Myoelectric Control for Hand Prostheses
Abstract: An investigation of improvements of myoelectric prostheses has been undertaken. The primary aims of this thesis were (1) to generate an accurate prediction of as many hand movement as possible, (2) to produce a training setup for subjects allowing intuitive and instant control over multiple movements, and (3) to reduce the training cycle for the control system to a maximum of a couple of minutes to enable optimizations, e.g., electrode placement. A median of six movements has been predicted with a 100% accuracy. At the initial predictions, a new set-up for training amputees using a data glove has been proposed, and training of less than 30 seconds of off-line learning, as well as direct online learning, has been conducted. Thus, the initial goals were fulfilled. Further, an online learning system has proved to further increase the accuracy and the number of movements performed while the response time for prediction decreased to 50–100 ms.
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