Wearable Data Collection System

Wearable data collection systems aim to continuously monitor physiological and behavioral data for various applications, from sleep analysis and activity recognition to cardiovascular health monitoring and even e-scooter safety research. Current research emphasizes developing efficient and accurate machine learning models, including convolutional neural networks (CNNs), transformers, and spiking neural networks (SNNs), often tailored for low-power devices and addressing challenges like data scarcity, missing data, and algorithmic bias. These advancements are significant for improving healthcare diagnostics, personalized medicine, and understanding human behavior in naturalistic settings, while also driving innovation in energy-efficient hardware and software for wearable technology.