Jain Sparsh, Perez Miguel A
Division of Data and Analytics, Virginia Tech Transportation Institute, 3500 Transportation Research Plaza, Blacksburg, VA 24061, USA.
Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA 24061, USA.
Sensors (Basel). 2025 Apr 26;25(9):2739. doi: 10.3390/s25092739.
In-vehicle physiological sensing is emerging as a vital approach to enhancing driver monitoring and overall automotive safety. This pilot study explores the feasibility of a pressure-based system, repurposing commonplace occupant classification electronics to capture respiration signals during real-world driving. Data were collected from a driver-seat-embedded, fluid-filled pressure bladder sensor during normal on-road driving. The sensor output was processed using simple filtering techniques to isolate low-amplitude respiratory signals from substantial background noise and motion artifacts. The experimental results indicate that the system reliably detects the respiration rate despite the dynamic environment, achieving a mean absolute error of 1.5 breaths per minute with a standard deviation of 1.87 breaths per minute (9.2% of the mean true respiration rate), thereby bridging the gap between controlled laboratory tests and real-world automotive deployment. These findings support the potential integration of unobtrusive physiological monitoring into driver state monitoring systems, which can aid in the early detection of fatigue and impairment, enhance post-crash triage through timely vital sign transmission, and extend to monitoring other vehicle occupants. This study contributes to the development of robust and cost-effective in-cabin sensor systems that have the potential to improve road safety and health monitoring in automotive settings.
车内生理传感正成为增强驾驶员监测及整体汽车安全的重要方法。这项初步研究探讨了基于压力的系统的可行性,该系统将常见的乘客分类电子设备重新用于在实际驾驶过程中捕捉呼吸信号。在正常道路驾驶期间,从嵌入驾驶座的充液压力气囊传感器收集数据。使用简单的滤波技术对传感器输出进行处理,以从大量背景噪声和运动伪影中分离出低幅度呼吸信号。实验结果表明,尽管环境动态变化,该系统仍能可靠地检测呼吸频率,平均绝对误差为每分钟1.5次呼吸,标准差为每分钟1.87次呼吸(占平均真实呼吸频率的9.2%),从而弥合了受控实验室测试与实际汽车应用之间的差距。这些发现支持将非侵入性生理监测潜在整合到驾驶员状态监测系统中,这有助于早期发现疲劳和损伤,通过及时传输生命体征增强碰撞后分诊,并扩展到监测其他车内乘客。本研究有助于开发强大且经济高效的车内传感器系统,这些系统有可能改善汽车环境中的道路安全和健康监测。