Advancing human activity recognition with quaternion-based recurrent neural networks
S. Gayathri Devi,
Ratnala Venkata Siva Harish,
N. Nalini,
K. D. V. Prasad,
N. Nagabhooshanam
Affiliations
S. Gayathri Devi
School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India
Ratnala Venkata Siva Harish
Department of Electronics & Communications Engineering, St.Ann’s College of Engineering and Technology, Chirala, India
N. Nalini
Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India
K. D. V. Prasad
Department of Research, Symbiosis Institute of Business Management, Hyderabad, India
N. Nagabhooshanam
Department of Mechanical Engineering, Aditya University, Surampalem, Peddapuram, Andhra Pradesh, India
Human activity recognition (HAR) stands as a vital nexus in the synthesis of healthcare, sports analytics, and human–computer interaction. This research introduces a groundbreaking approach to HAR by amalgamating the multidimensional strengths of quaternion algebra with the temporal sensitivity of recurrent neural networks, birthing the “Human Activity Recognition Utilizing Quaternion-Based Recurrent Neural Networks (QRNNs)” model. This innovative fusion targets the inherent challenges of high-dimensionality and temporal sequencing posed by wearable sensor data. The proposed QRNN model showcased promising results, achieving an accuracy rate of 98.46% after 20 training epochs, marking a significant advancement in HAR's state-of-the-art. The experimental results showcase the effectiveness and improved accuracy of HAR models with the utilization of quaternion algebra. Overall, this study offers an innovatiove way for wearable technology and human−machine synergy by ensuring an advanced mathematical and statistical framework for perceptual human activity identification.
