Advancing human activity recognition with quaternion-based recurrent neural networks
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 r...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-07-01
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| Series: | Automatika |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2025.2480419 |
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| Summary: | 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. |
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| ISSN: | 0005-1144 1848-3380 |