scholarly journals Predicting Collaborative Learning Quality through Physiological Synchrony Recorded by Wearable Biosensors

2020 ◽  
Author(s):  
Yang Liu ◽  
Tingting Wang ◽  
Kun Wang ◽  
Yu Zhang
Keyword(s):  
Heart Rate ◽  
Data Analysis ◽  
Collaborative Learning ◽  
Electrodermal Activity ◽  
Group Discussion ◽  
Student Learn ◽  
Classification Analysis ◽  
Physiological Synchrony ◽  
Collaboration Quality ◽  
The Difference

AbstractInterpersonal physiological synchrony has been consistently found during collaborative tasks. However, few studies have applied synchrony to predict collaborative learning quality in real classroom. This study collected electrodermal activity (EDA) and heart rate (HR) in naturalistic class sessions, and compared the physiological synchrony between independent task and group discussion task. Since each student learn differently and not everyone prefers collaborative learning, participants were sorted into collaboration and independent dyads based on collaborative behaviors before data analysis. The result showed that during groups discussions, high collaboration pairs produced significantly higher synchrony than low collaboration dyads (p = 0.010). Given the equivalent engagement level during independent and collaborative tasks, the difference of physiological synchrony between high and low collaboration dyads was triggered by collaboration quality. Building upon this result, the classification analysis was conducted, indicating that EDA synchrony can predict collaboration quality (AUC = 0.767, p = 0.015).

scholarly journals Collaborative Learning Quality Classification Through Physiological Synchrony Recorded by Wearable Biosensors

2021 ◽  
Vol 12 ◽  
Author(s):  
Yang Liu ◽  
Tingting Wang ◽  
Kun Wang ◽  
Yu Zhang
Keyword(s):  
Collaborative Learning ◽  
Electrodermal Activity ◽  
Group Discussion ◽  
Group Discussions ◽  
Student Learn ◽  
Quality Classification ◽  
Physiological Synchrony ◽  
Collaboration Quality ◽  
The Difference ◽  
The Relationship

Interpersonal physiological synchrony has been consistently found during collaborative tasks. However, few studies have applied synchrony to predict collaborative learning quality in real classroom. To explore the relationship between interpersonal physiological synchrony and collaborative learning activities, this study collected electrodermal activity (EDA) and heart rate (HR) during naturalistic class sessions and compared the physiological synchrony between independent task and group discussion task. The students were recruited from a renowned university in China. Since each student learn differently and not everyone prefers collaborative learning, participants were sorted into collaboration and independent dyads based on their collaborative behaviors before data analysis. The result showed that, during group discussions, high collaboration pairs produced significantly higher synchrony than low collaboration dyads (p = 0.010). Given the equivalent engagement level during independent and collaborative tasks, the difference of physiological synchrony between high and low collaboration dyads was triggered by collaboration quality. Building upon this result, the classification analysis was conducted, indicating that EDA synchrony can identify different levels of collaboration quality (AUC = 0.767 and p = 0.015).

scholarly journals Physiological Synchrony in EEG, Electrodermal Activity and Heart Rate Detects Attentionally Relevant Events in Time

2020 ◽  
Vol 14 ◽  
Author(s):  
Ivo V. Stuldreher ◽  
Nattapong Thammasan ◽  
Jan B. F. van Erp ◽  
Anne-Marie Brouwer
Keyword(s):  
Heart Rate ◽  
Stimulus Type ◽  
Electrodermal Activity ◽  
Physiological Signals ◽  
Independent Group ◽  
Top Down ◽  
Event Type ◽  
Physiological Synchrony ◽  
Electroencephalogram Eeg ◽  
Over Time

Interpersonal physiological synchrony (PS), or the similarity of physiological signals between individuals over time, may be used to detect attentionally engaging moments in time. We here investigated whether PS in the electroencephalogram (EEG), electrodermal activity (EDA), heart rate and a multimodal metric signals the occurrence of attentionally relevant events in time in two groups of participants. Both groups were presented with the same auditory stimulus, but were instructed to attend either to the narrative of an audiobook (audiobook-attending: AA group) or to interspersed emotional sounds and beeps (stimulus-attending: SA group). We hypothesized that emotional sounds could be detected in both groups as they are expected to draw attention involuntarily, in a bottom-up fashion. Indeed, we found this to be the case for PS in EDA or the multimodal metric. Beeps, that are expected to be only relevant due to specific “top-down” attentional instructions, could indeed only be detected using PS among SA participants, for EDA, EEG and the multimodal metric. We further hypothesized that moments in the audiobook accompanied by high PS in either EEG, EDA, heart rate or the multimodal metric for AA participants would be rated as more engaging by an independent group of participants compared to moments corresponding to low PS. This hypothesis was not supported. Our results show that PS can support the detection of attentionally engaging events over time. Currently, the relation between PS and engagement is only established for well-defined, interspersed stimuli, whereas the relation between PS and a more abstract self-reported metric of engagement over time has not been established. As the relation between PS and engagement is dependent on event type and physiological measure, we suggest to choose a measure matching with the stimulus of interest. When the stimulus type is unknown, a multimodal metric is most robust.

