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.

人类活动识别（HAR）是基于从各种传感器（例如，惯性传感器，生理传感器，位置传感器，照相机，时间以及更多环境传感器）获得的流数据自动识别人类活动的过程。事实证明，HAR在各种研究领域中都是有益的，尤其是在医疗保健，老年护理，环境生活，个人护理，社会科学，康复工程和许多其他领域。由于计算能力的最新发展，基于深度学习的算法已成为用于识别和解决HAR问题的算法的最有效和最有效的选择。在这项调查中，我们根据各种因素和措施对最近的研究工作进行了分类，以研究使用深度学习算法的HAR的最新趋势。文章从各个方面进行了分析，