Real time logistics monitoring system of packages during transportation using decision tree combined with clustering method

With the development of logistics industry recently, the problem of package damage has become more serious. Nonstandard driving can make packages turn into abnormal states. This article presents a package state monitoring system with inertial measurement units (IMUs) which can be easily installed and can detect states of packages correctly. The system uses decision tree method and clustering method to classify states with different features like stable as normal states, while turnover, move, shake, and knock as abnormal states. The experiment results show that the system can distinguish different states of packages with different weights.