ABSTRACT
Telehealth rehabilitation systems aimed at providing physical and occupational therapy in the home face considerable challenges in terms of clinician and therapist buy-in, system and training costs, and patient and caregiver acceptance. Understanding the optimal workflow process to support practitioners in delivering quality care in partnership with assistive technologies is significant. We describe the iterative co-development of our hybrid physical/digital workflow process for assisting therapists with the setup and calibration of a computer vision based system for remote rehabilitation. Through an interdisciplinary collaboration, we present promising preliminary concepts for streamlining the translation of research outcomes into everyday healthcare experiences.
- Craig Anderson, Sally Rubenach, Cliona Ni Mhurchu, Michael Clark, Carol Spencer, and Adrian Winsor. 2000. Home or Hospital for Stroke Rehabilitation? Results of a Randomized Controlled Trial. Stroke 31, 5 (2000), 1024–1031. https://doi.org/10.1161/01.STR.31.5.1024 arXiv:https://www.ahajournals.org/doi/pdf/10.1161/01.STR.31.5.1024Google ScholarCross Ref
- Hiroo Aoyama and Leila Aflatoony. 2020. HomeModAR: A Home Intervention Augmented Reality Tool for Occupational Therapists. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA) (CHI EA ’20). Association for Computing Machinery, New York, NY, USA, 1–7. https://doi.org/10.1145/3334480.3382993Google ScholarDigital Library
- L. Axelrod, G. Fitzpatrick, J. Burridge, S. Mawson, P. P. Smith, T. Rodden, and I. Ricketts. 2009. The reality of homes fit for heroes: design challenges for rehabilitation technology at home. Journal of Assistive Technologies 3 (2009), 35–43.Google ScholarCross Ref
- M. Baran, N. Lehrer, M. Duff, V. Venkataraman, P. Turaga, T. Ingalls, W. Z. Rymer, S. L. Wolf, and T. Rikakis. 2015. Interdisciplinary concepts for design and implementation of mixed reality interactive neurorehabilitation systems for stroke. Phys Ther 95, 3 (Mar 2015), 449–460.Google ScholarCross Ref
- Angelo Basteris and Farshid Amirabdollahian. 2014. Movement Recognition and Preference in Home-Based Robot-Assisted Stroke Rehabilitation. In Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare (Oldenburg, Germany) (PervasiveHealth ’14). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), Brussels, BEL, 432–435. https://doi.org/10.4108/icst.pervasivehealth.2014.255314Google ScholarDigital Library
- Y. Chen, M. Duff, N. Lehrer, S. M. Liu, P. Blake, S. L. Wolf, H. Sundaram, and T. Rikakis. 2011. A novel adaptive mixed reality system for stroke rehabilitation: principles, proof of concept, and preliminary application in 2 patients. Top Stroke Rehabil 18, 3 (2011), 212–230.Google ScholarCross Ref
- Yinpeng Chen, Margaret Duff, Nicole Lehrer, Hari Sundaram, Jiping He, Steven Wolf, and Thanassis Rikakis. 2011. A Computational Framework for Quantitative Evaluation of Movement during Rehabilitation. AIP Conference Proceedings 1371 (06 2011). https://doi.org/10.1063/1.3596656Google ScholarCross Ref
- Yinpeng Chen, Margaret Duff, Nicole Lehrer, Hari Sundaram, Jiping He, Steven L. Wolf, and Thanassis Rikakis. 2011. A computational framework for quantitative evaluation of movement during rehabilitation. In 2011 International Symposium on Computational Models for Life Sciences, CMLS-11(AIP Conference Proceedings). 317–326. https://doi.org/10.1063/1.3596656 Copyright: Copyright 2011 Elsevier B.V., All rights reserved.; 2011 International Symposium on Computational Models for Life Sciences, CMLS-11 ; Conference date: 11-10-2011 Through 13-10-2011.Google Scholar
- Juliet Clark and Aisling Kelliher. 2021. Understanding the needs and values of rehabilitation therapists in designing and implementing telehealth solutions. CHI Conference on Human Factors in Computing Systems Extended Abstracts. https://doi.org/10.1145/3411763.3451704Google ScholarDigital Library
- William W. Gaver. 1991. Technology Affordances. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (New Orleans, Louisiana, USA) (CHI ’91). Association for Computing Machinery, New York, NY, USA, 79–84. https://doi.org/10.1145/108844.108856Google ScholarDigital Library
- J. Gonzalez-Barbosa, T. Garcia-Ramirez, J. Salas, J. Hurtado-Ramos, and J. Rico-Jimenez. 2009. Optimal camera placement for total coverage. In 2009 IEEE International Conference on Robotics and Automation. 844–848. https://doi.org/10.1109/ROBOT.2009.5152761Google ScholarCross Ref
- Mostafa Haghi, Kerstin Thurow, and Regina Stoll. 2017. Wearable Devices in Medical Internet of Things: Scientific Research and Commercially Available Devices. Healthcare informatics research 23, 1 (2017), 4–15. https://doi.org/10.4258/hir.2017.23.1.4Google Scholar
- E. Hörster and R. Lienhart. 2006. On the Optimal Placement of Multiple Visual Sensors. In Proceedings of the 4th ACM International Workshop on Video Surveillance and Sensor Networks (Santa Barbara, California, USA) (VSSN ’06). Association for Computing Machinery, New York, NY, USA, 111–120. https://doi.org/10.1145/1178782.1178800Google ScholarDigital Library
