Abstract
This paper employs a Liquid State Machine (LSM) to classify inertial sensor data collected from horse riders into activities of interest. Since LSM was shown to be an effective classifier for spatio-temporal data and efficient hardware implementations on custom chips exist, we argue that LSM would be relative easy to integrate into wearable technologies. We explore here the general method of applying LSM technology to domain constrained activity recognition using a real-world data set. The aim of this study is to provide a proof of concept illustrating the applicability of LSM for the chosen problem domain.
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Schliebs, S., Kasabov, N., Parry, D., Hunt, D. (2013). Towards a Wearable Coach: Classifying Sports Activities with Reservoir Computing. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_24
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DOI: https://doi.org/10.1007/978-3-642-41013-0_24
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