Digital Machine Learning Circuit for Real-Time Stress Detection from Wearable ECG Sensor
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 frequen...
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Published in | 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS) pp. 978 - 981 |
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Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.08.2020
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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ISSN: | 1558-3899 |
DOI: | 10.1109/MWSCAS48704.2020.9184466 |