- R. Ibrahim. 2020. Home-Based Rehabilitation Coach for Stroke Survivors! A Critical Reflection. In 22nd International Conference on Human-Computer Interaction with Mobile Devices and Services (Oldenburg, Germany) (MobileHCI ’20). Association for Computing Machinery, New York, NY, USA, Article 55, 4 pages. https://doi.org/10.1145/3406324.3424597Google ScholarDigital Library
- Aisling Kelliher, Andrew Gibson, Eric Bottelsen, and Edward Coe. 2019. Designing Modular Rehabilitation Objects for Interactive Therapy in the Home. 251–257. https://doi.org/10.1145/3294109.3300983Google ScholarDigital Library
- Aisling Kelliher, Setor Zilevu, Thanassis Rikakis, Tamim Ahmed, Yen Truong, and Steven L. Wolf. 2020. Towards Standardized Processes for Physical Therapists to Quantify Patient Rehabilitation. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3313831.3376706Google ScholarDigital Library
- Aisling Kelliher, Setor Zilevu, Thanassis Rikakis, Tamim Ahmed, Yen Truong, and Steven L. Wolf. 2020. Towards Standardized Processes for Physical Therapists to Quantify Patient Rehabilitation. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3313831.3376706Google ScholarDigital Library
- Catherine Lang, Marghuretta Bland, Ryan Bailey, Sydney Schaefer, and Rebecca Birkenmeier. 2013. Assessment of upper extremity impairment, function, and activity after stroke: foundations for clinical decision making. Journal of hand therapy : official journal of the American Society of Hand Therapists 26 (April 2013), 104–14. https://doi.org/10.1016/j.jht.2012.06.005Google ScholarCross Ref
- Bruno Loureiro and Rui Rodrigues. 2014. Design Guidelines and Design Recommendations of Multi-Touch Interfaces for Elders. In The Seventh International Conference on Advances in Computer-Human Interactions. IARIA, Wilmington.Google Scholar
- A. Pantelopoulos and N. G. Bourbakis. 2010. A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 40, 1 (2010), 1–12. https://doi.org/10.1109/TSMCC.2009.2032660Google ScholarDigital Library
- Kelsey Picha and Dana Howell. 2018. A model to increase rehabilitation adherence to home exercise programmes in patients with varying levels of self-efficacy.Healthcare informatics research 16, 1 (2018), 233–237. https://doi.org/10.1002/msc.1194Google Scholar
- Thanassis Rikakis, Aisling Kelliher, Jinwoo Choi, Jia-Bin Huang, Kris Kitani, Setor Zilevu, and Steven L. Wolf. 2018. Semi-Automated Home-Based Therapy for the Upper Extremity of Stroke Survivors. (2018), 249–256. https://doi.org/10.1145/3197768.3197777Google ScholarDigital Library
- A. Schwarz, C. M. Kanzler, O. Lambercy, A. R. Luft, and J. M. Veerbeek. 2019. Systematic Review on Kinematic Assessments of Upper Limb Movements After Stroke. Stroke 50, 3 (03 2019), 718–727.Google Scholar
- Wei Shao, Le Xie, and Hailong Yu. 2011. A Convenient Home-Based Rehabilitation System for Patients with Paretic Upper-Limb. In Proceedings of the 5th International Conference on Rehabilitation Engineering & Assistive Technology (Bangkok, Thailand) (i-CREATe ’11). Singapore Therapeutic, Assistive & Rehabilitative Technologies (START) Centre, Midview City, SGP, Article 49, 3 pages.Google ScholarDigital Library
- Vinay Venkataraman, Pavan Turaga, Nicole Lehrer, Michael Baran, Thanassis Rikakis, and Steven Wolf. 2014. Decision support for stroke rehabilitation therapy via describable attribute-based decision trees. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 2014, 3154–9. https://doi.org/10.1109/EMBC.2014.6944292Google ScholarCross Ref
- A. Werner, A. Bayer, G. Schwarz, E. Zrenner, and W. Paulus. 2010. Effects of ageing on postreceptoral short-wavelength gain control: Transient tritanopia increases with age. Vision Research 50, 17 (2010), 1641–1648. https://doi.org/10.1016/j.visres.2010.05.004Google ScholarCross Ref
- Wolters Kluwer Health: Lippincott Williamsand Wilkins. 2014. Color vision problems become more common with age, study shows. Wolters Kluwer Health: Lippincott Williams and Wilkins. https://www.sciencedaily.com/releases/2014/02/140220102614.htmGoogle Scholar
Recommendations
Home-Based Rehabilitation System for Stroke Survivors: A Clinical Evaluation
AbstractRecently, a home-based rehabilitation system for stroke survivors (Baptista et al. Comput. Meth. Prog. Biomed. 176:111–120 2019), composed of two linked applications (one for the therapist and another one for the patient), has been introduced. The ...
Co-Design of a Patient Fall Risk Prevention Service Powered by Machine Learning
AfriCHI '23: Proceedings of the 4th African Human Computer Interaction ConferenceIn hospitals, patients often experience falls at night, especially when they’re under the influence of painkillers and anesthesia. Instead of seeking assistance, these patients, in their confused state, might impulsively try to get up, endangering their ...
Designing Interactive Systems for Balance Rehabilitation after Stroke
TEI '17: Proceedings of the Eleventh International Conference on Tangible, Embedded, and Embodied InteractionThis paper presents four different tangible interactive prototypes designed to support the continuation of balance rehabilitation at home. The interactive prototypes are designed to provide a more enjoyable and experience when performing balance ...
Comments