Prediction of the Ability to Develop the Relaxation Skill in Drivers of Locomotive Crews

N. V. Shcherbina
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Abstract

The effectiveness of biofeedback training for the development of relaxation skills using the NeuroDog hardware-software complex is investigated. The relaxation skill is one of the conditions for maintaining the functional state of drivers and assistant drivers of locomotive crews at an optimal level during a working trip, and it is also one of the factors for a successful inter-trip rest. A multiple regression analysis has been carried out, which allows to predict the indicators characterizing the success of developing the relaxation skill, depending on the severity of psychophysiological and personal indicators of drivers and assistant drivers of locomotive crews. Two regression models have been constructed to explain the dependence of the indicators of developing the skill of achieving relaxation on the psychophysiological and personal characteristics of drivers and assistant drivers of locomotive crews. Models are characterized by sufficient quality. Quality indicators: of the first regression model – coefficient of determination R2 = 0.71 (with F(40, 65) = 4.06, p = 0.00000027), Durbin-Watson statistics d = 1.81, of the second regression model – R2 = 0.75 (with F(49, 56) = 3.51, p = 0.0000043), d = 2.09. To predict the indicators of developing the skill of achieving relaxation, regression equations were obtained depending on the severity of psychophysiological and personal indicators of locomotive crew drivers.
机车车组驾驶员放松技能培养能力的预测
使用NeuroDog硬件-软件复合体的生物反馈训练对放松技能发展的有效性进行了调查。放松技巧是使机车乘员驾驶员和副驾驶员在工作行程中保持最佳功能状态的条件之一,也是确保车组间休息成功的因素之一。通过多元回归分析,可以根据机车乘务员驾驶员和辅助驾驶员的心理生理和个人指标的严重程度,预测表征放松技巧发展成功的指标。建立了两个回归模型来解释机车乘员驾驶员和辅助驾驶员的心理生理和个人特征对放松技能发展指标的依赖关系。模型的特点是有足够的质量。质量指标:第一个回归模型-决定系数R2 = 0.71(其中F(40,65) = 4.06, p = 0.00000027), Durbin-Watson统计量d = 1.81;第二个回归模型- R2 = 0.75(其中F(49,56) = 3.51, p = 0.0000043), d = 2.09。为了预测机车乘务员放松技能发展的指标,根据机车乘务员的心理生理严重程度和个人指标建立了回归方程。
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