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Carbohydrate Content Classification Using Postprandial Heart Rate Responses from Non-Invasive Wearables.

Lucy Chikwetu1, Rabih Younes1

  • 1Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA.

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Summary

This study shows non-invasive wearables can classify carbohydrate intake using heart rate responses. This technology aids diabetes management by tracking dietary carbohydrate content.

Keywords:
automated dietary monitoring (ADM) systemscarbohydrate content classificationdiabetes managementdietary monitoringmHealthmachine learning for healthpostprandial heart rate responsesprecision nutritionwearables

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Area of Science:

  • Biomedical Engineering
  • Wearable Technology
  • Diabetes Management

Background:

  • Rising type 2 diabetes incidence necessitates improved dietary monitoring tools.
  • Current technologies track meal timing and content, but non-invasive carbohydrate classification is underexplored.

Purpose of the Study:

  • To investigate carbohydrate content classification using postprandial heart rate (PHR) responses from non-invasive wearables.
  • To develop and validate a system for estimating dietary carbohydrate load.

Main Methods:

  • Developed the timeStampr iOS app for data labeling and ground truth collection.
  • Conducted a pilot study with 23 participants using Empatica E4 devices to record heart rate.
  • Collected data during consumption of low-carbohydrate and carbohydrate-rich meals.
  • Trained a Light Gradient Boosting Machine (LGBM) model for classification.

Main Results:

  • Classifiers achieved over 84% accuracy, precision, recall, and AUCROC within a 60-second window.
  • Demonstrated robust performance in distinguishing between low- and high-carbohydrate meals.
  • Successfully utilized PHR signals for dietary carbohydrate content classification.

Conclusions:

  • Postprandial heart rate responses from non-invasive wearables show potential for classifying dietary carbohydrate content.
  • This approach could enhance diabetes management by providing objective dietary intake data.
  • Further research is warranted to refine the technology and address limitations such as sensor performance across diverse skin tones.