Self-directed arm therapy at home after stroke with a sensor-based virtual reality training system

BackgroundThe effect of rehabilitative training after stroke is dose-dependent. Out-patient rehabilitation training is often limited by transport logistics, financial resources and a lack of motivation/compliance. We studied the feasibility of an unsupervised arm therapy for self-directed rehabilitation therapy in patients’ homes.MethodsAn open-label, single group study involving eleven patients with hemiparesis due to stroke (27 ± 31.5 months post-stroke) was conducted. The patients trained with an inertial measurement unit (IMU)-based virtual reality system (ArmeoSenso) in their homes for six weeks. The self-selected dose of training with ArmeoSenso was the principal outcome measure whereas the Fugl-Meyer Assessment of the upper extremity (FMA-UE), the Wolf Motor Function Test (WMFT) and IMU-derived kinematic metrics were used to assess arm function, training intensity and trunk movement. Repeated measures one-way ANOVAs were used to assess differences in training duration and clinical scores over time.ResultsAll subjects were able to use the system independently in their homes and no safety issues were reported. Patients trained on 26.5 ± 11.5 days out of 42 days for a duration of 137 ± 120 min per week. The weekly training duration did not change over the course of six weeks (p = 0.146). The arm function of these patients improved significantly by 4.1 points (p = 0.003) in the FMA-UE. Changes in the WMFT were not significant (p = 0.552). ArmeoSenso based metrics showed an improvement in arm function, a high number of reaching movements (387 per session), and minimal compensatory movements of the trunk while training.ConclusionsSelf-directed home therapy with an IMU-based home therapy system is safe and can provide a high dose of rehabilitative therapy. The assessments integrated into the system allow daily therapy monitoring, difficulty adaptation and detection of maladaptive motor patterns such as trunk movements during reaching.Trial registrationUnique identifier: NCT02098135.

[1]  Hyeon-Jeong Sin,et al.  Additional Virtual Reality Training Using Xbox Kinect in Stroke Survivors with Hemiplegia , 2013, American journal of physical medicine & rehabilitation.

[2]  N. Lannin,et al.  Telerehabilitation services for stroke. , 2013, The Cochrane database of systematic reviews.

[3]  W. McIlroy,et al.  Effectiveness of Virtual Reality Using Wii Gaming Technology in Stroke Rehabilitation: A Pilot Randomized Clinical Trial and Proof of Principle , 2010, Stroke.

[4]  Steven M. LaValle,et al.  Head tracking for the Oculus Rift , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

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

[6]  Naila Rahman,et al.  Design, development and deployment of a hand/wrist exoskeleton for home-based rehabilitation after stroke - SCRIPT project , 2014, Robotica.

[7]  G. Kwakkel Impact of intensity of practice after stroke: issues for consideration. , 2012, Disability and rehabilitation.

[8]  Eric J Lenze,et al.  Significance of poor patient participation in physical and occupational therapy for functional outcome and length of stay. , 2004, Archives of physical medicine and rehabilitation.

[9]  P. Veltink,et al.  Compensation of magnetic disturbances improves inertial and magnetic sensing of human body segment orientation , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  O. Celik,et al.  Systematic review of Kinect applications in elderly care and stroke rehabilitation , 2014, Journal of NeuroEngineering and Rehabilitation.

[11]  L. Connell,et al.  Patients' Use of a Home-Based Virtual Reality System to Provide Rehabilitation of the Upper Limb Following Stroke , 2014, Physical Therapy.

[12]  T. Schmitz-Rode,et al.  Introducing a feedback training system for guided home rehabilitation , 2010, Journal of NeuroEngineering and Rehabilitation.

[13]  Joel Stein,et al.  Combined Clinic-Home Approach for Upper Limb Robotic Therapy After Stroke: A Pilot Study. , 2015, Archives of physical medicine and rehabilitation.

[14]  M. Levin,et al.  Compensatory strategies for reaching in stroke. , 2000, Brain : a journal of neurology.

[15]  Francis L. Merat,et al.  Introduction to robotics: Mechanics and control , 1987, IEEE J. Robotics Autom..

