GSR Analysis for Stress: Development and Validation of an Open Source Tool for Noisy Naturalistic GSR Data

The stress detection problem is receiving great attention in related research communities. This is due to its essential part in behavioral studies for many serious health problems and physical illnesses. There are different methods and algorithms for stress detection using different physiological signals. Previous studies have already shown that Galvanic Skin Response (GSR), also known as Electrodermal Activity (EDA), is one of the leading indicators for stress. However, the GSR signal itself is not trivial to analyze. Different features are extracted from GSR signals to detect stress in people like the number of peaks, max peak amplitude, etc. In this paper, we are proposing an open-source tool for GSR analysis, which uses deep learning algorithms alongside statistical algorithms to extract GSR features for stress detection. Then we use different machine learning algorithms and Wearable Stress and Affect Detection (WESAD) dataset to evaluate our results. The results show that we are capable of detecting stress with the accuracy of 92 percent using 10-fold cross-validation and using the features extracted from our tool.Comment: 6 pages and 5 figures. Link to the github of the tool: https://github.com/HealthSciTech/pyED

Detection of COVID-19 Using Heart Rate and Blood Pressure: Lessons Learned from Patients with ARDS

The world has been affected by COVID-19 coronavirus. At the time of this study, the number of infected people in the United States is the highest globally (7.9 million infections). Within the infected population, patients diagnosed with acute respiratory distress syndrome (ARDS) are in more life-threatening circumstances, resulting in severe respiratory system failure. Various studies have investigated the infections to COVID-19 and ARDS by monitoring laboratory metrics and symptoms. Unfortunately, these methods are merely limited to clinical settings, and symptom-based methods are shown to be ineffective. In contrast, vital signs (e.g., heart rate) have been utilized to early-detect different respiratory diseases in ubiquitous health monitoring. We posit that such biomarkers are informative in identifying ARDS patients infected with COVID-19. In this study, we investigate the behavior of COVID-19 on ARDS patients by utilizing simple vital signs. We analyze the long-term daily logs of blood pressure and heart rate associated with 70 ARDS patients admitted to five University of California academic health centers (containing 42506 samples for each vital sign) to distinguish subjects with COVID-19 positive and negative test results. In addition to the statistical analysis, we develop a deep neural network model to extract features from the longitudinal data. Using only the first eight days of the data, our deep learning model is able to achieve 78.79% accuracy to classify the vital signs of ARDS patients infected with COVID-19 versus other ARDS diagnosed patients

Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings

Daily monitoring of stress is a critical component of maintaining optimal physical and mental health. Physiological signals and contextual information have recently emerged as promising indicators for detecting instances of heightened stress. Nonetheless, developing a real-time monitoring system that utilizes both physiological and contextual data to anticipate stress levels in everyday settings while also gathering stress labels from participants represents a significant challenge. We present a monitoring system that objectively tracks daily stress levels by utilizing both physiological and contextual data in a daily-life environment. Additionally, we have integrated a smart labeling approach to optimize the ecological momentary assessment (EMA) collection, which is required for building machine learning models for stress detection. We propose a three-tier Internet-of-Things-based system architecture to address the challenges. We utilized a cross-validation technique to accurately estimate the performance of our stress models. We achieved the F1-score of 70\% with a Random Forest classifier using both PPG and contextual data, which is considered an acceptable score in models built for everyday settings. Whereas using PPG data alone, the highest F1-score achieved is approximately 56\%, emphasizing the significance of incorporating both PPG and contextual data in stress detection tasks

Pregnant women's daily patterns of well-being before and during the COVID-19 pandemic in Finland: Longitudinal monitoring through smartwatch technology

