Haptic Rendering Modulates Task Performance, Physical Effort and Movement Strategy during Robot-Assisted Training

Research on neurorehabilitation has emphasized that somatosensory information about the interaction with the environment during physical training is crucial to provoke brain plasticity. Despite this, only a small number of robotic devices provide haptic rendering of the virtual environment during neurorehabilitation exercises, the majority with simple structures. However, to provide realistic haptic rendering while supporting neurological patients to perform motor tasks, a transparent robot with several degrees of freedom is needed. In this study, we employed Disturbance Observers to achieve high transparency and fine haptic capabilities on the six DoF exoskeleton ARMin. We incorporated arm weight compensation to reduce the excessive physical effort required to move against gravity, promoting movement performance and directing the participants’ effort to the interaction with the environment. The effect of haptic rendering and its interaction with arm weight compensation were evaluated with six healthy participants. The task consisted of inverting a virtual pendulum and keeping it inverted. We found that haptic rendering of the pendulum dynamics affects the movement strategy the participants follow, i.e., they covered a significantly larger workspace with the end-effector at a significantly higher speed, and required moderate physical effort. The inclusion of arm weight support increased task performance and reduced participants’ effort, while it did not change the movement strategy. Our results suggest that haptic rendering, together with arm weight support, are potential interventions to enhance neurorehabilitation due to the added somatosensory information during motor training.

[1]  H. Feys,et al.  How Do Somatosensory Deficits in the Arm and Hand Relate to Upper Limb Impairment, Activity, and Participation Problems After Stroke? A Systematic Review , 2014, Physical Therapy.

[2]  Olivier Lambercy,et al.  Performance comparison of interaction control strategies on a hand rehabilitation robot , 2015, 2015 IEEE International Conference on Rehabilitation Robotics (ICORR).

[3]  Olivier Lambercy,et al.  Neurocognitive Robot-Assisted Therapy of Hand Function , 2014, IEEE Transactions on Haptics.

[4]  D.J. Reinkensmeyer,et al.  Automating Arm Movement Training Following Severe Stroke: Functional Exercises With Quantitative Feedback in a Gravity-Reduced Environment , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[5]  Rahsaan J. Holley,et al.  Robotic Approaches for Rehabilitation of Hand Function After Stroke , 2012, American journal of physical medicine & rehabilitation.

[6]  Dagmar Sternad,et al.  Exploiting the geometry of the solution space to reduce sensitivity to neuromotor noise , 2018, PLoS Comput. Biol..

[7]  Robert Riener,et al.  The Effect of Haptic Guidance on Learning a Hybrid Rhythmic-Discrete Motor Task , 2015, IEEE Transactions on Haptics.

[8]  Marcia Kilchenman O'Malley,et al.  The Task-Dependent Efficacy of Shared-Control Haptic Guidance Paradigms , 2012, IEEE Transactions on Haptics.

[9]  C.-S. Poon,et al.  Sensorimotor learning and information processing by Bayesian internal models , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  Matteo Bianchi,et al.  Spatially Separating Haptic Guidance From Task Dynamics Through Wearable Devices , 2019, IEEE Transactions on Haptics.

[11]  Jaime E. Duarte,et al.  Evaluation of a mixed controller that amplifies spatial errors while reducing timing errors , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[12]  J B Myers,et al.  Proprioception and neuromuscular control of the shoulder after muscle fatigue. , 1999, Journal of athletic training.

[13]  Robert Riener,et al.  Exoskeleton transparency: feed-forward compensation vs. disturbance observer , 2018, Autom..

[14]  G. Savelsbergh,et al.  Transfer of motor learning from virtual to natural environments in individuals with cerebral palsy. , 2014, Research in developmental disabilities.

[15]  Robert Riener,et al.  Human arm weight compensation in rehabilitation robotics: efficacy of three distinct methods , 2020, Journal of NeuroEngineering and Rehabilitation.

[16]  Vicky Chan,et al.  Robotic Assistance for Training Finger Movement Using a Hebbian Model: A Randomized Controlled Trial , 2017, Neurorehabilitation and neural repair.

[17]  T. Aune,et al.  Effect of Physical Fatigue on Motor Control at Different Skill Levels , 2008, Perceptual and motor skills.

[18]  D. Reinkensmeyer,et al.  Review of control strategies for robotic movement training after neurologic injury , 2009, Journal of NeuroEngineering and Rehabilitation.

[19]  Robert Riener,et al.  Feedforward model based arm weight compensation with the rehabilitation robot ARMin , 2017, 2017 International Conference on Rehabilitation Robotics (ICORR).

[20]  V. Dietz,et al.  Rehabilitation robots for the treatment of sensorimotor deficits: a neurophysiological perspective , 2018, Journal of NeuroEngineering and Rehabilitation.

[21]  Robert Riener,et al.  The effectiveness of robotic training depends on motor task characteristics , 2017, Experimental Brain Research.

[22]  M. Bryden Measuring handedness with questionnaires , 1977, Neuropsychologia.