Title
Evaluation of the Influence of Time Synchronisation on Classification Learning Based Movement Detection with Accelerometers
Abstract
Context awareness is an important enabler of future applications in ubiquitous environments and has the potential to free the user from the control loop in a situation where he faces ever more connected devices. The context of a user can be accessed using data gathered from the user's personal devices and devices in his proximity. In this paper we examine the relation between time synchronisation of devices and context reasoning accuracy. For an application where we detect different activities of an office worker from acceleration data measured at the wrists, we show that insufficient time synchronisation leads to a significant decrease of reasoning accuracy. We show this for six commonly used classification learning algorithms that otherwise offer high accuracy provided devices are time synchronised. We also analyse the reasons that causes a lack of time synchronisation and why known synchronisation protocols are not applicable in ubiquitous environments. To solve the problems imposed by insufficient time synchronisation we propose a conceptual change in the communication paradigm used in architectures for context awareness.
Year
DOI
Venue
2011
10.1109/SAINT.2011.18
SAINT
Keywords
Field
DocType
insufficient time synchronisation,movement detection,synchronisation protocol,context reasoning accuracy,time synchronisation,communication paradigm,reasoning accuracy,classification learning,context awareness,acceleration data,ubiquitous environment,high accuracy,motion estimation,middleware,accelerometers,ubiquitous computing,accuracy,synchronization,synchronisation,learning artificial intelligence,accelerometer,sensors
Enabling,Synchronization,Accelerometer,Computer science,Real-time computing,Context awareness,Motion estimation,Control system,Ubiquitous computing,Conceptual change
Conference
Citations 
PageRank 
References 
1
0.36
13
Authors
3
Name
Order
Citations
PageRank
Bernd Niklas Klein1273.43
Sian Lun Lau28111.48
Klaus David37715.55