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Recognition of elementary upper limb movements in an activity of daily living using data from wrist mounted accelerometers

Recognition of elementary upper limb movements in an activity of daily living using data from wrist mounted accelerometers
Recognition of elementary upper limb movements in an activity of daily living using data from wrist mounted accelerometers
In this paper we present a methodology as a proof of concept for recognizing fundamental movements of the human
arm (extension, flexion and rotation of the forearm) involved in ‘making-a-cup-of-tea’, typical of an activity of daily-living (ADL). The movements are initially performed in a controlled environment as part of a training phase and the data are grouped into three clusters using k-means clustering. Movements performed during ADL, forming part of the testing phase, are associated with each cluster label using a minimum distance classifier in a multi-dimensional feature space, comprising of features selected from a ranked set of 30 features, using Euclidean and Mahalonobis distance as the metric. Experiments were performed with four healthy subjects and our results show that the proposed methodology can detect the three movements with an overall average accuracy of 88% across all subjects and arm movement types using Euclidean distance classifier.
232-237
Biswas, Dwaipayan
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Cranny, Andy
2ebc2ccb-7d3e-4a6a-91ac-9f089741939e
Gupta, Nayaab
2aa0a0a7-d58e-41f2-85ad-4146843607f3
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Ortmann, Steffen
dc43ef51-5657-45ed-b634-9a5e3cf6b321
Biswas, Dwaipayan
76983b74-d729-4aae-94c3-94d05e9b2ed4
Cranny, Andy
2ebc2ccb-7d3e-4a6a-91ac-9f089741939e
Gupta, Nayaab
2aa0a0a7-d58e-41f2-85ad-4146843607f3
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Ortmann, Steffen
dc43ef51-5657-45ed-b634-9a5e3cf6b321

Biswas, Dwaipayan, Cranny, Andy, Gupta, Nayaab, Maharatna, Koushik and Ortmann, Steffen (2014) Recognition of elementary upper limb movements in an activity of daily living using data from wrist mounted accelerometers. 2014 IEEE International Conference on Healthcare Informatics, Italy. 15 - 17 Sep 2014. pp. 232-237 . (doi:10.1109/ICHI.2014.40).

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper we present a methodology as a proof of concept for recognizing fundamental movements of the human
arm (extension, flexion and rotation of the forearm) involved in ‘making-a-cup-of-tea’, typical of an activity of daily-living (ADL). The movements are initially performed in a controlled environment as part of a training phase and the data are grouped into three clusters using k-means clustering. Movements performed during ADL, forming part of the testing phase, are associated with each cluster label using a minimum distance classifier in a multi-dimensional feature space, comprising of features selected from a ranked set of 30 features, using Euclidean and Mahalonobis distance as the metric. Experiments were performed with four healthy subjects and our results show that the proposed methodology can detect the three movements with an overall average accuracy of 88% across all subjects and arm movement types using Euclidean distance classifier.

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More information

Published date: 15 September 2014
Venue - Dates: 2014 IEEE International Conference on Healthcare Informatics, Italy, 2014-09-15 - 2014-09-17
Organisations: Electronic & Software Systems

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Local EPrints ID: 369163
URI: https://eprints.soton.ac.uk/id/eprint/369163
PURE UUID: 95b49732-13f6-4275-94e9-f1ba95df61ac

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Date deposited: 02 Oct 2014 12:38
Last modified: 19 Jul 2019 21:02

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Contributors

Author: Dwaipayan Biswas
Author: Andy Cranny
Author: Nayaab Gupta
Author: Steffen Ortmann

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