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Digital Twins for Food Nutrition and Health Based on Cloud Communication

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Image Based Computing for Food and Health Analytics: Requirements, Challenges, Solutions and Practices

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Abstract

The present work aims to meet the needs of human nutrition and health and realize multi-objective personalized intelligent recommendations of food. Firstly, human nutrition and health needs are analyzed through cloud communication technology. Besides, the optimized AlexNet is adopted to classify food images. The obtained image information and human nutrition and health data are mapped by Digital Twins (DTs) technology. Finally, the food DTs nutrition evaluation model is constructed based on cloud computing and AlexNet. Besides, the Sum Throughput Maximization (STM) algorithm is put forward to improve the objective function. The simulation results indicate that, compared with other algorithms, the STM algorithm has the best throughput. This algorithm improves the system fairness index and ensures the stability of human nutrition and health data identification and processing. Meanwhile, the improved AlexNet algorithm has an accuracy of 93.17% for food image classification and recognition, at least 2.01% higher than other neural network algorithms. Therefore, the algorithms reported here can intelligently recommend human nutrition and health needs and multi-target personalized nutrition matching. The research outcomes can reference the subsequently balanced intake and intellectual development in the dietary nutrition field.

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Acknowledgements

This study was supported by EU Commission (contract 2018-1-ES01-KA201-05093); Ministry of Science, Unversities and Innovation of the Spanish Kingdom (grant RTI2018-100683-B-I00); Ministerio de Economía y Competitividad (ES) (research project ECO2015-63880-R); Fundación Centro de Supercomputación de Castilla y León.

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Authors and Affiliations

  1. Department of Game Design, Faculty of Arts, Uppsala University, Uppsala, Sweden

    Zhihan Lv

  2. College of Computer Science & Technology, Qingdao University, Qingdao, China

    Liang Qiao

Authors
  1. Zhihan Lv
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  2. Liang Qiao
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Editor information

Editors and Affiliations

  1. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Rajeev Tiwari

  2. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Deepika Koundal

  3. School of Health Sciences and Technology, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Shuchi Upadhyay

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Lv, Z., Qiao, L. (2023). Digital Twins for Food Nutrition and Health Based on Cloud Communication. In: Tiwari, R., Koundal, D., Upadhyay, S. (eds) Image Based Computing for Food and Health Analytics: Requirements, Challenges, Solutions and Practices. Springer, Cham. https://doi.org/10.1007/978-3-031-22959-6_3

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