Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Semantic Annotation of Sports Videos
IEEE MultiMedia
Smart Cameras as Embedded Systems
Computer
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An analysis of Bayesian classifiers
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Are you looking at me, are you talking with me: multimodal classification of the focus of attention
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
In-shoe plantar pressure measurement and analysis system based on fabric pressure sensing array
IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
Hi-index | 0.10 |
In this presentation, we give a detailed analysis of the considerations needed for mapping the complete pattern classification chain to the restricted embedded system hardware environment. We describe the methodology of the design, realization and testing process that takes these hardware limitations into account. For this purpose, we consider a particular embedded application from the field of digital sports: a novel running shoe that is capable of sensing run-specific parameters and adapting the cushioning setting accordingly. Of utmost importance in this context is the classification of the current surface condition in order to enable optimal adaptation to the prevailing situation. Following our design approach, we provide a classification system with a runner-independent surface classification rate of more than 80%. This system is implemented in the current version of the aforementioned running shoe. The presented methodology is quite general as it makes no system-dependent assumptions and can thus be transferred to many other embedded classification applications.