Omar Abou Khaled

Interweaving knowledge management, argumentation and decision making in a collaborative setting: the KAD ontology model

Admitting that ontologies are a means to accomplish a shared understanding of different knowledge domains and to facilitate sharing and reuse of bodies of knowledge across groups and applications, this paper presents an ontology model, namely KAD, which applies to argumentative discourses carried out in collaborative decision making settings. The proposed model interweaves concepts from the Knowledge Management, Argumentation Theory, Decision Making, and Multicriteria Decision Aid disciplines. Through the case of manufacturing management, it is shown that KAD can be easily expanded in order to apply to any particular knowledge domain, after the proper definition of the relevant semantics.

A self-paced BCI system to control an electric wheelchair: Evaluation of a commercial, low-cost EEG device

The electrical cerebral activity has been already used in several applications aiming at improving the daily life of impaired people with strong motor disabilities. In particular the Electroencephalogram signals (EEG) have been used to provide new ways for communication and control. However, such kind of technology presents some important drawbacks such as the price and the difficulty to prepare the system without an experts support. This work intends to build a user-friendly, self-paced Brain-Computer Interface (BCI) system that allows using commercial EEG headsets in order to drive an electrical wheelchair with a motor imagery approach. Furthermore, the conceived system has been used for a first evaluation of a commercial, low-cost, EEG device compared with data coming from a professional device. The result shows that the low cost EEG device, at the actual state of the art, provide interesting results but can hardly be used for self-paced systems in error sensitive context.

Machine learning approaches for electric appliance classification

We report on the development of an innovative system which can automatically recognize home appliances based on their electric consumption profiles. The purpose of our system is to apply adequate rules to control electric appliance in order to save energy and money. The novelty of our approach is in the use of plug-based low-end sensors that measure the electric consumption at low frequency, typically every 10 seconds. Another novelty is the use of machine learning approaches to perform the classification of the appliances. In this paper, we present the system architecture, the data acquisition protocol and the evaluation framework. More details are also given on the feature extraction and classification models being used. The evaluation showed promising results with a correct rate of identification of 85%.

A Survey of Datasets for Human Gesture Recognition

This paper presents a survey on datasets created for the field of gesture recognition. The main characteristics of the datasets are presented on two tables to provide researchers a clear and rapid access to the information. This paper also provides a comprehensive description of the datasets and discusses their general strengths and limitations. Guidelines for creation and selection of datasets for gesture recognition are proposed. This survey should be a key-access point for researchers looking to create or use datasets in the field of human gesture recognition.

Humans and smart environments: a novel multimodal interaction approach

In this paper, we describe a multimodal approach for human-smart environment interaction. The input interaction is based on three modalities: deictic gestures, symbolic gestures and isolated-words. The deictic gesture is interpreted using the PTAMM (Parallel Tracking and Multiple Mapping) method exploiting a camera handheld or worn on the user arm. The PTAMM algorithm tracks in real-time the position and orientation of the hand in the environment. This information is used to point real or virtual objects, previously added to the environment, using the optical camera axis. Symbolic hand-gestures and isolated voice commands are recognized and used to interact with the pointed target. Haptic and acoustic feedbacks are provided to the user in order to improve the quality of the interaction. A complete prototype has been realized and a first usability evaluation, assessed with the help of 10 users has shown positive results.

Generic Framework for Transforming Everyday Objects into Interactive Surfaces

According to Mark Weiser, smart environments are physical worlds that are richly and invisibly interwoven with sensors, actuators, displays, and computational elements, embedded seamlessly in the everyday objects of our lives. At present however turn everyday objects into interactive ones is a very challenging issue and this limits their widespread diffusion. In order to address this issue we propose a framework to turn everyday objects, such as a table or a mirror, into interactive surfaces allowing to access and manipulate digital information. The framework integrates several interaction technologies such as electromagnetic, acoustic and optical one, supporting rapid prototype development. Two prototypes, an interactive table and an interactive tray, have been developed using the toolkit to validate the proposed approach.

PEGASO: Towards a Life Companion

In the frame of the PEGASO European project, we aim at promoting healthier lifestyles focusing on the alimentary education and physical activity. This paper presents the concept of health companion as the main tool to inform and push the user towards a healthier lifestyle. This companion is an advanced interface that assists and entertains the user, providing him an adequate knowledge about alimentary and physical education. The companion is based on a knowledge model of the user and its behavior; it is composed of three main facets: is tailored to the user, is based on affective design and is designed to be a life companion.

Gesture recognition corpora and tools

We present a review of the tools and corpora for gesture recognition.We introduce a framework supporting acquisition and management of corpora.We introduce a facilitating method for ground truthing corpora during acquisition. This article presents a framework supporting rapid prototyping of multimodal applications, the creation and management of datasets and the quantitative evaluation of classification algorithms for the specific context of gesture recognition. A review of the available corpora for gesture recognition highlights their main features and characteristics. The central part of the article describes a novel method that facilitates the cumbersome task of corpora creation. The developed method supports automatic ground truthing of the data during the acquisition of subjects by enabling automatic labeling and temporal segmentation of gestures through scripted scenarios. The temporal errors generated by the proposed method are quantified and their impact on the performances of recognition algorithm are evaluated and discussed. The proposed solution offers an efficient approach to reduce the time required to ground truth corpora for natural gestures in the context of close human-computer interaction.

Opportunistic synergy: a classifier fusion engine for micro-gesture recognition

In this paper, we present a novel opportunistic paradigm for in-vehicle gesture recognition. This paradigm allows using two or more subsystems in a synergistic manner: they can work in parallel but the lack of some of them does not compromise the functioning of the whole system. In order to segment and recognize micro-gestures performed by the user on the steering wheel, we combine a wearable approach based on the electromyography of the users forearm muscles, with an environmental approach based on pressure sensors integrated directly on the steering wheel. We present and analyze several fusion methods and gesture segmentation strategies. A prototype has been developed and evaluated with data from nine subjects. The results prove that the proposed opportunistic system performs equal or better than each stand-alone subsystem while increasing the interaction possibilities.

Towards an Anthropomorphic Lamp for Affective Interaction

This paper presents the concept of a lamp that allows displaying and collecting users emotional states. In particular, it displays the emotional information changing colors and facial expressions; in fact, the lamp is characterized by anthropomorphic form and behavior in order to make the interaction more natural and spontaneous. The user can interact with the lamp through tangible gestures typically used in social interactions by humans. Two different scenarios involving the use of the lamp as a companion and for computer-mediated communication are presented.