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JLR Heart: Employing Wearable Technology in Non-Intrusive Driver State Monitoring. Preliminary Study

机译:JLR心:在非侵入式驾驶员状态监测中采用可穿戴技术。初步研究

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This paper presents the results from a preliminary study where a wearable consumer electronic device was used to assess driver's state by capturing human physiological response in non-intrusive manner. Majority of state of the art studies have employed medical equipment drivers' state evaluation. Despite the potential gain in road safety this method of measuring physiology is unlikely to be accepted by private vehicle consumers due to its invasiveness, complexity, and high cost. This study was aiming to investigate possibility of employing a consumer grade wearable device to measure physiological parameters related to cognitive workload in real-time while driving i.e., drivers' heart rate. Furthermore, validity of captured heart activity metrics was analyzed to determine if wearable devices could be embedded into driving at its current technological state. The driving context was reproduced in desktop driving simulator, with 14 participants agreeing to take part in the study (μ = 28, σ = 8.5 years). Drivers were exposed to various road types, including pure Motorway, Rural, and Urban scenario modes. An accident was simulated in order to generate sudden cognitive arousal and capture participants' physiological response to the generated distress. It was found that a smartwatch is capable of reliable heart activity tracking in driving context. The results, supporting the relationship between cognitive workload level, generated by various complexity driving tasks, and Heart Rate Variability, were also presented.
机译:本文介绍了一种初步研究的结果,其中可穿戴消费者电子设备通过以非侵入性方式捕获人的生理反应来评估驾驶员状态。大多数艺术研究都采用医疗设备司机的国家评估。尽管道路安全潜在的潜力,但由于其侵袭性,复杂性和高成本,私人车辆消费者不太可能接受这种测量生理学方法。本研究旨在调查使用消费级可穿戴设备的可能性来测量与驾驶I.,驾驶员心率的实时与认知工作量相关的生理参数。此外,分析了捕获的心脏活动度量的有效性,以确定是否可以在其当前的技术状态下嵌入可穿戴设备。在桌面驾驶模拟器中转载了驾驶背景,14名参与者同意参加研究(μ= 28,σ= 8.5岁)。司机暴露在各种道路类型,包括纯粹的高速公路,农村和城市情景模式。模拟事故以产生突然认知唤起令人认知的唤醒,并捕获对产生的痛苦的生理反应。发现SmartWatch能够在驾驶环境中进行可靠的心脏活动跟踪。还提出了支持通过各种复杂性驱动任务生成的认知工作量水平与心率变异性之间的关系。

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