Human Activities Recognition in Android Smartphone Using Support Vector Machine

In this study, we designed and constructed a system to identify human actions using integrated sensors in smartphones. There are six actions that are selected for recognition include: walking, standing, sitting, lying down, up the stairs, down the stairs. In this system, Support Vector Machine (SVM) is used to classify and identify action. Collected data from sensors are analyzed for the classification model - the model file. The classification models are optimized to bring the best results for the identified human activity. After forming the classify model, the model will be integrated into the system to identify the human activities. Human activities recognition system is written on Windows and Android platforms and operate in real time. The accuracy of the system depends on selected features and the quality of the training model. On the Android system running on smartphone with 248 features achieve 89.59% accurate rate.