While a majority of older adults can live independently and enjoy a reasonable quality of life, there is a high prevalence of falls and frailty amongst this cohort. The proportion of community dwelling adults who sustain at least one fall over a 1-year period, ranges from 28 to 35 % in the ≥65 year age group to 32–42 % in the ≥75 year age group, with 15 % of older people falling at least twice [33, 34]. Within Ireland, falls are the leading cause of injury related visits to emergency departments. It has been estimated that while 80 % of falls in older persons are non-injurious, the remaining 20 % can have serious consequences [35]. These may include disability, mobility impairment, dependency, psychological problems, including fear of falling and social isolation [36]. The cost of falls each year among older adults in the US alone has been estimated to be in the region of US $20 billion [37].

Frailty is an issue that is central to ageing, transcending specific diseases and compromising quality of life [38]. By the age of 80, approximately 40 % of older adults have some degree of functional decline and 6–11 % of older people are considered frail [39]. The prevalence of frailty in community-dwelling older Europeans (>65) varies between 5.8 and 27.3 % and is reported at 22 % in the US; in addition, between 34.6 and 50.9 % are classified as ‘pre-frail’ in Europe and 28 % in the US [40, 41]. Research is beginning to emerge suggesting that frailty and emotional wellbeing are closely linked. The ‘frailty identity crisis’ is a psychological syndrome that may accompany the transition from robustness to the “next to last” stage of life [42]. The emotional and psychological challenges resulting from the development of frailty include sadness, regrets and depression, and can complicate frailty itself.

There is a large role for innovative technology to support monitoring, early detection and management of physical wellness in the home. Most diagnostic and treatment approaches are centered in clinical settings, and very few have focused on improving the self-management of physical wellbeing using novel in-home, ICT based systems.

As a result, there has been a surge in exercise and rehabilitation technology development for older adults, with the aim of making exercise fun and interactive. These include SilverFit a system that uses virtual reality video games to make exercising fun for this cohort [43], Motivating Mobility, a home-based technology solution for stroke rehabilitation, specifically helping people to reach and grasp with the shoulder, arm and fingers [44] and ‘Wii-habilitation’ which has become popular in retirement homes and day care centers, whereby groups of older adults come together to play Nintendo’s Wii, which both encourages physical exercise and social communication.

BASE (balance and strength exercises) is a technology solution to deliver the Otago strength and balance re-training program in the homes of a number of older adults [32]. The aim is to focus specifically on improving older adults’ lower limb strength and balance. Otago has been shown to reduce falls in older adults by over a third [45], but is typically not delivered through technology. BASE uses sensing technology to track older adults as they are performing their exercises and provides real-time feedback on correctness of exercises using animations, repetition counters and audio prompts, delivered on the person’s television (Fig. 9.2). It therefore overcomes some of the traditional problems associated with the delivery of Otago, and indeed physiotherapy in general, including lack of motivation to perform exercises or incorrect execution, which in some cases may lead to further problems.

Loneliness is prevalent among older adults for a number of reasons. As people age, social connections can be lost as a result of widowhood, dispersed family members or a shrinking peer network. Further factors affecting reduced social interaction include illness, a lack of mobility or a fear of falling, each of which can confine a person to their home, reducing their engagement in social activities and potentially resulting in a loss of independence. Given the associations amongst loneliness and significant health problems, including hypertension [46, 47], cognitive decline [48] and increased mortality [49], warrants alleviating interventions.

Technology can facilitate communication between older adults and their peers and family members, stimulating new relationships and maintaining existing ones [50]. A number of ICT-based approaches have developed to facilitate social connectedness; some use direct contact means (such as video conferencing software), while others use more ambient means of communication (also known as ambient awareness). Building Bridges is a project that uses communication technology to foster social connectedness amongst older adults, their peers and family and friends [28]. It does so through the shared experience of a video or radio broadcast, following which, listeners have the opportunity to take part in a group chat to discuss the broadcast. Other systems include photo sharing amongst family members [51] and social TV [52]. Examples of non-direct communication include MarkerClock [53], which is a communications appliance that allows users to improve their awareness of each other’s rhythms and routines. The interface is built inside a standard analog clock; the levels of activity per hour are highlighted over the preceding 6 h.

