Simple to Complex, Single to Concurrent Sensor-Based Human Activity Recognition: Perception and Open Challenges

Human activity recognition (HAR) has attracted considerable research attention due to its essential role in various domains, ranging from healthcare to security, safety, and entertainment. HAR has undergone a paradigm shift from simple single-task detection to the more complex task of identifying multiple simultaneous activities as technology advances. A wide range of methods, including sensing modalities, identification algorithms, a specified list of recognized activities, and end application goals, have been used in the literature to investigate activities carried out by single individuals. However, there appears to be a research gap when it comes to scenarios in which several people engage in individual or concurrent activities. Although numerous reviews and surveys have previously addressed HAR, with the continual expansion of literature, there is a necessity for an updated assessment of the status of HAR literature. The system encompasses various operational modules, including data acquisition, noise elimination, and distortion reduction through preprocessing, followed by feature extraction, feature selection, and classification. Recent advancements have introduced state-of-the-art techniques for feature extraction and selection, which are categorized using traditional machine learning classifiers. However, a notable limitation is observed, as many of these techniques rely on basic feature extraction processes, hindering their capability to recognize complex activities. This article reviews 190 articles with respect to data collection, segmentation, feature extraction, energy efficiency, personalized models, and machine learning (ML) and deep learning (DL) approaches for sensor-based HAR. Open challenges and future enhancements of HAR are also discussed in this article.