Abstract
This study demonstrates the feasibility of using a seat sensor designed for occupant classification from a production passenger vehicle to measure an occupant’s respiration rate (RR) and heart rate (HR) in a laboratory setting. Relaying occupant vital signs after a crash could improve emergency response by adding a direct measure of the occupant state to an Advanced Automatic Collision Notification (AACN) system. Data was collected from eleven participants with body weights ranging from 42 to 91 kg using a Ford Mustang passenger seat and seat sensor. Using a ballistocardiography (BCG) approach, the data was processed by time domain filtering and frequency domain analysis using the fast Fourier transform to yield RR and HR in a 1-min sliding window. Resting rates over the 30-min data collection and continuous RR and HR signals were compared to laboratory physiological instruments using the Bland-Altman approach. Differences between the seat sensor and reference sensor were within 5 breaths per minute for resting RR and within 15 beats per minute for resting HR. The time series comparisons for RR and HR were promising with the frequency analysis technique outperforming the peak detection technique. However, future work is necessary for more accurate and reliable real-time monitoring of RR and HR outside the laboratory setting.
Highlights
Advanced Automatic Collision Notification (AACN) systems have great potential for reducing mortality risk in car crashes
The purpose of this study was to assess the feasibility of seat sensors in production passenger vehicles to classify an occupant according to weight, but to monitor occupant respiration rate (RR) and heart rate (HR)
While the RR and HR calculated from the seat sensor fluctuate more than the reference rates, they generally agree with the expected values
Summary
Advanced Automatic Collision Notification (AACN) systems have great potential for reducing mortality risk in car crashes. AACN systems rely exclusively on vehicle-based measures of crash severity from which occupant crash response can only be estimated [1,2,3,4]. Non-invasive physiological monitoring of an occupant could prove extremely valuable in improving occupant safety for post-crash emergency response. Relaying vital signs of the occupants after a crash to the first responders, prior to arrival at the crash scene, could help prepare the appropriate response for transport as well as medical triage. Injury predictions could better prepare emergency room doctors for treatment of incoming crash victims. The purpose of this study was to assess the feasibility of seat sensors in production passenger vehicles to classify an occupant according to weight, but to monitor occupant respiration rate (RR) and heart rate (HR)
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