The world is addressing significant challenges due to the current and future demographic contexts. The number of people aged 65 years or over in Europe and the United States will almost double between 2015 and 2060 [1, 2]. This will be linked with an increase in people requiring long term care, i.e. a continuum of medical and social services designed to support the needs of people living with chronic health problems that affect their ability to perform everyday activities [3]. Currently, approximately 30% of people between 65 and 80 years of age require long-term care. This percentage reaches 50% for those over 80 [4]. Longevity of people combined with the decline in birth rate will also put pressure on the economic support of this care. The Statistical Office of the European Communities (EUROSTAT) projects that, in the next 30 years, the ratio between working and retired people, i.e. the old age support rate, will move from four-to-one to two-to-one in the EU [5]. Nowadays, EU Member States spend approximately a quarter of their GDP on social protection [6]. These demographic and economic situations raise significant challenges towards health and social care of the older population in terms of increased costs and lack of resources.

This paper seeks to note current contributors to AAL technology development and critically analyse how the field is progressing. These facilities now span the globe, especially in communities with well-established issues with an ageing population and a clinician crush. Having a good grasp of the current participants in the AAL field is important for anyone seeking to contribute to AAL projects.

The world's population is aging, and with age comes the potential for an increase in chronic health conditions, a decrease in mobility and impaired senses. This population shift and the desire to provide quality care has resulted in an increasing interest in developing technology-based solutions for enabling and enhancing healthy independent living among older adults, while improving the effectiveness of disease-prevention strategies and access to health care. This interest is paired with the increased availability of new wearable and ambient technologies and an everimproving health information technology infrastructure. Advances in bioengineering and computing, coupled with demographic shifts, suggest a ripe opportunity for the design and development of appropriate ambient and mobile technologies that enable functional independence and improve quality of life for older adults. Home-health and mobile-health technologies are expected to function not only as monitoring devices, but as essential components in the delivery of health services.

In this chapter, author is going to review how cameras are employed in AAL and what the current state of the art is related to applications and recognition techniques. Author will distinguish between traditional RGB cameras and depth sensors (or multimodal approaches, where both techniques are combined), whose recent great impact in the field deserves an individual overview. With the goal of introducing professionals of other AAL fields to computer vision in general and specifically to its application to assisted living, we will review the image processing pipeline, from different camera types that can be installed in care centres and people's homes to recent advances in image and video feature extraction and classification. For depth sensors, specific applications, feature estimation techniques and the most successful data representations are reviewed to detail how these differentiate from the traditional RGB approaches.

The motion sensor technology and the network of cameras have been explored separately as attractive solutions for building in-home monitoring systems for elderly care. Each of these technologies offers advantages over others while suffering from certain limitations. Motion detectors usually offer a privacy preserving solution, but do not yield granular information about the user's activities. Cameras, on the other hand, offer access to details of activities of daily life, but are regarded with caution in terms of coping with user privacy concerns. In this chapter, we provide an informative and a highly updated review of sensor fusion approaches. Then, we introduce an in-home monitoring system for elderly care, which is based on information collected from a network of PIR motion detection sensors and low-resolution cameras with 30 × 30 pixel arrays. The data fusion method we used for that system is based on different activity features. From the PIR sensors, the level of the daily occupation and the active level of the occupant are extracted. From the low-resolution cameras, other features such as activeness, sleep duration, visitors, and TV activity are extracted. Generally speaking, the proposed monitoring system fuses the heterogeneous sensor information to identify the occupant's lifestyle pattern. The system employs K-means clustering algorithm to group the observed days into different lifestyle patterns such as restful isolated days, restful social days, busy isolated days, and busy social days. To evaluate our system, experiments were conducted on six months of real-data. The results show promising performance to identify a certain lifestyle pattern or changes which occur over time.

