Department of Electronic and Computer Engineering, University of Limerick, Ireland.
Department of Electronic and Computer Engineering, University of Limerick, Ireland.
Med Eng Phys. 2014 Jun;36(6):779-85. doi: 10.1016/j.medengphy.2014.02.012. Epub 2014 Mar 11.
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.
身体活动对人们的健康有积极影响，已被证明可降低老年人群中慢性病的发生。迄今为止，已经有大量研究关注使用惯性传感器进行活动识别。这些研究中的许多都采用了单一传感器的方法，并专注于提出新的特征，结合复杂的分类器，以提高整体识别准确性。此外，先进的特征提取算法和复杂的分类器的实现超出了大多数当前可穿戴传感器平台的计算能力。本文提出了一种采用分布式身体位置的多个传感器的方法来克服这个问题。所提出系统的目标是通过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“轻量级”信号处理算法来实现更高的识别精度。为了分析和评估多传感器系统，招募了 8 名受试者在不同生活场景下执行 8 种正常脚本活动，每种活动重复 3 次。因此，共记录了 192 种活动，产生了 864 个单独的注释活动状态。设计这种多传感器系统的方法需要考虑以下几点：信号预处理算法、采样率、特征选择和分类器选择。已经对每个方面进行了研究，并选择了最合适的方法来在识别精度和计算执行时间之间取得平衡。本文介绍了六个不同系统的比较，这些系统采用了单一或多个传感器。实验结果表明，采用均值和方差特征，使用决策树分类器，所提出的多传感器系统可以达到 96.4%的整体识别准确率。结果表明，在多传感器系统上不需要复杂的分类器和特征集来实现高识别精度。
