Fadiga e sonolência em aviadores: análise de variações da voz, fala e linguagem

• Autor(a) principal: Carla Aparecida de Vasconcelos
• Data de Publicação: 2019
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Repositório Institucional da UFMG
• Texto Completo: http://hdl.handle.net/1843/33969
• topic: Neurociências; Fadiga; Fadiga mental; Sonolência; Qualidade da voz; Acústica da fala; Medida da produção da fala; Medicina; Fatigue; Mental fatigue; Sleepiness; Speech acoustics; Speech production measurement; Neurociências; Fadiga; Fadiga mental; Sonolência; Qualidade da voz; Acústica da fala; Medida da produção da fala; Medicina aeroespacial

Central human fatigue and sleepiness have aroused interest in aviation area worldwide. This is due to the number of accidents and the expressive involvement of human factors among the causes. In Brazil, according to CENIPA (Center for Research and Prevention of Aeronautical Accidents), the rate is 1 accident every 2 days and 90% are caused by human factors. According to NASA (National Aeronautics and Space Administration), fatigue/sleepiness would contribute to approximately 20% of air crashes in the world. But despite the risks that fatigue and sleepiness add to the safety, only 19 Brazilian aeronautical occurrences presented them as contributing factors. This is due to the absence of a methodology for detecting these signals/symptoms. In this sense, the objective of this study was to develop a method for detecting aviators human fatigue and sleepiness based on acoustic correlates of voice, speech and language. To this end, this research was subdivided into 5 substudies. In the first, speech samples from pilots complaining/suspected of fatigue/sleepiness were compared to a control group. The results were also compared to the Fatigue Avoidance Scheduling Tool (FAST). From the second to the fourth, the researchers analyzed 3 real cases of accident, with evidence of fatigue/sleepiness as contributing factors in 2 of these, and speech samples of pilots recorded before the accident were compared with those recorded during the crash. In the fifth, the aviators were screened through four fatigue/sleepiness scales (Karolinska Sleepiness Scale - KSS; Epworth Sleepiness Scale - ESS, Samn-Perelli Fatigue Scale - SPFS and Yoshitake Fatigue Scale - YFS) and speech evaluation was performed in two situations: on a day off when they were not complaining about sleepiness/fatigue and during a working day in which they were fatigued/sleepy. The data from the scales were statistically analyzed using the Friedman test (KSS and SPFS) and Wilcoxon test (ESS and YFS). It was observed that fatigue and sleepiness increased on the working day. For speech analysis, the paired GLM (General Linear Model) was used. Nine variables were extracted from speech: elocution rate, mean pause duration, total pause rate, fluent pause rate, disfluent pause rate, disfluent silent pause rate, disfluent filled pause rate, articulation rate and total silent pause rate. The first seven showed significant variation over time, when participants showed increased fatigue and sleepiness indexes. In addition, PCA (Principal Component Analysis) was applied to reduce the extracted variables to four. It was also found that it is possible to use Linear Discriminant Analysis (LDA) to group individuals and classify new cases (with or without fatigue and sleepiness) based on a database built for this purpose. There was quantitative and qualitative variation of voice, speech and language in the 2 out of the 3 cases where the accident occurred in the presence of signals of fatigue/sleepiness. In the first substudy, statistical and qualitative variations were also observed between the control group and the group with complaints. Through these studies, we found that the acoustic and perceptive parameters of voice, speech and language analyzed here are sufficiently robust to detect central fatigue and sleepiness.