Nowadays, deep learning is one of the popular research areas of the computer sciences, and many deep networks have been proposed to solve artificial intelligence and machine learning problems. Residual networks (ResNet) for instance ResNet18, ResNet50 and ResNet101 are widely used deep network in the literature. In this paper, a novel ResNet-based signal recognition method is presented. In this study, ResNet18, ResNet50 and ResNet101 are utilized as feature extractor and each network extracts 1000 features. The extracted features are concatenated, and 3000 features are obtained. In the feature selection phase, 1000 most discriminative features are selected using ReliefF, and these selected features are used as input for the third-degree polynomial (cubic) activation-based support vector machine. The proposed method achieved 99.96% and 99.61% classification accuracy rates for gender and activity recognitions, respectively. These results clearly demonstrate that the proposed pre-trained ensemble ResNet-based method achieved high success rate for sensors signals.

中文翻译：

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带信号的基于集成残差网络的性别和活动识别方法

如今，深度学习是计算机科学的热门研究领域之一，已经提出了许多深度网络来解决人工智能和机器学习问题。ResNet18、ResNet50 和 ResNet101 等残差网络 (ResNet) 是文献中广泛使用的深度网络。在本文中，提出了一种新的基于 ResNet 的信号识别方法。在本研究中，ResNet18、ResNet50 和 ResNet101 用作特征提取器，每个网络提取 1000 个特征。将提取的特征进行级联，得到3000个特征。在特征选择阶段，使用 ReliefF 选择 1000 个最具辨别力的特征，并将这些选择的特征用作基于三次多项式（三次）激活的支持向量机的输入。所提出的方法达到了 99.96% 和 99。性别和活动识别的分类准确率分别为 61%。这些结果清楚地表明，所提出的基于预训练集成 ResNet 的方法对传感器信号取得了很高的成功率。