SITAR: a system for independent task-oriented assessment and rehabilitation

Introduction Over recent years, task-oriented training has emerged as a dominant approach in neurorehabilitation. This article presents a novel, sensor-based system for independent task-oriented assessment and rehabilitation (SITAR) of the upper limb. Methods The SITAR is an ecosystem of interactive devices including a touch and force–sensitive tabletop and a set of intelligent objects enabling functional interaction. In contrast to most existing sensor-based systems, SITAR provides natural training of visuomotor coordination through collocated visual and haptic workspaces alongside multimodal feedback, facilitating learning and its transfer to real tasks. We illustrate the possibilities offered by the SITAR for sensorimotor assessment and therapy through pilot assessment and usability studies. Results The pilot data from the assessment study demonstrates how the system can be used to assess different aspects of upper limb reaching, pick-and-place and sensory tactile resolution tasks. The pilot usability study indicates that patients are able to train arm-reaching movements independently using the SITAR with minimal involvement of the therapist and that they were motivated to pursue the SITAR-based therapy. Conclusion SITAR is a versatile, non-robotic tool that can be used to implement a range of therapeutic exercises and assessments for different types of patients, which is particularly well-suited for task-oriented training.

[1]  Grant D. Huang,et al.  Robot-assisted therapy for long-term upper-limb impairment after stroke. , 2010, The New England journal of medicine.

[2]  E. Burdet,et al.  Instrumented sorting block box for children, a preliminary experiment , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[3]  Peter Langhorne,et al.  Effects of Augmented Exercise Therapy Time After Stroke: A Meta-Analysis , 2004, Stroke.

[4]  H. Krebs,et al.  Effects of Robot-Assisted Therapy on Upper Limb Recovery After Stroke: A Systematic Review , 2008, Neurorehabilitation and neural repair.

[5]  P. McNulty,et al.  The Prevalence and Magnitude of Impaired Cutaneous Sensation across the Hand in the Chronic Period Post-Stroke , 2014, PloS one.

[6]  R. Hébert,et al.  Validation of the Box and Block Test as a measure of dexterity of elderly people: reliability, validity, and norms studies. , 1994, Archives of physical medicine and rehabilitation.

[7]  A Melendez-Calderon,et al.  Force Field Adaptation Can Be Learned Using Vision in the Absence of Proprioceptive Error , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  A. Prochazka,et al.  A Fully Automated, Quantitative Test of Upper Limb Function , 2015, Journal of motor behavior.

[9]  David Webster,et al.  Experimental evaluation of Microsoft Kinect's accuracy and capture rate for stroke rehabilitation applications , 2014, 2014 IEEE Haptics Symposium (HAPTICS).

[10]  B. Phillips,et al.  The AsTex ®: clinimetric properties of a new tool for evaluating hand sensation following stroke , 2009, Clinical rehabilitation.

[11]  Esther Duarte,et al.  Erratum to: The visual amplification of goal-oriented movements counteracts acquired non-use in hemiparetic stroke patients , 2015, Journal of NeuroEngineering and Rehabilitation.

[12]  Etienne Burdet,et al.  Transfer of dynamic motor skills acquired during isometric training to free motion. , 2017, Journal of neurophysiology.

[13]  Sebastian Madgwick,et al.  Estimation of IMU and MARG orientation using a gradient descent algorithm , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

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

[15]  C. Lang,et al.  Assessment of upper extremity impairment, function, and activity after stroke: foundations for clinical decision making. , 2013, Journal of hand therapy : official journal of the American Society of Hand Therapists.

[16]  D. Hoang FLOW: The Psychology of Optimal Experience , 2018 .

[17]  Eric Foxlin,et al.  Pedestrian tracking with shoe-mounted inertial sensors , 2005, IEEE Computer Graphics and Applications.

[18]  A. Timmermans,et al.  Sensor-Based Arm Skill Training in Chronic Stroke Patients: Results on Treatment Outcome, Patient Motivation, and System Usability , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  A. Timmermans,et al.  Technology-assisted training of arm-hand skills in stroke: concepts on reacquisition of motor control and therapist guidelines for rehabilitation technology design , 2009, Journal of NeuroEngineering and Rehabilitation.

[20]  A. Prochazka,et al.  In-Home Tele-Rehabilitation Improves Tetraplegic Hand Function , 2011, Neurorehabilitation and neural repair.

