FuSE-MET addresses critical challenges in deploying human activity recognition (HAR) systems in uncontrolled environments by effectively managing noisy labels, sparse data, and undefined activity vocabularies. By integrating BERT-based word embeddings with domain-specific knowledge (i.e., MET values), FuSE-MET optimizes label merging, reducing label complexity and improving classification accuracy. Our approach outperforms the state-of-the-art techniques, including ChatGPT-4, by balancing semantic meaning and physical intensity.

Electronic fetal monitoring (EFM) is designed for the early detection of fetal risks and the prevention of serious neurological impairment but suffers from high false positive rates. The Fetal Reserve Index (FRI) is an expert-based system that combines EFM with maternal, obstetrical, and fetal risk factors and displays superior performance in risk detection than EFM alone. Towards translating the FRI into an automated risk prediction system that can make recommendations to clinicians in real-time, we here develop machine learning classifiers that calculate feature importance based on historical data from labor cases and predict the risk of developing neurological impairment. We train random forest and multilayer perceptron (MLP) models to classify abnormal and normal delivery cases and to assess the model performance using a dataset of 1462 labor cases. The random forest classifier achieves a macro average f-1 score of 0.82 with an abnormal case recall of 0.59. Alternatively, MLP classifiers provide higher abnormal case recall at a cost of lower accuracy and macro average f-1 score. Future work will aim to optimize weightings and trade-offs of statistical performance to achieve further improvements for clinical practice.

Physical activity is a cornerstone of chronic conditions and one of the most critical factors in reducing the risks of cardiovascular diseases, the leading cause of death in the United States. App-based lifestyle interventions have been utilized to promote physical activity in people with or at risk for chronic conditions. However, these mHealth tools have remained largely static and do not adapt to the changing behavior of the user. In a step toward designing adaptive interventions, we propose BeWell24Plus, a framework for monitoring activity and user engagement and developing computational models for outcome prediction and intervention design. In particular, we focus on devising algorithms that combine data about physical activity and engagement with the app to predict future physical activity performance. Knowing in advance how active a person is going to be in the next day can help with designing adaptive interventions that help individuals achieve their physical activity goals. Our technique combines the recent history of a person’s physical activity with app engagement metrics such as when, how often, and for how long the app was used to forecast the near future’s activity. We formulate the problem of multimodal activity forecasting and propose an LSTM-based realization of our proposed model architecture, which estimates physical activity outcomes in advance by examining the history of app usage and physical activity of the user. We demonstrate the effectiveness of our forecasting approach using data collected with 58 prediabetic people in a 9-month user study. We show that our multimodal forecasting approach outperforms single-modality forecasting by 2.2% to 11.1% in mean-absolute-error.