标杆管理
惯性测量装置
工程类
方向盘
噪音(视频)
高级驾驶员辅助系统
持续监测
计算机科学
加速度计
汽车工程
模拟
安全监测
实时计算
车辆动力学
信号(编程语言)
远程病人监护
驾驶模拟器
主动安全
光容积图
控制工程
可穿戴计算机
心跳
生命体征
结构健康监测
嵌入式系统
功能安全
智能传感器
可视化
作者
Branko Babušiak,Maroš Šmondrk,Lubomir Trpis,Tomáš Gajdošík,Rudolf Mádaj,Igor Gajdáč
出处
期刊:Sensors
[Multidisciplinary Digital Publishing Institute]
日期:2026-01-11
卷期号:26 (2): 477-477
被引量:6
摘要
Drowsy driving and sudden medical emergencies are major contributors to traffic accidents, necessitating continuous, non-intrusive driver monitoring. Since current technologies often struggle to balance accuracy with practicality, this study presents the design, fabrication, and validation of a smart steering wheel prototype. The device integrates dry-contact electrocardiogram (ECG), photoplethysmography (PPG), and inertial sensors to facilitate multimodal physiological monitoring. The system underwent a two-stage evaluation involving a single participant: laboratory validation benchmarking acquired signals against medical-grade equipment, followed by real-world testing in a custom electric research vehicle to assess performance under dynamic conditions. Laboratory results demonstrated that the prototype captured high-quality signals suitable for reliable heart rate variability analysis. Furthermore, on-road evaluation confirmed the system's operational functionality; despite increased noise from motion artifacts, the ECG signal remained sufficiently robust for continuous R-peak detection. These findings confirm that the multimodal smart steering wheel is a feasible solution for unobtrusive driver monitoring. This integrated platform provides a solid foundation for developing sophisticated machine-learning algorithms to enhance road safety by predicting fatigue and detecting adverse health events.
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