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On the sensor choice and data analysis for classification of elementary upper limb movements

On the sensor choice and data analysis for classification of elementary upper limb movements
On the sensor choice and data analysis for classification of elementary upper limb movements
In this paper we present a systematic exploration for determining the appropriate type of inertial sensor and the associated data processing techniques for classifying four fundamental movements of the upper limb. Our motivation was to explore classification techniques that are of low computational complexity enabling low power processing on body-worn sensor nodes for unhindered operation over a prolonged time. Kinematic data was collected from 18 healthy subjects, repeating 20 trials of each movement, using tri-axial accelerometers and tri-axial rate gyroscopes located near the wrist. Ten time-domain features extracted from data from individual sensor streams, their modulus and specific fused signals, were used to train classifiers based on three learning algorithms: LDA, QDA and SVM. Each classifier was evaluated using a leave-one-subject-out strategy. Our results show that we can correctly identify the four arm movements, with sensitivities in the range of 83–96%, using data from just a tri-axial gyroscope located near the wrist, and requiring only 12 features in combination with the lower complexity LDA learning algorithm.
Biswas, Dwaipayan
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Cranny, Andy
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Rahim, Ahmed
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Gupta, Nayaab
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Harris, Nick
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Maharatna, Koushik
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Ortmann, Steffen
dc43ef51-5657-45ed-b634-9a5e3cf6b321
Biswas, Dwaipayan
76983b74-d729-4aae-94c3-94d05e9b2ed4
Cranny, Andy
2ebc2ccb-7d3e-4a6a-91ac-9f089741939e
Rahim, Ahmed
8c66ee2e-7c05-4996-b847-836dad35895d
Gupta, Nayaab
2aa0a0a7-d58e-41f2-85ad-4146843607f3
Harris, Nick
237cfdbd-86e4-4025-869c-c85136f14dfd
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Ortmann, Steffen
dc43ef51-5657-45ed-b634-9a5e3cf6b321

Biswas, Dwaipayan, Cranny, Andy, Rahim, Ahmed, Gupta, Nayaab, Harris, Nick, Maharatna, Koushik and Ortmann, Steffen (2014) On the sensor choice and data analysis for classification of elementary upper limb movements. 2014 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Spain. 01 - 04 Jun 2014. 4 pp . (doi:10.1109/BHI.2014.6864471).

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper we present a systematic exploration for determining the appropriate type of inertial sensor and the associated data processing techniques for classifying four fundamental movements of the upper limb. Our motivation was to explore classification techniques that are of low computational complexity enabling low power processing on body-worn sensor nodes for unhindered operation over a prolonged time. Kinematic data was collected from 18 healthy subjects, repeating 20 trials of each movement, using tri-axial accelerometers and tri-axial rate gyroscopes located near the wrist. Ten time-domain features extracted from data from individual sensor streams, their modulus and specific fused signals, were used to train classifiers based on three learning algorithms: LDA, QDA and SVM. Each classifier was evaluated using a leave-one-subject-out strategy. Our results show that we can correctly identify the four arm movements, with sensitivities in the range of 83–96%, using data from just a tri-axial gyroscope located near the wrist, and requiring only 12 features in combination with the lower complexity LDA learning algorithm.

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Published date: 1 June 2014
Venue - Dates: 2014 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Spain, 2014-06-01 - 2014-06-04
Organisations: Electronic & Software Systems

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Local EPrints ID: 368054
URI: https://eprints.soton.ac.uk/id/eprint/368054
PURE UUID: 4eadd0a2-7565-4ffe-b51c-5e8ff67275db
ORCID for Nick Harris: ORCID iD orcid.org/0000-0003-4122-2219

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Date deposited: 12 Sep 2014 09:24
Last modified: 20 Jul 2019 01:21

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Contributors

Author: Dwaipayan Biswas
Author: Andy Cranny
Author: Ahmed Rahim
Author: Nayaab Gupta
Author: Nick Harris ORCID iD
Author: Steffen Ortmann

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