Ponnada Aditya, Cooper Seth, Tang Qu, Thapa-Chhetry Binod, Miller Josh Aaron, John Dinesh, Intille Stephen
Northeastern University, Boston, MA, USA.
Proc IEEE Int Conf Pervasive Comput Commun. 2021 Mar;2021. doi: 10.1109/percomworkshops51409.2021.9431110. Epub 2021 May 25.
Human activity recognition using wearable accelerometers can enable detection of physical activities to support novel human-computer interfaces. Many of the machine-learning-based activity recognition algorithms require multi-person, multi-day, carefully annotated training data with precisely marked start and end times of the activities of interest. To date, there is a dearth of usable tools that enable researchers to conveniently visualize and annotate multiple days of high-sampling-rate raw accelerometer data. Thus, we developed Signaligner Pro, an interactive tool to enable researchers to conveniently explore and annotate multi-day high-sampling rate raw accelerometer data. The tool visualizes high-sampling-rate raw data and time-stamped annotations generated by existing activity recognition algorithms and human annotators; the annotations can then be directly modified by the researchers to create their own, improved, annotated datasets. In this paper, we describe the tool's features and implementation that facilitate convenient exploration and annotation of multi-day data and demonstrate its use in generating activity annotations.
使用可穿戴加速度计进行人类活动识别能够实现对身体活动的检测，以支持新型人机界面。许多基于机器学习的活动识别算法需要多人多日、经过仔细标注且活动起始和结束时间精确标记的训练数据。迄今为止，缺乏可用工具使研究人员能够方便地可视化和标注多日的高采样率原始加速度计数据。因此，我们开发了Signaligner Pro，这是一种交互式工具，使研究人员能够方便地探索和标注多日的高采样率原始加速度计数据。该工具可视化高采样率原始数据以及由现有活动识别算法和人工标注者生成的带时间戳的标注；然后研究人员可以直接修改这些标注，以创建他们自己的、经过改进的带标注数据集。在本文中，我们描述了该工具便于对多日数据进行便捷探索和标注的功能及实现方式，并展示了其在生成活动标注中的应用。
