Wearable Sensing
Overview
Wearable sensing studies how devices such as watches, inertial sensors, physiological monitors, smart textiles, and glasses can measure movement, body signals, and interactions in daily life. Researchers use signal processing and machine learning—from handcrafted features and convolutional or recurrent networks to self-supervised and foundation models—to recognize activities, estimate health or movement states, and support assistive control. Evaluation commonly uses benchmarks such as PAMAP2, WISDM, WESAD, and SisFall, with subject-independent tests increasingly important for assessing performance on new people. A central challenge is making systems reliable across users, sensor placements, missing or noisy data, and real-world conditions while meeting privacy, battery, and on-device computing limits.
Latest papers
130 papers24 papers in the last four weeks, up 26% on the four weeks before. 0.1% of all new papers.