Ambient Intelligence and Future Trends - International Symposium on Ambient Intelligence (ISAmI 2010)
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Juan Manuel Corchado Rodríguez, Juan Carlos Augusto, Paulo Novais, Miguel Calejo
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Ambient Intelligence and Future Trends - International Symposium on Ambient Intelligence (ISAmI 2010)
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Springer-Verlag
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9783642132681
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Advances in Intelligent and Soft Computing
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1
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CHF 132.70
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Technik
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English
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260
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Wasserzeichen/DRM
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PC/MAC/eReader/Tablet
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PDF
ISAmI is the International Symposium on Ambient Intelligence, aiming to bring together researchers from various disciplines that constitute the scientific field of Ambient Intelligence to present and discuss the latest results, new ideas, projects and lessons obtained from recent experiences in building AmI systems. This volume presents the papers that have been accepted in this first edition. These papers reports on innovative results and advances achieved recently in this area.
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Context Triggered Experience Sampling
(p. 193-194)
David V. Keyson
Abstract.
As products and services have become more embedded in the everyday life and routines of users, the need to understand how the context of use influences product usage over time has increased. The Experience Sampling Method (ESM) aims to capture both the context and content of the daily life of individuals. Critical to the ESM methods is the notion of asking the appropriate set of experience related questions at the right moment. The current approach to ESM is based on provided the user with a set of qualitative questions, typically using scaled measures, at pre-defined intervals during the course of the day over a period of several days or longer. In the current paper the Context Aware Toolbox (CAT) is described as a means to rapidly equip an environment with sensors and actuators which in turn can trigger user input via formal questionnaires and indirect input based on the task at hand.
The main elements of the CAT is a database of algorithms, written in MAX MSP which can be rapidly adapted to particular needs, Phidget sensors and actuators, a MAX Mini containing instructions and the database, touch screens for displaying the qualitative measures on site, and installation tools, as well as a web based infrastructure for adding new content to the database. The CAT setup is currently being applied to design research cases in the area of social and environmental sustainability; an application design case focused on sustainable living is described.
Keywords:
Experience Sampling, Context Awareness, Prototyping.
1 Introduction
The emergence of products and services embedded in the everyday routine of people reflect the degree to which technology has become a part of lives. Increasingly, the only way to evaluate the usability of products and services, behind the immediate ease of use of the interface, is to study user interaction in context. For pure Internet based applications, this might mean launching a“beta” version of the software and observing what happens with large number of users and then fixing the problems over successive beta releases.
In the case of evaluating physical products in context, focus group sessions, or phone or mail based questionnaires may only provide brief glimpses into product adoption and usability issues. The two predominant methods to gather ongoing usage data are the Diary Recollection Method [1] and the Experience Sampling Method [2].
Both methods can also be combined [3]. In the case of DRMS the user may be given a diary to fill-in over a fixed time interval across several days or more, or maybe given a camera to take pictures of the context of use on a regular basis. Experience sampling may be conducted via asking the user to fill in a paper form of electronic form on the basis of timed requests which may generated via a watch, beeper, Website, or PDA, to name a few possible platforms.
It is generally argued that ESM combines the ecological validity of naturalistic behavioral observations with the nonintrusive nature of diaries and the precision of scaled questionnaire measures. While the limitations of ESM are acknowledged, in particular the demands it imposes on respondents, which may contribute to a self-selection bias and selective non-response, the method is regarded as some as the best alternative to capturing the context of use.
Central to the ESM is the notion that“by sampling experience the moment it occurs, it avoids the potential distortions associated with the use of daily or weekly retrospective diaries” [2]. While this seems at first glance a logical statement, taking a deeper look at ESM studies reveals that often ESM based questionnaires appear at pre-set time intervals, but are not necessarily triggered when the behavior under study occurs in a given context.
Furthermore the ESM assumes that experiences can only be recorded via qualitative scales, in some cases the way in which a product or service is engaged may in itself reveal experiential data. For example the degree to which a user applies force to while setting a physical control may be an indication of stress or the number of repeated efforts to make a“soft” selection on a screen could reflect a degree of confusion. Such data could be monitored in real-time and collected over an extended period. Taking things a step further the product or service itself could be remotely updated given the availability of online usability data. In some cases it may make sense deploy sensors and actuators close to the product or additionally in the vicinity of the product to capture experience data. To this extent the Context Aware Toolkit was developed."
Title Page
2
Preface
6
Organization
7
Contents
10
Long Papers
10
A Study on Autonomic Decision Method for Smart Gas Environments in Korea
14
Introduction
14
Collecting Status Data
15
The Necessity of Improving the Decision Approach
16
The Autonomic Decision Method for Smart City Gas
18
Conclusions and Future Works
20
References
21
Multiagent Systems and Wearable Devices: Helping People Live Healthier
23
Introduction
23
SWeDe Multiagent System
24
Case Study: Geriatric Residences Monitoring
27
Results and Conclusions
28
References
29
Accurate Temporal Relationships in Sequences of User Behaviours in Intelligent Environments
31
Introduction
31
Sequential Patterns of User Behaviour System
32
Related Work
34
Identifying Time Relations
34
Data Collection for Identifying Time Relations
35
Basic Algorithm for Identifying Time Relations
36
EM Algorithm for Identifying Time Relations
37
Validation and Results
37
Conclusions
38
References
38
A Framework to Enable Two-Layer Inference for Ambient Intelligence
40
Introduction
40
Standards and Technologies
41
Designing the Framework
42
AUseCase
44
Discussion
46
References
46
GLSS Group Learning in Shared Spaces Considering Aspects Like Emotion and Personality
48
Introduction
48
Background
49
Learning Styles
49
Intelligent Tutoring
50
Emotion
50
Personality
51
Proposed Architecture
51
Architecture
51
Scenario
53
Conclusions
53
References
54
Rewiring Strategies for Changing Environments
56
Introduction
56
Scenario: Parental Control on Media Content in Dynamic Pervasive Environment
57
Conceptual Models for Parental Con
