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Towards the Design of a Ring Sensor-based mHealth System to Achieve Optimal Motor Function in Stroke Survivors

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Published:14 September 2020Publication History
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Abstract

Maximizing the motor practice in stroke survivors' living environments may significantly improve the functional recovery of their stroke-affected upper-limb. A wearable system that can continuously monitor upper-limb performance has been considered as an effective clinical solution for its potential to provide patient-centered, data-driven feedback to improve the motor dosage. Towards that end, we investigate a system leveraging a pair of finger-worn, ring-type accelerometers capable of monitoring both gross-arm and fine-hand movements that are clinically relevant to the performance of daily activities. In this work, we conduct a mixed-methods study to (1) quantitatively evaluate the efficacy of finger-worn accelerometers in measuring clinically relevant information regarding stroke survivors' upper-limb performance, and (2) qualitatively investigate design requirements for the self-monitoring system, based on data collected from 25 stroke survivors and seven occupational therapists. Our quantitative findings demonstrate strong face and convergent validity of the finger-worn accelerometers, and its responsiveness to changes in motor behavior. Our qualitative findings provide a detailed account of the current rehabilitation process while highlighting several challenges that therapists and stroke survivors face. This study offers promising directions for the design of a self-monitoring system that can encourage the affected limb use during stroke survivors' daily living.

References

  1. Ryan R Bailey, Joseph W Klaesner, and Catherine E Lang. 2014. An accelerometry-based methodology for assessment of real-world bilateral upper extremity activity. PloS one 9, 7 (2014), e103135.Google ScholarGoogle ScholarCross RefCross Ref
  2. Ryan R Bailey, Joseph W Klaesner, and Catherine E Lang. 2015. Quantifying real-world upper-limb activity in nondisabled adults and adults with chronic stroke. Neurorehabilitation and neural repair 29, 10 (2015), 969--978.Google ScholarGoogle Scholar
  3. Ryan R Bailey and Catherine E Lang. 2014. Upper extremity activity in adults: referent values using accelerometry. Journal of rehabilitation research and development 50, 9 (2014), 1213.Google ScholarGoogle ScholarCross RefCross Ref
  4. Susan Barreca, Steven L Wolf, Susan Fasoli, and Richard Bohannon. 2003. Treatment interventions for the paretic upper limb of stroke survivors: a critical review. Neurorehabilitation and neural repair 17, 4 (2003), 220--226.Google ScholarGoogle Scholar
  5. Emelia J Benjamin, Paul Muntner, and Márcio Sommer Bittencourt. 2019. Heart disease and stroke statistics-2019 update: A report from the American Heart Association. Circulation 139, 10 (2019), e56--e528.Google ScholarGoogle ScholarCross RefCross Ref
  6. JHM Bergmann and AH McGregor. 2011. Body-worn sensor design: what do patients and clinicians want? Annals of biomedical engineering 39, 9 (2011), 2299--2312.Google ScholarGoogle ScholarCross RefCross Ref
  7. Julie Bernhardt, Kathryn S Hayward, Gert Kwakkel, Nick S Ward, Steven L Wolf, Karen Borschmann, John W Krakauer, Lara A Boyd, S Thomas Carmichael, Dale Corbett, et al. 2017. Agreed definitions and a shared vision for new standards in stroke recovery research: the stroke recovery and rehabilitation roundtable taskforce. International Journal of Stroke 12, 5 (2017), 444--450.Google ScholarGoogle ScholarCross RefCross Ref
  8. Luuk Beursgens, Freek Boesten, Annick Timmermans, Henk Seelen, and Panos Markopoulos. 2011. Us' em: motivating stroke survivors to use their impaired arm and hand in daily life. In CHI'11 Extended Abstracts on Human Factors in Computing Systems. ACM, 1279--1284.Google ScholarGoogle Scholar
