An Approach for Data Augmentation in HAR with Wearable Sensors Using TIMEGAN

Human Activity Recognition (HAR) is crucial in health and sports, where integrating wearable sensors with Artificial Intelligence (AI) leads to innovative solutions. However, data collection can be labor-intensive and slow, hindering project timelines. Data augmentation presents a viable solution to this challenge. Our study enhanced our original dataset using a TimeGAN-based neural network for wearable device sensors. The experimental outcomes confirm that it's feasible to construct efficient classifiers using the synthetic data generated, which closely resembles real data. This approach saves significant time in data collection and accelerates the development of wearable technologies. Consequently, we developed an effective classifier for HAR using synthetic data.