Human arm weight compensation in rehabilitation robotics: efficacy of three distinct methods

Background Arm weight compensation with rehabilitation robots for stroke patients has been successfully used to increase the active range of motion and reduce the effects of pathological muscle synergies. However, the differences in structure, performance, and control algorithms among the existing robotic platforms make it hard to effectively assess and compare human arm weight relief. In this paper, we introduce criteria for ideal arm weight compensation, and furthermore, we propose and analyze three distinct arm weight compensation methods ( Average , Full , Equilibrium ) in the arm rehabilitation exoskeleton ’ARMin’. The effect of the best performing method was validated in chronic stroke subjects to increase the active range of motion in three dimensional space. Methods All three methods are based on arm models that are generalizable for use in different robotic devices and allow individualized adaptation to the subject by model parameters. The first method Average uses anthropometric tables to determine subject-specific parameters. The parameters for the second method Full are estimated based on force sensor data in predefined resting poses. The third method Equilibrium estimates parameters by optimizing an equilibrium of force/torque equations in a predefined resting pose. The parameters for all three methods were first determined and optimized for temporal and spatial estimation sensitivity. Then, the three methods were compared in a randomized single-center study with respect to the remaining electromyography (EMG) activity of 31 healthy participants who performed five arm poses covering the full range of motion with the exoskeleton robot. The best method was chosen for feasibility tests with three stroke patients. In detail, the influence of arm weight compensation on the three dimensional workspace was assessed by measuring of the horizontal workspace at three different height levels in stroke patients. Results All three arm weight compensation methods reduced the mean EMG activity of healthy subjects to at least 49% compared with the no compensation reference. The Equilibrium method outperformed the Average and the Full methods with a highly significant reduction in mean EMG activity by 19% and 28% respectively. However, upon direct comparison, each method has its own individual advantages such as in set-up time, cost, or required technology. The horizontal workspace assessment in poststroke patients with the Equilibrium method revealed potential workspace size-dependence of arm height, while weight compensation helped maximize the workspace as much as possible. Conclusion Different arm weight compensation methods were developed according to initially defined criteria. The methods were then analyzed with respect to their sensitivity and required technology. In general, weight compensation performance improved with the level of technology, but increased cost and calibration efforts. This study reports a systematic way to analyze the efficacy of different weight compensation methods using EMG. Additionally, the feasibility of the best method, Equilibrium , was shown by testing with three stroke patients. In this test, a height dependence of the workspace size also seemed to be present, which further highlights the importance of patient-specific weight compensation, particularly for training at different arm heights. Trial registration ClinicalTrials.gov,NCT02720341 . Registered 25 March 2016

[1]  Steven L. Wolf,et al.  Partial weight support differentially affects corticomotor excitability across muscles of the upper limb , 2014, Physiological reports.

[2]  R. Richardson,et al.  Initial patient testing of iPAM - a robotic system for Stroke rehabilitation , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

[3]  Jules P. A. Dewald,et al.  Impairment-Based 3-D Robotic Intervention Improves Upper Extremity Work Area in Chronic Stroke: Targeting Abnormal Joint Torque Coupling With Progressive Shoulder Abduction Loading , 2009, IEEE Transactions on Robotics.

[4]  D.J. Reinkensmeyer,et al.  A pneumatic robot for re-training arm movement after stroke: rationale and mechanical design , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[5]  David A. Winter,et al.  Biomechanics and Motor Control of Human Movement , 1990 .

[6]  H. van der Kooij,et al.  Increased range of motion and decreased muscle activity during maximal reach with gravity compensation in stroke patients , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

[7]  Maria C. Bengtson,et al.  The Arm Movement Detection (AMD) test: a fast robotic test of proprioceptive acuity in the arm , 2017, Journal of NeuroEngineering and Rehabilitation.

[8]  Lakmal Seneviratne,et al.  Adaptive Control Of Robot Manipulators , 1992, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  J. Slotine,et al.  On the Adaptive Control of Robot Manipulators , 1987 .

[10]  J. Dewald,et al.  Progressive Shoulder Abduction Loading is a Crucial Element of Arm Rehabilitation in Chronic Stroke , 2009, Neurorehabilitation and neural repair.

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

[12]  Thomas Schauer,et al.  Adaptive Arm Weight Support Using a Cable-Driven Robotic System , 2017 .

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

[14]  Nicolas Schweighofer,et al.  Use It and Improve It or Lose It: Interactions between Arm Function and Use in Humans Post-stroke , 2012, PLoS Comput. Biol..

[15]  Julius P. A. Dewald,et al.  Robotic quantification of upper extremity loss of independent joint control or flexion synergy in individuals with hemiparetic stroke: a review of paradigms addressing the effects of shoulder abduction loading , 2016, Journal of NeuroEngineering and Rehabilitation.

