Perceiving Mass in Mixed Reality through Pseudo-Haptic Rendering of Newton's Third Law

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 pushing against virtual objects. The lack of reaction force during manipulation prevents users from perceiving the mass of virtual objects. Although this could be addressed by equipping real objects with force-feedback devices, such a solution remains complex and impractical.In this work, we present a technique to produce an illusion of mass without any active force-feedback mechanism. This is achieved by simulating the effects of this reaction force in a purely visual way. A first study demonstrates that our technique indeed allows users to differentiate light virtual objects from heavy virtual objects. In addition, it shows that the illusion is immediately effective, with no prior training. In a second study, we measure the lowest mass difference (JND) that can be perceived with this technique. The effectiveness and ease of implementation of our solution provides an opportunity to enhance mixed reality interaction at no additional cost

Incitations à l’offre de prévention et préférences en médecine générale : l’apport de la méthode DCE

L’objectif de cet article est de contribuer à la compréhension des préférences des médecins pour différents dispositifs, financiers ou non, susceptibles d’être implantés en France afin de favoriser la prévention en médecine de ville. La méthode du Discrete Choice Experiment (DCE) est mobilisée à travers une enquête menée auprès de 1 396 médecins qui permet d’analyser 3 390 observations. L’estimation économétrique de la fonction d’utilité des médecins est conduite dans le cadre d’un modèle Logit conditionnel, des dispositions à payer marginales sont également calculées. Nos résultats montrent que, si l’aspect financier est important dans les préférences des médecins, les aspects non financiers de leur environnement de travail (formation, guide de pratique, mode d’exercice, retour d’information) constituent des attributs très significatifs de leur fonction d’utilité. Ces résultats permettent à la fois de revenir sur le contenu d’une fonction d’utilité du médecin propre à modéliser ses choix d’activité et d’éclairer les politiques publiques à mettre en place en France pour accroître l’activité de prévention des médecins généralistes.The aim of this article is to help the comprehension of physicians’ preferences for different preventive devices, financial or not, likely to be implemented in France in order to facilitate preventive care supply. A discrete choice experiment is used, based on a postal survey among 1396 GPs, 3390 observations are available. The GPs utility is estimated using McFadden conditional Logit, willingness to pay are also calculated. Our results show the relevance of pecuniary and non-pecuniary arguments in GPs’ preferences. Non-pecuniary aspects of their work environment for prevention (continuing education, clinical guidelines, type of practice, information feedback) are highly significant attributes of their utility. These results provide a new insight into physicians’ utility function and could enlighten the public policy. in order to increase the preventive activity of French GPs

Model Fusion to Enhance the Clinical Acceptability of Long-Term Glucose Predictions

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 history of glucose predictions made by several models, the future glucose variation at a given horizon is predicted. Then, by accumulating the past predicted variations starting from a known glucose value, the fused glucose prediction is computed. A new loss function is introduced to make the DCP model learn to react faster to changes in glucose variations. The algorithm has been tested on 10 \textit{in-silico} type-1 diabetic children from the T1DMS software. Three initial predictors have been used: a Gaussian process regressor, a feed-forward neural network and an extreme learning machine model. The DCP and two other fusion algorithms have been evaluated at a prediction horizon of 120 minutes with the root-mean-squared error of the prediction, the root-mean-squared error of the predicted variation, and the continuous glucose-error grid analysis. By making a successful trade-off between prediction accuracy and predicted-variation accuracy, the DCP, alongside with its specifically designed loss function, improves the clinical acceptability of the predictions, and therefore the safety of the model for diabetic people

Study of Short-Term Personalized Glucose Predictive Models on Type-1 Diabetic Children

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 researchers do not share the same data between studies, progress is hard to assess. This paper aims at comparing the most promising algorithms in the field, namely Feedforward Neural Networks (FFNN), Long Short-Term Memory (LSTM) Recurrent Neural Networks, Extreme Learning Machines (ELM), Support Vector Regression (SVR) and Gaussian Processes (GP). They are personalized and trained on a population of 10 virtual children from the Type 1 Diabetes Metabolic Simulator software to predict future glucose values at a prediction horizon of 30 minutes. The performances of the models are evaluated using the Root Mean Squared Error (RMSE) and the Continuous Glucose-Error Grid Analysis (CG-EGA). While most of the models end up having low RMSE, the GP model with a Dot-Product kernel (GP-DP), a novel usage in the context of glucose prediction, has the lowest. Despite having good RMSE values, we show that the models do not necessarily exhibit a good clinical acceptability, measured by the CG-EGA. Only the LSTM, SVR and GP-DP models have overall acceptable results, each of them performing best in one of the glycemia regions