Harshe Karl, Williams Jack R, Hocking Toby D, Lerner Zachary F
Mechanical Engineering Department, Northern Arizona University, Flagstaff, AZ 86011 USA.
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011 USA.
IEEE Robot Autom Lett. 2023 Aug;8(8):5055-5060. doi: 10.1109/lra.2023.3291919. Epub 2023 Jul 3.
The clinical efficacy of robotic rehabilitation interventions hinges on appropriate neuromuscular recruitment from the patient. The first purpose of this study was to evaluate the use of supervised machine learning techniques to predict neuromuscular recruitment of the ankle plantar flexors during walking with ankle exoskeleton resistance in individuals with cerebral palsy (CP). The second goal of this study was to utilize the predictive models of plantar flexor recruitment in the design of a personalized biofeedback framework intended to improve (i.e., increase) user engagement when walking with resistance. First, we developed and trained multilayer perceptrons (MLPs), a type of artificial neural network (ANN), utilizing features extracted exclusively from the exoskeleton's onboard sensors, and demonstrated 85-87% accuracy, on average, in predicting muscle recruitment from electromyography measurements. Next, our participants completed a gait training session while receiving audio-visual biofeedback of their personalized real-time planar flexor recruitment predictions from the online MLP. We found that adding biofeedback to resistance elevated plantar flexor recruitment by 24 16% compared to resistance alone. This study highlights the potential for online machine learning frameworks to improve the effectiveness and delivery of robotic rehabilitation systems in clinical populations.
机器人康复干预的临床疗效取决于患者能否进行适当的神经肌肉募集。本研究的首要目的是评估使用监督机器学习技术来预测脑瘫（CP）患者在佩戴踝部外骨骼阻力行走时踝部跖屈肌的神经肌肉募集情况。本研究的第二个目标是在设计个性化生物反馈框架时利用跖屈肌募集的预测模型，旨在提高（即增加）患者在有阻力行走时的参与度。首先，我们开发并训练了多层感知器（MLP），这是一种人工神经网络（ANN），利用仅从外骨骼的机载传感器提取的特征，并证明平均而言，在根据肌电图测量预测肌肉募集方面，准确率达到85%-87%。接下来，我们的参与者在接受来自在线MLP的个性化实时跖屈肌募集预测的视听生物反馈的同时完成了一次步态训练。我们发现，与仅施加阻力相比，增加生物反馈后，跖屈肌募集增加了24%-16%。本研究突出了在线机器学习框架在提高临床人群中机器人康复系统的有效性和实施方面的潜力。
