使用变化点检测自动化日常活动分割

S. Aminikhanghahi, D. Cook
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引用次数: 29

摘要

基于传感器数据的活动之间转换的实时检测是一个有价值但尚未开发的挑战。检测这些转换对于活动分割、定时通知或干预以及分析人类行为都很有用。在这项工作中,我们设计和评估了基于实时机器学习的方法,用于对连续的人类日常活动进行自动分割和识别。我们检测活动转换,并将变化点检测算法与智能家居活动识别相结合,将人类日常活动分割为单独的动作,并正确识别每个动作。对现实世界智能家居数据集的实验表明,使用过渡感知的活动识别算法在检测活动边界和流式活动分割方面具有最佳性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using change point detection to automate daily activity segmentation
Real time detection of transitions between activities based on sensor data is a valuable but somewhat untapped challenge. Detecting these transitions is useful for activity segmentation, for timing notifications or interventions, and for analyzing human behavior. In this work, we design and evaluate real time machine learning-based methods for automatic segmentation and recognition of continuous human daily activity. We detect activity transitions and integrate the change point detection algorithm with smart home activity recognition to segment human daily activities into separate actions and correctly identify each action. Experiments with on real-world smart home datasets suggest that using transition aware activity recognition algorithms lead to best performance for detecting activity boundaries and streaming activity segmentation.
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