Embedded Controller for Artificial Limbs
Licentiate thesis, 2017

Promising developments are currently ongoing worldwide in the field of neuroprosthetics and artificial limb control. It is now possible to chronically connect a robotic limb to bone, nerves and muscles of a human being, and use the signals sourced from these connections to enable movements in the artificial limb. It is also possible to surgically redirect a nerve, deprived from its original target muscle due to amputation, to a new target in order to restore the original motor functionality. Intelligent signal processing algorithms can now utilize the bioelectric signals gathered from remaining muscles on the stump to decode the motor intention of the amputee, providing an intuitive control interface. Unfortunately for patients, clinical implementations still lag behind the advancements of research, and the conventional solutions for amputees remained basically unchanged since decades. More efforts are therefore needed from researchers to close the gap between scientific publications and hospital practices. The ultimate focus of this thesis is set on the intuitive control of a prosthetic upper limb. It was developed an embedded system capable of prosthetic control via the processing of bioelectric signals and pattern recognition algorithms. It includes a neurostimulator to provide direct neural feedback modulated by sensory information from artificial sensors. The system was designed towards clinical implementation and its functionality was proven by amputee subjects in daily life. It also constitutes a research platform to monitor prosthesis usage and training, machine learning based control algorithms, and neural stimulation paradigms.

Prosthetic Controller

Pattern Recognition

Osseointegrated Human-Machine Gateway (OHMG)

Electromyography (EMG)

Osseointegration.

Sensory Feedback

Room EF
Opponent: Associate Prof. Massimo Barbaro, Microelectronics and Bioengineering Lab., (EOLAB), Universita’ degli Studi di Cagliari, Italy

Author

Enzo Mastinu

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Biomedical Signals and Systems

Low-cost, open source bioelectric signal acquisition system

14th International Conference on Wearable and Implantable Body Sensor Networks,; (2017)p. 19-22

Paper in proceedings

Analog Front-Ends comparison: on the way to a portable, lowpower and low-cost EMG controller based on Pattern Recognition

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS,; (2015)p. 2111-2114

Paper in proceedings

Embedded System for Prosthetic Control Using Implanted Neuromuscular Interfaces Accessed Via an Osseointegrated Implant

IEEE Transactions on Biomedical Circuits and Systems,; Vol. 11(2017)p. 867-877

Journal article

Mastinu, E., Ahlberg, J., Lendaro, E., Håkansson, B., and Ortiz-Catalan, M., "A novel approach to myoelectric pattern recognition for the control of hand prostheses: A case study of use in daily life by a dysmelia subject"

Subject Categories

Other Medical Engineering

Neurosciences

Biomedical Laboratory Science/Technology

Embedded Systems

Control Engineering

Signal Processing

Publisher

Chalmers University of Technology

Room EF

Opponent: Associate Prof. Massimo Barbaro, Microelectronics and Bioengineering Lab., (EOLAB), Universita’ degli Studi di Cagliari, Italy

More information

Created

9/8/2017 8