Bringing Psychological Strategies to Robot-Assisted Physiotherapy for Enhanced Treatment Efficacy

Robotic technologies offer a range of functions to augment clinical rehabilitation practice. However, compliance with robot-assisted rehabilitation techniques has not been optimally achieved. Traditional approaches to improving the treatment efficacy are focusing more on the system function, while psychological factors have not been integrated comprehensively. In this perspective paper, eight key factors reflecting three conceptions-robot design, function design, and patients’ expectations have been evaluated and analyzed. Clinical results with 28 therapists and 84 patients indicate that integrating psychological strategies into robot-assisted physiotherapy may promote better trust and acceptance of rehabilitation robots.

[1]  Bruce A. MacDonald,et al.  Acceptance of Healthcare Robots for the Older Population: Review and Future Directions , 2009, Int. J. Soc. Robotics.

[2]  Andrew McDaid,et al.  Adaptive Trajectory Tracking Control of a Parallel Ankle Rehabilitation Robot With Joint-Space Force Distribution , 2019, IEEE Access.

[3]  G. Lenzi,et al.  Post-stroke depression. , 2008, Revue neurologique.

[4]  H.I. Krebs,et al.  Design and Characterization of Hand Module for Whole-Arm Rehabilitation Following Stroke , 2007, IEEE/ASME Transactions on Mechatronics.

[5]  Wei Meng,et al.  Adaptive Patient-Cooperative Control of a Compliant Ankle Rehabilitation Robot (CARR) With Enhanced Training Safety , 2018, IEEE Transactions on Industrial Electronics.

[6]  W. Feng,et al.  Factors affecting post-stroke motor recovery: Implications on neurotherapy after brain injury , 2018, Behavioural Brain Research.

[7]  M. Guadagnoli,et al.  Challenge Point: A Framework for Conceptualizing the Effects of Various Practice Conditions in Motor Learning , 2004, Journal of motor behavior.

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

[9]  Jessie Y. C. Chen,et al.  A Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction , 2011, Hum. Factors.

[10]  S. Barker-Collo.  Depression and anxiety 3 months post stroke: prevalence and correlates. , 2007, Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists.

[11]  C. Braun,et al.  Motor learning elicited by voluntary drive. , 2003, Brain : a journal of neurology.

[12]  Philipp Kellmeyer,et al.  Social robots in rehabilitation: A question of trust , 2018, Science Robotics.

[13]  G. Morone,et al.  Influence of Psychologic Features on Rehabilitation Outcomes in Patients with Subacute Stroke Trained with Robotic-Aided Walking Therapy , 2013, American journal of physical medicine & rehabilitation.

[14]  Marcia Kilchenman O'Malley,et al.  Minimal Assist-as-Needed Controller for Upper Limb Robotic Rehabilitation , 2016, IEEE Transactions on Robotics.

[15]  P. Feys,et al.  The Armeo Spring as training tool to improve upper limb functionality in multiple sclerosis: a pilot study , 2011, Journal of NeuroEngineering and Rehabilitation.

[16]  R. Riener,et al.  Psychological state estimation from physiological recordings during robot-assisted gait rehabilitation. , 2011, Journal of rehabilitation research and development.

[17]  Haoyong Yu,et al.  Multi-modal control scheme for rehabilitation robotic exoskeletons , 2017, Int. J. Robotics Res..

[18]  P. Dario,et al.  Design strategies to improve patient motivation during robot-aided rehabilitation , 2007, Journal of NeuroEngineering and Rehabilitation.

[19]  P. Pound,et al.  A critical review of the concept of patient motivation in the literature on physical rehabilitation. , 2000, Social science & medicine.