Ebook: Situational Awareness for Assistive Technologies

In many ways, the raison d'etre of computers is to perform tasks that humans either cannot do or do not want to do, from the mundane (such maintaining database records and sorting them into order) to the tedious (such as performing the millions of calculations required to produce even a short-term weather forecast). From this viewpoint, the assistance of particular sectors of society who are disadvantaged in some physical or mental capacity, thus performing some task that they cannot do easily, is a natural use of computers, from the development of speech synthesisers for those who cannot talk and automatic readers for the blind through to powered legs to assist those who have trouble walking.

As computers become more integrated into our everyday lives, and as we start to expect to interact with them in a more ‘natural’ way (that is, not just through a keyboard and mouse), so we start to expect real intelligence from them in the form of relevant responses and an understanding of what we are doing. It is this challenge of ‘ambient’ computing that can be seen as one of the hardest: as humans, we generally know instinctively what tasks other humans are engaged in, and can usually guess why, and so we respond appropriately in conversations and other social interactions. To date, our interactions with computers have been on their terms, and the assistance that they have rendered has been carefully specified to be particular to some specific task. We would not expect a database program to assist in writing a letter, nor a speech synthesiser to help us plan travel routes, and so we select the technology that we use for each task carefully.

While there is research in a great many aspects of ambient or ubiquitous computing, one area that is receiving particular interest is that of assisted living. This is an area of profound importance for human society at the current time: improvements in medicine mean that a ‘baby boomer’ now entering retirement in the Western world can expect to live for another 25 years on average, and as they enter the group of ‘oldest-old’ (those over 85 or 90) they will need significant assistance from carers and other medical professionals. As birth rates in the Western world drop, so the increasing proportion of the elderly in the population (sometimes called the ‘grey tsunami’) means that providing full-time human care for the elderly – even if it were economically viable – is not plausible. However, in order to be actually useful, an assisted living solution will need to have the ability to interact with, and interpret the actions of, the humans that it is assisting.

Thus, one of the particular challenges that ambient intelligence engenders is the requirement for the computer to understand the situation of the human, and therefore to choose and tailor the response appropriately. In addition to an interpretation of the requirements of the human—exactly what activity the human is attempting to perform, and what help they might require—a lot of this problem is concerned with context, i.e. the specific environment in which the humans are interacting with the computer and that modifies their activities and responses. While there are many aspects to context awareness, two of the most important are those of time and place: when and where activities happen.

This observation that spatial and temporal aspects of context awareness are important leads, as so often in human intellectual effort, to the possibility of knowledge transfer: there is a rich literature of spatio-temporal reasoning in symbolic Artificial Intelligence, and so this is a natural place to begin work on reasoning about context. From a sound basis there, it is to be hoped that the extension to other areas of context, whether environmental (e.g., temperature and humidity) to personal (e.g., emotion, boredom level) to the relationship with other activities (e.g., I just had went for a walk, so I don't want to go for another one just at the moment) will be simpler.

In the previous discussion an important and rather loaded word was used: reasoning. Looking at the problem of assisted living from the viewpoint of artificial intelligence – and it is hard to think of any reason why this is not the natural stance – it is clear that both logical and statistical methods will be needed: systems will need to be able to recognise what activities people are engaged in, deduce why that could be the case, identify relevant contextual variables and generalise them appropriately to other activities. They will also need to be able to identify the potential role that the system should play as assistant and reason about how their interactions will modify those of the human. In the papers in this book the approaches to context awareness, arising as they do from the spatiotemporal reasoning community, are primarily symbolic: witness the worlds ‘planning,’ ‘knowledge representation,’ and ‘reasoning’ in their titles. This leads to approaches that in some sense echo the concepts of spatial and temporal cognition, with the benefit of outputs that can be explained by the system and that are therefore more likely to be trusted, an important concept in all aspects of ambient intelligence, but most especially assisted living: unless the system can be queried to understand why it decided on the particular actions that it produced, it can be rather hard to identify defects in training and rectify them appropriately.

The more we expect a computer system to interact with us on our terms, the more understanding of human drives and behaviours the system needs. That is the implicit target behind commonsense situational awareness, one of the main aims of the papers in this book; we expect an artificially intelligent system to act (and react) in a way that we, with our human knowledge, would consider to be commonsensical. This is an ambitious goal, not least because we are tuned so well to recognise human behaviours and respond to them, and so anything that nearly, but not quite, meets our expectations produces a strong adverse reaction, even when it is other humans responding in ways that we do not expect. It can be hard to articulate the feeling we experience, let alone explain it: something is just ‘off’. Even defining what commonsense is turns out to be a challenge; like art, we might not know about it, but we know what we like.

The papers in this book tell us about the diversity of the field of ambient intelligence in the wide range of approaches and the variety of applications that they suggest. There is consideration of how space, time, and other context can be represented, modelled, and reasoned about, for use in mapping, multi-agent interactions, assisted living, and even emergency responses, using many techniques from the gamut of artificial intelligence. It is this variety that represents one of the strengths of the area, since the weaknesses of one approach can be offset by the capabilities of others.

To close, when thinking about the current state of commensensical in ambience intelligence I'm put in mind of the famous quotation from Samuel Johnson on a small part of the female bid for equality: “Sir, a woman's preaching is like a dog's walking on his hind legs. It is not done well; but you are surprised to find it done at all.” With developments such as those in this book, the equivalent of bipedal movement for assistive technologies comes a step closer.

