RecurrentHAR: A Novel Transfer Learning-Based Deep Learning Model for Sequential, Complex, Concurrent, Interleaved, and Heterogeneous Type Human Activity Recognition

Smartphone sensor-based Human Activity Recognition (HAR) is a significant facilitator for many real-world applications such as smart homes, personal healthcare, and illness detection because it has the potential to discover activity patterns in daily life. While solutions for detecting sequential activities like walking or running are already matured, however recognizing additional forms of complex, concurrent, interleaved, and heterogeneous human activities remains a research challenge. Furthermore, several solutions have been developed for specific types of activity complexity. According to the best of our knowledge, there is no common solution for detecting all types of action-based activities. In this paper, we address the problem of recognizing all types of action-based human activities i.e. sequential, complex, concurrent, interleaved, and heterogeneous activities. The intention behind this paper is to present a generic methodology for HAR by leveraging transfer learning and evaluating the performance on a plethora of HAR datasets. In this regard, we have proposed a model called RecurrentHAR, which stands for Recurrent layers for HAR, a novel adversarial knowledge transfer approach that uses the Gated Recurrent Units (GRUs) architecture for smartphone sensor-based HAR. The performance of the proposed model is evaluated through extensive experiments using three public datasets namely WISDM (sequential activities), PAMAP2 (complex, concurrent, and interleaved activities), and KU-HAR (Heterogeneous activities), and the RecurrentHAR model has outperformed compared with other state-of-the-art approaches. The proposed model achieved 96.26%, 94.77%, and 98.98% of F1-score for WISDM, PAMAP2, and KU-HAR datasets respectively. The experimental results provide insight into the applicability of the proposed model and future research possibilities.