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Assessment of Drivers' Workload: Performance and Subjective and Physiological Indexes
Published in Peter A. Hancock, Paula A. Desmond, Stress, Workload, and Fatigue, 2000
Karel A. Brookhuis, Dick de Waard
The final category of measures to be treated here for registering mental workload is the measurement of physiological parameters. Probably the most frequently applied measure in applied research is the electrocardiogram (ECG). The time between successive R-waves, the interbeat-interval time (IBI), as well as variability in the IBI are the prime measures. Variability in heart rate (HRV) can be computed in the time domain and is standardized by dividing the standard deviation of the IBI by the average IBI. HRV can also be analyzed in the frequency domain. When frequency analyses are performed on the IBI, the signal is decomposed into components that can be associated with biological mechanisms (Kramer, 1991). A frequency band that has been identified as sensitive to mental effort is the window between 0.07 and 0.14 hertz (the "0.10 Hz component" related to fluctuations in blood pressure), confusingly referred to as both low-frequency band (Berntson et al., 1997) and mid-frequency band (Mulder, 1992). IBI (or heart rate) has been found to be generally sensitive to both driver alertness level and computational effort, whereas the 0.10 hertz component is not sensitive to compensatory effort but exclusively to computational effort (De Waard & Brookhuis, 1997; Wiethoff, 1997). Although self-reports usually (and best) are collected after completion of a task, registration of physiology during task performance can reveal changes during performance without task interruption. Examples of changes in heart rate and 0.10 hertz heart-rate variability during task performance are given in Figs. 2.5.1 and 2.5.2. In the figures, the average heart rate of 22 subjects is computed over 30-second intervals and is displayed in "steps" of 10 seconds. Data were taken from a simulator study in which subjects "drove" through different road environments
Comparison of outlier heartbeat identification and spectral transformation strategies for deriving heart rate variability indices for drivers at different stages of sleepiness
Published in Traffic Injury Prevention, 2018
Fabio Forcolin, Ruben Buendia, Stefan Candefjord, Johan Karlsson, Bengt Arne Sjöqvist, Anna Anund
HRV is the physiological phenomenon of the beat-to-beat temporal variation of the heart; that is, the period of time between consecutive heartbeats varies slightly even when the average heart rate (beats per minute) is steady. HRV indices are derived from the heart interbeat interval (IBI) signal. HRV indices contain information about the state of the autonomic nervous system, which conveys information about the alertness of a person. There are many different indices, which can be categorized into different signal information domains: Time domain—for example, standard deviation of interbeat intervals; frequency—for example, the power spectral density (PSD); geometry—for example, triangular index; and nonlinear—for example, the Poincaré plot.
Deriving heart rate variability indices from cardiac monitoring—An indicator of driver sleepiness
Published in Traffic Injury Prevention, 2019
Ruben Buendia, Fabio Forcolin, Johan Karlsson, Bengt Arne Sjöqvist, Anna Anund, Stefan Candefjord
Driver sleepiness is a major factor contributing to road crashes (Connor et al. 2002; Horne and Reyner 1995). There is a need for countermeasures to reduce the number of crashes caused by driver fatigue (Abe et al. 2010). This study focuses on physiological measurements for detecting sleepiness, more specifically on the relation between heart rate variability (HRV) and driver sleepiness. HRV is the physiological phenomenon of the beat-to-beat temporal variation of the heart. HRV indices are derived from the heart interbeat interval (IBI) signal (Forcolin et al. 2018).
Objective stress monitoring based on wearable sensors in everyday settings
Published in Journal of Medical Engineering & Technology, 2020
Hee Jeong Han, Sina Labbaf, Jessica L. Borelli, Nikil Dutt, Amir M. Rahmani
Time-domain variables of HRV show the amount of variability in measurements of the interbeat interval (IBI), which is the time period between successive heartbeats [19]. In the time-domain analysis, Table 1 shows several time-domain parameters that we focus on.