Year: 2025
Type: article
Abstract: 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.
Source: Automatika
Authors: S. Gayathri Devi, Ratnala Venkata Siva Harish, N. Nalini, K. D. V. Prasad, N. Nagabhooshanam
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $850
OpenAlex ID: W4409656791