Combining multiple tinyML models for multimodal context-aware stress recognition on constrained microcontrollers

Gibbs, M. ORCID: 0009-0009-4754-7242, Woodward, K. ORCID: 0000-0003-3302-1345 and Kanjo, E. ORCID: 0000-0002-1720-0661, 2023. Combining multiple tinyML models for multimodal context-aware stress recognition on constrained microcontrollers. IEEE Micro. ISSN 0272-1732

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As stress continues to be a major health concern, there is growing interest in developing effective stress management systems that can detect and mitigate stress. Deep Neural Networks (DNNs) have shown their effectiveness in accurately classifying stress, but most existing solutions rely on the cloud or large obtrusive devices for inference. The emergence of tinyML provides an opportunity to bridge this gap and enable ubiquitous intelligent systems. In this paper, we propose a context-aware stress detection approach that uses a microcontroller to continuously infer physical activity to mitigate motion artifacts when inferring stress from heart rate and electrodermal activity. We deploy two DNNs onto a single resource-constrained microcontroller for real-world stress recognition, with the resultant stress and activity recognition models achieving 88% and 98% accuracy respectively. Our proposed context-aware approach improves the accuracy and privacy of stress detection systems while eliminating the need to store or transmit sensitive health data.

Item Type: Journal article
Publication Title: IEEE Micro
Creators: Gibbs, M., Woodward, K. and Kanjo, E.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2 November 2023
ISSN: 0272-1732
Rights: © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions: Schools > School of Science and Technology
Record created by: Jeremy Silvester
Date Added: 08 Nov 2023 10:50
Last Modified: 07 Mar 2024 09:46

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