skip to main content
10.1145/3313831.3376706acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
research-article

Towards Standardized Processes for Physical Therapists to Quantify Patient Rehabilitation

Authors Info & Claims
Published:23 April 2020Publication History

ABSTRACT

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.

References

  1. Gazihan Alankus, Rachel Proffitt, Caitlin Kelleher, and Jack Engsberg, Stroke Therapy through Motion-Based Games: A Case Study. ACM Trans. Access. Comput., 2011. 4(1): p. 1--35.Google ScholarGoogle Scholar
  2. Craig Anderson, et al., Home or hospital for stroke rehabilitation? results of a randomized controlled trial : I: health outcomes at 6 months. Stroke, 2000. 31(5): p. 1024--31.Google ScholarGoogle Scholar
  3. Craig Anderson, Cliona Ni Mhurchu, PM Brown, and K Carter, Stroke rehabilitation services to accelerate hospital discharge and provide homebased care: an overview and cost analysis. Pharmacoeconomics, 2002. 20(8): p. 537--52.Google ScholarGoogle Scholar
  4. Madeline Balaam, et al., Motivating mobility: designing for lived motivation in stroke rehabilitation, in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2011, ACM: Vancouver, BC, Canada. p. 3073--3082.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Michael Baran, et al., Design of a home-based adaptive mixed reality rehabilitation system for stroke survivors. Conf Proc IEEE Eng Med Biol Soc, 2011. 2011: p. 7602--5.Google ScholarGoogle ScholarCross RefCross Ref
  6. Michael Baran, et al., Interdisciplinary concepts for design and implementation of mixed reality interactive neurorehabilitation systems for stroke. Phys Ther, 2015. 95(3): p. 449--60.Google ScholarGoogle Scholar
  7. John Beard, et al., The World report on ageing and health: a policy framework for healthy ageing. Lancet, 2016. 387(10033): p. 2145--2154.Google ScholarGoogle ScholarCross RefCross Ref
  8. Emeilia Benjamin, et al., Heart Disease and Stroke Statistics-2017 Update: A Report From the American Heart Association. Circulation, 2017. 135(10): p. e146-e603.Google ScholarGoogle Scholar
  9. Yinpeng Chen, Michael Baran, Hari Sundaram, and Thanassis Rikakis, A low cost, adaptive mixed reality system for home-based stroke rehabilitation. Conf Proc IEEE Eng Med Biol Soc, 2011. 2011: p. 1827--30.Google ScholarGoogle ScholarCross RefCross Ref
  10. Yinpeng Chen, et al., A Computational Framework for Quantitative Evaluation of Movement during Rehabilitation, in AIP Conferernce. 2011. p. 317--326.Google ScholarGoogle Scholar
  11. Karen Chua and Christopher Kuah, Innovating With Rehabilitation Technology in the Real World: Promises, Potentials, and Perspectives. Am J Phys Med Rehabil, 2017. 96(10 Suppl 1): p. S150-S156.Google ScholarGoogle Scholar
  12. Carmen Cirstea and Mindy Levin, Improvement of arm movement patterns and endpoint control depends on type of feedback during practice in stroke survivors. Neurorehabil Neural Repair, 2007. 21(5): p. 398--411.Google ScholarGoogle Scholar
  13. Margaret Duff, et al., An adaptive mixed reality training system for stroke rehabilitation. IEEE Trans Neural Syst Rehabil Eng, 2010. 18(5): p. 531--41.Google ScholarGoogle Scholar
  14. Margaret Duff, et al., Adaptive mixed reality rehabilitation improves quality of reaching movements more than traditional reaching therapy following stroke. Neurorehabil Neural Repair, 2013. 27(4): p. 306--15.Google ScholarGoogle Scholar
  15. Pamela Duncan, et al., Adherence to Postacute Rehabilitation Guidelines Is Associated With Functional Recovery in Stroke. Stroke. 33(1): p. 167--178.Google ScholarGoogle ScholarCross RefCross Ref
  16. Aviv Elor, Mircea Teodorescu, and Sri Kurniawan, Project Star Catcher: A Novel Immersive Virtual Reality Experience for Upper Limb Rehabilitation. ACM Trans. Access. Comput., 2018. 11(4): p. 1--25.Google ScholarGoogle Scholar
  17. Elisa Ferrara, Sonia Nardotto, Serena Ponte, and Silvana G. Dellepiane, Infrastructure for data management and user centered rehabilitation in Rehab@Home project, in Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments. 2014, ACM: Rhodes, Greece. p. 1--8.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Axel Fugl-Meyer, Lisbeth Jaasko, Ingegerd Leyman, Sigyn Olsson, and Solveig Steglind, The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. Scand J Rehabil Med, 1975. 7(1): p. 13--31.Google ScholarGoogle Scholar
