A comparative study on human activity classification with miniature inertial and magnetic sensors
Author(s)
Advisor
Barshan, BillurDate
2011Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
This study provides a comparative assessment on the different techniques of
classifying human activities that are performed using body-worn miniature inertial
and magnetic sensors. The classification techniques compared in this
study are: naive Bayesian (NB) classifier, artificial neural networks (ANNs),
dissimilarity-based classifier (DBC), various decision-tree methods, Gaussian
mixture model (GMM), and support vector machines (SVM). The algorithms for
these techniques are provided on two commonly used open source environments:
Waikato environment for knowledge analysis (WEKA), a Java-based software;
and pattern recognition toolbox (PRTools), a MATLAB toolbox. 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. Three
different cross-validation techniques are employed to validate the classifiers. A
performance comparison of the classification techniques is provided in terms of
their correct differentiation rates, confusion matrices, and computational cost.
The methods that result in the highest correct differentiation rates are found to
be ANN (99.2%), SVM (99.2%), and GMM (99.1%). The magnetometer is the
best type of sensor to be used in classification whereas gyroscope is the least
useful. Considering the locations of the sensor units on body, the sensors worn
on the legs seem to provide the most valuable information.
Keywords
inertial sensorsgyroscope
accelerometer
magnetometer
activity recognition and classification
feature extraction and reduction
cross validation
Bayesian decision making
artificial neural networks
support vector machines
decision trees
dissimilarity-based classifier
Gaussian mixture model
WEKA
PRTools