Hypercomplex Neural Network Based Elderly Activity Recognition For Intelligent Healthcare Systems

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This system recognizes elderly activities in real-time using hypercomplex neural networks and PoseNet, suggesting improved monitoring capabilities.

The system accurately identifies five elderly activities: walking, sitting, running, fighting, and sleeping.
Using hypercomplex neural networks enhances spatial-temporal learning, leading to improved classification accuracy and robustness.
Real-time monitoring is enabled through video analysis, allowing for quick detection of abnormal behaviors.
Experimental evaluation shows that HCNN outperforms traditional models in recognition accuracy and response time.

Rashmi et al. (2025) studied this question.

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View Full Paper

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Authors

This system recognizes elderly activities in real-time using hypercomplex neural networks and PoseNet, suggesting improved monitoring capabilities.

The system accurately identifies five elderly activities: walking, sitting, running, fighting, and sleeping.
Using hypercomplex neural networks enhances spatial-temporal learning, leading to improved classification accuracy and robustness.
Real-time monitoring is enabled through video analysis, allowing for quick detection of abnormal behaviors.
Experimental evaluation shows that HCNN outperforms traditional models in recognition accuracy and response time.

Rashmi et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark