DocumentCode :
3667562
Title :
Distributional semantics and unsupervised clustering for sensor relevancy prediction
Author :
Myriam Leggieri;Brian Davis;John G. Breslin
Author_Institution :
Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland
fYear :
2015
Firstpage :
210
Lastpage :
215
Abstract :
The logging of Activities of Daily Living (ADLs) is becoming increasingly popular mainly thanks to wearable devices. Currently, most sensors used for ADLs logging are queried and filtered mainly by location and time. However, in an Internet of Things future, a query will return a large amount of sensor data. Therefore, existing approaches will not be feasible because of resource constraints and performance issues. Hence more fine-grained queries will be necessary. We propose to filter on the likelihood that a sensor is relevant for the currently sensed activity. Our aim is to improve system efficiency by reducing the amount of data to query, store and process by identifying which sensors are relevant for different activities during the ADLs logging by relying on Distributional Semantics over public text corpora and unsupervised hierarchical clustering. We have evaluated our system over a public dataset for activity recognition and compared our clusters of sensors with the sensors involved in the logging of manually-annotated activities. Our results show an average precision of 89% and an overall accuracy of 69%, thus outperforming the state of the art by 5% and 32% respectively. To support the uptake of our approach and to allow replication of our experiments, a Web service has been developed and open sourced.
Keywords :
"Sensors","Semantics","Cleaning","Art","Artificial intelligence","Hidden Markov models","Accuracy"
Publisher :
ieee
Conference_Titel :
Wireless Communications and Mobile Computing Conference (IWCMC), 2015 International
Type :
conf
DOI :
10.1109/IWCMC.2015.7289084
Filename :
7289084
Link To Document :
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