Wavelet-domain human activity recognition utilizing convolutional neural networks

Abstract

Human activity recognition (HAR) is a crucial area of research in human-computer interaction. Despite previous efforts in this field, there is still a need for more accurate and robust methods that can handle time-series data from different sensors. In this study, we propose a novel method that generates an image using wavelet transform to extract time-frequency features of the recorded signal. Our method employs convolutional neural networks (CNNs) for feature extraction and activity recognition, and a new loss function that produces denser representations for samples, improving the model’s generalization on unseen samples. To evaluate the effectiveness of our proposed method, we conducted experiments on multiple publicly available data sets. Our results demonstrate that our method outperforms previous methods in terms of activity classification accuracy. Specifically, our method achieves higher accuracy rates and demonstrates improved robustness in real-world settings. Overall, our proposed method addresses the research gap of accurate and robust activity recognition from time-series data recorded from different sensors. Our findings have the potential to improve the accuracy and robustness of human activity recognition systems in real-world applications.