Towards activity databases: Using sensors and statistical models to summarize people’s lives

Towards activity databases: Using sensors and statistical models to summarize people’s lives (2006)

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by Tanzeem Choudhury , Matthai Philipose , Danny Wyatt , Jonathan Lester

Venue:	IEEE Data Eng. Bull
Citations:	15 - 0 self

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Datum	Value	Source
TITLE	Towards activity databases: Using sensors and statistical models to summarize people’s lives	INFERENCE
AUTHOR NAME	Tanzeem Choudhury	SVM HeaderParse 0.2
AUTHOR AFFIL	2 Univ. of Washington, Seattle	SVM HeaderParse 0.2
AUTHOR ADDR	WA 98195	SVM HeaderParse 0.2
AUTHOR NAME	Matthai Philipose	SVM HeaderParse 0.2
AUTHOR AFFIL	2 Univ. of Washington, Seattle	SVM HeaderParse 0.2
AUTHOR ADDR	WA 98195	SVM HeaderParse 0.2
AUTHOR NAME	Danny Wyatt	SVM HeaderParse 0.2
AUTHOR NAME	Jonathan Lester	SVM HeaderParse 0.2
AUTHOR AFFIL	2 Univ. of Washington, Seattle	SVM HeaderParse 0.2
AUTHOR ADDR	WA 98195	SVM HeaderParse 0.2
ABSTRACT	Automated reasoning about human behavior is a central goal of artificial intelligence. In order to engage and intervene in a meaningful way, an intelligent system must be able to understand what humans are doing, their goals and intentions. Furthermore, as social animals, people’s interactions with each other underlie many aspects of their lives: how they learn, how they work, how they play and how they affect the broader community. Understanding people’s interactions and their social networks will play an important role in designing technology and applications that are “socially-aware”. This paper introduces some of the current approaches in activity recognition which use a variety of different sensors to collect data about users ’ activities, and probabilistic models and relational information that are used to transform the raw sensor data into higher-level descriptions of people’s behaviors and interactions. The end result of these methods is a richly structured dataset describing people’s daily patterns of activities and their evolving social networks. The potential applications of such datasets include mapping patterns of information-flow within an organization, predicting the spread of disease within a community, monitoring the health and activity-levels of elderly patients as well as healthy adults, and allowing “smart environments ” to respond proactively to the needs and intentions of their users. 1	SVM HeaderParse 0.2
YEAR	2006	INFERENCE
VENUE	IEEE Data Eng. Bull	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	2006	INFERENCE
VOLUME	29	INFERENCE
CITATIONS	22 found	ParsCit 1.0