[16]  TeichriebVeronica,et al.  Motor Rehabilitation Using Kinect: A Systematic Review , 2015 .

[17]  A. Eagger Rehabilitation , 1960 .

[18]  A. Mihailidis,et al.  Vision-based posture assessment to detect and categorize compensation during robotic rehabilitation therapy , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[19]  D. Roetenberg,et al.  Estimating Body Segment Orientation by Applying Inertial and Magnetic Sensing Near Ferromagnetic Materials , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[20]  Carlo Pozzilli,et al.  Home-Based Balance Training Using the Wii Balance Board , 2013, Neurorehabilitation and neural repair.

[21]  Pier Luca Lanzi,et al.  Computational Intelligence and Game Design for Effective At-Home Stroke Rehabilitation. , 2013, Games for health journal.

[22]  S. Page,et al.  Clinically Important Differences for the Upper-Extremity Fugl-Meyer Scale in People With Minimal to Moderate Impairment Due to Chronic Stroke , 2012, Physical Therapy.

[23]  Gazihan Alankus,et al.  Reducing Compensatory Motions in Motion-Based Video Games for Stroke Rehabilitation , 2015, Hum. Comput. Interact..

[24]  Darryl Charles,et al.  Optimising engagement for stroke rehabilitation using serious games , 2009, The Visual Computer.

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

[26]  Tim Johansson,et al.  Telerehabilitation in stroke care – a systematic review , 2011, Journal of telemedicine and telecare.

[27]  Olivier Lambercy,et al.  Assessment-driven arm therapy at home using an IMU-based virtual reality system , 2015, 2015 IEEE International Conference on Rehabilitation Robotics (ICORR).

[28]  Agnès Roby-Brami,et al.  Faster Reaching in Chronic Spastic Stroke Patients Comes at the Expense of Arm-Trunk Coordination , 2016, Neurorehabilitation and neural repair.

[29]  Nassir Navab,et al.  Motor Rehabilitation Using Kinect: A Systematic Review. , 2015, Games for health journal.

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

[31]  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.

[32]  D. Reisman,et al.  Observation of amounts of movement practice provided during stroke rehabilitation. , 2009, Archives of physical medicine and rehabilitation.

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

[34]  E. Lesaffre,et al.  Use of Time by Stroke Patients: A Comparison of Four European Rehabilitation Centers , 2005, Stroke.

[35]  E. Taub Movement In Nonhuman Primates Deprived Of Somatosensory Feedback , 1976, Exercise and sport sciences reviews.

[36]  Gert Kwakkel Senior Researcher Impact of intensity of practice after stroke: Issues for consideration , 2009 .

[37]  Xiaotian Wu,et al.  Long-term Effectiveness of Intensive Therapy in Chronic Stroke , 2016, Neurorehabilitation and neural repair.

[38]  Lara A. Boyd,et al.  Is More Better? Using Metadata to Explore Dose–Response Relationships in Stroke Rehabilitation , 2014, Stroke.

[39]  Per Backlund,et al.  Computer game-based upper extremity training in the home environment in stroke persons: a single subject design , 2012, Journal of NeuroEngineering and Rehabilitation.

[40]  Arno H. A. Stienen,et al.  Feasibility study into self-administered training at home using an arm and hand device with motivational gaming environment in chronic stroke , 2015, Journal of NeuroEngineering and Rehabilitation.

[41]  P. Verschure,et al.  Virtual reality based rehabilitation speeds up functional recovery of the upper extremities after stroke: a randomized controlled pilot study in the acute phase of stroke using the rehabilitation gaming system. , 2011, Restorative neurology and neuroscience.

[42]  P. L. Weiss,et al.  Kinematics of Reaching Movements in a 2-D Virtual Environment in Adults With and Without Stroke , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[43]  Steven C Cramer,et al.  Machine-Based, Self-guided Home Therapy for Individuals With Severe Arm Impairment After Stroke , 2015, Neurorehabilitation and neural repair.

[44]  Elaine Strachota,et al.  Low-cost monitoring of patients during unsupervised robot/computer assisted motivating stroke rehabilitation , 2011, Biomedizinische Technik. Biomedical engineering.