⚡ Quick Summary
This study introduces an IoT-enabled wearable device designed for fetal movement detection, utilizing accelerometer and gyroscope sensors. The device achieved impressive metrics, including a sensitivity of 90.00% and an F1-score of 88.56%, demonstrating its potential for enhancing fetal health monitoring.
🔍 Key Details
- 📊 Dataset: Data collected from 35 pregnant women at Suranaree University of Technology (SUT) Hospital
- ⚙️ Technology: Integration of accelerometer and gyroscope sensors with IoT technology
- 🏆 Performance: Sensitivity 90.00%, Precision 87.46%, F1-score 88.56%
- ⏱️ Latency: Average latency of 423.6 ms
- 🔋 Battery Life: Operates continuously for up to 48 hours on a single charge
🔑 Key Takeaways
- 👶 Fetal movement counting is crucial for assessing fetal health.
- 💡 Wearable technology offers a convenient solution for pregnant women.
- 🤖 Machine learning was employed to enhance classification performance.
- 🏆 Particle Swarm Optimization (PSO) was used for feature selection.
- 📈 Extreme Gradient Boosting (XGB) with PSO hyper-tuning yielded high performance.
- 🌐 Continuous monitoring is facilitated by IoT technology.
- 🔒 Data integrity and successful transmission were ensured throughout the study.
- 🌍 Study conducted at Suranaree University of Technology (SUT) Hospital.
📚 Background
Monitoring fetal movements is a vital aspect of prenatal care, as it provides insights into the health and well-being of the fetus. Traditional methods of counting these movements can be cumbersome and often rely on manual recording, which may lead to inaccuracies. The integration of technology into this process can significantly enhance the monitoring experience for expectant mothers.
🗒️ Study
The study was conducted at Suranaree University of Technology (SUT) Hospital, where researchers developed a wearable device capable of detecting fetal movements using advanced sensor technology. The device was tested on 35 pregnant women, and various signal extraction methods, machine learning algorithms, and feature selection techniques were evaluated to optimize its performance.
📈 Results
The results were promising, with the device achieving a sensitivity of 90.00%, precision of 87.46%, and an F1-score of 88.56%. These metrics indicate a high level of accuracy in differentiating between fetal and non-fetal movements. Additionally, the device demonstrated an average latency of 423.6 ms and could operate for up to 48 hours on a single charge, ensuring continuous monitoring without interruption.
🌍 Impact and Implications
The implications of this study are significant for prenatal care. By utilizing an IoT-enabled wearable device, healthcare providers can offer a more efficient and accurate method for monitoring fetal movements. This technology not only enhances the convenience for pregnant women but also has the potential to improve overall fetal health outcomes through timely interventions based on real-time data.
🔮 Conclusion
This study highlights the transformative potential of wearable technology in fetal movement detection. With its impressive performance metrics and the ability to operate continuously, the proposed device represents a valuable advancement in prenatal monitoring. As we move forward, further research and development in this area could lead to even more innovative solutions for enhancing maternal and fetal health.
💬 Your comments
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An IoT-Enabled Wearable Device for Fetal Movement Detection Using Accelerometer and Gyroscope Sensors.
Abstract
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.
Author: [‘Rattanasak A’, ‘Jumphoo T’, ‘Pathonsuwan W’, ‘Kokkhunthod K’, ‘Orkweha K’, ‘Phapatanaburi K’, ‘Tongdee P’, ‘Nimkuntod P’, ‘Uthansakul M’, ‘Uthansakul P’]
Journal: Sensors (Basel)
Citation: Rattanasak A, et al. An IoT-Enabled Wearable Device for Fetal Movement Detection Using Accelerometer and Gyroscope Sensors. An IoT-Enabled Wearable Device for Fetal Movement Detection Using Accelerometer and Gyroscope Sensors. 2025; 25:(unknown pages). doi: 10.3390/s25051552