Feature diversity for fall detection and human indoor activities classification using radar systems
Feature diversity for fall detection and human indoor activities classification using radar systems
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DOI：
10.1049/cp.2017.0381
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Aman Shrestha;J. Kernec;F. Fioranelli;Enea Cippitelli;E. Gambi;S. Spinsante
中科院分区：
文献类型：
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作者：
Aman Shrestha;J. Kernec;F. Fioranelli;Enea Cippitelli;E. Gambi;S. Spinsante
本文对用于跌倒检测和人类室内行为分类的雷达特征进行了初步分析，以监测有身体或认知健康恶化风险的个人的日常活动模式。已经收集了不同环境中的两个签名数据集，其中一个包括从雷达和 RGB-D Kinect 传感器同时收集的几个老年人身上的信号生成的签名。该初步分析显示了不同特征和分类器的潜在有效性，并强调需要进行额外的研究，以利用不同特征和分类方法在不同环境和数据集中实现的总体分类精度的多样性。
This paper presents a preliminary analysis of radar signatures for fall detection and classification of human indoor actions, to monitor the daily activity patterns of individuals at risk of deteriorating physical or cognitive health. Two datasets of signatures in different environments have been collected, one of which included signatures generated from signals simultaneously collected from a radar and an RGB-D Kinect sensor, on a couple of older individuals. This preliminary analysis shows the potential effectiveness of different features and classifiers, and highlights the need of additional investigation to exploit the diversity in terms of overall classification accuracy achieved with different features and classification methods, in different environments and datasets.