Testing and analysis of the proposed data driven method on the opportunity human activity dataset

Pouya Foudeh, A. Khorshidtalab, N. Salim
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引用次数: 2

Abstract

This paper proposes a data-driven method for constructing materials to be used in a probabilistic knowledge base for human activity recognition. The utilized dataset, challenge subset of Opportunity, is a publicly available dataset. It consists of a set of daily activities, which has been manually labeled as modes of locomotion and gestures. We applied several methods to extract proper features from sensors on bodies of subjects, then, chosen features are fed into two different classifiers. Finally, predicted labels for modes of locomotion and hand gestures are calculated. To evaluate the method, the recognition rates are benchmarked against the results of the competitors who have participated in Opportunity challenge as well as the baseline results provided by the Opportunity group. For modes of locomotion, our results surpass all of the available results and in some cases the recognition rate of our model is very close to the highest recognition rate. For gestures, regular or noisy data, in some cases our method is still higher than baseline or challenge participants but unlike locomotion, it is not capable to beat them all.
在机会人类活动数据集上对提出的数据驱动方法进行测试和分析
本文提出了一种数据驱动的方法来构建用于人类活动识别的概率知识库中的材料。所使用的数据集,机遇的挑战子集,是一个公开可用的数据集。它由一系列日常活动组成,这些活动被手工标记为运动和手势模式。我们应用了几种方法从受试者身体上的传感器中提取适当的特征,然后将选择的特征输入到两个不同的分类器中。最后,计算运动模式和手势的预测标签。为了评估该方法,识别率是根据参加机会挑战的竞争对手的结果以及机会小组提供的基线结果进行基准测试的。对于运动模式,我们的结果超过了所有可用的结果,在某些情况下,我们模型的识别率非常接近最高识别率。对于手势,常规或嘈杂的数据,在某些情况下,我们的方法仍然高于基线或挑战参与者,但与运动不同,它无法击败所有人。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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