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

2018 ◽  
Author(s):  
Youngjun Cho ◽  
Simon J. Julier ◽  
Nadia Bianchi-Berthouze
Keyword(s):  
Perceived Stress ◽  
Thermal Imaging ◽  
Mental Stress ◽  
Feature Learning ◽  
Apparent Temperature ◽  
Automated Inference ◽  
Artificial Neural ◽  
Thermal Signature ◽  
The Impact ◽  
Stress Recognition

AbstractBackgroundA smartphone is a promising tool for daily cardiovascular measurement and mental stress monitoring. Photoplethysmography (PPG) and low-cost thermography can be used to create cheap, convenient and mobile systems. However, to achieve robustness, a person has to remain still for several minutes while a measurement is being taken. This is very cumbersome, and limits the usage in applications such producing instant measurements of stress.ObjectiveWe propose to use smartphone-based mobile PPG and thermal imaging to provide a fast binary measure of stress responses to an event using dynamical physiological changes which occur within 20 seconds of the event finishing.MethodsWe propose a system that uses a smartphone and its physiological sensors to reliably and continuously measure over a short window of time a person’s blood volume pulse, the time interval between heartbeats (R-R interval) and the 1D thermal signature of the nose tip. 17 healthy participants, involved in a series of stress-inducing mental activities, measured their physiological response to stress in the 20 second-window immediately following each activity. A 10-cm Visual Analogue Scale was used by them to self-report their level of mental stress. As a main labeling strategy, normalized K-means clustering is used to better treat interpersonal differences in ratings. By taking an array of the R-R intervals and thermal directionality as a low-level feature input, we mainly use an artificial neural network to enable the automatic feature learning and the machine learning inference process. To compare the automated inference performance, we also extracted widely used high level features from HRV (e.g., LF/HF ratio) and the thermal signature and input them to a k-nearest neighbor to infer perceived stress levels.ResultsFirst, we tested the physiological measurement reliability. The measured cardiac signals were considered highly reliable (signal goodness probability used, Mean=0.9584, SD=0.0151). The proposed 1D thermal signal processing algorithm effectively minimized the effect of respiratory cycles on detecting the apparent temperature of the nose tip (respiratory signal goodness probability Mean=0.8998 to Mean=0). Second, we tested the 20 seconds instant perceived stress inference performance. The best results were obtained by using automatic feature learning and classification using artificial neural networks rather than using pre-crafted features. The combination of both modalities produced higher accuracy on the binary classification task using 17-fold leave-one-subject-out (LOSO) cross-validation (accuracy: HRV+Thermal: 76.96%; HRV: 60.29%; Thermal: 61.37%). The results are comparable with the state of the art automatic stress recognition methods requiring long term measurements (a minimum of 2 minutes for up to around 80% accuracy from LOSO). Lastly, we explored the impact of different data labeling strategies used in the field on the sensitivity of our inference methods and the need for normalization within individual.ConclusionsResults demonstrate the capability of smartphone biomedical imaging in instant mental stress recognition. Given that this approach does not require long measurements requiring attention and reduced mobility, it is more feasible for mobile mental healthcare solution in the wild.

scholarly journals Instant Stress: Detection of Perceived Mental Stress Through Smartphone Photoplethysmography and Thermal Imaging (Preprint)

2018 ◽  
Author(s):  
Youngjun Cho ◽  
Simon J Julier ◽  
Nadia Bianchi-Berthouze
Keyword(s):  
Perceived Stress ◽  
Stress Responses ◽  
Thermal Imaging ◽  
Mental Stress ◽  
Low Cost ◽  
The Self ◽  
Stress Detection ◽  
Thermal Camera ◽  
High Quality

