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Using bilateral lower limb kinematic and myoelectric signals to predict locomotor activities: A pilot study

机译:利用双侧下肢运动和肌电信号预测运动活动:一项初步研究

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Active lower limb exoskeletons can provide assistance to the lower extremities and may drastically improve the walking abilities of millions of individuals with gait impairments. However, most currently available control systems for these devices cannot predict the user's intended movements and have yet to enable walking with seamless transitions. Recent developments in intent recognition for active lower limb prostheses have demonstrated that using kinematic and kinetic signals from the device and myoelectric signals from the user can provide an intuitive control interface for seamlessly transitioning between different locomotor activities. In this work, we determined the baseline performance of intent recognition systems using neuromechanical signals presumably accessible for controlling active lower limb exoskeletons. We collected bilateral lower limb joint kinematics and muscle activity from three able-bodied subjects while they walked on level ground, ramps, and stairs in order to train an intent recognition system. We found that both combining kinematic and myoelectric signals and including signals from the contralateral leg significantly improved intent recognition performance. We achieved an average offline prediction error rate of 1.4 ± 0.90% using bilateral kinematic and myoelectric signals, demonstrating the promising potential of translating prosthesis-based intent recognition as an alternative control strategy for active lower limb exoskeletons.
机译:活跃的下肢外骨骼可以为下肢提供帮助,并可以大大改善数百万步态障碍者的步行能力。但是,用于这些设备的大多数当前可用的控制系统无法预测用户的预期运动,并且尚未能够无缝过渡地行走。主动式下肢假体的意图识别的最新发展表明,使用来自设备的运动学和动力学信号以及来自用户的肌电信号可以提供直观的控制界面,以在不同的运动活动之间无缝过渡。在这项工作中,我们使用可能可用于控制活动性下肢外骨骼的神经机械信号确定了意图识别系统的基线性能。我们收集了三个健壮受试者在水平地面,坡道和楼梯上行走时的双侧下肢关节运动学和肌肉活动,以训练他们的意图识别系统。我们发现,结合运动学信号和肌电信号,并包括对侧腿的信号,都可以显着改善意图识别性能。使用双边运动学和肌电信号,我们实现了平均离线预测错误率1.4±0.90 \%,证明了将基于假肢的意图识别翻译为活跃的下肢外骨骼控制策略的潜力。

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