Stephen Marsland

Massey University, New Zealand

Event management and response generation are two essential aspects of systems for Ambient Intelligence. In this regard, the context notion does also play an essential role, not only in determining the set of activities that take place in it, but also in devising the most appropriate response to those situations. Context is also essential for disambiguating knowledge and meaning. This work therefore proposes handling these issues by means of an approach with which to model and reason about actions and events which, under the umbrella of a philosophical and common-sense point of view, describes what actions and events are, how they are connected, and how computational systems should consider their meaning. Actions and events occur in the frame of situations. In order to leverage Ambient Intelligence systems to autonomously manage their environment these situations need to be characterized and understood. This work also describe an approach to tackle this challenge.

In recent years there has been growing interest in solutions for the delivery of clinical care for the elderly, due to the large increase in aging population. Monitoring a patient in his home environment is necessary to ensure continuity of care in home settings, but, to be useful, this activity must not be too invasive for patients and a burden for caregivers. In this chapter we want to consider how knowledge representation and reasoning techniques can be used in sensory-rich environments, where data about the person and the environment conditions are collected through pervasive Wireless Sensor Networks (WSN), and expressive knowledge representation and reasoning techniques can be used to combine these data with external knowledge sources at a symbolic level to support caregivers in understanding patients' well being and in predicting possible evolutions of their health. A hierarchical logic-based model of health is able to combine data from different sources, sensor data, tests results, commonsense knowledge and patient's clinical profile at the lower level, and correlation rules between health conditions across upper levels. The logical formalization and the reasoning process are based on non-monotonic reasoning and the implementation and testing of components is illustrated in the framework of Answer Set Programming. The expressive power of this logic programming paradigm makes it possible to reason about contextual aspects of health evolution even when the available information is incomplete and potentially incoherent, while declarativity simplifies rules specification by caregivers and allows automatic encoding of knowledge. Assuming the presence of such a complex and heterogenous intelligent monitoring system equipped with a pervasive sensor network and a non-monotonic reasoning engine, the rich set of sensors that can be used for monitoring in home environments and their sheer number make it quite complex to provide a correct interpretation of collected data for a particular patient. For this reason, we can take advantage of a logic-based context model for situation assessment, and we use logic programming techniques to reason about different pieces of knowledge for prevention of risky situation and proactive reaction of the system via appropriate policy rules expressed in an event-condition-action style and enforced via reasoning. A concrete example of how this functionality may represent a key aspect of assistive technology is represented by fall prevention for the elderly. This aspect will be illustrated in the chapter as a concrete application of the proposed methodologies.

Ambient Assisted Living (AAL) solutions can potentially reduce the problems faced by vulnerable people living independently. Software for AAL solutions can be implemented using a Multi-Agent System. This can be both context and situational aware so that interventions offered may be specifically tailored to the individual and their environment. In particular, sensory deficits in older users require significant attention to the issue of human computer interaction. An overview of current research in relation to situational awareness and assistive technologies is provided. A generalized architecture is proposed and a selection of implementation scenarios are documented. The implementation produces outputs which have been analysed with respect to context and situational awareness.

Situation awareness (SA) is an essential quality for humans performing complex tasks in dynamic environments. This quality has been regarded a useful feature in software agents that can improve agent performance by allowing more informed decisions about the choice of actions to be made. To achieve SA, it is necessary to alter agent behavior in ways such that perceptual attention is directed at the most relevant features of the environment and the decision-making concerning “what to do next” is closely linked to the agent's perceived state of the environment. The engineering effort of implementing SA in software agents can be substantial. It requires making changes to the agent's internal processes and the way agent designers capture a domain expert's knowledge and prescribe agent actions. This article discusses how these changes can be implemented using a software engineering approach, allowing the scope and impact of the changes to be analyzed and evaluated from a system architect's perspective and alongside with other desirable agent capabilities.

The present research addresses the problem of the investigation how potentialities of the technologies of smart space environments and Web-services can be exploited in emergency situations. The research purpose is to organize a resource community from an enormous amount of resources embedded in the smart space. The aims of the community members are planning emergency response actions and acting in accordance with the plan. Situation (or context) awareness is one of the major issues when dealing with emergency. In this connection, the research proposes a smart framework that supports organisation of a resource community in a context-aware manner. This framework defines a generic scheme for service-oriented context-aware organization of resource community and service-oriented architecture of smart environment. The framework is applied to three kinds of emergency events: fire, traffic accident, and traffic accident that produced car ignition.

Generic interactive model for buildings with an experimental hardware and software tool is presented. It is targeted to user groups such as building automation designers, system integrators and service engineers. It is characterized as an integrated software/hardware tool for design, fine-tuning and monitoring of building automation, supported with both simulation and real-time monitoring facilities. The designer of the automation needs to be supported with tools, to verify the technical solutions under development, as well as the service engineer should have tools to find optimal parameters for control. For the implementation of such designer/engineer tools, a generic Virtual Model of the Building (VMB) is developed. The VMB consists of active objects (e.g. agents) all construction elements (walls, floors, windows, etc.) of an indoor environment, as well as artifacts embedded (controllers, sensors, actuators, software agents, etc.). All VMB elements are supported (tagged) with space coordinates in a local reference frame, to allow implementation of zoning, spatially targeted environmental changes and impacts. The concept considers the application of proactive context-aware models of indoor environments and automation systems involved in a multifunctional building.