With the ageing of the world population, Do-ItYourself approaches and Smart Personal Assistant are combined to provide customised systems that help people in their daily lives, including fostering their self-reliance and promoting ageing in place, even when these people have cognitive disabilities or perceptual impairments such as “Alzheimer” disease. Ambient Assisted Living systems which rely on this combination can also help caregivers reduce their workload by giving them much relevant information about the health status and the situation of the patients at home, and, furthermore, by giving them facilities to create home automation scenarios to assist the patients. A scenario is the arrangement of daily activities in order to achieve a particular goal. In this paper, we propose a new approach to creating assistance scenarios through voice command, involving two types of ontologies, a smart home ontology (OntoDomus) and a knowledge graph of an activity/action extracted from a scenario in the form of structured knowledge. In particular, we leverage ontology alignment to process voice requests in order to create an assistance scenario for the elderly.

Abstract This work presents a real-time system for tracking multiple object in the context of meal preparation when using the Cognitive Orthosis for CoOKing (COOK). This system is called SafeCOOK. It aims to provide more capabilities to detect some dangerous situations that the current system does not consider. For example, it can locate a utensil or other kitchen object that has been left on the cooking surface of the stove while a meal is being prepared. This system uses a hybrid method based on YOLO and KCF to detect, track and drop cooking utensils as they enter and leave the cooking area, and is capable of monitoring an entire cooktop in real-time with a single camera. The software has been implemented on an embedded platform in the smart stove and has been added to it. The system produces good segmentation and tracking results at a frame rate of 1 to 4 frames per second, as demonstrated in extensive experiments using video sequences under different conditions.

In the last decade, advances in the field of ambient assisted living (AAL) have changed the way services can be provided in smart homes. New possibilities are now offered for addressing complex interaction problems between the technology and users with special needs. Within this context, this study addresses human-computer interactions for cognitive assistance to people with Traumatic Brain Injury (TBI). Interdisciplinary research combining computer science and occupational therapy was conducted to model the interaction between an AAL system (AALS) and a person with cognitive impairments due to TBI residing in an AAL environment. Cognitive assistance is modelled as an interactive exchange where an AALS spreads assistance cues that should induce appropriate behaviours from an assisted person as responses. After an assistance cue is delivered, the AALS should evaluate the user reaction and, stop the interaction if the intended reaction is observed or resume by adapting the assistance. To do so, evidence-based cognitive rehabilitation and speech acts are used to model the interaction content i.e., assistive cues and user feedback, while an ontology formalizes the semantics of this knowledge in a computer readable format. A behavioural model based on behaviour trees informed by the ontological model is then used to enable the cognitive assistant to plan the sequence of cues to be delivered adaptively depending on the circumstances, the assistance goal and the user's behaviour. To show that the proposed interaction model can help an AALS to provide adaptive assistance to people with cognitive impairments, it is exemplified on a cooking safety assistant designed for people with TBI.

In this paper, we present a method that uses the signal strength indication of RFID antennas with statistical features to perform relative positioning in a smart home. The goal of the proposed method is to enable the tracking of most objects inside a smart home in real-time, allowing activity recognition based on this tracking. This paper also introduce a new dataset of 4 100 000 RFID data collected in a real full-scale smart home setting. The dataset is available for the community. The method has an accuracy of 95.5% which is similar to previous work but require a fifth of the time to compute.

Abstract User-centered design (UCD) facilitates the creation of technologies that are specifically designed to answer users’ needs. This paper presents the first step of a UCD using a persona, a fictitious character representing the targeted population, which in this case is people having sustained a traumatic brain injury (TBI). The persona is used to better understand the possible interactions of a TBI population with a prototype of a technology that we wish to develop, namely the Cognitive Orthosis for coOKing (COOK). COOK is meant to be an assistive technology that will be designed to promote independence for cooking within a supported-living residence. More specifically, this paper presents the persona’s creation methodology based on the first four phases of the persona’s lifecycle. It also describes how the persona methodology served as a facilitator to initiate an interdisciplinary collaboration between a clinical team and a computer science team. Creation of personas relied on a clinical model (Disability Creation Process) that contextualized the needs of this population and an evaluation tool [Instrumental Activities of Daily Living (IADL) Profile] that presented a wide range of cognitive assistance needs found in this same population. This paper provides an in-depth description of some of the most frequent everyday difficulties experienced by individuals with TBI as well as the persona’s abilities, limitations and social participation during the realization of IADL, and an evaluation of the manifestations of these difficulties during IADL performance as represented through scenarios. The interdisciplinary team used the persona to complete a first description of the interactions of a persona with TBI with COOK. This work is an attempt at offering a communication tool, the persona, to facilitate interdisciplinary research among diverse disciplines who wish to learn to develop a common language, models and methodologies at the beginning of the design process.

