Latifa Oukhellou

TL;DR: A review of different classification techniques used to recognize human activities from wearable inertial sensor data shows that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms.

TL;DR: A new unsupervised approach for human activity recognition from raw acceleration data measured using inertial wearable sensors based upon joint segmentation of multidimensional time series using a Hidden Markov Model (HMM) in a multiple regression context.

TL;DR: Two approaches to cluster smart card data, which can be used to extract mobility patterns in a public transportation system, are proposed and illustrated how they can help reveal valuable insights about urban mobility.

TL;DR: A two-level generative model that applies the Gaussian mixture model to regroup passengers based on their temporal habits in their public transportation usage to demonstrate the efficiency of this approach in identifying a reduced set of passenger clusters linked to their fare types.

TL;DR: Using the generalized Bayesian theorem, an extension of Bayes' theorem in the belief function framework, a criterion generalizing the likelihood function is derived, allowing the ability of this approach to exploit partial information about class labels.