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Using Graphs to Improve Activity Prediction in Smart Environments Based on Motion Sensor Data

机译：使用图改善基于运动传感器数据的智能环境中的活动预测

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Activity Recognition in Smart Environments presents a difficult learning problem. The focus of this paper is a 10-class activity recognition problem using motion sensor events over time involving multiple residents and non-scripted activities. This paper presents the results of using three different graph-based approaches to this problem, and compares them to a non-graph SVM approach. The graph-based approaches are generating feature vectors using frequent subgraphs for classification by an SVM, an SVM using a graph kernel and nearest neighbor approach using a graph comparison measure. None demonstrate significantly superior accuracy compared to the non-graph SVM, but all demonstrate strongly uncorrelated error both against the base SVM and each other. An ensemble is created using the non-graph SVM, Frequent Subgraph SVM, Graph Kernel SVM, and Nearest Neighbor. Error is shown to be highly uncorrelated between these four. This ensemble substantially outperforms all of the approaches alone. Results are shown for a 10-class problem arising from smart environments, and a 2-class one-vs-all version of the same problem.
机译：智能环境中的活动识别提出了一个困难的学习问题。本文的重点是一个10类活动识别问题，该问题使用随时间变化的运动传感器事件，涉及多个居民和非脚本活动。本文介绍了使用三种不同的基于图的方法解决此问题的结果，并将它们与非图SVM方法进行了比较。基于图的方法正在使用频繁的子图生成特征向量，以通过SVM进行分类，使用图核的SVM以及使用图比较度量的最近邻方法。与非图形SVM相比，没有一个显示出明显更高的准确性，但是所有显示都与基础SVM以及彼此之间都存在严重不相关的误差。使用非图SVM，频繁子图SVM，图内核SVM和最近邻居创建集合。错误显示这四个之间高度不相关。该集成明显优于所有方法。显示了由智能环境引起的10类问题的结果，以及同一问题的2类一对多版本的结果。

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