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Found 20 papers, 9 papers with code
Designing User-Centric Behavioral Interventions to Prevent Dysglycemia with Novel Counterfactual Explanations
GlyCoach achieves 87\% sensitivity in the simulation-aided validation, surpassing 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 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.
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.
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.
