eHomeSeniors Dataset: An Infrared Thermal Sensor Dataset for Automatic Fall Detection Research
eHomeSeniors Dataset: An Infrared Thermal Sensor Dataset for Automatic Fall Detection Research
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DOI：
10.3390/s19204565
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发表时间：
2019-10-02
期刊：
影响因子：
3.9
通讯作者：
Taramasco, Carla
中科院分区：
文献类型：
--
作者：
Riquelme, Fabian;Espinoza, Cristina;Taramasco, Carla
自动跌倒检测是一个非常活跃的研究领域，自 2010 年代以来呈爆炸式增长，尤其关注老年人护理。快速发现跌倒有利于受伤者及早意识到，减少对老年人健康的一系列负面影响。目前，有几种跌倒检测系统（FDS），大多基于预测和机器学习方法。这些算法基于不同的数据源，例如可穿戴设备、基于环境的传感器或基于视觉/相机的方法。虽然惯性测量单元 (IMU) 和智能手机等可穿戴设备对其使用有依赖性，但大多数基于图像的设备（如 Kinect 传感器）都会生成视频记录，这可能会影响用户的隐私。无论使用何种设备，这些 FDS 中的大多数仅在受控实验室环境中进行了测试，并且仍然没有大规模商业 FDS。后者的部分原因是出于道德原因，无法对真实老年人跌倒产生的数据集进行计数。实验室生成的所有公共数据集都是由年轻人执行的，没有考虑老年人加速和跌倒特征的差异。鉴于上述情况，本文提出了eHomeSeniors数据集，这是一个新的公共数据集，它至少在三个方面进行了创新：首先，它从两个不同的隐私友好型红外热传感器收集数据；其次，它是由两类志愿者建造的：普通年轻人（像往常一样）和表演艺术家，后者在物理治疗师的协助下模仿老年人的真实跌倒情况；第三，所选择的跌倒类型是彻底文献综述的结果。
Automatic fall detection is a very active research area, which has grown explosively since the 2010s, especially focused on elderly care. Rapid detection of falls favors early awareness from the injured person, reducing a series of negative consequences in the health of the elderly. Currently, there are several fall detection systems (FDSs), mostly based on predictive and machine-learning approaches. These algorithms are based on different data sources, such as wearable devices, ambient-based sensors, or vision/camera-based approaches. While wearable devices like inertial measurement units (IMUs) and smartphones entail a dependence on their use, most image-based devices like Kinect sensors generate video recordings, which may affect the privacy of the user. Regardless of the device used, most of these FDSs have been tested only in controlled laboratory environments, and there are still no mass commercial FDS. The latter is partly due to the impossibility of counting, for ethical reasons, with datasets generated by falls of real older adults. All public datasets generated in laboratory are performed by young people, without considering the differences in acceleration and falling features of older adults. Given the above, this article presents the eHomeSeniors dataset, a new public dataset which is innovative in at least three aspects: first, it collects data from two different privacy-friendly infrared thermal sensors; second, it is constructed by two types of volunteers: normal young people (as usual) and performing artists, with the latter group assisted by a physiotherapist to emulate the real fall conditions of older adults; and third, the types of falls selected are the result of a thorough literature review.