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

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

doi:10.1109/MWSCAS48704.2020.9184466

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 proceeding › Conference contribution

6 Scopus citations

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 language	English (US)
Title of host publication	2020 IEEE 63rd International Midwest Symposium on Circuits and Systems, MWSCAS 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	978-981
Number of pages	4
ISBN (Electronic)	9781538629161
DOIs	https://doi.org/10.1109/MWSCAS48704.2020.9184466
State	Published - Aug 2020
Externally published	Yes
Event	63rd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2020 - Springfield, United States Duration: Aug 9 2020 → Aug 12 2020

Publication series

Name	Midwest Symposium on Circuits and Systems
Volume	2020-August
ISSN (Print)	1548-3746

Conference

Conference	63rd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2020
Country/Territory	United States
City	Springfield
Period	8/9/20 → 8/12/20

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Electrical and Electronic Engineering

Access to Document

10.1109/MWSCAS48704.2020.9184466

Cite this

Prashant Bhanushali, S., Sadasivuni, S., Banerjee, I., & Sanyal, A. (2020). Digital Machine Learning Circuit for Real-Time Stress Detection from Wearable ECG Sensor. In 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems, MWSCAS 2020 - Proceedings (pp. 978-981). Article 9184466 (Midwest Symposium on Circuits and Systems; Vol. 2020-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MWSCAS48704.2020.9184466

Digital Machine Learning Circuit for Real-Time Stress Detection from Wearable ECG Sensor. / Prashant Bhanushali, Sumukh; Sadasivuni, Sudarsan; Banerjee, Imon et al.
2020 IEEE 63rd International Midwest Symposium on Circuits and Systems, MWSCAS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 978-981 9184466 (Midwest Symposium on Circuits and Systems; Vol. 2020-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Prashant Bhanushali, S, Sadasivuni, S, Banerjee, I & Sanyal, A 2020, Digital Machine Learning Circuit for Real-Time Stress Detection from Wearable ECG Sensor. in 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems, MWSCAS 2020 - Proceedings., 9184466, Midwest Symposium on Circuits and Systems, vol. 2020-August, Institute of Electrical and Electronics Engineers Inc., pp. 978-981, 63rd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2020, Springfield, United States, 8/9/20. https://doi.org/10.1109/MWSCAS48704.2020.9184466

Prashant Bhanushali S, Sadasivuni S, Banerjee I, Sanyal A. Digital Machine Learning Circuit for Real-Time Stress Detection from Wearable ECG Sensor. In 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems, MWSCAS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. p. 978-981. 9184466. (Midwest Symposium on Circuits and Systems). doi: 10.1109/MWSCAS48704.2020.9184466

Prashant Bhanushali, Sumukh ; Sadasivuni, Sudarsan ; Banerjee, Imon et al. / Digital Machine Learning Circuit for Real-Time Stress Detection from Wearable ECG Sensor. 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems, MWSCAS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 978-981 (Midwest Symposium on Circuits and Systems).

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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.",

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

Abstract

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