HAR Datasets

Human Activity Recognition (HAR) datasets are crucial for developing algorithms that automatically identify human actions from various sensor data, such as wearable IMUs or WiFi signals. Current research focuses on improving accuracy and efficiency, particularly through deep learning models like temporal convolutional networks and hypergraph learning frameworks, often addressing challenges like limited labeled data via techniques such as contrastive learning and weak supervision. These advancements are driving progress in applications ranging from healthcare monitoring and security to assistive technologies, with a growing emphasis on real-time performance and data efficiency for deployment on resource-constrained devices.