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Lorin Werthen-Brabants¹, Geethika Bhavanasi², Ivo Couckuyt²

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Summary

This summary is machine-generated.

This study introduces a dual-processing framework for radar-based human activity recognition. It uses on-premise Recurrent Neural Networks (RNNs) for initial detection and cloud-based RNNs for enhanced accuracy, improving privacy-preserving monitoring.

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Area of Science:

Computer Science
Artificial Intelligence
Signal Processing

Background:

Radar systems offer privacy-preserving human activity recognition.
Deep Neural Networks (DNNs) process complex radar data but struggle with large-scale deployment.
Scaling DNNs for multi-room monitoring presents significant computational challenges.

Purpose of the Study:

To develop a scalable framework for radar-based human activity recognition.
To enhance the accuracy and efficiency of privacy-preserving activity monitoring systems.
To enable timely alerts for critical events like falls.

Main Methods:

A two-part processing framework utilizing Recurrent Neural Networks (RNNs).
On-premise RNNs perform initial data processing and prediction for time-sensitive use-cases.
Off-premise machines conduct backward RNN calculations to refine predictions and improve accuracy.

Main Results:

The framework enables real-time notifications for critical events via on-premise processing.
Off-premise processing successfully corrects missed or misclassified activities from the on-premise device.
The system demonstrates improved accuracy in human activity recognition using radar data.

Conclusions:

The proposed framework effectively balances real-time processing needs with enhanced accuracy for radar-based activity recognition.
This approach addresses the scalability limitations of traditional DNNs in multi-sensor environments.
It offers a robust solution for privacy-preserving monitoring in applications like healthcare.