ArticleSensors (Basel, Switzerland)2020
A Usability Study of Physiological Measurement in School Using Wearable Sensors.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 47 citations in OpenAlex.
- Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Detecting Emotions through Electrodermal Activity in Learning Contexts: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Measuring Arousal: Promises and Pitfalls.Affective science · 2025Review
- Monitoring audience engagement using electrodermal activity during an inaugural lecture.PloS one · 2025Article
- "It's Different for Girls!" The Role of Anxiety, Physiological Arousal, and Subject Preferences in Primary School Children's Math and Mental Rotation Performance.Behavioral sciences (Basel, Switzerland) · 2024Article
- Disturbances in Electrodermal Activity Recordings Due to Different Noises in the Environment.Sensors (Basel, Switzerland) · 2024Article
- Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review.Sensors (Basel, Switzerland) · 2024Article
- Physiological Profiling of Agitation in Dementia: Insights From Wearable Sensor Data.Innovation in aging · 2024Article
- Robustness of Physiological Synchrony in Wearable Electrodermal Activity and Heart Rate as a Measure of Attentional Engagement to Movie Clips.Sensors (Basel, Switzerland) · 2023Article
- Electrodermal activity as an index of food neophobia outside the lab.Frontiers in neuroergonomics · 2023Article
- Multimodal measurements enhance insights into emotional responses to immediate feedback.Frontiers in psychology · 2023Article
- Emotion Recognition from Physiological Channels Using Graph Neural Network.Sensors (Basel, Switzerland) · 2022Article
- Integrating Cognitive Developmental Neuroscience in Society: Lessons Learned From a Multidisciplinary Research Project on Education and Social Safety of Youth.Frontiers in integrative neuroscience · 2021Article
- A Novel Mixed Methods Approach to Synthesize EDA Data with Behavioral Data to Gain Educational Insight.Sensors (Basel, Switzerland) · 2020Article
- Comparison of Wearable and Clinical Devices for Acquisition of Peripheral Nervous System Signals.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.