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Sriranga, A.K.; Lu, Q.; Birrell, S. Physiological Mental Workload Indicators. Encyclopedia. Available online: https://encyclopedia.pub/entry/44422 (accessed on 15 April 2024).
Sriranga AK, Lu Q, Birrell S. Physiological Mental Workload Indicators. Encyclopedia. Available at: https://encyclopedia.pub/entry/44422. Accessed April 15, 2024.
Sriranga, Ashwini Kanakapura, Qian Lu, Stewart Birrell. "Physiological Mental Workload Indicators" Encyclopedia, https://encyclopedia.pub/entry/44422 (accessed April 15, 2024).
Sriranga, A.K., Lu, Q., & Birrell, S. (2023, May 17). Physiological Mental Workload Indicators. In Encyclopedia. https://encyclopedia.pub/entry/44422
Sriranga, Ashwini Kanakapura, et al. "Physiological Mental Workload Indicators." Encyclopedia. Web. 17 May, 2023.
Physiological Mental Workload Indicators
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Mental workload (MWL), sometimes referred to as cognitive workload, is a dynamic concept that acquires different meanings depending on the outcome desired.  Physiological indicators of MWL have gained an immense amount of attention in several domains, considering their objective nature. The response of the human body to external sources of workloads can be effectively observed through physiological signal markers that are not heavily affected by subjective opinions. Overall, it can be considered an indirect measure that can be related to MWL and has a relatively quicker response to sudden shifts. Physiological processes that include heart activity, respiration, digestion, and sexual arousal are involuntarily regulated by the peripheral component of the autonomic nervous system. The autonomic nervous system has three distinct divisions, namely, the sympathetic (SNS), parasympathetic (PNS), and enteric. The activation of the SNS and PNS can be directly observed in HR and HRV variations. Some commonly used SNS- and PNS-related physiological indicators are heart rate (HR), heartrate variability (HRV), respiratory rate (RR), galvanic skin response (GSR), and electrodermal activity (EDA). Eye-related data such as pupil size are also a result of autonomic activity and have been an important component of MWL research, as visual and mental tasks are highly correlated. Previous research has suggested that an increase in the cognitive demand or an increase in the MWL can result in increased blood flow in the frontal cortex of the brain.

HR HRV respiration mental workload conditional automation beat

1. Cardiovascular Measures

Heart rate (HR) is a cardiac activity metric that corresponds to the number of heartbeats per unit time (per minute). HR is commonly derived from the electrocardiogram (ECG) signal, which represents the electrical activity of the heart. HR can also be retrieved from photoplethysmography (PPG) devices that use light and photodetectors to observe volumetric changes of the blood through human skin. In the task of mental workload assessment, the topic of interest would be the variation in the interval between successive heartbeats. Heartrate variability (HRV) is a term that generally refers to changes in the inter-beat interval (IBI). The heart is known to be supplied with nerves from the sympathetic and the parasympathetic branches of the autonomic nervous system (ANS). The two systems show opposing effects on the HR, where activation of sympathetic system increases the HR and activation of parasympathetic system decreases the HR [1]. Several initial studies that were been conducted as early as in the 1980s have concluded that changes in the MWL can be observed through changes in the cardiovascular responses [2].

Heartrate Variability (HRV)

The traditional method for measuring mental workload using HRV metrics uses the time domain and frequency domain components [3][4]. The time domain category comprises statistical and geometrical measures that determine the time variability between heartbeats. Time domain analysis through statistical methods is further classified into two categories: (a) Direct measurements of Normal-to-Normal (NN) intervals, and (b) Differences between NN intervals. The NN intervals can also be derived as a geometrical pattern that can be studied using different approaches: (1) basic measurement of the pattern converted into a HRV measure, (2) interpolation of the geometrical pattern, and (3) categorisation of the geometrical pattern into classes of HRV. The analysis of HRV in the frequency domain usually involves the study of the power spectral density (PSD), which provides information about how power distributes as a function of frequency in the HRV signal. The spectral analysis of HRV is based on the Fourier theory, where a signal can be represented as the sum of sinusoidal signals consisting of amplitude, phase, and frequency components. The literature has confirmed that the spectral components of HRV can be categorised into very low frequency (VLF), low frequency (LF), high frequency (HF), and ultra-low frequency (ULF) components in short-term and long-term readings [5]. Short-term readings generally consider 2–5 min of HRV data, whereas long-term readings consider HRV data up to 24 h. The time and frequency domain components of HRV are summarised in Table 1.

