Wearable Devices for the Assessment of Cognitive Effort for Human–Robot Interaction

Villani, Valeria; Righi, Massimiliano; Secchi, Cristian

doi:10.1109/jsen.2020.3001635

Cited by 33 publications

(19 citation statements)

References 45 publications

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“…Furthermore, the resistance of ECG and eye‐tracking technologies to environmental noise indicates that they are more suitable for noisy cockpits environment than other commonly used measures (EEG and fNIRS). With the emergence of nonobtrusive measurement techniques, such as video‐based eye‐tracking, helmet‐mounted ECG sensors, and wearable ECG recording devices (Kapitaniak et al, 2015; Rodrigues et al, 2018; Villani et al, 2020; N. Wilson et al, 2020), the invasiveness of ECG and ocular measurements have been greatly reduced, and their feasibility in complex operational environments such as aircraft cockpits have been boosted. In the future, certain online monitoring systems could be developed, such as commonly used biofeedback systems (Chen et al, 2002; Fitzharris et al, 2017; Freer, 2002), integrating mental fatigue detection training models based on HRV and eye metrics, as well as unobtrusive ECG and eye‐tracking devices to effectively monitor pilot fatigue.…”

Section: Discussionmentioning

confidence: 99%

“…Heart rate variability (HRV) is a metric derived from ECG signals and is commonly used for investigating autonomic nervous system activity (Forcolin et al, 2018; C. Zhang et al, 2008). Since HRV is easy to collect and analyze, it has been utilized in a variety of contexts, such as surgery, car driving, and robot systems, as a measurement of mental fatigue and workload (Forcolin et al, 2018; Patel et al, 2011; Pimentel et al, 2019; Villani et al, 2020; Zhao et al, 2020). In the aviation field, a prior work conducted by Cheng et al (2018) suggested that HRV indicators were capable of distinguishing pilots' normal functional states from the fatigue states induced by sleep deprivation, which indicated the potentiality of HRV in pilot fatigue detection.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, several studies suggested that HRV could reflect pilots' mental workload and stress level (Alaimo et al, 2020; Cao et al, 2019; Goffeng et al, 2019; Mansikka et al, 2016), both of which were known contributors to in‐flight mental fatigue (Dai et al, 2020; Caldwell et al, 2009). From the device aspect, the fast‐developing unobtrusive ECG sensors, such as wristbands, armbands, chest straps, and embedded sensors (Kranjec et al, 2014; Rodrigues et al, 2018; Villani et al, 2020; N. Wilson et al, 2020), could replace traditional recorder systems which use contact ECG electrodes, thus further enhancing HRV's feasibility in real‐life conditions.…”

Section: Introductionmentioning

confidence: 99%

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Detection of mental fatigue state using heart rate variability and eye metrics during simulated flight

Qin

Liu

et al. 2021

Hum Ftrs & Erg Mfg Svc

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Pilot mental fatigue is a growing concern in the aviation field due to its significant contributions to human errors and aviation accidents. Long work hours, sleep loss, circadian rhythm disruption, and workload are well‐known reasons, but there is a need to accurately detect pilot mental fatigue to improve aviation safety. However, due to the highly restricted cockpit environment and the complex nature of mental fatigue, feasible in‐flight detection remains under‐investigated. The objective of this study is to define a promising approach for mental fatigue detection based on psychophysiological measurements in flying‐relevant environments. Eleven participants engaged in a simulated flight experiment, where several conventional heart rate variability (HRV) and ocular indices were examined to study their relevance to mental fatigue. Additionally, a Toeplitz Inverse Covariance‐Based Clustering (TICC) method was performed to determine the ground truth, after which supervised machine learning was adopted to enable automated mental fatigue detection using HRV and eye metrics. Results showed that HRV and eye metrics were sensitive to the mental fatigue induced by prolonged flight‐relevant tasks. The TICC method helped determine the ground truth for mental fatigue and identify its three distinct levels. Furthermore, a supervised learning‐based detection of mental fatigue was achieved using a support vector machine with the greatest detection accuracy of 91.8%. The findings and methodology of this study provide new insights into the fatigue countermeasures in restricted cockpit environment and lay the groundwork for further explorations into the mental fatigue induced by prolonged flight missions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of mental fatigue state using heart rate variability and eye metrics during simulated flight

Qin

Liu

et al. 2021

Hum Ftrs & Erg Mfg Svc

View full text Add to dashboard Cite

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“…The selected was 3, meaning that the participant needs to check if the number currently indicated was repeated 3 mentions before [13]. We selected the -back task as it was previously used in several cognitive load tests with many variations [26], and can now be considered a baseline.…”

Section: Methodsmentioning

confidence: 99%

Measuring Cognitive Load: Heart-rate Variability and Pupillometry Assessment

Urrestilla

St-Onge

2020

Companion Publication of the 2020 International Conference on Multimodal Interaction

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“…The stimulation of the nervous system is reflected, among other factors, by an increased heart rate and stimulation of sweat glands. Therefore, the changes in heart rate, galvanic skin response and skin temperature can be used to evaluate stress and, in turn, satisfaction level [53].…”

Section: Evaluation Of Job Satisfactionmentioning

confidence: 99%

Worker satisfaction with adaptive automation and working conditions: a theoretical model and questionnaire as an assessment tool

Villani

Żołnierczyk-Zreda

Mockałło

et al. 2021

International Journal of Occupational Safety and Ergonomics

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This article focuses on methods and tools to measure worker satisfaction with reference to industrial automation. Despite technological advances in automation, the role of human workers on industrial shop floors remains crucial. To promote humans' roles, production systems should be designed and organized so workers are valued and get satisfactory jobs. The article presents a novel holistic model of worker satisfaction with adaptive automation and working conditions. The model takes into account psychosocial and physical working conditions and the characteristics of the automation system the worker interacts with and its user interface. We propose a questionnaire to be used as a practical tool to assess worker satisfaction with industrial automation, considering also the case of adaptive automation. The proposed version of the questionnaire is the result of pilot testing carried out among shop floor operators and takes into account adjustments derived from end-user feedback.

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Wearable Devices for the Assessment of Cognitive Effort for Human–Robot Interaction

Cited by 33 publications

References 45 publications

Detection of mental fatigue state using heart rate variability and eye metrics during simulated flight

Detection of mental fatigue state using heart rate variability and eye metrics during simulated flight

Measuring Cognitive Load: Heart-rate Variability and Pupillometry Assessment

Worker satisfaction with adaptive automation and working conditions: a theoretical model and questionnaire as an assessment tool

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