The vast interest in the IoT has led to the development of many solutions in multiple sectors. In this chapter we concentrated on AAL and Smart Homes which are two domains that are closely interrelated. Of particular interest is the interoperability between the solutions deployed which as expected rely on different software and hardware platforms. Our attention has focused on consolidated proposals, evolved over the time, which are a base for other projects. In particular, we presented three major projects and their evolution: UniversAAL, Domoinstant and AllJoyn.

Human activity recognition is a key enabler for Ambient Assisted Living and provides a paradigmatic example of how reasoning capacities can be used in such scenarios. In order to detect and recognise human activities, first of all, humans have to be monitored using sensors. The information grabbed by those sensors feed the modelling and inference layers, where the reasoning takes place. In this chapter, the most used inference and reasoning techniques have been introduced. Reasoning systems for AAL have been divided into three different categories, namely, the data-driven approach, the knowledge-driven approach, and the hybrid approach. The most representative examples of those approaches have been presented, describing the advantages and disadvantages. Due to the emergence of the Semantic Web and its application to AAL scenarios, has been devoted to describe the features of OWL and associated semantic reasoners, which can be classified as a knowledge-driven approach.

This chapter presents an overview of human-computer interaction and design issues for person-environment interaction that are applied in Ambient Assisted Living (AAL), with consideration of the accessibility needs of people with age-related physical and cognitive impairments when designing interfaces. A review of the recent technologies that have been used to facilitate interaction via different modalities is presented, and the advantages and disadvantages they offer in relation to different physical and cognitive impairments. In addition, this review also considers the impact of stress, attention, social context, user experience and cognitive load on the usability of these technologies within specific AAL scenarios. This chapter also presents some key interaction models and considers how the recent AAL technologies designed for specific application areas can be assessed against these to ensure effectiveness and efficiency of user interaction. This chapter concludes with a set ofbest practice guidelines for designing person- environment interactions in the AAL context.

This chapter has shown that ML is a powerful method of identifying the activities of daily living. This has key implications for telehealth and Ambient Assisted Living in that the activities of a person at home can be monitored autonomously in order to provide a safe and secured environment.

In the last few years the amount of research regarding gait analysis has increased considerably. The emergence of new technologies makes it possible to accurately measure a wide amount of gait aspects, detecting multiple physical disabilities and syndromes. In this sense, frailty syndrome describes elderly people who are dependent on others to perform basic needs. Obviously, the study and detection of frailty considers a set of parameters from different domains mainly from a clinical viewpoint. However, the functional domain has been classically appreciated as the independence level of a person, and the gait activity as the main predictor of functional disorders. Thus, gait analysis can be used in the detection and diagnosis of frailty. Development of mobile technologies and sensor devices facilitates the gait study, providing relevant information about the health condition of the person. Acquisition, segmentation, filtering, and depth analysis are the main stages in the treatment of the gait signal. This chapter presents a detailed study of gait activity from a quantitative viewpoint, taking into account multiple sensors and devices, as well as several analysis techniques. We study the use of accelerometer-enabled devices, pressure sensors, and cameras to collect gait data, which provides details according to our experience about the pros and cons of using specific and general purpose devices. Also, the estimation of gait parameters and the application of classification algorithms in gait analysis are discussed. We present three experimental systems that analyse gait parameters by using different devices and procedures and consider other relevant factors to support frailty detection. From the deployment of these systems, some results and conclusions are shown.

In both research and clinical settings a fall incident is commonly defined as unintentionally coming to rest on the ground, floor, or other lower level. Such a fall incident is experienced at least once a year by approximately one in three adults older than 65. This number increases with age and frailty level. Yearly, 37.3 million fall incidents have physiological consequences. Additionally, psychological consequences, such as loss of mobility and independence often restrict the ability of older adults to perform daily activities. By detecting when a person has an elevated fall risk, and taking preventive measures when necessary, the number of fall incidents can be reduced. The first part of this chapter will therefore focus on automatic fall risk assessment techniques. Following this, the second part focuses on fall detection systems. As not all fall incidents can be prevented these systems can reduce the consequences of a fall incident by ensuring that timely aid is given.