Dementia can affect people of any age, but it is most common in older people. Ferri et al. [54] provided dementia prevalence estimates for every World Health Organisation (WHO) region, for men and women, in 5-year age bands from 60 to 84 years, and for 85 years and older. It was estimated that worldwide (in 2005) approximately 24.3 million people had been diagnosed with dementia, with 4.6 million new cases every year (one new case every 7 s). Assuming that there were no changes in mortality and no effective prevention strategies or curative treatments; the number of people affected was estimated to double every 20 years to 42.3 million by 2020, and 81.1 million by 2040.

Technologies to support dementia can ease the burden on caregivers, providing peace of mind concerning the dementia patient’s security in addition to improving the quality of life for both caregiver and patient. When considering the design of technologies to support dementia patients and their caregivers, there are additional challenges and thus additional guidelines that should be adhered to beyond what has been discussed previously in this chapter. It is often difficult to involve dementia patients in the design process given their memory impairments and cognitive and social difficulties; many caregivers are themselves older adults who might not be familiar with technology and paid care staff are often not aware of the benefits of technology and how it can be integrated into care practice [55].

ICT has significant potential to address the unmet needs of dementia sufferers and their caregivers. Research in this space examines how technologies can support:

Typical ways in which such activities are supported by caregivers include the use of verbal cues such as prompts and notes, visual cues such as making items visible and increasing lighting, and familiarity of surroundings, appliances and routines. Technologies for dementia patients should integrate these supports. Furthermore, there are additional usability issues that must be considered keeping in mind the unique needs of dementia sufferers. When designing technologies for dementia patients, it is important to present affordances for action – for example ‘do what this message says’ or ‘touch this picture’ – that do not require the learning of multi-step procedures.

Monitoring technologies are widely used to support dementia patients, primarily for purposes of safety and security. Infrared sensors are commonly used with nursing homes and specialized dementia units to detect wandering or to alert care staff when a patient gets out of bed at night. But people in the early stages of dementia who are still living at home typically do not have such technology. Furthermore, such sensors can be used for many additional purposes, such as monitoring activities of daily living, ensuring someone is getting up out of bed, has turned the stove off etc.

ICT has been used to support communication between people with dementia and their caregivers. The CIRCA system is a multimedia interactive reminiscence and conversational aid to support satisfying, meaningful communication between people with dementia and their caregivers [55]. Other work has examined the use of cognitive ‘prostheses’ to support diminished cognitive capabilities [56, 57]. Cooking safety and kitchen tasks have also been examined [58]. Wherton and Monk [59] provide a detailed description of the opportunities for technology to support people with dementia to live at home.

There are many therapies for people with dementia that are suitable to be applied using multiple technologies. Reminiscence therapy for dementia patients discusses past events, experiences and activities between people with dementia and their caregivers and has been associated with improvements in cognition, mood and behavioral function as well as decreased caregiver strain [60]. Traditionally, this has been performed directly with the person often recording stories in a reminiscence diary for the person with dementia to review afterwards.

However ongoing research is examining technology-based approaches, such as creating personalized, computerized interventions [61] or using video-based therapies such as life-logging [62, 63]. The personalized computer interventions may take the form of displaying comforting messages or videos from close friends or family, pictures from their childhood, and/or a computerized reminiscence diary. The video-based therapies may include capturing images from throughout a person’s day and allowing them to review it at the end of each day. The images may be recorded using a body-worn camera or cameras embedded in their environment.

Caregivers themselves commonly experience depression, sleep disturbances, anxiety and loss of companionship as a result of providing continuous care. Technological support provided to these caregivers may serve to alleviate stress, reduce the feeling of being over-burdened, or provide helpful advice. Two examples of this include an online teleconferencing schedule [64] and a caregiver support tool [65] which included videos and support content provided by caregiver ‘champions’ and experienced health care experts. The EU project Discover for Carers [66] also aims to support caregivers of people with dementia.

