Instant Automated Inference of Perceived Mental Stress through Smartphone PPG and Thermal Imaging

TL;DR

This paper introduces a novel smartphone-based system combining PPG and thermal imaging to quickly and reliably infer perceived mental stress from physiological signals, enabling practical real-life stress monitoring.

Contribution

The study presents a new integrated system using smartphone PPG and thermal imaging, along with neural network-based analysis, to improve instant mental stress detection.

Findings

Effective stress inference within 20 seconds post-task

Improved accuracy over traditional hand-engineered features

Reliable physiological measurement with minimal motion constraints

Abstract

Background: A smartphone is a promising tool for daily cardiovascular measurement and mental stress monitoring. A smartphone camera-based PhotoPlethysmoGraphy (PPG) and a low-cost thermal camera can be used to create cheap, convenient and mobile monitoring systems. However, to ensure reliable monitoring results, a person has to remain still for several minutes while a measurement is being taken. This is very cumbersome and makes its use in real-life mobile situations quite impractical. Objective: We propose a system which combines PPG and thermography with the aim of improving cardiovascular signal quality and capturing stress responses quickly. Methods: Using a smartphone camera with a low cost thermal camera added on, we built a novel system which continuously and reliably measures two different types of cardiovascular events: i) blood volume pulse and ii) vasoconstriction/dilation-induced temperature changes of the nose tip. 17 healthy participants, involved in a series of stress-inducing mental workload tasks, measured their physiological responses to stressors over a short window of time (20 seconds) immediately after each task. Participants reported their level of perceived mental stress using a 10-cm Visual Analogue Scale (VAS). We used normalized K-means clustering to reduce interpersonal differences in the self-reported ratings. For the instant stress inference task, we built novel low-level feature sets representing variability of cardiovascular patterns. We then used the automatic feature learning capability of artificial Neural Networks (NN) to improve the mapping between the extracted set of features and the self-reported ratings. We compared our proposed method with existing hand-engineered features-based machine learning methods. Results, Conclusions: ... due to limited space here, we refer to our manuscript.