Digital Machine Learning Circuit for Real-Time Stress Detection from Wearable ECG Sensor

Sumukh Prashant Bhanushali, Sudarsan Sadasivuni, Imon Banerjee, Arindam Sanyal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a digital machine learning circuit for classifying stress condition from chest ECG signal from a wearable sensor. To minimize hardware cost, we use only 5 time-domain features that have much lower area and power consumption than frequency domain or combination of time and frequency domain features as is used conventionally. We test the time-domain features on several machine learning algorithms. Random Forest classifier shows the best classification accuracy of 0.96 with the time-domain features at an estimated power consumption of only 1.16mW at 65nm CMOS process which demonstrates feasibility of embedding a machine learning classifier in a wearable ECG sensor for real-time, continuous stress detection.

Original languageEnglish (US)
Title of host publication2020 IEEE 63rd International Midwest Symposium on Circuits and Systems, MWSCAS 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages978-981
Number of pages4
ISBN (Electronic)9781538629161
DOIs
StatePublished - Aug 2020
Externally publishedYes
Event63rd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2020 - Springfield, United States
Duration: Aug 9 2020Aug 12 2020

Publication series

NameMidwest Symposium on Circuits and Systems
Volume2020-August
ISSN (Print)1548-3746

Conference

Conference63rd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2020
Country/TerritoryUnited States
CitySpringfield
Period8/9/208/12/20

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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