Comparative study on classifying human activities with miniature inertial and magnetic sensors

Altun K., Barshan B., Tunçel O.

Pattern Recognition, cilt.43, sa.10, ss.3605-3620, 2010 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 43 Sayı: 10
  • Basım Tarihi: 2010
  • Doi Numarası: 10.1016/j.patcog.2010.04.019
  • Dergi Adı: Pattern Recognition
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.3605-3620
  • Anahtar Kelimeler: Accelerometer, Activity recognition and classification, Artificial neural networks, Bayesian decision making, Decision tree, Dynamic time warping, Feature extraction, Feature reduction, Gyroscope, Inertial sensors, k-Nearest neighbor, Least-squares method, Magnetometer, 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 activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Human activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. A feature set extracted from the raw sensor data using principal component analysis (PCA) is used in the classification process. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost, as well as their pre-processing, training, and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that in general, BDM results in the highest correct classification rate with relatively small computational cost. © 2010 Elsevier Ltd. All rights reserved.