SARP - Sensing at Risk Populations

It is important for healthcare providers and caregivers to have a precise and detailed understanding of a patient’s functional, mental and psychological well-being, particularly among patients with serious illnesses or high risk comorbidities. A thorough patient assessment based on continuous data collected over longer periods of time will allow for improved risk stratification, treatment selection, and monitoring for adverse events.


The ability to detect decline or improvement in real time allows for faster intervention, improving resource utilization, outcomes and quality of life.  As health systems move towards both personalized care and accountable care, providing high quality precision medicine will require efficient and accurate risk stratification and monitoring through technology that is innovative and cost-effective. Until now, the opportunity to study day-to-day activities and patient clinical status have required direct contact between patient and healthcare organization while the data collected from patients diaries are frequently subject to reporting biases. With the advent of various mobile applications and wearable devices, it is practical to constantly track and assess physical activity, blood pressure, mood, weight and other indices that are indicative of general well-being. It is also feasible to measure psychosocial constructs (mood, motivation, social network, and social support) as well as quality of life and symptom management of individuals. SARP is low cost, easy-to-install integrated smart system to remotely assess elderly patients during inpatient rehabilitation and then at home. The system is made up of several modules: (1) wearable technology (2) environmental sensors and devices and (3) a data processing and analytics engine. These modules are harnessed, in concert, to provide a visual dashboard that reflects the daily “storyline” of an individual including to their indoor position, location, mobility and activities. In addition, SARP system is embedded with smart algorithms for early prediction of rehab failure or success, enabling staff to efficiently triage, admit and discharge patients. 

Los Angeles Pediatric Research Integrating Sensor Monitoring Systems (LA PRISMS)

The Los Angeles Pediatric Research Integrating Sensor Monitoring Systems (LA PRISMS) Center develops mobile health (mHealth) technologies for the scientific understanding and self-management of pediatric asthma. As part of a collaboration between UCLA and USC under the NIH's PRISMS community, this collaborative effort focuses on the creation of an innovative end-to-end infrastructure for pediatric sensor-based health monitoring. To answer the question "what if you could predict for a specific asthma patient the potential of an asthma attack, and thus mitigate if not prevent the event from occurring?", the Biomedical REAl-Time Health Evaluation for pediatric asthma (BREATHE) platform was developed. This system provides an extensible software infrastructure to communicate with a growing array of physiological and environmental sensors, and places the data into context to understand the patient's state. Through the use of sensors, smartphones, smartwatches, cloud-based servers, and data warehouses, the infrastructure developed defined the application programming interfaces for data exchange, secured all aspects of data collection and transmission to ensure privacy and security of health information,  while optimizing the power usage on the sensors and smart devices.


Inference Methodology for Pipeline System Integrity Management

Sponsor: Petroleum Institute (PI) of Abu Dhabi, United Arab Emirate (UAE), In Partnership with University of Maryland, Department of Mechanical Engineering


Project Title: Inference Methodology for Pipeline System Integrity Management 

PI: Professor Ali Mosleh
Funding Level: $860,000

Project Description:
The objective of the project is to develop and demonstrate an inference methodology as part of broader objectives of a proposed project to be led by University of Maryland (UMD) to develop a systems approach to pipeline integrity and health management submitted to the Petroleum Institute (PI) of Abu Dhabi, United Arab Emirate (UAE).

