Estimation of Worker Stress Considering Differences in Listening Tempo

Abstract: In recent years, a stressful society wherein various stresses accumulate only by living has become a problem. This accumulation adversely affects our physical and mental health. In particular, psychosocial stress is considered to be related to the onset and pathology of lifestyle-related diseases such as diabetes and myocardial infarction. Recently, there has been a growing interest in the problems of increased daily mental stress and decreased productivity. Many studies have been conducted with the aim of alleviating mental stress and improving productivity. In particular, it is acknowledged that listening to music during work improves concentration and facilitates work. In addition, as the term "music therapy" indicates, it has been reported that listening to music can relieve mental stress. To further verify this effect, research is being conducted actively using various types of music. Furthermore, analysis is being performed based on a number of classification methods, such as classification based on genre, by instrument, and presence or absence of lyrics. However, the music used as representative music for these classifications also varies, and consistent observations have not been obtained. In addition, individual differences significantly impact the effectiveness of music listening. This problem has prevented the acquisition of consistent observations. Solving the above two problems, (complexity of classification and individual differences, will be very) is highly important for gaining new knowledge regarding music therapy and, beyond that, in relieving daily stress and improving productivity. Machine learning classification is effective in addressing the complexity of music. The structure of music has rules such as musical scales, chord structures, and chord progressions based on music theory. It is considered feasible to analyze the structure using methods such as machine learning. In addition, incorporating biometric information into the parameters is considered effective for solving the problem of individual differences. A previous study of emotion induction using music reported that the accuracy of the linear regression model was exceeded by incorporating EEG information into the parameters of the generative model. Therefore, in this study, we utilize "machine learning" (which is good at classification) and personalization by "biometric feedback" to construct a personalized music generation model for alleviating mental stress and improving productivity. The target tasks are VDT tasks. The demand for these has been increasing recently with the introduction of work-at-home, IoT, and DX, etc. VDT tasks are tasks performed on a PC or other devices on a display Although these enable working without time or place constraints, these are considered to be a cause of the progression of a stressful society because individuals cannot leave their work. Listening to music generated by the model constructed in this study is likely to alleviate daily mental stress and improve productivity.

Keywords: Mental stress, Music therapy, Productivity, Biometric measurements, Machine Learning

DOI: 10.54941/ahfe1004674

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