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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\u2010based 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.<\/jats:p>","DOI":"10.1155\/int\/5533092","type":"journal-article","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T04:46:22Z","timestamp":1747284382000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ARIMA\u2010Based Virtual Data Generation Using Deepfake for Robust Physique Test"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7099-8302","authenticated-orcid":false,"given":"Bo","family":"Fan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0490-8425","authenticated-orcid":false,"given":"Kangrong","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9753-3760","authenticated-orcid":false,"given":"Peng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9419-3258","authenticated-orcid":false,"given":"Andia","family":"Foroughi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,5,15]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.22689"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.26599\/bdma.2024.9020025"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102182"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.26599\/bdma.2022.9020021"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.22840"},{"key":"e_1_2_12_6_2","doi-asserted-by":"publisher","DOI":"10.26599\/tst.2024.9010033"},{"key":"e_1_2_12_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2024.3446551"},{"key":"e_1_2_12_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2021.3083596"},{"key":"e_1_2_12_9_2","doi-asserted-by":"publisher","DOI":"10.26599\/bdma.2024.9020008"},{"key":"e_1_2_12_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.11.030"},{"key":"e_1_2_12_11_2","doi-asserted-by":"publisher","DOI":"10.26599\/tst.2023.9010137"},{"key":"e_1_2_12_12_2","first-page":"229","article-title":"Continuous Vital Signs Monitoring Using Wireless Wearable Devices in Patients After Video-Assisted Thoracoscopic Surgery for Lung Cancer: A Prospective Self-Control Study","author":"Mei X.","year":"2024","journal-title":"Chinese Journal of Thoracic Surgery"},{"key":"e_1_2_12_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.atech.2024.100609"},{"key":"e_1_2_12_14_2","volume-title":"Time-series Analyses of Cardiorespiratory Responses in Relation to Physical Activity for Advancing Athlete Monitoring and Technology Integration","author":"Gielen J.","year":"2024"},{"key":"e_1_2_12_15_2","doi-asserted-by":"publisher","DOI":"10.35882\/ijeeemi.v6i3.8"},{"key":"e_1_2_12_16_2","volume-title":"Development and Validation of the Motion Shirt: A Wearable Sensor System for Monitoring Upper Limb Motion in Shoulder Joint Replacement Patients","author":"MilaniZadeh S.","year":"2024"},{"key":"e_1_2_12_17_2","doi-asserted-by":"publisher","DOI":"10.5162\/eurosensorsxxxvi\/ot10.264"},{"key":"e_1_2_12_18_2","first-page":"11530","article-title":"What\u2019s a Good Imputation to Predict With Missing Values?","volume":"34","author":"Le Morvan M.","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_12_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2020.109922"},{"key":"e_1_2_12_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-05901-2"},{"key":"e_1_2_12_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3066551"},{"key":"e_1_2_12_22_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17086"},{"key":"e_1_2_12_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bdr.2021.100296"},{"key":"e_1_2_12_24_2","doi-asserted-by":"publisher","DOI":"10.3390\/e25010137"},{"key":"e_1_2_12_25_2","article-title":"Multivariate Time Series Imputation by Graph Neural Networks","author":"Cini A.","year":"2021","journal-title":"CoRR abs\/2108.00298"},{"key":"e_1_2_12_26_2","doi-asserted-by":"publisher","DOI":"10.3389\/fvets.2021.740253"},{"key":"e_1_2_12_27_2","doi-asserted-by":"crossref","unstructured":"KhichiM.andYadavR. 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