A Usability Study of Physiological Measurement in School Using Wearable Sensors

Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students’ physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps.

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User Experience Evaluation in Virtual Reality based on Subjective Feelings and Physiological Signals

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2019.63.6.060413 ◽

2019 ◽

Vol 63 (6) ◽

pp. 60413-1-60413-11

Author(s):

Yunfang Niu ◽

Danli Wang ◽

Ziwei Wang ◽

Fan Sun ◽

Kang Yue ◽

...

Keyword(s):

Virtual Reality ◽

Positive Emotions ◽

Electrodermal Activity ◽

Self Report ◽

Physiological Signals ◽

Physiological Data ◽

Self Assessment ◽

Emotion Experience ◽

Eye Fatigue ◽

Electrocardiogram Ecg

Abstract At present, the research on emotion in the virtual environment is limited to the subjective materials, and there are very few studies based on objective physiological signals. In this article, the authors conducted a user experiment to study the user emotion experience of virtual reality (VR) by comparing subjective feelings and physiological data in VR and two-dimensional display (2D) environments. First, they analyzed the data of self-report questionnaires, including Self-assessment Manikin (SAM), Positive And Negative Affect Schedule (PANAS) and Simulator Sickness Questionnaire (SSQ). The result indicated that VR causes a higher level of arousal than 2D, and easily evokes positive emotions. Both 2D and VR environments are prone to eye fatigue, but VR is more likely to cause symptoms of dizziness and vertigo. Second, they compared the differences of electrocardiogram (ECG), skin temperature (SKT) and electrodermal activity (EDA) signals in two circumstances. Through mathematical analysis, all three signals had significant differences. Participants in the VR environment had a higher degree of excitement, and the mood fluctuations are more frequent and more intense. In addition, the authors used different machine learning models for emotion detection, and compared the accuracies on VR and 2D datasets. The accuracies of all algorithms in the VR environment are higher than that of 2D, which corroborated that the volunteers in the VR environment have more obvious skin electrical signals, and had a stronger sense of immersion. This article effectively compensated for the inadequacies of existing work. The authors first used objective physiological signals for experience evaluation and used different types of subjective materials to make contrast. They hope their study can provide helpful guidance for the engineering reality of virtual reality.

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

Journal of Applied Physiology ◽

10.1152/japplphysiol.00714.2018 ◽

2019 ◽

Vol 126 (3) ◽

pp. 717-729 ◽

Cited By ~ 6

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.

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Longitudinal Classification of Mental Effort Using Electrodermal Activity, Heart Rate, and Skin Temperature Data from a Wearable Sensor

10.36227/techrxiv.13607291 ◽

2021 ◽

Author(s):

William Romine ◽

Noah Schroeder ◽

Anjali Edwards ◽

Tanvi Banerjee

Keyword(s):

Heart Rate ◽

Skin Temperature ◽

Electrodermal Activity ◽

Wearable Sensors ◽

Mental Effort ◽

Physiological Data ◽

Cognitive State ◽

Switching Regression ◽

Longitudinal Case Study ◽

Time Duration

Recent studies show that physiological data can detect changes in mental effort, making way for the development of wearable sensors to monitor mental effort in school, work, and at home. We have yet to explore how such a device would work with a single participant over an extended time duration. We used a longitudinal case study design with ~38 hours of data to explore the efficacy of electrodermal activity, skin temperature, and heart rate for classifying mental effort. We utilized a 2-state Markov switching regression model to understand the efficacy of these physiological measures for predicting self-reported mental effort during logged activities. On average, a model with state-dependent relationships predicted within one unit of reported mental effort (training RMSE = 0.4, testing RMSE = 0.7). This automated sensing of mental effort can have applications in various domains including student engagement detection and cognitive state assessment in drivers, pilots, and caregivers.

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OpiTrack

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies ◽

10.1145/3478107 ◽

2021 ◽

Vol 5 (3) ◽

pp. 1-29

Author(s):

Bhanu Teja Gullapalli ◽

Stephanie Carreiro ◽

Brittany P. Chapman ◽

Deepak Ganesan ◽

Jan Sjoquist ◽

...

