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Stress Modelling Using Transfer Learning in Presence of Scarce Data

机译:在缺乏数据的情况下使用转移学习进行应力建模

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Stress at work is a significant occupational health concern nowadays. Thus, researchers are looking to find comprehensive approaches for improving wellness interventions relevant to stress. Recent studies have been conducted for inferring stress in labour settings; they model stress behaviour based on non-obtrusive data obtained from smartphones. However, if the data for a subject is scarce, a good model cannot be obtained. We propose an approach based on transfer learning for building a model of a subject with scarce data. It is based on the comparison of decision trees to select the closest subject for knowledge transfer. We present an study carried out on 30 employees within two organisations. The results show that the in the case of identifying a "similar" subject, the classification accuracy is improved via transfer learning.
机译:如今,工作压力是一个重要的职业健康问题。因此,研究人员正在寻找能够改善与压力有关的健康干预措施的综合方法。为了推断劳动环境中的压力,最近进行了研究。他们基于从智能手机获得的非干扰性数据对压力行为进行建模。但是,如果对象的数据稀少,则无法获得良好的模型。我们提出了一种基于迁移学习的方法,用于建立数据稀缺的主题模型。它基于决策树的比较来选择最接近的主题进行知识转移。我们介绍了一项针对两个组织的30名员工进行的研究。结果表明,在识别“相似”主题的情况下,通过转移学习可以提高分类准确性。

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