Ambient Assisted Living (AAL) environments encompass technical systems and the Internet of Things (IoT) tools to support seniors in their daily routines. They aim to enable seniors to live independently and safely for as long as possible when faced declining physical or cognitive capacities. This work presents the design, development and deployment of an AAL system in the context of smart cities. The proposed architecture is based on microservices and software components. We examined the requirements and specifications of AAL systems in smart homes, in efforts to describe and evaluate how they would be transposable in the case of smart cities. The system has been tested and evaluated in the laboratory; it has been deployed in real life settings within city and is still in use by five elderly people.

The aging of the world population is accompanied by a substantial increase in neurodegenerative disorders, such as dementia. Early detection of mild cognitive impairment (MCI), a clinical diagnostic that comes with an increased chance to develop dementias, could be an essential condition for promoting quality of life and independent living, as it would provide a critical window for the implementation of early pharmacological and nonpharmacological interventions. This systematic review aims to investigate the current state of knowledge on the effectiveness of smart home sensors technologies for the early detection of MCI through the monitoring of everyday life activities. This approach offers many advantages, including the continuous measurement of functional abilities in ecological environments. A systematic search of publications in MEDLINE, EMBASE, and CINAHL, before November 2017, was conducted. Seventeen studies were included in this review. Thirteen studies were based on real-life monitoring, with several sensors installed in participants' actual homes, and four studies included scenario-based assessments, in which participants had to complete various tasks in a research lab apartment. In real-life monitoring, the most used indicators of MCI were walking speed and activity/motion in the house. In scenario-based assessment, time of completion, quality of activity completion, number of errors, amount of assistance needed, and task-irrelevant behaviors during the performance of everyday activities predicted MCI in participants. Despite technological limitations and the novelty of the field, smart home technologies represent a promising potential for the early screening of MCI and could support clinicians in geriatric care.

The Assistive living technologies provide good results for the support of specific activities transforming a home into a smart home. In this paper, we present a personalized ambient support system for elderly suffering from Alzheimer's dementia and nighttime wandering. Our goal is to help the person stay at home as long as possible and regain a regular circadian cycle while providing more comfort to the caregiver. The intervention proceeds in two phases. During the monitoring phase, the system determines the resident profile based on nighttime routines. Data is gathered from sensors dispatched in the smart home, coupled with physiological data obtained from worn sensors. Data is then classified to determine engine rules that will provide assistance to the resident to satisfy his needs. In the second phase, assistance is provided to the person by triggering rules depending on the activities occurring during night. It offers a calm environment with music and visual icons to soothe the person then encourage it to return to bed. The system is installed at the Alzheimer's home using wireless technologies. Multiple heterogeneous technologies are put in common to achieve it. Reliabilities and robustness tests were carried out in a 4 1/2 room apartment for 3 months with over 3.78 million collected data tuples. These tests have established three clusters of activities necessary for the recognition of nighttime wandering activities. This helped start an ongoing experiment in homes.

One of the greatest challenge of research on smart homes is to be able to recognize the ongoing Activity of Daily Living (ADL) in real-time and make prediction on the future action of the resident. To accomplish this task, it is important to have accurate information. In this paper, we present a novel passive RFID Indoor Tracking System (ITS). The goal of this ITS was to create a simple solution from data mining that could be easily deployed in any smart environment without requiring specialized human expertise on RFID. The tracking average 80% of accuracy and show promising results for large scale deployment.