The main project involves a multi-disciplinary science, engineering, and operational approach to realize a comprehensive and state-of-the-art solution to pipeline integrity. The intent is to leverage existing technologies and methods, and invent new ones as needed.  The approach is innovative and unique in its comprehensive integrative perspective, and in its focus on providing practical solutions while advancing the critical scientific and engineering foundations. The various scientific and technological challenges of proposed project will be tackled through three parallel but tightly coupled thrust areas:

Thrust Area I, Data Gathering and Monitoring Technologies
Thrust Area II, Failure Mechanism Sciences
Thrust Area III, Predictive Models and System-Level Pipeline Health Monitoring

UCLA work scope resides in Area III. The aim is to integrate the data, methods, models and technologies developed in Thrust Areas I and II into a total system health management support tool to aid in decision making and planning by the pipeline operators. This is done by developing (a) probabilistic evaluation and modeling of information from sensing, inspection and monitoring and probabilistic integration of mechanistic models and NDI for assessment of the pipe segment health (remaining life), and (b) dynamic pipeline network probabilistic health assessment model software for optimal risk-based prioritization of inspection and proactive management. Input to the proposed integrated health management system (IPHM) includes data from sensors and other inspection and monitoring methods from Thrust Area I. The output is online or offline updates on the reliability state of various segments of the pipeline system, and dynamically updated suggestions on when and where to take action (e.g., increase or decrease inspection frequency).

Methodological and Software Enhancements of Dynamic PRA Platforms for Event Assessment Applications

Sponsor: US Nuclear Regulatory Commission 

Project Title: Methodological and Software Enhancements of Dynamic PRA Platforms for Event Assessment Applications

PI: Professor Ali Mosleh

Funding Level : $217,000 

Scope: 

The main objective of the research is to develop needed features to make the ADS-IDAC dynamic PRA platform a more practical and realistic analysis tool for specific applications, primarily event assessments, and as a supplementary tool to analyze highly dynamic and complex accident scenarios in support of conventional PRAs. The modeling enhancements envisaged include more advanced system (hardware) and crew modeling capabilities, more comprehensive quantification features, and new post processing capabilities to extract risk insights from dynamic simulation runs. All these capabilities also require additional user-friendly graphical interfaces.
More specifically, the features will include
• Comprehensive quantification rules (full scenario dynamic probability calculations)
• More realistic model of team characteristic including those related to communication, tasking, and decision making (based on results from PI’s previous NRC grants)
• Extension of IDAC model to account for potential complexities in the action execution phase of crew response (a major post-Fukushima concern)
• Capability to use traditional fault tree models to explicitly define the impact of support system failures on accident progression (through their impact on frontline systems)
• Post processing rules and software capability for extracting risk insights from a large number of dynamic event tree scenarios generated typical by simulation runs
• Graphical User Interface to support the above features and also facilitate analysis of precursor events. This will include improved emergency operating proceduces editing capabilities.

Physics-Based Probabilistic Model Of The Effects Of Ionizing Radiation On Polymeric Insulators Of Electric Cables Used In Nuclear Power Plants

Sponsor: US Department of Energy 

Project Title: Physics-Based Probabilistic Model Of The Effects Of Ionizing Radiation On Polymeric Insulators Of Electric Cables Used In Nuclear Power Plants

PI: Professor Ali Mosleh

Funding Level : $800,000

Proposed Scope: 

The goal of the project is to develop a probabilistic prediction model of time-to-failure of cable jackets subject to radiation caused polymer degradation manifested as change in tensile strength and resistivity.  The main deliverable of the program will be a C++ based simulation code for the probabilistic prediction of the degradation of polymeric insulators of electric cables due to ionizing radiation. The code will be readily compatible with the MOOSE/Grizzly framework developed at the Idaho National Laboratory (INL) for multi-physics simulations. The project will combine theoretical and experimental findings during the course of the research to build radiation caused degradation models for environments consisting of air at different levels of relative humidity, various temperatures, as well as liquid water. Acknowledging the complexities and limitations of fully physics based models, the approach will take two promising technical steps. First, based on more fundamental understanding at molecular levels, we will introduce the notion of “damage precursor” as a key element in developing functional relation between dose levels and mechanical and electrical failure criteria. A damage precursor can be defined as any deviation from normal characteristics of micro-structural properties or any recognizable physical trend towards a failure-inducing threshold. Second, we use a hybrid modeling approach that fuses physics-based and data-driven approaches to produce better predictive power with reduced uncertainty.