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Understanding Reflection Needs for Personal Health Data in Diabetes
Accepted
,
2020
Temiloluwa Prioleau, Ashutosh Sabharwal, Madhuri Vasudevan
In this work, we conducted a two-phase user-study involving patients, caregivers, and clinicians to understand gaps in current approaches that support reflection and user needs for new solutions.
Predicting Brain Functional Connectivity Using Mobile Sensing
Published
,
2020
Mikio Obuchi, Jeremy F. Huckins, Weichen Wang, Alex Dasilva, Courtney Rogers, Eilis Murphy, Elin Hedlund, Paul Holtzheimer, Shayan Mirjafari, Andrew Campbell
We study the brain resting-state functional connectivity (RSFC) between the ventromedial prefrontal cortex (vmPFC) and the amygdala, which has been shown by neuroscientists to be associated with mental illness such as anxiety and depression
Neural Physiological Model: A Simple Module for Blood Glucose Prediction
Under Review
,
2020
Kang Gu, Ruoqi Dang, Temiloluwa Prioleau
In this paper, we present Neural Physiological Encoder (NPE), a simple module that leverages decomposed convolutional filters to automatically generate effective features that can be used with a downstream neural network for blood glucose prediction.
Data-Driven Insights on Behavioral Factors that Affect Diabetes Management
Accepted
,
2020
Samuel Morton, Rui Li, Sayanton Dibbo, Temiloluwa Prioleau
In this paper, we employ a data-driven approach to study the relationship between key behavioral factors (sleep, meal size, insulin dose) and proximal diabetic management indicators.
Adherence to Personal Health Devices: A Case Study in Diabetes Management
Accepted
,
2020
Sudip Vhaduri, Temiloluwa Prioleau
This paper takes a data mining approach to study adherence to continuous glucose monitors in diabetes management.
Exploring the State-of-Receptivity for mHealth Interventions
Published
,
2019
Florian Kunzler, Varun Mishra, Jan-Niklas Kramer, Davis Kotz, Elgar Fleisch, Tobias Kowatsch
In this work, we explore the factors affecting users’ receptivity towards Just-In-Time Adaptive Interventions (JITAI).
Noise-robust Bioimpedance Approach for Cardiac Output Measurement
Published
,
2019
Ethan K Murphy, Justice Amoh, Saaid H Arshad, Ryan J Halter, Kofi Odame
Machine Learning algorithms trained on electrical-impedance tomography data are presented for portable cardiac monitoring. The approach was validated on a simulated thorax and a measured tank experiment.
An Optimized Recurrent Unit For Ultra-Low-Power Keyword Spotting
Published
,
2019
Justin Amoh, Kofi Odame
Our work introduces a new recurrent unit architecture that is specifically adapted for on-device low power acoustic event detection.
Auracle: Detecting Eating Episodes with an Ear-Mounted Sensor
Published
,
2018
Shengjie Bi, Tao Wang, Nicole Tobias, Josephine Nordrum, Shang Wang, George Halvorsen, Sougata Sen, Ronald Peterson, Kofi Odame, Kelly Caine, Ryan Halter, Jacob Sorber, David Kotz
In this paper, we propose Auracle, a wearable earpiece that can automatically recognize eating behavior. More specifically, in free-living conditions, we can recognize when and for how long a person is eating.
An Analog Front End ASIC for Cardiac Electrical Impedance Tomography
Published
,
2018
Arun Rao, Yueh-Ching Teng, Chris Schaef, Ethan K. Murphy, Saaid Arshad, Ryan J. Halter, Kofi Odame
In this paper an end-to-end CMOS application specific integrated circuit (ASIC) for readout channel in a cardiac electrical impedance tomography (EIT) system is presented.
Toward a Wearable Sensor for Eating Detection
Published
,
2017
Shengie Bi, Tao Wang, Ellen Davenport, Ronald Peterson, Ryan Halter, Jacob Sorber, David Kotz
In this paper, we evaluate sensors and algorithms designed to detect eating activities, more specifically, when people eat.
Deep Neural Networks For Identifying Cough Sounds
Published
,
2016
Justice Amoh, Kofi Odame
In this paper, we consider two different approaches of using deep neural networks for cough detection. The cough detection task is cast as a visual recognition problem and as a sequence-to-sequence labeling problem.

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