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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/21854

Title: The application of machine learning in multi sensor data fusion for activity recognition in mobile device space
Authors: Al-Marhoubi, Asmaa H.A.
Saravi, Sara
Edirisinghe, Eran A.
Keywords: Activity Recognition
Mobile phone
Mobile Sensors
Multi Sensor
Issue Date: 2015
Publisher: © 2015 Society of Photo-Optical Instrumentation Engineers
Citation: MARHOUBI, A.H., SARAVI, S. and EDIRISINGHE, E.A., 2015. The application of machine learning in multi sensor data fusion for activity recognition in mobile device space. Proceedings of SPIE 9481, Image Sensing Technologies: Materials, Devices, Systems, and Applications II, 94810G.
Abstract: The present generation of mobile handheld devices comes equipped with a large number of sensors. The key sensors include the Ambient Light Sensor, Proximity Sensor, Gyroscope, Compass and the Accelerometer. Many mobile applications are driven based on the readings obtained from either one or two of these sensors. However the presence of multiple-sensors will enable the determination of more detailed activities that are carried out by the user of a mobile device, thus enabling smarter mobile applications to be developed that responds more appropriately to user behavior and device usage. In the proposed research we use recent advances in machine learning to fuse together the data obtained from all key sensors of a mobile device. We investigate the possible use of single and ensemble classifier based approaches to identify a mobile device’s behavior in the space it is present. Feature selection algorithms are used to remove non-discriminant features that often lead to poor classifier performance. As the sensor readings are noisy and include a significant proportion of missing values and outliers, we use machine learning based approaches to clean the raw data obtained from the sensors, before use. Based on selected practical case studies, we demonstrate the ability to accurately recognize device behavior based on multi-sensor data fusion.
Description: One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Version: Published
DOI: 10.1117/12.2177115
URI: https://dspace.lboro.ac.uk/2134/21854
Publisher Link: http://dx.doi.org/10.1117/12.2177115
ISSN: 0277-786X
Appears in Collections:Published Articles (Computer Science)
Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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