scholarly journals Physiological synchrony in EEG, electrodermal activity and heart rate reflects shared selective auditory attention

2020 ◽  
Vol 17 (4) ◽  
pp. 046028 ◽  
Author(s):  
Ivo V Stuldreher ◽  
Nattapong Thammasan ◽  
Jan B F van Erp ◽  
Anne-Marie Brouwer
Keyword(s):  
Heart Rate ◽  
Electrodermal Activity ◽  
Auditory Attention ◽  
Physiological Synchrony

scholarly journals Unsupervised Clustering of Individuals Sharing Selective Attentional Focus Using Physiological Synchrony

2022 ◽  
Vol 2 ◽  
Author(s):  
Ivo V. Stuldreher ◽  
Alexandre Merasli ◽  
Nattapong Thammasan ◽  
Jan B. F. van Erp ◽  
Anne-Marie Brouwer
Keyword(s):  
Heart Rate ◽  
Real World ◽  
Electrodermal Activity ◽  
Clustering Algorithms ◽  
Attentional Focus ◽  
Unsupervised Clustering ◽  
Limited Information ◽  
Attentional State ◽  
Sensory Stimuli ◽  
Physiological Synchrony

Research on brain signals as indicators of a certain attentional state is moving from laboratory environments to everyday settings. Uncovering the attentional focus of individuals in such settings is challenging because there is usually limited information about real-world events, as well as a lack of data from the real-world context at hand that is correctly labeled with respect to individuals' attentional state. In most approaches, such data is needed to train attention monitoring models. We here investigate whether unsupervised clustering can be combined with physiological synchrony in the electroencephalogram (EEG), electrodermal activity (EDA), and heart rate to automatically identify groups of individuals sharing attentional focus without using knowledge of the sensory stimuli or attentional focus of any of the individuals. We used data from an experiment in which 26 participants listened to an audiobook interspersed with emotional sounds and beeps. Thirteen participants were instructed to focus on the narrative of the audiobook and 13 participants were instructed to focus on the interspersed emotional sounds and beeps. We used a broad range of commonly applied dimensionality reduction ordination techniques—further referred to as mappings—in combination with unsupervised clustering algorithms to identify the two groups of individuals sharing attentional focus based on physiological synchrony. Analyses were performed using the three modalities EEG, EDA, and heart rate separately, and using all possible combinations of these modalities. The best unimodal results were obtained when applying clustering algorithms on physiological synchrony data in EEG, yielding a maximum clustering accuracy of 85%. Even though the use of EDA or heart rate by itself did not lead to accuracies significantly higher than chance level, combining EEG with these measures in a multimodal approach generally resulted in higher classification accuracies than when using only EEG. Additionally, classification results of multimodal data were found to be more consistent across algorithms than unimodal data, making algorithm choice less important. Our finding that unsupervised classification into attentional groups is possible is important to support studies on attentional engagement in everyday settings.

The Effects of a Caffeine Placebo and Experimenter Expectation on Blood Pressure, Heart Rate, Well-Being, and Cognitive Performance

2001 ◽  
Vol 6 (1) ◽  
pp. 15-25 ◽  
Author(s):  
Harald Walach ◽  
Stefan Schmidt ◽  
Yvonne-Michelle Bihr ◽  
Susanne Wiesch
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Cognitive Task ◽  
Well Being ◽  
Control Group ◽  
Double Blind ◽  
Main Effect ◽  
The Difference ◽  
Being There ◽  
To Receive

We studied the effect of experimenter expectations and different instructions in a balanced placebo design. 157 subjects were randomized into a 2 × 4 factorial design. Two experimenters were led to expect placebos either to produce physiological effects or not (pro- vs. antiplacebo). All subjects except a control group received a caffeine placebo. They were either made to expect coffee, no coffee, or were in a double-blind condition. Dependent measures were blood pressure, heart rate, well-being, and a cognitive task. There was one main effect on the instruction factor (p = 0.03) with the group “told no caffeine” reporting significantly better well-being. There was one main effect on the experimenter factor with subjects instructed by experimenter “proplacebo” having higher systolic blood pressure (p = 0.008). There was one interaction with subjects instructed by experimenter “proplacebo” to receive coffee doing worse in the cognitive task than the rest. Subjects instructed by experimenter “antiplacebo” were significantly less likely to believe the experimental instruction, and that mostly if they had been instructed to receive coffee. Contrary to the literature we could not show an effect of instruction, but there was an effect of experimenters. It is likely, however, that these experimenter effects were not due to experimental manipulations, but to the difference in personalities.

Heart rate reveals the difference between disgust and anger in the domain of morality.