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 must remain still for several minutes while a measurement is being taken. This is cumbersome and makes its use in real-life situations impractical. OBJECTIVE We proposed a system that combines PPG and thermography with the aim of improving cardiovascular signal quality and detecting stress responses quickly. METHODS Using a smartphone camera with a low-cost thermal camera added on, we built a novel system that continuously and reliably measures 2 different types of cardiovascular events: (1) blood volume pulse and (2) vasoconstriction/dilation-induced temperature changes of the nose tip. 17 participants, involved in stress-inducing mental workload tasks, measured their physiological responses to stressors over a short time period (20 seconds) immediately after each task. Participants reported their perceived stress levels on a 10-cm visual analog scale. For the instant stress inference task, we built novel low-level feature sets representing cardiovascular variability. We then used the automatic feature learning capability of artificial neural networks to improve the mapping between the extracted 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 (mean pSQI: 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 (mean pSQI: from 0.714 to 0.157). Second, we found low correlations between the self-reported stress scores and the existing metrics of the cardiovascular signals (ie, heart rate variability and thermal directionality) 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 precrafted features-based methods. In addition, the 17-fold leave-one-subject-out cross-validation results showed that combining both modalities produced higher accuracy than using 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 stress recognition methods that require long-term measurements. Finally, we explored effects of different data labeling strategies on the sensitivity of our inference methods. Our results showed the need for separation of and normalization between individual data. CONCLUSIONS The results demonstrate the feasibility of using smartphone-based imaging for instant stress detection. Given that this approach does not need long-term measurements requiring attention and reduced mobility, we believe it is more suitable for mobile mental health care solutions in the wild.

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

10.2196/10140 ◽  
2019 ◽  
Vol 6 (4) ◽  
pp. e10140 ◽  
Author(s):  
Youngjun Cho ◽  
Simon J Julier ◽  
Nadia Bianchi-Berthouze
Keyword(s):  
Perceived Stress ◽  
Stress Responses ◽  
Thermal Imaging ◽  
Mental Stress ◽  
Low Cost ◽  
The Self ◽  
Stress Detection ◽  
Thermal Camera ◽  
High Quality

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 must remain still for several minutes while a measurement is being taken. This is cumbersome and makes its use in real-life situations impractical. Objective We proposed a system that combines PPG and thermography with the aim of improving cardiovascular signal quality and detecting stress responses quickly. Methods Using a smartphone camera with a low-cost thermal camera added on, we built a novel system that continuously and reliably measures 2 different types of cardiovascular events: (1) blood volume pulse and (2) vasoconstriction/dilation-induced temperature changes of the nose tip. 17 participants, involved in stress-inducing mental workload tasks, measured their physiological responses to stressors over a short time period (20 seconds) immediately after each task. Participants reported their perceived stress levels on a 10-cm visual analog scale. For the instant stress inference task, we built novel low-level feature sets representing cardiovascular variability. We then used the automatic feature learning capability of artificial neural networks to improve the mapping between the extracted 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 (mean pSQI: 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 (mean pSQI: from 0.714 to 0.157). Second, we found low correlations between the self-reported stress scores and the existing metrics of the cardiovascular signals (ie, heart rate variability and thermal directionality) 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 precrafted features-based methods. In addition, the 17-fold leave-one-subject-out cross-validation results showed that combining both modalities produced higher accuracy than using 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 stress recognition methods that require long-term measurements. Finally, we explored effects of different data labeling strategies on the sensitivity of our inference methods. Our results showed the need for separation of and normalization between individual data. Conclusions The results demonstrate the feasibility of using smartphone-based imaging for instant stress detection. Given that this approach does not need long-term measurements requiring attention and reduced mobility, we believe it is more suitable for mobile mental health care solutions in the wild.

scholarly journals Stress Perceived by University Health Sciences Students, 1 Year after COVID-19 Pandemic

2021 ◽  
Vol 18 (10) ◽  
pp. 5233
Author(s):  
Yolanda Marcén-Román ◽  
Angel Gasch-Gallen ◽  
Irene Isabel Vela Martín de la Mota ◽  
Estela Calatayud ◽  
Isabel Gómez-Soria ◽  
...  
Keyword(s):  
Nursing Students ◽  
University Students ◽  
Perceived Stress ◽  
Psychological Health ◽  
Health Sciences ◽  
Online Questionnaire ◽  
Cross Sectional ◽  
Occupational Therapy Students ◽  
Health Sciences Students ◽  
The Impact

