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Found 33 papers, 18 papers with code
GlyTwin: Digital Twin for Glucose Control in Type 1 Diabetes Through Optimal Behavioral Modifications Using Patient-Centric Counterfactuals
Similarly, most digital twin approaches in diabetes management simulate only physiological processes.
CAN-STRESS: A Real-World Multimodal Dataset for Understanding Cannabis Use, Stress, and Physiological Responses
Therefore, it is hypothesized that cannabis users exhibit distinct physiological stress responses compared to non-users, and these differences would be more pronounced during moments of consumption.
AIMI: Leveraging Future Knowledge and Personalization in Sparse Event Forecasting for Treatment Adherence
However, effective forecasting systems for treatment adherence based on wearable sensors are still not widely available.
GlucoLens: Explainable Postprandial Blood Glucose Prediction from Diet and Physical Activity
In this paper, we propose GlucoLens, an explainable machine learning approach to predict PAUC and hyperglycemia from diet, activity, and recent glucose patterns.
NutriGen: Personalized Meal Plan Generator Leveraging Large Language Models to Enhance Dietary and Nutritional Adherence
We demonstrate that LLMs have strong potential in generating accurate and user-friendly food recommendations, addressing key limitations in existing dietary recommendation systems by providing structured, practical, and scalable meal plans.
Type 1 Diabetes Management using GLIMMER: Glucose Level Indicator Model with Modified Error Rate
We address this need with GLIMMER, Glucose Level Indicator Model with Modified Error Rate, a machine learning approach for forecasting blood glucose levels.
Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors
Addressing these gaps, we present FOGSense, a novel FOG detection system designed for uncontrolled, free-living conditions.
Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting
The availability of continuous glucose monitors as over-the-counter commodities have created a unique opportunity to monitor a person's blood glucose levels, forecast blood glucose trajectories and provide automated interventions to prevent devastating chronic complications that arise from poor glucose control.
Self-Supervised Learning and Opportunistic Inference for Continuous Monitoring of Freezing of Gait in Parkinson's Disease
Parkinson's disease (PD) is a progressive neurological disorder that impacts the quality of life significantly, making in-home monitoring of motor symptoms such as Freezing of Gait (FoG) critical.
Use of What-if Scenarios to Help Explain Artificial Intelligence Models for Neonatal Health
AIMEN uses an ensemble of fully-connected neural networks as the backbone for its classification with the data augmentation supported by either ADASYN or CTGAN.
Multimodal Physical Activity Forecasting in Free-Living Clinical Settings: Hunting Opportunities for Just-in-Time Interventions
Objective: This research aims to develop a lifestyle intervention system, called MoveSense, that forecasts a patient's activity behavior to allow for early and personalized interventions in real-world clinical environments.
Wearable-Based Real-time Freezing of Gait Detection in Parkinson's Disease Using Self-Supervised Learning
LIFT-PD is an innovative self-supervised learning framework developed for real-time detection of Freezing of Gait (FoG) in Parkinson's Disease (PD) patients, using a single triaxial accelerometer.
CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments
Wearable sensor systems have demonstrated a great potential for real-time, objective monitoring of physiological health to support behavioral interventions.
Designing User-Centric Behavioral Interventions to Prevent Dysglycemia with Novel Counterfactual Explanations
With $82. 8\%$ average validity in the simulation-aided validation, ExAct surpasses the state-of-the-art techniques for generating counterfactual explanations by at least $10\%$.
The Scope of In-Context Learning for the Extraction of Medical Temporal Constraints
Medications often impose temporal constraints on everyday patient activity.
ActSafe: Predicting Violations of Medical Temporal Constraints for Medication Adherence
It also addresses the challenges of accurately predicting RHBs central to MTCs (e. g., medication intake).
Stress Classification and Personalization: Getting the most out of the least
Stress detection and monitoring is an active area of research with important implications for the personal, professional, and social health of an individual.
Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error
However, extracting IBIs from noisy signals is challenging since the morphology of the signal is distorted in the presence of the noise.
Single Run Action Detector over Video Stream -- A Privacy Preserving Approach
This paper takes initial strides at designing and evaluating a vision-based system for privacy ensured activity monitoring.
Linear Mode Connectivity in Multitask and Continual Learning
Continual (sequential) training and multitask (simultaneous) training are often attempting to solve the same overall objective: to find a solution that performs well on all considered tasks.
Transfer Learning for Activity Recognition in Mobile Health
While activity recognition from inertial sensors holds potential for mobile health, differences in sensing platforms and user movement patterns cause performance degradation.
Pervasive Lying Posture Tracking
To answer these important research questions, in this article, we propose a comprehensive approach to design a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification.
Understanding the Role of Training Regimes in Continual Learning
However, there has been limited prior work extensively analyzing the impact that different training regimes -- learning rate, batch size, regularization method-- can have on forgetting.
Dropout as an Implicit Gating Mechanism For Continual Learning
However, it is more reliable to preserve the knowledge it has learned from the previous tasks.
Personality Assessment from Text for Machine Commonsense Reasoning
Our analysis of data collected with 300 participants demonstrate that PerSense predicts answers to commonsense questions with 82. 3% accuracy using a Random Forest classifier.
Proximity-Based Active Learning on Streaming Data: A Personalized Eating Moment Recognition
To facilitate development of personalized models, we propose PALS, Proximity-based Active Learning on Streaming data, a novel proximity-based model for recognizing eating gestures with the goal of significantly decreasing the need for labeled data with new users.
Adversarial Transferability in Wearable Sensor Systems
In this work, we take the first stride in studying adversarial transferability in wearable sensor systems from the following perspectives: 1) transferability between machine learning systems, 2) transferability across subjects, 3) transferability across sensor body locations, and 4) transferability across datasets.
ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition
Sensor-based human activity recognition has become a critical component of many emerging applications ranging from behavioral medicine to gaming.
Mindful Active Learning
This capacity constraint is manifested not only in the number of queries that a person can respond to in a given time-frame but also in the lag between the time that a query is made and when it is responded to.
Resource-Efficient Computing in Wearable Systems
We propose two optimization techniques to minimize memory usage and computation while meeting system timing constraints for real-time classification in wearable systems.
Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification
We propose to transform the activity recognition problem from a multi-class classification problem to a hierarchical model of binary decisions using cascading online binary classifiers.
Improved Knowledge Distillation via Teacher Assistant
To alleviate this shortcoming, we introduce multi-step knowledge distillation, which employs an intermediate-sized network (teacher assistant) to bridge the gap between the student and the teacher.
Personalized Human Activity Recognition Using Convolutional Neural Networks
A major barrier to the personalized Human Activity Recognition using wearable sensors is that the performance of the recognition model drops significantly upon adoption of the system by new users or changes in physical/ behavioral status of users.
