Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Gesture spotting with body-worn inertial sensors to detect user activities
Pattern Recognition
Integrated feature and parameter optimization for an evolving spiking neural network
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Constructing robust liquid state machines to process highly variable data streams
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
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This paper proposes to use a Liquid State Machine (LSM) to classify inertial sensor data collected from horse riders into activities of interest. LSM was shown to be an effective classifier for spatio-temporal data and efficient hardware implementations on custom chips have been presented in literature that would enable relative easy integration into wearable technologies. We explore here the general method of applying LSM technology to domain constrained activity recognition using a synthetic data set. The aim of this study is to provide a proof of concept illustrating the applicability of LSM for the chosen problem domain.