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Proceedings Paper

The application of machine learning in multi sensor data fusion for activity recognition in mobile device space
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Paper 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.

Paper Details

Date Published: 13 May 2015
PDF: 9 pages
Proc. SPIE 9481, Image Sensing Technologies: Materials, Devices, Systems, and Applications II, 94810G (13 May 2015); doi: 10.1117/12.2177115
Show Author Affiliations
Asmaa H. Marhoubi, Loughborough Univ. (United Kingdom)
Sara Saravi, Loughborough Univ. (United Kingdom)
Eran A. Edirisinghe, Loughborough Univ. (United Kingdom)


Published in SPIE Proceedings Vol. 9481:
Image Sensing Technologies: Materials, Devices, Systems, and Applications II
Nibir K. Dhar; Achyut K. Dutta, Editor(s)

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