Development of a Real-Time Stress Detection System for Older Adults with Heart Rate Data

Di Campli San Vito, P., Shakeri, G., Ross, J., Yang, X. and Brewster, S. (2023) Development of a Real-Time Stress Detection System for Older Adults with Heart Rate Data. In: 16th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA '23), Corfu, Greece, 5-7 Jul 2023, ISBN 9798400700699 (doi: 10.1145/3594806.3594817)

Abstract

Stress is one of the factors considerably contributing to older adult’s decreasing overall health. Detecting stress in real-time could aid family members to intervene more timely and keep older adults healthier. However, many stress detection systems are not detecting in real-time, depend on multiple devices, capture a plethora of inconveniently sampled data, or use data from younger adults. In this paper, we built a real-time stress detection system for older adults using only heart beats per minute (BPM), which can be easily obtained with most single, comfortable devices. We collected data from people over 60 (N=15), evaluating the Mannheim Multicomponent Stress Test (MMST) for older adults, then built a machine learning model with a classification performance of 76% (AUC) on BPM alone and tested it in real-time in another experiment, comparing the model’s effectiveness with four different heart rate devices. Detection performance decreased considerably (51%) when using the model in another experiment and could not be used successfully with other devices, while a reduced MMST induced stress comparable to the full test suite.

• Item Type: Conference Proceedings
• Additional Information: This research was part of the RadioMe project (EPSRC (EP/S026991/1)).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Di Campli San Vito, Dr Patrizia and Shakeri, Dr Gozel and Brewster, Professor Stephen and Yang, Dr Xiaochen
• Authors: Di Campli San Vito, P., Shakeri, G., Ross, J., Yang, X., and Brewster, S.
• College/School: College of Science and Engineering > School of Computing Science; College of Science and Engineering > School of Mathematics and Statistics > Statistics
• ISBN: 9798400700699
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA '23)
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
304275		Radio Me: Real-time Radio Remixing for people with mild to moderate dementia who live alone, incorporating Agitation Reduction, and Reminders.	Stephen Brewster	Engineering and Physical Sciences Research Council (EPSRC)	EP/S027491/1	Computing Science