Radar-Based Human-Motion Recognition With Deep Learning Promising applications for indoor monitoring
Radar-Based Human-Motion Recognition With Deep Learning Promising applications for indoor monitoring
复制标题
DOI：
10.1109/msp.2018.2890128
复制
发表时间：
2019-07-01
影响因子：
14.9
通讯作者：
Amin, Moeness G.
中科院分区：
文献类型：
--
作者：
Gurbuz, Sevgi Zubeyde;Amin, Moeness G.
深度学习（DL）在涉及目标分类和成像的雷达应用中显示出巨大的前景。在室内监测领域，研究人员对DL用于分类日常人类活动、检测福尔斯和监测步态异常表现出兴趣。推动这种兴趣的是与智能和安全家居、辅助生活和医疗诊断相关的新兴应用。DL在提供所观察到的人体运动关节的准确实时会计方面的成功从根本上取决于神经网络结构、输入数据表示和适当的训练。本文将DL置于数据驱动的运动分类方法的上下文中，并将其性能与采用手工特征的其他方法进行比较。我们讨论了最近提出的DL分类性能增强，并报告了重要的挑战和未来可能的研究，以实现其全部潜力。
Deep learning (DL) has shown tremendous promise in radar applications that involve target classification and imaging. In the field of indoor monitoring, researchers have shown an interest in DL for classifying daily human activities, detecting falls, and monitoring gait abnormalities. Driving this interest are emerging applications related to smart and secure homes, assisted living, and medical diagnosis. The success of DL in providing an accurate real-time accounting of observed human-motion articulations fundamentally depends on the neural network structure, input data representation, and proper training. This article puts DL in the context of data-driven approaches for motion classification and compares its performance with other approaches employing handcrafted features. We discuss recent proposed enhancements of DL classification performance and report on important challenges and possible future research to realize its full potential.