IoT powered RNN for improved human activity recognition with enhanced localization and classification

Human activity recognition (HAR) and localization are green research areas of the modern era that are being propped up by smart devices. But the data acquired from the sensors embedded in smart devices, contain plenty of noise that makes it indispensable to design robust systems for HAR and localization. In this article, a system is presented endowed with multiple algorithms that make it impervious to signal noise and efficient to recognize human activities and their respective locations. The system begins by denoising the input signal using a Chebyshev type-I filter and then performs windowing. Then, working in parallel branches, respective features are extracted for the performed activity and human's location. The Boruta algorithm is then implemented to select the most informative features among the extracted ones. The data is optimized using a particle swarm optimization (PSO) algorithm, and two recurrent neural networks (RNN) are trained in parallel, one for HAR and other for localization. The system is comprehensively evaluated using two publicly available benchmark datasets i.e., the Extrasensory dataset and the Sussex Huawei locomotion (SHL) dataset. The evaluation results advocate the system's exceptional performance as it outperformed the state-of-the-art methods by scoring respective accuracies of 89.25% and 90.50% over the former dataset and 95.75% and 91.50% over the later one for HAR and localization.