Francesco Carrino
University of Applied Sciences Western Switzerland
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Publication
Featured researches published by Francesco Carrino.
issnip biosignals and biorobotics conference biosignals and robotics for better and safer living | 2012
Francesco Carrino; Joël Dumoulin; Elena Mugellini; Omar Abou Khaled; Rolf Ingold
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.
automotive user interfaces and interactive vehicular applications | 2013
Leonardo Angelini; Francesco Carrino; Stefano Carrino; Maurizio Caon; Denis Lalanne; Omar Abou Khaled; Elena Mugellini
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.
international conference on human-computer interaction | 2013
Leonardo Angelini; Maurizio Caon; Francesco Carrino; Stefano Carrino; Denis Lalanne; Omar Abou Khaled; Elena Mugellini
This paper presents WheelSense, a system for non-distracting and natural interaction with the In-Vehicle Information and communication System (IVIS). WheelSense embeds pressure sensors in the steering wheel in order to detect tangible gestures that the driver can perform on its surface. In this application, the driver can interact by means of four gestures that have been designed to allow the execution of secondary tasks without leaving the hands from the steering wheel. Thus, the proposed interface aims at minimizing the distraction of the driver from the primary task. Eight users tested the proposed system in an evaluation composed of three phases: gesture recognition test, gesture recognition test while driving in a simulated environment and usability questionnaire. The results show that the accuracy rate is 87% and 82% while driving. The system usability scale scored 84 points out of 100.
ambient intelligence | 2013
Francesco Carrino; Maria Sokhn; Anne Le Calvé; Elena Mugellini; Omar Abou Khaled
Due to the development of new technologies (digital cameras, smart phones, new data formats, etc.) people are led to deal with an increasing amount of information. Moreover, the advent of the web as a main distribution channel imposes to manage heterogeneous types of data and to deal with the issue of finding efficiently the pertinent information becomes more and more important. Several approaches such as semantic annotation, data mining, virtual and interactive visualizations are nowadays available to address these specific issues. However, so far, they have not been jointly exploited in order to take advantage of their respective strong points. In this paper we introduces a novel approach that combines semantic annotation, data mining, virtual queries and interactive visualization techniques aiming to provide the user with a personal information manager capable of dealing with heterogeneous database formats (relational, semantic, etc.). As a proof of the concept and its feasibility, in this paper we present a first prototype of the framework Memoria-Mea, that integrates these different approaches.
management of emergent digital ecosystems | 2009
Maria Sokhn; Francesco Carrino; Elena Mugellini; Omar Abou Khaled; Ahmed Serhrouchni
The evolution of the web in the last decades has created the need for new requirements towards intelligent information retrieval capabilities and advanced user interfaces. Nowadays, effective retrieval and usage of multimedia resources have to deal with the issues of creating efficient indexes, developing retrieval tools and improving user oriented visualization interfaces. To that end we put forward an integrated framework named CALIMERA. The framework is based on a High-level modEL for cOnference (HELO) and aims at enhancing the information management, retrieval and visualization of recorded talks of scientific conferences. This paper presents the conference model HELO developed to perform high level annotation of scientific talk recordings, to allow granular search facilities and complex queries, and to enhance knowledge retrieval and visualization of the recordings. As a proof-of-concept a prototype has been implemented and is presented in this paper.
Future Internet | 2016
Francesco Carrino; Elena Mugellini; Omar Abou Khaled; Nabil Ouerhani; Juergen Ehrensberger
Internet of Things (IoT) seems a viable way to enable the Smart Cities of the future. iNUIT (Internet of Things for Urban Innovation) is a multi-year research program that aims to create an ecosystem that exploits the variety of data coming from multiple sensors and connected objects installed on the scale of a city, in order to meet specific needs in terms of development of new services (physical security, resource management, etc.). Among the multiple research activities within iNUIT, we present two projects: SmartCrowd and OpEc. SmartCrowd aims at monitoring the crowd’s movement during large events. It focuses on real-time tracking using sensors available in smartphones and on the use of a crowd simulator to detect possible dangerous scenarios. A proof-of-concept of the application has been tested at the Paleo Festival (Switzerland) showing the feasibility of the approach. OpEc (Optimisation de l’Eclairage public) aims at using IoT to implement dynamic street light management and control with the goal of reducing street light energy consumption while guaranteeing the same level of security of traditional illumination. The system has been tested during two months in a street in St-Imier (Switzerland) without interruption, validating its stability and resulting in an overall energy saving of about 56%.
