Robotic Rehabilitation and Assistance for Individuals With Movement Disorders Based on a Kinematic Model of the Upper Limb

Design and development of robotic-assistance must consider the abilities of individuals with disabilities. In this article, a 8-DOF kinematic model of the upper limb complex is derived to evaluate the reachable workspace of the arm during interaction with a planar robot and to serve as the basis for rehabilitation strategies and assistive robotics. Through inverse differential kinematics and by taking account the physical limits of each arm joint, the model determines workspaces where the individual is able to perform tasks and those regions where robotic assistance is required. Next, a learning-from-demonstration strategy via a nonparametric potential field function is derived to teach the robot the required assistance based on demonstrations of functional tasks. This article investigates two applications. First, in the context of rehabilitation, robotic assistance is only provided if the individual is required to move her arm in regions that are not reachable via voluntary motion. Second, in the context of assistive robotics, the demonstrated trajectory is scaled down to match the individual’s voluntary range of motion through a nonlinear workspace mapping. Assistance is provided within that workspace only. Experimental results in 5 different experimental scenarios with a person with cerebral palsy confirm the suitability of the proposed framework.

[1]  J. Denavit,et al.  A kinematic notation for lower pair mechanisms based on matrices , 1955 .

[2]  M. Kiely,et al.  Descriptive epidemiology of cerebral palsy. , 1984, Public health reviews.

[3]  Richard W. Bohannon,et al.  Interrater reliability of a modified Ashworth scale of muscle spasticity. , 1987, Physical therapy.

[4]  Bruno Siciliano,et al.  A solution algorithm to the inverse kinematic problem for redundant manipulators , 1988, IEEE J. Robotics Autom..

[5]  Fred L. Bookstein,et al.  Principal Warps: Thin-Plate Splines and the Decomposition of Deformations , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  J. Lenarcic,et al.  Simple Model of Human Arm Reachable Workspace , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[7]  Kazuhiko Kawamura,et al.  Intelligent robotic systems in service of the disabled , 1995 .

[8]  R. McN. Alexander,et al.  A minimum energy cost hypothesis for human arm trajectories , 1997, Biological Cybernetics.

[9]  R. Price,et al.  Selective dorsal rhizotomy: efficacy and safety in an investigator‐masked randomized clinical trial , 1998, Developmental medicine and child neurology.

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

[11]  R A Prokopenko,et al.  Assessment of the accuracy of a human arm model with seven degrees of freedom. , 2001, Journal of biomechanics.

[12]  Jun Wang,et al.  A dual neural network for kinematic control of redundant robot manipulators , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[13]  C. Burgar,et al.  Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke. , 2002, Archives of physical medicine and rehabilitation.

[14]  R. D. Schraft,et al.  Robotic home assistant Care-O-bot II , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[15]  N. Hogan,et al.  Effects of robotic therapy on motor impairment and recovery in chronic stroke. , 2003, Archives of physical medicine and rehabilitation.

[16]  M. Kawato,et al.  Formation and control of optimal trajectory in human multijoint arm movement , 1989, Biological Cybernetics.

[17]  Zhiwei Luo,et al.  Optimal trajectory formation of constrained human arm reaching movements , 2004, Biological Cybernetics.

[18]  W. Mechelen,et al.  Physical Activity for People with a Disability , 2004 .

[19]  Hermano I Krebs,et al.  Rehabilitation robotics: pilot trial of a spatial extension for MIT-Manus , 2004, Journal of NeuroEngineering and Rehabilitation.

[20]  P. Morasso,et al.  Anthropomorphic robotics , 1980, Biological Cybernetics.

[21]  Yoshiyuki Tanaka,et al.  Tracking control properties of human-robotic systems based on impedance control , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[22]  Maarten J. IJzerman,et al.  Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke. , 2006, Journal of rehabilitation research and development.

[23]  A. Eliasson,et al.  The Manual Ability Classification System (MACS) for children with cerebral palsy: scale development and evidence of validity and reliability. , 2006, Developmental medicine and child neurology.

