A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Introduction <s> In recent years, we have witnessed a rapid surge in assisted living technologies due to a rapidly aging society. The aging population, the increasing cost of formal health care, the caregiver burden, and the importance that the individuals place on living independently, all motivate development of innovative-assisted living technologies for safe and independent aging. In this survey, we will summarize the emergence of `ambient-assisted living” (AAL) tools for older adults based on ambient intelligence paradigm. We will summarize the state-of-the-art AAL technologies, tools, and techniques, and we will look at current and future challenges. <s> BIB001 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Introduction <s> Ambient Assisted Living (AAL) is an emerging multi-disciplinary field aiming at exploiting information and communication technologies in personal healthcare and telehealth systems for countering the effects of growing elderly population. AAL systems are developed for personalized, adaptive, and anticipatory requirements, necessitating high quality-of-service to achieve interoperability, usability, security, and accuracy. The aim of this paper is to provide a comprehensive review of the AAL field with a focus on healthcare frameworks, platforms, standards, and quality attributes. To achieve this, we conducted a literature survey of state-of-the-art AAL frameworks, systems and platforms to identify the essential aspects of AAL systems and investigate the critical issues from the design, technology, quality-of-service, and user experience perspectives. In addition, we conducted an email-based survey for collecting usage data and current status of contemporary AAL systems. We found that most AAL systems are confined to a limited set of features ignoring many of the essential AAL system aspects. Standards and technologies are used in a limited and isolated manner, while quality attributes are often addressed insufficiently. In conclusion, we found that more inter-organizational collaboration, user-centered studies, increased standardization efforts, and a focus on open systems is needed to achieve more interoperable and synergetic AAL solutions. <s> BIB002 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Introduction <s> Life expectancy has continuously increased in most countries over the last decades and will probably continue to increase in the future. This leads to new challenges relative to the autonomy and the independence of elderly. The development of Smart Homes is a direction to face these challenges and to enable people to live longer in a safe and comfortable environment. Making a home smart consists in placing sensors, actuators and a controller in the house in order to take into account the behavior of their inhabitants and to act on their environment to improve their safety, health and comfort. Most of these approaches are based on the real-time indoor Location Tracking of the inhabitants. In this thesis, a whole new approach for model-based Location Tracking of an a priori unknown number of inhabitants is proposed. This approach is based on Discrete Event Systems paradigms, theory and tools. The usage of Finite Automata (FA) to model the detectable motion of the inhabitants as well as different methods to create such FA models have been developed. Based on these models, algorithms to perform efficient Location Tracking are defined. Finally, several approaches aiming at evaluating the relevance of the instrumentation of a Smart Home with the objective of Location Tracking are proposed. The approach has also been fully implemented and tested. Throughout the thesis, the different contributions are illustrated on case studies. <s> BIB003 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Introduction <s> Recent advances in the areas of pervasive computing, data mining, and machine learning offer unique opportunities to provide health monitoring and assistance for individuals facing difficulties to live independently in their homes. Several components have to work together to provide health monitoring for smart home residents including, but not limited to, activity recognition, activity discovery, activity prediction, and prompting system. Compared to the significant research done to discover and recognize activities, less attention has been given to predict the future activities that the resident is likely to perform. Activity prediction components can play a major role in design of a smart home. For instance, by taking advantage of an activity prediction module, a smart home can learn context-aware rules to prompt individuals to initiate important activities. In this paper, we propose an activity prediction model using Bayesian networks together with a novel two-step inference process to predict both the next activity features and the next activity label. We also propose an approach to predict the start time of the next activity which is based on modeling the relative start time of the predicted activity using the continuous normal distribution and outlier detection. To validate our proposed models, we used real data collected from physical smart environments. <s> BIB004 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Introduction <s> Monitoring the activities of daily living (ADLs) and detection of deviations from previous patterns is crucial to assessing the ability of an elderly person to live independently in their community and in early detection of upcoming critical situations. "Aging in place" for an elderly person is one key element in ambient assisted living (AAL) technologies. <s> BIB005 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Introduction <s> Smart Home technologies may improve the comfort and the safety of frail people into their home. To achieve this goal, models of Activities of Daily Living (ADL) are often used to detect dangerous situations or behavioral changes in the habits of these persons. In this paper, an approach is proposed to build a model of ADLs, under the form of Hidden Markov Models (HMMs), from a training database of observed events emitted by binary sensors. The main advantage of our approach is that no knowledge of actions really performed during the learning period is required. Finally, we apply our approach to a real case study and we discuss the quality of the results obtained. <s> BIB006
According to the World Health Organization, the world's population percentage of people aged over 60 is expected to double in the next decades to increase from 12% in 2015 to 22% in 2050. This phenomenon, known as Ageing Population, can be already witnessed in high-income countries. This demographic shift will induce new challenges to the countries, e.g., preparing the health care and social systems to deal with higher capacities  . Focusing on healthy ageing is an essential investment for facing that shift. Taking care of the elderlies would decrease the chance of further complications to their health status. This can be achieved by providing care in nursing homes or hospitals. However, it is costly and the costs increase greatly if the person needs specialized care due to immobilization or other health problems. A cost-effective alternative is using technology for independent living of the elderlies  . Ambient assisted living (AAL) is defined as "the use of information and communication technologies (ICT) in a person's daily living and working environment to enable them to stay active longer, remain socially connected and live independently into old age"  . AAL technologies can monitor the behavior of elderly people at home and provide support whenever required, and hence, improve the quality of life BIB002 . This would cast some burden away from the family members of the elderlies, decrease the need for qualified caregivers and have a positive impact on the psychological status of the elderlies, as they would live independently at their homes longer and safer  . Smart homes and ambient assisted living (AAL) terms were found to be interchangeably used in scientific articles, however, AAL is a special form of a smart home. AAL tools range between health and activity monitoring tools, wandering prevention tools, and cognitive orthotics tools BIB001 . The technology of those tools are based on ambient intelligence, a paradigm that integrates technology in people's environment to help them in their everyday lives by learning and adaptively responding to their behaviour BIB005 . Researchers are interested in investigating approaches to track the location and the activities of the residents, prompting the residents, discovering the abnormal behavior, and predicting the future activities BIB004 . Integrating sensors in an unobtrusive intelligent way in the residents' homes, allow monitoring their activities of daily living (ADL) to track their health status, and to detect early signs of diseases BIB006 . The sensors used to monitor and locate the resident can be classified into intrusive sensors (e.g., camera, microphone) and non-intrusive sensors (e.g., motion detectors, pressure sensors). In practice, the sensors installed in the inhabitant's place of residence may produce wrong output, e.g., false positives or negatives. A failure in one of the sensors of the AAL could lead to misleading result in activity recognition, or in location tracking. This can have dramatic consequences to the health of the inhabitant BIB003 . This survey paper aims to review the research work done in the sensor failure detection and fault tolerance in the presence of sensor failures in AAL systems equipped with non-intrusive binary sensors. The paper is organized as follows; Section 2 provides an overview of sensor failures, Section 3 presents an overview of the typical publicly available datasets used in the reviewed works, Section 4 outlines the methodology used to conduct the literature survey, Section 5 presents the research work found in the survey, Section 6 discusses the reviewed works and Section 7 discusses the status of research and highlights the gaps.