Machine learning-powered wearable interface for distinguishable and predictable sweat sensing.

Journal: Biosensors & bioelectronics
PMID: 39208509

Abstract

The constrained resources on wearable devices pose a challenge in meeting the demands for comprehensive sensing information, and current wearable non-enzymatic sensors face difficulties in achieving specific detection in biofluids. To address this issue, we have developed a highly selective non-enzymatic sweat sensor that seamlessly integrates with machine learning, ensuring reliable sensing and physiological monitoring of sweat biomarkers during exercise. The sensor consists of two electrodes supported by a microsystem that incorporates signal processing and wireless communication. The device generates four explainable features that can be used to accurately predict tyrosine and tryptophan concentrations, as well as sweat pH. The reliability of this device has been validated through rigorous statistical analysis, and its performance has been tested in subjects with and without supplemental amino acid intake during cycling trials. Notably, a robust linear relationship has been identified between tryptophan and tyrosine concentrations in the collected samples, irrespective of the pH dimension. This innovative sensing platform is highly portable and has significant potential to advance the biomedical applications of non-enzymatic sensors. It can markedly improve accuracy while decreasing costs.

Authors

  • Zhongzeng Zhou
    College of Chemistry and Environmental Engineering, School of Biomedical Engineering of Health Science Center, Shenzhen University, Shenzhen, Guangdong, China518060.
  • Xuecheng He
    College of Chemistry and Environmental Engineering, School of Biomedical Engineering of Health Science Center, The Institute for Advanced Study (IAS), Shenzhen University, Shenzhen, Guangdong, 518060, China.
  • Jingyu Xiao
    College of Chemistry and Environmental Engineering, School of Biomedical Engineering of Health Science Center, The Institute for Advanced Study (IAS), Shenzhen University, Shenzhen, Guangdong, 518060, China.
  • Jiuxiang Pan
    College of Materials Science and Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China.
  • MengMeng Li
    Key Laboratory of Chinese Materia Medica, Ministry of Education of Heilongjiang University of Chinese Medicine, No. 24 Haping Road, Xiangfang District, Harbin, 150040, PR China.
  • Tailin Xu
    School of Biomedical Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China; Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University, Guangdong, 518060, China. Electronic address: xutailin@ustb.edu.cn.
  • Xueji Zhang
    Research Center for Bioengineering and Sensing Technology, University of Science and Technology Beijing, Beijing 100083, China. josephwang@ucsd.edu zhang@ucsd.edu zhangxueji@ustb.edu.cn.

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