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Personal productivity monitoring through smartphones
Journal of Ambient Intelligence and Smart Environments ( IF 1.8 ) Pub Date : 2020-07-09 , DOI: 10.3233/ais-200567
Soban Ahmed Khan 1 , Asma Ahmad Farhan 1 , Labiba Gillani Fahad 1 , Syed Fahad Tahir 2
Affiliation  

Smartphones, with built-in array of sensors, provide an opportunity to ubiquitously collect user’s behavioral data. This leads to variety of founding applications that identifies interesting patterns in the smartphone data to learn human behavior. In this paper, we propose an approach that enhancesthe productivity of individual’s by unobtrusively learning their routine through smartphones. We design and develop a non-intrusive smartphone app – Prodmapp that periodically collects sensing data from user’s smartphone. We extract several potentially useful behavioral features from the data and perform correlation analysis among the features and user’s productivity score (ground truth). We collect 15 days sensing data from 10 users through Prodmapp. Ground truth is collected from the users in the form of questionnaires to quantify their productivity. The results showed that there exists a significant correlation among several behavioral features and user’s productivity score. Finally, we train and evaluate a prediction model using significantly correlated features that can predict the change in productivity of users by analyzing the variation in feature values. We train three classifiers i.e., logistic regression, SVM and KNN to compare their performance on the two benchmark datasets, one collected through Prodmapp and other from CASAS smart home project. Results shows that our proposed approach performs well and all three classifiers achieve good prediction accuracy on both datasets.

中文翻译:

通过智能手机监控个人生产力

带有内置传感器阵列的智能手机为广泛收集用户的行为数据提供了机会。这导致了各种各样的创始应用程序,这些应用程序识别智能手机数据中的有趣模式以学习人类行为。在本文中,我们提出了一种方法,可以通过智能手机毫不费力地学习他们的例程来提高他们的生产力。我们设计和开发了一种非侵入式智能手机应用程序– Prodmapp,该应用程序会定期从用户的智能手机收集感应数据。我们从数据中提取了一些潜在有用的行为特征,并在这些特征与用户的生产率得分(真实情况)之间进行了相关性分析。我们通过Prodmapp从10个用户那里收集了15天的传感数据。以调查表的形式从用户那里收集地面实况,以量化他们的生产力。结果表明,几种行为特征与用户生产率得分之间存在显着相关性。最后,我们使用显着相关的特征训练和评估预测模型,这些特征可以通过分析特征值的变化来预测用户生产力的变化。我们训练了三个分类器,即逻辑回归,SVM和KNN,以比较它们在两个基准数据集上的性能,一个是通过Prodmapp收集的,另一个是从CASAS智能家居项目收集的。结果表明,我们提出的方法表现良好,并且所有三个分类器均在两个数据集上均实现了良好的预测准确性。我们使用显着相关的特征训练和评估预测模型,这些特征可以通过分析特征值的变化来预测用户生产力的变化。我们训练了三个分类器,即逻辑回归,SVM和KNN,以比较它们在两个基准数据集上的性能,一个是通过Prodmapp收集的,另一个是从CASAS智能家居项目收集的。结果表明,我们提出的方法表现良好,并且所有三个分类器均在两个数据集上均实现了良好的预测准确性。我们使用显着相关的特征训练和评估预测模型,这些特征可以通过分析特征值的变化来预测用户生产力的变化。我们训练了三个分类器,即逻辑回归,SVM和KNN,以比较它们在两个基准数据集上的性能,一个是通过Prodmapp收集的,另一个是从CASAS智能家居项目收集的。结果表明,我们提出的方法表现良好,并且所有三个分类器均在两个数据集上均实现了良好的预测准确性。
更新日期:2020-07-10
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