Medication adherence is a challenging problem, particularly when multiple medications need to be taken or for those with a reduced cognitive capacity. Pill boxes labelled for each day have been traditionally used to alleviate some issues, however problems still arise when individuals forget to take their medication, or when different medications are required to be taken at various times of the day. Many technologies have been developed to prompt the user to take particular medications at certain times of the day; some of which have been based on using reminder messages on mobile phones and/or customized pill boxes. However, context-sensitive prompting (e.g. reminding the user to take their medication while they are in their bathroom) can be more effective as individuals may not adhere to the reminder using standard reminder-based prompting (e.g. their medication prompts occur while they are not in the house, on the telephone, or with a visitor) [67]. Furthermore an instrumented pillbox can also report if/when a person does take their medication and this can be used to highlight and quantify forgetfulness.

Recent times have seen the advent of the quantified-self movement, whereby individuals have begun to use computers, mobile phones, tablets etc. to ‘self-track’. Self-tracking typically involves recording some aspect of your life, including your work, sleep, exercise, diet or mood and supports self-management of such through the provision of informative and educational feedback. This feedback is typically persuasive and aims to help the person to change their behavior to live the lifestyle they wish to lead.

UbiFit is an application that uses on-body sensing and activity inference to encourage and promote physical activity [68]. It uses the screen of a mobile phone to display a wallpaper that consists of a garden that blooms as the person performs physical activities throughout the week. The user can set goals (outlining the amount of activity they would like to achieve) and if they meet their goals, a butterfly appears in the garden. However, UbiFit only supports tracking of one single parameter of wellbeing. BeWell is a smartphone application to monitor, model and promote wellbeing across three parameters – quantity of sleep, physical activity and social interactions [69]. All sensing happens through the smartphone, for example levels of social interaction are determined by microphone measurement of ambient conversations and duration of sleep is inferred by examining phone usage patterns. Similar to UbiFit, BeWell uses the wallpaper of a mobile phone to display a metaphor of an aquarium, For example, a turtle depicts the user’s sleep habits. If the person has had enough sleep, the turtle ‘wakes up’ and comes out of its shell. If the turtle remains in its shell, it means the user should try to get more sleep. Both BeWell and UbiFit are targeted at the general population rather than older adults. They are stand-alone applications rather than subsets of a larger integrated home self-management system. Furthermore, evaluations carried out at CASALA have indicated that older adults find it difficult to interpret such metaphors, i.e. they would find it difficult to remember what the butterfly or turtle represent [70].

Other more commercial applications include MindBloom³ – an application that supports people in improving their quality of life by helping them focus on what aspects of their life are important to them and motivating them to improve these areas. While there are a number of smartphone and tablet applications that support monitoring and tracking of mood, for example MoodJam⁴ and MoodPanda,⁵ they fall short in providing an effective, clinical tool for monitoring and supporting emotional wellbeing of older adults. However, a number of research efforts have begun to appear in this space. Monarca is a persuasive monitoring and feedback system for mental illness and is an excellent example of a closed feedback loop to patients [71]. With Monarca, mental health patients self-report on their mood, sleep, activity and medication adherence on a daily basis, using a mobile phone. The patient has access to an overview of their data on their phone and their clinician also has access to this data through a Web interface. Together, they can explore historical data. Triggers can also alert both the patient and the clinician as to potential warning signals, for example if the person has not taken their medication or if their mood is particularly low.

Ambient Assisted Living (AAL) refers to noninvasive systems placed within people’s homes to monitor their behavior and to facilitate independent living. AAL often consists of sensor systems that include presence/ movement Passive InfraRed (PIR) sensors, door and window latch sensors and resource usage sensors. A PIR sensor can be seen in Fig. 9.3. These sensors are very common in homes, as they are used in home security systems to detect intruders – simply, they detect movement in the home. The reason for choosing ambient sensors over body worn sensors, for example, is generally to reduce the impact of the sensing on peoples’ lives, to remove the stigma of healthcare devices in the home and also to remove the poor compliance often associated with body worn sensors and pendent alarms. For example this poor compliance can often be due to the pendant alarm being left on a bed-post whilst taking a shower (a high falls risk scenario), but it can also be related to conditions such as dementia. The data recorded from ambient sensors in AAL is used to trigger alerts about either emergency scenarios in the person’s home or to indicate changes in their regular patterns of behavior that might be indicative of a health decline. AAL makes this possible without the need for active input from the person.

