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Estimation of Energy Expenditure Using a Patch-Type Sensor Module with an Incremental Radial Basis Function Neural

Meina Li1, Keun-Chang Kwak2, Youn Tae Kim3

  • 1College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, China. lmn@jlu.edu.cn.

Sensors (Basel, Switzerland)
|September 27, 2016
PubMed
Summary

This study introduces a wearable patch sensor to estimate energy expenditure, replacing uncomfortable calorimetry masks. The device uses an embedded neural network for accurate, real-time human energy expenditure monitoring.

Keywords:
context-based fuzzy c-means clusteringenergy expenditurelinguistic regressionradial basis function neural network

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Area of Science:

  • Biomedical Engineering
  • Wearable Technology
  • Artificial Intelligence in Health

Background:

  • Indirect calorimetry is the standard for measuring human energy expenditure but requires cumbersome masks.
  • Existing methods lack convenience and comfort, hindering widespread, long-term monitoring.
  • Need for non-invasive, user-friendly solutions for accurate energy expenditure assessment.

Purpose of the Study:

  • To develop a novel patch-type sensor module for estimating human energy expenditure.
  • To integrate an incremental radial basis function neural network (RBFNN) for real-time calculations.
  • To enhance user comfort and convenience in energy expenditure monitoring.

Main Methods:

  • Developed a wearable sensor module with ECG electrodes and a three-axis accelerometer.
  • Implemented an embedded incremental learning network combining linear regression (LR) and RBFNN.
  • Utilized context-based fuzzy c-means (CFCM) clustering to guide network construction based on LR error distribution.

Main Results:

  • The sensor module enables real-time monitoring of heart rate (HR) and movement index (MI).
  • The embedded RBFNN effectively estimates energy expenditure.
  • The system offers a comfortable and convenient alternative to traditional calorimetry.

Conclusions:

  • The developed patch-type sensor with an embedded RBFNN provides a viable and user-friendly method for estimating human energy expenditure.
  • This technology has the potential to improve personal health monitoring and activity tracking.
  • Future research can further refine the algorithm for enhanced accuracy and broader applications.