Cover Image

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Selamat, Nur Asmiza, Md. Ali, Sawal Hamid, Minhad, Khairun Nisa’, Ahmad, Siti Anom
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Online Access:http://eprints.utem.edu.my/id/eprint/26774/2/A_NOVEL_PEAK_02025.PDF
http://eprints.utem.edu.my/id/eprint/26774/
https://ieeexplore.ieee.org/document/9942809/authors#authors
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utem.eprints.26774
record_format eprints
spelling my.utem.eprints.267742023-04-14T14:55:22Z http://eprints.utem.edu.my/id/eprint/26774/ 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 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 Inc. 2022-11-08 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/26774/2/A_NOVEL_PEAK_02025.PDF Selamat, Nur Asmiza and Md. Ali, Sawal Hamid and Minhad, Khairun Nisa’ and Ahmad, Siti Anom (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. pp. 1-12. ISSN 0018-9456 https://ieeexplore.ieee.org/document/9942809/authors#authors 10.1109/TIM.2022.3220281
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
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
spellingShingle Selamat, Nur Asmiza
Md. Ali, Sawal Hamid
Minhad, Khairun Nisa’
Ahmad, Siti Anom
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
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 Inc.
publishDate 2022
url http://eprints.utem.edu.my/id/eprint/26774/2/A_NOVEL_PEAK_02025.PDF
http://eprints.utem.edu.my/id/eprint/26774/
https://ieeexplore.ieee.org/document/9942809/authors#authors
_version_ 1763300019859881984
score 13.211869

Similar Items