Towards Standardized Processes for Physical Therapists to Quantify Patient Rehabilitation

Physical rehabilitation typically requires therapists to make judgements about patient movement and functional improvement using subjective observation. This process makes it challenging to quantitatively track, compute and predict long-term patient improvement. We therefore propose a novel methodical approach to the standardized and interpretable quantification of patient movement during rehabilitation. We describe the expert-led development of a movement assessment rubric and an accompanying quantitative rating system. We present our movement capture and annotation computational tools designed to implement the rubric and assist therapists in the quantitative documentation and assessment of rehabilitation. We describe results from a movement capture study of the tool with nine stroke survivors and a movement rating study with four therapists. Findings from these studies highlight potential optimal methodical process paths for individuals engaged in capturing, understanding and predicting human movement performance.

[1]  Sri Hastuti Kurniawan,et al.  Project Star Catcher , 2018, ACM Trans. Access. Comput..

[2]  Bilge Mutlu,et al.  How Do Humans Teach: On Curriculum Learning and Teaching Dimension , 2011, NIPS.

[3]  Ritu Sadana,et al.  The World report on ageing and health: a policy framework for healthy ageing , 2016, The Lancet.

[4]  G. Kwakkel,et al.  Understanding the pattern of functional recovery after stroke: facts and theories. , 2004, Restorative neurology and neuroscience.

[5]  J. Reinbolt,et al.  Influence of Total Knee Arthroplasty on Gait Mechanics of the Replaced and Non-Replaced Limb During Stair Negotiation. , 2016, The Journal of arthroplasty.

[6]  Proceedings of the Thirteenth International Conference on Tangible, Embedded, and Embodied Interaction , 2019, Tangible and Embedded Interaction.

[7]  Silvia Koton,et al.  Stroke Incidence and Mortality Trends in US Communities, 1987 to 2011 , 2014 .

[8]  Athanassios Bissas,et al.  Differences between motion capture and video analysis systems in calculating knee angles in elite-standard race walking , 2018, Journal of sports sciences.

[9]  Jiping He,et al.  A Computational Framework for Quantitative Evaluation of Movement during Rehabilitation , 2011 .

[10]  J. Krakauer,et al.  Computational neurorehabilitation: modeling plasticity and learning to predict recovery , 2016, Journal of NeuroEngineering and Rehabilitation.

[11]  M. Levin,et al.  What Do Motor “Recovery” and “Compensation” Mean in Patients Following Stroke? , 2009, Neurorehabilitation and neural repair.

[12]  Thanassis Rikakis,et al.  Adaptive Mixed Reality Rehabilitation Improves Quality of Reaching Movements More Than Traditional Reaching Therapy Following Stroke , 2013, Neurorehabilitation and neural repair.

[13]  Aisling Kelliher,et al.  Semi-automated home-based therapy for the upper extremity of stroke survivors , 2018, PETRA.

[14]  A. Danielsson,et al.  Intra-rater and inter-rater reliability at the item level of the Action Research Arm Test for patients with stroke. , 2014, Journal of rehabilitation medicine.

[15]  M. Levin,et al.  Improvement of Arm Movement Patterns and Endpoint Control Depends on Type of Feedback During Practice in Stroke Survivors , 2007, Neurorehabilitation and neural repair.

[16]  R Langton-Hewer,et al.  The hemiplegic arm after stroke: measurement and recovery. , 1983, Journal of neurology, neurosurgery, and psychiatry.

[17]  Geoffroy Saussez,et al.  Rehabilitation of Motor Function after Stroke: A Multiple Systematic Review Focused on Techniques to Stimulate Upper Extremity Recovery , 2016, Front. Hum. Neurosci..

[18]  David K. McGookin,et al.  Using Both Hands: Tangibles for Stroke Rehabilitation in the Home , 2019, CHI.

[19]  Todd Ingalls,et al.  Interdisciplinary Concepts for Design and Implementation of Mixed Reality Interactive Neurorehabilitation Systems for Stroke , 2014, Physical Therapy.

[20]  Janice J Eng,et al.  Paretic Upper-Limb Strength Best Explains Arm Activity in People With Stroke , 2007, Physical Therapy.

