Smith, MikeDukelow, SeanKhan, Alhamd2021-09-212021-09-212021-09Khan, A. (2021). Study of an at-home upper-extremity stroke rehabilitation system (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/113917Increased awareness of the signs of stroke and better stroke management has increased survival rate close to 90% in Canada. However, this means each year close to 4000 Albertans require rehabilitation to re-learn skills for performing daily activities. A major challenge for therapists carrying out rehabilitation is the assessment of motor and sensory functions, especially of the upper extremities. Assessment tools range from therapists using simplistic observer based ordinal scales to more quantitative research-based tools such as the NEOFECT data gloves, using which patients can perform virtual reality-based exercises displayed on a computer screen or using virtual reality headsets. However, existing smart-gloves are marketed towards a clinic, not home, environment. Apart from their high initial $15,000USD cost, they require additional interfacing including costly and precise installation of cameras/base-stations for position tracking in addition to headsets to interact with the virtual reality environments. We propose a prototype of a data glove and arm tracking system with a variety of low-cost, position, orientation, and feedback sensors to supplement clinic assessments, and enable their continued use at home. Preliminary results for gamified rehabilitation exercises to encourage participation in a wider variety of motor and sensory tasks using smart-glove monitoring will be presented. Covid-19 restrictions have limited the ability to evaluate the accuracy and effectiveness of the glove monitoring by comparison to existing assessment approaches for both simplistic and complex therapeutic activities in the clinic. An initial optimization of the real-time capture of smart-glove measurements will initiate our longer term research goal of implementing machine learning models for user’s hand performance evaluation. We believe our prototype has the potential to lead to a new device that could assist therapists, patients, and families by enabling an adjunctive route for monitoring and evaluating stroke rehabilitation and recovery of the post-stroke hand when used in home-based environments.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Wearable technologyStroke rehabilitationGamificationData gloveEducation--HealthRehabilitation and TherapyEngineeringEngineering--Electronics and Electricalmaster thesis10.11575/PRISM/39234