Formal modeling and analysis of human behavior can properly advance disciplines ranging from psychology to economics. The ability to perform such modeling has been limited by a lack of ecologically-valid data collected regarding human daily activity. We propose a formal model of indoor routine behavior based on data from automatically-sensed and recognized activities. A mechanistic description of behavior patterns for identical activity is offered to both investigate behavioral norms with 99 smart homes and compare these norms between subgroups. We identify and model the patterns of human behaviors based on inter-arrival times, the time interval between two successive activities, for selected activity classes in the smart home dataset with diverse participants. We also explore the inter-arrival times of sequence of activities in one smart home. To demonstrate the impact such analysis can have on other disciplines, we use this same smart home data to examine the relationship between the formal model and resident health status. Our study reveals that human indoor activities can be described by non-Poisson processes and that the corresponding distribution of activity inter-arrival times follows a Pareto distribution. We further discover that the combination of activities in certain subgroups can be described by multivariate Pareto distributions. These findings will help researchers understand indoor activity routine patterns and develop more sophisticated models of predicting routine behaviors and their timings. Eventually, the findings may also be used to automate diagnoses and design customized behavioral interventions by providing activity-anticipatory services that will benefit both caregivers and patients.

中文翻译：

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使用连续的传感器数据来规范家庭活动模式的模型

对人类行为的形式化建模和分析可以适当地推进从心理学到经济学的学科。由于缺乏关于人类日常活动的生态有效数据的收集，执行此类建模的能力受到了限制。我们基于来自自动感知和识别的活动的数据，提出了一个室内日常行为的形式化模型。提供了针对相同活动的行为模式的机械描述，以调查99个智能家居的行为规范并比较子组之间的这些规范。我们根据到达间隔时间（两次连续活动之间的时间间隔），为智能家居数据集中具有不同参与者的选定活动类别识别并建模人类行为模式。我们还探索了一个智能家居中一系列活动的到来时间。为了证明这种分析对其他学科可能产生的影响，我们使用了相同的智能家居数据来检查正式模型与居民健康状况之间的关系。我们的研究表明，人类室内活动可以用非泊松过程来描述，活动到来时间的相应分布遵循帕累托分布。我们进一步发现，某些子组中活动的组合可以通过多元帕累托分布来描述。这些发现将有助于研究人员了解室内活动的常规模式，并开发出更复杂的预测常规行为及其时间的模型。最终，