Sensor-based activity recognition (AR) depends on effective feature representation and classification. However, many recent studies focus on recognition methods, but largely ignore feature representation. Benefitting from the success of Convolutional Neural Networks (CNN) in feature extraction, we propose to improve the feature representation of activities. Specifically, we use a reversed CNN to generate the significant data based on the original features and combine the raw training data with significant data to obtain to enhanced training data. The proposed method can not only train better feature extractors but also help better understand the abstract features of sensor-based activity data. To demonstrate the effectiveness of our proposed method, we conduct comparative experiments with CNN Classifier and CNN-LSTM Classifier on five public datasets, namely the UCIHAR, UniMiB SHAR, OPPORTUNITY, WISDM, and PAMAP2. In addition, we evaluate our proposed method in comparison with traditional methods such as Decision Tree, Multi-layer Perceptron, Extremely randomized trees, Random Forest, and k-Nearest Neighbour on a specific dataset, WISDM. The results show our proposed method consistently outperforms the state-of-the-art methods.

基于传感器的活动识别（AR）取决于有效的特征表示和分类。然而，许多最近的研究集中在识别方法上，但是在很大程度上忽略了特征表示。受益于卷积神经网络（CNN）在特征提取方面的成功，我们建议改进活动的特征表示。具体来说，我们使用反向CNN根据原始特征生成重要数据，并将原始训练数据与重要数据结合以获得增强的训练数据。所提出的方法不仅可以训练更好的特征提取器，而且可以帮助更好地理解基于传感器的活动数据的抽象特征。为了证明我们提出的方法的有效性，我们使用CNN分类器和CNN-LSTM分类器对五个公共数据集（即UCIHAR，UniMiB SHAR，OPPORTUNITY，WISDM和PAMAP2）进行了对比实验。此外，在特定数据集WISDM上，我们与传统方法（例如决策树，多层感知器，极随机树，随机森林和k最近邻）进行了比较，评估了我们提出的方法。结果表明，我们提出的方法始终优于最新方法。