Lab

Health SciTech Group

About the lab

Our mission is to support health sciences based on a community-, data-, and technology-driven approach to health, thereby enhancing the mental and physical health of people and societies. The Health SciTech’s focus is defined by “Technology” which refers to preventive care, patient care, and patient safety through the application of sensing technologies (e.g., Internet of Things and Wearable Technology), mobile computing and effective data management methodologies.

Featured projects (5)

Project
Quality of Experience (QoE) is a key metric for successful delivery of end-user services for IoT-enabled applications. Achieving consistent end-user QoE poses tremendous challenges in the face of resource constraints and dynamic variations at multiple scales of the IoT system stack: at the application, network, resource, and device levels. This proposal outlines a self-aware cognitive architecture – the Internet of Cognitive Things (IoCT) – that delivers acceptable QoE by adapting to dynamic variations in infrastructural compute, communication and resource needs, while also synergistically learning and adapting to end user behavior. The approach leverages edge (i.e., Fog) computing architectures to introduce intelligence and adaptability in integrated multi-scale IoT systems. The objective is to efficiently manage information acquisition, communication and processing across different scales of the IoT systems, while synergistically coupling learning of end-user behaviors to deliver efficient and customized services. The proposed IoCT system is the first example of architecture where a network of algorithms communicates and collaborates synergistically to achieve a system-wide objective. Cognition and edge computing architectures are leveraged to introduce intelligence and adaptability in integrated multi-scale IoT systems, through a Personal Holistic Cognitive Optimization (PHCO) framework. To this aim, the IoCT will adopt recently proposed learning and control techniques (i.e. Deep Q-Networks), and exploit self-awareness principles to achieve effective system optimization. The project leverages on-going collaboration with the Turku University Hospital to demonstrate a personalized ubiquitous healthcare framework using the Early Warning Score (EWS) system for human health monitoring. Healthcare spending accounts for almost 17% of the GDP in the US. In healthcare, effective monitoring and observation of patients plays a key role in detecting a deteriorating patient. This project’s exemplar application on efficient early detection of these life-threatening signs can potentially save lives through better quality of care, and timely delivery of critical/urgent health indicators. The framework and services are also applicable to a broad range of other IoT application domains.
Project
The emerging field of Internet-of-Things (IoT) blends mobile computing systems, advanced communication technologies, and cloud computing. If merged with wearable technologies, IoT could potentially provide personalized interventions to anyone, anytime, and anywhere. Our lab has designed a unique Wearable IoT framework that interconnects wearable sensors, smartphones and cloud servers to enable personalized healthcare. WIoT helps physicians to leverage wearable sensors in their interventions to monitor multidimensional symptoms of patients from their body, brain and behaviors.
Project
Preterm birth (PTB) is the most common cause of neonatal deaths. Due to the high rate of PTBs (15M/y), it is extremely beneficial to identify the women at risk at early stage and prevent PTB. Physiological parameters could help us to uncover and model multifactorial processes that lead to PTB. Continuous monitoring of such parameters holds significant promise to successful prevention. Internet of Things (IoT) technologies can be leveraged to create the ability to perform such monitoring throughout pregnancy. In this project, we tackle PTB issues by proposing an IoT platform tailored for PTB prevention for everyday settings. Our core contributions are 1) a customized architecture including a set of wearable electronic devices that are feasible for 7-9 months of continuous monitoring, 2) a personalized PTB prevention solution using artificial intelligence methods, and 3) a comprehensive performance assessment via implementation of this monitoring in clinical trials. Home page: http://iot4health.utu.fi/prevent-info/ Funded by Academy of Finland

Featured research (6)

