A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors
A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors
复制标题
使用智能手机传感器进行现实生活人类活动识别的公共领域数据集
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
M. R. Luaces
中科院分区：
文献类型：
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作者：
Daniel Garcia;D. Rivero;Enrique Fernández;M. R. Luaces
近年来，人类活动识别已成为科学界的热门话题。受到关注的原因是它在多个领域的直接应用，例如医疗保健或健身。此外，当前智能手机在全球范围内的使用使得以非侵入性且更便宜的方式从人们那里获取此类数据变得特别容易，而无需其他可穿戴设备。在本文中，我们介绍了与方向无关、与位置无关和与主题无关的人类活动识别数据集。该数据集中的信息是来自智能手机的加速计、陀螺仪、磁力计和 GPS 的测量结果。此外，每项测量都与四种可能的注册活动之一相关联：不活动、活动、步行和驾驶。这项工作还提出了一种支持向量机（SVM）模型来对数据集进行一些初步实验。考虑到该数据集是在实际使用中从智能手机中获取的，与其他数据集不同，在此类数据上开发良好的模型是一个开放的问题，对研究人员来说也是一个挑战。通过这样做，我们将能够缩小模型和现实应用程序之间的差距。
In recent years, human activity recognition has become a hot topic inside the scientific community. The reason to be under the spotlight is its direct application in multiple domains, like healthcare or fitness. Additionally, the current worldwide use of smartphones makes it particularly easy to get this kind of data from people in a non-intrusive and cheaper way, without the need for other wearables. In this paper, we introduce our orientation-independent, placement-independent and subject-independent human activity recognition dataset. The information in this dataset is the measurements from the accelerometer, gyroscope, magnetometer, and GPS of the smartphone. Additionally, each measure is associated with one of the four possible registered activities: inactive, active, walking and driving. This work also proposes asupport vector machine (SVM) model to perform some preliminary experiments on the dataset. Considering that this dataset was taken from smartphones in their actual use, unlike other datasets, the development of a good model on such data is an open problem and a challenge for researchers. By doing so, we would be able to close the gap between the model and a real-life application.