scholarly journals A Context-Aware System Infrastructure for Monitoring Activities of Daily Living in Smart Home

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Qin Ni ◽  
Ana Belén García Hernando ◽  
Iván Pau de la Cruz
Keyword(s):  
Daily Living ◽  
Smart Home ◽  
Context Aware ◽  
Level Data ◽  
Common Activity ◽  
Context Knowledge ◽  
Monitoring Activities ◽  
Upper Level ◽  
Context Aware System ◽  
High Level

We propose a three-layered context-aware architecture for monitoring activities of daily life in smart home. This architecture provides for the inclusion of functionalities that range from low-level data collection to high-level context knowledge extraction. We have also devised an upper-level ontology to model the context in which the activities take place. This enables having a common activity-related context representation, on which to infer and share knowledge. Furthermore, we have begun to implement a platform that realizes our architecture and ontology, making use of Microsoft’s Lab of Things (LoT) platform, being the preliminary results on this task also described in the paper.

scholarly journals A non-intrusive context-aware system for ambient assisted living in smart home

2013 ◽  
Vol 3 (2) ◽  
pp. 129-138 ◽  
Author(s):  
Willy Allègre ◽  
Thomas Burger ◽  
Jean-Yves Antoine ◽  
Pascal Berruet ◽  
Jean-Paul Departe
Keyword(s):  
Assisted Living ◽  
Smart Home ◽  
Ambient Assisted Living ◽  
Context Aware ◽  
Context Aware System

Machine Learning Based Adaptive Context-Aware System for Smart Home Environment

2015 ◽  
Vol 9 (11) ◽  
pp. 55-62 ◽  
Author(s):  
M. Humayun Kabir ◽  
M. Robiul Hoque ◽  
Hyungyu Seo ◽  
Sung-Hyun Yang
Keyword(s):  
Machine Learning ◽  
Home Environment ◽  
Smart Home ◽  
Context Aware ◽  
Smart Home Environment ◽  
Context Aware System

ConChi

2016 ◽  
pp. 798-820
Author(s):  
Luca Cagliero
Keyword(s):  
Contextual Information ◽  
Extraction Process ◽  
Mobile User ◽  
Mobile Service ◽  
Context Aware ◽  
Time Periods ◽  
Context Aware System ◽  
Mobile Context ◽  
High Level ◽  
Abstraction Levels

Mobile context-aware systems focus on adapting mobile service provisions to the actual user needs. They offer personalized services based on the context in which mobile users' requests have been submitted. Since contextual information changes over time, the application of established itemset change mining algorithms to context-aware data is an appealing research issue. Change itemset discovery focuses on discovering patterns which represent the temporal evolution of frequent itemsets in consecutive time periods. However, the sparseness of the analyzed data may bias the extraction process, because itemsets are likely to become infrequent at certain time periods. This chapter presents ConChI, a novel context-aware system that performs change itemset mining from context-aware data with the aim at supporting mobile expert decisions. To counteract data sparseness itemset change mining is driven by an analyst-provided taxonomy which allows analyzing data correlation changes at different abstraction levels. In particular, taxonomy is exploited to represent the knowledge that becomes infrequent in certain time periods by means of high level (generalized) itemsets. Experiments performed on real contextual data coming from a mobile application show the effectiveness of the proposed system in supporting mobile user and service profiling.

ConChi

2014 ◽  
pp. 263-285
Author(s):  
Luca Cagliero
Keyword(s):  
Contextual Information ◽  
Extraction Process ◽  
Mobile User ◽  
Mobile Service ◽  
Context Aware ◽  
Time Periods ◽  
Context Aware System ◽  
Mobile Context ◽  
High Level ◽  
Abstraction Levels

Mobile context-aware systems focus on adapting mobile service provisions to the actual user needs. They offer personalized services based on the context in which mobile users’ requests have been submitted. Since contextual information changes over time, the application of established itemset change mining algorithms to context-aware data is an appealing research issue. Change itemset discovery focuses on discovering patterns which represent the temporal evolution of frequent itemsets in consecutive time periods. However, the sparseness of the analyzed data may bias the extraction process, because itemsets are likely to become infrequent at certain time periods. This chapter presents ConChI, a novel context-aware system that performs change itemset mining from context-aware data with the aim at supporting mobile expert decisions. To counteract data sparseness itemset change mining is driven by an analyst-provided taxonomy which allows analyzing data correlation changes at different abstraction levels. In particular, taxonomy is exploited to represent the knowledge that becomes infrequent in certain time periods by means of high level (generalized) itemsets. Experiments performed on real contextual data coming from a mobile application show the effectiveness of the proposed system in supporting mobile user and service profiling.

Representing and reasoning with situations for context-aware pervasive computing: a logic programming perspective

2004 ◽  
Vol 19 (3) ◽  
pp. 213-233 ◽  
Author(s):  
SENG W LOKE
Keyword(s):  
Logic Programming ◽  
Pervasive Computing ◽  
Contextual Information ◽  
Physical World ◽  
Context Aware ◽  
Pervasive Systems ◽  
Computational Environment ◽  
Meta Programming ◽  
Context Aware System ◽  
High Level

Context-aware pervasive systems are emerging as an important class of applications. Such systems can respond intelligently to contextual information about the physical world acquired via sensors and information about the computational environment. A declarative approach to building context-aware pervasive systems is presented, and the notion of the situation program is introduced, which highlights the primacy of the situation abstraction for building context-aware pervasive systems. There is also a demonstration of how to manipulate situation programs using meta-programming within an extension of the Prolog logic programming language which is called LogicCAP. Such meta-reasoning enables complex situations to be described in terms of other situations. Furthermore, a discussion is given on how the design of situation programs can affect the properties of a context-aware system. The approach encourages a high-level of abstraction for representing and reasoning with situations, and supports building context-aware systems incrementally by providing modularity and separation of concerns.

Inference of Human Intentions in Smart Home Environments

2013 ◽  
Vol 1 (2) ◽  
pp. 26-42
Author(s):  
Katsunori Oyama ◽  
Carl K. Chang ◽  
Simanta Mitra
Keyword(s):  
Home Environment ◽  
Smart Home ◽  
Context Aware ◽  
Different Types ◽  
Human Intention ◽  
Smart Home Environment ◽  
System Requirements ◽  
Context Aware System ◽  
Context Models

Most of context models have limited capability in involving human intention for system evolvability and self-adaptability. Human intention in context aware systems can evolve at any time; however, context aware systems based on these context models can provide only standard services that are often insufficient for specific user needs. Consequently, evolving human intentions result in changes in system requirements. Moreover, an intention must be analyzed from tangled relations with different types of contexts. In the past, this complexity has prevented researchers from using computational methods for analyzing or specifying human intention in context aware system design. The authors investigated the possibility for inferring human intentions from contexts and situations, and deploying appropriate services that users require during system run-time. This paper presents an inference ontology to represent stepwise inference tasks, and then evaluate contexts surrounding a user who accesses PCs through a case study of the smart home environment.

Sign in / Sign up

Export Citation Format

Share Document