An IoT-Enabled Wearable Device for Fetal Movement Detection Using Accelerometer and Gyroscope Sensors
Counting fetal movements is essential for assessing fetal health, but manually recording these movements can be challenging and inconvenient for pregnant women. This study presents a wearable device designed to detect fetal movements across various settings, both within and outside medical facilitie...
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MDPI AG
2025-03-01
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| author | Atcharawan Rattanasak Talit Jumphoo Wongsathon Pathonsuwan Kasidit Kokkhunthod Khwanjit Orkweha Khomdet Phapatanaburi Pattama Tongdee Porntip Nimkuntod Monthippa Uthansakul Peerapong Uthansakul |
| author_facet | Atcharawan Rattanasak Talit Jumphoo Wongsathon Pathonsuwan Kasidit Kokkhunthod Khwanjit Orkweha Khomdet Phapatanaburi Pattama Tongdee Porntip Nimkuntod Monthippa Uthansakul Peerapong Uthansakul |
| author_sort | Atcharawan Rattanasak |
| collection | DOAJ |
| description | Counting fetal movements is essential for assessing fetal health, but manually recording these movements can be challenging and inconvenient for pregnant women. This study presents a wearable device designed to detect fetal movements across various settings, both within and outside medical facilities. The device integrates accelerometer and gyroscope sensors with Internet of Things (IoT) technology to accurately differentiate between fetal and non-fetal movements. Data were collected from 35 pregnant women at Suranaree University of Technology (SUT) Hospital. This study evaluated ten signal extraction methods, six machine learning algorithms, and four feature selection techniques to enhance classification performance. The device utilized Particle Swarm Optimization (PSO) for feature selection and Extreme Gradient Boosting (XGB) with PSO hyper-tuning. It achieved a sensitivity of 90.00%, precision of 87.46%, and an F1-score of 88.56%, reflecting commendable results. The IoT-enabled technology facilitated continuous monitoring with an average latency of 423.6 ms. It ensured complete data integrity and successful transmission, with the capability to operate continuously for up to 48 h on a single charge. The findings substantiate the efficacy of the proposed approach in detecting fetal movements, thereby demonstrating a practical and valuable technology for fetal movement detection applications. |
| format | Article |
| id | doaj-art-b5d8fd04347a4161bced67423f4e0875 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-b5d8fd04347a4161bced67423f4e08752025-08-20T02:52:49ZengMDPI AGSensors1424-82202025-03-01255155210.3390/s25051552An IoT-Enabled Wearable Device for Fetal Movement Detection Using Accelerometer and Gyroscope SensorsAtcharawan Rattanasak0Talit Jumphoo1Wongsathon Pathonsuwan2Kasidit Kokkhunthod3Khwanjit Orkweha4Khomdet Phapatanaburi5Pattama Tongdee6Porntip Nimkuntod7Monthippa Uthansakul8Peerapong Uthansakul9School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, ThailandInstitute of Research and Development, Suranaree University of Technology, Nakhon Ratchasima 30000, ThailandInstitute of Research and Development, Suranaree University of Technology, Nakhon Ratchasima 30000, ThailandInstitute of Research and Development, Suranaree University of Technology, Nakhon Ratchasima 30000, ThailandDepartment of Integrated Engineering, Rajamangala University of Technology Tawan-Ok, Chanthaburi 22210, ThailandDepartment of Telecommunication Engineering, Faculty of Engineering and Technology, Rajamangala University of Technology Isan (RMUTI), Nakhon Ratchasima 30000, ThailandSchool of Obstetrics and Gynecology, Institute of Medicine, Suranaree University of Technology, Nakhon Ratchasima 30000, ThailandSchool of Medicine, Institute of Medicine, Suranaree University of Technology, Nakhon Ratchasima 30000, ThailandSchool of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, ThailandSchool of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, ThailandCounting fetal movements is essential for assessing fetal health, but manually recording these movements can be challenging and inconvenient for pregnant women. This study presents a wearable device designed to detect fetal movements across various settings, both within and outside medical facilities. The device integrates accelerometer and gyroscope sensors with Internet of Things (IoT) technology to accurately differentiate between fetal and non-fetal movements. Data were collected from 35 pregnant women at Suranaree University of Technology (SUT) Hospital. This study evaluated ten signal extraction methods, six machine learning algorithms, and four feature selection techniques to enhance classification performance. The device utilized Particle Swarm Optimization (PSO) for feature selection and Extreme Gradient Boosting (XGB) with PSO hyper-tuning. It achieved a sensitivity of 90.00%, precision of 87.46%, and an F1-score of 88.56%, reflecting commendable results. The IoT-enabled technology facilitated continuous monitoring with an average latency of 423.6 ms. It ensured complete data integrity and successful transmission, with the capability to operate continuously for up to 48 h on a single charge. The findings substantiate the efficacy of the proposed approach in detecting fetal movements, thereby demonstrating a practical and valuable technology for fetal movement detection applications.https://www.mdpi.com/1424-8220/25/5/1552fetal movement detectioninternet of thingswearable devicemachine learning |
| spellingShingle | Atcharawan Rattanasak Talit Jumphoo Wongsathon Pathonsuwan Kasidit Kokkhunthod Khwanjit Orkweha Khomdet Phapatanaburi Pattama Tongdee Porntip Nimkuntod Monthippa Uthansakul Peerapong Uthansakul An IoT-Enabled Wearable Device for Fetal Movement Detection Using Accelerometer and Gyroscope Sensors Sensors fetal movement detection internet of things wearable device machine learning |
| title | An IoT-Enabled Wearable Device for Fetal Movement Detection Using Accelerometer and Gyroscope Sensors |
| title_full | An IoT-Enabled Wearable Device for Fetal Movement Detection Using Accelerometer and Gyroscope Sensors |
| title_fullStr | An IoT-Enabled Wearable Device for Fetal Movement Detection Using Accelerometer and Gyroscope Sensors |
| title_full_unstemmed | An IoT-Enabled Wearable Device for Fetal Movement Detection Using Accelerometer and Gyroscope Sensors |
| title_short | An IoT-Enabled Wearable Device for Fetal Movement Detection Using Accelerometer and Gyroscope Sensors |
| title_sort | iot enabled wearable device for fetal movement detection using accelerometer and gyroscope sensors |
| topic | fetal movement detection internet of things wearable device machine learning |
| url | https://www.mdpi.com/1424-8220/25/5/1552 |
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