利用毫米波雷达和深度学习进行活动识别：祈祷跟踪案例研究
Activity Recognition Using mm-Wave Radar and Deep Learning: Prayer Tracker Case Study
AI总结：
本文提出基于毫米波雷达的隐私保护活动识别框架，通过点云信息与深度学习结合，实现高精度的祈祷跟踪与定位，验证了ResNet在未知数据上的95.4%准确率。
摘要：
隐私问题近年来备受关注，许多现实应用需要敏感数据，如监控或跟踪辅助系统。为应对这些挑战，我们提出基于毫米波雷达技术的框架，满足隐私要求并提供可靠的位置跟踪、序列跟踪和用户反馈能力。虽然雷达技术用于监控的应用日益增多，但至今尚无研究将其应用于祈祷跟踪和辅助系统。此外，缺乏涵盖此类系统实施所有方面的综合研究。所提方法提供了一种灵活的解决方案，可应用于广泛场景。我们不使用原始I-Q数据，而是利用传统频率调制连续波雷达处理链生成的点云信息进行分类挑战。该信息包含相应距离、反射幅度、多普勒和角度值。我们开发并比较了不同的机器学习分类算法以确定最有效的。我们的发现表明，卷积神经网络ResNet在未知数据上的准确率高达95.4%。开发系统的演示视频可在以下链接查看：https://youtu.be/PnpGQZWqCr4。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
