Human Activity Recognition

Human activity recognition (HAR) aims to automatically identify and classify human actions from various data sources, such as video, sensor data (e.g., accelerometers, inertial measurement units), and even WiFi signals. Current research emphasizes developing robust and efficient HAR systems using deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs, particularly LSTMs and GRUs), and transformers, often incorporating multimodal fusion techniques to leverage complementary information from different data modalities. This field is significant for its potential applications in healthcare monitoring, smart homes, human-robot interaction, and security, driving advancements in both machine learning algorithms and sensor technologies.