Biosignal Analysis to Assess Mental Stress in Automatic Driving of Trucks: Palmar Perspiration and Masseter Electromyography

Abstract

Nowadays insight into human-machine interaction is a critical topic with the large-scale development of intelligent vehicles. Biosignal analysis can provide a deeper understanding of driver behaviors that may indicate rationally practical use of the automatic technology. Therefore, this study concentrates on biosignal analysis to quantitatively evaluate mental stress of drivers during automatic driving of trucks, with vehicles set at a closed gap distance apart to reduce air resistance to save energy consumption. By application of two wearable sensor systems, a continuous measurement was realized for palmar perspiration and masseter electromyography, and a biosignal processing method was proposed to assess mental stress levels. In a driving simulator experiment, ten participants completed automatic driving with 4, 8, and 12 m gap distances from the preceding vehicle, and manual driving with about 25 m gap distance as a reference. It was found that mental stress significantly increased when the gap distances decreased, and an abrupt increase in mental stress of drivers was also observed accompanying a sudden change of the gap distance during automatic driving, which corresponded to significantly higher ride discomfort according to subjective reports.

Brake Operation and Palmar Perspiration Reflect Older Adult Drivers' Ability to Predict Hazards: Driving Simulation Research

“…In literature where PSR is an index, reports involving videos of a truck coming toward participants showed that PSR and sense of tension vary with the distance between their own car and the truck. Specifically, the closer the truck is to the participant, the higher these values become [36]. Previous research using similar driving simulation tests has reported that while a clear increase in PSR is observed in both hazard and hazard prediction scenes, the timing of brake operation and PSR and/or SPR responses in older adults with impaired cognitive function does not correspond with the average responses seen in healthy older adults [33].…”

A Critical Review of Proactive Detection of Driver Stress Levels Based on Multimodal Measurements

“…Stressful situations can affect this reaction by increasing muscle activity (Lundberg et al 1994), which can be measured using electromyography (EMG) signals. These signals are recorded from the drivers' shoulders (e.g., the trapezius muscle) or facial muscles (e.g., the masseter muscle) to analyze their stress levels (Healey and Picard 2005;Katsis et al 2011;Zheng et al 2015).…”

“…Statistical features such as mean, root-mean square (RMS), zero crossing rate (ZCR), and rootmean quad (RMQ) are extracted from the EMG signals to detect drivers' stress levels Soman et al 2014;Zheng et al 2015). (See Table 1.)…”

“…However, recording stress levels of drivers in real-time environments using intrusive physiological sensors may affect driving safety and cause vehicle crashes. For safety and ethical reasons, it is therefore very hard to evaluate drivers' stress levels in real-time driving situations (Zheng et al 2015). Today's advanced driving simulators have the advantage of well-designed visual, rotation, and sound systems that provide a highly realistic sensation that is close to a real driving feeling (Yamaguchi et al 2009).…”

EEG Based Classification of Long-Term Stress Using Psychological Labeling

“…Psychological methods alone are not enough to assess stress-related conditions [10]. Stress can be quantified objectively from bio-markers like electroencephalography (EEG), galvanic skin response, and electrocardiography [11]. Recently, wearable systems were developed that can record electro-physiological signals (such as EEG and heart rate variability) to detect acute stress [12].…”