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A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection

This article uses a proximity sensor to perform noncontact-based (sensing) chewing activity detection, capturing the temporalis muscle movement during food intake. The proposed approach is validated using data from a larger number of participants, 20, and different food types, eight. The proposed ch...

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Main Authors: Selamat, Nur Asmiza, Md. Ali, Sawal Hamid, Minhad, Khairun Nisa’, Ahmad, Siti Anom, Sampe, Jahariah
Format: Article
Published: Institute of Electrical and Electronics Engineers 2022
Online Access:http://psasir.upm.edu.my/id/eprint/100267/
https://ieeexplore.ieee.org/document/9942809
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spelling my.upm.eprints.1002672024-07-09T03:48:01Z http://psasir.upm.edu.my/id/eprint/100267/ A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection Selamat, Nur Asmiza Md. Ali, Sawal Hamid Minhad, Khairun Nisa’ Ahmad, Siti Anom Sampe, Jahariah This article uses a proximity sensor to perform noncontact-based (sensing) chewing activity detection, capturing the temporalis muscle movement during food intake. The proposed approach is validated using data from a larger number of participants, 20, and different food types, eight. The proposed chewing detection classifies the chewing activity with an overall accuracy of 96.4% using a medium Gaussian support vector machine (SVM). In accordance with the result, this article proposes a novel chew count estimation based on particle swarm optimization (PSO). First, the base of the algorithm is developed based on counting the peak of the chewing signal. Next, the insignificant peak is removed by introducing an argument of minimum peak prominence and maximum peak width where the value of the parameters needs to be determined. As the individual chewing pattern varies from person to person, this article uses a novel parameter search using the PSO method to find the multiplier (parameter values) according to the average peak prominence and width value within each chewing episode. The proposed estimation approach simplifies the typical trial-and-error method. During optimization, within 100 iterations, the chewing count is reduced by 12.9% from its first iteration. Overall, the proposed methods achieve a 4.26% mean absolute error of chewing count estimation. Institute of Electrical and Electronics Engineers 2022-11-08 Article PeerReviewed Selamat, Nur Asmiza and Md. Ali, Sawal Hamid and Minhad, Khairun Nisa’ and Ahmad, Siti Anom and Sampe, Jahariah (2022) A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection. IEEE Transactions on Instrumentation and Measurement, 71. art. no. 9512712. pp. 1-12. ISSN 0018-9456; ESSN: 1557-9662 https://ieeexplore.ieee.org/document/9942809 10.1109/TIM.2022.3220281
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description This article uses a proximity sensor to perform noncontact-based (sensing) chewing activity detection, capturing the temporalis muscle movement during food intake. The proposed approach is validated using data from a larger number of participants, 20, and different food types, eight. The proposed chewing detection classifies the chewing activity with an overall accuracy of 96.4% using a medium Gaussian support vector machine (SVM). In accordance with the result, this article proposes a novel chew count estimation based on particle swarm optimization (PSO). First, the base of the algorithm is developed based on counting the peak of the chewing signal. Next, the insignificant peak is removed by introducing an argument of minimum peak prominence and maximum peak width where the value of the parameters needs to be determined. As the individual chewing pattern varies from person to person, this article uses a novel parameter search using the PSO method to find the multiplier (parameter values) according to the average peak prominence and width value within each chewing episode. The proposed estimation approach simplifies the typical trial-and-error method. During optimization, within 100 iterations, the chewing count is reduced by 12.9% from its first iteration. Overall, the proposed methods achieve a 4.26% mean absolute error of chewing count estimation.
format Article
author Selamat, Nur Asmiza
Md. Ali, Sawal Hamid
Minhad, Khairun Nisa’
Ahmad, Siti Anom
Sampe, Jahariah
spellingShingle Selamat, Nur Asmiza
Md. Ali, Sawal Hamid
Minhad, Khairun Nisa’
Ahmad, Siti Anom
Sampe, Jahariah
A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection
author_facet Selamat, Nur Asmiza
Md. Ali, Sawal Hamid
Minhad, Khairun Nisa’
Ahmad, Siti Anom
Sampe, Jahariah
author_sort Selamat, Nur Asmiza
title A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection
title_short A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection
title_full A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection
title_fullStr A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection
title_full_unstemmed A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection
title_sort novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection
publisher Institute of Electrical and Electronics Engineers
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/100267/
https://ieeexplore.ieee.org/document/9942809
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score 13.211869

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