使用霍克斯点过程从大规模无标签可穿戴时间序列数据流中学习日常行为表征
Learning Behavioral Representations of Routines From Large-scale Unlabeled Wearable Time-series Data Streams using Hawkes Point Process
AI总结：
提出HOT-ROD框架，结合协方差时间序列聚类与霍克斯点过程，从完全无标签的可穿戴数据中发现日常行为作息并捕捉活动状态转换，且能反映个体性格与情感。
摘要：
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日常生活真实活动的生物行为时间序列记录，为在日常作息中推断新颖的人类行为模式提供了前所未有的机会。现有的通过生物行为数据进行日常作息发现的方法要么依赖于预定义的活动概念，要么使用额外的非行为测量作为上下文，例如GPS位置或家庭内的定位，这对用户隐私构成风险。在这项工作中，我们提出了一种新颖的可穿戴时间序列挖掘框架，即用于日常作息发现的霍克斯点过程时间序列聚类框架（HOT-ROD），用于从完全无标签的可穿戴记录中揭示行为日常作息。我们利用基于协方差的方法生成时间序列簇，并通过霍克斯点过程学习算法发现日常作息。我们使用在十周时间内从100多名个体在工作场所内外连续收集的完全无标签时间序列，实证验证了我们提取日常行为的方法。此外，我们证明了该方法在不使用先验知识的情况下直观地捕捉了身体活动状态之间的日常转换关系。我们还表明，学习到的行为模式有助于揭示个体的性格和情感。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
