A Perspective on the Use of Error Augmentation in Robot-Assisted Gait Training of StrokeSurvivors

Robot-Assisted Gait Training (RAGT) has gathered significant attention in the past years, nevertheless the results of its application on the stroke population are inconsistent. One of the reasons behind the mixed success of RAGT is believed to be its failure in promoting active participation from the patients. Herein is discussed the potential use of the Error Augmentation paradigm to RAGT. Error Augmentation is a training paradigm which utilize perturbations to amplify biomechanical errors (or their associated feedback). The augmented error leads to the generation of a motor adaptation that compensates the perturbation and, once the perturbation is removed, reduces the original error. So far the Error Augmentation paradigm has been applied to gait rehabilitation only in the split-belt treadmill paradigm, where it is used to induce compensation for altered step symmetry. The application of such paradigm to RAGT, although technically challenging, has the potential of increasing the gait parameters that can be targeted in a training modality that, by design, is highly patient-specific and demands the active participation of the patients during the therapy sessions.

[1]  Paolo Bonato,et al.  Advanced Robotic Therapy Integrated Centers (ARTIC): an international collaboration facilitating the application of rehabilitation technologies , 2018, Journal of NeuroEngineering and Rehabilitation.

[2]  Paolo Bonato,et al.  Robot-induced perturbations of human walking reveal a selective generation of motor adaptation , 2017, Science Robotics.

[3]  M. Morari,et al.  Robotic Orthosis Lokomat: A Rehabilitation and Research Tool , 2003, Neuromodulation : journal of the International Neuromodulation Society.

[4]  A. Bastian Understanding sensorimotor adaptation and learning for rehabilitation , 2008, Current opinion in neurology.

[5]  Donald Hedeker,et al.  Error Augmentation Enhancing Arm Recovery in Individuals With Chronic Stroke , 2014, Neurorehabilitation and neural repair.

[6]  J. Higginson,et al.  Assist-as-Needed Robot-Aided Gait Training Improves Walking Function in Individuals Following Stroke , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  Kelly A Danks,et al.  Repeated Split-Belt Treadmill Training Improves Poststroke Step Length Asymmetry , 2013, Neurorehabilitation and neural repair.

[8]  Robert Riener,et al.  Patient-cooperative control increases active participation of individuals with SCI during robot-aided gait training , 2010, Journal of NeuroEngineering and Rehabilitation.

[9]  T. Takken,et al.  Effects of a high-intensity task-oriented training on gait performance early after stroke: a pilot study , 2010, Clinical rehabilitation.

[10]  C. Walsh,et al.  A soft robotic exosuit improves walking in patients after stroke , 2017, Science Translational Medicine.

[11]  Hannah J. Block,et al.  Interlimb coordination during locomotion: what can be adapted and stored? , 2005, Journal of neurophysiology.

[12]  J. Hidler,et al.  Multicenter Randomized Clinical Trial Evaluating the Effectiveness of the Lokomat in Subacute Stroke , 2009, Neurorehabilitation and neural repair.