Research on the Intelligent Evaluation of Addiction Degree and New Method of Rehabilitation of Drug Addicts

Brain function of the drug addict will be changed after long-term use of drugs. The article designs a method for assessing the degree of addiction to physiological indicators based on the forehead close to infrared device. The experiment paradigm is designed to include three stages: the first stage is the resting state. The second stage is task stage where the picture contains the drug picture and the neutral pictures. The third stage, resting state is using the neutral pictures. Near-infrared data collection was done in three stages of this paradigm, and then classification based on the personal information of drug users and consultation with hospital experts to determine the mild, moderate and severe classification. First, a statistical analysis was conducted to find that there are indeed differences in the three types of addiction degree. Then, the Convolutional Neural Network (CNN) model was used to classify the data of 30 people, and the classification accuracy rate reached 77.04%, which verified that the distinguished method of the three types of addiction degree is completely reasonable, and the VR closed-loop sports imaging and rehabilitation training program was designed specifically to improve the self-control training of drug addicts through sports imaging and rehabilitation training.