Transfer learning and its extensive appositeness in human activity recognition: A survey

In this competitive world, the supervision and monitoring of human resources are primary and necessary tasks to drive context-aware applications. Advancement in sensor and computational technology has cleared the path for automatic human activity recognition (HAR). First, machine learning and later deep learning play a cardinal role in this automation process. Classical machine learning approaches follow the hypothesis that the training, validation, and testing data belong to the same domain, where data distribution characteristics and the input feature space are alike. However, during real-time HAR, the above hypothesis does not always true. Transfer learning helps in an extended manner to transfer the required knowledge among heterogeneous data of various activities. To display the hierarchical advancements in transfer learning-enhanced HAR, we have shortlisted the 150 most influential works and articles from 2014–2021 based on their contribution, citation score, and year of publication. These selected articles are collected from IEEE Xplore, Web of Science, and Google Scholar digital libraries. We have also analyzed the statistical research interest related to this topic to substantiate the significance of our survey. We have found a significant growth of 10% in research publications related to this domain every year. Our survey provides a unique classification model to delineate the diversity in transfer learning-based HAR. This survey delves into the world of HAR datasets, exploring their types, specifications, advantages, and limitations. We also examine the steps involved in HAR, including the various transfer learning techniques and performance metrics, as well as the computational complexity associated with these methods. Additionally, we identify the challenges and gaps in HAR related to transfer learning and provide insights into future directions for researchers in this field. Based on the survey findings, researchers prefer the inductive transfer method, feature learning transfer mode, and cross-action transfer domain more over others due to their superior performance, with respective popularity scores of 55%, 40.8%, and 50.2%. This review aims to equip readers with a comprehensive understanding of HAR and transfer learning mechanisms, while also highlighting areas that require further research.