Amir M. Rahmani

University of California, Irvine | UCI

Background Emotional stress of family caregivers of persons with dementia (PWD) is often a topic of concern, as many experience negative health outcomes resulting from their years‐long caregiving role. A home‐visit based educational intervention focused on improving caregiving skills and managing stress was delivered to ethnically diverse dementia...

Multi-modal machine learning (MMML) applications combine results from different modalities in the inference phase to improve prediction accuracy. Existing MMML fusion strategies use static modality weight assignment, based on the intrinsic value of sensor modalities determined during the training phase. However, input data perturbations in practica...

Objectives: The autonomic nervous system (ANS) plays a central role in dynamic adaptation during pregnancy in accordance with the pregnancy demands which otherwise can lead to various pregnancy complications. Despite the importance of understanding the ANS function during pregnancy, the literature lacks sufficiency in the ANS assessment. In this st...

Smart eHealth applications deliver personalized and preventive digital healthcare services to clients through remote sensing, continuous monitoring, and data analytics. Smart eHealth applications sense input data from multiple modalities, transmit the data to edge and/or cloud nodes, and process the data with compute-intensive machine learning (ML)...

Background: There are indisputable health benefits to physical activity (PA). By collecting and displaying individual exercise behaviors via wearable trackers, the Internet of Things (IoT) and mobile health (mHealth) have made it possible to correlate users' physiological data and daily activity information with their fitness requirements. Objectiv...

The proliferation of Internet-connected health devices and the widespread availability of mobile connectivity have resulted in a wealth of reliable digital health data and the potential for delivering just-in-time interventions. However, leveraging these opportunities for health research requires the development and deployment of mobile health (mHe...

Photoplethysmography (PPG) is a non-invasive optical method to acquire various vital signs, including heart rate (HR) and heart rate variability (HRV). The PPG method is highly susceptible to motion artifacts and environmental noise. Unfortunately, such artifacts are inevitable in ubiquitous health monitoring, as the users are involved in various a...

Pain assessment is essential for pain diagnosis and treatment. Automating the assessment process from pain behaviors could be an alternative to self-report; however, inter-subject and time-dynamic differences in pain behaviors hinder pain recognition as generic patterns. To address this problem, we proposed a neural network method integrating pain...

The proliferation of Internet-connected health devices and the widespread availability of mobile connectivity have resulted in a wealth of reliable digital health data and the potential for delivering just-in-time interventions. However, leveraging these opportunities for health research requires the development and deployment of mobile health (mHe...

Loneliness is linked to wide ranging physical and mental health problems, including increased rates of mortality. Understanding how loneliness manifests is important for targeted public health treatment and intervention. With advances in mobile sending and wearable technologies, it is possible to collect data on human phenomena in a continuous and...

Objective To develop and evaluate a multimodal machine learning-based objective pain assessment algorithm on data collected from post-operative patients. Methods The proposed method addresses the major challenges that come with using data from such patients like the imbalanced distribution of pain classes and the scarcity of ground-truth labels. Sp...

The adverse effects of loneliness on both physical and mental well-being are profound. Although previous research has utilized mobile sensing techniques to detect mental health issues, few studies have utilized state-of-the-art wearable devices to forecast loneliness and comprehend the physiological manifestations of loneliness and its predictive n...

Sleep quality is crucial to both mental and physical well-being. The COVID-19 pandemic, which has notably affected the population's health worldwide, has been shown to deteriorate people's sleep quality. Numerous studies have been conducted to evaluate the impact of the COVID-19 pandemic on sleep efficiency, investigating their relationships using...

Background: The development and quality assurance of perinatal eHealth self-monitoring systems is an upcoming area of inquiry in health science. Building patient engagement into eHealth development as a core component has potential to guide process evaluation. Access, 1 attribute of patient engagement, is the focus of study here. Access to eHealth...

Background 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 conte...

California was the first state to implement statewide public health measures, including lockdowns and curfews, to mitigate the transmission of SARS-CoV-2. The implementation of these public health measures may have had unintended consequences related to mental health for persons in California. This study is a retrospective review of electronic heal...

