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Smart piezoelectric-based wearable system for calorie intake estimation using machine learning

Eating an appropriate food volume, maintaining the required calorie count, and making good nutritional choices are key factors for reducing the risk of obesity, which has many consequences such as Osteoarthritis (OA) that affects the patient’s knee. In this paper, we present a wearable sensor in the...

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Main Authors: Hussain, Ghulam, Al-rimy, Bander Ali Saleh, Hussain, Saddam, Albarrak, Abdullah M., Qasem, Sultan Noman, Ali, Zeeshan
Format: Article
Language:English
Published: MDPI 2022
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/100974/1/BanderAliSaleh2022_SmartPiezoelectricBasedWearableSystem.pdf
http://eprints.utm.my/id/eprint/100974/
http://dx.doi.org/10.3390/app12126135
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spelling my.utm.1009742023-05-18T06:12:52Z http://eprints.utm.my/id/eprint/100974/ Smart piezoelectric-based wearable system for calorie intake estimation using machine learning Hussain, Ghulam Al-rimy, Bander Ali Saleh Hussain, Saddam Albarrak, Abdullah M. Qasem, Sultan Noman Ali, Zeeshan QA75 Electronic computers. Computer science Eating an appropriate food volume, maintaining the required calorie count, and making good nutritional choices are key factors for reducing the risk of obesity, which has many consequences such as Osteoarthritis (OA) that affects the patient’s knee. In this paper, we present a wearable sensor in the form of a necklace embedded with a piezoelectric sensor, that detects skin movement from the lower trachea while eating. In contrast to the previous state-of-the-art piezoelectric sensor-based system that used spectral features, our system fully exploits temporal amplitude-varying signals for optimal features, and thus classifies foods more accurately. Through evaluation of the frame length and the position of swallowing in the frame, we found the best performance was with a frame length of 30 samples (1.5 s), with swallowing located towards the end of the frame. This demonstrates that the chewing sequence carries important information for classification. Additionally, we present a new approach in which the weight of solid food can be estimated from the swallow count, and the calorie count of food can be calculated from their estimated weight. Our system based on a smartphone app helps users live healthily by providing them with real-time feedback about their ingested food types, volume, and calorie count. MDPI 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/100974/1/BanderAliSaleh2022_SmartPiezoelectricBasedWearableSystem.pdf Hussain, Ghulam and Al-rimy, Bander Ali Saleh and Hussain, Saddam and Albarrak, Abdullah M. and Qasem, Sultan Noman and Ali, Zeeshan (2022) Smart piezoelectric-based wearable system for calorie intake estimation using machine learning. Applied Sciences (Switzerland), 12 (12). pp. 1-18. ISSN 2076-3417 http://dx.doi.org/10.3390/app12126135 DOI : 10.3390/app12126135
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Hussain, Ghulam
Al-rimy, Bander Ali Saleh
Hussain, Saddam
Albarrak, Abdullah M.
Qasem, Sultan Noman
Ali, Zeeshan
Smart piezoelectric-based wearable system for calorie intake estimation using machine learning
description Eating an appropriate food volume, maintaining the required calorie count, and making good nutritional choices are key factors for reducing the risk of obesity, which has many consequences such as Osteoarthritis (OA) that affects the patient’s knee. In this paper, we present a wearable sensor in the form of a necklace embedded with a piezoelectric sensor, that detects skin movement from the lower trachea while eating. In contrast to the previous state-of-the-art piezoelectric sensor-based system that used spectral features, our system fully exploits temporal amplitude-varying signals for optimal features, and thus classifies foods more accurately. Through evaluation of the frame length and the position of swallowing in the frame, we found the best performance was with a frame length of 30 samples (1.5 s), with swallowing located towards the end of the frame. This demonstrates that the chewing sequence carries important information for classification. Additionally, we present a new approach in which the weight of solid food can be estimated from the swallow count, and the calorie count of food can be calculated from their estimated weight. Our system based on a smartphone app helps users live healthily by providing them with real-time feedback about their ingested food types, volume, and calorie count.
format Article
author Hussain, Ghulam
Al-rimy, Bander Ali Saleh
Hussain, Saddam
Albarrak, Abdullah M.
Qasem, Sultan Noman
Ali, Zeeshan
author_facet Hussain, Ghulam
Al-rimy, Bander Ali Saleh
Hussain, Saddam
Albarrak, Abdullah M.
Qasem, Sultan Noman
Ali, Zeeshan
author_sort Hussain, Ghulam
title Smart piezoelectric-based wearable system for calorie intake estimation using machine learning
title_short Smart piezoelectric-based wearable system for calorie intake estimation using machine learning
title_full Smart piezoelectric-based wearable system for calorie intake estimation using machine learning
title_fullStr Smart piezoelectric-based wearable system for calorie intake estimation using machine learning
title_full_unstemmed Smart piezoelectric-based wearable system for calorie intake estimation using machine learning
title_sort smart piezoelectric-based wearable system for calorie intake estimation using machine learning
publisher MDPI
publishDate 2022
url http://eprints.utm.my/id/eprint/100974/1/BanderAliSaleh2022_SmartPiezoelectricBasedWearableSystem.pdf
http://eprints.utm.my/id/eprint/100974/
http://dx.doi.org/10.3390/app12126135
_version_ 1768006591797264384
score 13.211869

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