Assessing mental workload with wearable devices – Reliability and applicability of heart rate and motion measurements

Mach, S; Storozynski, Pamela; Halama, Josephine; Krems, Josef F.

doi:10.1016/j.apergo.2022.103855

Cited by 16 publications

(9 citation statements)

References 46 publications

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“…Specifically, the results of the feature selection are aligned with the other studies that have emphasized the importance of the selected features. It is noteworthy that despite the differences in the devices employed in the previous studies, the HRV-based features have emerged as the most robust indicator of stress, along with the SCR information [ 24 , 36 ]. The results obtained from the current study indicate that both feature evaluation methods employed, particularly the chi-test method, possess considerable strength in selecting stress-related characteristics.…”

Section: Discussionmentioning

confidence: 99%

“…Support Vector Machine, Random Forest, AdaBoost, Gradient Boosting, and Logistic Regression classifiers were used to detect stressed or non-stressed periods in both objective and subjective stress models. In the study carried out by Mach et al [ 24 ] in 2022, a laboratory experiment consisting of an arithmetic task which is counting down or up steadily, and physical activity (sitting vs. stepping) with 52 participants was conducted. This study aimed to assess mental workload via heart rate measurement and a chest strap with a 1-channel ECG.…”

Section: Introductionmentioning

confidence: 99%

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A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques

Campanella

Altaleb

Belli

et al. 2023

Sensors

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In response to challenging circumstances, the human body can experience marked levels of anxiety and distress. To prevent stress-related complications, timely identification of stress symptoms is crucial, necessitating the need for continuous stress monitoring. Wearable devices offer a means of real-time and ongoing data collection, facilitating personalized stress monitoring. Based on our protocol for data pre-processing, this study proposes to analyze signals obtained from the Empatica E4 bracelet using machine-learning algorithms (Random Forest, SVM, and Logistic Regression) to determine the efficacy of the abovementioned techniques in differentiating between stressful and non-stressful situations. Photoplethysmographic and electrodermal activity signals were collected from 29 subjects to extract 27 features which were then fed into three different machine-learning algorithms for binary classification. Using MATLAB after applying the chi-square test and Pearson’s correlation coefficient on WEKA for features’ importance ranking, the results demonstrated that the Random Forest model has the highest stability (accuracy of 76.5%) using all the features. Moreover, the Random Forest applying the chi-test for feature selection reached consistent results in terms of stress evaluation based on precision, recall, and F1-measure (71%, 60%, 65%, respectively).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques

Campanella

Altaleb

Belli

et al. 2023

Sensors

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“…Today, these electronics are capable of recording user data in real time. The most important part of this sector today is mainly smartwatches [21].…”

Section: Gsrmentioning

confidence: 99%

“…Another advantage of smartwatches would be within the measurement of cognitive load, where they would not add additional problems to the participant of the experiment, such as ECG electrodes [21].…”

Section: Gsrmentioning

confidence: 99%

Possibilities of using commercial electronics to measure driver physiological functions in a vehicle simulator

Lehet

2023

APP

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The aim of this paper is to explore the potential of using inexpensive, commercial electronics such as a smartwatch or chest belt to measure human physiological functions while driving in vehicle simulator, both to improve safety and to use these devices in other experiments to facilitate their conduct and replace more complex laboratory equipment. In the first part of the paper, the signs of stress and cognitive load through physiological functions are researched, then the possibilities of measuring these signs are explored. This is followed by an experiment in which two commonly available devices are compared in terms of accuracy and usefulness for measurement during the experiment.

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“…Eye tracking-for attention recognition and state detection, by assessment of some parameters [15], e.g., pupil dilation [16,17], saccade and fixation duration [18]; • Facial expression recognition-for emotional and cognitive state detection [19][20][21]; • Electrocardiography (ECG), electrodermal activity (EDA) [22][23][24][25][26], and skin temperature sensors [27]-for assessment of the human body's physiological reactions related to stress, anxiety, agitation, cognitive load, etc. It was also shown that the detection of heart rate variability could be based on photoplethysmography (PPG) sensors integrated into wearable devices [28,29], as well as on traditional electrode-based technology integrated into plasters [30] or smart textiles [31,32]; • Electroencephalography (EEG) [33][34][35][36][37][38]-for capturing the cognition processes, even in cases of a lack of behavioral reaction caused by a specific stimulus. The potential of this technology is promising, primarily through the development of brain-computer interfaces (BCIs) [39][40][41][42] in that number those with direct brain connection [43].…”

mentioning

confidence: 99%

Towards Implementation of Emotional Intelligence in Human–Machine Collaborative Systems

Markov,

Kalinin,

Markova

et al. 2023

Electronics

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Social awareness and relationship management components can be seen as a form of emotional intelligence. In the present work, we propose task-related adaptation on the machine side that accounts for a person’s momentous cognitive and emotional state. We validate the practical significance of the proposed approach in person-specific and person-independent setups. The analysis of results in the person-specific setup shows that the individual optimal performance curves for that person, according to the Yerkes–Dodson law, are displaced. Awareness of these curves allows for automated recognition of specific user profiles, real-time monitoring of the momentous condition, and activating a particular relationship management strategy. This is especially important when a deviation is detected caused by a change in the person’s state of mind under the influence of known or unknown factors.

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Assessing mental workload with wearable devices – Reliability and applicability of heart rate and motion measurements

Cited by 16 publications

References 46 publications

A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques

A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques

Possibilities of using commercial electronics to measure driver physiological functions in a vehicle simulator

Towards Implementation of Emotional Intelligence in Human–Machine Collaborative Systems

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