Continuously-worn wearable sensors enable researchers to collect copious amounts of rich bio-behavioral time series recordings of real-life activities of daily living, offering unprecedented opportunities to infer novel human behavior patterns during daily routines. Existing approaches to routine discovery through bio-behavioral data rely either on pre-defined notions of activities or use additional non-behavioral measurements as contexts, such as GPS location or localization within the home, presenting risks to user privacy. In this work, we propose a novel wearable time-series mining framework, Hawkes point process On Time series clusters for ROutine Discovery (HOT-ROD), for uncovering behavioral routines from completely unlabeled wearable recordings. We utilize a covariance-based method to generate time-series clusters and discover routines via the Hawkes point process learning algorithm. We empirically validate our approach for extracting routine behaviors using a completely unlabeled time-series collected continuously from over 100 individuals both in and outside of the workplace during a period of ten weeks. Furthermore, we demonstrate this approach intuitively captures daily transitional relationships between physical activity states without using prior knowledge. We also show that the learned behavioral patterns can assist in illuminating an individual's personality and affect.

翻译：持续佩戴的可穿戴传感器使研究人员能够收集大量丰富的生物行为时间序列记录，涵盖日常生活的真实活动，为推断日常规律中的新型人类行为模式提供了前所未有的机会。现有通过生物行为数据发现日常规律的方法要么依赖预定义的活动概念，要么使用额外的非行为测量作为上下文（如GPS位置或室内定位），这存在用户隐私风险。本研究提出了一种新颖的可穿戴时间序列挖掘框架——基于时间序列聚类的霍克斯点过程日常规律发现方法（HOT-ROD），用于从完全无标签的可穿戴记录中揭示行为规律。我们利用基于协方差的方法生成时间序列聚类，并通过霍克斯点过程学习算法发现日常规律。通过对100多名个体在十周内（包括工作场所内外）持续采集的完全无标签时间序列进行实证验证，证明了该方法在提取规律行为方面的有效性。进一步地，我们展示了该方法无需先验知识即可直观捕捉体力活动状态之间的日常转换关系，并表明所学到的行为模式有助于揭示个体的个性与情感特征。