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LabelSens: enabling real-time sensor data labelling at the point of collection using an artificial intelligence-based approach
Personal and Ubiquitous Computing ( IF 3.006 ) Pub Date : 2020-06-27 , DOI: 10.1007/s00779-020-01427-x
Kieran Woodward , Eiman Kanjo , Andreas Oikonomou , Alan Chamberlain

In recent years, machine learning has developed rapidly, enabling the development of applications with high levels of recognition accuracy relating to the use of speech and images. However, other types of data to which these models can be applied have not yet been explored as thoroughly. Labelling is an indispensable stage of data pre-processing that can be particularly challenging, especially when applied to single or multi-model real-time sensor data collection approaches. Currently, real-time sensor data labelling is an unwieldy process, with a limited range of tools available and poor performance characteristics, which can lead to the performance of the machine learning models being compromised. In this paper, we introduce new techniques for labelling at the point of collection coupled with a pilot study and a systematic performance comparison of two popular types of deep neural networks running on five custom built devices and a comparative mobile app (68.5–89% accuracy within-device GRU model, 92.8% highest LSTM model accuracy). These devices are designed to enable real-time labelling with various buttons, slide potentiometer and force sensors. This exploratory work illustrates several key features that inform the design of data collection tools that can help researchers select and apply appropriate labelling techniques to their work. We also identify common bottlenecks in each architecture and provide field tested guidelines to assist in building adaptive, high-performance edge solutions.

中文翻译:

LabelSens:使用基于人工智能的方法在采集点实现实时传感器数据标记

近年来,机器学习得到了迅速的发展,从而使得与语音和图像的使用相关的具有高识别精度的应用程序的开发成为可能。但是,尚未对这些模型可以应用到的其他类型的数据进行彻底探讨。标记是数据预处理必不可少的阶段,可能特别具有挑战性,尤其是在应用于单模型或多模型实时传感器数据收集方法时。当前,实时传感器数据标记是一个笨拙的过程,可用的工具范围有限且性能特征较差,这可能会导致机器学习模型的性能受到损害。在本文中,我们在收集时介绍了用于标记的新技术,并进行了先期研究,并对在五个定制设备上运行的两种流行类型的深度神经网络和一个比较的移动应用进行了系统性能比较(设备内GRU的准确度为68.5–89%模型,LSTM模型的最高准确度为92.8%)。这些设备旨在通过各种按钮,滑动电位计和力传感器进行实时标记。这项探索性工作说明了一些关键功能,这些功能可为数据收集工具的设计提供信息,这些工具可以帮助研究人员选择合适的标签技术并将其应用于他们的工作。我们还将确定每种体系结构中的常见瓶颈,并提供经过现场测试的准则,以帮助构建自适应的高性能边缘解决方案。
更新日期:2020-06-27
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