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Detecting driver’s fatigue, distraction and activity using a non-intrusive ai-based monitoring system

Costa M.

Journal of Artificial Intelligence and Soft Computing Research

2019

Vol. 9, No. 4

247--266

The lack of attention during the driving task is considered as a major risk factor for fatal road accidents around the world. Despite the ever-growing trend for autonomous driving which promises to bring greater road-safety benefits, the fact is today’s vehicles still only feature partial and conditional automation, demanding frequent driver action. Moreover, the monotony of such a scenario may induce fatigue or distraction, reducing driver awareness and impairing the regain of the vehicle’s control. To address this challenge, we introduce a non-intrusive system to monitor the driver in terms of fatigue, distraction, and activity. The proposed system explores state-of-the-art sensors, as well as machine learning algorithms for data extraction and modeling. In the domain of fatigue supervision, we propose a feature set that considers the vehicle’s automation level. In terms of distraction assessment, the contributions concern (i) a holistic system that covers the full range of driver distraction types and (ii) a monitoring unit that predicts the driver activity causing the faulty behavior. By comparing the performance of Support Vector Machines against Decision Trees, conducted experiments indicated that our system can predict the driver’s state with an accuracy ranging from 89% to 93%.

Assessment of isometric strength of the shoulder rotators in swimmers using a handheld dynamometer : a reliability study

Coinceicao A., Parraca J., Marinho D., Costa M., Louro H., Silva A., Batalha N.

Acta of Bioengineering and Biomechanics

2018

Vol. 20, no. 4

113--119

The purpose of this study was to determine the reliability of shoulder isometric strength assessment using the microfet 2™ dynamometer in adolescent swimmers. Methods: Twenty-nine participants (16.2 ± 1.2 years old; 59.05 ± 6.98 kg of body mass) were tested using the microfet 2™ dynamometer. Swimmers performed an isometric strength test (IST) in two distinct occasions with 7 days apart in order to calculate the reliability. All participants were asked to perform a maximal isometric contraction from the external and internal shoulder rotators in a prone body position. Results: The external and internal shoulder rotators showed an excellent intraclass correlation coefficients for both shoulders, with more than 0.90 and a low percentage of method error variation. The external/internal ratios reliability was good in dominant (ICC 0.80) and non-dominant (ICC 0.81) shoulders. The reliability using Bland–Altman method showed that systematic errors (mean difference between test-retest) were nearly zero and the 95% limits of agreement narrow, indicating a good reliability. Conclusions: It can be concluded that microfet 2™ is a reliable apparatus for measuring the strength of the external and internal rotation of the shoulder in swimmers. Its light weight and easy portable characteristics can help swimming coaches monitoring specific dry-land strength training programs for their swimmers.