A deep learning based method for intelligent detection of seafarers' mental health condition

Abstract

Abstract Mental health monitoring of seafarers is an important part of achieving normal development of the ocean shipping industry. In this paper, a dual subjective–objective testing scheme is proposed to achieve a more effective and intelligent assessment of seafarers' mental health status. Firstly, a new seafarers' mental health test scale (SMHT) is revised based on fuzzy factor analysis and the test data of 283 marine practitioners are analyzed using SPSS v24 software; secondly, this paper proposes an intelligent framework module for immersive subjective emotion extraction based on natural language processing, namely semantic summary extraction (SSE), speech emotion extraction (SEE), using hybrid scoring mechanism to obtain semantic and emotion matching values and assist the seafarer mental health scale to obtain the final correction score. The results showed that the assessment results of the SMHT scale exhibited good reliability (Cronbach's alpha of 0.852 $$\in (0.80{-}0.90)$$ ∈ ( 0.80 - 0.90 ) and retest reliability R of $$0.873\in (0.85{-}0.90)$$ 0.873 ∈ ( 0.85 - 0.90 ) ) and scale association validity (for SCL-90, ( $$\text{r}= 0.468{-}0.841)>0.45$$ r = 0.468 - 0.841 ) > 0.45 ). In addition, the calibration rate of the subject-object dual test method was improved by approximately 12.05% compared to the traditional mental health scale. Finally, the advantages and disadvantages of this solution were compared with mental health testing techniques such as CAT, machine learning, SCL-90, and fMRI, and the method demonstrated more accurate psychological testing results, providing a simple and intelligent solution for standardized psychological testing of seafarers.