Today’s COVID-19 situation can affect university Health Sciences students’ psychological health. This study aimed to analyze the stress caused by the impact of the COVID-19 pandemic on Health Sciences students from the University of Zaragoza (Spain) almost 1 year after the pandemic began. This cross-sectional descriptive study was conducted with a sample of 252 university students who completed a self-administered online questionnaire. It evaluated the impact of perceived stress with a modified scale (PSS-10-C), and assessed anxiety and depression on the Goldberg scale. Students presented stress (13.1%), anxiety (71.4%) and depression (81%). Females (81.7%) and the third-year Occupational Therapy students (p = 0.010) reported perceived stress. Nursing students perceived less stress (OR: 0.148; 95% CI: 0.026 to 0.842). University students developed stress and anxiety due to COVID-19 almost 1 year after the pandemic began. Psychological support measures for these groups should be prioritized.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

scholarly journals Family Physicians’ Standpoint and Mental Health Assessment in the Light of COVID-19 Pandemic—A Nationwide Survey Study

2021 ◽  
Vol 18 (4) ◽  
pp. 2093
Author(s):  
Tina Vilovic ◽  
Josko Bozic ◽  
Marino Vilovic ◽  
Doris Rusic ◽  
Sanja Zuzic Furlan ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Perceived Stress ◽  
Depression Scale ◽  
Family Physicians ◽  
Knowledge Score ◽  
Well Being ◽  
Anxiety And Depression ◽  
Event Scale ◽  
The Impact

During the coronavirus disease 2019 (COVID-19) outbreak, family physicians (FPs) are the backbone of the healthcare system with considerable impact on the general population, and their well-being is of great importance. The aim of this investigation was to assess FPs mental health, as well as knowledge, attitudes and practices (KAPs) regarding the pandemic, and opinions on non-communicable disease (NCD) health care provided to patients. A cross-sectional study was carried out with a sample of 613 FPs. Anxiety and depression levels were estimated with the Hospital Anxiety and Depression Scale, subjective perceived stress with the Perceived Stress Scale, while trauma-related symptoms were assessed using the Impact on Event Scale-COVID19. KAPs toward the pandemic and opinions regarding NCD patients were evaluated with questionnaires accordingly. Results have shown that age (β = −0.02, p = 0.013) and personal risk of COVID‑19 (β = 1.05, p < 0.001) were significant independent correlates of the knowledge score. A total of 87.7% FPs expressed moderate/high perceived stress, 45.2% moderate/severe trauma-related symptoms, 60.4% borderline/abnormal anxiety levels, and 52.4% borderline/abnormal depression levels. Knowledge score was an independent predictor of perceived stress (β = −0.33, p = 0.023) and anxiety (β = −0.31, p = 0.006) levels. Limited accessibility to healthcare services and decreased number of newly-diagnosed NCD cases were mostly agreed on. The pandemic puts a considerable strain on FPs mental health, as well as on public health measures, due to the decreased overall quality of NCD patient health care. Educational programs may bridge the gaps between FPs’ knowledge. Thus lowering anxiety and improving patient care.

scholarly journals O52: THE IMPACT OF STUDENT BACKGROUND ON PERFORMANCE AND STRESS DURING SURGICAL SIMULATION

2021 ◽  
Vol 108 (Supplement_1) ◽  
Author(s):  
A Soualhi ◽  
MSA Amin ◽  
A Aydin ◽  
MS Khan ◽  
P Dasgupta ◽  
...  
Keyword(s):  
Medical Students ◽  
Medical School ◽  
Perceived Stress ◽  
Surgical Simulation ◽  
Demographic Factors ◽  
Skills Acquisition ◽  
Laparoscopic Skills ◽  
Student Background ◽  
Simulation Performance ◽  
The Impact

