Deep Learning and AI for Human Activity Recognition

Abstract

Human Activity Recognition (HAR) is a key AI field that uses sensor, video, wearable, and environmental data to identify and predict human activities. Advances in deep learning, including CNNs, RNNs, LSTMs, Transformers, and hybrid models, have improved recognition accuracy through automatic feature extraction. The growth of wearable devices, smartphones, IoT sensors, and computer vision systems has expanded HAR applications in healthcare, smart homes, industrial safety, sports analytics, surveillance, and human-computer interaction. Despite these advances, challenges such as data heterogeneity, privacy, explainability, computational complexity, and real-time deployment remain. This chapter reviews HAR concepts, data collection methods, deep learning techniques, applications, challenges, and future directions, while highlighting explainable and trustworthy AI frameworks for developing accurate, scalable, and human-centered HAR systems.