This paper describes SRCB-LUL team's unsupervised relapse detection system submitted to the 2nd E-Prevention Challenge (Psychotic and Non-Psychotic Relapse Detection using Wearable-Based Digital Phenotyping). In our system, a person identification task is added to make the feature extraction network better distinguish between different behavior patterns. Three different structures of the feature extraction network are adopted. Then, the extracted features are used to train an Elliptic Envelope model of each patient for anomaly detection. Finally, the feature extraction network with the highest score was selected through the parameter experiment on the validation dataset. In the test dataset, we ranked 3rd in the 2nd track of the challenge with an average AUC score of 0.4964.