Tabor, Aaron2023-03-012023-03-012017https://unbscholar.lib.unb.ca/handle/1882/13343For new myoelectric prosthesis users, muscle training is a critical step that promotes effective use and long-term adoption of the prosthesis. Training, however, currently has several problems: 1) existing approaches require expensive tools and clinical expertise, restricting their use to the clinical environment, 2) exercises are boring, repetitive, and uninformative, making it difficult for patients to stay motivated, 3) assessment tools focus exclusively on improvements in functional, real-world prosthesis tasks, which conflicts with other therapeutic goals in early training, and 4) little is known about the effects of longer-term training because existing studies have subjected participants to a very short series of training sessions. While myoelectric training games have been proposed to create a more motivating training environment, commercially available games still exhibit many of these issues. Furthermore, current research presents inconsistent findings and conflicting results, making it unclear whether games hold therapeutic value. This research demonstrates that training games can be designed to address these issues by developing a low-cost, easy-to-use training game that targets the therapeutic goals of myoelectric training. Guidelines for promoting a fun, engaging, and informative training experience were identified by engaging prosthesis users and clinical experts throughout the design of a myoelectric training game. Furthermore, a newly developed set of metrics was used to demonstrate improvement in participants’ underlying muscle control throughout a series of game-based training sessions, further suggesting that games can be designed to provide therapeutic value. This work introduces an open-source training game, demonstrates the therapeutic value of games for myoelectric training, and presents insight that will be applicable to both future research on myoelectric training as well as aspects of training in clinical practice.text/xmlxvi, 180 pageselectronicen-CAhttp://purl.org/coar/access_right/c_abf2Game-based myoelectric muscle trainingmaster thesis2023-03-01Bateman, ScottScheme, ErikComputer Science