Evaluating the Impact of Noisy Point Clouds on Wireless Gesture Recognition Systems
Evaluating the Impact of Noisy Point Clouds on Wireless Gesture Recognition Systems
复制标题
DOI：
10.1145/3565287.3617626
复制
发表时间：
2023-10
期刊：
影响因子：
--
通讯作者：
Paul Jiang;Ellie Fassman;Amit Singha;Yimin Chen;Tao Li
中科院分区：
文献类型：
--
作者：
Paul Jiang;Ellie Fassman;Amit Singha;Yimin Chen;Tao Li
通过无线传感器收集的点云数据在其关键应用中获得了越来越多的关注，包括汽车雷达、安全系统，尤其是手势识别。它为人机交互提供了一种非侵入性和鲁棒性的方法。然而，它对实时数据的依赖使得弹性成为最重要的问题，对这些传感器的攻击或缺陷可能会产生灾难性的影响。从实时欺骗到数据中毒攻击，甚至只是错误的数据，基于2D和3D点云机器学习模型的系统可能非常脆弱。尽管如此，有一些研究优先评估这些系统的鲁棒性超过嘈杂的时间敏感的点云。这项研究提出了一个深入的检查噪声数据的影响，用于训练各种毫米波的手势识别系统。噪声点云可能在训练阶段引入，其中不完美的数据被馈送到模型，导致模型错误分类测试时的样本并降低其整体准确性。我们阶段和评估四个不同的，简单的数据噪声场景的影响，以观察这些系统中的潜在漏洞。我们的研究结果揭示了Transformer、长短期记忆和卷积模型各自的可扩展性和可扩展性，强调了不仅要将时间和研究投入到无线手势识别的创新中，而且要优化这些系统以主动防止不良影响的重要性。
Point cloud data gathered through wireless sensors has garnered increasing attention for its critical applications, including automotive radars, security systems, and notably, gesture recognition. It provides a non-intrusive and robust approach towards humancomputer interactions. However, its reliance on real-time data makes resilience of paramount concern and attacks on or imperfections with these sensors can have catastrophic effects. From real-time spoofing to data poisoning attacks or even just faulty data, systems based on 2D and 3D point cloud machine learning models can be extremely vulnerable. Despite this, there exist few studies prioritizing evaluations on the robustness of these systems over noisy time-sensitive point clouds. This study presents an in-depth examination on the effects of noisy data being used in training various millimeter wave based gesture recognition systems. Noisy point clouds can be introduced during the training stage where imperfect data is fed to a model, causing the model to misclassify test-time samples and lowering its overall accuracy. We stage and evaluate the impact of four different, simple data noising scenarios to observe potential vulnerabilities within these systems. Our findings reveal the respective susceptibilities and resiliencies of transformer, long-short term memory, and convolutional models, highlighting the importance to not only dedicate time and research towards innovations in wireless gesture recognition, but also towards optimizing these systems in order to proactively prevent undesirable effects.