Wearable Sensors: Improving AI for Walking Activities Through GAN-Based Data Augmentation

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.