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Found 16 papers, 0 papers with code
Enhancing Wearable based Real-Time Glucose Monitoring via Phasic Image Representation Learning based Deep Learning
In the U. S., over a third of adults are pre-diabetic, with 80\% unaware of their status.
Crowdotic: A Privacy-Preserving Hospital Waiting Room Crowd Density Estimation with Non-speech Audio
We propose a non-speech audio-based approach for crowd analytics, leveraging a transformer-based model.
Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding
To overcome these challenges, researchers have proposed encoding of wearable temporal sensor data in images using recurrent plots.
Unbiased Pain Assessment through Wearables and EHR Data: Multi-attribute Fairness Loss-based CNN Approach
The combination of diverse health data (IoT, EHR, and clinical surveys) and scalable-adaptable Artificial Intelligence (AI), has enabled the discovery of physical, behavioral, and psycho-social indicators of pain status.
Internet of Things Fault Detection and Classification via Multitask Learning
This paper presents a comprehensive investigation into developing a fault detection and classification system for real-world IIoT applications.
Recent Trend of Nanotechnology Applications to Improve Bio-accessibility of Lycopene by Nanocarrier: A Review
By manipulating the size or hydrodynamic diameter, zeta potential value or stability, polydispersity index or homogeneity and functional activity or retention of antioxidant properties observed to be the most prominent physicochemical properties to evaluate beneficial effect of implementation of nanotechnology on bioaccessibility study.
PhysioGait: Context-Aware Physiological Context Modeling for Person Re-identification Attack on Wearable Sensing
Person re-identification is a critical privacy breach in publicly shared healthcare data.
Enabling Heterogeneous Domain Adaptation in Multi-inhabitants Smart Home Activity Learning
Domain adaptation for sensor-based activity learning is of utmost importance in remote health monitoring research.
Semi-Supervised Domain Adaptation with Auto-Encoder via Simultaneous Learning
We present a new semi-supervised domain adaptation framework that combines a novel auto-encoder-based domain adaptation model with a simultaneous learning scheme providing stable improvements over state-of-the-art domain adaptation models.
College Student Retention Risk Analysis From Educational Database using Multi-Task Multi-Modal Neural Fusion
We develop a Multimodal Spatiotemporal Neural Fusion network for Multi-Task Learning (MSNF-MTCL) to predict 5 important students' retention risks: future dropout, next semester dropout, type of dropout, duration of dropout and cause of dropout.
PALMAR: Towards Adaptive Multi-inhabitant Activity Recognition in Point-Cloud Technology
With the advancement of deep neural networks and computer vision-based Human Activity Recognition, employment of Point-Cloud Data technologies (LiDAR, mmWave) has seen a lot interests due to its privacy preserving nature.
Estimating Heterogeneous Causal Effect of Polysubstance Usage on Drug Overdose from Large-Scale Electronic Health Record
We apply our framework to answer a critical question, can concurrent usage of benzodiazepines and opioids have heterogeneous causal effects on the opioid overdose epidemic?
Activity-Aware Deep Cognitive Fatigue Assessment using Wearables
Cognitive fatigue has been a common problem among workers which has become an increasing global problem since the emergence of COVID-19 as a global pandemic.
AutoCogniSys: IoT Assisted Context-Aware Automatic Cognitive Health Assessment
Cognitive impairment has become epidemic in older adult population.
LAXARY: A Trustworthy Explainable Twitter Analysis Model for Post-Traumatic Stress Disorder Assessment
Veteran mental health is a significant national problem as large number of veterans are returning from the recent war in Iraq and continued military presence in Afghanistan.
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
Reflecting After Learning for Understanding
Using the framework on images from ImageNet, we demonstrate systems that unify 41% to 46% of predictions in general and unify 67% to 75% of predictions when the systems can explain their conceptual differences.