  9. Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative research in psychology 3, 2 (2006), 77--101.Google ScholarGoogle Scholar
  10. Tamara Bushnik. 2011. Motor Activity Log. In Encyclopedia of Clinical Neuropsychology. Springer, 1664--1665.Google ScholarGoogle Scholar
  11. Samprit Chatterjee and Ali S Hadi. 2009. Sensitivity analysis in linear regression. Vol. 327. John Wiley & Sons.Google ScholarGoogle Scholar
  12. Hao-ling Chen, Keh-chung Lin, Yu-wei Hsieh, Ching-yi Wu, Rong-jiuan Liing, and Chia-ling Chen. 2018. A study of predictive validity, responsiveness, and minimal clinically important difference of arm accelerometer in real-world activity of patients with chronic stroke. Clinical rehabilitation 32, 1 (2018), 75--83.Google ScholarGoogle Scholar
  13. Eun Kyoung Choe, Saeed Abdullah, Mashfiqui Rabbi, Edison Thomaz, Daniel A Epstein, Felicia Cordeiro, Matthew Kay, Gregory D Abowd, Tanzeem Choudhury, James Fogarty, et al. 2017. Semi-automated tracking: a balanced approach for self-monitoring applications. IEEE Pervasive Computing 16, 1 (2017), 74--84.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Angela Coulter and Alf Collins. 2011. Making shared decision-making a reality. London: King's Fund (2011).Google ScholarGoogle Scholar
  15. Leanne S Cowin, Cecily Hengstberger-Sims, Sandy C Eagar, Linda Gregory, Sharon Andrew, and John Rolley. 2008. Competency measurements: testing convergent validity for two measures. Journal of advanced nursing 64, 3 (2008), 272--277.Google ScholarGoogle ScholarCross RefCross Ref
  16. Leah Daniel, Whitney Howard, Danielle Braun, and Stephen J Page. 2012. Opinions of constraint-induced movement therapy among therapists in southwestern Ohio. Topics in stroke rehabilitation 19, 3 (2012), 268--275.Google ScholarGoogle Scholar
  17. Stefan Rennick Egglestone, Lesley Axelrod, Thomas Nind, Ruth Turk, Anna Wilkinson, Jane Burridge, Geraldine Fitzpatrick, Sue Mawson, Zoe Robertson, Ann Marie Hughes, et al. 2009. A design framework for a home-based stroke rehabilitation system: Identifying the key components. In 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare. IEEE, 1--8.Google ScholarGoogle ScholarCross RefCross Ref
  18. Iza Faria-Fortini, Stella Maris Michaelsen, Janine Gomes Cassiano, and Luci Fuscaldi Teixeira-Salmela. 2011. Upper extremity function in stroke subjects: relationships between the international classification of functioning, disability, and health domains. Journal of Hand Therapy 24, 3 (2011), 257--265.Google ScholarGoogle ScholarCross RefCross Ref
  19. Nizan Friedman, Justin B Rowe, David J Reinkensmeyer, and Mark Bachman. 2014. The manumeter: a wearable device for monitoring daily use of the wrist and fingers. IEEE journal of biomedical and health informatics 18, 6 (2014), 1804--1812.Google ScholarGoogle Scholar
  20. JM Geddes, Jon Fear, Alan Tennant, Ann Pickering, Micky Hillman, and M Anne Chamberlain. 1996. Prevalence of self reported stroke in a population in northern England. Journal of Epidemiology & Community Health 50, 2 (1996), 140--143.Google ScholarGoogle ScholarCross RefCross Ref
  21. Shefali Haldar, Sonali R. Mishra, Maher Khelifi, Ari H. Pollack, and Wanda Pratt. 2019. Beyond the Patient Portal: Supporting Needs of Hospitalized Patients. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19). ACM, New York, NY, USA, Article 366, 14 pages. https://doi.org/10.1145/3290605.3300596Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. James Hallam. 2015. Haptic mirror therapy glove: aiding the treatment of a paretic limb after a stroke. In Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers. ACM, 459--464.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Kathryn S Hayward, Janice J Eng, Lara A Boyd, Bimal Lakhani, Julie Bernhardt, and Catherine E Lang. 2016. Exploring the role of accelerometers in the measurement of real world upper-limb use after stroke. Brain Impairment 17, 1 (2016), 16--33.Google ScholarGoogle ScholarCross RefCross Ref