BACKGROUND Maternal loneliness is associated with adverse physical and mental health outcomes for both the mother and her child. Detecting maternal loneliness non-invasively through wearable devices and passive sensing provides opportunities to prevent or reduce the impact of loneliness on the health and well-being of the mother and her child. OBJ...

Background Maternal loneliness is associated with adverse physical and mental health outcomes for both the mother and her child. Detecting maternal loneliness noninvasively through wearable devices and passive sensing provides opportunities to prevent or reduce the impact of loneliness on the health and well-being of the mother and her child. Objec...

Traditional machine learning (ML) approaches learn to recognize patterns in the data but fail to go beyond observing associations. Such data-driven methods can lack generalizability when the data is outside the independent and identically distributed (i.i.d) setting. Using causal inference can aid data-driven techniques to go beyond learning spurio...

SARS-CoV-2 (COVID-19) has caused over 80 million infections 973,000 deaths in the United States, and mutations are linked to increased transmissibility. This study aimed to determine the effect of SARS-CoV-2 variants on respiratory features, mortality, and to determine the effect of vaccination status. A retrospective review of medical records (n =...

Objective: The aim of this study was to compare subjectively and objectively measured stress during pregnancy and the three months postpartum in women with previous adverse pregnancy outcomes and women with normal obstetric histories. Methods: We recruited two cohorts in southwestern Finland for this longitudinal study: (1) pregnant women (n = 3...

Objectives To assess, in terms of self-efficacy in weight management, the effectiveness of the SLIM lifestyle intervention among overweight or obese women during pregnancy and after delivery, and further to exploit machine learning and event mining approaches to build personalized models. Additionally, the aim is to evaluate the implementation of t...

Family caregivers of persons with dementia (PWD) suffer from stress and negative health outcomes related to round‐the‐clock caregiving responsibilities. Often caregivers lack awareness of community resources. Community Health Workers (CHW) have proven effective in educating families and can implement interventions that aim to reduce the impact of s...

Background Photoplethysmography (PPG) is a low-cost and easy-to-implement method to measure vital signs, including heart rate (HR) and pulse rate variability (PRV) which widely used as a substitute of heart rate variability (HRV). The method is used in various wearable devices. For example, Samsung smartwatches are PPG-based open-source wristbands...

Background: The autonomic nervous system (ANS) is known as a critical regulatory system for pregnancy-induced adaptations. If it fails to function, life-threatening pregnancy complications could occur. Hence, understanding and monitoring the underlying mechanism of action for these complications are necessary. Objective: We aimed to systematical...

BACKGROUND The autonomic nervous system (ANS) is known as a critical regulatory system for pregnancy-induced adaptations. If it fails to function, life-threatening pregnancy complications could occur. Hence, understanding and monitoring the underlying mechanism of action for these complications are necessary. OBJECTIVE We aimed to systematically r...

BACKGROUND Access to eHealth self-monitoring has the potential to influence receival of appropriate health resources during pregnancy. Little is known about influences of the process of accessing eHealth systems on use patterns. OBJECTIVE Here, we examined processes occurring during the adaption of eHealth self-monitoring use from a socio-material...

Food plays a central role in agriculture, public wellness, public health, culinary art, and culture. Food-related data is available in varying formats and with different access levels ranging from private datasets to publicly downloadable data. Every food-related query, in principle, is a food recommendation problem. We analyze the components of a...

Background Maternal loneliness is associated with adverse physical and mental health outcomes for both the mother and her child. Detecting maternal loneliness non-invasively through wearable devices and passive sensing provides opportunities to prevent or reduce the impact of loneliness on the health and well-being of the mother and her child. Obj...

Resource Management is an important research area of distributed systems with a wide range of applications due to the Internet of Things (IoT) insurgence. Collaborative End-Edge-Cloud computing for deep learning provides a range of performance and efficiency advancements that can address application requirements through computation offloading. Opti...

Aims and objectives: To determine the frequency, timing, and duration of post-acute sequelae of SARS-CoV-2 infection (PASC) and their impact on health and function. Background: Post-acute sequelae of SARS-CoV-2 infection is an emerging major public health problem that is poorly understood and has no current treatment or cure. PASC is a new syndr...

