Computer Science ›› 2016, Vol. 43 ›› Issue (12): 297-301.doi: 10.11896/j.issn.1002-137X.2016.12.055

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Human Motion Activity Recognition Model Based on Multi-classifier Fusion

WANG Zhong-min, WANG Ke and HE Yan   

  • Online:2018-12-01 Published:2018-12-01

Abstract: To improve the accuracy of human activity recognition based on the triaxial acceleration data from mobile sensors,an activity recognition model based on multiple classifier fusion (MCF) was proposed.The features which are high correlated with each daily activity (staying,walking,running,going upstairs and going downstairs) are extracted from the original acceleration data to generate the five feature data sets to train the five base classifiers.The input of the five base classifiers are these feature data sets,and their output are processed using multi-classifier fusion algorithm to produce the final activity recognition result.The experimental results show that the average activity recognition accuracy and the reliability by using MCF are respectively 96.84% and 97.41%,and it can effectively identify human activities.

Key words: Activity recognition,Triaxial acceleration,Base classifier,Multi-classifier fusion

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