209 research outputs found
ZotCare: a flexible, personalizable, and affordable mhealth service provider
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 (mHealth) applications, which present significant technical challenges for researchers. While existing mHealth solutions have made progress in addressing some of these challenges, they often fall short in terms of time-to-use, affordability, and flexibility for personalization and adaptation. ZotCare aims to address these limitations by offering ready-to-use and flexible services, providing researchers with an accessible, cost-effective, and adaptable solution for their mHealth studies. This article focuses on ZotCare’s service orchestration and highlights its capabilities in creating a programmable environment for mHealth research. Additionally, we showcase several successful research use cases that have utilized ZotCare, both in the past and in ongoing projects. Furthermore, we provide resources and information for researchers who are considering ZotCare as their mHealth research solution
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Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice.
Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today's world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios
Intelligent Management of Mobile Systems through Computational Self-Awareness
Runtime resource management for many-core systems is increasingly complex.
The complexity can be due to diverse workload characteristics with conflicting
demands, or limited shared resources such as memory bandwidth and power.
Resource management strategies for many-core systems must distribute shared
resource(s) appropriately across workloads, while coordinating the high-level
system goals at runtime in a scalable and robust manner.
To address the complexity of dynamic resource management in many-core
systems, state-of-the-art techniques that use heuristics have been proposed.
These methods lack the formalism in providing robustness against unexpected
runtime behavior. One of the common solutions for this problem is to deploy
classical control approaches with bounds and formal guarantees. Traditional
control theoretic methods lack the ability to adapt to (1) changing goals at
runtime (i.e., self-adaptivity), and (2) changing dynamics of the modeled
system (i.e., self-optimization).
In this chapter, we explore adaptive resource management techniques that
provide self-optimization and self-adaptivity by employing principles of
computational self-awareness, specifically reflection. By supporting these
self-awareness properties, the system can reason about the actions it takes by
considering the significance of competing objectives, user requirements, and
operating conditions while executing unpredictable workloads
GSR Analysis for Stress: Development and Validation of an Open Source Tool for Noisy Naturalistic GSR Data
The stress detection problem is receiving great attention in related research
communities. This is due to its essential part in behavioral studies for many
serious health problems and physical illnesses. There are different methods and
algorithms for stress detection using different physiological signals. Previous
studies have already shown that Galvanic Skin Response (GSR), also known as
Electrodermal Activity (EDA), is one of the leading indicators for stress.
However, the GSR signal itself is not trivial to analyze. Different features
are extracted from GSR signals to detect stress in people like the number of
peaks, max peak amplitude, etc. In this paper, we are proposing an open-source
tool for GSR analysis, which uses deep learning algorithms alongside
statistical algorithms to extract GSR features for stress detection. Then we
use different machine learning algorithms and Wearable Stress and Affect
Detection (WESAD) dataset to evaluate our results. The results show that we are
capable of detecting stress with the accuracy of 92 percent using 10-fold
cross-validation and using the features extracted from our tool.Comment: 6 pages and 5 figures. Link to the github of the tool:
https://github.com/HealthSciTech/pyED
Detection of COVID-19 Using Heart Rate and Blood Pressure: Lessons Learned from Patients with ARDS
The world has been affected by COVID-19 coronavirus. At the time of this
study, the number of infected people in the United States is the highest
globally (7.9 million infections). Within the infected population, patients
diagnosed with acute respiratory distress syndrome (ARDS) are in more
life-threatening circumstances, resulting in severe respiratory system failure.
Various studies have investigated the infections to COVID-19 and ARDS by
monitoring laboratory metrics and symptoms. Unfortunately, these methods are
merely limited to clinical settings, and symptom-based methods are shown to be
ineffective. In contrast, vital signs (e.g., heart rate) have been utilized to
early-detect different respiratory diseases in ubiquitous health monitoring. We
posit that such biomarkers are informative in identifying ARDS patients
infected with COVID-19. In this study, we investigate the behavior of COVID-19
on ARDS patients by utilizing simple vital signs. We analyze the long-term
daily logs of blood pressure and heart rate associated with 70 ARDS patients
admitted to five University of California academic health centers (containing
42506 samples for each vital sign) to distinguish subjects with COVID-19
positive and negative test results. In addition to the statistical analysis, we
develop a deep neural network model to extract features from the longitudinal
data. Using only the first eight days of the data, our deep learning model is
able to achieve 78.79% accuracy to classify the vital signs of ARDS patients
infected with COVID-19 versus other ARDS diagnosed patients
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