Enabling Eating Detection in a Free-living Environment: Integrative Engineering and Machine Learning Study.

Journal: Journal of medical Internet research
PMID: 35230244

Abstract

BACKGROUND: Monitoring eating is central to the care of many conditions such as diabetes, eating disorders, heart diseases, and dementia. However, automatic tracking of eating in a free-living environment remains a challenge because of the lack of a mature system and large-scale, reliable training set.

Authors

  • Bo Zhang
    Department of Clinical Pharmacology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China.
  • Kaiwen Deng
    State Key Laboratory of Urban Water Resource and Environment, School of Municipal and Environmental Engineering, Harbin Institute of Technology, 73 Huanghe Road, Harbin 150090, PR China.
  • Jie Shen
    Anhui Provincial Center for Drug Clinical Evaluation, Yijishan Hospital, Wannan Medical College, Wuhu, Anhui 241001, China; Pharmacy School, Wannan Medical College, Wuhu, Anhui 241002, China; Department of Clinical Pharmacy, Yijishan Hospital, Wannan Medical College, Wuhu, Anhui 241001, China; Anhui Provincial Engineering Research Center for Polysaccharides Drugs, Wannan Medical College, Wuhu, Anhui 241001, China.
  • Lingrui Cai
    Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA.
  • Bohdana Ratitch
    Eli Lilly and Company, Indianapolis, IN, United States.
  • Haoda Fu
    Eli Lilly and Company.
  • Yuanfang Guan
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. gyuanfan@umich.edu.

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