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 energy requirements of computationally-intensive deep learning services. 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 a communication-computation co-optimization problem that varies with both system parameters (e.g., network condition) and workload characteristics (e.g., inputs). On the other hand, deep learning model optimization provides another source of tradeoff between latency and model accuracy. An end-to-end decision-making solution that considers such computation-communication problem is required to synergistically find the optimal offloading policy and model for deep learning services. To this end, we propose a reinforcement-learning-based computation offloading solution that learns optimal offloading policy considering deep learning model selection techniques to minimize response time while providing sufficient accuracy. We demonstrate the effectiveness of our solution for edge devices in an end-edge-cloud system and evaluate with a real-setup implementation using multiple AWS and ARM core configurations. Our solution provides 35% speedup in the average response time compared to the state-of-the-art with less than 0.9% accuracy reduction, demonstrating the promise of our online learning framework for orchestrating DL inference in end-edge-cloud systems.
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 Photoplethysmograph (PPG) and electrocardiographic (ECG) signals using different machine and deep learning approaches to non-invasively estimate BP; however, they fail to reconstruct the complete signal, leading to less accurate models. In this paper, 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 theGAN architecture for domain translation, and outperforms state-of-the-art approaches by up to 2x in BP estimation.
Background The physical and emotional well-being of women is critical for healthy pregnancy and birth outcomes. The Two Happy Hearts intervention is a personalized mind-body program coached by community health workers that includes monitoring and reflecting on personal health, as well as practicing stress management strategies such as mindful breathing and movement. Objective The aims of this study are to (1) test the daily use of a wearable device to objectively measure physical and emotional well-being along with subjective assessments during pregnancy, and (2) explore the user’s engagement with the Two Happy Hearts intervention prototype, as well as understand their experiences with various intervention components. Methods A case study with a mixed design was used. We recruited a 29-year-old woman at 33 weeks of gestation with a singleton pregnancy. She had no medical complications or physical restrictions, and she was enrolled in the Medi-Cal public health insurance plan. The participant engaged in the Two Happy Hearts intervention prototype from her third trimester until delivery. The Oura smart ring was used to continuously monitor objective physical and emotional states, such as resting heart rate, resting heart rate variability, sleep, and physical activity. In addition, the participant self-reported her physical and emotional health using the Two Happy Hearts mobile app–based 24-hour recall surveys (sleep quality and level of physical activity) and ecological momentary assessment (positive and negative emotions), as well as the Perceived Stress Scale, Center for Epidemiologic Studies Depression Scale, and State-Trait Anxiety Inventory. Engagement with the Two Happy Hearts intervention was recorded via both the smart ring and phone app, and user experiences were collected via Research Electronic Data Capture satisfaction surveys. Objective data from the Oura ring and subjective data on physical and emotional health were described. Regression plots and Pearson correlations between the objective and subjective data were presented, and content analysis was performed for the qualitative data. Results Decreased resting heart rate was significantly correlated with increased heart rate variability (r=–0.92, P<.001). We found significant associations between self-reported responses and Oura ring measures: (1) positive emotions and heart rate variability (r=0.54, P<.001), (2) sleep quality and sleep score (r=0.52, P<.001), and (3) physical activity and step count (r=0.77, P<.001). In addition, deep sleep appeared to increase as light and rapid eye movement sleep decreased. The psychological measures of stress, depression, and anxiety appeared to decrease from baseline to post intervention. Furthermore, the participant had a high completion rate of the components of the Two Happy Hearts intervention prototype and shared several positive experiences, such as an increased self-efficacy and a normal delivery. Conclusions The Two Happy Hearts intervention prototype shows promise for potential use by underserved pregnant women.
BACKGROUND The physical and emotional well-being of women is critical for healthy pregnancy and birth outcomes. The Two Happy Hearts (THH) intervention is a personalized mind-body program, coached by community health workers (CHWs), that includes monitoring and reflecting on personal health, as well as practicing stress management strategies such as mindful breathing and movement. OBJECTIVE The study objectives were to 1) test the daily use of a wearable device to objectively measure physical and emotional well-being along with subjective assessments during pregnancy, and 2) explore the user’s engagement with the THH intervention prototype, as well as understand her experiences with the THH intervention prototype components. METHODS We recruited a 29-year-old woman, at 33 weeks gestation with a singleton pregnancy, and no medical complications or physical activity restrictions. She reported some college education and was identified as low income. The participant engaged in the THH intervention prototype from her third trimester until delivery. The Oura ring was used to continuously monitor objective physical and emotional states, such as heart rate, heart rate variability, sleep, and physical activity. In addition, the participant reported physical and emotional health using the Perceived Stress Scale, Center for Epidemiologic Studies Depression Scale, State Trait Anxiety Inventory, as well as the app-based 24-hour recall surveys and Ecological Momentary Assessment. Objective data from the Oura ring and subjective self-reported data on physical and emotional health were described. Furthermore, the participant’s engagement in the THH intervention prototype was recorded using both the smart ring and THH mobile phone app, and her experiences collected via survey. Descriptive statistics and Pearson correlations were conducted. RESULTS Decreased resting heart rate was significantly and negatively correlated with increased heart rate variability (r=-0.92, P<.001). We found significant associations between self-reported responses and Oura ring measures: i) positive emotions and RMSSD (r=0.54., P<.001), ii) sleep quality and sleep score (r=0.52, P<.001), and iii) physical activity and step-count (r=0.23, P=.097). In addition, deep sleep appeared to increase as light and REM sleep decreased. The psychological measures of stress, depression, and anxiety decreased from baseline to post-intervention. Furthermore, the participant had a high completion rate of the THH intervention prototype components and shared several positive experiences, such as increased self-efficacy and an uncomplicated delivery. CONCLUSIONS The Two Happy Hearts intervention prototype shows promise for potential use by underserved pregnant women.

Lab head

Amir M. Rahmani
Department
  • Institute for Future Health

Members (4)

Iman Azimi
  • University of California, Irvine
Milad Asgari Mehrabadi
  • University of California, Irvine
Seyed Amir Hossein Aqajari
  • University of California, Irvine
Maximilian Götzinger
  • University of Turku
Delaram Amiri
Delaram Amiri
  • Not confirmed yet
Mohammad R. Nakhkash
Mohammad R. Nakhkash
  • Not confirmed yet