Integration of autonomous and pattern recognition controls in hand prostheses

Medientyp: Dissertation; E-Book; Elektronische Hochschulschrift

Titel: Integration of autonomous and pattern recognition controls in hand prostheses

Beteiligte: Mouchoux, Jeremy [VerfasserIn]

Erschienen: Georg-August-Universität Göttingen: eDiss, 2022-02-08

Sprache: Englisch

DOI: https://doi.org/10.53846/goediss-33

ISBN: 1794694560

Schlagwörter: Informatik (PPN619939052) ; Upper Limb ; Myoelectric ; Semi-autonomous ; Artificial Propriocepetion ; Artificial Exteroception ; Prosthesis ; Computer Vision ; Sensor Fusion

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Beschreibung: Thanks to the progress in the robotic and mechatronic fields, upper limb prostheses became more and more dexterous, getting closer to closing the gap between the natural limb and the medical device replacing it. However, the standard prosthesis controller spread on the market is still limited in the number of functions it can efficiently control and constitute a bottleneck in prosthesis use. Two leading solutions are currently under research to tackle this discrepancy: machine learning algorithms and autonomous controllers. Myo-controllers based on machine learning algorithms enable the user to control a high but still limited number of functions directly. On the other hand, autonomous controllers are based on a diversification of the sensor modalities to integrate the context and the user's intention in control and automatise part of the grasping process, relieving the user from the physical and potentially cognitive workload associated with it. This thesis focuses on the impact of the combination of these two solutions. Therefore, two studies investigated the gain of performance gain and the range of applicability of an association of a semi-autonomous system to a machine learning myo-controller for upper limb prostheses. This dissertation introduces first a method to preshape the prosthesis based on the prediction of the user’s intended grasping strategy. This method system supports the user in real-time by preshaping the prosthetic device's hand and wrist during a reaching phase of a prehensile action. This result is achieved by merging data from inertial measurement units, computer vision, and positions and pressure sensors to reproduce artificial proprioception, artificial exteroception, and short-term memory. The autonomous controller developed was designed to support the user in dynamic objects-crowded conditions. In a second phase, it has been assessed whether a semi-autonomous system associated with a machine learning myo-controller could improve the performance compared to the same machine learning ...

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