scholarly journals Interpersonal Physiological Synchrony for Detecting Moments of Connection in Persons With Dementia: A Pilot Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Dannie Fu ◽  
Natalia Incio-Serra ◽  
Rossio Motta-Ochoa ◽  
Stefanie Blain-Moraes
Keyword(s):  
Electrodermal Activity ◽  
Wearable Sensors ◽  
Single Session ◽  
Time Resolved ◽  
Video Recordings ◽  
Interpersonal Synchrony ◽  
Physiological Synchrony ◽  
Symbolic Transfer Entropy ◽  
Communicative Abilities ◽  
Synchrony Measure

Interpersonal physiological synchrony has been successfully used to characterize social interactions and social processes during a variety of interpersonal interactions. There are a handful of measures of interpersonal physiological synchrony, but those that exist have only been validated on able-bodied adults. Here, we present a novel information-theory based measure of interpersonal physiological synchrony—normalized Symbolic Transfer Entropy (NSTE)—and compare its performance with a popular physiological synchrony measure—physiological concordance and single session index (SSI). Using wearable sensors, we measured the electrodermal activity (EDA) of five individuals with dementia and six able-bodied individuals as they participated in a movement activity that aimed to foster connection in persons with dementia. We calculated time-resolved NSTE and SSI measures for case studies of three dyads and compared them against moments of observed interpersonal connection in video recordings of the activity. Our findings suggest that NSTE-based measures of interpersonal physiological synchrony may provide additional advantages over SSI, including resolving moments of ambiguous SSI and providing information about the direction of information flow between participants. This study also investigated the feasibility of using interpersonal synchrony to gain insight into moments of connection experienced by individuals with dementia and further encourages exploration of these measures in other populations with reduced communicative abilities.

scholarly journals CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 52
Author(s):  
Tianyi Zhang ◽  
Abdallah El Ali ◽  
Chen Wang ◽  
Alan Hanjalic ◽  
Pablo Cesar
Keyword(s):  
Emotion Recognition ◽  
Electrodermal Activity ◽  
Short Form ◽  
Recognition Performance ◽  
Binary Classification ◽  
Wearable Sensors ◽  
Recognition Algorithm ◽  
Fine Grained ◽  
Physiological Sensors ◽  
Valence And Arousal

Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion recognition algorithm (CorrNet) to recognize the valence and arousal (V-A) of each instance (fine-grained segment of signals) using only wearable, physiological signals (e.g., electrodermal activity, heart rate). CorrNet takes advantage of features both inside each instance (intra-modality features) and between different instances for the same video stimuli (correlation-based features). We first test our approach on an indoor-desktop affect dataset (CASE), and thereafter on an outdoor-mobile affect dataset (MERCA) which we collected using a smart wristband and wearable eyetracker. Results show that for subject-independent binary classification (high-low), CorrNet yields promising recognition accuracies: 76.37% and 74.03% for V-A on CASE, and 70.29% and 68.15% for V-A on MERCA. Our findings show: (1) instance segment lengths between 1–4 s result in highest recognition accuracies (2) accuracies between laboratory-grade and wearable sensors are comparable, even under low sampling rates (≤64 Hz) (3) large amounts of neutral V-A labels, an artifact of continuous affect annotation, result in varied recognition performance.

scholarly journals Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning

2019 ◽  
Vol 25 (3) ◽  
pp. 1785-1802 ◽  
Author(s):  
Muhterem Dindar ◽  
Jonna Malmberg ◽  
Sanna Järvelä ◽  
Eetu Haataja ◽  
Paul A. Kirschner
Keyword(s):  
Academic Achievement ◽  
Electrodermal Activity ◽  
Learning Processes ◽  
Self Report ◽  
Self Regulated Learning ◽  
Cognitive Regulation ◽  
Physiological Synchrony ◽  
Regulated Learning ◽  
Report Data ◽  
Self Report Data

