Assisted Labeling Visualizer (ALVI): A Semi-Automatic Labeling System For Time-Series Data
Assisted Labeling Visualizer (ALVI): A Semi-Automatic Labeling System For Time-Series Data
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DOI：
10.1109/icasspw59220.2023.10193169
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Lee B. Hinkle;Tristan Pedro;Tyler Lynn;G. Atkinson;V. Metsis
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文献类型：
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作者：
Lee B. Hinkle;Tristan Pedro;Tyler Lynn;G. Atkinson;V. Metsis
机器学习应用程序可以从大量标记数据中显著受益，尽管标记数据的任务是众所周知的具有挑战性和耗时的。这在涉及人类受试者的领域尤其明显，在这些领域，标记时间序列信号往往需要训练有素的专业人员。在这项工作中，我们介绍了辅助标记可视化器（ALVI），该系统通过提供交互式用户界面来可视化同步视频，特征图表示和原始时间序列信号，从而简化了标记数据的过程。ALVI还利用深度学习和自监督学习技术来促进对大量未标记数据的半自动标记。我们在人类活动识别数据集上展示了ALVI的功能，以展示其增强时间序列传感器数据标记过程的潜力。
Machine learning applications can significantly benefit from large amounts of labeled data, although the task of labeling data is notoriously challenging and time-consuming. This is particularly evident in domains involving human subjects, where labeling time-series signals often necessitates trained professionals. In this work, we introduce the Assisted Labeling Visualizer (ALVI), a system that simplifies the process of labeling data by offering an interactive user interface that visualizes synchronized video, feature-map representations, and raw time-series signals. ALVI also leverages deep learning and self-supervised learning techniques to facilitate the semi-automatic labeling of large amounts of unlabeled data. We demonstrate the capabilities of ALVI on a human activity recognition dataset to showcase its potential for enhancing the labeling process of time-series sensor data.