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Special Issue "Sensor Based Pattern Recognition and Signal Processing"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 1 April 2023 | Viewed by 1336

Special Issue Editors

Prof. Dr. Xun Chen
E-Mail Website
Guest Editor
Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China
Interests: artificial intelligence in medicine; human-machine interaction; multimodal image analysis; mobile health monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Juan Cheng
E-Mail Website
Guest Editor
Department of Biomedical Engineering, Hefei University of Technology, Hefei 230009, China
Interests: bioelectrical signal processing; pattern recognition; video-based intelligent perception
Dr. Rencheng Song
E-Mail Website
Guest Editor
Department of Biomedical Engineering, Hefei University of Technology, Hefei 230009, China
Interests: video/electromagnetic-based intelligent perceptions
Dr. Xinrui Cui
E-Mail Website
Guest Editor
Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China
Interests: explainable AI; AI in medicine
Dr. Yao Guo
E-Mail Website
Guest Editor
Institute of Medical Robotics, Shanghai Jiaotong University, Shanghai 200240, China
Interests: robotic vision; human-centric perception; cognition, and behavior analysis; rehabilitation and assistive robotics; social robotics

Special Issue Information

Dear Colleagues,

With demographic shift associated with the aging population, there are growing demand on pervasive health monitoring and management for both cognitive and physical declines. Applying various wearable, implantable, or peripheric sensors, intelligent perception systems can provide a 24/7 monitoring of users at any environment, recognize intents, emotions and behaviors of the users, and predict potential risks for diseases at the early stage. Besides, dedicated AI technologies designed for intelligent perception systems can promote the performance of healthcare applications. This special issue aims to highlight new achievements in intelligent perception areas including but not limited to the following topics such as signal denoising, multi-modal data fusion, pattern recognition, AI algorithms etc.

Prof. Dr. Xun Chen
Dr. Juan Cheng
Dr. Rencheng Song
Dr. Xinrui Cui
Dr. Yao Guo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Bioelectrical signal (EEG, EMG, ECG) processing

  • Video-based measurement

  • Biomedical image fusion

  • Advanced sensing technologies for physical parameters, emotion, metal status detection 

  • Human behavior analysis from cameras, bioelectrical signals or other relevant sensor data

  • Pattern recognition algorithms for prediction and decision making in healthcare

  • Secure and data privacy of sensing data 

  • AI algorithms in healthcare applications

Published Papers (2 papers)

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Research

Article
Research on Emotion Recognition Method Based on Adaptive Window and Fine-Grained Features in MOOC Learning
Sensors 2022, 22(19), 7321; https://doi.org/10.3390/s22197321 - 27 Sep 2022
Viewed by 106
Abstract
In MOOC learning, learners’ emotions have an important impact on the learning effect. In order to solve the problem that learners’ emotions are not obvious in the learning process, we propose a method to identify learner emotion by combining eye movement features and [...] Read more.
In MOOC learning, learners’ emotions have an important impact on the learning effect. In order to solve the problem that learners’ emotions are not obvious in the learning process, we propose a method to identify learner emotion by combining eye movement features and scene features. This method uses an adaptive window to partition samples and enhances sample features through fine-grained feature extraction. Using an adaptive window to partition samples can make the eye movement information in the sample more abundant, and fine-grained feature extraction from an adaptive window can increase discrimination between samples. After adopting the method proposed in this paper, the four-category emotion recognition accuracy of the single modality of eye movement reached 65.1% in MOOC learning scenarios. Both the adaptive window partition method and the fine-grained feature extraction method based on eye movement signals proposed in this paper can be applied to other modalities. Full article
(This article belongs to the Special Issue Sensor Based Pattern Recognition and Signal Processing)
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Article
Impact of Label Noise on the Learning Based Models for a Binary Classification of Physiological Signal
Sensors 2022, 22(19), 7166; https://doi.org/10.3390/s22197166 - 21 Sep 2022
Viewed by 164
Abstract
Label noise is omnipresent in the annotations process and has an impact on supervised learning algorithms. This work focuses on the impact of label noise on the performance of learning models by examining the effect of random and class-dependent label noise on a [...] Read more.
Label noise is omnipresent in the annotations process and has an impact on supervised learning algorithms. This work focuses on the impact of label noise on the performance of learning models by examining the effect of random and class-dependent label noise on a binary classification task: quality assessment for photoplethysmography (PPG). PPG signal is used to detect physiological changes and its quality can have a significant impact on the subsequent tasks, which makes PPG quality assessment a particularly good target for examining the impact of label noise in the field of biomedicine. Random and class-dependent label noise was introduced separately into the training set to emulate the errors associated with fatigue and bias in labeling data samples. We also tested different representations of the PPG, including features defined by domain experts, 1D raw signal and 2D image. Three different classifiers are tested on the noisy training data, including support vector machine (SVM), XGBoost, 1D Resnet and 2D Resnet, which handle three representations, respectively. The results showed that the two deep learning models were more robust than the two traditional machine learning models for both the random and class-dependent label noise. From the representation perspective, the 2D image shows better robustness compared to the 1D raw signal. The logits from three classifiers are also analyzed, the predicted probabilities intend to be more dispersed when more label noise is introduced. From this work, we investigated various factors related to label noise, including representations, label noise type, and data imbalance, which can be a good guidebook for designing more robust methods for label noise in future work. Full article
(This article belongs to the Special Issue Sensor Based Pattern Recognition and Signal Processing)
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