Classifying human leg motions with uniaxial piezoelectric gyroscopes

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Tunçel O., Altun K., Barshan B.

Sensors, cilt.9, sa.11, ss.8508-8546, 2009 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 9 Sayı: 11
  • Basım Tarihi: 2009
  • Doi Numarası: 10.3390/s91108508
  • Dergi Adı: Sensors
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.8508-8546
  • Anahtar Kelimeler: Artificial neural networks, Bayesian decision making, Dynamic time warping, Gyroscope, Inertial sensors, K-nearest neighbor, Least-squares method, Motion classification, Rule-based algorithm, Support vector machines
  • Yıldız Teknik Üniversitesi Adresli: Hayır

Özet

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. © 2009 by the authors.