Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems
Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems
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DOI：
10.1016/j.medengphy.2014.02.012
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发表时间：
2014-06-01
影响因子：
2.2
通讯作者：
Nelson, John
中科院分区：
文献类型：
--
作者：
Gao, Lei;Bourke, A. K.;Nelson, John
体育活动对人们的福祉有积极影响，而且已证明可以减少老年人慢性病的发病率。迄今为止，存在大量的研究，其集中于使用惯性传感器的活动识别。其中许多研究采用单传感器方法，并重点提出与复杂分类器相结合的新特征，以提高整体识别准确性。此外，高级特征提取算法和复杂分类器的实现超过了当前大多数可穿戴传感器平台的计算能力。本文提出了一种方法，采用多个传感器的分布式身体位置，以克服这个问题。该系统的目标是通过“轻量级”信号处理算法实现更高的识别准确度，该算法在由计算效率高的节点组成的基于分布式计算的传感器系统上运行。为了分析和评估多传感器系统，招募了八名受试者在不同的生活场景中执行八个正常的脚本活动，每个重复三次。因此，总共记录了192项活动，形成了864个单独的附加说明的活动状态。设计这种多传感器系统的方法需要考虑以下因素：信号预处理算法、采样率、特征选择和分类器选择。每一个都进行了研究，并选择最合适的方法来实现识别精度和计算执行时间之间的权衡。六个不同的系统，采用单个或多个传感器的比较。实验结果表明，建议的多传感器系统可以达到96.4%的整体识别准确率，采用均值和方差的功能，使用决策树分类器。结果表明，不需要精心设计的分类器和特征集，以实现高识别精度的多传感器系统。(C)2014年IPEM。由爱思唯尔有限公司出版。保留所有权利。
Physical activity has a positive impact on people's well-being and it had been shown to decrease the occurrence of chronic diseases in the older adult population. To date, a substantial amount of research studies exist, which focus on activity recognition using inertial sensors. Many of these studies adopt a single sensor approach and focus on proposing novel features combined with complex classifiers to improve the overall recognition accuracy. In addition, the implementation of the advanced feature extraction algorithms and the complex classifiers exceed the computing ability of most current wearable sensor platforms. This paper proposes a method to adopt multiple sensors on distributed body locations to overcome this problem. The objective of the proposed system is to achieve higher recognition accuracy with "light-weight" signal processing algorithms, which run on a distributed computing based sensor system comprised of computationally efficient nodes. For analysing and evaluating the multi-sensor system, eight subjects were recruited to perform eight normal scripted activities in different life scenarios, each repeated three times. Thus a total of 192 activities were recorded resulting in 864 separate annotated activity states. The methods for designing such a multi-sensor system required consideration of the following: signal pre-processing algorithms, sampling rate, feature selection and classifier selection. Each has been investigated and the most appropriate approach is selected to achieve a trade-off between recognition accuracy and computing execution time. A comparison of six different systems, which employ single or multiple sensors, is presented. The experimental results illustrate that the proposed multi-sensor system can achieve an overall recognition accuracy of 96.4% by adopting the mean and variance features, using the Decision Tree classifier. The results demonstrate that elaborate classifiers and feature sets are not required to achieve high recognition accuracies on a multi-sensor system. (C) 2014 IPEM. Published by Elsevier Ltd. All rights reserved.