A photoplethysmography (PPG) is an uncomplicated and inexpensive optical technique widely used in the healthcare domain to extract valuable health-related information, e.g., heart rate variability, blood pressure, and respiration rate. PPG signals can easily be collected continuously and remotely using portable wearable devices. However, these meas...

Post-acute sequelae of SARS-CoV-2 (PASC) is defined as persistent symptoms after apparent recovery from acute COVID-19 infection, also known as COVID-19 long-haul. We performed a retrospective review of electronic health records (EHR) from the University of California COvid Research Data Set (UC CORDS), a de-identified EHR of PCR-confirmed SARS-CoV...

Objective To develop a machine learning algorithm utilizing heart rate variability (HRV) and salivary cortisol to detect the presence of acute stress among pregnant women that may be applied to future clinical research. Methods ECG signals and salivary cortisol were analyzed from 29 pregnant women as part of a crossover study involving a standardi...

Cortisol is a steroid hormone that regulates a wide range of vital signs throughout the body. However, current cortisol monitoring methods are inconvenient for everyday settings. Heart Rate (HR) and Heart Rate Variability (HRV) are easily collected biological parameters whose fluctuations highly correlate with cortisol, however, there does not exis...

Current digital mental healthcare solutions conventionally take on a reactive approach, requiring individuals to self-monitor and document existing symptoms. These solutions are unable to provide comprehensive, wrap-around, customized treatments that capture an individual’s holistic mental health model as it unfolds over time. Recognizing that each...

Aim This paper proposes a novel, trauma‐informed, conceptual model of care for Post‐Acute Sequelae of COVID‐19 illness (PASC). Design This paper describes essential elements, linkages and dimensions of the model that affect PASC patient experiences and the potential impact of trauma‐informed care on outcomes. Data sources PASC is a consequence of...

How women experience pregnancy as uplifting or a hassle is related to their mental and physical health and birth outcomes. Pregnancy during a pandemic introduces new hassles, but may offer benefits that could affect how women perceive their pregnancy. Surveying 118 ethnically and racially diverse pregnant women, we explore (1) women’s traditional a...

Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate. Conventional methods are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resilienc...

Descriptive Comparative Pilot study: Self-monitoring in pregnancy 1 1 2 Abstract 3 Pregnancy is a challenging time for maintaining quality sleep and managing stress. 4 Digital self-monitoring technologies are popular due to assumed increased patient 5 engagement leading to an impact on health outcomes. However, the actual association 6 between wear...

Smart eHealth applications deliver personalized and preventive digital healthcare services to clients through remote sensing, continuous monitoring, and data analytics. Smart eHealth applications sense input data from multiple modalities, transmit the data to edge and/or cloud nodes, and process the data with compute intensive machine learning (ML)...

Health monitoring applications increasingly rely on machine learning techniques to learn end-user physiological and behavioral patterns in everyday settings. Considering the significant role of wearable devices in monitoring human body parameters, on-device learning can be utilized to build personalized models for behavioral and physiological patte...

Electrocardiogram (ECG) signals provide rich information on individuals' potential cardiovascular conditions and disease, ranging from coronary artery disease to the risk of a heart attack. While health providers store and share these information for medical and research purposes, such data is highly vulnerable to privacy concerns, similar to many...

Background SARS-CoV-2 (COVID-19) has caused over 80 million infections and 973,000 deaths in the United States, and mutations are linked to increased transmissibility. This study aimed to determine the effect of SARS-CoV-2 variants on respiratory features and mortality and to determine the effect of vaccination status. Method A retrospective revie...

Long-haul COVID-19, also called Post-Acute Sequelae of SARS-CoV-2 (PASC), is a new illness caused by SARS-CoV-2 infection and characterized by the persistence of symptoms. The purpose of this cross-sectional study was to identify a distinct and significant temporal pattern of PASC symptoms (symptom type and onset) among a nationwide sample of PASC...

