Recognition of driver’s mental workload based on physiological signals, a comparative study

Huang, Jing; Liu, Yu; Peng, Xiaoyan

doi:10.1016/j.bspc.2021.103094

Cited by 28 publications

(22 citation statements)

References 30 publications

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“…Moreover, the use of biofeedback has proven results in the medical area with applications in the control of HRV showing that through external excitations such as harmonic sounds it is possible to improve HRV, and thus the homeostasis or self-regulatory process of the organism [ 43 ], in this solution HRV and biofeedback are integrated to generate self-control of mental stress in drivers [ 3 ].…”

Section: Related Workmentioning

confidence: 99%

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Heart Rate Variability Control Using a Biofeedback and Wearable System

Viera

Kaschel

Valencia

2022

Sensors

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Heart rate variability is an important physiological parameter in medicine. This parameter is used as an indicator of physiological and psychological well-being and even of certain pathologies. Research on biofeedback integrates the fields of biological application (physiological behavior), system modeling, and automated control. This study proposes a new method for modeling and controlling heart rate variability as heart rate acceleration, a model expressed in the frequency domain. The model is obtained from excitation and response signals from heart rate variability, which through the instrumental variables method and the minimization of a cost function delivers a transfer function that represents the physiological phenomenon. This study also proposes the design of an adaptive controller using the reference model. The controller controls heart rate variability based on the light actuators designed here, generating a conditioned reflex that allows individuals to self-regulate their state through biofeedback, synchronizing this action to homeostasis. Modeling is conducted in a target population of middle-aged men who work as firefighters and forest firefighters. This study validates the proposed model, as well as the design of the controllers and actuators, through a simple experiment based on indoor cycling. This experiment has different segments, namely leaving inertia, non-controlled segment, and actively controlled segment.

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Section: Related Workmentioning

confidence: 99%

“…The use of HRV is widespread and is used in areas of medicine for the detection and recognition of stress [ 3 , 4 ]; cardiovascular health [ 5 ]; insomnia [ 6 ], and autoimmune diseases, such as ulcerative colitis [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Heart Rate Variability Control Using a Biofeedback and Wearable System

Viera

Kaschel

Valencia

2022

Sensors

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“…[37,38]. The measurement methods of the driver's mental workload include subjective evaluation method, driving task performance method, physiological parameter evaluation method and so on [31,33,[39][40][41]. Studies have found that when a person's mental load changes, the related physiological indicators will also change, so the physiological parameter evaluation method can provide real-time, objective and highly sensitive evaluation of mental load [31,42,43].…”

Section: Figure 1 the Four Components Of The Seev Modelmentioning

confidence: 99%

“…Since divers' fixation behaviours are generally considered to be effective to reflect the collection of road traffic information and identify the driver's intention., and the blinking behaviour and ECG of drivers are often used to evaluate driving fatigue and driving load [31,39,[40][41][42], the attention allocation characteristics is measured by fixation duration, the number of fixations and the transferring of it from one interest of area to another, and the mental workload is determined using the blink duration, the growth rate of heart rate and the RMSSD, a time domain indicator of HRV in this study. Table 3 presents the description of all these dependent variables.…”

Section: E Indicator Selection and Data Extractionmentioning

confidence: 99%

“…Many research results show that the blink behavior of drivers' eye movement and the heart rate growth rate and heart rate variability (heart rate variability, HRV) of the ECG signal are effective indicators in the driver's mental workload [31,33,41,[57][58][59].So, the blink duration of the eye movement, the heart rate growth rate (HRGR) and the RMSSD in the HRV time domain index are selected to analyze the change of drivers' mental workload at intersections of prairie highways with different shapes and priority of way.…”

Section: B the Mental Workload Of Drivers At Intersectionsmentioning

confidence: 99%

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The Visual Scanning Behavior and Mental Workload of Drivers at Prairie Highway Intersections With Different Characteristics

Lyu

Zhu

et al. 2022

IEEE Access

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Highway intersections are crash prone locations, and drivers' improper attention allocation and sudden increase of mental workload are main contributing factors. To explore the visual scanning characteristics and mental workload of drivers at prairie highway intersections with different characteristics, an on-road driving test was taken at 6 intersections scattered on a typical prairie highway with 3 different shapes and 2 different priority rules, and drivers' eye movement and ECG data were collected. The results show that there is significant difference in fixation allocation and transferring among intersections with different shapes and priority rules. The X-shaped intersection without the right of way shows the longest average fixation duration and the most gaze shifts between the drivers' lane and the intersection road, whereas the three-way intersection with the right of way shows the shortest fixation duration and the gaze transfer path is relatively simple and the transfers are less. The mental workload implies almost the same conclusion, with the X-shaped intersection without right of way shows the highest mental workload which is manifested by largest heart rate growth rate and the lowest RMSSD, whereas the three-way with priority of way shows the opposite trend. This study revealed that both intersection types and priority rule made differences in drivers' scanning behavior and mental workload at prairie highway intersections, which can support the development of appropriate countermeasures regarding the applications in advanced driver assistance systems and the design of intersection constructions to warn drivers about potential critical events at different types of intersections.

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Real‐Time Non‐Driving Behavior Recognition Using Deep Learning‐Assisted Triboelectric Sensors in Conditionally Automated Driving

Zhang

Tan

Wang

et al. 2022

Adv Funct Materials

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Real-time recognition of non-driving behaviors is of great importance in conditionally automated driving, as it determines the takeover time budget, which in turn has a huge impact on the performance of the takeover. Here, a novel real-time non-driving behavior recognition system (RNBRS) integrating self-powered, low-cost, easy-to-manufacture triboelectric sensors, and a deep learning model is proposed. The structure, working mechanism, and electrical characteristics of triboelectric sensors are investigated and analyzed. Through the ingenious structural design of single-electrode triboelectric sensors and driving simulation experiments under conditional automated driving, nondriving behaviors are captured in the form of electrical signals. A well-trained long short-term memory network model is adopted to recognize the five most typical non-driving behaviors, including phone, console touchpad, driving, monitoring driving, and no operation, and test accuracy of 93.5% is achieved. Demonstration of a set of controlled experiments shows that RNBRS enables vehicles with conditional automation to dynamically adjust takeover time budget based on driver behavior, therefore significantly improving both safety and stability of takeover. This study opens new frontiers for the development of self-powered electronics and inspires new thoughts on human-machine interaction and the safety of autonomous vehicles.

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Recognition of driver’s mental workload based on physiological signals, a comparative study

Cited by 28 publications

References 30 publications

Heart Rate Variability Control Using a Biofeedback and Wearable System

Heart Rate Variability Control Using a Biofeedback and Wearable System

The Visual Scanning Behavior and Mental Workload of Drivers at Prairie Highway Intersections With Different Characteristics

Real‐Time Non‐Driving Behavior Recognition Using Deep Learning‐Assisted Triboelectric Sensors in Conditionally Automated Driving

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