Activity Recognition Using mm-Wave Radar and Deep Learning: Prayer Tracker Case Study

TL;DR

This paper presents a privacy-preserving activity recognition system using mm-wave radar and deep learning, specifically for prayer tracking, achieving high accuracy with CNNs like ResNet.

Contribution

It introduces a novel application of radar-based activity recognition for prayer tracking, utilizing point cloud data and deep learning, filling a research gap.

Findings

ResNet CNN achieves up to 95.4% accuracy on unknown data.

Radar-based classification outperforms traditional raw data approaches.

The system demonstrates effective privacy-preserving activity recognition.

Abstract

The issue of privacy has gained significant attention in recent times. Many real-world applications increasingly require the use of sensitive data, such as in surveillance or tracking and assistance systems. To address these concerns, we propose a framework based on mm-wave radar technology that not only meets privacy requirements but also provides the necessary capabilities for these systems, including reliable current position tracking, sequence tracking, and feedback to the user. While the use of radar technology for surveillance purposes is gaining momentum, there has been no research to date on its application for prayer tracking and assistance systems. Furthermore, there is a lack of comprehensive research that covers all aspects of implementing such a system. Proposed approach offers a versatile solution that can be applied to a broad range of scenarios. Instead of utilizing raw I-Q data, we addressed the challenge of classification based on point cloud information generated by the conventional processing chain of the frequency-modulated continuous wave radar. This information contains corresponding range, reflection amplitude, Doppler and angular values. We have developed and compared different machine-learning classification algorithms to identify the most effective one. Our findings reveal that the convolutional neural network ResNet achieves the best results, with accuracy rates reaching up to 95.4 percent when applied to unknown data. The demonstration video of the developed system can be viewed at the following link: https://youtu.be/PnpGQZWqCr4.