AbstractThis study investigated the interplay of temporal changes in self-regulated learning processes (i.e., behavioral, cognitive, motivational and emotional) and their relationship with academic achievement in computer-supported collaborative learning. The study employed electrodermal activity and self-report data to capture the dynamicity of self-regulated learning processes during 15 sessions of collaborative learning activities. Our findings revealed that the changes in motivational regulation was related to academic achievement. However, academic achievement was not related to behavioral regulation, cognitive regulation or emotional regulation. Physiological synchrony among the collaborating students was found to be related only to cognitive regulation. The results also showed that the concordance of self-report data among the collaborating students was related to higher physiological synchrony among them in the behavioral, cognitive, and motivational dimensions of self-regulated learning. The findings reflect the complexity of the relationships between self-regulated learning constructs and demonstrates the potential value of physiological measures in self-regulated learning research.

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 Ski Jumping Trajectory Reconstruction Using Wearable Sensors via Extended Rauch-Tung-Striebel Smoother with State Constraints

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1995
Author(s):  
Xiang Fang ◽  
Benedikt Grüter ◽  
Patrick Piprek ◽  
Veronica Bessone ◽  
Johannes Petrat ◽  
...  
Keyword(s):  
State Constraints ◽  
Wearable Sensors ◽  
Measurement Data ◽  
Velocity Estimation ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Shape Information ◽  
Video Recordings ◽  
Ski Jumping ◽  
Increasing Demand

To satisfy an increasing demand to reconstruct an athlete’s motion for performance analysis, this paper proposes a new method for reconstructing the position and velocity in the context of ski jumping trajectories. Therefore, state-of-the-art wearable sensors, including an inertial measurement unit, a magnetometer, and a GPS logger are used. The method employs an extended Rauch-Tung-Striebel smoother with state constraints to estimate state information offline from recorded raw measurements. In comparison to the classic inertial navigation system and GPS integration solution, the proposed method includes additional geometric shape information of the ski jumping hill, which are modeled as soft constraints and embedded into the estimation framework to improve the position and velocity estimation accuracy. Results for both simulated measurement data and real measurement data demonstrate the effectiveness of the proposed method. Moreover, a comparison between jump lengths obtained from the proposed method and video recordings shows the relative root-mean-square error of the reconstructed jump length is below 1.5 m depicting the accuracy of the algorithm.

scholarly journals Physiological synchrony predicts observational threat learning in humans

2020 ◽  
Vol 287 (1927) ◽  
pp. 20192779 ◽  
Author(s):  
Philip Pärnamets ◽  
Lisa Espinosa ◽  
Andreas Olsson
Keyword(s):  
Nervous System ◽  
Autonomic Nervous System ◽  
Autonomic Nervous System Activity ◽  
System Activity ◽  
Autonomic Nervous ◽  
Nervous System Activity ◽  
Interpersonal Synchrony ◽  
Skin Conductance Responses ◽  
Physiological Synchrony ◽  
Conditioned Responses

Understanding how information about threats in the environment is shared and transmitted between individuals is crucial for explaining adaptive, survival-related behaviour in humans and other animals, and for developing treatments for phobias and other anxiety disorders. Research across species has shown that observing a conspecific’s, a ‘demonstrator’s,’ threat responses causes strong and persistent threat memories in the ‘observer’. Here, we examined if physiological synchrony between demonstrator and observer can serve to predict the strength of observationally acquired conditioned responses. We measured synchrony between demonstrators’ and observers’ phasic electrodermal signals during learning, which directly reflects autonomic nervous system activity. Prior interpersonal synchrony predicted the strength of the observer’s later skin conductance responses to threat predicting stimuli, in the absence of the demonstrator. Dynamic coupling between an observer’s and a demonstrator’s autonomic nervous system activity may reflect experience sharing processes facilitating the formation of observational threat associations.

scholarly journals Evaluating physiological signal salience for estimating metabolic energy cost from wearable sensors

2019 ◽  
Vol 126 (3) ◽  
pp. 717-729 ◽  
Author(s):  
Kimberly A. Ingraham ◽  
Daniel P. Ferris ◽  
C. David Remy
Keyword(s):  
Heart Rate ◽  
Steady State ◽  
Indirect Calorimetry ◽  
Energy Cost ◽  
Electrodermal Activity ◽  
Wearable Sensors ◽  
Metabolic Energy ◽  
Physiological Signals ◽  
Physiological Signal ◽  
Signal Salience