  24. Jeremia PO Held, Andreas R Luft, and Janne M Veerbeek. 2018. Encouragement-induced real-World upper limb use after Stroke by a tracking and Feedback device: a Study Protocol for a Multi-Center, assessor-Blinded, randomized Controlled trial. Frontiers in neurology 9 (2018), 13.Google ScholarGoogle Scholar
  25. Amey Holden, Róisín McNaney, Madeline Balaam, Robin Thompson, Nils Hammerla, Thomas Ploetz, Dan Jackson, Christopher Price, Lianne Brkic, and Patrick Olivier. 2015. CueS: cueing for upper limb rehabilitation in stroke. In Proceedings of the 2015 British HCI Conference. ACM, 18--25.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Sharon L Kilbreath and Robert C Heard. 2005. Frequency of hand use in healthy older persons. Australian Journal of Physiotherapy 51, 2 (2005), 119--122.Google ScholarGoogle ScholarCross RefCross Ref
  27. Yoojung Kim, Eunyoung Heo, Hyunjeong Lee, Sookyoung Ji, Jueun Choi, Jeong-Whun Kim, Joongseek Lee, and Sooyoung Yoo. 2017. Prescribing 10,000 Steps Like Aspirin: Designing a Novel Interface for Data-Driven Medical Consultations. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI '17). ACM, New York, NY, USA, 5787--5799. https://doi.org/10.1145/3025453.3025570Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Annett Kunkel, Bruno Kopp, Gudrun Müller, Kersten Villringer, Arno Villringer, Edward Taub, and Herta Flor. 1999. Constraint-induced movement therapy for motor recovery in chronic stroke patients. Archives of physical medicine and rehabilitation 80, 6 (1999), 624--628.Google ScholarGoogle Scholar
  29. Gert Kwakkel, Boudewijn Kollen, and Eline Lindeman. 2004. Understanding the pattern of functional recovery after stroke: facts and theories. Restorative neurology and neuroscience 22, 3-5 (2004), 281--299.Google ScholarGoogle Scholar
  30. Gert Kwakkel, Boudewijn J Kollen, and Hermano I Krebs. 2008. Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review. Neurorehabilitation and neural repair 22, 2 (2008), 111--121.Google ScholarGoogle Scholar
  31. Catherine E Lang, Kimberly J Waddell, Joseph W Klaesner, and Marghuretta D Bland. 2017. A method for quantifying upper limb performance in daily life using accelerometers. JoVE (Journal of Visualized Experiments) 122 (2017), e55673.Google ScholarGoogle Scholar
  32. Catherine E Lang, Joanne M Wagner, Dorothy F Edwards, and Alexander W Dromerick. 2007. Upper extremity use in people with hemiparesis in the first few weeks after stroke. Journal of Neurologic Physical Therapy 31,2 (2007), 56--63.Google ScholarGoogle ScholarCross RefCross Ref
  33. Peter Langhorne, Julie Bernhardt, and Gert Kwakkel. 2011. Stroke rehabilitation. The Lancet 377, 9778 (2011), 1693--1702.Google ScholarGoogle Scholar
  34. Amanda Lazar, Christian Koehler, Joshua Tanenbaum, and David H Nguyen. 2015. Why we use and abandon smart devices. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 635--646.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Sunghoon I Lee, Catherine P Adans-Dester, Matteo Grimaldi, Ariel V Dowling, Peter C Horak, Randie M Black-Schaffer, Paolo Bonato, and Joseph T Gwin. 2018. Enabling stroke rehabilitation in home and community settings: a wearable sensor-based approach for upper-limb motor training. IEEE Journal of Translational Engineering in Health and Medicine 6 (2018), 1--11.Google ScholarGoogle ScholarCross RefCross Ref
  36. Sunghoon Ivan Lee, Xin Liu, Smita Rajan, Nathan Ramasarma Eun Kyoung Choe, and Paolo Bonato. 2019. A novel upper-limb function measure derived from finger-worn sensor data collected in a free-living setting. PloS one 14, 3 (2019), e0212484.Google ScholarGoogle Scholar
  37. Kaspar Leuenberger, Roman Gonzenbach, Susanne Wachter, Andreas Luft, and Roger Gassert. 2017. A method to qualitatively assess arm use in stroke survivors in the home environment. Medical & biological engineering & computing 55, 1 (2017), 141--150.Google ScholarGoogle Scholar
  38. Wan-wen Liao, Ching-yi Wu, Yu-wei Hsieh, Keh-chung Lin, and Wan-ying Chang. 2012. Effects of robot-assisted upper limb rehabilitation on daily function and real-world arm activity in patients with chronic stroke: a randomized controlled trial. Clinical Rehabilitation 26, 2 (2012), 111--120.Google ScholarGoogle ScholarCross RefCross Ref
