Abstract
For a user of a hand prosthesis, it is very important to be able to control his/her prosthetic hand when achieving goals in daily life. Lack of control increases the chance that people will not use their prosthesis. Our research focused on training strategies for multi-articulating myoelectric hand prostheses, which are prosthesis that are controlled using muscle signals collected on the skin of the forearm. These so-called myosignals are not only used to open and close the hand, but also for switching between between grips types. Different grip types are practical to perform different tasks, such as grasping a small object, such as a pencil, versus a large object, such as a cup of tea. The goal of this thesis was to examine how the production of triggers, needed to switch between the grip types, can be trained. Training usually consists of a lot of repetitions of a fixed set of tasks, and therefore can be perceived as boring and tiring. We examined the possibilities of using a computer game for training. Besides studying improvement within the training sessions with the game, we examined the period after training to see whether improvement in the training game would lead to improvements in prosthesis use of activities in daily life. Our results showed that motor learning related to arm prosthesis control is very complex and that training should be adjusted to the learning individual. The developed game had specific benefits and can be a promising addition to rehabilitation practice.
Original language | English |
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Qualification | Doctor of Philosophy |
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Award date | 17-May-2023 |
Place of Publication | [Groningen] |
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Publication status | Published - 2023 |