Body-in-the-loop optimization algorithms have the capability to automatically tune the parameters of robotic prostheses and exoskeletons to minimize the metabolic energy expenditure of the user. However, current body-in-the-loop algorithms rely on indirect calorimetry to obtain measurements of energy cost, which are noisy, sparsely sampled, time-delayed, and require wearing a respiratory mask. To improve these algorithms, the goal of this work is to predict a user’s steady-state energy cost quickly and accurately using physiological signals obtained from portable, wearable sensors. In this paper, we quantified physiological signal salience to discover which signals, or groups of signals, have the best predictive capability when estimating metabolic energy cost. We collected data from 10 healthy individuals performing 6 activities (walking, incline walking, backward walking, running, cycling, and stair climbing) at various speeds or intensities. Subjects wore a suite of physiological sensors that measured breath frequency and volume, limb accelerations, lower limb EMG, heart rate, electrodermal activity, skin temperature, and oxygen saturation; indirect calorimetry was used to establish the ‘ground truth’ energy cost for each activity. Evaluating Pearson’s correlation coefficients and single and multiple linear regression models with cross validation (leave-one- subject-out and leave-one- task-out), we found that 1) filtering the accelerations and EMG signals improved their predictive power, 2) global signals (e.g., heart rate, electrodermal activity) were more sensitive to unknown subjects than tasks, while local signals (e.g., accelerations) were more sensitive to unknown tasks than subjects, and 3) good predictive performance was obtained combining a small number of signals (4–5) from multiple sensor modalities. NEW & NOTEWORTHY In this paper, we systematically compare a large set of physiological signals collected from portable sensors and determine which sensor signals contain the most salient information for predicting steady-state metabolic energy cost, robust to unknown subjects or tasks. This information, together with the comprehensive data set that is published in conjunction with this paper, will enable researchers and clinicians across many fields to develop novel algorithms to predict energy cost from wearable sensors.

scholarly journals A Super-Bagging Method for Volleyball Action Recognition Using Wearable Sensors

2020 ◽  
Vol 4 (2) ◽  
pp. 33 ◽  
Author(s):  
Fasih Haider ◽  
Fahim A. Salim ◽  
Dees B.W. Postma ◽  
Robby van Delden ◽  
Dennis Reidsma ◽  
...  
Keyword(s):  
Wearable Sensors ◽  
Sensor Data ◽  
Dominant Hand ◽  
Imbalanced Learning ◽  
Time Intervals ◽  
Video Recordings ◽  
Potential Applications ◽  
Individual Player ◽  
Measurement Units ◽  
Bagging Method

Access to performance data during matches and training sessions is important for coaches and players. Although there are many video tagging systems available which can provide such access, these systems require manual effort. Data from Inertial Measurement Units (IMU) could be used for automatically tagging video recordings in terms of players’ actions. However, the data gathered during volleyball sessions are generally very imbalanced, since for an individual player most time intervals can be classified as “non-actions” rather than “actions”. This makes automatic annotation of video recordings of volleyball matches a challenging machine-learning problem. To address this problem, we evaluated balanced and imbalanced learning methods with our newly proposed ‘super-bagging’ method for volleyball action modelling. All methods are evaluated using six classifiers and four sensors (i.e., accelerometer, magnetometer, gyroscope and barometer). We demonstrate that imbalanced learning provides better unweighted average recall, (UAR = 83.99%) for the non-dominant hand using a naive Bayes classifier than balanced learning, while balanced learning provides better performance (UAR = 84.18%) for the dominant hand using a tree bagger classifier than imbalanced learning. Our super-bagging method provides the best UAR (84.19%). It is also noted that the super-bagging method provides better averaged UAR than balanced and imbalanced methods in 8 out of 10 cases, hence demonstrating the potential of the super-bagging method for IMU’s sensor data. One of the potential applications of these novel models is fatigue and stamina estimation e.g., by keeping track of how many actions a player is performing and when these are being performed.

