Paper Info
Title
A study on instance-based learning with reduced training prototypes for device-context-independent activity recognition on a mobile phone.
Abstract
This paper presents a study of two simple methods for reducing the complexity of the instance-based classification technique and demonstrates their use in device-context independent activity recognition on a mobile phone. A projection-based method for signal rectification has been implemented on an iPhone in order to handle with variation in device orientations. The transformation matrix is estimated on a ten-second dynamic data buffer. To search for a suitable set of training prototypes for iPhone implementation, an activity recognition experiment is conducted with twenty different device contexts performed by eight subjects. With the developed mobile application, the recognition results along with the user's location can be displayed on both iPhone and the web application in real time.
Year
DOI
Venue
2013
10.1109/BSN.2013.6575462
BSN
Keywords
Field
DocType
data collection,accelerometer,feature extraction,instance based learning,prototypes,accuracy,activity recognition,acceleration,real time systems
Computer vision,Data collection,Instance-based learning,Activity recognition,Computer science,Feature extraction,Dynamic data,Artificial intelligence,Context independent,Mobile phone,Transformation matrix,Embedded system
Conference
ISSN
ISBN
Citations 
2325-1425
978-1-4799-0331-3
8
PageRank 
References 
Authors
0.53
17
3
Name
Order
Citations
PageRank
Surapa Thiemjarus119215.64
Apiwat Henpraserttae2441.74
Sanparith Marukatat313117.05