Motion recognition in wearable sensor system using an ensemble artificial neuro-molecular system

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. © 2011 Springer-Verlag.