Hierarchical domain adaptation for SEMG signal classification across multiple subjects

Large variations in Surface Electromyogram (SEMG) signal across different subjects make the process of automated signal classification as a generalized tool, challenging. In this paper, we propose a domain adaptation methodology that addresses this challenge. In particular we propose a hierarchical...

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Bibliographic Details
Published in2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2011; pp. 7853 - 7856
Main Authors Chattopadhyay, R., Krishnan, N. C., Panchanathan, S.
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.01.2011
Subjects
Accuracy
Adaptation models
Adult
Algorithms
Data models
Educational institutions
Electromyography - methods
Fatigue
Female
Hand Strength - physiology
Humans
Kernel
Male
Middle Aged
Muscles
Signal Processing, Computer-Assisted
Surface Properties
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Summary:Large variations in Surface Electromyogram (SEMG) signal across different subjects make the process of automated signal classification as a generalized tool, challenging. In this paper, we propose a domain adaptation methodology that addresses this challenge. In particular we propose a hierarchical sample selection methodology, that selects samples from multiple training subjects, based on their similarity with the target subject at different levels of granularity. We have validated our framework on SEMG data collected from 8 people during a fatiguing exercise. Comprehensive experiments conducted in the paper demonstrate that the proposed method improves the subject independent classification accuracy by 21% to 23% over the cases without domain adaptation methods and by 14% to 20% over the existing state-of-the-art domain adaptation methods.
ISBN:9781424441211
1424441218
ISSN:1094-687X
1557-170X
DOI:10.1109/IEMBS.2011.6091935