Wearable sensors have the intriguing potential to continuously evaluate human physiological characteristics in real-time without being obtrusive. This thesis aims to incorporate physiological sensors data to investigate the Media Perception and Activity Recognition. Our primary research goals include (a) neural encoding-based psycho-acoustic attribute analysis for data sonification, (b) empirical evidence for perceptual subjectivity in neural encoding during human-media interactions, the impact of incorporating behavioral ratings, and (c) the efficacy of attention-based transformer models on physiological data on human activity recognition problems.