Keyword(s):

Time Window ◽

Electrodermal Activity ◽

Clinical Care ◽

Opioid Use Disorder ◽

Physiological Signals ◽

Physiological Data ◽

Opioid Use ◽

Painful Condition ◽

Targeted Interventions ◽

Opioid Administration

Opioid use disorder is a medical condition with major social and economic consequences. While ubiquitous physiological sensing technologies have been widely adopted and extensively used to monitor day-to-day activities and deliver targeted interventions to improve human health, the use of these technologies to detect drug use in natural environments has been largely underexplored. The long-term goal of our work is to develop a mobile technology system that can identify high-risk opioid-related events (i.e., development of tolerance in the setting of prescription opioid use, return-to-use events in the setting of opioid use disorder) and deploy just-in-time interventions to mitigate the risk of overdose morbidity and mortality. In the current paper, we take an initial step by asking a crucial question: Can opioid use be detected using physiological signals obtained from a wrist-mounted sensor? Thirty-six individuals who were admitted to the hospital for an acute painful condition and received opioid analgesics as part of their clinical care were enrolled. Subjects wore a noninvasive wrist sensor during this time (1-14 days) that continuously measured physiological signals (heart rate, skin temperature, accelerometry, electrodermal activity, and interbeat interval). We collected a total of 2070 hours (≈ 86 days) of physiological data and observed a total of 339 opioid administrations. Our results are encouraging and show that using a Channel-Temporal Attention TCN (CTA-TCN) model, we can detect an opioid administration in a time-window with an F1-score of 0.80, a specificity of 0.77, sensitivity of 0.80, and an AUC of 0.77. We also predict the exact moment of administration in this time-window with a normalized mean absolute error of 8.6% and R2 coefficient of 0.85.

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User Experience Evaluation in Virtual Reality based on Subjective Feelings and Physiological Signals

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.13.ervr-382 ◽

2020 ◽

Vol 2020 (13) ◽

pp. 60413-1-60413-11

Author(s):

Yunfang Niu ◽

Danli Wang ◽

Ziwei Wang ◽

Fan Sun ◽

Kang Yue ◽

...

Keyword(s):

Virtual Reality ◽

Positive Emotions ◽

Electrodermal Activity ◽

Self Report ◽

Physiological Signals ◽

Physiological Data ◽

Self Assessment ◽

Emotion Experience ◽

Eye Fatigue ◽

Electrocardiogram Ecg

At present, the research on emotion in the virtual environment is limited to the subjective materials, and there are very few studies based on objective physiological signals. In this article, the authors conducted a user experiment to study the user emotion experience of virtual reality (VR) by comparing subjective feelings and physiological data in VR and two-dimensional display (2D) environments. First, they analyzed the data of self-report questionnaires, including Self-assessment Manikin (SAM), Positive And Negative Affect Schedule (PANAS) and Simulator Sickness Questionnaire (SSQ). The result indicated that VR causes a higher level of arousal than 2D, and easily evokes positive emotions. Both 2D and VR environments are prone to eye fatigue, but VR is more likely to cause symptoms of dizziness and vertigo. Second, they compared the differences of electrocardiogram (ECG), skin temperature (SKT) and electrodermal activity (EDA) signals in two circumstances. Through mathematical analysis, all three signals had significant differences. Participants in the VR environment had a higher degree of excitement, and the mood fluctuations are more frequent and more intense. In addition, the authors used different machine learning models for emotion detection, and compared the accuracies on VR and 2D datasets. The accuracies of all algorithms in the VR environment are higher than that of 2D, which corroborated that the volunteers in the VR environment have more obvious skin electrical signals, and had a stronger sense of immersion. This article effectively compensated for the inadequacies of existing work. The authors first used objective physiological signals for experience evaluation and used different types of subjective materials to make contrast. They hope their study can provide helpful guidance for the engineering reality of virtual reality.

