Falls are a major problem in older adults worldwide with an estimated 30% of elderly adults over 65 years of age falling each year. The direct and indirect societal costs associated with falls are enormous. A system that could provide an accurate automated assessment of falls risk prior to falling would allow timely intervention and ease the burden on overstretched healthcare systems worldwide. An objective method for assessing falls risk using body-worn kinematic sensors is reported. The gait and balance of 349 community-dwelling elderly adults was assessed using body-worn sensors while each patient performed the "timed up and go" (TUG) test. Patients were also evaluated using the Berg balance scale (BBS). Of the 44 reported parameters derived from body-worn kinematic sensors, 29 provided significant discrimination between patients with a history of falls and those without. Cross-validated estimates of retrospective falls prediction performance using logistic regression models yielded a mean sensitivity of 77.3% and a mean specificity of 75.9%. This compares favorably to the cross-validated performance of logistic regression models based on the time taken to complete the TUG test (manually timed TUG) and the Berg balance score. These models yielded mean sensitivities of 58.0% and 57.8%, respectively, and mean specificities of 64.8% and 64.2%, respectively. Results suggest that this method offers an improvement over two standard falls risk assessments (TUG and BBS) and may have potential for use in supervised assessment of falls risk as part of a longitudinal monitoring protocol.

Rapid technological advances have prompted the development of a wide range of telemonitoring systems to enable the prevention, early diagnosis and management, of chronic conditions. Remote monitoring can reduce the amount of recurring admissions to hospital, facilitate more efficient clinical visits with objective results, and may reduce the length of a hospital stay for individuals who are living at home. Telemonitoring can also be applied on a long-term basis to elderly persons to detect gradual deterioration in their health status, which may imply a reduction in their ability to live independently. Mobility is a good indicator of health status and thus by monitoring mobility, clinicians may assess the health status of elderly persons. This article reviews the architecture of health smart home, wearable, and combination systems for the remote monitoring of the mobility of elderly persons as a mechanism of assessing the health status of elderly persons while in their own living environment.

Approximately one in three people over the age of 65 will fall each year, resulting in significant financial, physical, and emotional cost on the individual, their family, and society. Currently, falls are managed using on-body sensors and alarm pendants to notify others when a falls event occurs. However these technologies do not prevent a fall from occurring. There is now a growing focus on falls risk assessment and preventative interventions. Falls risk is currently assessed in a clinical setting by expert physiotherapists, geriatricians, or occupational therapists following the occurrence of an injurious fall. As the population ages, this reactive model of care will become increasingly unsatisfactory, and a proactive community-based prevention strategy will be required. Recent advances in technology can support this new model of care by enabling community-based practitioners to perform tests that previously required expensive technology or expert interpretation. Gait and balance impairment is one of the most common risk factors for falls. This paper reviews the current technical and non-technical gait and balance assessments, discusses how low-cost technology can be applied to objectively administer and interpret these tests in the community, and reports on recent research where body-worn sensors have been utilized. It also discusses the barriers to adoption in the community and proposes ethnographic research as a method to investigate solutions to these barriers.

