Towards clinically viable neuromuscular control of bone-anchored prosthetic arms with sensory feedback
Doktorsavhandling, 2019

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 to use the signals sourced from these connections to enable movements of 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, clinical implementations still lag behind the advancements made in research, and the conventional solutions for amputees have remained largely unchanged for decades. More efforts are needed from researchers to close the gap between scientific developments and clinical practices.

This thesis ultimately focuses on the intuitive control of a prosthetic upper limb. In the first part of this doctoral project, an embedded system capable of prosthetic control via the processing of bioelectric signals and pattern recognition algorithms was developed. The design included 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 its use by amputee subjects in daily life. This system was then used during the second part of the doctoral project as a research platform to monitor prosthesis usage and training, machine learning based control algorithms, and neural stimulation paradigms for tactile sensory feedback. Within this work, a novel method for interfacing a multi-grip prosthetic hand to facilitate posture selection via pattern recognition was proposed. Moreover, the need for tactile sensory feedback was investigated in order to restore natural grasping behavior in amputees. Notably, the benefit for motor coordination of somatotopic tactile feedback achieved via direct neural stimulation was demonstrated. The findings and the technology developed during this project open to the clinical use of a new class of prosthetic arms that are directly connected to the neuromusculoskeletal system, intuitively controlled and capable of tactile sensory feedback.

Electromyography

prosthetics

sensory feedback

closed-loop

osseointegration

embedded system

pattern recognition

neurostimulation

Room EE, Hörsalsvägen 11
Opponent: Associate Professor Levi Hargrove, Northwestern University, USA

Författare

Enzo Mastinu

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Medicinska signaler och system

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

Artikel i vetenskaplig tidskrift

An Alternative Myoelectric Pattern Recognition Approach for the Control of Hand Prostheses: A Case Study of Use in Daily Life by a Dysmelia Subject

IEEE Journal of Translational Engineering in Health and Medicine,; Vol. 6(2018)

Artikel i vetenskaplig tidskrift

Myoelectric signals and pattern recognition from implanted electrodes in two TMR subjects with an osseointegrated communication interface

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS,; Vol. 2018-July(2018)p. 5174-5177

Paper i proceeding

Grip control and motor coordination with implanted and surface electrodes while grasping with an osseointegrated prosthetic hand

Journal of NeuroEngineering and Rehabilitation,; (2019)

Artikel i vetenskaplig tidskrift

Motor coordination in closed-loop control of osseo-neuromuscular upper limb prostheses

The human hand is an incredibly complex system consisting of bones, muscles, nerves, tendons, as well as arteries, veins and other soft tissues. It has a wide spectrum of functionality. Hands are essential not only to manipulate different objects in daily life, but also necessary for social interactions, such communication and the arts. The loss of a hand is a terribly traumatic experience, usually followed by significant psychological and rehabilitation challenges. The interaction between engineering and science has, since a long time, been pointed towards the restauration of the functionality of a lost hand. Currently, promising developments are ongoing worldwide in the field of neuroprosthetics and the replacement of human limbs with robotic devices is advancing considerably. It is now possible to permanently connect a robotic arm to bone, nerves and muscles of a human being. Signals sourced from muscles can be decoded to enable intuitive control of the robotic limb. At the same time, connections in the nerves can be used as a path to transfer information from the external world to the subject’s brain, with the result of allowing amputees to feel again when they touch objects.

Unfortunately, robotic arms as seen in science-fiction are still far from reality. Clinical implementations lag behind the advancements in research, and the conventional solutions for amputees have remained basically unchanged since decades. More efforts are needed to close the gap between research findings within the lab and actual hospital practice. This thesis sets its efforts toward this direction.

This thesis ultimately focuses on the intuitive control of a prosthetic arm. An embedded system capable of prosthetic control by processing of bioelectric signals and pattern recognition algorithms was developed in the first part of this doctoral project. It includes a neurostimulator to provide tactile feedback modulated by sensory information from artificial sensors. The system functionality was proven by its successful use by amputee subjects outside the laboratory in daily life. Said system was then used during the second part of the doctoral project as a research platform to monitor prosthesis usage and training, machine learning based control algorithms, and neural stimulation paradigms for tactile sensory feedback. Within this work, a novel method for interfacing a multi-grip prosthetic hand to facilitate posture selection via pattern recognition was proposed. Moreover, the need for tactile sensory feedback to restore natural grasping behavior in amputees was investigated. In particular, the benefit for motor coordination of somatotopic tactile feedback achieved via direct neural stimulation was demonstrated. The findings and the technology developed during this project open to the clinical use of a new class of prosthetic arms which are directly connected to the neuromusculoskeletal system, intuitively controlled and capable of tactile sensory feedback.

Ämneskategorier

Medicinteknik

Programvaruteknik

Inbäddad systemteknik

Signalbehandling

ISBN

978-91-7905-120-4

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4587

Utgivare

Chalmers tekniska högskola

Room EE, Hörsalsvägen 11

Opponent: Associate Professor Levi Hargrove, Northwestern University, USA

Mer information

Senast uppdaterat

2019-05-09