[21]  Colin Potts,et al.  Design of Everyday Things , 1988 .

[22]  Eric E. Adelman,et al.  Persistent ischemic stroke disparities despite declining incidence in Mexican Americans , 2013, Annals of neurology.

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

[24]  L. Tickle-Degnen,et al.  Effects of object affordances on reaching performance in persons with and without cerebrovascular accident. , 1998, The American journal of occupational therapy : official publication of the American Occupational Therapy Association.

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

[26]  Stefan Rennick Egglestone,et al.  Motivating mobility: designing for lived motivation in stroke rehabilitation , 2011, CHI.

[27]  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.

[28]  J. Krakauer Motor learning: its relevance to stroke recovery and neurorehabilitation. , 2006, Current opinion in neurology.

[29]  Aisling Kelliher,et al.  HOMER: An Interactive System for Home Based Stroke Rehabilitation , 2017, ASSETS.

[30]  C. Anderson,et al.  Home or hospital for stroke rehabilitation? results of a randomized controlled trial : I: health outcomes at 6 months. , 2000, Stroke.

[31]  Xing-Dong Yang,et al.  Physio@Home: Exploring Visual Guidance and Feedback Techniques for Physiotherapy Exercises , 2015, CHI.

[32]  D. Reinkensmeyer,et al.  Technologies and combination therapies for enhancing movement training for people with a disability , 2012, Journal of NeuroEngineering and Rehabilitation.

[33]  Christopher Wee Keong Kuah,et al.  Innovating With Rehabilitation Technology in the Real World , 2017, American journal of physical medicine & rehabilitation.

[34]  H. Sundaram,et al.  Novel Design of Interactive Multimodal Biofeedback System for Neurorehabilitation , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[35]  P. Duncan,et al.  Adherence to Postacute Rehabilitation Guidelines Is Associated With Functional Recovery in Stroke , 2002, Stroke.

[36]  Todd Ingalls,et al.  Design of a home-based adaptive mixed reality rehabilitation system for stroke survivors , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[37]  L. Snyder-Mackler,et al.  Do patients achieve normal gait patterns 3 years after total knee arthroplasty? , 2012, The Journal of orthopaedic and sports physical therapy.

[38]  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.

[39]  Klaus Krippendorff,et al.  Computing Krippendorff's Alpha-Reliability , 2011 .

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

[41]  John W Krakauer,et al.  Arm function after stroke: from physiology to recovery. , 2005, Seminars in neurology.

[42]  G. Kwakkel,et al.  Probability of regaining dexterity in the flaccid upper limb: impact of severity of paresis and time since onset in acute stroke. , 2003, Stroke.

[43]  Pavan K. Turaga,et al.  Decision support for stroke rehabilitation therapy via describable attribute-based decision trees , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[44]  Alberto L. Morán,et al.  Design Factors of Virtual Environments for Upper Limb Motor Rehabilitation of Stroke Patients , 2014, MexIHC '14.

[45]  S. Wolf,et al.  Exploring the bases for a mixed reality stroke rehabilitation system, Part I: A unified approach for representing action, quantitative evaluation, and interactive feedback , 2011, Journal of NeuroEngineering and Rehabilitation.

[46]  Silvana G. Dellepiane,et al.  Infrastructure for data management and user centered rehabilitation in Rehab@Home project , 2014, PETRA '14.

[47]  Gazihan Alankus,et al.  Stroke Therapy through Motion-Based Games: A Case Study , 2010, TACC.

[48]  Jiping He,et al.  An Adaptive Mixed Reality Training System for Stroke Rehabilitation , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[49]  Aisling Kelliher,et al.  Designing Modular Rehabilitation Objects for Interactive Therapy in the Home , 2019, TEI.

[50]  Olivier Lambercy,et al.  Systematic Review on Kinematic Assessments of Upper Limb Movements After Stroke , 2019, Stroke.

[51]  Dae-Hyeong Kim,et al.  Multifunctional wearable devices for diagnosis and therapy of movement disorders. , 2014, Nature nanotechnology.

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

[53]  Thanassis Rikakis,et al.  A low cost, adaptive mixed reality system for home-based stroke rehabilitation , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.