With great advancements in pervasive sensing and sensor data analytics, ambient assisted living (AAL) environment technologies are becoming increasingly feasible and popular. This book presents research design, implementation case studies, and next challenges for AAL with regard to smart homes. The most effective application of AAL through smart home systems is for the health and well-being of elderly people. According to the authors, the level of well-being of elderly residents can be known from following principal values of AAL: location (S), time (T), and context (C). Based on these (computed from unobtrusive, noninvasive, wearable or nonwearable sensor data streams), the complex behavioral pattern of the elderly can be quantified through a function Z (spatio-temporal functions). In this regard, the main functional blocks of smart home monitoring are presented, where a health informatics system consists of the following functionalities: activities of daily living (ADL) monitoring, physiological monitoring, wellness determination, and environmental monitoring. In the introductory chapter, the authors also discuss research directions, novel research contributions, and the research significance of AAL in smart homes. The remainder of the book consists of three parts, each containing one or more chapters. Part 1 discusses smart home-related research works for elderly people and independent living. Part 2 presents works in wireless sensor networks (WSNs) for AAL. Part 3 mainly deals with data analytics for the assessment of user activities and behavior patterns.

Part 1 presents existing research works related to smart home monitoring systems and assistive technologies for elderly people. It first discusses the issue of the rapid increase of the elderly population around the world, and its effect on limited healthcare resources (budget and available facilities). The healthcare costs for aging-related diseases and disabilities are becoming a major concern. Then, the chapter focuses on components of smart home monitoring systems (SHMS): physical components, communication mechanisms, and information processing. The physical components consist of the instrumentation of various sensing modes: ambient, motion and presence, biochemical agents, and multimedia. The communication mechanism mostly consists of various wireless protocols: Bluetooth, Wi-Fi, Wi-Max, and Zigbee. The key functionalities of information processing are described as compatibility, flexibility, robustness, and real-time processing. Then, the authors present and compare a range of smart home research works and testbeds. These comparisons are based on sensor and actuator usage, home networking mechanisms, user graphical user interface (GUI) and interaction, and used procedures and corresponding functionalities. Finally, Part 1 surveys works on ADL recognition.

Part 2 studies various works on WSNs for AAL in smart homes. These works are classified as follows: (1) monitoring and controlling electrical household objects; (2) monitoring nonelectrical household objects; (3) contact sensing systems; (4) passive infrared (PIR) sensor-based motion detection; (5) environment parameter sensing; and (6) human physiological sensing. It also discusses various human emotion recognition systems through heart rate, skin temperature, and galvanic skin response sensors. Then, the authors present various sensing and wireless networking factors for the discussed six classes of systems: XBee communication, network topologies, placement of sensors across homes, number of sensor requirements for elderly care, data acquisition, heterogeneous sensor data fusion, sensor network data storage, query processing, sampling rate for sensors, quality of service (QoS), system reliability, data throughput, statistical analysis, and troubleshooting.

Finally, in Part 3, the authors discuss various data analytics methods and systems for elderly care and well-being services. First, the components or levels of an ADL recognition system are discussed: sensor event level, context recognition level, and ADL recognition level. Then, it is described how the degree of well-being in the elderly at home is evaluated based on household appliance usage data and various wellness function definitions. The authors then focus on forecasting the behavior of the elderly through time series modeling. In the last chapter, the authors discuss sensor activity pattern (SAP) matching algorithms and outlier detection methods.

Overall, this book is very resourceful for readers interested in smart home-based elderly care research as well as system design.