Human Activity Recognition Using Smart-Phone Sensors

Abstract:

Activity recognition is a challenging problem for context-aware systems and applications. Many studies in this field has mainly adopted techniques based on supervised or semi-supervised learning algorithms to recognize activities by movement patterns gathered through sensors, but these existing systems suffer from complex issues for feature representations of sensor data and multi-sensors integration. In this paper, we propose a novel feature learning method for activity recognition based on entropy and construct an activity recognition model with multi-class AdaBoost algorithm. Experiments on sensor data from a real dataset demonstrate the significant potential of our method to extract features for activity recognition. The experimental results also show recognition model based on multi-class AdaBoost is effective. The average precision and recall for six activities are 95.9% and 95.9%, respectively, which are higher than results obtained by using other methods such as Support Vector Machine (SVM) or K-Nearest Neighbor (KNN).

• Periodical: Applied Mechanics and Materials (Volumes 571-572)
• Pages: 1019-1029
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.571-572.1019
• Online since: June 2014
• Authors: De Feng Guo*, Bin Liu, Xiao Tian Jin, Hong Jian Liu
• Keywords: Activity Recognition, Feature Learning, Multi-Class AdaBoost, Smart Phone

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