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.
|Title of host publication
|2020 IEEE 63rd International Midwest Symposium on Circuits and Systems, MWSCAS 2020 - Proceedings
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - Aug 2020
|63rd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2020 - Springfield, United States
Duration: Aug 9 2020 → Aug 12 2020
|Midwest Symposium on Circuits and Systems
|63rd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2020
|8/9/20 → 8/12/20
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Electrical and Electronic Engineering