Human Activity Recognition: Evolution, Techniques, Applications, and Future Challenges

Human Activity Recognition (HAR) is becoming a hot topic for research at the intersection of artificial intelligence, computer vision, and sensor analysis. It analyses and classifies different human behaviors from diverse data inputs in an automated way. This paper provides a thorough study of Human Activity Recognition (HAR), following its development from early machine learning models which were handcrafted feature-based systems to modern deep learning models which process multimodal inputs. A number of methods such as wearable sensor-based techniques, vision-based approaches, radar and non-contact sensing, transfer learning techniques and domain adaptation are surveyed. An overview of real-world applications which include healthcare, sports, surveillance, and human-computer interaction is provided. These real-world applications reveal HAR’s visible impact on society. Lastly, we discuss important future challenges such as robustness and generalizability, explainability and interpretability, multi-activity and complex behavior recognition, privacy concerns, and real-world & open-world recognition. This overview focuses on the current advancements and recognizes open research directions mandatory for reliable, interpretable, and ethically responsible HAR systems