ARIMA-Based Virtual Data Generation Using Deepfake for Robust Physique Test

Abstract

Physique testing plays a crucial role in health monitoring and fitness assessment, with wearable devices becoming an essential tool to collect real-time data. However, incomplete or missing data from wearable devices often hamper the accuracy and reliability of such tests. Existing methods struggle to address this challenge effectively, leading to gaps in the analysis of physical conditions. To overcome this limitation, we propose a novel framework that combines ARIMA-based virtual data generation with deepfake technology. ARIMA is used to predict and reconstruct missing physique data from historical records, while deepfake technology synthesizes virtual data that mimic the physical attributes of the test subjects. This hybrid approach enhances the robustness and accuracy of physique tests, especially in scenarios where data are incomplete. The experimental results demonstrate significant improvements in the accuracy and reliability of data prediction and test reliability, offering a new avenue to advance the monitoring of health and fitness.