Kevin Bouchard

Accurate passive RFID localization system for smart homes

The smart home paradigm is a promising new trend of research aiming to propose an alternative to postpone the institutionalization of cognitively-impaired silver-aged people. These habitats are intended to provide security, guidance and direct support services to its resident. To be able to fulfill this important mission, a smart home system first has to identify the ongoing activities of its user by tracking, in real time, the position of the main daily living objects. Many researchers addressed this issue by proposing systems based on ultrasonic wave sensors, video cameras, and radio-frequency identification (RFID). However, because of its robustness and its low price, RFID constitutes the most viable technology for smart homes. Recently, several RFID localization algorithms have been developed, mainly for commercial and industrial uses, but they are not precise enough to be used in an assistive recognition context or they focus on active tags, which need batteries and are much more expensive. We present, in this paper, a new algorithmic approach for passive RFID localization in smart homes based on elliptical trilateration and fuzzy logic. This new algorithm has been implemented in a real smart home infrastructure and has been rigorously tested. We also analyze and compare the obtained results with the main existing approaches.

Guidelines to efficient smart home design for rapid AI prototyping: a case study

Advances in ubiquitous technology have moved us towards the dream of creating intelligent houses that can help human in their everyday life. The next step in the completion of this vision is to make major breakthroughs in artificial intelligence. In fact, it is the key component for allowing sensors and effectors to give useful services when it is appropriate. In consequence, researchers need to conduct more experiments in realistic setting (e.g. smart home). In order to face this challenge, many research teams try to build new experimental infrastructures without any background experience, guidance or even a real idea of their research needs and issues. Our team is composed of specialists in AI for cognitive assistance and has worked with four major smart home infrastructures. From that experience, we propose, in this paper, a set of guidelines for designing and implementing an efficient smart home architecture on both hardware and software perspective. This paper aims to be a major step toward the AI development (rapid prototyping) and smart home research. Moreover, we share our recent experience with the construction of a new smart home and clinical trials conducted at our laboratory with real Alzheimers subjects.

SIMACT: a 3D open source smart home simulator for activity recognition

Smart home technologies have become, in the last few years, a very active topic of research. However, many scientists working in this field do not possess smart home infrastructure allowing them to conduct satisfactory experiments in a concrete environment with real data. To address this issue, this paper presents a new flexible 3D smart home infrastructure simulator developed in Java specifically to help researchers working in the field of activity recognition. A set of pre-recorded scenarios, made with data extracted from clinical trials, will be included with the simulator in order to give a common foundation for testing activity recognition algorithms. The goal is to release the SIMACT simulator as an open source component that will benefit the whole smart home research community.

Human activity recognition in smart homes based on passive RFID localization

Modern societies are facing an important ageing of their population leading to arising economical and sociological challenges such as the pressure on health support services for semi-autonomous persons. Smart home technology is considered by many researchers as a promising potential solution to help supporting the needs of elders. It aims to provide cognitive assistance by taking decisions, such as giving hints, suggestions and reminders, with different kinds of effectors (light, sound, screen, etc.) to a resident suffering from cognitive deficits in order to foster their autonomy. To implement such technology, the first challenge we need to overcome is the recognition of the ongoing inhabitant activity of daily living (ADL). Moreover, to assist him correctly, we also need to be able to detect the cognitive errors he performs. Therefore, we present in this paper a new affordable activity recognition system, based on passive RFID technology, able to detect errors related to cognitive impairment in morning routines. The entire system relies on an innovative model of elliptical trilateration with several filters, and on an ingenious representation of activities with spatial zones. This system has been implemented and deployed in a real smart home prototype. We also present the promising results of a first experiment conducted on this new activity recognition system with real cases scenarios about morning routines.

Discovery of topological relations for spatial Activity Recognition

Human Activity Recognition (HAR) is a challenging problem that could enable an outstanding number of applications in pervasive computing. Many approaches have been developed to overcome this issue, but they all suffer from major drawbacks. While some use invasive sensors such as video-cameras and wearable technology, other exploit complex models to only recognize coarse-grained activities. In this paper, we propose to exploit the largely neglected spatial aspects in the smart home to recognize the activity of daily living (ADLs) of a resident in a noninvasive fashion. To do so, we designed an extension to well-known data mining algorithms that we exploit to automatically learn the models of the resident ADLs. The models are built from the retrieval of spatial patterns corresponding to the topological relationships of the smart home entities. We demonstrate the advantages of our new semi-supervised system through comprehensive experiments inside a smart home and compare the results with expert defined models of activity.

