Maternal health includes health during pregnancy and childbirth. Each stage during pregnancy should be a positive experience, ensuring that women and their babies reach their full potential in health and well-being. However, this cannot always be achieved. According to UNFPA (United Nations Population Fund), approximately 800 women die every day from avoidable causes related to pregnancy and childbirth, so it is important to monitor mother and fetal health throughout the pregnancy. Many wearable sensors and devices have been developed to monitor both fetal and the mother’s health and physical activities and reduce risk during pregnancy. Some wearables monitor fetal ECG or heart rate and movement, while others focus on the mother’s health and physical activities. This study presents a systematic review of these analyses. Twelve scientific articles were reviewed to address three research questions oriented to (1) sensors and method of data acquisition; (2) processing methods of the acquired data; and (3) detection of the activities or movements of the fetus or the mother. Based on these findings, we discuss how sensors can help effectively monitor maternal and fetal health during pregnancy. We have observed that most of the wearable sensors were used in a controlled environment. These sensors need more testing in free-living conditions and to be employed for continuous monitoring before being recommended for mass implementation.

EEG (electroencephalogram) signals could be used reliably to extract critical information regarding ADHD (attention deficit hyperactivity disorder), a childhood neurodevelopmental disorder. The early detection of ADHD is important to lessen the development of this disorder and reduce its long-term impact. This study aimed to develop a computer algorithm to identify children with ADHD automatically from the characteristic brain waves. An EEG machine learning pipeline is presented here, including signal preprocessing and data preparation steps, with thorough explanations and rationale. A large public dataset of 120 children was selected, containing large variability and minimal measurement bias in data collection and reproducible child-friendly visual attentional tasks. Unlike other studies, EEG linear features were extracted to train a Gaussian SVM-based model from only the first four sub-bands of EEG. This eliminates signals more than 30 Hz, thus reducing the computational load for model training while keeping mean accuracy of ~94%. We also performed rigorous validation (obtained 93.2% and 94.2% accuracy, respectively, for holdout and 10-fold cross-validation) to ensure that the developed model is minimally impacted by bias and overfitting that commonly appear in the ML pipeline. These performance metrics indicate the ability to automatically identify children with ADHD from a local clinical setting and provide a baseline for further clinical evaluation and timely therapeutic attempts.

A crucial component for developing an automated speaker or speech recognition system is voice preprocessing, which filters unwanted noise and detects the speech part. This study aimed to develop a computerized model to remove background noise and improve signal quality for future applications. This model was developed on a large longitudinal dataset containing varying real-world noise and validated on both a public dataset and a locally collected test dataset in different environments. An overall voice pre-processing pipeline is presented in this study, including denoising, segmentation, feature extraction, and ease of storage. The backbone of the denoising model is a Kalman filter, where the parameters were obtained from a grid search method; the signal-to-noise ratio (SNR) was used as the performance metric. Also, the segmentation was done to remove the pauses from the audio signal before the feature extraction. Finally, the data was stored as a template with the most common features. The SNR result suggested that the Kalman filter-based proposed method performed successfully across diverse datasets. Thus, this model provides a robust and adaptable solution for real-world scenarios and also ensures the data storing quality for future applications.