This chapter has provided an overview of the technologies and applications involved within the domain of AAL. It has introduced the concept of ADL along with the concept of a smart environment and how technology can be applied to provide support in order to improve QoL for those that would normally require full time care or institutionalisation. A brief overview of the technology used has also been presented along with a comparison of the major techniques that can be applied to AmI, namely data, knowledge, and context driven approaches. Current methods of supporting ADL suffer from common problems such as the cost of retro fitting an environment along with the intrusiveness such an installation will incur. Other problems exist with current support of ADL such as the `cold start' problem were a large amount of data needs to be collected for pattern recognition through data mining, when the system is initially installed there is no data to be processed therefore support will not be available. A number of challenges that AAL faces have also been presented and discussed, such as those of multiple occupancy, future challenges include the personalisation of support to each individual occupants needs, Each occupants condition will deteriorate at differing rates therefore support will need to be tailored to the individual. These challenges will need to be addressed in the future if the vision of AAL is to be achieved in a real-world setting.

This chapter on outdoor mobility showed that the realisation of seamless and intermodal mobility requires not only a lot of technologies but also the systematic integration and combination of different navigational methods. First of all the positioning of pedestrians is constrained by a high inaccuracy of GNSS in urban areas. By entering public or private buildings, there is a need for a seamless change to indoor positioning technologies, which in turn are only available in selected areas. Additionally, both-the indoor as well as the outdoor environment-need to be modelled precisely and with a high level of detail. At last, the lack of interoperability still remains. Although the use of crowdsourcing concepts could lead to a higher data quality in the meantime, there are only just a few standardisation approaches. Even efficient and intuitive human machine interfaces for pedestrian navigation are still subject to research.

In this chapter we review the developments made in the technology of location and positioning inside buildings and pilot tests that have been carried. First, as a general framework we will begin by reviewing the key elements that influence in technological development (the activity, the operator, the environment, and the technology). In the next section, we describe the most important advances and development carried out on people with different disabilities. Finally, in the concluding section we try to draw the lines that we believe should continue future development.

Moving beyond obvious markets of entertainment and security systems, home technology can enable greater independence and safety across the lifespan. New developments in technology can enable older adults to live independently and safely at home. These innovations also empower and provide peace of mind for caregivers. Increasing access to technologies that are embedded within the environment allows for remote control of our homes, while the expansion of home health technologies permits all generations to be actively involved in their own, as well as their loved one's health. This chapter will discuss and explore ambient-assisted living (AAL) technologies that support productive ageing vis-à-vis universal design (UD), sensory needs and changes which occur with age and disability, as well as in the context of the societal and demographic drivers creating demand. Product usability analysis through an occupational therapist lens, considering UD principles to feature match solutions to enable health, well-being, social connectedness and engagement, is to be addressed.

This review provides an overview of the literature on a broad range of tablet-based healthcare applications in hospital care settings. Most of the systems described are either in their development stage or simulation stage but not actually implemented in the hospital care (exceptions to systems conducted clinical trials). Further focused research is required in such applications with respect to their clinical implementation, end-user acceptability, evaluation by medical professionals and security and privacy.

The examples presented in this chapter show the multidimensionality of the implementation of SAFCC. This concept is based on the synergy of the human factor of an ageing population (particularly various capitals of older citizens), technical solutions including AAL addressed to not only older adults, and the factors uniting technology and society such as local government and leaders, NGOs and commercial entities offering technological innovation.

