Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
2,995
result(s) for
"Physiological measures"
Sort by:
Objective and subjective responses to motion sickness: the group and the individual
We investigated and modeled the temporal evolution of motion sickness in a highly dynamic sickening drive. Slalom maneuvers were performed in a passenger vehicle, resulting in lateral accelerations of 0.4 g at 0.2 Hz, to which participants were subjected as passengers for up to 30 min. Subjective motion sickness was recorded throughout the sickening drive using the MISC scale. In addition, physiological and postural responses were evaluated by recording head roll, galvanic skin response (GSR) and electrocardiography (ECG). Experiment 1 compared external vision (normal view through front and side car windows) to internal vision (obscured view through front and side windows). Experiment 2 tested hypersensitivity with a second exposure a few minutes after the first drive and tested repeatability of individuals’ sickness responses by measuring these two exposures three times in three successive sessions. An adapted form of Oman’s model of nausea was used to quantify sickness development, repeatability, and motion sickness hypersensitivity at an individual level. Internal vision was more sickening compared to external vision with a higher mean MISC (4.2 vs. 2.3), a higher MISC rate (0.59 vs. 0.10 min−1) and more dropouts (66% vs. 33%) for whom the experiment was terminated due to reaching a MISC level of 7 (moderate nausea). The adapted Oman model successfully captured the development of sickness, with a mean model error, including the decay during rest and hypersensitivity upon further exposure, of 11.3%. Importantly, we note that knowledge of an individuals’ previous motion sickness response to sickening stimuli increases individual modeling accuracy by a factor of 2 when compared to group-based modeling, indicating individual repeatability. Head roll did not vary significantly with motion sickness. ECG varied slightly with motion sickness and time. GSR clearly varied with motion sickness, where the tonic and phasic GSR increased 42.5% and 90%, respectively, above baseline at high MISC levels, but GSR also increased in time independent of motion sickness, accompanied with substantial scatter.
Journal Article
Test Anxiety and Physiological Arousal
Test anxiety is a widespread and mostly detrimental emotion in learning and achievement settings. Thus, it is a construct of high interest for researchers and its measurement is an important issue. So far, test anxiety has typically been assessed using self-report measures. However, physiological measures (e.g., heart rate or skin conductance level) have gained increasing attention in educational research, as they allow for an objective and often continuous assessment of students’ physiological arousal (i.e., the physiological component of test anxiety) in real-life situations, such as a test. Although theoretically one would assume self-report measures of test anxiety and objective physiological measures would converge, empirical evidence is scarce and findings have been mixed. To achieve a more coherent picture of the relationship between these measures, this systematic review and meta-analysis investigated whether higher self-reported test anxiety is associated with expected increases in objectively measured physiological arousal. A systematic literature search yielded an initial 231 articles, and a structured selection process identified 29 eligible articles, comprising 31 studies, which met the specified inclusion criteria and provided sufficient information about the relationship under investigation. In line with theoretical models, in 21 out of the 31 included studies, there was a significant positive relationship between self-reported test anxiety and physiological arousal. The strengths of these correlations were of medium size. Moderators influencing the relation between these two measures are discussed, along with implications for the assessment of physiological data in future classroom-based research on test anxiety.
Journal Article
Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection
Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving.
Journal Article
A Systematic Review of International Affective Picture System (IAPS) around the World
Standardized Emotion Elicitation Databases (SEEDs) allow studying emotions in laboratory settings by replicating real-life emotions in a controlled environment. The International Affective Pictures System (IAPS), containing 1182 coloured images as stimuli, is arguably the most popular SEED. Since its introduction, multiple countries and cultures have validated this SEED, making its adoption on the study of emotion a worldwide success. For this review, 69 studies were included. Results focus on the discussion of validation processes by combining self-report and physiological data (Skin Conductance Level, Heart Rate Variability and Electroencephalography) and self-report only. Cross-age, cross-cultural and sex differences are discussed. Overall, IAPS is a robust instrument for emotion elicitation around the world.
Journal Article
A Systematic Review of Physiological Measures of Mental Workload
Mental workload (MWL) can affect human performance and is considered critical in the design and evaluation of complex human-machine systems. While numerous physiological measures are used to assess MWL, there appears no consensus on their validity as effective agents of MWL. This study was conducted to provide a comprehensive understanding of the use of physiological measures of MWL and to synthesize empirical evidence on the validity of the measures to discriminate changes in MWL. A systematical literature search was conducted with four electronic databases for empirical studies measuring MWL with physiological measures. Ninety-one studies were included for analysis. We identified 78 physiological measures, which were distributed in cardiovascular, eye movement, electroencephalogram (EEG), respiration, electromyogram (EMG) and skin categories. Cardiovascular, eye movement and EEG measures were the most widely used across varied research domains, with 76%, 66%, and 71% of times reported a significant association with MWL, respectively. While most physiological measures were found to be able to discriminate changes in MWL, they were not universally valid in all task scenarios. The use of physiological measures and their validity for MWL assessment also varied across different research domains. Our study offers insights into the understanding and selection of appropriate physiological measures for MWL assessment in varied human-machine systems.
