Du är här

Myoelectric Control for Hand Prostheses

Författare:
Publiceringsår: 2004
Språk: Engelska
Sidor: 155
Volym: 04
Dokumenttyp: Doktorsavhandling
Förlag: Department of Electrical Measurements, Box 118, 221 00 LUND, Sweden,
Övrig information: 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.

Sammanfattning

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.

Disputation

2004-04-02
10:15
Room E:1406, E-building, Lund Institute of Technology
  • Johan Wessberg (Docent)

Nyckelord

  • Technology and Engineering
  • Social Sciences
  • 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

Övrigt

  • ISSN: 0346-6222
  • ISRN:LUTEDX/TEEM--1078--SE

Box 117, 221 00 LUND
Telefon 046-222 00 00 (växel)
Telefax 046-222 47 20
lu [at] lu [dot] se

Fakturaadress: Box 188, 221 00 LUND
Organisationsnummer: 202100-3211
Om webbplatsen

LERU logo U21 logo