Physical Activity Recognition Using Continuous Wave Radar With Deep Neural Network

Yiyuan Zhang, O. J. Babarinde, B. Vanrumste, D. Schreurs
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引用次数: 2

Abstract

In this study, we investigated the feasibility of using a continuous-wave radar sensor for detecting physical activities. The transfer learning method, applying a pre-trained deep neural network (Alexnet), was used to perform the classification task. Doppler signatures of these activities were converted to spectrogram figures as the input of the classifier. The classifier was tested in five-fold cross-validation and leave-one-person-out. The Fl-score of five-fold cross-validation had higher score, which ranged from 71.11 % to 82.05%.
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基于深度神经网络的连续波雷达运动识别
在这项研究中,我们探讨了使用连续波雷达传感器来检测身体活动的可行性。使用迁移学习方法,应用预训练的深度神经网络(Alexnet)来执行分类任务。这些活动的多普勒特征被转换成光谱图作为分类器的输入。分类器在五重交叉验证和留一人之外进行了测试。五重交叉验证的fl得分较高,为71.11% ~ 82.05%。
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