Pain is a subjective experience with interpersonal perception sensitivity differences. Pain sensitivity is of scientific and clinical interest, as it is a risk factor for several pain conditions. Resting heart rate variability (HRV) is a potential pain sensitivity measure reflecting the parasympathetic tone and baroreflex function, but it remains u...

Photoplethysmography (PPG) is a non-invasive technique used in wearable devices to collect various vital signs, including heart rate and heart rate variability. The signal is highly susceptible to motion artifacts, which is inevitable in health monitoring and may lead to inaccurate decision-making. Studies in the literature proposed time series ana...

Continuous monitoring of blood pressure (BP) can help individuals manage their chronic diseases such as hypertension, requiring non-invasive measurement methods in free-living conditions. Recent approaches fuse Photoplethys-mograph (PPG) and electrocardiographic (ECG) signals using different machine and deep learning approaches to non-invasively es...

Machine learning and deep learning algorithms have paved the way for improved analysis of biomedical data which has led to a better understanding of various biological conditions. However, one major hindrance to leveraging the potential of machine learning models is the requirement of huge datasets. In the biomedical domain, this becomes extremely...

Background: Sleep disturbance is a transdiagnostic risk factor so prevalent among young adults it is considered a public health epidemic, exacerbated by the COVID-19 pandemic. Sleep may contribute to mental health via affect dynamics. Prior literature on contribution of sleep to affect is largely based on correlational studies or experiments that...

Background Affective states are important aspects of healthy functioning; as such, monitoring and understanding affect is necessary for the assessment and treatment of mood-based disorders. Recent advancements in wearable technologies have increased the use of such tools in detecting and accurately estimating mental states (eg, affect, mood, and st...

Mobile health technology is a rapidly growing field with numerous promises to make substantial impact in our lives. To open this special issue, which brings to you many exciting research results in mobile health technology, we discuss two important aspects of this technology. One is how they can be integrated in our daily lives as important care de...

Background Photoplethysmography (PPG) is a low-cost and easy-to-implement method to measure vital signs, including heart rate (HR) and heart rate variability (HRV). The method is widely used in various wearable devices. For example, Samsung smartwatches are PPG-based open-source wristbands used in remote well-being monitoring and fitness applicatio...

Continuous monitoring of perinatal women in a descriptive case study allowed us the opportunity to examine the time during which the COVID-19 infection led to physiological changes in two low-income pregnant women. An important component of this study was the use of a wearable sensor device, the Oura ring, to monitor and record vital physiological...

Health and wellness applications increasingly rely on machine learning techniques to learn end-user physiological and behavioral patterns in everyday settings, posing two key challenges: inability to perform on-device online learning for resource-constrained wearables, and learning algorithms that support privacy-preserving personalization. We expl...

Cardiovascular diseases are one of the world's major causes of loss of life. The vital signs of a patient can indicate this up to 24 hours before such an incident happens. Healthcare professionals use Early Warning Score (EWS) as a common tool in healthcare facilities to indicate the health status of a patient. However , the chance of survival of a...

Smart rings, such as the Oura ring, might have potential in health monitoring. To be able to identify optimal devices for healthcare settings, validity studies are needed. The aim of this study was to compare the Oura smart ring estimates of steps and sedentary time with data from the ActiGraph accelerometer in a free-living context. A cross-sectio...

Deep-learning-based intelligent services have become prevalent in cyber-physical applications including smart cities and health-care. Deploying deep-learning-based intelligence near the end-user enhances privacy protection, responsiveness, and reliability. Resource-constrained end-devices must be carefully managed in order to meet the latency and e...

Deep-learning-based intelligent services have become prevalent in cyber-physical applications including smart cities and health-care. Deploying deep-learning-based intelligence near the end-user enhances privacy protection, responsiveness, and reliability. Resource-constrained end-devices must be carefully managed in order to meet the latency and e...

Deep-learning-based intelligent services have become prevalent in cyber-physical applications including smart cities and health-care. Collaborative end-edge-cloud computing for deep learning provides a range of performance and efficiency that can address application requirements through computation offloading. The decision to offload computation is...