2020 ◽  
Vol 14 (3) ◽  
pp. 284-298 ◽  
Author(s):  
Naoki Konishi ◽  
Toshiyuki Himichi ◽  
Yohsuke Ohtsubo
Keyword(s):  
Heart Rate ◽  
The Difference

PENGARUH TERAPI TERTAWA TERHADAP PENURUNAN TEKANAN DARAH LANSIA DI UPT PANTI WREDHA BUDHI DHARMA YOGYAKARTA

2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Niken Setyaningrum ◽  
Andri Setyorini ◽  
Fachruddin Tri Fitrianta
Keyword(s):  
Blood Pressure ◽  
Data Analysis ◽  
The Elderly ◽  
Control Group ◽  
Group Comparison ◽  
Control Blood Pressure ◽  
Men And Women ◽  
Treatment Of Hypertension ◽  
Before And After ◽  
The Difference

ABSTRACTBackground: Hypertension is one of the most common diseases, because this disease is suffered byboth men and women, as well as adults and young people. Treatment of hypertension does not onlyrely on medications from the doctor or regulate diet alone, but it is also important to make our bodyalways relaxed. Laughter can help to control blood pressure by reducing endocrine stress andcreating a relaxed condition to deal with relaxation.Objective: The general objective of the study was to determine the effect of laughter therapy ondecreasing elderly blood pressure in UPT Panti Wredha Budhi Dharma Yogyakarta.Methods: The design used in this study is a pre-experimental design study with one group pre-posttestresearch design where there is no control group (comparison). The population in this study wereelderly aged over> 60 years at 55 UPT Panti Wredha Budhi Dharma Yogyakarta. The method oftaking in this study uses total sampling. The sample in this study were 55 elderly. Data analysis wasused to determine the difference in blood pressure before and after laughing therapy with a ratio datascale that was using Pairs T-TestResult: There is an effect of laughing therapy on blood pressure in the elderly at UPT Panti WredhaBudhi Dharma Yogyakarta marked with a significant value of 0.000 (P <0.05)

scholarly journals Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3461
Author(s):  
Blake Anthony Hickey ◽  
Taryn Chalmers ◽  
Phillip Newton ◽  
Chin-Teng Lin ◽  
David Sibbritt ◽  
...  
Keyword(s):  
Mental Health ◽  
Systematic Review ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Electrodermal Activity ◽  
Smart Devices ◽  
Stress Detection ◽  
Wearable Technologies ◽  
Detection Of Stress ◽  
Anxiety Depression

Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.

Does body position, age, and heart rate induce IOP’s changes?

2021 ◽  
pp. 112067212110233
Author(s):  
Marcelina Sobczak ◽  
Magdalena Asejczyk ◽  
Malwina Geniusz
Keyword(s):  
Heart Rate ◽  
Body Position ◽  
Age Groups ◽  
Cardiovascular Parameters ◽  
Entire Cohort ◽  
Significant Difference ◽  
Group A ◽  
The Difference ◽  
Group B ◽  
Effect Of Age

Objectives: The main goal of this research was to determine the differences between the values of intraocular pressure (IOP) in the supine and sitting positions, and to assess the effect of age and cardiovascular parameters. Methods: Seventy-two healthy adults were enrolled and classified into age groups: 20–30 years (group A), 31–40 years (group B), and 41–71 years (group C). Corneal biometry and cardiovascular parameters, such as heart rate (HR), were measured. IOP measurements were taken in the sitting position (IOPS) and in the supine position (IOPL) using the iCare® Pro tonometer. Results: A significant difference between the IOPS and IOPL in the entire cohort was found ( p < 0.001). Regarding the age subgroups, a significant difference ( p < 0.001) between the IOPS and IOPL was obtained in group A (2.6 ± 1.6 mmHg) and group C (1.5 ± 1.3 mmHg). There were no significant differences in the IOPS between groups. The highest IOP values were obtained for group A. The correlations between HR and IOPS are statistically significant for group A and group B, and for HR and IOPL-S for group B only. Multivariate analysis showed that HR has a significant influence on the difference in IOP in the two body positions. Conclusion: A statistically significant difference between the effect of age and the values of IOPS and IOPL was shown. Cardiovascular parameters showed some relevant statistical dependencies, but with a rather marginal significance in young people. The influence of body position for the measurement of IOP for healthy subjects does not seem to matter, despite the fact that there are some dependencies that are statistically significant.

scholarly journals What happens when data are fitted to the wrong equation?

1978 ◽  
Vol 171 (3) ◽  
pp. 513-517 ◽  
Author(s):  
K J Ellis ◽  
R G Duggleby
Keyword(s):  
Data Analysis ◽  
Random Errors ◽  
Mathematical Equation ◽  
Error Structure ◽  
Weighting Factors ◽  
The Difference ◽  
Residual Plots

In many problems of data analysis it is necessary to fit the data to a mathematical equation. Random errors of measurement will be responsible for deviations between the data and the equation, but superimposed on this there may be deviations that result from the equation being an inadequate description of the system from which the data were obtained. Plots of the residual (i.e. the difference between the experimental and calculated values of the dependent variable) against each of the experimental variables have been previously used to detect a misfit between the data and the equation. In the present paper, we show that the shape of the residual plots may be used as a guide in choosing a more appropriate equation. In addition, residual plots give useful information on the error structure of the data, and hence the weighting factors that should be used in the analysis.

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