Abstract
Anthropoid images encode reliable biometric information in abundance. Recent research on image-based screening drives this effort to investigate the feasibility of interpreting the inherent nutritional state from multidimensional human body images. However, anthropometric databases are becoming increasingly essential and grow in parallel to achieve efficient system designs. Typically, learning models on anthropometric databases require voluminous datasets, and obtaining huge volumes of labeled data for supervised algorithms can be challenging due to the time and cost complexity required to classify data points. This paper presents a novel imaging-based strategy in an augmented environment to quantify the human anthropometric features with blockchain-based transfer learning to generate a diagnosis report. It includes evaluating the attributes such as height, weight, waist, knee-length from an image using augmented reality and blockchain-based transfer learning for diagnostic accuracy. We developed a novel skeleton known as FETTLE with ARKit to determine the role of body measures for assessing nutritional conditions and body weight from human body images. It forms an instantly applicable technique aimed at evaluating children’s growth patterns all through their initial ages. The FETTLE app can also be operated on bedridden people as a screening mechanism to spot their risk of pressure ulcers and undernutrition, followed by a more structured examination. Our approach is superior in accuracy measures with consortium blockchain-based learning context with privacy-preserved medical data sharing at 1014 transactions per second and high-end user experience and interaction. Our framework is proved to gain about 97.26% validation accuracy on anthropoid images.
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Data analyzed in this study were a re-analysis of existing data, which are openly available at locations cited in the reference section. World Bank Health Nutrition and Population Statistics. Available online: https://datacatalog.worldbank.org/dataset/health-nutrition-and-population-statistics (accessed on 1 November 2020).
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Acknowledgements
This work concerns an expansion of a previous work presented by the same Corresponding Author in an International Conference on Paradigms of Computing, Communication and Data Sciences (PCCDS-2020) whose proceedings can be accessed at https://link.springer.com/book/10.1007%2F978-981-15-7533-4.
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Priya, J.C., Choudhury, T., Khanna, A. et al. Blockchain-based transfer learning for health screening with digital anthropometry from body images. Netw Model Anal Health Inform Bioinforma 11, 23 (2022). https://doi.org/10.1007/s13721-022-00363-5
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DOI: https://doi.org/10.1007/s13721-022-00363-5