Latifa Oukhellou

Physical Human Activity Recognition Using Wearable Sensors.

This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors’ placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show 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. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.

Dedicated sensor and classifier of rail head defects

Abstract This paper presents an original system based on a specialized eddy-current sensor for the inspection of railway tracks. The device aims at the detection of broken rails and large head spalls of the rail, and it has been designed to be mounted underneath automatic driving railway vehicles. In fact, less important types of defects can also be detected. Another use of the sensor would be for predictive management of the rail. All these applications need a procedure for parametrization of the output signals. Then a classification procedure is performed by a set of neural networks which is able to assign each ‘defect’ into one particular class.

Mixture Model Estimation with Soft Labels

This paper addresses classification problems in which the class membership of training data is only partially known. Each learning sample is assumed to consist in a feature vector and an imprecise and/or uncertain “soft” label m i defined as a Dempster-Shafer basic belief assignment over the set of classes. This framework thus generalizes many kinds of learning problems including supervised, unsupervised and semi-supervised learning. Here, it is assumed that the feature vectors are generated from a mixture model. Using the General Bayesian Theorem, we derive a criterion generalizing the likelihood function. A variant of the EM algorithm dedicated to the optimization of this criterion is proposed, allowing us to compute estimates of model parameters. Experimental results demonstrate the ability of this approach to exploit partial information about class labels.

Partially supervised Independent Factor Analysis using soft labels elicited from multiple experts: application to railway track circuit diagnosis

Using a statistical model in a diagnosis task generally requires a large amount of labeled data. When ground truth information is not available, too expensive or difficult to collect, one has to rely on expert knowledge. In this paper, it is proposed to use partial information from domain experts expressed as belief functions. Expert opinions are combined in this framework and used with measurement data to estimate the parameters of a statistical model using a variant of the EM algorithm. The particular application investigated here concerns the diagnosis of railway track circuits. A noiseless Independent Factor Analysis model is postulated, assuming the observed variables extracted from railway track inspection signals to be generated by a linear mixture of independent latent variables linked to the system component states. Usually, learning with this statistical model is performed in an unsupervised way using unlabeled examples only. In this paper, it is proposed to handle this learning process in a soft-supervised way using imperfect information on the system component states. Fusing partially reliable information about cluster membership is shown to significantly improve classification results.

Comparison of Supervised Self-Organizing Maps Using Euclidian or Mahalanobis Distance in Classification Context

The supervised self-organizing map consists in associating output vectors to input vectors through a map, after self-organizing it on the basis of both input and desired output given altogether. This paper compares the use of Euclidian distance and Mahalanobis distance for this model. The distance comparison is made on a data classification application with either global approach or partitioning approach. The Mahalanobis distance in conjunction with the partitioning approach leads to interesting classification results.

Combined use of partial least squares regression and neural network for diagnosis tasks

This work deals with a diagnosis system, based on a combined use of partial least squares regression (PLS) and neural network (NN). An application concerning the French railway track/vehicle transmission system illustrates this approach. It is shown that a reliable selection of a reduced set of relevant descriptors is made by the PLS regression. Moreover, the projection of the data on the first PLS plane allows to highlight trajectories of the evolution of the system state between different classes. The modeling of the process state is performed by a multilayer NN. In this case, the PLS algorithm provides also a suitable approach to initialize the NN weights and to determine the optimal number of hidden nodes.

HYBRID TRAINING OF RADIAL BASIS FUNCTION NETWORKS IN A PARTITIONING CONTEXT OF CLASSIFICATION

Abstract The design of radial basis function networks is rather complex because of the great number of parameters that must be adjusted: positioning and number of kernels, choice of the distance type and centre widths, weight values. This article details these points in the framework of classification tasks with a partitioning approach. An adaptation of the orthogonal least-squares method is presented in order to select the centres of each sub-classifier in connection with a particular stopping criterion based on the addition of a random centre. Moreover, different choices of distance and centre widths are compared and illustrated by a 4-class problem in the non-destructive evaluation domain.

Dedicated Sensor and Classifier of Rail Head Defects for Railway Systems

Abstract This article presents an original system based on specialized eddy current sensor for the inspection of the railway tracks. The device aims at the detection of broken rails and large head spalls of the rail and it has been designed in order to be mounted underneath automatic driving railway vehicles. In fact, less important types of defects can also be detected. Other uses of the sensor are predictive management of the rail. All the applications need a parametrization procedure of the output signals of the sensor. Then a classification is performed by a set of Neural Networks which is able to split the “defects” into several classes.

Clustering the Vélib' Dynamic Origin/Destination flows using a family of Poisson Mixture Models

Studies on human mobility, including Bike Sharing System Analysis, have expanded over the past few years. They aim to give insight into the underlying urban phenomena linked to city dynamics and generally rely on data-mining tools to extract meaningful patterns from the huge volume of data recorded by such complex systems. This paper presents one such tool through the introduction of a family of generative models based on Poisson mixtures to automatically analyse and find temporal-based clusters in Origin/Destination flow-data. Such an approach may provide latent factors that reveal how regions of different usage interact over time. More generally, the proposed methodology can be used to cluster edges of temporal valued-graphs with respect to their temporal profiles and is thus particularly suited to mine patterns in dynamic Origin/Destination matrices commonly encountered in the field of transport. An in-depth analysis of the results of the proposed models was carried out on two months of trips data recorded on the Velib׳ Bike-Sharing System of Paris to validate the approach.

Activity recognition using body mounted sensors: An unsupervised learning based approach

Unsupervised learning approaches are used in various applications such as speech recognition, image compression, information retrieval and activity recognition. This paper introduces a novel unsupervised approach for clustering multi-dimensional time series that present the 3-d acceleration data measured with body-worn accelerometers. More specifically, the proposed approach uses a statistical model based on Multiple Hidden Markov Model Regression (MHMMR) to automatically analyze the human activity. This method takes into account the sequential appearance and temporal evolution of the data to easily detect static and dynamic activities. Comparisons with existing unsupervised approaches, including the standard Gaussian Mixture Model, the k-means algorithm, the DBSCAN algorithm and the standard HMM, demonstrate the effectiveness of the proposed approach.