Download Full-text

Longitudinal Classification of Mental Effort Using Electrodermal Activity, Heart Rate, and Skin Temperature Data from a Wearable Sensor

10.36227/techrxiv.13607291.v1 ◽

2021 ◽

Author(s):

William Romine ◽

Noah Schroeder ◽

Anjali Edwards ◽

Tanvi Banerjee

Keyword(s):

Heart Rate ◽

Skin Temperature ◽

Electrodermal Activity ◽

Wearable Sensors ◽

Mental Effort ◽

Physiological Data ◽

Cognitive State ◽

Switching Regression ◽

Longitudinal Case Study ◽

Time Duration

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

Sensors ◽

10.3390/s21010052 ◽

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.

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Assessment of construction workers’ perceived risk using physiological data from wearable sensors: A machine learning approach

Journal of Building Engineering ◽

10.1016/j.jobe.2021.102824 ◽

2021 ◽

pp. 102824

Author(s):

By Gaang Leei ◽

Byungjoo Choi ◽

Houtan Jebelli ◽

SangHyun Lee

Keyword(s):

Machine Learning ◽

Perceived Risk ◽

Wearable Sensors ◽

Construction Workers ◽

Physiological Data ◽

Learning Approach ◽

Machine Learning Approach

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Quantitative assessment of the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousness

10.1101/2020.11.03.367607 ◽

2020 ◽

Author(s):

Sandya Subramanian ◽

Patrick L. Purdon ◽

Riccardo Barbieri ◽

Emery N. Brown

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Prediction Models ◽

Electrodermal Activity ◽

Strong Relationship ◽

Pulse Amplitude ◽

Physiological Data ◽

Statistical Tool ◽

Autonomic Changes ◽

Before And After

ABSTRACTDuring general anesthesia, both behavioral and autonomic changes are caused by the administration of anesthetics such as propofol. Propofol produces unconsciousness by creating highly structured oscillations in brain circuits. The anesthetic also has autonomic effects due to its actions as a vasodilator and myocardial depressant. Understanding how autonomic dynamics change in relation to propofol-induced unconsciousness is an important scientific and clinical question since anesthesiologists often infer changes in level of unconsciousness from changes in autonomic dynamics. Therefore, we present a framework combining physiology-based statistical models that have been developed specifically for heart rate variability and electrodermal activity with a robust statistical tool to compare behavioral and multimodal autonomic changes before, during, and after propofol-induced unconsciousness. We tested this framework on physiological data recorded from nine healthy volunteers during computer-controlled administration of propofol. We studied how autonomic dynamics related to behavioral markers of unconsciousness: 1) overall, 2) during the transitions of loss and recovery of consciousness, and 3) before and after anesthesia as a whole. Our results show a strong relationship between behavioral state of consciousness and autonomic dynamics. All of our prediction models showed areas under the curve greater than 0.75 despite the presence of non-monotonic relationships among the variables during the transition periods. Our analysis highlighted the specific roles played by fast versus slow changes, parasympathetic vs sympathetic activity, heart rate variability vs electrodermal activity, and even pulse rate vs pulse amplitude information within electrodermal activity. Further advancement upon this work can quantify the complex and subject-specific relationship between behavioral changes and autonomic dynamics before, during, and after anesthesia. However, this work demonstrates the potential of a multimodal, physiologically-informed, statistical approach to characterize autonomic dynamics.

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Kansei’s Physiological Measurement and Its Application (2)

Advances in Computer and Electrical Engineering - Kansei Engineering and Soft Computing ◽

10.4018/978-1-61692-797-4.ch018 ◽

2010 ◽

pp. 319-329 ◽

Cited By ~ 1

Author(s):

Santoso Handri ◽

Shusaku Nomura

Keyword(s):

Beating Heart ◽

Physiological Signals ◽

Kansei Engineering ◽

Internal Organs ◽

Physiological Measurement ◽

New Approach ◽

Engineering Research ◽

Physiological Signal ◽

Research Fields ◽

Metabolic Activities

Physiological signals or biosignals are electrical, chemical, or mechanical signals that created by biological events such as a beating heart or a contracting muscle producing signals that can be measured and analyzed. These signals are generated from the metabolic activities of human internal organs. Therefore, in certain conditions, physiological signals have different pattern between healthy and unhealthy individuals. Based on this information, generally, physicians take some action and treat their patients. However, utilizing physiological signals is a new approach in Kansei engineering research fields for coping with human sensitivity. This study focuses on the possibility of physiological signal application in Kansei engineering.

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