Novel chewing cycle approach for peak detection algorithm of chew count estimation
Chew count is a critical parameter in analyzing mastication signals, yet traditional methods of manual counting by trained clinicians are often labor-intensive and prone to errors. As a result, there has been a growing interest among researchers in developing automated methods for estimating chew co...
| Main Authors: | , , , , |
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| Format: | Article |
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Institute of Electrical and Electronics Engineers Inc.
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/118880/ |
| _version_ | 1848867812197531648 |
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| author | Selamat, Nur Asmiza Md Ali, Sawal Hamid Ismail, Ahmad Ghadafi Ahmad, Siti Anom Minhad, Khairun Nisa' |
| author_facet | Selamat, Nur Asmiza Md Ali, Sawal Hamid Ismail, Ahmad Ghadafi Ahmad, Siti Anom Minhad, Khairun Nisa' |
| author_sort | Selamat, Nur Asmiza |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Chew count is a critical parameter in analyzing mastication signals, yet traditional methods of manual counting by trained clinicians are often labor-intensive and prone to errors. As a result, there has been a growing interest among researchers in developing automated methods for estimating chew count. This article reviews the existing approaches, evaluates their effectiveness, and proposes a new approach based on optimization technique. This work proposes a novel approach to chew count estimation using particle swarm optimization (PSO) combined with a peak detection algorithm. The chewing dataset comprises signals collected from 20 participants consuming eight different food types, with proximity sensors (PSs) detecting temporalis muscle activity. The peak detection algorithm identifies key signal features, while PSO optimizes the peak prominence and width parameters to minimize the mean absolute error (MAE) in chew count estimation. Two specific chewing cycle approaches were implemented: a participants-based (P) cycle and a participants-food type-based (PF) cycle. These approaches were compared to the traditional All (A) chewing cycle method, which evaluates chew count across the entire dataset in a single analysis. Results demonstrate that the PF method yields the lowest MAE at 1.25%, followed by the P method at 3.46%, and the A method at 4.26%. Moreover, the PF method required the least computational time at 8012.2 s, compared to 9392.0 s for the P method and 36621.4 s for the A. |
| first_indexed | 2025-11-15T14:42:26Z |
| format | Article |
| id | upm-118880 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T14:42:26Z |
| publishDate | 2025 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1188802025-07-29T02:59:11Z http://psasir.upm.edu.my/id/eprint/118880/ Novel chewing cycle approach for peak detection algorithm of chew count estimation Selamat, Nur Asmiza Md Ali, Sawal Hamid Ismail, Ahmad Ghadafi Ahmad, Siti Anom Minhad, Khairun Nisa' Chew count is a critical parameter in analyzing mastication signals, yet traditional methods of manual counting by trained clinicians are often labor-intensive and prone to errors. As a result, there has been a growing interest among researchers in developing automated methods for estimating chew count. This article reviews the existing approaches, evaluates their effectiveness, and proposes a new approach based on optimization technique. This work proposes a novel approach to chew count estimation using particle swarm optimization (PSO) combined with a peak detection algorithm. The chewing dataset comprises signals collected from 20 participants consuming eight different food types, with proximity sensors (PSs) detecting temporalis muscle activity. The peak detection algorithm identifies key signal features, while PSO optimizes the peak prominence and width parameters to minimize the mean absolute error (MAE) in chew count estimation. Two specific chewing cycle approaches were implemented: a participants-based (P) cycle and a participants-food type-based (PF) cycle. These approaches were compared to the traditional All (A) chewing cycle method, which evaluates chew count across the entire dataset in a single analysis. Results demonstrate that the PF method yields the lowest MAE at 1.25%, followed by the P method at 3.46%, and the A method at 4.26%. Moreover, the PF method required the least computational time at 8012.2 s, compared to 9392.0 s for the P method and 36621.4 s for the A. Institute of Electrical and Electronics Engineers Inc. 2025-01 Article PeerReviewed Selamat, Nur Asmiza and Md Ali, Sawal Hamid and Ismail, Ahmad Ghadafi and Ahmad, Siti Anom and Minhad, Khairun Nisa' (2025) Novel chewing cycle approach for peak detection algorithm of chew count estimation. IEEE Sensors Journal, 25 (1). pp. 803-812. ISSN 1530-437X; eISSN: 1558-1748 https://ieeexplore.ieee.org/document/10752885/ 10.1109/JSEN.2024.3487150 |
| spellingShingle | Selamat, Nur Asmiza Md Ali, Sawal Hamid Ismail, Ahmad Ghadafi Ahmad, Siti Anom Minhad, Khairun Nisa' Novel chewing cycle approach for peak detection algorithm of chew count estimation |
| title | Novel chewing cycle approach for peak detection algorithm of chew count estimation |
| title_full | Novel chewing cycle approach for peak detection algorithm of chew count estimation |
| title_fullStr | Novel chewing cycle approach for peak detection algorithm of chew count estimation |
| title_full_unstemmed | Novel chewing cycle approach for peak detection algorithm of chew count estimation |
| title_short | Novel chewing cycle approach for peak detection algorithm of chew count estimation |
| title_sort | novel chewing cycle approach for peak detection algorithm of chew count estimation |
| url | http://psasir.upm.edu.my/id/eprint/118880/ http://psasir.upm.edu.my/id/eprint/118880/ http://psasir.upm.edu.my/id/eprint/118880/ |