Abstract Introduction Early exposure of medical students to surgical skills has been associated with an increased interest in pursuing a career in surgery. This study aimed to investigate the impact of student background on basic laparoscopic skills acquisition and perceived stress to workload during simulation. Method Students were recruited to a course modelled on the European Basic Laparoscopic Urological Skills (E-BLUS) programme. Students filled out a pre-course demographic survey, and a post-course survey that evaluated perceived stress to workload (SURG-TLX). Pre- and post-course confidence in laparoscopic skills, performance in the course, as well as SURG-TLX parameters were statistically compared for all demographic factors. Result Twenty-three participants completed the course, fifteen (65%) of which were female. Ten students were junior (first and second year), and thirteen were senior (third year and above). Hobbies were grouped into either requiring hand-eye coordination (n=9), manual dexterity (n=9), or no relevance to surgery (n=5). No significant differences were found in demographic factors for course performance or pre- and post-course confidence. No significant differences were found between male and female students, and between different hobby groups, in all SURG-TLX parameters. Senior students reported a significantly higher Physical Demand of the SURG-TLX compared to juniors (p=0.042). No differences were found in other parameters. Conclusion Our study showed no correlation between student background (notably sex and stage of medical school) and basic laparoscopic skills acquisition or stress during simulation. We therefore suggest that medical students are provided with more exposure to acquire basic laparoscopic skills regardless of their background. Take-home message Medical student background is not a discriminator in surgical simulation performance and stress. All medical students, regardless of their sex or stage of medical school, should be given the opportunity to practice laparoscopic skills.

Prognosis of water levels in a moor groundwater system influenced by hydrology and water extraction using an artificial neural network 

2021 ◽  
Author(s):  
Sascha Flaig ◽  
Timothy Praditia ◽  
Alexander Kissinger ◽  
Ulrich Lang ◽  
Sergey Oladyshkin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Meteorological Data ◽  
Water Levels ◽  
Typical Structure ◽  
Water Abstraction ◽  
Physical Knowledge ◽  
Artificial Neural ◽  
The Impact

&lt;p&gt;In order to prevent possible negative impacts of water abstraction in an ecologically sensitive moor south of Munich (Germany), a &amp;#8220;predictive control&amp;#8221; scheme is in place. We design an artificial neural network (ANN) to provide predictions of moor water levels and to separate hydrological from anthropogenic effects. As the moor is a dynamic system, we adopt the &amp;#8222;Long short-term memory&amp;#8220; architecture.&lt;/p&gt;&lt;p&gt;To find the best LSTM setup, we train, test and compare LSTMs with two different structures: (1) the non-recurrent one-to-one structure, where the series of inputs are accumulated and fed into the LSTM; and (2) the recurrent many-to-many structure, where inputs gradually enter the LSTM (including LSTM forecasts from previous forecast time steps). The outputs of our LSTMs then feed into a readout layer that converts the hidden states into water level predictions. We hypothesize that the recurrent structure is the better structure because it better resembles the typical structure of differential equations for dynamic systems, as they would usually be used for hydro(geo)logical systems. We evaluate the comparison with the mean squared error as test metric, and conclude that the recurrent many-to-many LSTM performs better for the analyzed complex situations. It also produces plausible predictions with reasonable accuracy for seven days prediction horizon.&lt;/p&gt;&lt;p&gt;Furthermore, we analyze the impact of preprocessing meteorological data to evapotranspiration data using typical ETA models. Inserting knowledge into the LSTM in the form of ETA models (rather than implicitly having the LSTM learn the ETA relations) leads to superior prediction results. This finding aligns well with current ideas on physically-inspired machine learning.&lt;/p&gt;&lt;p&gt;As an additional validation step, we investigate whether our ANN is able to correctly identify both anthropogenic and natural influences and their interaction. To this end, we investigate two comparable pumping events under different meteorological conditions. Results indicate that all individual and combined influences of input parameters on water levels can be represented well. The neural networks recognize correctly that the predominant precipitation and lower evapotranspiration during one pumping event leads to a lower decrease of the hydrograph.&lt;/p&gt;&lt;p&gt;To further demonstrate the capability of the trained neural network, scenarios of pumping events are created and simulated.&lt;/p&gt;&lt;p&gt;In conclusion, we show that more robust and accurate predictions of moor water levels can be obtained if available physical knowledge of the modeled system is used to design and train the neural network. The artificial neural network can be a useful instrument to assess the impact of water abstraction by quantifying the anthropogenic influence.&lt;/p&gt;

Sign in / Sign up

Export Citation Format

Share Document