RAM: Radar-based activity monitor
RAM: Radar-based activity monitor
复制标题
DOI：
10.1109/infocom.2016.7524361
复制
发表时间：
2016-04
期刊：
影响因子：
--
通讯作者：
Md Abdullah Al Hafiz Khan;Ruthvik Kukkapalli;Piyush Waradpande;Sekar Kulandaivel;Nilanjan Banerjee;Nirmalya Roy-N
中科院分区：
文献类型：
--
作者：
Md Abdullah Al Hafiz Khan;Ruthvik Kukkapalli;Piyush Waradpande;Sekar Kulandaivel;Nilanjan Banerjee;Nirmalya Roy-N
活动认可在各种人类环境中的应用，例如智能家庭健康监测，绿色建筑能源和占用管理，智能运输和参与性感应。虽然细粒度的活动识别系统和方法有助于实现多种新颖的应用程序，但通过非侵入性环境传感器系统发现它们构成挑战性设计，以及数据处理，采矿和活动识别问题。在本文中，我们开发了低成本的异质雷达活动监测（RAM）系统，用于识别细粒活性。我们利用了使用一系列异质微多普勒雷达来识别低级活动的可行性。我们原型是一个短距离和远程雷达系统，并评估了使用该系统进行细粒活性识别的可行性。在我们的评估中，使用真实的数据跟踪，我们表明我们的系统可以以92.84％的精度检测细粒度的用户活动。
Activity recognition has applications in a variety of human-in-the-loop settings such as smart home health monitoring, green building energy and occupancy management, intelligent transportation, and participatory sensing. While fine-grained activity recognition systems and approaches help enable a multitude of novel applications, discovering them with non-intrusive ambient sensor systems pose challenging design, as well as data processing, mining, and activity recognition issues. In this paper, we develop a low-cost heterogeneous Radar based Activity Monitoring (RAM) system for recognizing fine-grained activities. We exploit the feasibility of using an array of heterogeneous micro-doppler radars to recognize low-level activities. We prototype a short-range and a long-range radar system and evaluate the feasibility of using the system for fine-grained activity recognition. In our evaluation, using real data traces, we show that our system can detect fine-grained user activities with 92.84% accuracy.