Embedded System for Prosthetic Control Using Implanted Neuromuscular Interfaces Accessed Via an Osseointegrated Implant
Artikel i vetenskaplig tidskrift, 2017

Despite the technological progress in robotics achieved in the last decades, prosthetic limbs still lack functionality, reliability, and comfort. Recently, an implanted neuromusculoskeletal interface built upon osseointegration was developed and tested in humans, namely the Osseointegrated Human-Machine Gateway. Here, we present an embedded system to exploit the advantages of this technology. Our artificial limb controller allows for bioelectric signals acquisition, processing, decoding of motor intent, prosthetic control, and sensory feedback. It includes a neurostimulator to provide direct neural feedback based on sensory information. The system was validated using real-time tasks characterization, power consumption evaluation, and myoelectric pattern recognition performance. Functionality was proven in a first pilot patient from whom results of daily usage were obtained. The system was designed to be reliably used in activities of daily living, as well as a research platform to monitor prosthesis usage and training, machine-learning-based control algorithms, and neural stimulation paradigms.

Electromyography (EMG)

pattern recognition

osseointegration

sensory feedback

osseointegrated human-machine gateway (OHMG)

prosthetic controller

Författare

Enzo Mastinu

Signaler och system, Signalbehandling och medicinsk teknik, Medicinska signaler och system

P. Doguet

Synergia Medical

Y. Botquin

Synergia Medical

Bo Håkansson

Signaler och system, Signalbehandling och medicinsk teknik, Medicinska signaler och system

Max Jair Ortiz Catalan

Signaler och system, Signalbehandling och medicinsk teknik, Medicinska signaler och system

IEEE Transactions on Biomedical Circuits and Systems

1932-4545 (ISSN)

Vol. 11 867-877 7932919

Ämneskategorier

Medicinsk apparatteknik

Inbäddad systemteknik

Robotteknik och automation

Signalbehandling

DOI

10.1109/TBCAS.2017.2694710