Background: Technology enables the continuous monitoring of personal health parameter data during pregnancy regardless of the disruption of normal daily life patterns. Our research group has established a project investigating the usefulness of an Internet of Things-based system and smartwatch technology for monitoring women during pregnancy to explore variations in stress, physical activity and sleep. The aim of this study was to examine daily patterns of well-being in pregnant women before and during the national stay-at-home restrictions related to the COVID-19 pandemic in Finland.Methods: A longitudinal cohort study design was used to monitor pregnant women in their everyday settings. Two cohorts of pregnant women were recruited. In the first wave in January-December 2019, pregnant women with histories of preterm births (gestational weeks 22-36) or late miscarriages (gestational weeks 12-21); and in the second wave between October 2019 and March 2020, pregnant women with histories of full-term births (gestational weeks 37-42) and no pregnancy losses were recruited. The final sample size for this study was 38 pregnant women. The participants continuously used the Samsung Gear Sport smartwatch and their heart rate variability, and physical activity and sleep data were collected. Subjective stress, activity and sleep reports were collected using a smartphone application developed for this study. Data between February 12 to April 8, 2020 were included to cover four-week periods before and during the national stay-at-home restrictions. Hierarchical linear mixed models were exploited to analyze the trends in the outcome variables.Results: The pandemic-related restrictions were associated with changes in heart rate variability: the standard deviation of all normal inter-beat intervals (p = 0.034), low-frequency power (p = 0.040) and the low-frequency/high-frequency ratio (p = 0.013) increased compared with the weeks before the restrictions. Women's subjectively evaluated stress levels also increased significantly. Physical activity decreased when the restrictions were set and as pregnancy proceeded. The total sleep time also decreased as pregnancy proceeded, but pandemic-related restrictions were not associated with sleep. Daily rhythms changed in that the participants overall started to sleep later and woke up later.Conclusions: The findings showed that Finnish pregnant women coped well with the pandemic-related restrictions and lockdown environment in terms of stress, physical activity and sleep.</div

COVID Symptoms, Symptom Clusters, and Predictors for Becoming a Long-Hauler: Looking for Clarity in the Haze of the Pandemic

Emerging data suggest that the effects of infection with SARS-CoV-2 are far reaching extending beyond those with severe acute disease. Specifically, the presence of persistent symptoms after apparent resolution from COVID-19 have frequently been reported throughout the pandemic by individuals labeled as “long-haulers”. The purpose of this study was to assess for symptoms at days 0-10 and 61+ among subjects with PCR-confirmed SARS-CoV-2 infection. The University of California COvid Research Data Set (UC CORDS) was used to identify 1407 records that met inclusion criteria. Symptoms attributable to COVID-19 were extracted from the electronic health record. Symptoms reported over the previous year prior to COVID-19 were excluded, using nonnegative matrix factorization (NMF) followed by graph lasso to assess relationships between symptoms. A model was developed predictive for becoming a long-hauler based on symptoms. 27% reported persistent symptoms after 60 days. Women were more likely to become long-haulers, and all age groups were represented with those aged 50 ± 20 years comprising 72% of cases. Presenting symptoms included palpitations, chronic rhinitis, dysgeusia, chills, insomnia, hyperhidrosis, anxiety, sore throat, and headache among others. We identified 5 symptom clusters at day 61+: chest pain-cough, dyspnea-cough, anxiety-tachycardia, abdominal pain-nausea, and low back pain-joint pain. Long-haulers represent a very significant public health concern, and there are no guidelines to address their diagnosis and management. Additional studies are urgently needed that focus on the physical, mental, and emotional impact of long-term COVID-19 survivors who become long-haulers

Holistic Health Monitoring and Personalized Intervention for Well-being Promotion

