Towards optimal sensor deployment for location tracking in smart home

International audienceAmbient Assisted Living (AAL) aims to ease the daily living and working environmentfor disabled/elderly peopleat home. AAL use information and communication technology based on sensors data. These sensors are generally placed randomly without taking into account the layout of buildings and rooms. In this paper, we develop a mathematical model foroptimal sensor placement in order (i) to optimize the sensor number with regard to room features, (ii) to ensure a reliability level in sensor networkconsidering a sensor failure rate. This placement ensures the targettracking in smart home sinceoptimizing sensorplacement allow us to distinguish different zonesand consequently, to identify the target location, according to the activated sensors

A Discrete event model for multiple inhabitants location tracking

6 pagesInternational audienceSmart Home technologies are aiming to improve the comfort and safety of the inhabitants into their houses. To achieve this goal, online indoor location tracking of the inhabitants is often used to monitor the air conditioning, to detect dangerous situations and for many other applications. In this paper, it is proposed an approach to build a model allowing dynamic tracking of several persons in their house. A method to construct such a model by using finite automata and Discrete Event System (DES) paradigms is presented. An approach to reduce the size of the model is also introduced. Finally, an efficient algorithm for location tracking is proposed. For the sake of better understanding, an illustrative example is used throughout the paper

Sensor Network-based and User-friendly User Location Discovery for Future Smart Homes

User location is crucial context information for future smart homes where a lot of location based services will be proposed. This location necessarily means that User Location Discovery (ULD) will play an important role in future smart homes. Concerns about privacy and the need to carry a mobile or a tag device within a smart home currently makes conventional ULD systems uncomfortable for users. Future smart homes will need a ULD system to consider these challenges. This paper addresses to design such a ULD system for context-aware services in future smart homes stressing on the following challenges: (i) users’ privacy, (ii) device/tag-free, and (iii) fault tolerance and accuracy. On the other hand, emerging new technologies such as Internet of Things, embedded systems, intelligent devices and machine-to-machine communication are penetrating into our daily life with more and more sensors available for use in our homes. Considering this opportunity, we propose a ULD system that is capitalizing on the prevalence of sensors or home while satisfying the aforementioned challenges. The proposed sensor network-based and user-friendly ULD system relies on different types of cheap sensors as well as a context broker with a fuzzy-based decision maker. The context broker receives context information from different types of sensors and evaluates that data using the fuzzy set theory. We demonstrate the performance of the proposed system by illustrating a use case, utilizing both an analytical model and simulation

Localisation of humans, objects and robots interacting on load-sensing floors

International audienceLocalisation, tracking and recognition of objects and humans are basic tasks that are of high value in applications of ambient intelligence. Sensing floors were introduced to address these tasks in a non-intrusive way. To recognize the humans moving on the floor, they are usually first localized, and then a set of gait features are extracted (stride length, cadence, pressure profile over a footstep). However, recognition generally fails when several people stand or walk together, preventing successful tracking. This paper presents a detection, tracking and recognition technique which uses objects' weight. It continues working even when tracking individual persons becomes impossible. Inspired by computer vision, this technique processes the floor pressure-image by segmenting the blobs containing objects, tracking them, and recognizing their contents through a mix of inference and combinatorial search. The result lists the probabilities of assignments of known objects to observed blobs. The concept was successfully evaluated in daily life activity scenarii, involving multi-object tracking and recognition on low resolution sensors, crossing of user trajectories, and weight ambiguity. This technique can be used to provide a probabilistic input for multi-modal object tracking and recognition systems

Human Localization and Activity Recognition Using Distributed Motion Sensors

The purpose of this thesis is to localize a human and recognize his/her activities in indoor environments using distributed motion sensors. We propose to use a test bed simulated as mock apartment for conducting our experiments. The two parts of the thesis are localization and activity recognition of the elderly person. We explain complete hardware and software setup used to provide these services. The hardware setup consists of two types of sensor end nodes and two sink nodes. The two types of end nodes are Passive Infrared sensor node and GridEye sensor node. Passive Infrared sensor nodes consist of Passive Infrared sensors for motion detection. GridEye sensor nodes consist of thermal array sensors. Data from these sensors are acquired using Arduino boards and transmitted using Xbee modules to the sink nodes. The sink nodes consist of receiver Xbee modules connected to a computer. The sensor nodes were strategically placed at different place inside the apartment. The thermal array sensor provides 64 pixel temperature values, while the PIR sensor provides binary information about motion in its field of view. Since the thermal array sensor provides more information, they were placed in large rooms such as living room and bed room. While PIR sensors were placed in kitchen and bathroom. Initially GridEye sensors are calibrated to obtain the transformation between pixel and real world coordinates. Data from these sensors were processed on computer and we were able to localize the human inside the apartment. We compared the location accuracy using ground truth data obtained from the OptiTrack system. GridEye sensors were also used for activity recognition. Basic human activities such as sitting, sleeping, standing and walking were recognized. We used Support Vector Machine (SVM) to recognize sitting and sleeping activities. Gait speed of human was used to recognize the standing and walking activities. Experiments were performed to obtain the accuracy of classification for these activities.Electrical Engineerin

