244 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
Robust CNN-based Respiration Rate Estimation for Smartwatch PPG and IMU
Respiratory rate (RR) serves as an indicator of various medical conditions,
such as cardiovascular diseases and sleep disorders. These RR estimation
methods were mostly designed for finger-based PPG collected from subjects in
stationary situations (e.g., in hospitals). In contrast to finger-based PPG
signals, wrist-based PPG are more susceptible to noise, particularly in their
low frequency range, which includes respiratory information. Therefore, the
existing methods struggle to accurately extract RR when PPG data are collected
from wrist area under free-living conditions. The increasing popularity of
smartwatches, equipped with various sensors including PPG, has prompted the
need for a robust RR estimation method. In this paper, we propose a
convolutional neural network-based approach to extract RR from PPG,
accelerometer, and gyroscope signals captured via smartwatches. Our method,
including a dilated residual inception module and 1D convolutions, extract the
temporal information from the signals, enabling RR estimation. Our method is
trained and tested using data collected from 36 subjects under free-living
conditions for one day using Samsung Gear Sport watches. For evaluation, we
compare the proposed method with four state-of-the-art RR estimation methods.
The RR estimates are compared with RR references obtained from a chest-band
device. The results show that our method outperforms the existing methods with
the Mean-Absolute-Error and Root-Mean-Square-Error of 1.85 and 2.34, while the
best results obtained by the other methods are 2.41 and 3.29, respectively.
Moreover, compared to the other methods, the absolute error distribution of our
method was narrow (with the lowest median), indicating a higher level of
agreement between the estimated and reference RR values
Conversational Health Agents: A Personalized LLM-Powered Agent Framework
Conversational Health Agents (CHAs) are interactive systems that provide
healthcare services, such as assistance and diagnosis. Current CHAs, especially
those utilizing Large Language Models (LLMs), primarily focus on conversation
aspects. However, they offer limited agent capabilities, specifically lacking
multi-step problem-solving, personalized conversations, and multimodal data
analysis. Our aim is to overcome these limitations. We propose openCHA, an
open-source LLM-powered framework, to empower conversational agents to generate
a personalized response for users' healthcare queries. This framework enables
developers to integrate external sources including data sources, knowledge
bases, and analysis models, into their LLM-based solutions. openCHA includes an
orchestrator to plan and execute actions for gathering information from
external sources, essential for formulating responses to user inquiries. It
facilitates knowledge acquisition, problem-solving capabilities, multilingual
and multimodal conversations, and fosters interaction with various AI
platforms. We illustrate the framework's proficiency in handling complex
healthcare tasks via three demonstrations. Moreover, we release openCHA as open
source available to the community via GitHub.Comment: 23 pages, 6 figures, 3 tables, journal pape
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
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