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Found 23 papers, 1 papers with code
Integrating Wearable Sensor Data and Self-reported Diaries for Personalized Affect Forecasting
Emotional states, as indicators of affect, are pivotal to overall health, making their accurate prediction before onset crucial.
Impact of Physical Activity on Quality of Life During Pregnancy: A Causal ML Approach
Our result shows that performing adequate physical activity during pregnancy and postpartum improves the QoL by units of 7. 3 and 3. 4 on average in physical health and psychological domains, respectively.
ChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Framework
The personal model leverages causal discovery and inference techniques to assess personalized nutritional effects for a specific user, whereas the population model provides generalized information on food nutritional content.
Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection
Mental health conditions, prevalent across various demographics, necessitate efficient monitoring to mitigate their adverse impacts on life quality.
Optimizing Warfarin Dosing Using Contextual Bandit: An Offline Policy Learning and Evaluation Method
Warfarin, an anticoagulant medication, is formulated to prevent and address conditions associated with abnormal blood clotting, making it one of the most prescribed drugs globally.
Knowledge-Infused LLM-Powered Conversational Health Agent: A Case Study for Diabetes Patients
We compare the proposed CHA with GPT4.
Food Recommendation as Language Processing (F-RLP): A Personalized and Contextual Paradigm
State-of-the-art rule-based and classification-based food recommendation systems face significant challenges in becoming practical and useful.
Robust CNN-based Respiration Rate Estimation for Smartwatch PPG and IMU
The increasing popularity of smartwatches, equipped with various sensors including PPG, has prompted the need for a robust RR estimation method.
Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings
We achieved the F1-score of 70\% with a Random Forest classifier using both PPG and contextual data, which is considered an acceptable score in models built for everyday settings.
Reducing Intraspecies and Interspecies Covariate Shift in Traumatic Brain Injury EEG of Humans and Mice Using Transfer Euclidean Alignment
We tested the robustness of this transfer learning technique on various rule-based classical machine learning models as well as the EEGNet-based deep learning model by evaluating on different datasets, including human and mouse data in a binary classification task of detecting individuals with versus without traumatic brain injury (TBI).
Conversational Health Agents: A Personalized LLM-Powered Agent Framework
openCHA includes an orchestrator to plan and execute actions for gathering information from external sources, essential for formulating responses to user inquiries.
Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AI
The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare.
Edge-centric Optimization of Multi-modal ML-driven eHealth Applications
Smart eHealth applications deliver personalized and preventive digital healthcare services to clients through remote sensing, continuous monitoring, and data analytics.
Efficient Personalized Learning for Wearable Health Applications using HyperDimensional Computing
Considering the significant role of wearable devices in monitoring human body parameters, on-device learning can be utilized to build personalized models for behavioral and physiological patterns, and provide data privacy for users at the same time.
Hybrid Learning for Orchestrating Deep Learning Inference in Multi-user Edge-cloud Networks
Furthermore, we deploy Hybrid Learning (HL) to accelerate the RL learning process and reduce the number of direct samplings.
Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks
To this end, we propose a reinforcement-learning-based computation offloading solution that learns optimal offloading policy considering deep learning model selection techniques to minimize response time while providing sufficient accuracy.
Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks
Continuous monitoring of blood pressure (BP)can help individuals manage their chronic diseases such as hypertension, requiring non-invasive measurement methods in free-living conditions.
Personalized Stress Monitoring using Wearable Sensors in Everyday Settings
Since stress contributes to a broad range of mental and physical health problems, the objective assessment of stress is essential for behavioral and physiological studies.
An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGAN
A photoplethysmography (PPG) is an uncomplicated and inexpensive optical technique widely used in the healthcare domain to extract valuable health-related information, e. g., heart rate variability, blood pressure, and respiration rate.
An End-to-End and Accurate PPG-based Respiratory Rate Estimation Approach Using Cycle Generative Adversarial Networks
In this paper, we present an end-to-end and accurate pipeline for RR estimation using Cycle Generative Adversarial Networks (CycleGAN) to reconstruct respiratory signals from raw PPG signals.
Personal Mental Health Navigator: Harnessing the Power of Data, Personal Models, and Health Cybernetics to Promote Psychological Well-being
Traditionally, the regime of mental healthcare has followed an episodic psychotherapy model wherein patients seek care from a provider through a prescribed treatment plan developed over multiple provider visits.
The Causality Inference of Public Interest in Restaurants and Bars on COVID-19 Daily Cases in the US: A Google Trends Analysis
Our results showed for states/territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly happened after re-opening, significantly affect the daily new cases, on average.
GSR Analysis for Stress: Development and Validation of an Open Source Tool for Noisy Naturalistic GSR Data
There are different methods and algorithms for stress detection using different physiological signals.
