Human Activity Recognition (HAR) Using Deep Learning: Review, Methodologies, Progress and Future Research Directions

Human activity recognition is essential in many domains, including the medical and smart home sectors. Using deep learning, we conduct a comprehensive survey of current state and future directions in human activity recognition (HAR). Key contributions of deep learning to the advancement of HAR, including sensor and video modalities, are the focus of this review. A wide range of databases and performance metrics used in the implementation of HAR methodologies are described in depth. This paper explores the wide range of HAR's potential uses, from healthcare, emotion calculation and assisted living to security and education. The paper provides an in-depth analysis of the most significant works that employ deep learning techniques for a variety of HAR downstream tasks across both the video and sensor domains including the most recent advances. Finally, it addresses problems and limitations in the current state of HAR research and proposes future research avenues for advancing the field.

Automated summary

This paper conducts a comprehensive survey and analysis of the application of deep learning in human activity recognition. The focus is on the key contributions of deep learning and the description of various databases and performance metrics used in HAR methodologies. The paper explores the potential uses of HAR in domains such as healthcare, emotion calculation, assisted living, security, and education.