Personalized Activity Recognition Using Molecular Complex Detection Clustering

Human activity recognition is widely used in medical rehabilitation, self-management system and social network. In recent years, with the rise of smartphone and the development of sensor technology, mobile devices with embedded sensors become an important source of data collection. There are many studies use dataset collected from tri-axial accelerometer. In the study of activity recognition, MCODE algorithms are applied on accelerometer data for data analysis and processing, and experimental results show the effectiveness of the method.