کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
534998 | 870311 | 2008 | 8 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers](/preview/png/534998.png)
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.
Journal: Pattern Recognition Letters - Volume 29, Issue 16, 1 December 2008, Pages 2213–2220