Year: 2025
Type: article
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.
DOI: 10.1155/int/5533092
Authors: Bo Fan, Kangrong Luo, Peng Wang, Andia Foroughi
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,700
OpenAlex ID: W4410419437