Skip to main content

LUP Student Papers

LUND UNIVERSITY LIBRARIES

Artificial Neural Networks for Enhanced Exoskeleton Grip Movement

Horal, Malcolm LU (2018) In Master's Theses in Mathematical Sciences FMAM05 20181
Mathematics (Faculty of Engineering)
Abstract
The idea to artificially enhance our physical abilities has always fascinated humankind and over the cause of history it has resulted in countless important innovations such as wheelchairs and carbon-fiber-reinforced polymer leg prosthetics. By combining electronics and Artificial Intelligence the step towards artificially enhanced limbs has been greatly reduced. This master’s thesis describes the development of a Machine Learning solution to control an upper extremity exoskeleton. A Recurrent Neural Network is developed and trained on Electromyography data from the forearm. Two different networks and two data sets are tested. The results show that this approach is very promising for classification of grip movements if implemented... (More)
The idea to artificially enhance our physical abilities has always fascinated humankind and over the cause of history it has resulted in countless important innovations such as wheelchairs and carbon-fiber-reinforced polymer leg prosthetics. By combining electronics and Artificial Intelligence the step towards artificially enhanced limbs has been greatly reduced. This master’s thesis describes the development of a Machine Learning solution to control an upper extremity exoskeleton. A Recurrent Neural Network is developed and trained on Electromyography data from the forearm. Two different networks and two data sets are tested. The results show that this approach is very promising for classification of grip movements if implemented correctly. (Less)
Please use this url to cite or link to this publication:
author
Horal, Malcolm LU
supervisor
organization
course
FMAM05 20181
year
type
H2 - Master's Degree (Two Years)
subject
keywords
AI, ML, ANN, RNN, Exoskeleton, Electromyography
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3365-2018
ISSN
1404-6342
other publication id
2018:E65
language
English
id
8964188
date added to LUP
2018-12-28 14:42:09
date last changed
2019-07-12 10:04:06
@misc{8964188,
  abstract     = {{The idea to artificially enhance our physical abilities has always fascinated humankind and over the cause of history it has resulted in countless important innovations such as wheelchairs and carbon-fiber-reinforced polymer leg prosthetics. By combining electronics and Artificial Intelligence the step towards artificially enhanced limbs has been greatly reduced. This master’s thesis describes the development of a Machine Learning solution to control an upper extremity exoskeleton. A Recurrent Neural Network is developed and trained on Electromyography data from the forearm. Two different networks and two data sets are tested. The results show that this approach is very promising for classification of grip movements if implemented correctly.}},
  author       = {{Horal, Malcolm}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Artificial Neural Networks for Enhanced Exoskeleton Grip Movement}},
  year         = {{2018}},
}