This chapter has proposed the following steps towards achieving AAL environments accessible and adaptable by design: The elaboration and adoption of deeply human-centered design approaches directly and actively involving target users in all development phases. Such approaches may be facilitated by physical as well as virtual AAL environments simulators; The elaboration and adoption of ontology-based user and interaction models, capturing of user requirements and of the appropriateness of different solutions for different combinations of user characteristics/functional limitations and environment characteristics/function; The provision and seamless integration of ready-to-use accessibility solutions supporting alternative interaction techniques suitable for various combinations of human abilities and contextual factors in a pervasive, natural, and nonobtrusive fashion; The development of a reference architectural model that addresses user needs for inclusive design in smart environments, allowing for the provision of accessible multimodal interaction in a natural way.

As we move towards a community based, person-centred model of ageing well at home, invasive sensor technologies such as cameras present significant opportunities to enhance self-management and caregiving processes but at the same time raise concern regards data protection and privacy for end users. Adhering to fundamental ethical principles (including; integrity, objectivity, beneficence, respect for autonomy, fairness, non-maleficence, truthfulness and justice) when developing and implementing invasive sensor technologies, particularly within health and community care is pivotal to their adoption. Regional (e.g. across EU member states) legal and data protection differences, must be addressed by industry and research programmes to ensure that the human rights are protected. A key question we must consistently address is: “Does the technology introduced do good and improve the quality of life for the end user?” To effectively answer this, developers and researchers must carefully weigh risks and benefits of any proposed technology carefully, considering all issues (e.g. user mental/cognitive capacity) to protect the most vulnerable in society. With rapidly evolving invasive sensor technologies and related research fields (e.g. `big data', `wearables' and the `Internet of Things (IoT)'), paralleled by ever changing ethical and legal processes to their use, it is important to encourage best practice design processes.As such technology, policy, regulatory and research funding bodies are increasingly endorsing the use of Privacy Enhancing Technologies (PETs) and Fair Information Practices (FIPs). Fundamentally derived from the `privacy by design' approach their aim is to ensure the strongest protection possible for users of invasive sensor technology.

As the population ages, the European Union (EU) seeks technological solutions that will be able to assist older adults in leading healthy, active, joyful and dignified lives. In its call to action the EU has also, namely through the Active Assisted Living Programme (AALP)1, encouraged the groups engaged in the development of these new solutions to involve end-users in the design process. Literature is profuse in examples of the application of human-centred design (HCD) principles, participatory design or co-creation with different types of end-users. On the other hand, it might be difficult for researchers and developers to find concentrated examples of, and reflexions on, the application of these methods and techniques with older adults.

This chapter focused on the issues of designing feasible smart home technological platforms for the delivery of Ambient Assisted Living services, providing a review of the most relevant research projects in the field, and discussing in details the real-world experiences performed by the authors in a similar scenario. Matching the complexity of user requirements to the available consumer devices and technologies means a hard work to be performed on integration of different components, harmonization of services and applications. As understood by the authors, through personal experience, the approach to the real-world implementation often brings out different aspects and problems with respect to the results of a laboratory or experimental development. Referring to the main issues to overcome, some general considerations have been made, such as: the advantages of a user-centred design, the need of monitoring the correct operation of the system, and the importance of checking the real interoperability among different components. All these factors should have influence on the design solutions adopted, with the aim to bring benefits to all the involved stakeholders, from final users to designers and developers, to business entities.

This goal is pursued by covering all major technologies used in the field, including but not limited to smart homes, environmental and wearable sensors, visual monitoring, information fusion, standards and interoperability, reasoning systems and person-environment interaction. On the other hand, AAL applications are important also to healthcare professionals and caregivers. In this part, the book has reviewed tele-care and tele-health, as well as gait analysis, fall detection, support of ADLs, outdoor and indoor mobility, well-being and social interaction and decision support systems. Finally, associated issues such as accessibility, privacy and human-centred design; and study cases for smart cities and smart homes are presented. In this sense, this book gives a unified view of the significant amount of scientific fields that are emerging from and coming together in AAL. As such, it can provide not only a way of precise and direct initiation in the field, but it may also serve as reference for similar state-of-the-art reviews and retrospections in the future.