At DOMUS for the past 14 years, several cognitive orthotics were designed and implemented and evaluated, most of the time using participatory design. The resulting set of orthotics can support a wide variety of activities of daily living (ADL) to foster autonomy at home for people with cognitive impairments, e.g. medication, meal preparation, or budget. DOMUS benefits from a rich and versatile research infrastructure to design, implement, and evaluate such orthotics, namely a smart apartment on the campus, a living lab in an alternative housing unit for people with traumatic brain injury (TBI), seniors' residences, personal residences (apartments and houses). To different extents, all these places can be considered as living labs. In this paper, building on our extensive experience, we first show that participative design is the best-suited methodology for developing cognitive orthotics in living labs. Clinical researchers, caregivers, and end users are involved from the start. This ensures that design is user driven and that assistive technologies will satisfy users needs. Then we propose a classification of living labs according to the levels of control one can have on which and how much technology is deployed, on how space is organized and how it may vary from experiment to experiment, and on how the progression and execution of a scenario can be constrained or not. For each category of living labs, we discuss their different yet complementary characteristics, highlighting their pros and cons. For instance, our smart apartment provides tight control over technology, space, and execution of predefined scenarios. Accordingly evaluations then provide a lot of useful and reliable information about technology and the ability for people to use it, but less on acceptance of the technology and its integration at one's real home in her daily life habits.

In smart homes, a few millions of data are sent by different sensors and RFID per day. It thus constitutes a Big Data warehouse with all the problems that come from and in our work, we are interested in the problem of managing huge data in streaming. In this paper, we propose to use very fast decision tree(VFDT) for activity recognition. We formulate Activity Recognition as a classification problem where classes correspond to activities. The performance of the VFDT and other classifiers is compared.

Complexity in ubiquitous environments is an important problem that leads to high implementation and management costs. In some cases, the complexity of the environments' systems are so important that it become impossible for a single human to understand or manage them. The software self-organization simplifies this complexity by reducing the user manipulations and reasoning on the ubiquitous environments' system during software management and organization. This paper presents the self-organization middleware model and architecture, as well as results coming from tests and validation scenarios carried out on the middleware implementation.

Autonomic Computing Self-Configuration /Organization can simplify the complexity of software deployment, management and utilization in pervasive environments such as smart spaces. Software management complexity in pervasive environments is a serious problem, which leads to high management costs and slow down the broad utilization of the pervasive technologies. This paper presents our work on a self-organization middleware that reduce user manipulations and reasoning during software deployment and organization in smart spaces. Our approach is based on Fuzzy Logic and semantic description in order to support the self-organization reasoning on the environment context.

Context-aware software provides adapted services to users or other software components. On the other hand, Autonomic Pervasive Computing uses Context to reduce the complexity of pervasive system utilization, management and maintenance. This paper introduces two Context-awareness models, the macro and micro approaches, that define and integrate contextual views of individual pervasive components (micro level) and global knowledge of the system (macro level). These models are presented and compared with respect to their ability to meet the requirements of the Autonomic Pervasive Computing concept of the four selves.

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In the context of an intelligent habitat assisting an occupant with Alzheimer's disease, the goal of plan recognition is to predict the patient's behavior in order to identify the various ways of supporting him in carrying out his daily activities. H

This paper presents a hybrid plan recognition model, based on probabilistic description logic, which addresses the issue of recognizing the activities and the errors of Alzheimer's patients at an early stage of the disease. This model has been implemented to be a new extension of the COACH system, an emerging prototype of cognitive device for persons with Alzheimer's disease that offers assistance in task completion. We present an initial experimentation done on this new recognition module for COACH, which is based on the results of two sets of clinical trials.

The Autonomic Pervasive Computing can simplify the complexity of the configuration, maintenance and management of pervasive environments such as smart spaces. The Pervasive Computing is a paradigm where the information processes are distributed in the environment components, bringing processes more closer to the information context. On the other hand, the Autonomic Computing allows to simplify the complexity caused by the heterogeneity of the space components, there large number, the dynamic relations between them, etc.

Smart living spaces provide autonomy and assistance to people with disabilities and elderly people in order to increase their independence. In this paper we present our approach to personalize a complex living environment for users with special needs. It provides an appropriate middleware for supporting assistive services in dynamic environments. It is built upon formalisms based on Description logic (DL) named Semantic Matching Framework (SMF) and designed on the matching of two models: an environment model, and a user model. SMF was implemented and integrated into a demonstrator, which is validated under laboratory conditions. Performance results are presented to highlight the time response of SMF.