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Physical fitness is not only one of the most important keys to a healthy body; it is the basis of dynamicand creative intellectual activity. -John F. Kennedy, 35th President of the United StatesChapter 9 considered how sensors are playing an increasingly important role in health-related applications, such as chronic disease management. Medically, health is sometimes described as the absence of one or more of the "five Ds": death, disease, discomfort, disability, and dissatisfaction. Consequently, the focus is on determining whether disease is present and, when present, managing that condition (Edlin et al., 2000). Wellness takes a different perspective on health. It looks at the entire person, the manner in which they live their life, and lifestyle influences on their well-being. Wellness encompasses six distinct dimensions of well-being: emotional, intellectual, spiritual, occupational, social, and physical (Hettler, 1976). Collectively, these dimensions are often referred to as the holistic model of wellness. Sensors can be applied to quantify all dimensions of wellness to a certain extent. Of these dimensions, only physical wellness is monitored by individuals in the consumer domain. This chapter focuses on physical well-being and how sensing can be used to monitor and maintain physical wellness. Positively influencing physical well-being can also have significant benefits for other aspects of well-being, such as socializing with others during physical activities and helping to reduce emotional stress.A variety of factors can influence personal wellness, including diet, exercise, poor habits, proactive self-care, and seeking medical intervention when appropriate (Edlin et al., 2014). As such, wellness is a dynamic process that is constantly changing based on the daily decisions we make about what we eat, drink, how much exercise we do, and so on. It is easy to lose track of wellness with the demands of busy, modern lifestyles. Technology is now having a positive effect on individuals by helping them to manage their physical wellness. This trend will continue to grow in the future as sensing and supporting technologies are seamlessly integrated into our daily lives. Discrete sensors and sensors integrated into smartphones are already enabling us to monitor our activity levels, fitness, performance levels, and calorie burn/consumption through smartphone apps and web portals. In a broader context, pervasive sensing in our homes and leisure areas will provide passive monitoring on a long-term basis of our physical activities, interactions with our environment, and other physiological, cognitive, and biochemical parameters of interest without activity restriction and behavior modification. The collected data can be used to notify us of immediate risk or to identify trends in parameters that are outside of normative ranges. Sensing sleep quality, in babies and adults, is a now a common application of pervasive sensing. The consumer does not actively track their sleep in real time but they need to be alerted immedi...

This paper draws attention to the need for further understanding of the fine details of routine and taken-for-granted daily activities and mobility. It argues that such understanding is critical if technologies designed to mitigate the negative impacts of falls and fear-of-falling are to provide unobtrusive support for independent living. The reported research was part of a large, multidisciplinary, multi-site research programme into responses to population ageing in Ireland, Technologies for Independent Living (TRIL). A small, exploratory, qualitative life-space diary study was conducted. Working with eight community-dwelling older adults with different experiences of falls or of fear-of-falls, data were collected through weekly life-space diaries, daily-activity logs, two-dimensional house plans and a pedometer. For some participants, self-recording of their daily activities and movements revealed routine, potentially risky behaviour about which they had been unaware, which may have implications for falls-prevention advice. The findings are presented and discussed around four key themes: ‘being pragmatic’, ‘not just a faller’, ‘heightened awareness and blind spots’ and ‘working with technology’. The findings suggest a need to think creatively about how technological and other solutions best fit with people's everyday challenges and needs and of critical importance, that their installation does not reduce an older adult to ‘just a faller’ or a person with a fear-of-falls.

Sensors utilize a wide spectrum of transducer and signal transformation approaches with corresponding variations in technical complexity. These range from relatively simple temperature measurement based on a bimetallic thermocouple, to the detection of specific bacteria species using sophisticated optical systems. Within the healthcare, wellness, and environmental domains, there are a variety of sensing approaches, including microelectromechanical systems (MEMS), optical, mechanical, electrochemical, semiconductor, and biosensing. As outlined in Chapter 1, the proliferation of sensor-based applications is growing across a range of sensing targets such as air, water, bacteria, movement, and physiology. As with any form of technology, sensors have both strengths and weaknesses. Operational performance may be a function of the transduction method, the deployment environment, or the system components. In this chapter, we review the common sensing mechanisms that are used in the application domains of interest within the scope of this book, along with their respective strengths and weaknesses. Finally, we describe the process of selecting and specifying sensors for an application.

Independent living technologies are fast gaining interest within both academia and industry, amid the realisation that the world's population is ageing. Technology can increase the quality of life of older people, allowing them to age in-place and helping them to remain physically, cognitively and socially engaged with their environment. However, little research in this area is applied. We argue the necessity of moving such technology out of the research lab and into the home, where its real impact on the lives of older adults can be assessed. This paper outlines a series of recommendations encompassing the life cycle of independent living technologies, from ethnographic assessment, through to design, deployment and evaluation. This work is based on lessons we have learned deploying such technologies to older people in over 200 homes. We believe this paper can act as a guide for other researchers interested in developing technologies with older people.