A Multisensory Approach for Remote Health Monitoring of Older People
A Multisensory Approach for Remote Health Monitoring of Older People
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DOI：
10.1109/jerm.2018.2827099
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发表时间：
2018-06-01
影响因子：
3.2
通讯作者：
Fioranelli, Francesco
中科院分区：
文献类型：
--
作者：
Li, Haobo;Shrestha, Aman;Fioranelli, Francesco
预期寿命的延长和多种慢性疾病发病率的增加是重大的社会挑战。已经提出了不同的技术来解决这些问题，检测诸如中风或福尔斯之类的关键事件，并自动监测人类活动以进行健康状况推断和异常检测。本文旨在研究两种类型的传感技术提出的辅助生活：可穿戴和雷达传感器。首先，不同的特征选择方法进行了验证和比较的准确性和计算负荷。然后，信息融合，以提高活动的分类精度相结合的两个传感器。支持向量机（SVM）和最近邻（KNN）分类器的分类精度提高了约12%，使用特征级融合。决策级融合方案也进行了研究，产生的分类精度在97%-98%的顺序。
Growing life expectancy and increasing incidence of multiple chronic health conditions are significant societal challenges. Different technologies have been proposed to address these issues, detect critical events, such as stroke or falls, and monitor automatically human activities for health condition inference and anomaly detection. This paper aims to investigate two types of sensing technologies proposed for assisted living: wearable and radar sensors. First, different feature selection methods are validated and compared in terms of accuracy and computational loads. Then, information fusion is applied to enhance activity classification accuracy combining the two sensors. Improvements in classification accuracy of approximately 12% using feature level fusion are achieved with both support vector machine s (SVMs) and knearest neighbor (KNN) classifiers. Decision-level fusion schemes are also investigated, yielding classification accuracy in the order of 97%-98%.