Van Laerhoven, Kristof and Aidoo, Kofi A. and Lowette, Steven (2001) Real-Time Analysis of Data from Many Sensors with Neural Networks. In: Fifth International Symposium on Wearable Computers (ISWC'01), 1900-01-01.Full text not available from this repository.
Much research has been conducted that uses sensor-based modules with dedicated software to automatically distinguish the user's situation or context. The best results were obtained when powerful sensors (such as cameras or GPS systems) and/or sensor-specific algorithms (like sound analysis) were applied. A somewhat new approach is to replace the one smart sensor by many simple sensors. We argue that neural networks are ideal algorithms to analyze the data coming from these sensors and describe how we came to one specific algorithm that gives good results, by giving an overview of several requirements. Finally, wearable implementations are given to show the feasibility and benefits of this approach and its implications.
|Item Type:||Conference or Workshop Item (Paper)|
|Journal or Publication Title:||Fifth International Symposium on Wearable Computers (ISWC'01)|
|Uncontrolled Keywords:||cs_eprint_id ; 1523 cs_uid ; 382|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Departments:||Faculty of Science and Technology > School of Computing & Communications|
|Deposited On:||25 Jun 2008 11:03|
|Last Modified:||26 Feb 2017 01:20|
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