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Automatic hand phantom map detection methods

Huang, Huaiqi; Li, Tao; Antfolk, Christian LU ; Bruschini, Claudio; Enz, Christian; Justiz, Jorn and Koch, Volker M. (2015) 11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015 In IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings
Abstract

Many amputees have maps of referred sensation from their missing hand on their residual limb (phantom maps). This skin area can serve as a target for providing amputees with tactile sensory feedback. Providing tactile feedback on the phantom map can improve the object manipulation ability, enhance embodiment of myoelectric prostheses users and help reduce phantom limb pain. The distribution of the phantom map varies with the individual. Here, we investigate a fast and accurate method for hand phantom map shape detection. We present three elementary (group testing, adaptive edge finding and support vector machines (SVM)) and two combined methods (SVM with majority-pooling and SVM with active learning) tested with different types of... (More)

Many amputees have maps of referred sensation from their missing hand on their residual limb (phantom maps). This skin area can serve as a target for providing amputees with tactile sensory feedback. Providing tactile feedback on the phantom map can improve the object manipulation ability, enhance embodiment of myoelectric prostheses users and help reduce phantom limb pain. The distribution of the phantom map varies with the individual. Here, we investigate a fast and accurate method for hand phantom map shape detection. We present three elementary (group testing, adaptive edge finding and support vector machines (SVM)) and two combined methods (SVM with majority-pooling and SVM with active learning) tested with different types of phantom map models and compare the classification error rates. The results show that SVM with majority-pooling has the smallest classification error rate.

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Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings
publisher
Institute of Electrical and Electronics Engineers Inc.
conference name
11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015
external identifiers
  • scopus:84962710828
DOI
10.1109/BioCAS.2015.7348315
language
English
LU publication?
yes
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6ab5088c-0443-4a24-8374-383ea103b78f
date added to LUP
2016-09-22 10:42:20
date last changed
2017-02-03 10:00:44
@inproceedings{6ab5088c-0443-4a24-8374-383ea103b78f,
  abstract     = {<p>Many amputees have maps of referred sensation from their missing hand on their residual limb (phantom maps). This skin area can serve as a target for providing amputees with tactile sensory feedback. Providing tactile feedback on the phantom map can improve the object manipulation ability, enhance embodiment of myoelectric prostheses users and help reduce phantom limb pain. The distribution of the phantom map varies with the individual. Here, we investigate a fast and accurate method for hand phantom map shape detection. We present three elementary (group testing, adaptive edge finding and support vector machines (SVM)) and two combined methods (SVM with majority-pooling and SVM with active learning) tested with different types of phantom map models and compare the classification error rates. The results show that SVM with majority-pooling has the smallest classification error rate.</p>},
  author       = {Huang, Huaiqi and Li, Tao and Antfolk, Christian and Bruschini, Claudio and Enz, Christian and Justiz, Jorn and Koch, Volker M.},
  booktitle    = {IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings},
  language     = {eng},
  month        = {12},
  publisher    = {Institute of Electrical and Electronics Engineers Inc.},
  title        = {Automatic hand phantom map detection methods},
  url          = {http://dx.doi.org/10.1109/BioCAS.2015.7348315},
  year         = {2015},
}