Atcharawan Rattanasak: School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand. ORCID
Talit Jumphoo: Institute of Research and Development, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand.
Wongsathon Pathonsuwan: Institute of Research and Development, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand.
Kasidit Kokkhunthod: Institute of Research and Development, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand. ORCID
Khwanjit Orkweha: Department of Integrated Engineering, Rajamangala University of Technology Tawan-Ok, Chanthaburi 22210, Thailand.
Khomdet Phapatanaburi: Department of Telecommunication Engineering, Faculty of Engineering and Technology, Rajamangala University of Technology Isan (RMUTI), Nakhon Ratchasima 30000, Thailand.
Pattama Tongdee: School of Obstetrics and Gynecology, Institute of Medicine, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand.
Porntip Nimkuntod: School of Medicine, Institute of Medicine, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand.
Monthippa Uthansakul: School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand.
Peerapong Uthansakul: School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand. ORCID
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.
