Myoelectric Control for Hand Prostheses

Fredrik Sebelius

Myoelectric Control for Hand Prostheses

Division for Biomedical Engineering

Research output: Thesis › Doctoral Thesis (compilation)

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.

Original language	English
Qualification	Doctor
Awarding Institution	Division for Biomedical Engineering
Supervisors/Advisors	Laurell, Thomas, Supervisor
Award date	2004 Apr 2
Publisher	Department of Electrical Measurements, Lund University
Publication status	Published - 2004

Bibliographical note

Defence details

Date: 2004-04-02
Time: 10:15
Place: Room E:1406, E-building, Lund Institute of Technology

External reviewer(s)

Name: Wessberg, Johan
Title: Docent
Affiliation: Inst. för fysiologi och farmakologi, Göteborgs universitet, Box 432, 405 30 Göteborg

---

Article: I Tele-Operation of Multi-Finger Virtual/Robot Hand Using Self-Organizing Tree Structured Network on EMGSebelius F, Eriksson L, Balkenius C, Laurell TProceedings of First International ICSC Conference on Neuro-Fuzzy Technologies NF'2002, 16–19 January, Havana, Cuba, 2002.

Article: II Classification of Motor Commands Using a Modified Self-Organizing Feature MapSebelius F, Eriksson L, Holmberg H, Levinsson A, Lundborg G,Danielsen N, Schouenborg J, Balkenius C, Laurell T, Montelius LSubmitted (2004) to Medical Engineering & Physics.

Article: III Motor Control of an Artificial Hand by Tree-Structured Network: A New Concept Based on the Combined Use of ANNs and a Data GloveSebelius F, Eriksson L, Balkenius C, Laurell TSubmitted (2003) to Journal of Medical Engineering & Technology.

Article: IV Refined Motor Control of Artificial Hands: Preliminary Results from Six PatientsSebelius FCP, Rosén BN, Lundborg GNSubmitted (2003) to The Journal of Hand Surgery.

Article: V Real-time Control of a Virtual HandSebelius F, Axelsson M, Danielsen N, Schouenborg J, Laurell TSubmitted (2004) to Technology and Disability.

Subject classification (UKÄ)

Medical Engineering

Free keywords

Mät- och instrumenteringsteknik
Care and help to handicapped
Handikappade
vård och rehabilitering
Instrumentation technology
Virtual
Recognition
Real time
On-line learning
Myoelectric
Hand prosthesis
EMG
ANN
Artificial hands

Cite this

@phdthesis{d0b393066c5c402e9a091a812b4b324b,

title = "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.",

keywords = "M{\"a}t- och instrumenteringsteknik, Care and help to handicapped, Handikappade, v{\aa}rd och rehabilitering, Instrumentation technology, Virtual, Recognition, Real time, On-line learning, Myoelectric, Hand prosthesis, EMG, ANN, Artificial hands",

author = "Fredrik Sebelius",

note = "Defence details Date: 2004-04-02 Time: 10:15 Place: Room E:1406, E-building, Lund Institute of Technology External reviewer(s) Name: Wessberg, Johan Title: Docent Affiliation: Inst. f{\"o}r fysiologi och farmakologi, G{\"o}teborgs universitet, Box 432, 405 30 G{\"o}teborg --- Article: I Tele-Operation of Multi-Finger Virtual/Robot Hand Using Self-Organizing Tree Structured Network on EMGSebelius F, Eriksson L, Balkenius C, Laurell TProceedings of First International ICSC Conference on Neuro-Fuzzy Technologies NF'2002, 16–19 January, Havana, Cuba, 2002. Article: II Classification of Motor Commands Using a Modified Self-Organizing Feature MapSebelius F, Eriksson L, Holmberg H, Levinsson A, Lundborg G,Danielsen N, Schouenborg J, Balkenius C, Laurell T, Montelius LSubmitted (2004) to Medical Engineering & Physics. Article: III Motor Control of an Artificial Hand by Tree-Structured Network: A New Concept Based on the Combined Use of ANNs and a Data GloveSebelius F, Eriksson L, Balkenius C, Laurell TSubmitted (2003) to Journal of Medical Engineering & Technology. Article: IV Refined Motor Control of Artificial Hands: Preliminary Results from Six PatientsSebelius FCP, Ros{\'e}n BN, Lundborg GNSubmitted (2003) to The Journal of Hand Surgery. Article: V Real-time Control of a Virtual HandSebelius F, Axelsson M, Danielsen N, Schouenborg J, Laurell TSubmitted (2004) to Technology and Disability.",

year = "2004",

language = "English",

publisher = "Department of Electrical Measurements, Lund University",

type = "Doctoral Thesis (compilation)",

school = "Division for Biomedical Engineering",

}

TY - THES

T1 - Myoelectric Control for Hand Prostheses

AU - Sebelius, Fredrik

N1 - Defence details Date: 2004-04-02 Time: 10:15 Place: Room E:1406, E-building, Lund Institute of Technology External reviewer(s) Name: Wessberg, Johan Title: Docent Affiliation: Inst. för fysiologi och farmakologi, Göteborgs universitet, Box 432, 405 30 Göteborg --- Article: I Tele-Operation of Multi-Finger Virtual/Robot Hand Using Self-Organizing Tree Structured Network on EMGSebelius F, Eriksson L, Balkenius C, Laurell TProceedings of First International ICSC Conference on Neuro-Fuzzy Technologies NF'2002, 16–19 January, Havana, Cuba, 2002. Article: II Classification of Motor Commands Using a Modified Self-Organizing Feature MapSebelius F, Eriksson L, Holmberg H, Levinsson A, Lundborg G,Danielsen N, Schouenborg J, Balkenius C, Laurell T, Montelius LSubmitted (2004) to Medical Engineering & Physics. Article: III Motor Control of an Artificial Hand by Tree-Structured Network: A New Concept Based on the Combined Use of ANNs and a Data GloveSebelius F, Eriksson L, Balkenius C, Laurell TSubmitted (2003) to Journal of Medical Engineering & Technology. Article: IV Refined Motor Control of Artificial Hands: Preliminary Results from Six PatientsSebelius FCP, Rosén BN, Lundborg GNSubmitted (2003) to The Journal of Hand Surgery. Article: V Real-time Control of a Virtual HandSebelius F, Axelsson M, Danielsen N, Schouenborg J, Laurell TSubmitted (2004) to Technology and Disability.

PY - 2004

Y1 - 2004

N2 - 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.

AB - 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.

KW - Mät- och instrumenteringsteknik

KW - Care and help to handicapped

KW - Handikappade

KW - vård och rehabilitering

KW - Instrumentation technology

KW - Virtual

KW - Recognition

KW - Real time

KW - On-line learning

KW - Myoelectric

KW - Hand prosthesis

KW - EMG

KW - ANN

KW - Artificial hands

M3 - Doctoral Thesis (compilation)

PB - Department of Electrical Measurements, Lund University

ER -

Myoelectric Control for Hand Prostheses

Abstract

Bibliographical note

Subject classification (UKÄ)

Free keywords

Fingerprint

Cite this