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Deep learning-based elderly gender classification using Doppler radar
Personal and Ubiquitous Computing ( IF 3.006 ) Pub Date : 2021-01-07 , DOI: 10.1007/s00779-020-01490-4
ZhiChen Wang , Zelin Meng , Kenshi Saho , Kazuki Uemura , Naoto Nojiri , Lin Meng

Society today is facing a rapidly aging population. While various monitoring systems have been proposed for protecting elderly persons in their daily lives, concerns relating to privacy limit the effectiveness of these systems. In response to this issue, we investigate the use of Doppler radar images for monitoring the elderly, as these images are known to protect privacy very well. As the first step, we investigate the use of Doppler radar images for the gender classification of the elderly. We used sit-to-stand Doppler radar images of elderly persons, obtained eleven groups of images through image processing, and applied five state-of-the-art deep learning models to classify the gender. The classification results revealed a classification accuracy rate as high as 90%, which indicates that sit-to-stand Doppler radar images of the elderly can indeed reflect their gender to a certain extent.



中文翻译:

多普勒雷达基于深度学习的老年人性别分类

当今社会正面临人口迅速老龄化的问题。尽管已经提出了各种监视系统来保护老年人的日常生活,但是与隐私有关的担忧限制了这些系统的有效性。针对此问题,我们调查了使用多普勒雷达图像监视老年人的情况,因为众所周知这些图像可以很好地保护隐私。第一步,我们调查使用多普勒雷达图像对老年人进行性别分类。我们使用了老年人的从站到站多普勒雷达图像,通过图像处理获得了11组图像,并应用了五个最新的深度学习模型对性别进行分类。分类结果显示分类准确率高达90%,

更新日期:2021-01-07
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