Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers

This paper presents a systematic design approach for constructing neural classifiers that are capable of classifying human activities using a triaxial accelerometer. The philosophy of our design approach is to apply a divide-and-conquer strategy that separates dynamic activities from static activiti...

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Bibliographic Details
Published in:Pattern recognition letters 2008-12, Vol.29 (16), p.2213-2220
Main Authors: Yang, Jhun-Ying, Wang, Jeen-Shing, Chen, Yen-Ping
Format: Article
Language:English
Subjects:
Acceleration data
Activity recognition
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Connectionism. Neural networks
Exact sciences and technology
Feature extraction
Feature subset selection
Information, signal and communications theory
Miscellaneous
Neural network
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
Telecommunications and information theory
Triaxial accelerometer
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Summary:This paper presents a systematic design approach for constructing neural classifiers that are capable of classifying human activities using a triaxial accelerometer. The philosophy of our design approach is to apply a divide-and-conquer strategy that separates dynamic activities from static activities preliminarily and recognizes these two different types of activities separately. Since multilayer neural networks can generate complex discriminating surfaces for recognition problems, we adopt neural networks as the classifiers for activity recognition. An effective feature subset selection approach has been developed to determine significant feature subsets and compact classifier structures with satisfactory accuracy. Experimental results have successfully validated the effectiveness of the proposed recognition scheme.
ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2008.08.002