Journal Article
Does size matter? Exploring the effect of cobot size on user experience in human–robot collaboration
In the vision of Industry 5.0, collaborative robots (or cobots) play a central supporting role in various industries, especially manufacturing. Close interaction with cobots requires special attention to user experience to fully exploit the benefits of this paradigm. Consequently, understanding the impact of a cobot’s physical size on user experience becomes critical to optimizing human–robot collaboration (HRC). This research aims to investigate the relationship between cobot size (UR3e – small cobot vs. UR10e – large cobot) and user experience in HRC contexts, in conjunction with other factors (i.e., cobot movement speed and product assembly complexity). Through a series of controlled experiments involving 32 participants, user experience data were obtained by collecting physiological measures (i.e., electro-dermal activity, heart activity, eye-tracking metrics) and subjective responses with questionnaires (i.e., perceived workload, interaction quality, and affective state). Results showed that the large cobot was generally perceived to be safer, more natural, efficient, fluid, and trustworthy. With the large cobot, there was a decrease in dominance; however, it was offset by the learning effect. Perceived workload was mainly influenced by product complexity. No clear difference in terms of mental strain emerged from the physiological data comparing the cobot sizes. In addition, the interaction term between cobot size and cobot movement speed never emerged as significant. The results of this research can offer practical insights to improve the effectiveness and acceptance of cobots during the implementation phase.
Journal Article
Pattern Recognition of Cognitive Load Using EEG and ECG Signals
The matching of cognitive load and working memory is the key for effective learning, and cognitive effort in the learning process has nervous responses which can be quantified in various physiological parameters. Therefore, it is meaningful to explore automatic cognitive load pattern recognition by using physiological measures. Firstly, this work extracted 33 commonly used physiological features to quantify autonomic and central nervous activities. Secondly, we selected a critical feature subset for cognitive load recognition by sequential backward selection and particle swarm optimization algorithms. Finally, pattern recognition models of cognitive load conditions were constructed by a performance comparison of several classifiers. We grouped the samples in an open dataset to form two binary classification problems: (1) cognitive load state vs. baseline state; (2) cognitive load mismatching state vs. cognitive load matching state. The decision tree classifier obtained 96.3% accuracy for the cognitive load vs. baseline classification, and the support vector machine obtained 97.2% accuracy for the cognitive load mismatching vs. cognitive load matching classification. The cognitive load and baseline states are distinguishable in the level of active state of mind and three activity features of the autonomic nervous system. The cognitive load mismatching and matching states are distinguishable in the level of active state of mind and two activity features of the autonomic nervous system.
Journal Article
Exploring the induction and measurement of positive affective state in equines through a personality-centred lens
There is increasing focus on how to induce and measure positive affective states in animals and the development of social license to operate has brought this to the forefront within equestrianism. This study aimed to utilise a range of methods to induce and measure positive affect in horses in real-world settings. Twenty healthy horses were scored for personality, exposed to four induction methods (wither scratching, high value food provision, positive reinforcement training and the addition of an affiliative conspecific), and data collected on their behaviour (QBA and ethograms) and physiology (heart and respiratory rate, heart rate variability, eye and ear thermography and salivary cortisol). Analyses identified potentially sensitive and specific behavioural (ear and eye position, QBA items, frustration items) and physiological (RR mean, HF power, LF power, LF/HF ratio, mean HR, RMSSD and pNN50) measures of affective state across the four quadrants of core affect. Individual difference effects were found, and personality traits such as unfriendly, nervous and unresponsive were associated with differing responses to induction stimuli indicating that all four induction stimuli are potentially useful for inducing positive affect depending on their salience to the individual. Research measuring and inducing positive affect in animals rarely considers personality, but this study underscores its importance. The dimensional approach taken allowed for assessment of the broad arousal and valence components of affect without ascribing measures to discrete emotions. Accurate, real-world measures of affect could benefit 116 million equines globally, and exploring ways to promote positive affect in horses can significantly enhance their welfare.
Journal Article
The Evaluation of Emotional Intelligence by the Analysis of Heart Rate Variability
Emotional intelligence (EI) is a critical social intelligence skill that refers to an individual’s ability to assess their own emotions and those of others. While EI has been shown to predict an individual’s productivity, personal success, and ability to maintain positive relationships, its assessment has primarily relied on subjective reports, which are vulnerable to response distortion and limit the validity of the assessment. To address this limitation, we propose a novel method for assessing EI based on physiological responses—specifically heart rate variability (HRV) and dynamics. We conducted four experiments to develop this method. First, we designed, analyzed, and selected photos to evaluate the ability to recognize emotions. Second, we produced and selected facial expression stimuli (i.e., avatars) that were standardized based on a two-dimensional model. Third, we obtained physiological response data (HRV and dynamics) from participants as they viewed the photos and avatars. Finally, we analyzed HRV measures to produce an evaluation criterion for assessing EI. Results showed that participants’ low and high EI could be discriminated based on the number of HRV indices that were statistically different between the two groups. Specifically, 14 HRV indices, including HF (high-frequency power), lnHF (the natural logarithm of HF), and RSA (respiratory sinus arrhythmia), were significant markers for discerning between low and high EI groups. Our method has implications for improving the validity of EI assessment by providing objective and quantifiable measures that are less vulnerable to response distortion.
Journal Article