  19. Brian Hanley, Catherine Tucker, and Athanassios Bissas, Differences between motion capture and video analysis systems in calculating knee angles in elite-standard race walking. J Sports Sci., 2018. 36(11): p. 1250 - 1255.Google ScholarGoogle Scholar
  20. Jocelyn Harris and Janice Eng, Paretic upper-limb strength best explains arm activity in people with stroke. . Phys Ther, 2007. 87: p. 88--97.Google ScholarGoogle Scholar
  21. Samar Hatem, et al., Rehabilitation of Motor Function after Stroke: A Multiple Systematic Review Focused on Techniques to Stimulate Upper Extremity Recovery. Front Hum Neurosci, 2016. 10: p. 442.Google ScholarGoogle Scholar
  22. Sarah Housman, Kelly Scott, and David Reinkensmeyer, A randomized controlled trial of gravity-supported, computer-enhanced arm exercise for individuals with severe hemiparesis. Neurorehabil Neural Repair, 2009. 23(5): p. 50514.Google ScholarGoogle ScholarCross RefCross Ref
  23. Huey Huang, et al., Novel design of interactive multimodal biofeedback system for neurorehabilitation. Conf Proc IEEE Eng Med Biol Soc, 2006. 1: p. 4925--8.Google ScholarGoogle ScholarCross RefCross Ref
  24. Aisling Kelliher, Jinwoo Choi, Jia-Bin Huang, Thanassis Rikakis, and Kris Kitani, HOMER: An Interactive System for Home Based Stroke Rehabilitation, in Proceedings of the 19th International ACM SIGACCESS Conference on Computers and Accessibility. 2017, ACM: Baltimore, Maryland, USA. p. 379--380.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Aisling Kelliher and Barbara Barry, Designing Therapeutic Experiences with AI in Mind, in AAAI 2018 Symposium, Designing the User Experience of Artificial Intelligence. 2018: Stanford, CA.Google ScholarGoogle Scholar
  26. Aisling Kelliher, Andrew Gibson, Eric Bottelsen, and Edward Coe, Designing Modular Rehabilitation Objects for Interactive Therapy in the Home, in Proceedings of the Thirteenth International Conference on Tangible, Embedded, and Embodied Interaction. 2019, ACM: Tempe, Arizona, USA. p. 251--257.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Faisal Khan, Bilge Mutlu, and Zhu Jerry. How Do Humans Teach: On Curriculum Learning and Teaching Dimension. in Advances in Neural Information Processing Systems 2011. 2011.Google ScholarGoogle Scholar
  28. Silvia Koton, et al., Stroke incidence and mortality trends in US communities, 1987 to 2011. JAMA, 2014. 312(3): p. 259--68.Google ScholarGoogle Scholar
  29. John Krakauer, Arm function after stroke: from physiology to recovery. Semin Neurol, 2005. 25(4): p. 384--95.Google ScholarGoogle Scholar
  30. John Krakauer, Motor learning: its relevance to stroke recovery and neurorehabilitation. Curr Opin Neurol, 2006. 19(1): p. 84--90.Google ScholarGoogle Scholar
  31. Klaus Krippendorff Computing Krippendorff's alpha reliability. 2007. 43.Google ScholarGoogle Scholar
  32. Gert Kwakkel, Boudewijn Kollen, Jay van der Grond, and Arie Prevo, Probability of regaining dexterity in the flaccid upper limb: impact of severity of paresis and time since onset in acute stroke. Stroke, 2003. 34(9): p. 2181--6.Google ScholarGoogle Scholar
  33. Gert Kwakkel, Boudewijn Kollen, and Eline Lindeman, Understanding the pattern of functional recovery after stroke: facts and theories. Restor Neurol Neurosci, 2004. 22(3--5): p. 281--99.Google ScholarGoogle Scholar
  34. Mikko Kytö, Laura Maye, and David McGookin, Using Both Hands: Tangibles for Stroke Rehabilitation in the Home, in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019, ACM: Glasgow, Scotland Uk. p. 1--14.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. N. Lehrer, S. Attygalle, S. L. Wolf, and T. Rikakis, Exploring the bases for a mixed reality stroke rehabilitation system, part I: a unified approach for representing action, quantitative evaluation, and interactive feedback. J Neuroeng Rehabil, 2011. 8: p. 51.Google ScholarGoogle Scholar
  36. Mindy Levin, Jeff Kleim, and Steven Wolf, What do motor recovery and compensation mean in patients following stroke? Neurorehabilitation and neural repair, 2008.Google ScholarGoogle Scholar
  37. Michelle McDonnell, Action research arm test. Aust J Physiother, 2008. 54(3): p. 220.Google ScholarGoogle Scholar
  38. Lewis Morgenstern, et al., Persistent ischemic stroke disparities despite declining incidence in Mexican Americans. Ann Neurol, 2013. 74(6): p. 778--85.Google ScholarGoogle Scholar
  39. A. Nordin, M. Alt Murphy, and A. Danielsson, Intra-rater and inter-rater reliability at the item level of the Action Research Arm Test for patients with stroke. J Rehabil Med, 2014. 46(8): p. 73845.Google ScholarGoogle Scholar
