A new qualitative spatial recognition model based on Egenhofer topological approach using C4.5 algorithm : experiment and results

Ambient technologies and ubiquitous computing constitute together an emerging trend of research bringing new possible solutions to many problems of human life. One of them is the technological assistance of the elders suffering from cognitive deficit with their everyday life activities inside what is called a smart home. The main issue in implementing such technology is the recognition of the activities of the resident. This problem consists in inferring the minimal set of possible ongoing activities using models defined in a plans library. To achieve that, most works propose to exploit different types of constraints (logical, temporal, etc.) in order to eliminate a maximum of incoherent hypotheses. However, very few works considered exploiting the spatial aspect related to the movement of objects and to their relations in space. In this paper, we propose to add a spatial pre-filter based on a topological approach from Egenhofer to discriminate implausible ongoing activities before applying a C4.5 decision tree to choose from the remaining hypotheses. Furthermore, this paper presents promising results we obtained from an experiment on that model using real case scenarios built from clinical trials that we conducted with Alzheimer's patients

A 3D simulator for intelligent environment experiments

The advances in sensor networks, electronics and ambient intelligence make creation of intelligent environments (IEs) possible. However, on account of economic and logistic issues the implementation of physical IEs is difficult in research domain. That makes it harder for researchers to experiment new approaches in IE domain. In this article, we propose a simulator to build virtual IEs. Simulators are a good alternative to physical IEs. Indeed, virtual IEs does not require expensive resources. Moreover, researchers and designers can conduct experiments anytime and repeat scenarios easily. Our simulator provides users with a set of virtual sensors and actuators. Our virtual sensors try to reproduce behavior of physical sensors and to produce datasets with the same properties as those generated by real sensors. Our proposition contains a tool to build a home from scratch and a model to define scenarios and behaviors of occupants. It also proposes an interface to control occupants directly. Virtual sensors collect data and generate datasets. Scientists and designers can use these datasets to evaluate and design new approaches in IE domain

Spatiotemporal knowledge representation and reasoning under uncertainty for action recognition in smart homes

We apply artificial intelligence techniques to perform data analysis and activity recognition in smart homes. Sensors embedded in smart home provide primary data to reason about observations and provide appropriate assistance for residents to complete their Activities Daily Livings (ADLs). These residents may suffer from different levels of Alzheimer disease. In this paper, we introduce a qualitative approach that considers spatiotemporal specifications of activities in the Activity Recognition Agent (ARA) to do knowledge representation and reasoning about the observations. In this paper, we consider different existing uncertainties within sensors observations and Observed Agent?s activities. In the introduced approach if the more details about environment context be provided, the less activity recognition process complexity and more precise functionality is expected

Activity recognition in the city using embedded systems and anonymous sensors

This paper presents an embedded system that performs activity recognition in the city. Arduino Due boards with infrared, distance and sound sensors are used to collect data in the city and the activity, profile, and group size recognition performance of different machine learning algorithms (RF, SVM, MLP) are compared. The features were extracted based on fixed-size windows around the observations. We show that it is possible to achieve a high accuracy for binary activity recognition with simple features, and we discuss the optimization of different parameters such as the sensors collection frequency, and the storage buffer size. We highlight the challenges of activity recognition using anonymous sensors in the environment, its possible applications and advantages compared to classical smartphone and wearable based approaches, as well as the improvements that will be made in future versions of this system. This work is a first step towards real-time online activity recognition in smart cities, with the long-term goal of monitoring and offering extended assistance for semi-autonomous people

Method for monitoring an activity of a cognitively impaired user and device therefore

The method for monitoring an activity of a cognitively impaired user using a device having a body resting on a plurality of support areas and loadable by the cognitively impaired user, generally comprises the step of measuring a plurality of force values exerted by a weight of a load to corresponding ones of the support areas; the step of obtaining a state of an activity of the cognitively impaired user based on the measured values; and the step of generating a signal indicative of the state of the activity. The state of the activity typically can be a given step in a recipe to be cooked on a smart stove in order to assist a cognitively impaired person in the completion of a cooking activity

Unsupervised mining of activities for smart home prediction

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 Alzheimer's 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

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

Light Node Communication Framework : a new way to communicate inside a Smart Home

The Internet of things has profoundly changed the way we imagine information science and architecture, and smart homes are an important part of this domain. Created a decade ago, the few existing prototypes use technologies of the day, forcing designers to create centralized and costly architectures that raise some issues concerning reliability, scalability, and ease of access which cannot be tolerated in the context of assistance. In this paper, we briefly introduce a new kind of architecture where the focus is placed on distribution. More specifically, we respond to the first issue we encountered by proposing a lightweight and portable messaging protocol. After running several tests, we observed a maximized bandwidth, whereby no packets were lost and good encryption was obtained. These results tend to prove that our innovation may be employed in a real context of distribution with small entities