[24]  H. van der Kooij,et al.  Design and Evaluation of the LOPES Exoskeleton Robot for Interactive Gait Rehabilitation , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[25]  T. Sinkjaer,et al.  Spastic movement disorder: impaired reflex function and altered muscle mechanics , 2007, The Lancet Neurology.

[26]  S. Rossi,et al.  Repetitive transcranial magnetic stimulation of the motor cortex ameliorates spasticity in multiple sclerosis , 2007, Neurology.

[27]  B. McDowell.  The Gross Motor Function Classification System – Expanded and Revised , 2008, Developmental medicine and child neurology.

[28]  D. Weber,et al.  The role of assistive robotics in the lives of persons with disability. , 2010, American journal of physical medicine & rehabilitation.

[29]  Francois Routhier,et al.  Evaluation of the JACO robotic arm: Clinico-economic study for powered wheelchair users with upper-extremity disabilities , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[30]  Cristian Secchi,et al.  A tank-based approach to impedance control with variable stiffness , 2013, 2013 IEEE International Conference on Robotics and Automation.

[31]  Matteo Malosio,et al.  SafeNet: A methodology for integrating general-purpose unsafe devices in safe-robot rehabilitation systems , 2014, Comput. Methods Programs Biomed..

[32]  Carlos Rossa,et al.  Design and Control of a Dual Unidirectional Brake Hybrid Actuation System for Haptic Devices , 2014, IEEE Transactions on Haptics.

[33]  A. U. Pehlivan,et al.  Current Trends in Robot-Assisted Upper-Limb Stroke Rehabilitation: Promoting Patient Engagement in Therapy , 2014, Current Physical Medicine and Rehabilitation Reports.

[34]  D. Corbett,et al.  Prevalence of Individuals Experiencing the Effects of Stroke in Canada: Trends and Projections , 2015, Stroke.

[35]  Chien Chern Cheah,et al.  Passivity and Stability of Human–Robot Interaction Control for Upper-Limb Rehabilitation Robots , 2015, IEEE Transactions on Robotics.

[36]  Hassan Zohoor,et al.  Design and prototype of an active assistive exoskeletal robot for rehabilitation of elbow and wrist , 2016 .

[37]  A. Cook,et al.  Playfulness in Children with Limited Motor Abilities When Using a Robot , 2016, Physical & occupational therapy in pediatrics.

[38]  Mahdi Tavakoli,et al.  Robotic assistance for children with cerebral palsy based on learning from tele-cooperative demonstration , 2017, International Journal of Intelligent Robotics and Applications.

[39]  G. Gelderblom,et al.  Robots and ICT to support play in children with severe physical disabilities: a systematic review , 2016, Disability and rehabilitation. Assistive technology.

[40]  Oussama Khatib,et al.  Learning potential functions from human demonstrations with encapsulated dynamic and compliant behaviors , 2017, Auton. Robots.

[41]  Mahdi Tavakoli,et al.  Telerobotics-Assisted Platform for Enhancing Interaction with Physical Environments for People Living with Cerebral Palsy , 2017, J. Medical Robotics Res..

[42]  Carlos Rossa,et al.  Nonlinear workspace mapping for telerobotic assistance of upper limb in patients with severe movement disorders , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[43]  Bin Yao,et al.  Modular Development of Master-Slave Asymmetric Teleoperation Systems With a Novel Workspace Mapping Algorithm , 2018, IEEE Access.

[44]  Mahdi Tavakoli,et al.  A Computational-Model-Based Study of Supervised Haptics-Enabled Therapist-in-the-Loop Training for Upper-Limb Poststroke Robotic Rehabilitation , 2018, IEEE/ASME Transactions on Mechatronics.

[45]  Chen Wang,et al.  A Greedy Assist-as-Needed Controller for Upper Limb Rehabilitation , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[46]  T. Koeppel,et al.  Test-Retest Reliability of Kinematic Assessments for Upper Limb Robotic Rehabilitation , 2020, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[47]  Mojtaba Sharifi,et al.  Assist-as-needed policy for movement therapy using telerobotics-mediated therapist supervision , 2020 .