A multimodal physiological dataset for non-invasive blood glucose estimation
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Data"
DOI: 10.1038/s41597-025-06090-6
Abstract: Diabetes is a major health challenge that affects millions of people worldwide. Managing diabetes effectively requires monitoring blood glucose levels continuously, typically through invasive sensing devices such as continuous glucose monitors (CGMs). Blood glucose excursions… read more here.
Keywords: blood glucose; physiological dataset; multimodal physiological; blood ... See more keywords
Intelligent emotion recognition in product design using multimodal physiological signals and machine learning
Sign Up to like & getrecommendations! Published in 2024 at "Journal of Engineering Design"
DOI: 10.1080/09544828.2024.2362589
Abstract: Identifying emotional responses in products is essential for product design and user research. Traditional methods, such as interviews and surveys, for gathering product experience data are time-consuming and resource-intensive, and often fail to capture users’… read more here.
Keywords: product; machine learning; multimodal physiological; physiological signals ... See more keywords
Deep Learning-Based Automated Emotion Recognition Using Multimodal Physiological Signals and Time-Frequency Methods
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2024.3420349
Abstract: Accurate prediction and recognition of human emotions are crucial for effective human-computer interfaces. An automated emotion recognition (AER) method is highly desirable, and multimodal approaches have gained scientific attention due to their ability to leverage… read more here.
Keywords: emotion; time; recognition; multimodal physiological ... See more keywords
PhysioFormer: Integrating multimodal physiological signals and symbolic regression for explainable affective state prediction
Sign Up to like & getrecommendations! Published in 2024 at "PLOS One"
DOI: 10.1371/journal.pone.0335221
Abstract: As affective computing becomes increasingly crucial in health monitoring and psychological intervention, accurately identifying affective states is a key challenge. While traditional machine learning models have achieved some success in affective computation, their ability to… read more here.
Keywords: affective computing; multimodal physiological; model; symbolic regression ... See more keywords