Instant Stress: Detection of Perceived Mental Stress Through Smartphone Photoplethysmography and Thermal Imaging

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: First, we found that the measured PPG signals presented high quality cardiac cyclic information (relative power Signal Quality Index, pSQI: M=0.755, SD=0.068). We also found that the measured thermal changes of the nose tip presented high quality breathing cyclic information and filtering helped extract vasoconstriction/dilation-induced patterns with fewer respiratory effects (respiratory pSQI: from M=0.714 to M=0.157). Second, we found low correlations between the self-reported stress scores and the existing metrics of the two cardiovascular signals (i.e. heart rate variability and thermal directionality metrics) from short measurements, suggesting they were not very dependent upon one another. Third, we tested the performance of the instant perceived stress inference method. The proposed method achieved significantly higher accuracies than existing pre-crafted features based-methods. In addition, the 17-fold Leave-One-Subject-Out (LOSO) cross-validation results showed that combination of both modalities produced higher accuracy in comparison with the use of PPG or thermal imaging only (PPG+Thermal: 78.33%; PPG: 68.53%; Thermal: 58.82%). The multimodal results are comparable to the state-of-the-art automatic stress recognition methods that require long term measurements (usually, at least a period of 2 minutes is required for an accuracy of around 80% from LOSO). Lastly, we explored effects of different widely-used data labeling strategies on the sensitivity of our inference methods. Our results showed the need for separation of and normalization between individual data. // Conclusions: Results demonstrate the feasibility of using smartphone-based imaging for instant mental stress recognition. Given that this approach does not need long-term measurements requiring attention and reduced mobility, we believe it is more suitable for mobile mental healthcare solutions in the wild

Mobile Thermography-based Physiological Computing for Automatic Recognition of a Person’s Mental Stress

This thesis explores the use of Mobile Thermography1, a significantly less investigated sensing capability, with the aim of reliably extracting a person’s multiple physiological signatures and recognising mental stress in an automatic, contactless manner. Mobile thermography has greater potentials for real-world applications because of its light-weight, low computation-cost characteristics. In addition, thermography itself does not necessarily require the sensors to be worn directly on the skin. It raises less privacy concerns and is less sensitive to ambient lighting conditions. The work presented in this thesis is structured through a three-stage approach that aims to address the following challenges: i) thermal image processing for mobile thermography in variable thermal range scenes; ii) creation of rich and robust physiology measurements; and iii) automated stress recognition based on such measurements. Through the first stage (Chapter 4), this thesis contributes new processing techniques to address negative effects of environmental temperature changes upon automatic tracking of regions-of-interest and measuring of surface temperature patterns. In the second stage (Chapters 5,6,7), the main contributions are: robustness in tracking respiratory and cardiovascular thermal signatures both in constrained and unconstrained settings (e.g. respiration: strong correlation with ground truth, r=0.9987), and investigation of novel cortical thermal signatures associated with mental stress. The final stage (Chapters 8,9) contributes automatic stress inference systems that focus on capturing richer dynamic information of physiological variability: firstly, a novel respiration representation-based system (which has achieved state-of-the-art performance: 84.59% accuracy, two stress levels), and secondly, a novel cardiovascular representation-based system using short-term measurements of nasal thermal variability and heartrate variability from another sensing channel (78.33% accuracy achieved from 20seconds measurements). Finally, this thesis contributes software libraries and incrementally built labelled datasets of thermal images in both constrained and everyday ubiquitous settings. These are used to evaluate performance of our proposed computational methods across the three-stages

Your blush gives you away: detecting hidden mental states with remote photoplethysmography and thermal imaging

Multimodal emotion recognition techniques are increasingly essential for assessing mental states. Image-based methods, however, tend to focus predominantly on overt visual cues and often overlook subtler mental state changes. Psychophysiological research has demonstrated that HR and skin temperature are effective in detecting ANS activities, thereby revealing these subtle changes. However, traditional HR tools are generally more costly and less portable, while skin temperature analysis usually necessitates extensive manual processing. Advances in remote-PPG and automatic thermal ROI detection algorithms have been developed to address these issues, yet their accuracy in practical applications remains limited. This study aims to bridge this gap by integrating r-PPG with thermal imaging to enhance prediction performance. Ninety participants completed a 20-minute questionnaire to induce cognitive stress, followed by watching a film aimed at eliciting moral elevation. The results demonstrate that the combination of r-PPG and thermal imaging effectively detects emotional shifts. Using r-PPG alone, the prediction accuracy was 77% for cognitive stress and 61% for moral elevation, as determined by SVM. Thermal imaging alone achieved 79% accuracy for cognitive stress and 78% for moral elevation, utilizing a RF algorithm. An early fusion strategy of these modalities significantly improved accuracies, achieving 87% for cognitive stress and 83% for moral elevation using RF. Further analysis, which utilized statistical metrics and explainable machine learning methods including SHAP, highlighted key features and clarified the relationship between cardiac responses and facial temperature variations. Notably, it was observed that cardiovascular features derived from r-PPG models had a more pronounced influence in data fusion, despite thermal imaging's higher predictive accuracy in unimodal analysis.Comment: 28 pages, 6 figure

