Visual and Haptic Error Modulating Controllers for Robotic Gait Training

Tsangaridis, Panagiotis; Obwegeser, David; Maggioni, Serena; Riener, Robert; Marchal Crespo, Laura (11 October 2018). Visual and Haptic Error Modulating Controllers for Robotic Gait Training. In: 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob) (pp. 1050-1055). IEEE 10.1109/BIOROB.2018.8488011

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Robotic algorithms that augment movement errors have been proposed as promising training strategies to enhance motor training and neurorehabilitation. However, research effort has mainly focused on rehabilitation of upper limbs. In this study, we investigated the effect of training with novel error modulating strategies on learning an asymmetric gait pattern. Thirty healthy young participants walked in the robotic exoskeleton Lokomat, while learning a foot target-tracking task which required an increased hip and knee flexion in the dominant leg. Learning with three different strategies was evaluated: (i) No guidance: no disturbance/guidance was applied, (ii) Haptic error amplification: dangerous and discouraging large errors were limited with haptic guidance, while awareness of task relevant errors was enhanced with error amplification, and (iii) Visual error amplification: visually perceived errors were amplified in a virtual reality environment. We also evaluated whether increasing the movement variability during training by adding randomly-varying haptic disturbances on top of the other training strategies further enhanced learning. We found that training with the novel haptic error amplification strategy limited large errors during training, did not hamper learning and enhanced transfer of the learned asymmetric gait pattern. Training with visual error amplification, on the other hand, increased errors during training and hampered motor learning. Adding haptic disturbances did not have a significant effect on learning. The novel haptic error modulating controller that amplifies small task-relevant errors while limiting large errors provided the best framework to enhance motor learning.

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