A preliminary study for remote healthcare system: Activity classification for elder people with on body sensors
Özet
Development of intelligent care system for elder people have been investigated in recent years. In this study, to detect emergency situations for elder people, activity classification was aimed using on body sensor data. Multi-layer perceptron, radial basis function networks, k- nearest neighbor and support vector machines were used in classification. In feature selection process principal component analysis and ReliefF were used. Accuracy of classification was above 85% for every classifier and the best performance was acquired with 3-NN with 99.8% accuracy. When feature selection was applied 5- NN was showed the highest performance with 99.4%. This study shows that it is possible to develop remote care system by using sensors and classifiers for a more secure life for elder people. © 2018 IEEE.
