Wearable Sensor

Wearable sensors are revolutionizing data collection for human activity recognition (HAR) and health monitoring, aiming to provide objective, real-time insights into various physiological and behavioral aspects. Current research emphasizes developing robust machine learning models, including deep learning architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer networks, often incorporating multimodal data fusion (e.g., combining IMU data with video or other sensor modalities) and techniques like self-supervised learning to address data scarcity and improve model generalizability. This field holds significant promise for advancing healthcare, particularly in early disease detection, personalized medicine, and remote patient monitoring, as well as improving workplace safety and optimizing athletic performance.