2 citations · 4 across the 6 of their papers we have counts for
6 papers
Feasibility of assessing cognitive impairment via distributed camera network and privacy-preserving edge computing
Chaitra Hegde, Yashar Kiarashi, Allan I Levey +3
INTRODUCTION: Mild cognitive impairment (MCI) is characterized by a decline in cognitive functions beyond typical age and education-related expectations. Since, MCI has been linked…
Explainable Artificial Intelligence for Quantifying Interfering and High-Risk Behaviors in Autism Spectrum Disorder in a Real-World Classroom Environment Using Privacy-Preserving Video Analysis
Barun Das, Conor Anderson, Tania Villavicencio +6
Rapid identification and accurate documentation of interfering and high-risk behaviors in ASD, such as aggression, self-injury, disruption, and restricted repetitive behaviors, are…
Benchmarking changepoint detection algorithms on cardiac time series
Ayse Cakmak, Erik Reinertsen, Shamim Nemati +1
The pattern of state changes in a biomedical time series can be related to health or disease. This work presents a principled approach for selecting a changepoint detection algorit…
Point-of-Care Real-Time Signal Quality for Fetal Doppler Ultrasound Using a Deep Learning Approach
Mohsen Motie-Shirazi, Reza Sameni, Peter Rohloff +2
In this study, we present a deep learning framework designed to integrate with our previously developed system that facilitates large-scale 1D fetal Doppler data collection, aiming…
Indoor Localization using Bluetooth and Inertial Motion Sensors in Distributed Edge and Cloud Computing Environment
Yashar Kiarashi, Chaitra Hedge, Venkata Siva Krishna Madala +5
Spatial navigation of indoor space usage patterns reveals important cues about the cognitive health of individuals. In this work, we present a low-cost, scalable, open-source edge…
Mythological Medical Machine Learning: Boosting the Performance of a Deep Learning Medical Data Classifier Using Realistic Physiological Models
Ismail Sadiq, Erick A. Perez-Alday, Amit J. Shah +3
Objective: To determine if a realistic, but computationally efficient model of the electrocardiogram can be used to pre-train a deep neural network (DNN) with a wide range of morph…