Abstract
This paper proposes an ensemble artificial neuro-molecular system for motion recognition for a wearable sensor system with 3-axis accelerometers. Human motions can be distinguished through classification algorithms for the wearable sensor system of two 3-axis accelerometers attached to both forearms. Raw data from the accelerometers are pre-processed and forwarded to the classification algorithm designed using the proposed ensemble artificial neuro-molecular(ANM) system. The ANM system is a kind of bio-inspired algorithm like neural network. It is composed of many artificial neurons that are linked together according to a specific network architecture. For comparison purpose, other algorithms such as artificial neuro-molecular system, artificial neural networks support vector machine, k-nearest neighbor algorithm and k-means clustering, are tested. In experiments, eight kinds of motions are randomly selected in a daily life to test the performance of the proposed system and to compare its performance with that of existing algorithms.
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Ryu, SJ., Kim, JH. (2011). Motion Recognition in Wearable Sensor System Using an Ensemble Artificial Neuro-Molecular System. In: Li, TH.S., et al. Next Wave in Robotics. FIRA 2011. Communications in Computer and Information Science, vol 212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23147-6_10
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DOI: https://doi.org/10.1007/978-3-642-23147-6_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23146-9
Online ISBN: 978-3-642-23147-6
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