Entwicklung und Integration von Interaktionsstrategien zur Erkennung und Behandlung von Notfallsituationen im häuslichen Umfeld durch Service-Roboter

Service-Roboter werden genutzt, um Senioren bei der Verrichtung von Aktivitäten des alltäglichen Lebens zu unterstützen und ihnen damit ein selbständiges Leben bis ins hohe Alter zu ermöglichen. Neben der Bereitstellung der Services muss der Roboter in der Lage sein, Notsituationen der Nutzer zu erkennen und in angemessener Weise darauf zu reagieren. Ziel dieser Masterarbeit ist es daher, eine Interaktionsstrategie zur Erkennung und Behandlung von Notfällen im häuslichen Umfeld zu entwickeln. Im Rahmen des SYMPARTNER-Projektes des Fachgebiets für Neuroinformatik und kognitive Robotik wurde der EmergencyService entwickelt. Dieser umfasst Module zur Erkennung von Notfällen, zur Personensuche innerhalb der Wohnung, zum Auffinden und zur Verifikation des Nutzers sowie einem anschließenden Dialog zur Klärung der Situation. Zur Realisierung des Ablaufs wurden bereits existierende Module mit Modulen kombiniert, deren Erstellung Teil dieser Arbeit war. Im Anschluss an die Implementierung des Konzepts auf der genutzten mobilen Roboterplattform wurde in durchgeführten Tests die Funktionsfähigkeit der Modulkette überprüft. Dazu wurden verschiedene Szenarien mit liegenden, stehenden und sitzenden Personen konzipiert. In 18 der 25 Versuche konnte der Roboter die Personen in den Bereichen der Testumgebung erfolgreich detektieren und über einen situationsabhängigen Dialog mit ihnen in Kontakt treten. Die Erkennungsleistung bei liegenden und stehenden Personen betrug 90% bzw. 80%. Im Gegensatz dazu konnten sitzende Personen nur in 20% der Fälle erkannt werden. Es zeigte sich, dass die Erfassungsbereiche der verwendeten Kamerasysteme die Erkennungsrate beeinflussten. Die Versuchsergebnisse wurden diskutiert und Empfehlungen für zukünftige Arbeiten gegeben.Service robots are used to assist elderly persons in performing activities of daily life and supporting them to live independently up to old ages. Besides providing services, the robots need to be able to recognize emergency situations and react to them in a proper way. This master thesis aims to establish a strategy to identify and handle emergencies in domestic environments. The proposed emergency service is part of the SYMPARTNER project at the department of Neuroinformatics and Cognitive Robotics. The service includes modules to detect emergencies, search for persons, person detection and verification as well as using dialogues for communication purposes. To realize the desired behavior, the service combines existing modules with modules developed within this master thesis. To examine the functionality of the created emergency service, several tests were conducted with lying, standing and sitting people. The results show that 18 of 25 tests were finished successfully, indicating that the robot detected them correctly in the test environment and started the corresponding dialogue. The detection rates for lying and standing persons were 90% and 80% respectively. The tests with sitting persons resulted in a detection rate of 20%. Further results show that the detection rate is influenced by the utilized camera systems' field of view. Eventually, the results are discussed and goals for prospective research are suggested

Modèle fonctionnel d'un sol intelligent

L'assistance aux personnes ayant des troubles cognitifs, au laboratoire DOMUS, se fait selon deux approches. La première met en oeuvre des outils d'assistance personnelle portés par la personne, comme le panic button ou encore une application déployée sur un téléphone intelligent. La seconde consiste à équiper l'environnement de la personne afin de lui proposer une aide discrète qui ne modifie que très peu ses habitudes de vie. Ce présent projet de recherche s'inscrit dans la seconde approche d'assistance. Ce mémoire a pour but de proposer un prototype de sol intelligent permettant la localisation et même l'identification des personnes présentes dans un espace intelligent. Les sols intelligents jusqu'alors développés présentent tous la même particularité : ils nécessitent, pour les déployer et les exploiter, des connaissances avancées dans les domaines de l'informatique et de l'électronique ainsi que la mise en place d'un processus complexe pour leur déploiement et leur exploitation. L'architecture proposée pour ce prototype de sol intelligent vise à faciliter au maximum sa configuration et son utilisation afin de réduire au maximum les coûts qui y sont liés. Pour faciliter le développement des algorithmes d'auto-configuration et d'exploitation des noeuds constituant le sol intelligent, un simulateur a été réalisé. Il permet de confirmer le comportement de ces algorithmes dans un réseau de grande dimension sans avoir à mettre en place une réalisation matérielle qui représente une étape complexe et coûteuse. La mise en oeuvre, d'un point de vue matériel, au niveau matériel n'a été réalisée que sur un nombre limité de noeuds afin d'en démontrer la faisabilité