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Complex Activity Recognition Using Context-Driven Activity Theory and Activity Signatures

机译:使用上下文驱动的活动理论和活动签名的复杂活动识别

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摘要

In pervasive and ubiquitous computing systems, human activity recognition has immense potential in a large number of application domains. Current activity recognition techniques (ⅰ) do not handle variations in sequence, concurrency and interleaving of complex activities; (ⅱ) do not incorporate context; and (ⅲ) require large amounts of training data. There is a lack of a unifying theoretical framework which exploits both domain knowledge and data-driven observations to infer complex activities. In this article, we propose, develop and validate a novel Context-Driven Activity Theory (CDAT) for recognizing complex activities. We develop a mechanism using probabilistic and Markov chain analysis to discover complex activity signatures and generate complex activity definitions. We also develop a Complex Activity Recognition (CAR) algorithm. It achieves an overall accuracy of 95.73% using extensive experimentation with real-life test data. CDAT utilizes context and links complex activities to situations, which reduces inference time by 32.5% and also reduces training data by 66%.
机译:在无处不在的计算系统中,人类活动识别在众多应用领域中具有巨大潜力。当前的活动识别技术(ⅰ)无法处理复杂活动的顺序,并发性和交错性变化; (ⅱ)不包含上下文; (ⅲ)需要大量的训练数据。缺乏一个统一的理论框架,可以利用领域知识和数据驱动的观察来推断复杂的活动。在本文中,我们提出,开发和验证了一种新颖的上下文驱动活动理论(CDAT),用于识别复杂活动。我们使用概率和马尔可夫链分析开发一种机制,以发现复杂的活动签名并生成复杂的活动定义。我们还开发了复杂活动识别(CAR)算法。通过对真实测试数据进行广泛的实验,它可以达到95.73%的整体精度。 CDAT利用上下文并将复杂的活动与情况联系起来,从而将推理时间减少了32.5%,并将培训数据减少了66%。

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