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A Survey of Deep Learning Based Models for Human Activity Recognition

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Abstract

Human Activity Recognition (HAR) is a process of recognizing human activities automatically based on streaming data obtained from various sensors, such as, inertial sensors, physiological sensors, location sensors, camera, time and many more environmental sensors. HAR has proven to be beneficial in various fields of study especially in healthcare, aged-care, ambient living, personal care, social science, rehabilitation engineering and many other domains. Due to the recent advancements in computing power, deep learning-based algorithms have become most effective and efficient choice of algorithms for recognizing and solving HAR problems. In this survey, we categorize recent research work with respect to various factors and measures to investigate the recent trends in HAR using deep learning algorithms. The articles are analyzed in various aspects, such as those related to HAR, time series analysis, machine learning models, methods of dataset creation, and use of various other new trends such as transfer learning, active learning, etc.

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  1. Telecommunication Research Lab, Department of Computer Science, Institute of Business Administration, Garden/ Kayani Shaheed Road, Karachi, 74400, Pakistan

    Nida Saddaf Khan & Muhammad Sayeed Ghani

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Khan, N.S., Ghani, M.S. A Survey of Deep Learning Based Models for Human Activity Recognition. Wireless Pers Commun 120, 1593–1635 (2021). https://doi.org/10.1007/s11277-021-08525-w

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