  39. Xin Liu, Smita Rajan, Nathan Ramasarma, Paolo Bonato, and Sunghoon Ivan Lee. 2019. The Use of A Finger-Worn Accelerometer for Monitoring of Hand Use in Ambulatory Settings. IEEE journal of biomedical and health informatics 23, 2 (2019), 599--606.Google ScholarGoogle Scholar
  40. Eric L Luster, Troy McDaniel, Bijan Fakhri, Jim Davis, Morris Goldberg, Shantanu Bala, and Sethuraman Panchanathan. 2013. Vibrotactile cueing using wearable computers for overcoming learned non-use in chronic stroke. In Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare. ICST (Institute for Computer Sciences, Social-Informatics and ..., 378--381.Google ScholarGoogle ScholarCross RefCross Ref
  41. Niall Maclean, Pandora Pound, Charles Wolfe, and Anthony Rudd. 2000. Qualitative analysis of stroke patients' motivation for rehabilitation. Bmj 321, 7268 (2000), 1051--1054.Google ScholarGoogle Scholar
  42. Helena M Mentis, Anita Komlodi, Katrina Schrader, Michael Phipps, Ann Gruber-Baldini, Karen Yarbrough, and Lisa Shulman. 2017. Crafting a view of self-tracking data in the clinical visit. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 5800--5812.Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Nicholas Micallef, Lynne Baillie, and Stephen Uzor. 2016. Time to exercise!: an aide-memoire stroke app for post-stroke arm rehabilitation. In Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services. ACM, 112--123.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Marian E Michielsen, Ruud W Selles, Henk J Stam, Gerard M Ribbers, and Johannes B Bussmann. 2012. Quantifying nonuse in chronic stroke patients: a study into paretic, nonparetic, and bimanual upper-limb use in daily life. Archives of physical medicine and rehabilitation 93, 11 (2012), 1975--1981.Google ScholarGoogle Scholar
  45. Sonali R. Mishra, Andrew D. Miller, Shefali Haldar, Maher Khelifi, Jordan Eschler, Rashmi G. Elera, Ari H. Pollack, and Wanda Pratt. 2018. Supporting Collaborative Health Tracking in the Hospital: Patients' Perspectives. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18). ACM, New York, NY, USA, Article 650, 14 pages. https://doi.org/10.1145/3173574.3174224Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Hossein Mousavi Hondori and Maryam Khademi. 2014. A review on technical and clinical impact of microsoft kinect on physical therapy and rehabilitation. Journal of medical engineering 2014 (2014).Google ScholarGoogle Scholar
  47. Marika Noorkõiv, Helen Rodgers, and Christopher I Price. 2014. Accelerometer measurement of upper extremity movement after stroke: a systematic review of clinical studies. Journal of neuroengineering and rehabilitation 11, 1 (2014), 144.Google ScholarGoogle ScholarCross RefCross Ref
  48. Norbert Noury, A Galay, J Pasquier, and M Ballussaud. 2008. Preliminary investigation into the use of autonomous fall detectors. In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2828--2831.Google ScholarGoogle ScholarCross RefCross Ref
  49. NJ O'dwyer, L Ada, and PD Neilson. 1996. Spasticity and muscle contracture following stroke. Brain 119, 5 (1996), 1737--1749.Google ScholarGoogle ScholarCross RefCross Ref
  50. Shyamal Patel, Richard Hughes, Todd Hester, Joel Stein, Metin Akay, Jennifer G Dy, and Paolo Bonato. 2010. A novel approach to monitor rehabilitation outcomes in stroke survivors using wearable technology. Proc. IEEE 98, 3 (2010), 450--461.Google ScholarGoogle ScholarCross RefCross Ref
  51. Betsy Phillips and Hongxin Zhao. 1993. Predictors of assistive technology abandonment. Assistive technology 5, 1 (1993), 36--45.Google ScholarGoogle Scholar
  52. Wullianallur Raghupathi and Viju Raghupathi. 2014. Big data analytics in healthcare: promise and potential. Health information science and systems 2, 1 (2014), 3.Google ScholarGoogle Scholar
