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Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification

Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification
Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification
In this paper we present a methodology for recognizing three fundamental movements of the human forearm (extension, flexion and rotation) using pattern recognition applied to the data from a single wrist-worn, inertial sensor. We propose that this technique could be used as a clinical tool to assess rehabilitation progress in neurodegenerative pathologies such as stroke or cerebral palsy by tracking the number of times a patient performs specific arm movements (e.g. prescribed exercises) with their paretic arm throughout the day. We demonstrate this with healthy subjects and stroke patients in a simple proof of concept study in which
these arm movements are detected during an archetypal activity of daily-living (ADL) – ‘making-a-cup-of-tea’. Data is collected from a tri-axial accelerometer and a tri-axial gyroscope located proximal to the wrist. In a training phase, movements are initially performed in a controlled environment which are represented by a ranked set of 30 time-domain features. Using a sequential forward selection technique, for each set of feature combinations three clusters are formed using k-means clustering followed by 10 runs of 10-fold cross validation on the training data to determine the best feature combinations. For the testing phase, movements performed during the ADL are associated with each cluster label using a minimum distance classifier in a multi-dimensional feature space, comprised of the best ranked features, using Euclidean or Mahalanobis distance as the metric. Experiments were performed with four healthy subjects and four stroke survivors and our results show
that the proposed methodology can detect the three movements performed during the ADL with an overall average accuracy of 88% using the accelerometer data and 83% using the gyroscope data across all healthy subjects and arm movement types. The average accuracy across all stroke survivors was 70% using accelerometer data and 66% using gyroscope data. We also use a Linear Discriminant Analysis (LDA) classifier and a Support Vector Machine (SVM) classifier in association with the same set of features to detect the three arm movements and compare the results to demonstrate the effectiveness of our proposed methodology.
0167-9457
59-76
Biswas, Dwaipayan
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Cranny, Andy
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Gupta, Nayaab
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Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Achner, Josy
1eb12fc1-6d4e-41e6-8e2e-6b7f4fec7daf
Klemke, Jasmin
0c095460-4faf-4d56-bfcc-c04a26e70249
Jobges, Michael
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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
Gupta, Nayaab
2aa0a0a7-d58e-41f2-85ad-4146843607f3
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Achner, Josy
1eb12fc1-6d4e-41e6-8e2e-6b7f4fec7daf
Klemke, Jasmin
0c095460-4faf-4d56-bfcc-c04a26e70249
Jobges, Michael
56f78929-cd51-4837-930d-57efaee6d6ef
Ortmann, Steffen
dc43ef51-5657-45ed-b634-9a5e3cf6b321

Biswas, Dwaipayan, Cranny, Andy, Gupta, Nayaab, Maharatna, Koushik, Achner, Josy, Klemke, Jasmin, Jobges, Michael and Ortmann, Steffen (2015) Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification. Human Movement Science, 40, 59-76.

Record type: Article

Abstract

In this paper we present a methodology for recognizing three fundamental movements of the human forearm (extension, flexion and rotation) using pattern recognition applied to the data from a single wrist-worn, inertial sensor. We propose that this technique could be used as a clinical tool to assess rehabilitation progress in neurodegenerative pathologies such as stroke or cerebral palsy by tracking the number of times a patient performs specific arm movements (e.g. prescribed exercises) with their paretic arm throughout the day. We demonstrate this with healthy subjects and stroke patients in a simple proof of concept study in which
these arm movements are detected during an archetypal activity of daily-living (ADL) – ‘making-a-cup-of-tea’. Data is collected from a tri-axial accelerometer and a tri-axial gyroscope located proximal to the wrist. In a training phase, movements are initially performed in a controlled environment which are represented by a ranked set of 30 time-domain features. Using a sequential forward selection technique, for each set of feature combinations three clusters are formed using k-means clustering followed by 10 runs of 10-fold cross validation on the training data to determine the best feature combinations. For the testing phase, movements performed during the ADL are associated with each cluster label using a minimum distance classifier in a multi-dimensional feature space, comprised of the best ranked features, using Euclidean or Mahalanobis distance as the metric. Experiments were performed with four healthy subjects and four stroke survivors and our results show
that the proposed methodology can detect the three movements performed during the ADL with an overall average accuracy of 88% using the accelerometer data and 83% using the gyroscope data across all healthy subjects and arm movement types. The average accuracy across all stroke survivors was 70% using accelerometer data and 66% using gyroscope data. We also use a Linear Discriminant Analysis (LDA) classifier and a Support Vector Machine (SVM) classifier in association with the same set of features to detect the three arm movements and compare the results to demonstrate the effectiveness of our proposed methodology.

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e-pub ahead of print date: April 2015
Organisations: Electronic & Software Systems

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Local EPrints ID: 372914
URI: https://eprints.soton.ac.uk/id/eprint/372914
ISSN: 0167-9457
PURE UUID: 3a8c8c16-9390-47eb-8cf2-cdc5f2bde5ff

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Date deposited: 24 Dec 2014 14:44
Last modified: 11 Nov 2017 00:17

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Contributors

Author: Dwaipayan Biswas
Author: Andy Cranny
Author: Nayaab Gupta
Author: Josy Achner
Author: Jasmin Klemke
Author: Michael Jobges
Author: Steffen Ortmann

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