Enabling Stroke Rehabilitation in Home and Community Settings: A Wearable Sensor-Based Approach for Upper-Limb Motor Training

High-dosage motor practice can significantly contribute to achieving functional recovery after a stroke. Performing rehabilitation exercises at home and using, or attempting to use, the stroke-affected upper limb during Activities of Daily Living (ADL) are effective ways to achieve high-dosage motor practice in stroke survivors. This paper presents a novel technological approach that enables 1) detecting goal-directed upper limb movements during the performance of ADL, so that timely feedback can be provided to encourage the use of the affected limb, and 2) assessing the quality of motor performance during in-home rehabilitation exercises so that appropriate feedback can be generated to promote high-quality exercise. The results herein presented show that it is possible to detect 1) goal-directed movements during the performance of ADL with a <inline-formula> <tex-math notation="LaTeX">$c$ </tex-math></inline-formula>-statistic of 87.0% and 2) poorly performed movements in selected rehabilitation exercises with an <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula>-score of 84.3%, thus enabling the generation of appropriate feedback. In a survey to gather preliminary data concerning the clinical adequacy of the proposed approach, 91.7% of occupational therapists demonstrated willingness to use it in their practice, and 88.2% of stroke survivors indicated that they would use it if recommended by their therapist.

[1]  Geoffrey I. Webb,et al.  Dynamic Time Warping Averaging of Time Series Allows Faster and More Accurate Classification , 2014, 2014 IEEE International Conference on Data Mining.

[2]  C. Lang,et al.  An Accelerometry-Based Methodology for Assessment of Real-World Bilateral Upper Extremity Activity , 2014, PloS one.

[3]  Silvia Conforto,et al.  Early recognition of upper limb motor tasks through accelerometers: real-time implementation of a DTW-based algorithm , 2011, Comput. Biol. Medicine.

[4]  Malcolm H. Granat,et al.  Continuous monitoring of upper-limb activity in a free-living environment: a validation study , 2007, Medical & Biological Engineering & Computing.

[5]  David M. W. Powers,et al.  Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.

[6]  E. Taub,et al.  Constraint-induced movement therapy to enhance recovery after stroke , 2001, Current atherosclerosis reports.

[7]  Brian Caulfield,et al.  Rehabilitation exercise assessment using inertial sensors: a cross-sectional analytical study , 2014, Journal of NeuroEngineering and Rehabilitation.

[8]  R. Teasell,et al.  What’s New in Stroke Rehabilitation , 2004, Stroke.

[9]  Brian Caulfield,et al.  Evaluating rehabilitation exercise performance using a single inertial measurement unit , 2013, 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops.

[10]  Paolo Bonato,et al.  Development of a Body Sensor Network to Detect Motor Patterns of Epileptic Seizures , 2012, IEEE Transactions on Biomedical Engineering.

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

[12]  Privender Saini,et al.  Philips stroke rehabilitation exerciser: a usability test , 2008 .

[13]  J. Eng,et al.  Exploring the Role of Accelerometers in the Measurement of Real World Upper-Limb Use After Stroke , 2015, Brain Impairment.

[14]  Catherine E. Lang,et al.  Upper Extremity Use in People with Hemiparesis in the First Few Weeks After Stroke , 2007, Journal of neurologic physical therapy : JNPT.

[15]  J. Verbunt,et al.  Assessment of arm activity using triaxial accelerometry in patients with a stroke. , 2011, Archives of physical medicine and rehabilitation.

[16]  S Studenski,et al.  A randomized, controlled pilot study of a home-based exercise program for individuals with mild and moderate stroke. , 1998, Stroke.

[17]  E. Roth,et al.  Physical Activity and Exercise Recommendations for Stroke Survivors: A Statement for Healthcare Professionals From the American Heart Association/American Stroke Association , 2014, Stroke.

[18]  P. Veltink,et al.  Objective Evaluation of the Quality of Movement in Daily Life after Stroke , 2016, Front. Bioeng. Biotechnol..

[19]  Shyamal Patel,et al.  A Novel Approach to Monitor Rehabilitation Outcomes in Stroke Survivors Using Wearable Technology , 2010, Proceedings of the IEEE.

[20]  Ailie Turton,et al.  The Use of Home Therapy Programmes for Improving Recovery of the Upper Limb following Stroke , 1990 .

[21]  J. Kleim,et al.  Principles of experience-dependent neural plasticity: implications for rehabilitation after brain damage. , 2008, Journal of speech, language, and hearing research : JSLHR.

[22]  Feng Hong,et al.  MGRA: Motion Gesture Recognition via Accelerometer , 2016, Sensors.

[23]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  E. Taub,et al.  Constraint-Induced Movement Therapy: a new family of techniques with broad application to physical rehabilitation--a clinical review. , 1999, Journal of rehabilitation research and development.

[25]  Gert Kwakkel,et al.  Early Prediction of Outcome of Activities of Daily Living After Stroke: A Systematic Review , 2011, Stroke.

