Human activity recognition using inertial/magnetic sensor units
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
38 - 51
Item Usage Stats
MetadataShow full item record
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), 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). Daily and sports activities are classified using five sensor units worn by eight subjects on the chest, the arms, and the legs. Each sensor unit comprises a triaxial gyroscope, a triaxial accelerometer, and a triaxial magnetometer. Principal component analysis (PCA) and sequential forward feature selection (SFFS) methods are employed for feature reduction. For a small number of features, SFFS demonstrates better performance and should be preferable especially in real-time applications. The classifiers are validated using different cross-validation techniques. Among the different classifiers we have considered, BDM results in the highest correct classification rate with relatively small computational cost. © 2010 Springer-Verlag Berlin Heidelberg.
Keywordsfeature selection and reduction
human activity recognition and classification
Artificial Neural Network
Dynamic time warping
feature selection and reduction
Human activity recognition
K-nearest neighbor algorithm
Least squares methods
Inertial navigation systems
Principal component analysis
Support vector machines
Published Version (Please cite this version)http://dx.doi.org/10.1007/978-3-642-14715-9_5
Showing items related by title, author, creator and subject.
The analysis of wearable motion sensors in human activity recognition based on mutual information criterion Dobrucali O.; Barshan, B. (IEEE Computer Society, 2014)Selecting a suitable sensor configuration is an important aspect of recognizing human activities with wearable motion sensors. This problem encompasses selecting the number and type of the sensors, their position on the ...
Investigating inter-subject and inter-activity variations in activity recognition using wearable motion sensors Barshan, B.; Yurtman, A. (Oxford University Press, 2016)This work investigates inter-subject and inter-activity variability of a given activity dataset and provides some new definitions to quantify such variability. The definitions are sufficiently general and can be applied ...
Yurtman, Aras (Bilkent University, 2012)We address the problem of detecting and classifying human activities using two different types of wearable sensors. In the first part of the thesis, a comparative study on the different techniques of classifying human ...