Physiological Change

Physiological change research focuses on understanding and modeling how internal bodily states and external stimuli interact, impacting behavior and health. Current research employs diverse methods, including deep learning architectures (like Bi-LSTMs and CNNs) applied to multimodal data (physiological signals, images, and behavioral data) to detect and predict physiological changes related to health conditions (e.g., cardiorespiratory disease, anxiety, depression) and human-robot interaction. This work is significant for advancing early disease detection, improving healthcare monitoring, and creating more robust and human-centered technologies, particularly in areas like autonomous vehicles and assistive robotics.