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Electronics 2017, 6(2), 44; doi:10.3390/electronics6020044

On the Generality of Codebook Approach for Sensor-Based Human Activity Recognition

1
Pattern Recognition Group, University of Siegen, Hoelderlinstr. 3, D-57076 Siegen, Germany
2
Department of Knowledge Engineering, University of Economics in Katowice, Bogucicka 3 Str., 40-226 Katowice, Poland
*
Author to whom correspondence should be addressed.
Received: 29 April 2017 / Revised: 23 May 2017 / Accepted: 26 May 2017 / Published: 1 June 2017
(This article belongs to the Special Issue Data Processing and Wearable Systems for Effective Human Monitoring)
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Abstract

With the recent spread of mobile devices equipped with different sensors, it is possible to continuously recognise and monitor activities in daily life. This sensor-based human activity recognition is formulated as sequence classification to categorise sequences of sensor values into appropriate activity classes. One crucial problem is how to model features that can precisely represent characteristics of each sequence and lead to accurate recognition. It is laborious and/or difficult to hand-craft such features based on prior knowledge and manual investigation about sensor data. To overcome this, we focus on a feature learning approach that extracts useful features from a large amount of data. In particular, we adopt a simple but effective one, called codebook approach, which groups numerous subsequences collected from sequences into clusters. Each cluster centre is called a codeword and represents a statistically distinctive subsequence. Then, a sequence is encoded as a feature expressing the distribution of codewords. The extensive experiments on different recognition tasks for physical, mental and eye-based activities validate the effectiveness, generality and usability of the codebook approach. View Full-Text
Keywords: sensor-based human activity recognition; sequence classification; feature learning; codebook approach sensor-based human activity recognition; sequence classification; feature learning; codebook approach
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Shirahama, K.; Grzegorzek, M. On the Generality of Codebook Approach for Sensor-Based Human Activity Recognition. Electronics 2017, 6, 44.

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