  53. Shriti Raj, Joyce M Lee, Ashley Garrity, and Mark W Newman. 2019. Clinical Data in Context: Towards Sensemaking Tools for Interpreting Personal Health Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 1 (2019), 22.Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Debbie Rand and Janice J Eng. 2012. Disparity between functional recovery and daily use of the upper and lower extremities during subacute stroke rehabilitation. Neurorehabilitation and neural repair 26, 1 (2012), 76--84.Google ScholarGoogle Scholar
  55. Justin B Rowe, Nizan Friedman, Vicky Chan, Steven C Cramer, Mark Bachman, and David J Reinkensmeyer. 2014. The variable relationship between arm and hand use: a rationale for using finger magnetometry to complement wrist accelerometry when measuring daily use of the upper extremity. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 4087--4090.Google ScholarGoogle ScholarCross RefCross Ref
  56. Julie Sanford, Julie Moreland, Laurie R Swanson, Paul W Stratford, and Carolyn Gowland. 1993. Reliability of the Fugl-Meyer assessment for testing motor performance in patients following stroke. Physical therapy 73, 7 (1993), 447--454.Google ScholarGoogle Scholar
  57. Gustavo Saposnik, Robert Teasell, Muhammad Mamdani, Judith Hall, William McIlroy, Donna Cheung, Kevin E Thorpe, Leonardo G Cohen, and Mark Bayley. 2010. Effectiveness of virtual reality using Wii gaming technology in stroke rehabilitation: a pilot randomized clinical trial and proof of principle. Stroke 41, 7 (2010), 1477--1484.Google ScholarGoogle ScholarCross RefCross Ref
  58. Fátima de NAP Shelton and Michael J Reding. 2001. Effect of lesion location on upper limb motor recovery after stroke. Stroke 32, 1 (2001), 107--112.Google ScholarGoogle ScholarCross RefCross Ref
  59. Robert Steele, Amanda Lo, Chris Secombe, and Yuk Kuen Wong. 2009. Elderly persons' perception and acceptance of using wireless sensor networks to assist healthcare. International journal of medical informatics 78, 12 (2009), 788--801.Google ScholarGoogle Scholar
  60. Nicholas Stergiou and Leslie M Decker. 2011. Human movement variability, nonlinear dynamics, and pathology: is there a connection? Human movement science 30, 5 (2011), 869--888.Google ScholarGoogle Scholar
  61. Edward Taub, Gitendra Uswatte, Mary H Bowman, Victor W Mark, Adriana Delgado, Camille Bryson, David Morris, and Staci Bishop-McKay. 2013. Constraint-induced movement therapy combined with conventional neurorehabilitation techniques in chronic stroke patients with plegic hands: a case series. Archives of physical medicine and rehabilitation 94, 1 (2013), 86--94.Google ScholarGoogle Scholar
  62. Gyrd Thrane, Nina Emaus, Torunn Askim, and Audny Anke. 2011. Arm use in patients with subacute stroke monitored by accelerometry: association with motor impairment and influence on self-dependence. Journal of rehabilitation medicine 43, 4 (2011), 299--304.Google ScholarGoogle ScholarCross RefCross Ref
  63. Shanbao Tong. 2016. Virtual Reality: The New Era of Rehabilitation Engineering [From the Regional Editor]. IEEE pulse 7, 3 (2016), 5--5.Google ScholarGoogle Scholar
  64. Jarugool Tretriluxana, James Gordon, Beth E Fisher, and Carolee J Winstein. 2009. Hemisphere specific impairments in reach-to-grasp control after stroke: effects of object size. Neurorehabilitation and neural repair 23, 7 (2009), 679--691.Google ScholarGoogle Scholar
  65. MA Urbin, Ryan R Bailey, and Catherine E Lang. 2015. Validity of body-worn sensor acceleration metrics to index upper extremity function in hemiparetic stroke. Journal of neurologic physical therapy: JNPT 39, 2 (2015), 111.Google ScholarGoogle ScholarCross RefCross Ref
  66. Gitendra Uswatte, Carol Giuliani, Carolee Winstein, Angelique Zeringue, Laura Hobbs, and Steven L Wolf. 2006. Validity of accelerometry for monitoring real-world arm activity in patients with subacute stroke: evidence from the extremity constraint-induced therapy evaluation trial. Archives of physical medicine and rehabilitation 87, 10 (2006), 1340--1345.Google ScholarGoogle Scholar
