130 citations · 329 across the 14 of their papers we have counts for
14 papers
Integration of Clinical Criteria into the Training of Deep Models: Application to Glucose Prediction for Diabetic People
Maxime De Bois, Mounîm A. El Yacoubi, Mehdi Ammi
Standard objective functions used during the training of neural-network-based predictive models do not consider clinical criteria, leading to models that are not necessarily clinic…
Interpreting Deep Glucose Predictive Models for Diabetic People Using RETAIN
Maxime De Bois, Mounîm A. El Yacoubi, Mehdi Ammi
Progress in the biomedical field through the use of deep learning is hindered by the lack of interpretability of the models. In this paper, we study the RETAIN architecture for the…
Model Fusion to Enhance the Clinical Acceptability of Long-Term Glucose Predictions
Maxime De Bois, Mounîm A. El Yacoubi, Mehdi Ammi
This paper presents the Derivatives Combination Predictor (DCP), a novel model fusion algorithm for making long-term glucose predictions for diabetic people. First, using the histo…
Study of Short-Term Personalized Glucose Predictive Models on Type-1 Diabetic Children
Maxime De Bois, Mounîm A. El Yacoubi, Mehdi Ammi
Research in diabetes, especially when it comes to building data-driven models to forecast future glucose values, is hindered by the sensitive nature of the data. Because researcher…
Enhancing the Interpretability of Deep Models in Heathcare Through Attention: Application to Glucose Forecasting for Diabetic People
Maxime De Bois, Mounîm A. El Yacoubi, Mehdi Ammi
The adoption of deep learning in healthcare is hindered by their "black box" nature. In this paper, we explore the RETAIN architecture for the task of glusose forecasting for diabe…
Prediction-Coherent LSTM-based Recurrent Neural Network for Safer Glucose Predictions in Diabetic People
Maxime De Bois, Mounîm A. El Yacoubi, Mehdi Ammi
In the context of time-series forecasting, we propose a LSTM-based recurrent neural network architecture and loss function that enhance the stability of the predictions. In particu…
Energy Expenditure Estimation Through Daily Activity Recognition Using a Smart-phone
Maxime De Bois, Hamdi Amroun, Mehdi Ammi
This paper presents a 3-step system that estimates the real-time energy expenditure of an individual in a non-intrusive way. First, using the user's smart-phone's sensors, we build…
GLYFE: Review and Benchmark of Personalized Glucose Predictive Models in Type-1 Diabetes
Maxime De Bois, Mehdi Ammi, Mounîm A. El Yacoubi
Due to the sensitive nature of diabetes-related data, preventing them from being shared between studies, progress in the field of glucose prediction is hard to assess. To address t…
Adversarial Multi-Source Transfer Learning in Healthcare: Application to Glucose Prediction for Diabetic People
Maxime De Bois, Mounîm A. El Yacoubi, Mehdi Ammi
Deep learning has yet to revolutionize general practices in healthcare, despite promising results for some specific tasks. This is partly due to data being in insufficient quantiti…
Preference Between Allocentric and Egocentric 3D Manipulation in a Locally Coupled Configuration
Paul Issartel, Lonni Besançon, Florimond Guéniat +2
We study user preference between allocentric and egocentric 3D manipulation on mobile devices, in a configuration where the motion of the device is applied to an object displayed o…
Usability Comparison of Mouse, Touch and Tangible Inputs for 3D Data Manipulation
Lonni Besançon, Paul Issartel, Mehdi Ammi +1
We evaluate the performance and usability of mouse-based, touch-based, and tangible interaction for manipulating objects in a 3D virtual environment. This comparison is a step towa…
A Tangible Volume for Portable 3D Interaction
Paul Issartel, Lonni Besançon, Tobias Isenberg +1
We present a new approach to achieve tangible object manipulation with a single, fully portable and self-contained device. Our solution is based on the concept of a "tangible volum…
Analysis of Locally Coupled 3D Manipulation Mappings Based on Mobile Device Motion
Paul Issartel, Florimond Guéniat, Tobias Isenberg +1
We examine a class of techniques for 3D object manipulation on mobile devices, in which the device's physical motion is applied to 3D objects displayed on the device itself. This "…
Perceiving Mass in Mixed Reality through Pseudo-Haptic Rendering of Newton's Third Law
Paul Issartel, Florimond Guéniat, Sabine Coquillart +1
In mixed reality, real objects can be used to interact with virtual objects. However, unlike in the real world, real objects do not encounter any opposite reaction force when pushi…