Michael P. Poland

Using Event Calculus for Behaviour Reasoning and Assistance in a Smart Home

Smart Homes (SH) have emerged as a viable solution capable of providing assistive living for the elderly and disabled. Nevertheless, it still remains a challenge to assist the inhabitants of a SH in performing the correct action(s) at the correct time in the correct place. To address this challenge, this paper introduces a novel logic-based approach to cognitive modeling based on a highly developed logical theory of actions - the Event Calculus. Cognitive models go beyond behavioral models in that they govern an inhabitants behavior by reasoning about its knowledge, actions and events. We present a formal cognitive model for a SH and describe the mechanisms for its use in facilitating assistive living. In addition we present a system architecture and demonstrate the use of the proposed approach through a real world daily activity.

Smart Home Research: Projects and Issues

Smart Homes are environments facilitated with technology that act in a protective and proactive function to assist an inhabitant in managing their daily lives specific to their individual needs. A typical Smart Home implementation would include sensors and actuators to detect changes in status and to initiate beneficial interventions. This paper aims to introduce the diversity of recent Smart Home research and to present the challenges that are faced not only by engineers and potential inhabitants; but also by policy makers and healthcare professionals

HomeCI - A visual editor for healthcare professionals in the design of home based care

The demands of introducing a more practical means of managing and monitoring technology within the home environment to support independent living are increasing. Within this paper we present a prototype solution, referred to as HomeCI, which allows healthcare professionals to establish the conditions/rules within which technology in the home should operate. The HomeCI concept is based on the use of visual notation and has been designed for use by healthcare professionals with a non technical background. Within the paper we present the design of the first version of the HomeCI visual editor and present the results of a usability study conducted on 4 healthcare professionals.

Genetic algorithm and pure random search for exosensor distribution optimisation

The positioning, amount(s) and field of view(s) of exosensors are a fundamental characteristic of a smart home environment. Contemporary smart home sensor distribution is aligned to either: a) a total coverage approach; b) a human assessment approach. These methods for sensor arrangement are not data driven strategies, are unempirical, and frequently irrational. Little research has been conducted in relation to optimal resource allocation in smart homes environments. This study aimed to generate globally optimal sensor distributions for a smart home replica-kitchen using two distinct methodologies, namely a genetic algorithm (GA) and a pure random search algorithm (PRS), to ascertain which method is appropriate for this task. GA outperformed PRS consistently, with a coverage percentage that encapsulated an average of 43.6% more inhabitant spatial frequency data. The results of this study indicate that GA provides more optimal solutions than PRS for exosensor distributions in a smart home environment.

Development of a smart home simulator for use as a heuristic tool for management of sensor distribution

Smart Homes offer potential solutions for various forms of independent living for the elderly. The assistive and protective environment afforded by smart homes offer a safe, relatively inexpensive, dependable and viable alternative to vulnerable inhabitants. Nevertheless, the success of a smart home rests upon the quality of information its decision support system receives and this in turn places great importance on the issue of correct sensor deployment. In this article we present a software tool that has been developed to address the elusive issue of sensor distribution within smart homes. Details of the tool will be presented and it will be shown how it can be used to emulate any real world environment whereby virtual sensor distributions can be rapidly implemented and assessed without the requirement for physical deployment for evaluation. As such, this approach offers the potential of tailoring sensor distributions to the specific needs of a patient in a non-evasive manner. The heuristics based tool presented here has been developed as the first part of a three stage project.

Spatiotemporal Data Acquisition Modalities for Smart Home Inhabitant Movement Behavioural Analysis

In current Smart Home implementations pressure sensors within the environment are normally deployed in a uniform pattern. Nevertheless, in order to create an optimised pressure sensor deployment paradigm it is necessary to correlate the positions of sensors with the high frequency movement behaviours of the inhabitant. The locations of furniture and other objects in the environment should also be taken into consideration. To create a paradigm for optimised sensor deployment, data pertaining to inhabitant movement behaviour first needs to be collected. This paper outlines the evaluation of two movement behaviour capture methods and assesses them for practical issues such as ease of installation and feasibility of use.

Embedding Self-Awareness into Objects of Daily Life -- The Smart Kettle

Intelligent Environments on varying scales and for different purposes are slowly becoming a reality. In the near future, global smart world infrastructures will become a commodity that will support various activities of daily life at different degrees of realism. Such infrastructures have the potential to offer dedicated, context- and situation-aware information and services by simultaneously providing the next-generation of data collection, execution and service provisioning layers. One key aspect of this vision is the correct monitoring and understanding of how people interact with their environment; how they can actually benefit from the added intelligence; and finally how future services can be improved or better personalized to enhance human environment interaction as a whole. This level of intelligence is of particular relevance in the health and social care domain where person-centric services can be deployed to assist or even enable a person in performing activities of daily living. This paper discusses the concept of embedded self-aware profiles for smart devices that can be used to gain a deeper contextual understanding of their use and also discusses the emergence of a general model of Ambient Intelligence that is based on the collective existence and behavior of such smart devices. Although generic in principle, the proposed concepts have been exemplified by a distinct use case, namely a smart kettle.

Spatial-frequency data acquisition using rotational invariant pattern matching in smart environments

This article details the development and testing of an empirical data capture system with the ability to collect spatial-frequency statistics relating to the movement behaviour of a smart home inhabitant. This is achieved using a greyscale normalised cross-correlation pattern matching algorithm. Environmental obstructions on the floor space can also be inferred from a visual representation of the accumulated data. Whilst this methodology itself is not novel, its application to person tracking specifically within a smart home environment does not appear in the literature and is considered a novel approach. The results of tests performed on the pattern matching technique show a tracking competency rate of 94.45% with a standard deviation of 0.009027, indicating high fidelity across a wide variety of environmental factors.

Human positioning and tracking in smart environments using colour pattern matching

The gold standard for detection and tracking of a human target in an indoor setting is Background Subtraction (BS). However BS has numerous problems when utilised indoors. Colour pattern matching may offer a viable and superior alternative to BS whilst enhancing privacy and person-device independence. Results obtained from this study demonstrate that, when using colour pattern matching to target a persons clothing, detection rates of 97.28% can be maintained across the electromagnetic spectrum of visible light, and during instances of acute differentiation in ambient illumination whilst allowing for a privacy-enabling blurring technique to be applied to all frames.

Stopping Criterion Impact on Pure Random Search Optimisation for Intelligent Device Distribution

The number of intelligent environment implementations such as smart homes is set to increase dramatically within the next 40 years. This is predicted using forecasts of demographic data which indicates an expansion of the aged population. It has also been predicted that governments will struggle to meet the demand for resources such as sensor technology due to costs. Optimisation of limited resources involves physically positioning devices to maximise pertinent data gathering potential. Currently the most utilised methodology of distributing limited spatial detection sensors such as pressure mats within smart homes is via ad-hoc deployments performed by a human being. In this study idiosyncratic inhabitant spatial-frequency data was processed using a Pure Random Search (PRS) algorithm to uncover probabilistic future regions of interest, alluding to optimal sensor distributions under resource constraint. With PRS a null hypothesis was stated: ‘using lower iteration stopping criteria produce less optimal sensor distributions than when using higher iteration stopping criteria’. A student t-test between 1000 and 5000 iterations was statistically significant at 5% (p = 0.016852) whereby the null hypothesis was rejected. Similar results were obtained between other iteration criteria. These data demonstrate that the iteration stopping criterion is not as critical as sensor size or number of sensors; and that comparable results could be obtained when lower stopping parameters are specified when using PRS.