Chen Ji, Damiano Diane L, Lerner Zachary F, Bulea Thomas C
IEEE Int Conf Rehabil Robot. 2019 Jun;2019:778-783. doi: 10.1109/ICORR.2019.8779513.
Advanced control strategies that can adjust assistance based volitional effort from the user may be beneficial for deploying exoskeletons for overground gait training in ambulatory populations, such as children with cerebral palsy (CP). In this study, we evaluate the ability to predict biological knee moment during stance phase of walking with an exoskeleton in two children subjects with crouch gait from CP. The predictive model characterized the knee as a rotational spring with the addition of correction factors at knee extensor moment extrema to predict the instantaneous knee moment profile from the knee angle. Our model prediction performance was comparable to previous studies for weight acceptance (WA) and mid-stance (MS) phases in both assisted (Assist) and non-assisted (Zero) modes based on normalized root mean square error (RMSE), demonstrating the feasibility of joint moment estimation during exoskeleton walking. RMSE was highest in late stance phase, likely due to the non-linear knee stiffness exhibited during this phase in one participant. Overall, our results support real-time implementation of the joint moment prediction model for control of exoskeleton knee extension assistance in children with CP.
能够根据用户的自主努力来调整辅助程度的先进控制策略，对于在诸如脑性瘫痪（CP）儿童等可行走人群中部署用于地面步态训练的外骨骼可能是有益的。在本研究中，我们评估了在两名患有蹲伏步态的CP儿童受试者中，使用外骨骼行走时预测站立阶段生物膝关节力矩的能力。该预测模型将膝关节表征为一个旋转弹簧，并在膝关节伸肌力矩极值处添加校正因子，以根据膝关节角度预测瞬时膝关节力矩曲线。基于归一化均方根误差（RMSE），我们的模型预测性能在辅助（Assist）和非辅助（Zero）模式下，对于负重（WA）和站立中期（MS）阶段与先前的研究相当，这表明在外骨骼行走过程中进行关节力矩估计是可行的。在站立后期阶段RMSE最高，这可能是由于一名参与者在此阶段表现出非线性膝关节刚度。总体而言，我们的结果支持实时实施关节力矩预测模型，以控制CP儿童外骨骼膝关节伸展辅助。
