Deep Learning and Recurrent Signature Based Classification for Sensor-Based HAR: Addressing Explainability and Complexity in 5G Networks

When it comes to clinical applications, sensor-based human activity recognition (HAR) is invaluable, and numerous machine learning algorithms have effectively used to obtain excellent presentation. Using a variety of on-body sensors, these systems attempt to ascertain the subject's status relative to their immediate surroundings. There was a time when feature extraction was done by hand, but now more and more people are using Artificial Neural Networks (ANNs). A number of innovative approaches to HAR have surfaced since the advent of deep learning. Problems arise, however, for sensor-based HAR classification algorithms in today's communication networks. Among these, you can find solutions to problems like deal with complicated and large-scale data signals, extract characteristics from complicated datasets, and meet explainability standards. For complicated 5G networks, these difficulties become even more apparent. In particular, explainability is now critical for the broad use of sensor-based HAR in 5G networks and beyond. The research suggests a classification approach based on path signatures, recurrent signature (ReS), to address these issues. This cutting-edge model employs deep-learning (DL) approaches to circumvent the tedious feature selection challenge. Furthermore, the study investigates how to improve the ReS model's classification accuracy by using graph-based optimisation methods. To test how well the suggested framework worked, to dug deep into the publicly available dataset, which included a separate set of tasks. The paper's empirical results on AReM datasets achieved an average accuracy of 96%.