Projects

iHurt

Intelligent and Automatic Pain Assessment Tool Employing Behavioral and Physiologic Indicators

Intelligent and Automatic Pain Assessment Tool Employing Behavioral and Physiologic Indicators

Pain is an unpleasant sensory and emotional experience associated with actual or potential tissue damage or described in terms of such damage. It is a subjective sensation and patients self-report is considered the most reliable indicator of pain. However, assessment of pain is particularly difficult when the ability of the patient to communicate is limited or impossible e.g. during critical illness, under sedation and anesthesia or for infants.

The objective of this project is to benefit from the offered features of the IoT and sensor networks to provide an automatic tool which can detect and assess pain employing behavioral and physiologic indicators such as facial muscle activity, heart rate, blood pressure, and breathing rate. The aim of this project is to develop a system based on Internet of Things to detect and assess pain in a reliable and objective way by enabling the pain diagnoses in the case when the patient is unable to communicate and express the pain sensations.


Research Partners:

School of Nursing, University of California, Irvine, USA
UCI Medical Center, University of California, Irvine, USA
Department of Computer Science, University of California, Irvine, USA
Department of Informatics, University of California, Irvine, USA
Department of Future Technologies, University of Turku, Finland
Department of Nursing Science, University of Turku, Finland
Department of Anesthesiology and Intensive Care Medicine, University of Turku, Finland
Division of Perioperative Services, Intensive Care Medicine and Pain Management, Turku University Hospital, Finland
Center for Functional Materials (FUNMAT), Åbo Akademi University, Finland


IoCT-CARE

Internet of Cognitive Things for Personalized Healthcare

Internet of Cognitive Things for Personalized Healthcare

Quality of Experience (QoE) is a key metric for the 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.


A joint project between the Academy of Finland and National Science Foundation (NSF), US.


Link to Academy of Finland press release (available only in Finnish)
http://www.aka.fi/fi/akatemia/media/Tiedotteet1/2017/suomen-akatemia-ja-yhdysvaltojen-kansallinen-tiedesaatio-nsf-rahoittavat-yhdessa-langattoman-tietoliikenteen-tutkimushankkeita/

PREVENT

Preterm Birth Prevention in Everyday Settings

Preterm Birth Prevention in Everyday Settings

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 an 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 the implementation of this monitoring in clinical trials.

Funded by Academy of Finland

SLIM

Supporting Lifestyle Change in Obese Pregnant Mothers through Wearable Internet-of-Things

Supporting Lifestyle Change in Obese Pregnant Mothers through Wearable Internet-of-Things

Pregnant women with obesity have indisputably increased risk for gestational diabetes mellitus, depression, miscarriage, and preterm birth, just to mention few. These pregnancy complications clearly have negative effects on their unborn children. Due to the magnitude of this global challenge it calls for immediate action. During the course of this project, an Internet-of-Things-based intelligent monitoring system will be developed to detect and predict obesity-related pregnancy complications as early as possible. Cybernetic health concept will be utilized by intertwining lifestyle and environmental data together with bio-signals associated with medical knowledge to develop a closed-loop system to make maternity care more effective, dynamic and end-user driven. This is done via a platform that leverages portable devices and inexpensive wearable sensors, coupled with a multimodal event modeling, activity recognition, and life-logging engine. This research will deliver a ubiquitous pregnancy monitoring service to end-users, mothers, and healthcare providers, enabling pregnancy events detection, prediction, assessment, and prevention.

Funded by Academy of Finland

Objective Stress Monitoring

Objective Stress Monitoring based on Wearable Sensors in Everyday Settings

Objective Stress Monitoring based on Wearable Sensors in Everyday Settings

Monitoring people’s stress has become an essential part of behavioral studies for physical and mental illnesses conducted within the biopsychosocial framework. There have been several stress assessment studies in laboratory-based controlled settings. However, the results of these studies do not always translate effectively into an everyday context. The current state of wearable sensor technology allows us to develop systems measuring the physiological signals reflecting stress 24/7 while capturing the context. In this project, we develop a stress monitoring system that provides objective daily stress measurements in everyday settings based on wearable sensors. This research is conducted in collaboration with the UCI THRIVE LAB at the Department of Psychological Science.

UNITE

Smart, Connected, and Coordinated Maternal Care for Underserved Communities

Smart, Connected, and Coordinated Maternal Care for Underserved Communities

UNITE (UNderserved communITiEs) presents a community engagement model that is smart, deploying ubiquitous monitoring and lifelogging; connected, bringing together a diverse cast of community members including mothers, families, care providers, and outreach resources; and coordinated, using technology to proactively reach out to the community and use personalized intervention and education for improved self-management by the women. The UNITE project champions a model that is scalable in size, portable across different ethnic communities, and promises improved outcomes through better self-management and community enhanced motivational factors. The UNITE project performs a controlled study using a community of underserved Orange County mothers together with non-profit agencies, hospitals, and local support organizations to evaluate the efficacy of this new community-enhanced self-management approach, and its impact on community building. The project builds larger communities of healthcare providers, insurance providers, and governmental agencies that can work in concert to enhance the well-being and lifestyles of mothers and families across a diverse spectrum of socio-economically disadvantaged groups. The UNITE project also trains the next generation of healthcare providers to deploy socio-economically relevant Internet-of-Things (IoT) technology in a cost-effective and user-friendly manner.

UNITE is funded through the National Science Foundation grant CNS-1831918 within the NSF Smart and Connected Communities (S&CC) program.


Research Partners:

MOMS Orange County
School of Nursing, University of California, Irvine, USA
School of Information and Computer Sciences, University of California, Irvine, USA
School of Education, University of California, Irvine, USA
School of Social Ecology, University of California, Irvine, USA
UCI Medical Center, Orange, CA, USA
St. Joseph’s Hospital of Orange, CA, USA
Children & Families Commission of Orange County, CA, USA
Community Health Initiative of Orange County, CA, USA
Department of Future Technologies, University of Turku, Finland
Department of Nursing Science, University of Turku, Finland
Turku University Hospital, Finland


Family Caregiver

A Monitoring-Intervention System for Dementia Caregivers Using Wearable IoT

A Monitoring-Intervention System for Dementia Caregivers Using Wearable IoT

Our program aims to build caregiving and stress-management skills in family caregivers of persons with dementia or mild cognitive impairment. The study involves remote monitoring using wearable sensors, home visits, and follow-ups. This research is conducted in collaboration with Dr. Jung-Ah Lee’s group at UCI (https://faculty.sites.uci.edu/caregiverstudy/).