Analysing the performance of stress detection models on consumer-grade wearable devices
Ninh, Van-TuORCID: 0000-0003-0641-8806, Smyth, SinéadORCID: 0000-0002-8736-0505, Tran, Minh-TrietORCID: 0000-0003-3046-3041 and Gurrin, CathalORCID: 0000-0003-2903-3968
(2021)
Analysing the performance of stress detection models on consumer-grade wearable devices.
In: International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, 21-23 Septr 2021, Cancun, Mexico.
ISBN 978-1-64368-195-5
Identifying stress levels can provide valuable data for mental health analytics as well as labels for annotation systems. Although much research has been conducted into stress detection models using heart rate variability at a higher cost of data collection, there is a lack of research on the potential of using low-resolution Electrodermal Activity (EDA) signals from consumer-grade wearable devices to identify stress patterns. In this paper, we concentrate on performing statistical analyses on the stress detection capability of two popular approaches of training stress detection models with stress-related biometric signals: user-dependent and userindependent models. Our research manages to show that user-dependent models are statistically more accurate for stress detection. In terms of effectiveness assessment, the balanced accuracy (BA) metric is employed to evaluate the capability of distinguishing stress and non-stress conditions of the models trained on either low-resolution or high-resolution Electrodermal Activity (EDA) signals. The results from the experiment show that training the model with (comparatively lowcost) low-resolution EDA signal does not affect the stress detection accuracy of the model significantly compared to using a high-resolution EDA signal. Our research results demonstrate the potential of attaching the user-dependent stress detection model trained on personal low-resolution EDA signal recorded to collect data in daily life to provide users with personal stress level insight and analysis.
Metadata
Item Type:
Conference or Workshop Item (Paper)
Event Type:
Conference
Refereed:
Yes
Uncontrolled Keywords:
Stress detection using Electrodermal Activity signal; Model Selection; Statistical Analysis; Hypothesis Testing
Fujita, Hamido and Perez-Meana, Hector, (eds.)
New Trends in Intelligent Software Methodologies, Tools and Techniques. Frontiers in Artificial Intelligence and Applications
337.
IOS Press BV. ISBN 978-1-64368-195-5
This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:
ADAPT Core under Grant Agreement No. 13/RC/2106, Science Foundation Ireland (SF) Research Centres Programme and is co-funded under the European Regional Development Fund (ERDF) through Grant Number 13/RC/2106 References P2.
ID Code:
26428
Deposited On:
03 Nov 2021 13:03 by
Van Tu Ninh
. Last Modified 20 Apr 2022 11:12