Fully Connected Generative Adversarial Network for Human Activity Recognition

Abstract

Conditional Generative Adversarial Networks (CGAN) have shown great promise in generating synthetic data for sensor-based activity recognition. However, one key issue concerning existing CGAN is the design of the network architecture that affects sample quality. This study proposes an effective CGAN architecture that synthesizes higher quality samples than state-of-the-art CGAN architectures. This is achieved by combining convolutional layers with multiple fully connected networks in the generator's input and discriminator's output of the CGAN. We show the effectiveness of the proposed approach using elderly data for sensor-based activity recognition. Visual evaluation, similarity measure, and usability evaluation is used to assess the quality of generated samples by the proposed approach and validate its performance in activity recognition. In comparison to the state-of-the-art CGAN, the visual evaluation and similarity measure demonstrate that the proposed models' synthetic data more accurately represents actual data and creates more variations in each synthetic data than the state-of-the-art approach respectively. The experimental stages of the usability evaluation, on the other hand, show a performance gain of 2.5%, 2.5%, 3.1%, and 4.4% over the state-of-the-art CGAN when using synthetic samples by the proposed architecture.

Smart-Data-Glove-Based Gesture Recognition for Amphibious Communication

“…Specifically, the experiments collected samples of 5, 10, 20, 30, 40, and 50 instances for each gesture category. To verify the superiority of our proposed -DSNbased gesture recognition transfer model, we also selected several state-ofthe-art transfer learning models for comparison, including generative adversarial network (GAN)- [40] and conditional generative adversarial networks (CGAN)-based [41] transfer learning models. The transfer process involved utilizing our proposed DSN-based gesture recognition transfer model and selected state-of-the-art transfer learning models, with incremental updates applied to enhance the model's performance.…”