  67. Gitendra Uswatte, Wolfgang HR Miltner, Benjamin Foo, Maneesh Varma, Scott Moran, and Edward Taub. 2000. Objective measurement of functional upper-extremity movement using accelerometer recordings transformed with a threshold filter. Stroke 31, 3 (2000), 662--667.Google ScholarGoogle ScholarCross RefCross Ref
  68. Gitendra Uswatte and Edward Taub. 2005. Implications of the learned nonuse formulation for measuring rehabilitation outcomes: Lessons from constraint-induced movement therapy. Rehabilitation psychology 50, 1 (2005), 34.Google ScholarGoogle Scholar
  69. Gitendra Uswatte, Edward Taub, David Morris, Mary Vignolo, and Karen McCulloch. 2005. Reliability and validity of the upper-extremity Motor Activity Log-14 for measuring real-world arm use. Stroke 36, 11 (2005), 2493--2496.Google ScholarGoogle ScholarCross RefCross Ref
  70. Sanne C van der Pas, Jeanine A Verbunt, Dorien E Breukelaar, Rachma van Woerden, and Henk A Seelen. 2011. Assessment of arm activity using triaxial accelerometry in patients with a stroke. Archives of physical medicine and rehabilitation 92, 9 (2011), 1437--1442.Google ScholarGoogle Scholar
  71. Viswanath Venkatesh and Hillol Bala. 2008. Technology acceptance model 3 and a research agenda on interventions. Decision sciences 39, 2 (2008), 273--315.Google ScholarGoogle Scholar
  72. Ricardo Viana and Robert Teasell. 2012. Barriers to the implementation of constraint-induced movement therapy into practice. Topics in stroke rehabilitation 19, 2 (2012), 104--114.Google ScholarGoogle Scholar
  73. Tien-ni Wang, Keh-chung Lin, Ching-yi Wu, Chia-ying Chung, Yu-cheng Pei, and Yu-kuei Teng. 2011. Validity, responsiveness, and clinically important difference of the ABILHAND questionnaire in patients with stroke. Archives of physical medicine and rehabilitation 92, 7 (2011), 1086--1091.Google ScholarGoogle Scholar
  74. David Webster and Ozkan Celik. 2014. Systematic review of Kinect applications in elderly care and stroke rehabilitation. Journal of neuroengineering and rehabilitation 11, 1 (2014), 108.Google ScholarGoogle ScholarCross RefCross Ref
  75. Peter West, Max Van Kleek, Richard Giordano, Mark J Weal, and Nigel Shadbolt. 2018. Common barriers to the use of patient-generated data across clinical settings. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 484.Google ScholarGoogle ScholarDigital LibraryDigital Library
  76. Steven L Wolf, Pamela A Catlin, Michael Ellis, Audrey Link Archer, Bryn Morgan, and Aimee Piacentino. 2001. Assessing Wolf motor function test as outcome measure for research in patients after stroke. Stroke 32, 7 (2001), 1635--1639.Google ScholarGoogle ScholarCross RefCross Ref
  77. Steven L Wolf, Carolee J Winstein, J Philip Miller, Edward Taub, Gitendra Uswatte, David Morris, Carol Giuliani, Kathye E Light, Deborah Nichols-Larsen, Excite Investigators, et al. 2006. Effect of constraint-induced movement therapy on upper extremity function 3 to 9 months after stroke: the EXCITE randomized clinical trial. Jama 296, 17 (2006), 2095--2104.Google ScholarGoogle ScholarCross RefCross Ref
  78. Hong Kai Yap, Jeong Hoon Lim, Fatima Nasrallah, and Chen-Hua Yeow. 2017. Design and preliminary feasibility study of a soft robotic glove for hand function assistance in stroke survivors. Frontiers in neuroscience 11 (2017), 547.Google ScholarGoogle Scholar
  79. Haining Zhu, Joanna Colgan, Madhu Reddy, and Eun Kyoung Choe. 2016. Sharing patient-generated data in clinical practices: an interview study. In AMIA Annual Symposium Proceedings, Vol. 2016. American Medical Informatics Association, 1303.Google ScholarGoogle Scholar

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      cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
      Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 3, Issue 4
      December 2019
      873 pages
      EISSN:2474-9567
      DOI:10.1145/3375704
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      • Published: 14 September 2020
      Published in imwut Volume 3, Issue 4

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