Accurate Trilateration for Passive RFID Localization in Smart Homes

The smart home as emerged in recent years as a new trend of research aiming to propose an alternative to postpone the institutionalization of cognitively-impaired people. These habitats are intended to provide security, guidance and direct support services to its resident. To fulfill this important mission, an algorithm first has to identify the ongoing activities of its user by tracking, in real time, the position of the main daily living objects. Many researchers addressed this issue by proposing systems based ultrasonic wave sensors, video cameras, and radio-frequency identification (RFID). However, the RFID technology, constitutes the most viable technology for smart homes. Recently, several RFID localization algorithms have been developed, mainly for commercial and industrial uses, but they are not precise enough to be used in an assistive context. Furthermore, the majority of them focuses on systems exploiting active RFID tags, which need batteries and are much more expensive. We present, in this paper, a new algorithmic approach for passive RFID localization in smart homes based on elliptical trilateration and fuzzy logic. This new algorithm has been implemented in a real smart home infrastructure. It has been rigorously tested and outperformed the comparable approaches.

Accurate RFID Trilateration to Learn and Recognize Spatial Activities in Smart Environment

The rapid adoption of wireless communication and sensors technology has raised the awareness of many laboratories about the field of network embedded system. Most researchers aim to exploit these advances to enable technological assistance of frail persons in smart homes. However, to reach the full potential of applications using network embedded systems such as assistive smart home, scientists need to work toward the creation of support services. In this paper, we present an accurate passive RFID localization technique, which can easily be implemented and deployed in various environments, coupled to a complete human activity recognition model. The goal of this paper is to demonstrate, through concrete experiments, that support services can enable powerful solution to long-lived challenges of the network embedded system community. Particularly, the model exploits qualitative spatial reasoning from RFID localization of objects in the smart home to learn and recognize the basic and instrumental activities of daily living of a resident. Our system was deployed in a real smart home, and the results obtained were quite encouraging. The developed RFID technique gives an average precision of ±14.12 cm, and the recognition algorithm recognizes up to 92% activities.

Unsupervised Mining of Activities for Smart Home Prediction

Abstract This paper addresses the problem of learning the Activities of Daily Living (ADLs) in smart home for cognitive assistance to an occupant suffering from some type of dementia, such as Alzheimers disease. We present an extension of the Flocking algorithm for ADL clustering analysis. The Flocking based algorithm does not require an initial number of clusters, unlike other partition algorithms such as K-means. This approach allows us to learn ADL models automatically (without human supervision) to carry out activity recognition. By simulating a set of real case scenarios, an implementation of this model was tested in our smart home laboratory, the LIARA.

Qualitative spatial activity recognition using a complete platform based on passive RFID tags: experimentations and results

Smart home has become a very active topic of research in the past few years. The problem of recognizing activity inside a smart home is one of the biggest challenge researchers have to face in this discipline. Many of them have presented approaches exploiting temporal constraints in order to maximize the efficiency and the precision of their recognition model. However, only few works investigate the spatial aspects characterizing the habitat context. In this paper, we present a new algorithm and a complete experiment showing the importance of taking into account spatial constraints in the recognition process. The goal of this paper is to demonstrate that recognition algorithms will benefit from exploiting spatial constraints.

Gesture recognition in smart home using passive RFID technology

Gesture recognition is a well-establish topic of research that is widely adopted for a broad range of applications. For instance, it can be exploited for the command of a smart environment without any remote control unit or even for the recognition of human activities from a set of video cameras deployed in strategic position. Many researchers working on assistive smart home, such as our team, believe that the intrusiveness of that technology will prevent the future adoption and commercialization of smart homes. In this paper, we propose a novel gesture recognition algorithm that is solely based on passive RFID technology. This technology enables the localization of small tags that can be embedded in everyday life objects (a cup or a book, for instance) while remaining non intrusive. However, until now, this technology has been largely ignored by researchers on gesture recognition, mostly because it is easily disturbed by noise (metal, human, etc.) and offer limited precision. Despite these issues, the localization algorithms have improved over the years, and our recent efforts resulted in a real-time tracking algorithm with a precision approaching 14cm. With this, we developed a gesture recognition algorithm able to perform segmentation of gestures and prediction on a spatio-temporal data series. Our new model, exploiting works on qualitative spatial reasoning, achieves recognition of 91%. Our goal is to ultimately use that knowledge for both human activity recognition and errors detection.