2. Respiratory Measures

The biggest oscillator in the human body that is involved in the maintenance of homeostasis is the phenomenon of respiration. It is well known that respiratory activation additionally influences psychological and behavioural processes along with metabolic changes [6]. RR also exhibits a close relationship with HR, as the coupling between them can provide information to be used as an index to study the vagal control of the heart [7]. Several studies have indicated that the respiratory rate (RR) is affected by emotional and cognitive demands reflecting limbic and paralimbic influences [8][9]. The psychophysiology effects of respiration are generally studied based on measures related to time, volume, and gas exchange [10][11]. The components and features of the RR are generally analysed using time, volume, and spectral parameters. The most popularly used features are the respiratory rate, inspiratory time, expiratory time, timing ratio, tidal volume, minute ventilation, and spectral power. A summary of RR features has been presented in Table 2.

3. Other Physiological Measures

Mental workload (MWL) and driver distraction are often related to each other, such that the same secondary task used to measure distraction can be used to measure MWL [12]. As the trend towards in-vehicle infotainment systems has modernised vehicles, distractions are often induced visually or cognitively [13]. Eye-related measures such as blink frequency, blink duration, and pupil dilation are recorded using head-mounted gear that cannot be used outside experimental conditions, as it causes discomfort. Further, if one opts for optical instruments such as cameras to gather eye-related data, multiple installations may be required, considering head movement and occluded situations. EDA (Electrodermal Activity) is a collective term used to define the bio-electrical changes that occur in the skin, which is one of the most useful indices of the sympathetic activity impacting sweat gland activity.
The functional concepts of EDA closely relate to psychophysiological activity, which makes it an important objective measure for understanding arousal, attention, and emotional responses. EDA is classified into two categories: skin conductance level (SCL) and skin conductance responses (SCR). SCR data are obtained through the placement of electrodes in positions such as below the distal phalanx of the index and middle finger. Similarly to with eye-related measures, the signal acquisition of the EDA components might become problematic, as data are generally affected by noise artifacts induced by driving movement.

References

  1. Backs, R.W.; Lenneman, J.K.; Wetzel, J.M.; Green, P. Cardiac Measures of Driver Workload during Simulated Driving with and without Visual Occlusion. Hum. Factors J. Hum. Factors Ergon. Soc. 2003, 45, 525–538.
  2. Hidalgo-Muñoz, A.R.; Béquet, A.J.; Astier-Juvenon, M.; Pépin, G.; Fort, A.; Jallais, C.; Tattegrain, H.; Gabaude, C. Respiration and Heart Rate Modulation Due to Competing Cognitive Tasks While Driving. Front. Hum. Neurosci. 2019, 12, 525.
  3. Henelius, A.; Hirvonen, K.; Holm, A.; Korpela, J.; Muller, K. Mental Workload Classification Using Heart Rate Metrics. In Proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, USA, 2–6 September 2009; pp. 1836–1839.
  4. Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology, Heart Rate Variability. Circulation 1996, 93, 1043–1065.
  5. Li, K.; Rüdiger, H.; Ziemssen, T. Spectral Analysis of Heart Rate Variability: Time Window Matters. Front. Neurol. 2019, 10, 545.
  6. Wientjes, C.J.E.; Grossman, P.; Gaillard, A.W.K. Influence of Drive and Timing Mechanisms on Breathing Pattern and Ventilation during Mental Task Performance. Biol. Psychol. 1998, 49, 53–70.
  7. Evans, K.C.; Dougherty, D.D.; Schmid, A.M.; Scannell, E.; McCallister, A.; Benson, H.; Dusek, J.A.; Lazar, S.W. Modulation of Spontaneous Breathing via Limbic/Paralimbic–Bulbar Circuitry: An Event-Related FMRI Study. NeuroImage 2009, 47, 961–971.
  8. Homma, I.; Masaoka, Y. Breathing Rhythms and Emotions: Breathing and Emotion. Exp. Physiol. 2008, 93, 1011–1021.
  9. Grassmann, M.; Vlemincx, E.; von Leupoldt, A.; Mittelstädt, J.M.; Van den Bergh, O. Respiratory Changes in Response to Cognitive Load: A Systematic Review. Neural Plast. 2016, 2016, 1–16.
  10. Vlemincx, E.; Van Diest, I.; Van den Bergh, O. A Sigh Following Sustained Attention and Mental Stress: Effects on Respiratory Variability. Physiol. Behav. 2012, 107, 1–6.
  11. Yasuma, F.; Hayano, J. Respiratory Sinus Arrhythmia. Chest 2004, 125, 683–690.
  12. Mehler, B.; Reimer, B.; Zec, M. Defining Workload in the Context of Driver State Detection and HMI Evaluation. In Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications—AutomotiveUI ’12; ACM Press: Portsmouth, New Hampshire, 2012; p. 187.
  13. Phansalkar, S.; Edworthy, J.; Hellier, E.; Seger, D.L.; Schedlbauer, A.; Avery, A.J.; Bates, D.W. A Review of Human Factors Principles for the Design and Implementation of Medication Safety Alerts in Clinical Information Systems. J. Am. Med. Inform. Assoc. 2010, 17, 493–501.
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