Work

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

Year: 2024

Type: conference-paper

Abstract: 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.

DOI: 10.1109/sbesc65055.2024.10771916

Authors: Jonathan C. F. Silva, Mateus C. Silva, Vicente J. P. Amorim, Pedro S. O. Lazaroni, Ricardo A R Oliveira

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green