In Journal of affective disorders ; h5-index 79.0
BACKGROUND : The psychological and emotional problems of drug users are a focus of research. However, quick and effective emotion assessment tools were scarce. We aimed to use facial expression recognition to assess the emotional states of drug users.
METHODS : Our study was conducted in Chengdu City, Sichuan Province, China from January 1, 2020 to June 30, 2020. The 69 drug users who were undergoing community-based treatment were recruited. We developed an app to collect their images and videos, and trained the deep learning model to assess their emotional states. We also explored the correlation between emotional changes and treatment time, and investigated the impact factors associated with emotional changes.
RESULTS : Based on the continuous 6-month follow-up study, the emotional distribution of drug users was still dominated by negative emotions during community treatment (72.85%). Nevertheless, with the increase of treatment time, 17.39% of drug users' emotions were changing better. Results also showed that compared with the females, males were less likely to have positive emotion change. In addition, the females were more willing to read reply messages from social workers.
LIMITATIONS : The relatively short observation period could be extended, and voice data should be considered for analysis in the future.
CONCLUSIONS : Social workers should pay attention to emotional states of drug users, and provide effective and gender-specific psychological interventions for them. In addition, as a more powerful "medicine", it is essential to strengthen the accessibility of humanistic care and services to help drug users maintain a positive attitude.
Li Yongjie, Yan Xiangyu, Wang Zekun, Zhang Bo, Jia Zhongwei
Affective computing, Convolutional neural networks, Drug users, Facial expression recognition, Machine learning, Negative emotions