[26]  Leonid Churilov,et al.  Exercise Preferences Are Different after Stroke , 2011, Stroke research and treatment.

[27]  Anna M. Bianchi,et al.  User-Independent Recognition of Sports Activities From a Single Wrist-Worn Accelerometer: A Template-Matching-Based Approach , 2016, IEEE Transactions on Biomedical Engineering.

[28]  Shyamal Patel,et al.  Using Wearable Motion Sensors to Estimate Longitudinal Changes in Movement Quality in Stroke and Traumatic Brain Injury Survivors Undergoing Rehabilitation , 2016 .

[29]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[30]  Catherine E. Lang,et al.  Validity of Body-Worn Sensor Acceleration Metrics to Index Upper Extremity Function in Hemiparetic Stroke , 2015, Journal of neurologic physical therapy : JNPT.

[31]  J. Bussmann,et al.  Quantifying nonuse in chronic stroke patients: a study into paretic, nonparetic, and bimanual upper-limb use in daily life. , 2012, Archives of physical medicine and rehabilitation.

[32]  J. P. Miller,et al.  Effect of constraint-induced movement therapy on upper extremity function 3 to 9 months after stroke: the EXCITE randomized clinical trial. , 2006, JAMA.

[33]  Ashutosh Kumar Singh,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .

[34]  Roger Gassert,et al.  A method to qualitatively assess arm use in stroke survivors in the home environment , 2016, Medical & Biological Engineering & Computing.

[35]  Ariel Linden Measuring diagnostic and predictive accuracy in disease management: an introduction to receiver operating characteristic (ROC) analysis. , 2006, Journal of evaluation in clinical practice.

[36]  H. Rodgers,et al.  Accelerometer measurement of upper extremity movement after stroke: a systematic review of clinical studies , 2014, Journal of NeuroEngineering and Rehabilitation.

[37]  S. Engelborghs,et al.  Actigraphic Measurement of Motor Deficits in Acute Ischemic Stroke , 2008, Cerebrovascular Diseases.

[38]  A Villringer,et al.  Constraint-induced movement therapy for motor recovery in chronic stroke patients. , 1999, Archives of physical medicine and rehabilitation.

[39]  Richard W. Bohannon,et al.  Treatment Interventions for the Paretic Upper Limb of Stroke Survivors: A Critical Review , 2003, Neurorehabilitation and neural repair.

[40]  C. Lang,et al.  Real-world affected upper limb activity in chronic stroke: an examination of potential modifying factors , 2015, Topics in stroke rehabilitation.

[41]  J. Bussmann,et al.  The stroke upper-limb activity monitor: its sensitivity to measure hemiplegic upper-limb activity during daily life. , 2007, Archives of physical medicine and rehabilitation.

[42]  J. Leith,et al.  Review Article: Validity of the KT-1000 Knee Ligament Arthrometer , 2009, Journal of orthopaedic surgery.

[43]  Shyamal Patel,et al.  A review of wearable sensors and systems with application in rehabilitation , 2012, Journal of NeuroEngineering and Rehabilitation.

[44]  C. Winstein,et al.  Validity of accelerometry for monitoring real-world arm activity in patients with subacute stroke: evidence from the extremity constraint-induced therapy evaluation trial. , 2006, Archives of physical medicine and rehabilitation.

[45]  E. Taub,et al.  The learned nonuse phenomenon: implications for rehabilitation. , 2006, Europa medicophysica.

[46]  C. Lang,et al.  Quantifying Real-World Upper-Limb Activity in Nondisabled Adults and Adults With Chronic Stroke , 2015, Neurorehabilitation and neural repair.

[47]  P. Stratford,et al.  Reliability of the Fugl-Meyer assessment for testing motor performance in patients following stroke. , 1993, Physical therapy.

[48]  Dah-Jye Lee,et al.  Converting non-parametric distance-based classification to anytime algorithms , 2008, Pattern Analysis and Applications.

[49]  S. Johnston,et al.  Thirty-year projections for deaths from ischemic stroke in the United States. , 2003, Stroke.

[50]  Catherine E Lang,et al.  Counting Repetitions: An Observational Study of Outpatient Therapy for People with Hemiparesis Post-Stroke , 2007, Journal of neurologic physical therapy : JNPT.

[51]  Eric Wade,et al.  Upper extremity post-stroke motion quality estimation with decision trees and bagging forests , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[52]  Shyamal Patel,et al.  Estimating fugl-meyer clinical scores in stroke survivors using wearable sensors , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[53]  J. Eng,et al.  Investigating measures of intensity during a structured upper limb exercise program in stroke rehabilitation: an exploratory study. , 2014, Archives of physical medicine and rehabilitation.

[54]  Catherine E Lang,et al.  Acceleration metrics are responsive to change in upper extremity function of stroke survivors. , 2015, Archives of physical medicine and rehabilitation.

[55]  M. Fornage,et al.  Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association , 2017, Circulation.