time wearable sensing and exoskeleton control design to provide assistance for the industrial workers under awkward gaits. A wearable sensor-based gait activity detection and pose estimation scheme is designed to predict the human motion and lower-limb joint angles in real time during a sequence of walking, standing, squatting, and kneeling gaits. Wearable bilateral exoskeletons provide assistive torques at various gaits under a multi-level controller. Human-subject experiments are presented to demonstrate the gait detection and exoskeleton control performance. The results show that the overall accuracy of human gait recognition is up to 95% and the average detection latency is around 50 ms. The exoskeleton control strategy reduces muscle activation in knee extension/flexion up to 25% during various stationary gaits and posture transitions.