Wang Tinghui, Cook Diane J
Amazon, Seattle, WA 98121.
School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164.
IEEE Trans Emerg Top Comput. 2022 Apr-Jun;10(2):1130-1141. doi: 10.1109/tetc.2021.3072980. Epub 2021 Apr 15.
Activity recognizers are challenging to design for continuous, in-home settings. However, they are notoriously difficult to create when there is more than one resident in the home. Despite recent efforts, there remains a need for an algorithm that can estimate the number of residents in the house, split a time series stream into separate substreams, and accurately identify each resident's activities. To address this challenge, we introduce Gamut. This novel unsupervised method jointly estimates the number of residents and associates sensor readings with those residents, based on a multi-target Gaussian mixture probability hypothesis density filter. We hypothesize that the proposed method will offer robust recognition for homes with two or more residents. In experiments with labeled data collected from 50 single-resident and 11 multi-resident homes, we observe that Gamut outperforms previous unsupervised and supervised methods, offering a robust strategy to track behavioral routines in complex settings.
活动识别器对于连续的家庭环境来说设计颇具挑战性。然而，当家中有不止一位居住者时，创建活动识别器更是出了名的困难。尽管最近人们付出了努力，但仍需要一种算法，它能够估计房屋内居住者的数量，将时间序列流拆分为单独的子流，并准确识别每个居住者的活动。为应对这一挑战，我们引入了Gamut。这种新颖的无监督方法基于多目标高斯混合概率假设密度滤波器，联合估计居住者数量并将传感器读数与这些居住者关联起来。我们假设所提出的方法将为有两名或更多居住者的家庭提供强大的识别能力。在对从50个单居住者家庭和11个多居住者家庭收集的标记数据进行的实验中，我们观察到Gamut优于先前的无监督和有监督方法，为在复杂环境中跟踪行为习惯提供了一种强大的策略。
