Paper Info
Title
Elaborating Sensor Data using Temporal and Spatial Commonsense Reasoning
Abstract
Ubiquitous computing has established a vision of computation where computers are so deeply integrated into our lives that they become both invisible and everywhere. In order to have computers out of sight and out of mind, they will need a deeper understanding of human life. LifeNet [1] is a model that functions as a computational model of human life that attempts to anticipate and predict what humans do in the world from a first-person point of view. LifeNet utilizes a general knowledge storage [2] gathered from assertions about the world input by the web community at large. In this work, we extend this general knowledge with sensor data gathered in vivo. By adding these sensor-network data to LifeNet, we are enabling a bidirectional learning process: both bottom-up segregation of sensor data and top-down conceptual constraint propagation, thus correcting current metric assumptions in the LifeNet conceptual model by using sensor measurements. Also, in addition to having LifeNet learning general common sense metrics of physical time and space, it will also learn metrics to a specific lab space and chances for recognizing specific individual human activities, and thus be able to make both general and specific spatial/temporal inferences, such as predicting howmany people are in a given room and what they might be doing. This paper discusses the following topics: (1) details of the LifeNet probabilistic human model, (2) a description of the Plug sensor network used in this research, and (3) a description of an experimental design for evaluation of the LifeNet learning method.
Year
DOI
Venue
2006
10.1109/BSN.2006.22
BSN
Keywords
Field
DocType
sensor data,lifenet conceptual model,human life,general knowledge storage,spatial commonsense reasoning,general knowledge,lifenet probabilistic human model,sensor measurement,computational model,general common sense metrics,plug sensor network,ubiquitous computing,learning artificial intelligence,computational modeling,commonsense reasoning,computer vision,sensor fusion,sensor network,in vivo,wireless sensor networks,bottom up,experimental design,constraint propagation,conceptual model,top down,data gathering,pervasive computing,predictive models,computer model
Computer vision,Local consistency,Conceptual model,Computer science,Commonsense reasoning,Sensor fusion,Artificial intelligence,General knowledge,Ubiquitous computing,Probabilistic logic,Wireless sensor network,Embedded system
Conference
ISBN
Citations 
PageRank 
0-7695-2547-4
1
0.37
References 
Authors
5
2
Name
Order
Citations
PageRank
Bo Morgan131.08
Push Singh235831.13