This research began with literature reviews, which contributed to Journal Articles 1 [1]
and 3 [5] and Conference Article 3 [7]. Part I, which is presented in detail in Journal Article 1 [1], consists of a literature review of the technical aspects of the research and reviews the analytical methods used to assist older people who live alone. Part II focuses on the health care implications of the technical research and is discussed in Journal Article 3 [5]
and Conference Article 3 [7]. Journal Article 3 [5] is a systematic review of the ethics of using ‘smart house’ welfare technology for older people, and Conference Article 3 [7]
reviews the legal implications of using smart house welfare technology for elder care in Norway. The rest of this chapter briefly summarises the existing research based on these three review Articles.
2.1 Part I: Technical Research
Generally, welfare technology applications for older people seek to improve their lives and safety [28]–[30] by learning individuals’ activity patterns and adapting their homes to their needs [27]. The technique used to achieve this is usually called human activity recognition (HAR) [24], [25], [31] but may also be referred to as activities of daily living (ADL) recognition or detection [26], [32]. Both HAR and ADL recognition have the same aim of identifying an individual’s activities, as detailed further in Journal Article 2 [2]. In this thesis, the more common term of HAR is used.
Studies involving HBM have not received the same kind of attention as HAR more broadly.
This thesis therefore uses the knowledge derived from HAR’s state-of-the-art techniques as a foundation for studying HBM. As described further in Journal Article 1 [1], machine learning and statistical algorithms are generally used to learn to predict an individual’s activities. These algorithms include Bayesian methods, Markov chains, statistical inferential algorithms, neural networks, fuzzy logic and multi-agent system algorithms, among others.
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Following are some prominent examples of prior research on the use of smart houses for welfare technology, using machine learning or statistical methods, which are documented in Journal Article 1 [1]. The Adaptive Control of Home Environments (ACHE) project at the University of Colorado seeks to achieve a home that can programme itself, using a neural network model that observes a person’s lifestyle and desires and then learns to predict and adapt to their needs [33]. The Managing an Intelligent Versatile Home (MavHome) project at the University of Texas is based on the LeZi-update algorithm for tracking users and seeks to develop a home that functionally behaves as a
‘rational agent’ [34]. The GatorTech Smart House, developed at the University of Florida, is composed of several single smart devices connected to an operational platform, in order to optimise the comfort and safety of any older person [35].
Studies using decision trees in smart environments have also been successfully implemented [36], and one study that used decision trees achieved an 80% accuracy rate in recognising 20 everyday activities [37]. Similarly, decision trees were also used for activities recognition by Fan with good results [32]. Likewise, hidden Markov models (HMMs) have also been successful at recognising human actions. Since its first published application by Yamato et al. in 1992 [38], HMM has been used alone or in combination with other methods, such as neural networks and intelligent agents [39], [40]. For example, the Center for Advanced Studies in Adaptive Systems (CASAS) at Washington State University has used HMM with promising results in several residences in a smart house environment [41], while another study using HMM in a smart house achieved 98%
accuracy in assisting individuals with diabetes [39]. Similarly, a project in the Netherlands used HMM to track a person for 28 days in a smart house environment and made the resultant annotated dataset available for public use [40]. These examples show that HMM and decision trees have both been successfully implemented for HAR [41]–[45].
Therefore, in this thesis, HMM and decision trees were both tested for use in HBM.
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2.2 Part II: Health Care
The second part of this work focuses on the health care implications of HBM and its applications to welfare technology, with the goal of maintaining a person-centred approach. Person-centred approaches focus on the needs and values of the individual, which are central to health care practice and policy. These values include respect, autonomy, participation, justice, dignity, trust and patient safety and rights [46]. Some of the challenges of person-centred approaches deal with information and communication technology, including welfare technology. Literature reviews on the ethical and legal aspects of welfare technology were therefore conducted as a first step towards understanding some of the potential barriers to applying HBM to welfare technology.
These are reviewed in more detail in Journal Article 3 [5] and Conference Article 3 [7], but a short summary is also provided here.
Saranummi et al. [44] noted that older people should have the right to live independently in their own homes for as long as they wish and should have access to assistance services.
Understanding their needs is therefore a challenge to be addressed in the development of welfare technology. Nonetheless, the implementation of welfare technology, and particularly of smart houses, raises many ethical and legal concerns about privacy, autonomy, informed consent, dignity, safety, trust, legal obligations and stakeholders’
interests and technology acceptance. Sadri [45] and Rozo [47] both noted that a major ethical challenge related to technology is that technical developers, rather than the end-users, typically establish anomaly detection. Similarly, the functions of a smart house welfare system are usually set by the developers [48]. This may lead to data being interpreted only from the developers’ perspective, thereby limiting end-users’ privacy and freedom of choice [45], [47].
Developers thus play a key role in welfare technology that can affect the end-users positively or negatively. Moreover, Detweiler et al. [48] argued that a gap exists between developers’ values and the ethical implications of developing welfare technology for older people. In addition, potential users who are not involved in the research and development phases could ultimately reject the developed technology [47]. It is thus
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essential that developers take into consideration who the consumers are and what their needs might be [49]. Therefore, this thesis sought user feedback during the research and development stages to reduce the potential for errors in its final implementation of HBM [50].
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