scholarly journals Race and ethnic variation in college students’ allostatic regulation of racism-related stress

2020 ◽  
Vol 117 (49) ◽  
pp. 31053-31062 ◽  
Author(s):  
Jacob E. Cheadle ◽  
Bridget J. Goosby ◽  
Joseph C. Jochman ◽  
Cara C. Tomaso ◽  
Chelsea B. Kozikowski Yancey ◽  
...  
Keyword(s):  
Nervous System ◽  
Sympathetic Nervous System ◽  
Stress Reactivity ◽  
Negative Emotion ◽  
Electrodermal Activity ◽  
Wearable Sensors ◽  
Real Life ◽  
Sympathetic Nervous ◽  
Interpersonal Discrimination ◽  
Related Stress

Racism-related stress is thought to contribute to widespread race/ethnic health inequities via negative emotion and allostatic stress process up-regulation. Although prior studies document race-related stress and health correlations, due to methodological and technical limitations, they have been unable to directly test the stress-reactivity hypothesis in situ. Guided by theories of constructed emotion and allostasis, we developed a protocol using wearable sensors and daily surveys that allowed us to operationalize and time-couple self-reported racism-related experiences, negative emotions, and an independent biosignal of emotional arousal. We used data from 100 diverse young adults at a predominantly White college campus to assess racism-related stress reactivity using electrodermal activity (EDA), a biosignal of sympathetic nervous system activity. We find that racism-related experiences predict both increased negative emotion risk and heightened EDA, consistent with the proposed allostatic model of health and disease. Specific patterns varied across race/ethnic groups. For example, discrimination and rumination were associated with negative emotion for African American students, but only interpersonal discrimination predicted increased arousal via EDA. The pattern of results was more general for Latinx students, for whom interpersonal discrimination, vicarious racism exposure, and rumination significantly modulated arousal. As with Latinx students, African students were particularly responsive to vicarious racism while 1.5 generation Black students were generally not responsive to racism-related experiences. Overall, these findings provide support for allostasis-based theories of mental and physical health via a naturalistic assessment of the emotional and sympathetic nervous system responding to real-life social experiences.

scholarly journals Wearable Technologies for Mental Workload, Stress, and Emotional State Assessment during Working-Like Tasks: A Comparison with Laboratory Technologies

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2332
Author(s):  
Andrea Giorgi ◽  
Vincenzo Ronca ◽  
Alessia Vozzi ◽  
Nicolina Sciaraffa ◽  
Antonello di Florio ◽  
...  
Keyword(s):  
Emotional State ◽  
Mental Workload ◽  
Electrodermal Activity ◽  
Wearable Sensors ◽  
Mental States ◽  
State Assessment ◽  
Ease Of Use ◽  
Laboratory Equipment ◽  
Human Errors ◽  
Work Related

The capability of monitoring user’s performance represents a crucial aspect to improve safety and efficiency of several human-related activities. Human errors are indeed among the major causes of work-related accidents. Assessing human factors (HFs) could prevent these accidents through specific neurophysiological signals’ evaluation but laboratory sensors require highly-specialized operators and imply a certain grade of invasiveness which could negatively interfere with the worker’s activity. On the contrary, consumer wearables are characterized by their ease of use and their comfortability, other than being cheaper compared to laboratory technologies. Therefore, wearable sensors could represent an ideal substitute for laboratory technologies for a real-time assessment of human performances in ecological settings. The present study aimed at assessing the reliability and capability of consumer wearable devices (i.e., Empatica E4 and Muse 2) in discriminating specific mental states compared to laboratory equipment. The electrooculographic (EOG), electrodermal activity (EDA) and photoplethysmographic (PPG) signals were acquired from a group of 17 volunteers who took part to the experimental protocol in which different working scenarios were simulated to induce different levels of mental workload, stress, and emotional state. The results demonstrated that the parameters computed by the consumer wearable and laboratory sensors were positively and significantly correlated and exhibited the same evidences in terms of mental states discrimination.

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