[21]  Etienne Burdet,et al.  A modular sensor-based system for the Rehabilitation and Assessment of manipulation , 2012, 2012 IEEE Haptics Symposium (HAPTICS).

[22]  B. Volpe,et al.  Kinematic Robot-Based Evaluation Scales and Clinical Counterparts to Measure Upper Limb Motor Performance in Patients With Chronic Stroke , 2010, Neurorehabilitation and neural repair.

[23]  J. Fung,et al.  Effects of robot-assisted therapy on stroke rehabilitation in upper limbs: systematic review and meta-analysis of the literature. , 2012, Journal of rehabilitation research and development.

[24]  Heather Carnahan,et al.  Motor Learning Perspectives on Haptic Training for the Upper Extremities , 2014, IEEE Transactions on Haptics.

[25]  Janice I. Glasgow,et al.  Assessment of Upper-Limb Sensorimotor Function of Subacute Stroke Patients Using Visually Guided Reaching , 2010, Neurorehabilitation and neural repair.

[26]  W. Rymer,et al.  Robot-assisted reaching exercise promotes arm movement recovery in chronic hemiparetic stroke: a randomized controlled pilot study , 2006, Journal of NeuroEngineering and Rehabilitation.

[27]  Pankaj Sharma,et al.  Democratizing Neurorehabilitation: How Accessible are Low-Cost Mobile-Gaming Technologies for Self-Rehabilitation of Arm Disability in Stroke? , 2016, PloS one.

[28]  Michelle McDonnell,et al.  Action research arm test. , 2008, The Australian journal of physiotherapy.

[29]  F. Q. Ribeiro The meta-analysis , 2017, Brazilian journal of otorhinolaryngology.

[30]  E. Burdet,et al.  Learning to Design Rehabilitation Devices Through the H-CARD Course: Project-Based Learning of Rehabilitation Technology Design , 2012, IEEE Pulse.

[31]  R. Harvey,et al.  Improving poststroke recovery: Neuroplasticity and task-oriented training , 2009, Current treatment options in cardiovascular medicine.

[32]  Etienne Burdet,et al.  Quantitative assessment of motor deficit with an intelligent key Object: A Pilot Study , 2015, 2015 IEEE International Conference on Rehabilitation Robotics (ICORR).

[33]  S. K. Wee,et al.  Trunk Restraint to Promote Upper Extremity Recovery in Stroke Patients , 2014, Neurorehabilitation and neural repair.

[34]  Abderrahmane Kheddar,et al.  Pseudo-haptic feedback: can isometric input devices simulate force feedback? , 2000, Proceedings IEEE Virtual Reality 2000 (Cat. No.00CB37048).

[35]  G. Kwakkel,et al.  The impact of physical therapy on functional outcomes after stroke: what's the evidence? , 2004, Clinical rehabilitation.

[36]  E. Taub,et al.  Automated Constraint-Induced Therapy Extension (AutoCITE) for movement deficits after stroke. , 2004, Journal of rehabilitation research and development.

[37]  Etienne Burdet,et al.  Investigation of isometric strength and control of the upper extremities in multiple sclerosis , 2016, Journal of rehabilitation and assistive technologies engineering.

[38]  Günther Deuschl,et al.  Hand coordination following capsular stroke. , 2004, Brain : a journal of neurology.

[39]  Chee Leong Teo,et al.  Post-stroke training of a pick and place activity in a virtual environment , 2008, 2008 Virtual Rehabilitation.

[40]  N. Schweighofer,et al.  Task-Oriented Rehabilitation Robotics , 2012, American journal of physical medicine & rehabilitation.

[41]  Agnès Roby-Brami,et al.  Analysis of grasping strategies and function in hemiparetic patients using an instrumented object , 2013, 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR).

[42]  R. Riener,et al.  Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review , 2012, Psychonomic Bulletin & Review.

[43]  N. Hogan,et al.  Robotic Therapy and the Paradox of the Diminishing Number of Degrees of Freedom. , 2015, Physical medicine and rehabilitation clinics of North America.

[44]  P. Verschure,et al.  The visual amplification of goal-oriented movements counteracts acquired non-use in hemiparetic stroke patients , 2015, Journal of neuroengineering and rehabilitation.

[45]  E. Burdet,et al.  Robot-assisted rehabilitation of hand function. , 2010, Current opinion in neurology.