Human Activity Recognition Using Smart-Phone Sensors
De Feng Guo, Bin Liu, Xiao Tian Jin, Hong Jian Liu · Applied Mechanics and Materials · 2014
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).