Inertial Sensor Based Modelling of Human Activity Classes: Feature Extraction and Multi-sensor Data Fusion Using Machine Learning Algorithms

Zebin, T., Scully, PJ and Ozanyan, K B. 2017. Inertial Sensor Based Modelling of Human Activity Classes: Feature Extraction and Multi-sensor Data Fusion Using Machine Learning Algorithms. in: eHealth 360°. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Springer. pp. 306-314

Chapter titleInertial Sensor Based Modelling of Human Activity Classes: Feature Extraction and Multi-sensor Data Fusion Using Machine Learning Algorithms
AuthorsZebin, T., Scully, PJ and Ozanyan, K B.
Abstract

Wearable inertial sensors are currently receiving pronounced interest due to applications in unconstrained daily life settings, ambulatory monitoring and pervasive computing systems. This research focuses on human activity recognition problem, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are automatically classified human activities. A general-purpose framework has been presented for designing and evaluating activity recognition system with six different activities using machine learning algorithms such as support vector machine (SVM) and artificial neural networks (ANN). Several feature selection methods were explored to make the recognition process faster by experimenting on the features extracted from the accelerometer and gyroscope time series data collected from a number of volunteers. In addition, a detailed discussion is presented to explore how different design parameters, for example, the number of features and data fusion from multiple sensor locations - impact on overall recognition performance.

Book titleeHealth 360°. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Page range306-314
Year2017
PublisherSpringer
FileAS4405975940218891482058060256_content_1.pdf
Publication dates
Published22 Dec 2017
Published online01 Dec 2016
SeriesLecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
ISBN9783319496542
9783319496559
ISSN1867-8211
Digital Object Identifier (DOI)doi:10.1007/978-3-319-49655-9_38
Web address (URL)https://link.springer.com/chapter/10.1007/978-3-319-49655-9_38

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