Activity Recognition

Activity recognition (AR) aims to automatically identify and classify human actions from various data sources, such as wearable sensors, cameras, and microphones. Current research heavily utilizes deep learning, focusing on architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and graph convolutional networks (GCNs), often incorporating multimodal data fusion and techniques like contrastive learning and domain adaptation to improve robustness and accuracy. The field is significant for its potential applications in healthcare monitoring, human-computer interaction, and smart environments, driving advancements in both model explainability and efficient on-device processing.