Wearable Sensors: Improving AI for Walking Activities Through GAN-Based Data Augmentation - Silva, Jonathan C. F.; Silva, Mateus C.; Amorim, Vicente J. P.; Lazaroni, Pedro S. O.; Oliveira, Ricardo A. R. (Anais do Simpósio Brasileiro de Engenharia de Sistemas Computacionais (SBESC), 2024)
Human Activity Recognition (HAR) with artificial intelligence fosters the development of innovative solutions. However, building AI models often requires a substantial amount of data and can be time-consuming. In this context, our work adopted the TimeGAN technique for data augmentation, facilitating the construction of a more efficient model. We developed a classifier that integrates both synthetic and real data. This strategy significantly reduces the time required for data collection and may accelerate the development of new wearable technologies. This approach represents a promising step in optimizing development processes in AI applications for HAR, enhancing the speed and effectiveness of technological innovation.