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Remote Monitoring of Respiratory Health

ClinicalTrials.gov study NCT04739943. IPD Sharing: NO. Countries: 1. Publications: 0.

Mobile Health Monitoring Solution for Heart Failure Patients

ClinicalTrials.gov study NCT02594007. IPD Sharing: NO. Countries: 1. Publications: 0.

Quantum Menstrual Health Monitoring Study

ClinicalTrials.gov study NCT05936840. IPD Sharing: Not stated. Countries: 1. Publications: 0.

WATCH (Wearable Artificial inTelligence for Cardiac Function and Health Monitoring)

ClinicalTrials.gov study NCT07058064. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Study Evaluating Retinal Health Monitoring System Thickness Module

ClinicalTrials.gov study NCT04428242. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.

Application of Monitoring and Intervention Technologies in Suboptimal Health Status

ClinicalTrials.gov study NCT02441010. IPD Sharing: Not stated. Countries: 1. Publications: 0.

eRT Remote Health Monitoring

ClinicalTrials.gov study NCT01495780. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Skin Tracker: A Mobile Health App to Monitor Skin Disease Activity and Treatment Use

ClinicalTrials.gov study NCT04404075. IPD Sharing: NO. Countries: 1. Publications: 0.

Evaluation of the Concordance Between Measures Obtained by a Medical Device for Emotional Monitoring (EMOCARE) and the Patient Health Questionnaire (PHQ-9) Score in Patients With Mild to Severe Depres

ClinicalTrials.gov study NCT06601140. IPD Sharing: NO. Countries: 1. Publications: 0.

Investigating the Impact of Self-monitoring Feedback for Health Behaviors

ClinicalTrials.gov study NCT03940599. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Tele Health Monitoring Service for Patients With Chronic Obstructive Pulmonary Disease

ClinicalTrials.gov study NCT02615795. IPD Sharing: Not stated. Countries: 1. Publications: 0.

ClinicalTrials.gov20/100

Interventions for Enhancing Adherence to Syphilis Treatment and Follow-up: Study Protocol for the Health Information and Monitoring of Sexually Transmitted Infections (SIM) Randomized Controlled Trial

ClinicalTrials.gov study NCT04753125. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.

ClinicalTrials.gov20/100

A Study to Monitor the Health of Participants in HIVNET 014 Who Become Infected With HIV-1

ClinicalTrials.gov study NCT00001121. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Prognostic Techniques for Capacitor Degradation and Health Monitoring

This paper discusses our initial efforts in constructing physics of failure models for electrolytic capacitors subjected to electrical stressors in DC-DC power converters. Electrolytic capacitors and MOSFET’s are known to be the primary causes for degradation and failure in DC-DC converter systems. We have employed a topological energy based modeling scheme based on the bond graph (BG) modeling language for building parametric models of multi-domain systems, such as motors and pumps. In previous work, we have conducted experimental studies to validate an empirical physics of failure model based on Arrhenius Law for equivalent series resistance (ESR) increase in electrolytic capacitors operating under nominal conditions. In this paper, our focus shifts to deriving first principle models of capacitor degradation that explain both the ESR increase and the decrease in capacitance over time when the capacitor is operated under electrical stress conditions. Experimental studies are run in parallel, and data collected from these studies are used to validate the generated models. In the future, they will also be used to compute model parameters, so that the overall goal of deriving accurate models of capacitor degradation, and using them to predict performance changes in DC-DC converters is realized.

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Integrated Diagnostic/Prognostic Experimental Setup for Capacitor Degradation and Health Monitoring

This paper proposes the experiments and setups for studying diagnosis and prognosis of electrolytic capacitors in DC-DC power converters. Electrolytic capacitors and power MOSFET’s have higher failure rates than other components in DC-DC converter systems. Currently, our work focuses on experimental analysis and modeling electrolytic capacitors degradation and its effects on the output of DC-DC converter systems. The output degradation is typically measured by the increase in Equivalent series resistance and decrease in capacitance leading to output ripple currents.Typically, the ripple current effects dominate, and they can have adverse effects on downstream components. A model based approach to studying degradation phenomena enables us to combine the physics based modeling of the DC-DC converter with physics of failure models of capacitor degradation, and predict using stochastic simulation methods how system performance deteriorates with time. Degradation experiments were conducted where electrolytic capacitors were subjected to electrical and thermal stress to accelerate the aging of the system. This more systematic analysis may provide a more general and accurate method for computing the remaining useful life (RUL) of the component and the converter system.

DIAGNOSTIC/PROGNOSTIC EXPERIMENTS FOR CAPACITOR DEGRADATION AND HEALTH MONITORING IN DC-DC CONVERTERS

Studying and analyzing the ageing mechanisms of electronic components avionics in systems such as the GPS and INAV are of critical importance. In DC-DC power converter systems electrolytic capacitors and MOSFET’s have higher failure rates among the components. Degradation in the capacitors under varying operating conditions leads to high ripples output voltages and currents affecting downstream components leading to cascading faults. For example, in avionics systems where the power supply drives a GPS unit, ripple currents can cause glitches in the GPS position and velocity output, and this may cause errors in the Inertial Navigation (INAV) system, causing the aircraft to fly off course The work in this paper proposes a detail experimental and systematic study on analyzing the degradation phenomenon is electrolytic capacitors under high stress operating conditions. The output degradation is typically measured by an increase in ESR (Equivalent Series Resistance) and decrease in the capacitance value. We present the details of our accelerated ageing methodology along with analysis and comparison of the results.

Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework

This paper explores how the remaining useful life (RUL) can be assessed for complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. Consequently, inference and esti- mation techniques need to be applied on indirect measurements, anticipated operational conditions, and historical data for which a Bayesian statistical approach is suitable. Models of electrochem- ical processes in the form of equivalent electric circuit parame- ters were combined with statistical models of state transitions, aging processes, and measurement fidelity in a formal frame- work. Relevance vector machines (RVMs) and several different particle filters (PFs) are examined for remaining life prediction and for providing uncertainty bounds. Results are shown on battery data.1 Index Terms—Battery health, Bayesian learning, particle filter, prognostics, relevance vector machine, remaining useful life.

An Integrated Approach to Battery Health Monitoring using Bayesian Regression, Classification and State Estimation

The application of the Bayesian theory of managing uncertainty and complexity to regression and classification in the form of Relevance Vector Machine (RVM), and to state estimation via Particle Filters (PF), proves to be a powerful tool to integrate the diagnosis and prognosis of battery health. Accurate estimates of the state-of-charge (SOC), the state-of-health (SOH) and state-of- life (SOL) for batteries provide a significant value addition to the management of any operation involving electrical systems. This is especially true for aerospace systems, where unanticipated battery performance may lead to catastrophic failures. Batteries, composed of multiple electro- chemical cells, are complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. In addition, battery performance is strongly influenced by ambient environmental and load conditions. Consequently, inference and estimation techniques need to be applied on indirect measurements, anticipated operational conditions and historical data, for which a Bayesian statistical approach is suitable. Accurate models of electro-chemical processes in the form of equivalent electric circuit parameters need to be combined with statistical models of state transitions, aging processes and measurement fidelity, need to be combined in a formal framework to make the approach viable. The RVM, which is a Bayesian treatment of the Support Vector Machine (SVM), is used for diagnosis as well as for model development. The PF framework uses this model and statistical estimates of the noise in the system and anticipated operational conditions to provide estimates of SOC, SOH and SOL. Validation of this approach on experimental data from Li-ion batteries is presented.

Health Monitoring and Prognostics for Computer Servers

**Abstract** Prognostics solutions for mission critical systems require a comprehensive methodology for proactively detecting and isolating failures, recommending and guiding condition-based maintenance actions, and estimating in real time the remaining useful life of critical components and associated subsystems. A major challenge has been to extend the benefits of prognostics to include computer servers and other electronic components. The key enabler for prognostics capabilities is monitoring time series signals relating to the health of executing components and subsystems. Time series signals are processed in real time using pattern recognition for proactive anomaly detection and for remaining useful life estimation. Examples will be presented of the use of pattern recognition techniques for early detection of a number of mechanisms that are known to cause failures in electronic systems, including: environmental issues; software aging; degraded or failed sensors; degradation of hardware components; degradation of mechanical, electronic, and optical interconnects. Prognostics pattern classification is helping to substantially increase component reliability margins and system availability goals while reducing costly sources of "no trouble found" events that have become a significant warranty-cost issue. **Bios** Aleksey Urmanov is a research scientist at Sun Microsystems. He earned his doctoral degree in Nuclear Engineering at the University of Tennessee in 2002. Dr. Urmanov's research activities are centered around his interest in pattern recognition, statistical learning theory and ill-posed problems in engineering. His most recent activities at Sun focus on developing health monitoring and prognostics methods for EP-enabled computer servers. He is a founder and an Editor of the Journal of Pattern Recognition Research. Anton Bougaev holds a M.S. and a Ph.D. degrees in Nuclear Engineering from Purdue University. Before joining Sun Microsystems Inc. in 2007, he was a lecturer in Nuclear Engineering Department and a member of Applied Intelligent Systems Laboratory (AISL), of Purdue University, West Lafayette, USA. Dr. Bougaev is a founder and the Editor-in-Chief of the Journal of Pattern Recognition Research. His current focus is in reliability physics with emphasis on complex system analysis and the physics of failures which are based on the data driven pattern recognition techniques.

Prognostics Design Solutions in Structural Health Monitoring Systems

The chapter describes the application of prognostic techniques to the domain of structural health and demonstrates the efficacy of the methods using fatigue data from a graphite-epoxy composite coupon. Prognostics denotes the in-situ assessment of the health of a component and the repeated estimation of remaining life, conditional on anticipated future usage. The methods shown here use a physics-based modeling approach whereby the behavior of the damaged components is encapsulated via mathematical equations that describe the characteristics of the components as it experiences increasing degrees of degradation. Mathematical rigorous techniques are used to extrapolate the remaining life to a failure threshold. Additionally, mathematical tools are used to calculate the uncertainty associated with making predictions. The information stemming from the predictions can be used in an operational context for go/no go decisions, quantify risk of ability to complete a (set of) mission or operation, and when to schedule maintenance.