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.

Fetal movement monitoring (FMM) is crucial for assessing fetal well-being, traditionally relying on clinical assessments or maternal perception, each with inherent limitations. This study presents a novel lightweight deep learning framework for real-time FMM on edge devices. Data were collected from 120 participants using a wearable device equipped with an inertial measurement unit, which captured both accelerometer and gyroscope data, coupled with a rigorous two-stage labeling protocol integrating maternal perception and ultrasound validation. We addressed class imbalance using virtual-rotation-based augmentation and adaptive clustering-based undersampling. The data were transformed into spectrograms using the Short-Time Fourier Transform, serving as input for deep learning models. To ensure model efficiency suitable for resource-constrained microcontrollers, we employed knowledge distillation, transferring knowledge from larger, high-performing teacher models to compact student architectures. Post-training integer quantization further optimized the models, reducing the memory footprint by 74.8%. The final optimized model achieved a sensitivity (SEN) of 90.05%, a precision (PRE) of 87.29%, and an F1-score (F1) of 88.64%. Practical energy assessments showed continuous operation capability for approximately 25 h on a single battery charge. Our approach offers a practical framework adaptable to other medical monitoring tasks on edge devices, paving the way for improved prenatal care, especially in resource-limited settings.

Blood pressure (BP) monitoring is essential for cardiovascular health management, yet traditional cuff-based methods face limitations including patient discomfort and inapplicability for certain populations. This study presents a deep learning framework for cuffless BP estimation using phonocardiogram (PCG) signals. The proposed model integrates convolutional neural networks (CNNs) with Squeeze-and-Excitation (SE) blocks and demographic information to enhance prediction accuracy. Mel-Frequency Cepstral Coefficients (MFCCs), along with their delta and delta–delta coefficients, were employed to capture comprehensive acoustic characteristics of heart sounds. The results demonstrated that the proposed model achieved high predictive accuracy and strong consistency with reference BP measurements. Component analysis confirmed that the inclusion of SE blocks provided substantial performance gains, while demographic information further improved prediction stability. Clinical validation also verified that the model maintained close agreement with true BP values across the tested population, showing significant improvement over the baseline CNN implementation. These findings suggest potential for accessible, non-invasive BP monitoring systems suitable for continuous health tracking.

This dataset comprises phonocardiogram (PCG) signals, blood pressure measurements, and additional demographic collected from 78 participants under controlled conditions. The data were recorded using medical-grade digital stethoscope and automated blood pressure monitor to ensure precision and consistency. PCG signals were captured at the Pulmonic area (P), located at the second left intercostal space near the sternum, and segmented into 10-s intervals with 50 % overlap, enhancing signal processing and analysis capabilities. Blood pressure measurements include systolic and diastolic values, along with demographic data such as age, weight, height, and gender, providing comprehensive physiological data. The dataset is structured to support various applications, particularly the development of artificial intelligence models for non-invasive blood pressure prediction, leading to early hypertension screening and prevention. This resource enables researchers to explore the relationships between PCG, blood pressure values, and physical characteristics, facilitating advancements in blood pressure health monitoring and diagnostics.