Bulea Thomas C, Lerner Zachary F, Damiano Diane L
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2801-2804. doi: 10.1109/EMBC.2018.8512799.
Effective solutions for gait rehabilitation in children with cerebral palsy (CP) remain elusive. Wearable robotic exoskeletons offer the potential to greatly increase the dosage and intensity of gait training in this population, which may improve outcomes. We recently reported that a robotic exoskeleton significantly improved knee extension in children with crouch gait from CP. Longitudinal studies are necessary to fully understand long term biomechanical effects of exoskeleton gait training. Given that children's gait can change both as they develop and throughout their therapy, advanced control strategies which can adapt assistance over time may be beneficial. But, stride-to-stride variability makes it difficult to ascertain the effects of exoskeleton assistance and therefore complicates implementation of adaptable control algorithms. Here, we examine the use of the variance ratio (VR), a previously published measure, to assess the effect of exoskeleton assistance on knee extensor and flexor EMG variability in children with CP. Our results show that VR was significantly increased ($p<0.001)$ compared to baseline during walking with exoskeleton assistance. After five practice sessions, we found that VR was reduced though still greater than baseline levels. Given its sensitivity to exoskeleton assistance and ease of computation, VR may be a useful measure in the future for evaluating stride-to-stride variability in real time to inform algorithmic decision making for autonomous adaptable control.
针对脑瘫（CP）儿童的步态康复，有效的解决方案仍然难以捉摸。可穿戴机器人外骨骼有潜力大幅增加该人群步态训练的剂量和强度，这可能改善治疗效果。我们最近报告称，一种机器人外骨骼显著改善了患有蹲伏步态的CP儿童的膝关节伸展。需要进行纵向研究以充分了解外骨骼步态训练的长期生物力学效应。鉴于儿童的步态在发育过程中和整个治疗过程中都会发生变化，能够随时间调整辅助的先进控制策略可能会有所帮助。但是，步幅间的变异性使得难以确定外骨骼辅助的效果，因此使适应性控制算法的实施变得复杂。在此，我们研究使用方差比（VR）（一种先前发表的测量方法）来评估外骨骼辅助对CP儿童膝关节伸肌和屈肌肌电图变异性的影响。我们的结果表明，在有外骨骼辅助的行走过程中，与基线相比，VR显著增加（p<0.001）。经过五次练习后，我们发现VR有所降低，但仍高于基线水平。鉴于其对外骨骼辅助的敏感性和计算简便性，VR未来可能是一种有用的测量方法，用于实时评估步幅间变异性，为自主适应性控制的算法决策提供依据。