atlantic web intelligence conference | 2011
Francesco Carrino; Maria Sokhn; Elena Mugellini; Omar Abou Khaled
Thanks to the development of new technologies (such as PCs, PDAs, digital cameras, etc.) and with the advent of the Web, people are faced to deal with an increasing amount of information during their every day-life activities. As a consequence, the problem of finding the right information, at the right time, in a short period of time, becomes more and more crucial. Several technologies such as semantic web data mining and interactive visualizations are nowadays available to address these specific issues. However they have not been jointly exploited. With this respect, the paper presents a novel approach. Finally a prototype that validate our approach is presented.
international conference on distributed ambient and pervasive interactions | 2015
Joseph El Mallah; Francesco Carrino; Omar Abou Khaled; Elena Mugellini
Festivals and big scale events are becoming more and more popular, they can attract thousands of spectators. Ensuring the safety of the crowd has become a top priority to many organisers after the multitude of dramatic accidents that resulted in losses in human lives. Monitoring the crowd via smartphones is a relatively new technique that emerged recently with the capabilities of mobile phones to transmit their GPS location data. We present a novel approach, based on the local crowd pressure, combined with the detection of groups in a crowd, to detect critical situations and propose evacuation plans that does not separate groups of people that are together. Groups were detected using DBSCAN clustering algorithm with 80i¾?% accuracy. Location acquisition was tested during the Campus Fever event, and 87i¾?% of the collected data had an accuracy lower than 10i¾?m while 29i¾?% of the total data had 5i¾?m of accuracy. During 2i¾?h of monitoring, activity of the application, reduced the battery of 20i¾?%.
issnip biosignals and biorobotics conference biosignals and robotics for better and safer living | 2013
Francesco Carrino; Antonio Ridi; Maurizio Caon; Omar Abou Khaled; Elena Mugellini
We present the optimization of a wearable surface electromyography-based system for activity recognition in relation with the number of sensed muscles. The muscles of interest were four: Gastrocnemius, Tibialis Anterior, Vastus Lateralis and Erector Spinae. In particular, the system has been tested for the recognition of five everyday activities: “walking”, “running”, “cycling”, “sitting” and “standing”. We conducted two types of analysis: impersonal and subjective. The impersonal analysis aimed to evaluate the recognition rate when the system was trained over different users. On the opposite, during the subjective analysis the system has been trained using the data coming from a single user. Moreover, we computed the relative computational costs. Among the results, we can highlight that using the signals sensed from three opportunely selected muscles (Gastrocnemius, Tibialis Anterior and Vastus Lateralis) instead of four did not entail a sensible loss of accuracy, whereas it reduced the computational cost of the 24.1 %. In particular, sensing four and three muscles we achieved an activity recognition accuracy higher than 96% for the impersonal analysis; for the subjective analysis, the attained accuracy was higher than 99%.
web intelligence | 2012
Julien Tscherrig; Francesco Carrino; Maria Sokhn; Elena Mugellini; Omar Abou Khaled
This paper presents an original solution to visualize semantic information contained into an ontology. It is important to be able to record a large amount of data. On another hand, it is useful to be able to find a specific data among all the data recorded. The more data we have the higher is the difficulty to visualize them. The solution that is presented here below is based on a particular visualization of content based on filter. A Knowledge Management System provides the storage part. The storage is based on an ontology specifically designed for travels. The ontology provides a data structure with the creation of concepts. This structure allows at the same time the sorting of data and a clear visualization. The visualization is dynamic, it depends on the ontology relationship established.