Towards On-Device Dehydration Monitoring Using Machine Learning from Wearable Device’s Data
With the ongoing advances in sensor technology and miniaturization of electronic chips, more applications are researched and developed for wearable devices. Hydration monitoring is among the problems that have been recently researched. Athletes, battlefield soldiers, workers in extreme weather condi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/5/1887 |
| _version_ | 1797473745320804352 |
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| author | Farida Sabry Tamer Eltaras Wadha Labda Fatima Hamza Khawla Alzoubi Qutaibah Malluhi |
| author_facet | Farida Sabry Tamer Eltaras Wadha Labda Fatima Hamza Khawla Alzoubi Qutaibah Malluhi |
| author_sort | Farida Sabry |
| collection | DOAJ |
| description | With the ongoing advances in sensor technology and miniaturization of electronic chips, more applications are researched and developed for wearable devices. Hydration monitoring is among the problems that have been recently researched. Athletes, battlefield soldiers, workers in extreme weather conditions, people with adipsia who have no sensation of thirst, and elderly people who lost their ability to talk are among the main target users for this application. In this paper, we address the use of machine learning for hydration monitoring using data from wearable sensors: accelerometer, magnetometer, gyroscope, galvanic skin response sensor, photoplethysmography sensor, temperature, and barometric pressure sensor. These data, together with new features constructed to reflect the activity level, were integrated with personal features to predict the last drinking time of a person and alert the user when it exceeds a certain threshold. The results of applying different models are compared for model selection for on-device deployment optimization. The extra trees model achieved the least error for predicting unseen data; random forest came next with less training time, then the deep neural network with a small model size, which is preferred for wearable devices with limited memory. Embedded on-device testing is still needed to emphasize the results and test for power consumption. |
| first_indexed | 2024-03-09T20:21:43Z |
| format | Article |
| id | doaj.art-d1be2b5d50d148c8b9460d73c40ad0f6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T20:21:43Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d1be2b5d50d148c8b9460d73c40ad0f62023-11-23T23:47:46ZengMDPI AGSensors1424-82202022-02-01225188710.3390/s22051887Towards On-Device Dehydration Monitoring Using Machine Learning from Wearable Device’s DataFarida Sabry0Tamer Eltaras1Wadha Labda2Fatima Hamza3Khawla Alzoubi4Qutaibah Malluhi5Computer Science and Engineering Department, Faculty of Engineering, Qatar University, Doha 2713, QatarComputer Science and Engineering Department, Faculty of Engineering, Qatar University, Doha 2713, QatarComputer Science and Engineering Department, Faculty of Engineering, Qatar University, Doha 2713, QatarComputer Science and Engineering Department, Faculty of Engineering, Qatar University, Doha 2713, QatarEngineering Technology Department, Community College of Qatar, Doha 7344, QatarComputer Science and Engineering Department, Faculty of Engineering, Qatar University, Doha 2713, QatarWith the ongoing advances in sensor technology and miniaturization of electronic chips, more applications are researched and developed for wearable devices. Hydration monitoring is among the problems that have been recently researched. Athletes, battlefield soldiers, workers in extreme weather conditions, people with adipsia who have no sensation of thirst, and elderly people who lost their ability to talk are among the main target users for this application. In this paper, we address the use of machine learning for hydration monitoring using data from wearable sensors: accelerometer, magnetometer, gyroscope, galvanic skin response sensor, photoplethysmography sensor, temperature, and barometric pressure sensor. These data, together with new features constructed to reflect the activity level, were integrated with personal features to predict the last drinking time of a person and alert the user when it exceeds a certain threshold. The results of applying different models are compared for model selection for on-device deployment optimization. The extra trees model achieved the least error for predicting unseen data; random forest came next with less training time, then the deep neural network with a small model size, which is preferred for wearable devices with limited memory. Embedded on-device testing is still needed to emphasize the results and test for power consumption.https://www.mdpi.com/1424-8220/22/5/1887on-devicedehydration detectionwearable deviceshydration monitoringmachine learningskin response |
| spellingShingle | Farida Sabry Tamer Eltaras Wadha Labda Fatima Hamza Khawla Alzoubi Qutaibah Malluhi Towards On-Device Dehydration Monitoring Using Machine Learning from Wearable Device’s Data Sensors on-device dehydration detection wearable devices hydration monitoring machine learning skin response |
| title | Towards On-Device Dehydration Monitoring Using Machine Learning from Wearable Device’s Data |
| title_full | Towards On-Device Dehydration Monitoring Using Machine Learning from Wearable Device’s Data |
| title_fullStr | Towards On-Device Dehydration Monitoring Using Machine Learning from Wearable Device’s Data |
| title_full_unstemmed | Towards On-Device Dehydration Monitoring Using Machine Learning from Wearable Device’s Data |
| title_short | Towards On-Device Dehydration Monitoring Using Machine Learning from Wearable Device’s Data |
| title_sort | towards on device dehydration monitoring using machine learning from wearable device s data |
| topic | on-device dehydration detection wearable devices hydration monitoring machine learning skin response |
| url | https://www.mdpi.com/1424-8220/22/5/1887 |
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