Trial reportSensors (Basel, Switzerland)2020
The Design of CNN Architectures for Optimal Six Basic Emotion Classification Using Multiple Physiological Signals.
Trial report 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 13 papers, 1 of them a synthesis 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
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.
- Datasets for Automated Affect and Emotion Recognition from Cardiovascular Signals Using Artificial Intelligence- A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Emotion recognition with multiple physiological parameters based on ensemble learning.Scientific reports · 2025Article
- Deep learning hybrid model ECG classification using AlexNet and parallel dual branch fusion network model.Scientific reports · 2024Article
- An enhanced speech emotion recognition using vision transformer.Scientific reports · 2024Article
- A Domain Generalization and Residual Network-Based Emotion Recognition from Physiological Signals.Cyborg and bionic systems (Washington, D.C.) · 2024Article
- Building a Low-Cost Wireless Biofeedback Solution: Applying Design Science Research Methodology.Sensors (Basel, Switzerland) · 2023Article
- A Bimodal Emotion Recognition Approach through the Fusion of Electroencephalography and Facial Sequences.Diagnostics (Basel, Switzerland) · 2023Article
- A Study on the Role of Affective Feedback in Robot-Assisted Learning.Sensors (Basel, Switzerland) · 2023Article
- Multi-Level Classification of Driver Drowsiness by Simultaneous Analysis of ECG and Respiration Signals Using Deep Neural Networks.International journal of environmental research and public health · 2022Article
- Machine Learning Methods for Fear Classification Based on Physiological Features.Sensors (Basel, Switzerland) · 2021Article
- A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics.Frontiers in cardiovascular medicine · 2021Article
- Smart Sensor Based on Biofeedback to Measure Child Relaxation in Out-of-Home Care.Sensors (Basel, Switzerland) · 2020Article
- Physiological Sensors Based Emotion Recognition While Experiencing Tactile Enhanced Multimedia.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
3 authors at 2 institutions in 1 country.
Funding
Abstract
This study aimed to design an optimal emotion recognition method using multiple physiological signal parameters acquired by bio-signal sensors for improving the accuracy of classifying individual emotional responses. Multiple physiological signals such as respiration (RSP) and heart rate variability (HRV) were acquired in an experiment from 53 participants when six basic emotion states were induced. Two RSP parameters were acquired from a chest-band respiration sensor, and five HRV parameters were acquired from a finger-clip blood volume pulse (BVP) sensor. A newly designed deep-learning model based on a convolutional neural network (CNN) was adopted for detecting the identification accuracy of individual emotions. Additionally, the signal combination of the acquired parameters was proposed to obtain high classification accuracy. Furthermore, a dominant factor influencing the accuracy was found by comparing the relativeness of the parameters, providing a basis for supporting the results of emotion classification. The users of this proposed model will soon be able to improve the emotion recognition model further based on CNN using multimodal physiological signals and their sensors.
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What OpenQuestion holds
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.