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On the data analysis for classification of elementary upper limb movements

On the data analysis for classification of elementary upper limb movements
On the data analysis for classification of elementary upper limb movements
Purpose. Body worn inertial sensors could be used to assess rehabilitation of patients with impaired upper limb motor control by detecting and classifying how many times particular arm movements (exercises) are made during normal activities. We present a systematic exploration to determine such a system.
Methods. Kinematic data was collected from 18 healthy subjects using tri-axial inertial sensors (accelerometers and gyroscopes) located at two positions on the dominant arm as four fundamental arm movements were repeated 20 times each. Ten time domain features were extracted from individual and combinations of sensor axes data, and were used to train a classifier. Three different classifiers were investigated: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and support vector machine (SVM). Each was verified using a leave-one-subject-out technique for a generalized classification model, and a ten-fold cross validation technique for a personalized classification model.
Results. LDA repeatedly gave the better results when using features extracted from individual sensor axes data. When a personalized learning model is used with LDA, only a single tri-axial sensor (accelerometer or gyroscope) is required to classify all four of the upper limb movements with a sensitivity in the range 92-100%, using as few as 6-10 time-domain features. By comparison, the generalized model using LDA exhibited lower sensitivity and generally required more features (12-18), reflecting the greater variability inherent in a training set comprised of more than one individual’s data.
Conclusions. We demonstrate that body worn inertial sensors can classify elementary arm movements using a low complexity algorithm.
2093-9868
403-413
Biswas, Dwaipayan
76983b74-d729-4aae-94c3-94d05e9b2ed4
Cranny, Andy
2ebc2ccb-7d3e-4a6a-91ac-9f089741939e
Rahim, Ahmed F.
09553606-5ade-49c6-b384-2c0e912deaa9
Gupta, Nayaab
2aa0a0a7-d58e-41f2-85ad-4146843607f3
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Harris, Nick
237cfdbd-86e4-4025-869c-c85136f14dfd
Ortmann, Steffen
dc43ef51-5657-45ed-b634-9a5e3cf6b321
Biswas, Dwaipayan
76983b74-d729-4aae-94c3-94d05e9b2ed4
Cranny, Andy
2ebc2ccb-7d3e-4a6a-91ac-9f089741939e
Rahim, Ahmed F.
09553606-5ade-49c6-b384-2c0e912deaa9
Gupta, Nayaab
2aa0a0a7-d58e-41f2-85ad-4146843607f3
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Harris, Nick
237cfdbd-86e4-4025-869c-c85136f14dfd
Ortmann, Steffen
dc43ef51-5657-45ed-b634-9a5e3cf6b321

Biswas, Dwaipayan, Cranny, Andy, Rahim, Ahmed F., Gupta, Nayaab, Maharatna, Koushik, Harris, Nick and Ortmann, Steffen (2015) On the data analysis for classification of elementary upper limb movements. Biomedical Engineering Letters, 4 (4), 403-413. (doi:10.1007/s13534-014-0160-0).

Record type: Article

Abstract

Purpose. Body worn inertial sensors could be used to assess rehabilitation of patients with impaired upper limb motor control by detecting and classifying how many times particular arm movements (exercises) are made during normal activities. We present a systematic exploration to determine such a system.
Methods. Kinematic data was collected from 18 healthy subjects using tri-axial inertial sensors (accelerometers and gyroscopes) located at two positions on the dominant arm as four fundamental arm movements were repeated 20 times each. Ten time domain features were extracted from individual and combinations of sensor axes data, and were used to train a classifier. Three different classifiers were investigated: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and support vector machine (SVM). Each was verified using a leave-one-subject-out technique for a generalized classification model, and a ten-fold cross validation technique for a personalized classification model.
Results. LDA repeatedly gave the better results when using features extracted from individual sensor axes data. When a personalized learning model is used with LDA, only a single tri-axial sensor (accelerometer or gyroscope) is required to classify all four of the upper limb movements with a sensitivity in the range 92-100%, using as few as 6-10 time-domain features. By comparison, the generalized model using LDA exhibited lower sensitivity and generally required more features (12-18), reflecting the greater variability inherent in a training set comprised of more than one individual’s data.
Conclusions. We demonstrate that body worn inertial sensors can classify elementary arm movements using a low complexity algorithm.

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e-pub ahead of print date: 1 December 2014
Published date: 15 January 2015
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 375702
URI: https://eprints.soton.ac.uk/id/eprint/375702
ISSN: 2093-9868
PURE UUID: f8ec3b32-62ae-45b3-877c-65abe8972e7a
ORCID for Nick Harris: ORCID iD orcid.org/0000-0003-4122-2219

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Date deposited: 02 Apr 2015 13:31
Last modified: 15 Aug 2019 00:55

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