Inertial sensor based modelling of human activity classes:Feature extraction and multi-sensor data fusion using machine learning algorithms

Zebin, Tahmina, Scully, Patricia J. and Ozanyan, Krikor B. (2017) Inertial sensor based modelling of human activity classes:Feature extraction and multi-sensor data fusion using machine learning algorithms. In: eHealth 360° - International Summit on eHealth, Revised Selected Papers. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST . Springer, HUN, pp. 306-314. ISBN 9783319496542

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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.

Item Type: Book Section
Uncontrolled Keywords: accelerometer data,data-fusion,feature extraction,human activity recognition,inertial measurement unit,machine learning algorithms,computer networks and communications ,/dk/atira/pure/subjectarea/asjc/1700/1705
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Depositing User: LivePure Connector
Date Deposited: 24 May 2019 15:30
Last Modified: 07 Jun 2020 23:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/71133
DOI: 10.1007/978-3-319-49655-9_38

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