"Reading Between the Heat": Co-Teaching Body Thermal Signatures for Non-intrusive Stress Detection

Stress impacts our physical and mental health as well as our social life. A passive and contactless indoor stress monitoring system can unlock numerous important applications such as workplace productivity assessment, smart homes, and personalized mental health monitoring. While the thermal signatures from a user's body captured by a thermal camera can provide important information about the "fight-flight" response of the sympathetic and parasympathetic nervous system, relying solely on thermal imaging for training a stress prediction model often lead to overfitting and consequently a suboptimal performance. This paper addresses this challenge by introducing ThermaStrain, a novel co-teaching framework that achieves high-stress prediction performance by transferring knowledge from the wearable modality to the contactless thermal modality. During training, ThermaStrain incorporates a wearable electrodermal activity (EDA) sensor to generate stress-indicative representations from thermal videos, emulating stress-indicative representations from a wearable EDA sensor. During testing, only thermal sensing is used, and stress-indicative patterns from thermal data and emulated EDA representations are extracted to improve stress assessment. The study collected a comprehensive dataset with thermal video and EDA data under various stress conditions and distances. ThermaStrain achieves an F1 score of 0.8293 in binary stress classification, outperforming the thermal-only baseline approach by over 9%. Extensive evaluations highlight ThermaStrain's effectiveness in recognizing stress-indicative attributes, its adaptability across distances and stress scenarios, real-time executability on edge platforms, its applicability to multi-individual sensing, ability to function on limited visibility and unfamiliar conditions, and the advantages of its co-teaching approach.Comment: 29 page

PhysioKit: An Open-Source, Low-Cost Physiological Computing Toolkit for Single- and Multi-User Studies

The proliferation of physiological sensors opens new opportunities to explore interactions, conduct experiments and evaluate the user experience with continuous monitoring of bodily functions. Commercial devices, however, can be costly or limit access to raw waveform data, while low-cost sensors are efforts-intensive to setup. To address these challenges, we introduce PhysioKit, an open-source, low-cost physiological computing toolkit. PhysioKit provides a one-stop pipeline consisting of (i) a sensing and data acquisition layer that can be configured in a modular manner per research needs, and (ii) a software application layer that enables data acquisition, real-time visualization and machine learning (ML)-enabled signal quality assessment. This also supports basic visual biofeedback configurations and synchronized acquisition for co-located or remote multi-user settings. In a validation study with 16 participants, PhysioKit shows strong agreement with research-grade sensors on measuring heart rate and heart rate variability metrics data. Furthermore, we report usability survey results from 10 small-project teams (44 individual members in total) who used PhysioKit for 4–6 weeks, providing insights into its use cases and research benefits. Lastly, we discuss the extensibility and potential impact of the toolkit on the research community

Early Prediction of Hemodynamic Shock in Pediatric Intensive Care Units With Deep Learning on Thermal Videos

Shock is one of the major killers in intensive care units, and early interventions can potentially reverse it. In this study, we advance a noncontact thermal imaging modality for continuous monitoring of hemodynamic shock working on 1,03,936 frames from 406 videos recorded longitudinally upon 22 pediatric patients. Deep learning was used to preprocess and extract the Center-to-Peripheral Difference (CPD) in temperature values from the videos. This time-series data along with the heart rate was finally analyzed using Long-Short Term Memory models to predict the shock status up to the next 6 h. Our models achieved the best area under the receiver operating characteristic curve of 0.81 ± 0.06 and area under the precision-recall curve of 0.78 ± 0.05 at 5 h, providing sufficient time to stabilize the patient. Our approach, thus, provides a reliable shock prediction using an automated decision pipeline that can provide better care and save lives