Design of Virtual Guiding Tasks With Haptic Feedback for Assessing the Wrist Motor Function of Patients With Upper Motor Neuron Lesions

Impaired motor function is a common consequence of upper motor neuron lesions (UMNLs). Fine motor skills involved in small movements occurring in the fingers, hand, and wrist are usually regained by patient self-training at home. Most studies focus on the rehabilitation of the fingers but ignore the recovery of wrist motor function. In this paper, three virtual guiding tasks were designed to assess wrist motor functions, including the basic motor flexibility, motion stability, and a range of active motion. A haptic device was used to provide haptic feedback to users who performed virtual tasks in a virtual reality (VR) environment. In total, 46 healthy subjects and 10 UMNL patients were included to test the effectiveness of the designed tasks on improving wrist motor assessments. Quantitative performances, including the completion time, contact force, and motion trajectory, were automatically acquired during the tasks. Measurements for 95% of control subjects were used to establish normative references. Patient deficiencies in the wrist motor function were identified when their quantitative performances were outside the normative control ranges. The results suggest that the designed virtual tasks are sensitive for patients in the later period of rehabilitation, making the assessment suitable for using at home.

[1]  P. Giannoni,et al.  Wrist Rehabilitation in Chronic Stroke Patients by Means of Adaptive, Progressive Robot-Aided Therapy , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  P. Langhorne,et al.  Motor recovery after stroke: a systematic review , 2009, The Lancet Neurology.

[3]  A. Fugl-Meyer,et al.  The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. , 1975, Scandinavian journal of rehabilitation medicine.

[4]  Jonghyun Kim,et al.  Automated Evaluation of Upper-Limb Motor Function Impairment Using Fugl-Meyer Assessment , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[5]  K. Sunnerhagen,et al.  Virtual reality and haptics as a training device for movement rehabilitation after stroke: a single-case study. , 2004, Archives of Physical Medicine and Rehabilitation.

[6]  Zoran Duric,et al.  The feasibility of using haptic devices to engage people with chronic traumatic brain injury in virtual 3D functional tasks , 2014, Journal of NeuroEngineering and Rehabilitation.

[7]  H. Johansen-Berg,et al.  Evaluation of the Modifid Jebsen Test of Hand Function and the University of Maryland Arm Questionnaire for Stroke , 2004, Clinical rehabilitation.

[8]  G. Kwakkel,et al.  Probability of regaining dexterity in the flaccid upper limb: impact of severity of paresis and time since onset in acute stroke. , 2003, Stroke.

[9]  Richard G. Carson,et al.  Tele-Supervised FES-Assisted Exercise for Hemiplegic Upper Limb , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  Wei Yao,et al.  Design and Evaluation of a Soft and Wearable Robotic Glove for Hand Rehabilitation , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[11]  A. L. Albright Spastic Cerebral Palsy , 1995 .

[12]  Nazir Kamaldin,et al.  A Magnetic Resonance Compatible Soft Wearable Robotic Glove for Hand Rehabilitation and Brain Imaging , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[13]  Giulio Sandini,et al.  Journal of Neuroengineering and Rehabilitation Performance Adaptive Training Control Strategy for Recovering Wrist Movements in Stroke Patients: a Preliminary, Feasibility Study , 2009 .

[14]  M. Aisen,et al.  Cerebral palsy: clinical care and neurological rehabilitation , 2011, The Lancet Neurology.

[15]  M. White,et al.  Virtual Activities of Daily Living for Recovery of Upper Extremity Motor Function , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[16]  S. Leonhardt,et al.  A survey on robotic devices for upper limb rehabilitation , 2014, Journal of NeuroEngineering and Rehabilitation.

[17]  Oonagh M. Giggins,et al.  Biofeedback in rehabilitation , 2013, Journal of NeuroEngineering and Rehabilitation.

[18]  S. Black,et al.  The Fugl-Meyer Assessment of Motor Recovery after Stroke: A Critical Review of Its Measurement Properties , 2002, Neurorehabilitation and neural repair.

[19]  Martin Levesley,et al.  Home-based Computer Assisted Arm Rehabilitation (hCAAR) robotic device for upper limb exercise after stroke: results of a feasibility study in home setting , 2014, Journal of NeuroEngineering and Rehabilitation.

[20]  Sang Hyuk Son,et al.  A Framework to Automate Assessment of Upper-Limb Motor Function Impairment: A Feasibility Study , 2015, Sensors.

[21]  Abdulmotaleb El-Saddik,et al.  A Fuzzy-Based Adaptive Rehabilitation Framework for Home-Based Wrist Training , 2014, IEEE Transactions on Instrumentation and Measurement.

[22]  V. Feigin,et al.  Global and regional burden of stroke during 1990–2010: findings from the Global Burden of Disease Study 2010 , 2014, The Lancet.

[23]  R. H. Jebsen,et al.  An objective and standardized test of hand function. , 1969, Archives of physical medicine and rehabilitation.

[24]  Hubertus J.A. van Hedel,et al.  Weight-supported training of the upper extremity in children with cerebral palsy: a motor learning study , 2017, Journal of NeuroEngineering and Rehabilitation.

[25]  Abdulmotaleb El-Saddik,et al.  Haptic Virtual Rehabilitation Exercises for Poststroke Diagnosis , 2008, IEEE Transactions on Instrumentation and Measurement.

[26]  Nick F Ramsey,et al.  Review: Functional Neuroimaging Studies of Early Upper Limb Recovery After Stroke: A Systematic Review of the Literature , 2010, Neurorehabilitation and neural repair.

[27]  Jennifer A. Semrau,et al.  Robotic Identification of Kinesthetic Deficits After Stroke , 2013, Stroke.

[28]  Bogdan Gabrys,et al.  An overview of self-adaptive technologies within virtual reality training , 2016, Comput. Sci. Rev..

[29]  Daniel Simonsen,et al.  Design and test of an automated version of the modified Jebsen test of hand function using Microsoft Kinect , 2017, Journal of NeuroEngineering and Rehabilitation.

[30]  Carol A. Seger,et al.  Implicit learning. , 1994, Psychological bulletin.

[31]  Kang Xiang Khor,et al.  Portable and Reconfigurable Wrist Robot Improves Hand Function for Post-Stroke Subjects , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[32]  Ruben G. L. Real,et al.  Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research , 2017, The Lancet Neurology.

[33]  Matthew D. Lichter,et al.  Assessing Upper Extremity Motor Function in Practice of Virtual Activities of Daily Living , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[34]  S. Wolf,et al.  Assessing Wolf Motor Function Test as Outcome Measure for Research in Patients After Stroke , 2001, Stroke.

[35]  Ferdinando A. Mussa-Ivaldi,et al.  Robot-assisted adaptive training: custom force fields for teaching movement patterns , 2004, IEEE Transactions on Biomedical Engineering.

[36]  Corey J. Bohil,et al.  Virtual reality in neuroscience research and therapy , 2011, Nature Reviews Neuroscience.

[37]  D. Nichols,et al.  Home-Based Therapy After Stroke Using the Hand Spring Operated Movement Enhancer (HandSOME) , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[38]  P. Langhorne,et al.  Stroke rehabilitation , 2011, The Lancet.

[39]  G.C. Burdea,et al.  Virtual reality-enhanced stroke rehabilitation , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[40]  M. Levin,et al.  Virtual Reality in Stroke Rehabilitation: A Meta-Analysis and Implications for Clinicians , 2011, Stroke.