While more detail is provided in Section “Case study – independent living in great northern haven” on how AAL sensing technology operates within an assisted living environment for older adults, we introduce here the types of activities that can be identified by such sensing, indicating the power of such inexpensive, unobtrusive technology. Figure 9.4 shows a visualization of data taken from the PIR sensors in one of the apartments in Great Northern Haven. The data is from a 60-day period and shows the older resident’s movement/location within their home at certain times of the day. For example, we can see that they are typically in the bedroom between 10 at night and 9 in the morning. However, we can also see that this person sometimes moves around during the night, sometimes to use their en-suite bathroom and sometimes to go into the kitchen.

A second visualization showing data of a GNH resident’s movement in their home is shown in Fig. 9.5. This image shows that this particular resident has very regular patterns of behavior – they typically go to bed at the same time each night and get up at the same time each morning. They have little movement outside their bedroom during the night.

The power of AAL and such sensing technology is the ability to detect changes to a person’s normal behavior or activity that might be an indicator that something is wrong. For example, is a person begins to stay in bed much longer than they usually do, it could be an indicator of a number of things, such as illness or depression. In Great Northern Haven, one of the residents showed increased nighttime movement in the week, which led up to him being hospitalized for cardiac problems. Automatic detection of such an event would trigger an alert to an appropriate person, such as a caregiver or clinician, who might then check on the person. Further detail on AAL sensor monitoring in GNH is presented in the section on “Case study – independent living in great northern haven.

Smart Homes are domestic residences, augmented with AAL and ICT-based services that provide support to facilitate ageing-in-place [81, 82]. They can promote independent living and quality of life of older adults – particularly as they enable movement from a reactive to a more preventative model of healthcare. Smart homes that support longitudinal monitoring and behavior recognition enable a better understanding of the causes and the relative contributions and interactions between the different factors that contribute to illness. This is a pre-requisite for early detection, prevention and management and ultimately enables more individually tailored interventions that can be delivered in a timely fashion. This in turn results in a more individual patient-oriented treatment.

This section presents an overview of smart home initiatives, mostly aimed at enabling ageing-in-place, which have recorded data from residents living in their living labs over multiple weeks. Such studies range from a single highly sensed residence to a series of assisted living apartments kitted out with various unobtrusive activity detection technologies. Often these environments record the daily patterns and behaviors of its residents through a number of sensors, and intelligent algorithms have been created which automatically identify these behaviors. Significant research currently being undertaken is focusing on the extraction of activities of daily living (ADL) from such data, and most importantly in detecting deviations away from a person’s normal activity patterns that might indicate the onset of poor health. Specific examples of ADL detection and possible interventions based on changes from usual behavior are presented in the case study in the section on “Case study – independent living in great northern haven”.

A goal of much of the smart home research presented in this section is to integrate technologies that detect functional decline and/or alert care providers should an adverse event occur. Results from the analysis of this smart home data are mainly used to inform clinicians and care providers of changes in overall health status. Apart from Great Northern Haven (GNH) few support the provision of feedback to the older adult themselves, to support health self-management.

Table 9.1 provides an overview of smart home initiatives and highlights the type of monitoring they support. TigerPlace is a series of 32 private apartments designed to facilitate ageing-in-place [83]. The Managing an Adaptive Versatile Home (MavHome) project focuses on creating a smart home that maximizes comfort and minimizes cost through predicting the behavioral patterns of the inhabitant, the periodic monitoring of vital signs, water and device usage, use of food items, exercise regimen and medication intake [84–86]. The GatorTech Smart House is a smart home developed by the University of Florida [87]. The Adaptive House at the University of Colorado has been developed to dynamically predict future behavioral patterns of the use of lights, heating and temperature by its residents [88]. The Aware Home at Georgia Institute of Technology is a three story smart environment which investigates the design, development and evaluation of future domestic technologies with the overall aim of enhancing quality of life and lengthening lives [89, 90]. The MIT House_n/PlaceLab project investigated using a highly sensed smart apartment ADL detection, user prompting (for labelling of activities) and machine learning based inferencing engines [91–93]. The CASAS Smart Home Project at Washington State University is a three-bedroom apartment test bed whose priority is to improve the comfort, safety and/or productivity of the its resident(s) [81, 94, 95]. The ORCATECH Living Lab consists of a group of community dwelling older adults who have had unobtrusive monitoring technologies installed in their homes since 2006, who provide a test-bed for evaluating behavioral monitoring technology [96, 97].