Automatic hand phantom map detection methods

Huaiqi Huang; Tao Li; Christian Antfolk; Claudio Bruschini; Christian Enz; Jorn Justiz; Volker M. Koch

doi:10.1109/BioCAS.2015.7348315

Automatic hand phantom map detection methods

Huaiqi Huang, Tao Li, Christian Antfolk, Claudio Bruschini, Christian Enz, Jorn Justiz, Volker M. Koch

Department of Biomedical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

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 phantom map models and compare the classification error rates. The results show that SVM with majority-pooling has the smallest classification error rate.

Original language	English
Title of host publication	IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings
Publisher	IEEE - Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479972333
DOIs	https://doi.org/10.1109/BioCAS.2015.7348315
Publication status	Published - 2015 Dec 4
Event	11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015 - Atlanta, United States Duration: 2015 Oct 22 → 2015 Oct 24

Conference

Conference	11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015
Country/Territory	United States
City	Atlanta
Period	2015/10/22 → 2015/10/24

Subject classification (UKÄ)

Medical Materials

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10.1109/BioCAS.2015.7348315

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Huang, H., Li, T., Antfolk, C., Bruschini, C., Enz, C., Justiz, J., & Koch, V. M. (2015). Automatic hand phantom map detection methods. In IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings Article 7348315 IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BioCAS.2015.7348315

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title = "Automatic hand phantom map detection methods",

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 phantom map models and compare the classification error rates. The results show that SVM with majority-pooling has the smallest classification error rate.",

author = "Huaiqi Huang and Tao Li and Christian Antfolk and Claudio Bruschini and Christian Enz and Jorn Justiz and Koch, {Volker M.}",

year = "2015",

month = dec,

day = "4",

doi = "10.1109/BioCAS.2015.7348315",

language = "English",

booktitle = "IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015 ; Conference date: 22-10-2015 Through 24-10-2015",

}

Huang, H, Li, T, Antfolk, C, Bruschini, C, Enz, C, Justiz, J & Koch, VM 2015, Automatic hand phantom map detection methods. in IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings., 7348315, IEEE - Institute of Electrical and Electronics Engineers Inc., 11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015, Atlanta, United States, 2015/10/22. https://doi.org/10.1109/BioCAS.2015.7348315

Automatic hand phantom map detection methods. / Huang, Huaiqi; Li, Tao; Antfolk, Christian et al.
IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings. IEEE - Institute of Electrical and Electronics Engineers Inc., 2015. 7348315.

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

TY - GEN

T1 - Automatic hand phantom map detection methods

AU - Huang, Huaiqi

AU - Li, Tao

AU - Antfolk, Christian

AU - Bruschini, Claudio

AU - Enz, Christian

AU - Justiz, Jorn

AU - Koch, Volker M.

PY - 2015/12/4

Y1 - 2015/12/4

N2 - 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.

AB - 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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M3 - Paper in conference proceeding

AN - SCOPUS:84962710828

BT - IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 22 October 2015 through 24 October 2015

ER -

Automatic hand phantom map detection methods

Abstract

Conference

Subject classification (UKÄ)

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