Well-being is a crucial factor in human lives and society insofar as it is an indicator of satisfaction. Within the pillars of well-being, we favor sleep, physical activity, and mental health because these can represent body health. Furthermore, the entire world has been affected widely by a global virus pandemic which could significantly impact societies with vulnerable factors of well-being. Hence, we have investigated the effect of COVID-19 as one of the representatives of threats to social well-being. Parties interested in illness prevention and health promotion may find it helpful to measure, monitor, and promote well-being. Through the advancement of the Internet of the Things (IoT), it is now possible to monitor health outcomes and biomarkers in everyday free-living conditions without needing to proceed to labs or clinical settings. Taking the above into consideration, we can organize the main contribution of this dissertation into two components. First, we examine the trends and patterns of sleep and mental health disorders at a population level. To do so, we evaluate the sleep parameters of the Oura ring and the Samsung Gear Sport watch compared with a medically approved actigraphy device in a midterm everyday setting, where users engage in their daily routines. We used home-based sleep monitoring to examine the sleep characteristics of 45 healthy people (23 women and 22 men) for 7 days. Then we investigate the sleep trends of 38 pregnant women during the COVID-19 lockdown in Finland. The subjects used the Samsung Gear Sport smartwatch, and their sleep data was recorded. Subjective sleep reports were obtained using a smartphone app designed specifically for this study. Later, we analyze different mental health disorder reports before and during the pandemic and discuss the most vulnerable population. The benefit of such investigations is that capturing real-time information and public attitudes would facilitate policymakers to monitor public health and social wellness.In the second part, we focus on individual-level analyses. We use Machine Learning and Deep Learning techniques to monitor, reconstruct, evaluate, and forecast various tasks utilizing individuals' data and biomarkers. We begin by reconstructing the blood pressure signal. Continuous blood pressure (BP) monitoring can help individuals manage their chronic diseases such as hypertension, requiring non-invasive measurement methods in free-living conditions. Recent approaches fuse Photoplethysmograph (PPG) and electrocardiographic (ECG) signals using different machine and deep learning approaches to estimate BP non-invasively; however, they fail to reconstruct the complete signal, leading to less accurate models. We propose a cycle generative adversarial network (CycleGAN) based approach to extract a BP signal known as ambulatory blood pressure (ABP) from a clean PPG signal. Our approach uses a cycle generative adversarial network that extends the GAN architecture for domain translation and outperforms state-of-the-art methods by up to 2x in BP estimation. Next, we focus on patients diagnosed with acute respiratory distress syndrome (ARDS) who are in more life-threatening circumstances when it comes to COVID-19, resulting in severe respiratory system failure. We investigate the behavior of COVID19 on ARDS patients by utilizing simple vital signs. We analyze the long-term daily logs of blood pressure (BP), and heart rate (HR) associated with 150 ARDS patients admitted to five University of California academic health centers to distinguish subjects with COVID-19 positive and negative test results. In addition to the statistical analysis, we develop a deep neural network model to extract features from the longitudinal data. Our deep learning model achieved 0.81 area under the curve (AUC) to classify the vital signs of ARDS patients infected with COVID19 versus other ARDS diagnosed patients. Finally, we designed and developed a study to recommend personalized exercises to non-pregnant subjects to increase their physical activity level. We developed smartphone and smartwatch applications to collect, monitor, and suggest exercises using a contextual multi-arm bandit framework. This study includes constructing and developing a personalized model that predicts or recommends different actions depending on individual user biofeedback

A Recommendation System for Predicting Privacy Leaks in Mobile Traffic

Today’s smart phones have access to personal stored data, including personally identifiable information (PII) that can be used to uniquely identify users. It is well-known that a wide range of mobile applications transmit this data to remote servers, including their own servers, third-party advertisers, and trackers, which clearly poses a threat to user privacy. The present study’s goal is to detect PII in packets transmitted out of a mobile device, referred to as “privacy leaks”. This study build on prior work that developed systems for intercepting each network packet and inspecting it to detect PII, typically using deep-packet inspection (DPI) and/or machine learning techniques. This thesis, develop a lightweight mechanism that can predict if an outgoing packet contains any PII, based on minimal information, namely (i) the application name (package name) that generated the packet and (ii) the second-level destination domain. The problem is formulated as a recommendation system combining baseline and neighborhood predictors that exploit the similarity of mobile app behavior and PII leak types. Two different datasets of popular apps are used to get insights into privacy leak patterns. It is shown that the present framework can successfully detect 89% and 84% of PII in network packets on average while achieving F1 score as high as 0.97 and 0.91 in both datasets