Human Behaviour Recognition in Ambient Intelligent Environments

2015 ◽  
pp. 107-121
Author(s):  
Hans W. Guesgen ◽  
Stephen Marsland
Keyword(s):  
Probability Theory ◽  
Ambient Intelligence ◽  
Human Behaviour ◽  
Intelligent Environments ◽  
Dempster Shafer Theory ◽  
Ambient Intelligent ◽  
Local Patterns ◽  
Spatio Temporal ◽  
Shafer Theory ◽  
Behaviour Recognition

The recognition of human behaviour from sensor observations is an important area of research in smart homes and ambient intelligence. In this chapter, the authors introduce the idea of spatio-temporal footprints, which are local patterns in space and time that should be similar across repeated occurrences of the same behaviour. They discuss the spatial and temporal mapping requirements of these footprints, together with how they may be used. As possible formalisms for implementing spatio-temporal footprints, the authors discuss and evaluate probability theory, fuzzy sets, and the Dempster-Shafer theory.

Spatio-Temporal Footprints

2011 ◽  
pp. 51-57
Author(s):  
Hans W. Guesgen ◽  
Stephen Marsland
Keyword(s):  
Ambient Intelligence ◽  
Smart Homes ◽  
Human Behaviour ◽  
Space And Time ◽  
Local Patterns ◽  
Spatio Temporal

The recognition of human behaviour from sensor observations is an important area of research in smart homes and ambient intelligence. In this paper, we introduce the idea of spatio-temporal footprints, which are local patterns in space and time that should be similar across repeated occurrences of the same behaviour. We discuss the spatial and temporal mapping requirements of these footprints, together with how they may be used.

Spatio-Temporal Footprints

2010 ◽  
Vol 2 (1) ◽  
pp. 52-58
Author(s):  
Hans W. Guesgen ◽  
Stephen Marsland
Keyword(s):  
Ambient Intelligence ◽  
Smart Homes ◽  
Human Behaviour ◽  
Space And Time ◽  
Local Patterns ◽  
Spatio Temporal

The recognition of human behaviour from sensor observations is an important area of research in smart homes and ambient intelligence. In this paper, we introduce the idea of spatio-temporal footprints, which are local patterns in space and time that should be similar across repeated occurrences of the same behaviour. We discuss the spatial and temporal mapping requirements of these footprints, together with how they may be used.

Planning and Evaluation of Assurance Services for Sustainability Reporting: An Evidential Reasoning Approach

2013 ◽  
Vol 27 (2) ◽  
pp. 107-126 ◽  
Author(s):  
Rajendra P. Srivastava ◽  
Sunita S. Rao ◽  
Theodore J. Mock
Keyword(s):  
Probability Theory ◽  
Theoretical Model ◽  
Corporate Sustainability ◽  
Sustainability Reporting ◽  
Belief Function ◽  
Global Reporting Initiative ◽  
Bayesian Probability ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Different Levels

ABSTRACT This study develops a framework for planning, performing, and evaluating evidence obtained to assess and control the risks of providing assurance on sustainability reports. Sustainability reporting, or corporate sustainability reporting (CSR), provides stakeholders with important information on both financial and non-financial factors related to environmental, social, and economic performance. Importantly, the presented framework is developed from both a Bayesian (probability-based theory) and Belief Function (Dempster-Shafer theory) perspective. This facilitates application of the framework to cases where the assurance provider prefers to assess risk in terms of probability versus in terms of beliefs. To demonstrate the application of this framework we evaluate assertions, sub-assertions, and audit evidence relevant to CSR based on the G3 Reporting framework developed by the Global Reporting Initiative (GRI). The paper contributes to the literature in three main areas. First, it demonstrates how evidence-based reasoning can be used for engagements where different levels of assurance are provided for the assertions being audited. Second, it shows how various items of evidence at different levels may be aggregated. Third, it presents a generic theoretical model for assuring information based on belief-based assessments, which is then contrasted with a theoretical model based on probability theory. In contrasting the two approaches, we show that in cases where initial uncertainty is substantial, the use of Dempster-Shafer theory has advantages over probability theory in terms of efficiency in achieving a targeted low level of assurance.

Decision Support System for Advanced Traffic Management Through Data Fusion

2002 ◽  
Vol 1804 (1) ◽  
pp. 173-178 ◽  
Author(s):  
Lawrence A. Klein ◽  
Ping Yi ◽  
Hualiang Teng
Keyword(s):  
Data Fusion ◽  
Traffic Management ◽  
Information Sources ◽  
Partial Information ◽  
Weather Conditions ◽  
Field Testing ◽  
Traffic Operations ◽  
Dempster Shafer Theory ◽  
Classification Technique ◽  
Shafer Theory

The Dempster–Shafer theory for data fusion and mining in support of advanced traffic management is introduced and tested. Dempste–Shafer inference is a statistically based classification technique that can be applied to detect traffic events that affect normal traffic operations. It is useful when data or information sources contribute partial information about a scenario, and no single source provides a high probability of identifying the event responsible for the received information. The technique captures and combines whatever information is available from the data sources. Dempster’s rule is applied to determine the most probable event—as that with the largest probability based on the information obtained from all contributing sources. The Dempster–Shafer theory is explained and its implementation described through numerical examples. Field testing of the data fusion technique demonstrated its effectiveness when the probability masses, which quantify the likelihood of the postulated events for the scenario, reflect current traffic and weather conditions.

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