[16]  M. Bergamasco,et al.  Arm rehabilitation with a robotic exoskeleleton in Virtual Reality , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

[17]  Olivier Lambercy,et al.  Influence of Arm Weight Support on a Robotic Assessment of Upper Limb Function , 2018, 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob).

[18]  Bernhard Elsner,et al.  Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke. , 2018, The Cochrane database of systematic reviews.

[19]  Scott L. Delp,et al.  A Model of the Upper Extremity for Simulating Musculoskeletal Surgery and Analyzing Neuromuscular Control , 2005, Annals of Biomedical Engineering.

[20]  T. Olsen,et al.  Recovery of upper extremity function in stroke patients: the Copenhagen Stroke Study. , 1994, Archives of physical medicine and rehabilitation.

[21]  D. Robertson Body Segment Parameters , 2014 .

[22]  Maarten J. IJzerman,et al.  Influence of Gravity Compensation on Muscle Activation Patterns During Different Temporal Phases of Arm Movements of Stroke Patients , 2009, Neurorehabilitation and neural repair.

[23]  Jiping He,et al.  Design and Control of RUPERT: A Device for Robotic Upper Extremity Repetitive Therapy , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[24]  Frans C. T. van der Helm,et al.  Freebal: Design of a Dedicated Weight-Support System for Upper-Extremity Rehabilitation , 2009 .

[25]  Robert Riener,et al.  A robotic system to train activities of daily living in a virtual environment , 2011, Medical & Biological Engineering & Computing.

[26]  Sarah J. Housman,et al.  A Randomized Controlled Trial of Gravity-Supported, Computer-Enhanced Arm Exercise for Individuals With Severe Hemiparesis , 2009, Neurorehabilitation and neural repair.

[27]  Ashley N. Johnson,et al.  Dual-task motor performance with a tongue-operated assistive technology compared with hand operations , 2012, Journal of NeuroEngineering and Rehabilitation.

[28]  A.H.A. Stienen,et al.  Freebal: dedicated gravity compensation for the upper extremities , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

[29]  J. Dewald,et al.  Shoulder abduction-induced reductions in reaching work area following hemiparetic stroke: neuroscientific implications , 2007, Experimental Brain Research.

[30]  Maarten J. IJzerman,et al.  Influence of gravity compensation on muscle activity during reach and retrieval in healthy elderly. , 2009, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[31]  J. Kabat-Zinn,et al.  The clinical use of mindfulness meditation for the self-regulation of chronic pain , 1985, Journal of Behavioral Medicine.

[32]  Robert Riener,et al.  Online adaptive compensation of the ARMin Rehabilitation Robot , 2016, 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[33]  Jason M Wilken,et al.  Range of Motion Requirements for Upper-Limb Activities of Daily Living. , 2015, The American journal of occupational therapy : official publication of the American Occupational Therapy Association.

[34]  T. Hothorn,et al.  Simultaneous Inference in General Parametric Models , 2008, Biometrical journal. Biometrische Zeitschrift.

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

[36]  Frans C. T. van der Helm,et al.  Dampace: Design of an Exoskeleton for Force-Coordination Training in Upper-Extremity Rehabilitation , 2009 .

[37]  Herman van der Kooij,et al.  A Damper Driven Robotic End-Point Manipulator for Functional Rehabilitation Exercises After Stroke , 2014, IEEE Transactions on Biomedical Engineering.

[38]  E. Rocon,et al.  Locomotor training through a novel robotic platform for gait rehabilitation in pediatric population: short report , 2016, Journal of NeuroEngineering and Rehabilitation.

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

[40]  L Dipietro,et al.  Changing motor synergies in chronic stroke. , 2007, Journal of neurophysiology.

[41]  Yuichi Hirano,et al.  Effects of systemic hypoxia on human muscular adaptations to resistance exercise training , 2014, Physiological reports.

[42]  J. Buurke,et al.  Influence of gravity compensation training on synergistic movement patterns of the upper extremity after stroke, a pilot study , 2012, Journal of NeuroEngineering and Rehabilitation.

[43]  G. A. Jackson Survey of EMC measurement techniques , 1989 .

[44]  Dobrivoje S. Stokic,et al.  Weight compensation characteristics of Armeo®Spring exoskeleton: implications for clinical practice and research , 2017, Journal of NeuroEngineering and Rehabilitation.

[45]  Maarten J. IJzerman,et al.  An explorative, cross-sectional study into abnormal muscular coupling during reach in chronic stroke patients , 2010, Journal of NeuroEngineering and Rehabilitation.

[46]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[47]  J P Dewald,et al.  Upper-Limb Discoordination in Hemiparetic Stroke: Implications for Neurorehabilitation , 2001, Topics in stroke rehabilitation.