  40. Donald A. Norman, The design of everyday things. 1st Doubleday/Currency ed. 1990, New York: Doubleday. xv, 257 p.Google ScholarGoogle Scholar
  41. Cristina Ramírez-Fernández, Alberto L. Morán, Eloísa García-Canseco, and Felipe Orihue la Espina, Design Factors of Virtual Environments for Upper Limb Motor Rehabilitation of Stroke Patients, in Proceedings of the 5th Mexican Conference on Human-Computer Interaction. 2014, ACM: Oaxaca, Mexico, Mexico. p. 22--25.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. D. J. Reinkensmeyer, et al., Computational neurorehabilitation: modeling plasticity and learning to predict recovery. J Neuroeng Rehabil, 2016. 13(1): p. 42.Google ScholarGoogle Scholar
  43. David Reinkensmeyer and Michael Boninger, Technologies and combination therapies for enhancing movement training for people with a disability. J Neuroeng Rehabil, 2012. 9: p. 17.Google ScholarGoogle Scholar
  44. Thanassis Rikakis, et al., Semi-automated homebased therapy for the upper extremity of stroke survivors, in ACM PErvasive Technologies Related to Assistive Environments (PETRA '18). 2018: Corfu.Google ScholarGoogle Scholar
  45. Thanassis Rikakis, et al., Semi-automated homebased therapy for the upper extremity of stroke survivors, in Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference. 2018, ACM: Corfu, Greece. p. 249256.Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. A. Schwarz, C. M. Kanzler, O. Lambercy, A. R. Luft, and J. M. Veerbeek, Systematic Review on Kinematic Assessments of Upper Limb Movements After Stroke. Stroke, 2019. 50(3): p. 718--727.Google ScholarGoogle Scholar
  47. D. Son, et al., Multifunctional wearable devices for diagnosis and therapy of movement disorders. Nat Nanotechnol, 2014. 9(5): p. 397--404.Google ScholarGoogle Scholar
  48. Standifird TW, et al., Influence of Total Knee Arthroplasty on Gait Mechanics of the Replaced and Non-Replaced Limb During Stair Negotiation. J Arthroplasty, 2016. 31(1): p. 278--283.Google ScholarGoogle Scholar
  49. Richard Tang, Xing-Dong Yang, Scott Bateman, Joaquim Jorge, and Anthony Tang, Physio@Home: Exploring Visual Guidance and Feedback Techniques for Physiotherapy Exercises, in Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 2015, ACM: Seoul, Republic of Korea. p. 4123--4132.Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Vinay Venkataraman, et al., Decision support for stroke rehabilitation therapy via describable attribute-based decision trees. Conf Proc IEEE Eng Med Biol Soc, 2014. 2014: p. 3154--9.Google ScholarGoogle ScholarCross RefCross Ref
  51. David Wade, R. Langton-Hewer, V. A. Wood, Clive Skilbeck, and H. M. Ismail, The hemiplegic arm after stroke: measurement and recovery. J Neurol Neurosurg Psychiatry, 1983. 46(6): p. 5214.Google ScholarGoogle Scholar
  52. David Webster and Ozkan Celik, Systematic review of Kinect applications in elderly care and stroke rehabilitation. J Neuroeng Rehabil, 2014. 11: p. 108.Google ScholarGoogle Scholar
  53. Steven Wolf, et al., Assessing wolf motor function test as outcome measure for research in patients after stroke. Stroke, 2001. 32(7): p. 1635--1639.Google ScholarGoogle Scholar
  54. Steven Wolf, et al., Effect of constraint- induced movement therapy on upper extremity function 3 to 9 months after stroke: the excite randomized clinical trial. J. Jama, 2006. 296(17): p. 2095--2104.Google ScholarGoogle Scholar
  55. Ching-Yi Wu, Catherine Trombly, Keh-Chung Lin, and Linda Tickle-Degnen, Effects of object affordances on reaching performance in persons with and without cerebrovascular accident. Am J Occup Ther, 1998. 52(6): p. 447--56.Google ScholarGoogle Scholar
  56. Y. Yoshida, J. Zeni, and L. Snyder-Mackler, Do Patients Achieve Normal Gait Patterns 3 Years After Total Knee Arthroplasty? Journal of orthopaedic & sports physical therapy, 2012. 42(12): p. 1039--1049.Google ScholarGoogle Scholar

Index Terms

  1. Towards Standardized Processes for Physical Therapists to Quantify Patient Rehabilitation

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
      April 2020
      10688 pages
      ISBN:9781450367080
      DOI:10.1145/3313831

      Copyright © 2020 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 23 April 2020

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      Overall Acceptance Rate6,199of26,314submissions,24%

      Upcoming Conference

      CHI '24
      CHI Conference on Human Factors in Computing Systems
      May 11 - 16, 2024
      Honolulu , HI , USA

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format .

    View HTML Format