On Predicting Transitions to Compliant Surfaces in Human Gait via Neural and Kinematic Signals
On Predicting Transitions to Compliant Surfaces in Human Gait via Neural and Kinematic Signals
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DOI：
10.1109/tnsre.2023.3272355
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发表时间：
2023-01-01
影响因子：
4.9
通讯作者：
Artemiadis，Panagiotis
中科院分区：
文献类型：
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作者：
Angelidou，Charikleia;Artemiadis，Panagiotis
在日常生活中经常遇到不同顺应性的行走表面，并且对于大多数人来说，在它们之间的过渡通常不是一项具有挑战性的任务。人类大脑根据环境反馈以及先前的经验，控制下肢动力学，以过渡到新的表面，确保稳定性和安全性。然而，这对于下肢损伤的人来说并不总是可能的，特别是那些使用可穿戴（矫正）或假肢设备的人。目前用于下肢可穿戴设备和动力踝关节假体的控制方法已经成功地复制了在刚性表面上行走的条件。然而，在非平坦和顺应性表面上的敏捷性和行走稳定性对于步态残疾的个体来说仍然是一个重大挑战。因此，需要将人类佩戴者纳入环中，并主动调整其控制以过渡到不同顺应性的表面。这项工作提出了一个特定主题的模式识别（PR）和分类策略，使用运动学数据和表面肌电（EMG）信号，以识别用户的意图，从刚性过渡到一个顺应性的表面。使用k-近邻（k-NN）方法结合人工神经网络（ANN），我们的策略可以准确地预测即将到来的表面刚度转换C在真实的时间。C这将允许假体C或可穿戴设备的快速参数控制以及适应新的地形。采用该策略后的分类结果达到高达87.5%的预测精度，证明C预测过渡到顺应表面在真实的时间是可行的和有效的。所提出的框架可以提高下肢假肢C或可穿戴设备的鲁棒性和安全性，最终提高患有下肢损伤的C患者的生活质量。
Walking surfaces of varying compliance are encountered frequently in everyday life C, and transitions between them are usually not a challenging task for most people. The human brain, based on feedback from the environment, as well as previous experience, controls the lower limb dynamics to transition to new surfaces ensuring stability and safety. However, this is not always possible for people with lower limb impairments, especially those using wearable (orthotic) or prosthetic devices. Current control methodologies for lower limb wearables and powered ankle prostheses have successfully replicated conditions for walking on rigid surfaces. However, agility and walking stability on non-flat and compliant surfaces remain a significant challenge for individuals with gait disabilities. C There is therefore the need to incorporate the human wearer in the loop and proactively adjust their control to transition to surfaces of different compliance. This work proposes a subject-specific pattern recognition (PR) and classification strategy using kinematic data and surface electromyographic (EMG) signals to recognize user intent to transition from a rigid to a compliant surface. Using a k-Nearest Neighbors (k-NN) methodology in combination with an Artificial Neural Network (ANN), our strategy can accurately predict upcoming surface stiffness transitions C in real time. C This would allow for a fast parameter control of the prosthesis C or wearable device and for adaptation to the new terrain. Classification results after employing the proposed strategy reach a prediction accuracy of up to 87.5%, proving that C predicting transitions to compliant surfaces in real time is feasible and efficient. The proposed framework can lead to increased robustness and safety of lower-limb prosthetic C or wearable devices that will eventually improve the quality of life of individuals living with C a lower limb impairment.