Classifying Human Leg Motions with Uniaxial Piezoelectric Gyroscopes

Sensors

Orkun Tun๾l¹  Kerem Altun¹ 

[1] Department of Electrical and Electronics Engineering, Bilkent University, Bilkent 06800 Ankara, Turkey;

关键词: gyroscope;    inertial sensors;    motion classification;    Bayesian decision making;    rule-based algorithm;    least-squares method;    k-nearest neighbor;    dynamic time warping;    support vector machines;    artificial neural networks;   

DOI  :  10.3390/s91108508

来源: mdpi

【 摘 要 】

This paper provides a comparative study on the different techniques of classifying human leg motions that are performed using two low-cost uniaxial piezoelectric gyroscopes worn on the leg. A number of feature sets, extracted from the raw inertial sensor data in different ways, are used in the classification process. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, least-squares method (LSM), k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). A performance comparison of these classification techniques is provided in terms of their correct differentiation rates, confusion matrices, computational cost, and training and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that BDM, in general, results in the highest correct classification rate with relatively small computational cost.

【 授权许可】

CC BY   
© 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.