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

Deadline for manuscript submissions: closed (1 April 2023) | Viewed by 14217

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Special Issue Editors

Prof. Dr. Xun Chen

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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

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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

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Guest Editor

Department of Biomedical Engineering, Hefei University of Technology, Hefei 230009, China
Interests: video/electromagnetic-based intelligent perceptions

Dr. Xinrui Cui

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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

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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 (6 papers)

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Research

19 pages, 15756 KiB

Open AccessArticle

A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar

by Koji Endo, Kohei Yamamoto and Tomoaki Ohtsuki

Sensors 2022, 22(23), 9401; https://doi.org/10.3390/s22239401 - 02 Dec 2022

Cited by 2 | Viewed by 1352

Abstract

A Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide a range-angle map that expresses the signal power against each range and angle. It is possible to estimate object locations by detecting the signal power that exceeds a threshold using an algorithm, such as Constant False Alarm Rate (CFAR). However, noise and multipath components often exist over the range-angle map, which could produce false alarms for an undesired location depending on the threshold setting. In other words, the threshold setting is sensitive in noisy range-angle maps. Therefore, if the noise is reduced, the threshold can be easily set to reduce the number of false alarms. In this paper, we propose a method that improves the CFAR threshold tolerance by denoising a range-angle map using Deep Image Prior (DIP). DIP is an unsupervised deep-learning technique that enables image denoising. In the proposed method, DIP is applied to the range-angle map calculated by the Curve-Length (CL) method, and then the object location is detected over the denoised range-angle map based on Cell-Averaging CFAR (CA-CFAR), which is a typical threshold setting algorithm. Through the experiments to estimate human locations in indoor environments, we confirmed that the proposed method with DIP reduced the number of false alarms and estimated the human location accurately while improving the tolerance of the threshold setting, compared to the method without DIP. Full article

(This article belongs to the Special Issue Sensor Based Pattern Recognition and Signal Processing)

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27 pages, 6782 KiB

Open AccessArticle

M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity

by Sumya Akter, Rumman Ahmed Prodhan, Tanmoy Sarkar Pias, David Eisenberg and Jorge Fresneda Fernandez

Sensors 2022, 22(21), 8467; https://doi.org/10.3390/s22218467 - 03 Nov 2022

Cited by 5 | Viewed by 5180

Abstract

Emotion recognition, or the ability of computers to interpret people’s emotional states, is a very active research area with vast applications to improve people’s lives. However, most image-based emotion recognition techniques are flawed, as humans can intentionally hide their emotions by changing facial expressions. Consequently, brain signals are being used to detect human emotions with improved accuracy, but most proposed systems demonstrate poor performance as EEG signals are difficult to classify using standard machine learning and deep learning techniques. This paper proposes two convolutional neural network (CNN) models (M1: heavily parameterized CNN model and M2: lightly parameterized CNN model) coupled with elegant feature extraction methods for effective recognition. In this study, the most popular EEG benchmark dataset, the DEAP, is utilized with two of its labels, valence, and arousal, for binary classification. We use Fast Fourier Transformation to extract the frequency domain features, convolutional layers for deep features, and complementary features to represent the dataset. The M1 and M2 CNN models achieve nearly perfect accuracy of 99.89% and 99.22%, respectively, which outperform every previous state-of-the-art model. We empirically demonstrate that the M2 model requires only 2 seconds of EEG signal for 99.22% accuracy, and it can achieve over 96% accuracy with only 125 milliseconds of EEG data for valence classification. Moreover, the proposed M2 model achieves 96.8% accuracy on valence using only 10% of the training dataset, demonstrating our proposed system’s effectiveness. Documented implementation codes for every experiment are published for reproducibility. Full article

(This article belongs to the Special Issue Sensor Based Pattern Recognition and Signal Processing)

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19 pages, 4179 KiB

Open AccessArticle

Seizure Detection: A Low Computational Effective Approach without Classification Methods

by Neethu Sreenivasan, Gaetano D. Gargiulo, Upul Gunawardana, Ganesh Naik and Armin Nikpour

Sensors 2022, 22(21), 8444; https://doi.org/10.3390/s22218444 - 03 Nov 2022

Cited by 2 | Viewed by 1897

Abstract

Epilepsy is a severe neurological disorder that is usually diagnosed by using an electroencephalogram (EEG). However, EEG signals are complex, nonlinear, and dynamic, thus generating large amounts of data polluted by many artefacts, lowering the signal-to-noise ratio, and hampering expert interpretation. The traditional seizure-detection method of professional review of long-term EEG signals is an expensive, time-consuming, and challenging task. To reduce the complexity and cost of the task, researchers have developed several seizure-detection approaches, primarily focusing on classification systems and spectral feature extraction. While these methods can achieve high/optimal performances, the system may require retraining and following up with the feature extraction for each new patient, thus making it impractical for real-world applications. Herein, we present a straightforward manual/automated detection system based on the simple seizure feature amplification analysis to minimize these practical difficulties. Our algorithm (a simplified version is available as additional material), borrowing from the telecommunication discipline, treats the seizure as the carrier of information and tunes filters to this specific bandwidth, yielding a viable, computationally inexpensive solution. Manual tests gave 93% sensitivity and 96% specificity at a false detection rate of 0.04/h. Automated analyses showed 88% and 97% sensitivity and specificity, respectively. Moreover, our proposed method can accurately detect seizure locations within the brain. In summary, the proposed method has excellent potential, does not require training on new patient data, and can aid in the localization of seizure focus/origin. Full article

(This article belongs to the Special Issue Sensor Based Pattern Recognition and Signal Processing)

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10 pages, 797 KiB

Open AccessArticle

Assessing Walking Stability Based on Whole-Body Movement Derived from a Depth-Sensing Camera

by Arunee Promsri

Sensors 2022, 22(19), 7542; https://doi.org/10.3390/s22197542 - 05 Oct 2022

Cited by 3 | Viewed by 1101

Abstract

Stability during walking is considered a crucial aspect of assessing gait ability. The current study aimed to assess walking stability by applying principal component analysis (PCA) to decompose three-dimensional (3D) whole-body kinematic data of 104 healthy young adults (21.9 ± 3.5 years, 54 females) derived from a depth-sensing camera into a set of movement components/synergies called “principal movements” (PMs), forming together to achieve the task goal. The effect of sex as the focus area was tested on three PCA-based variables computed for each PM: the relative explained variance (rVAR) as a measure of the composition of movement structures; the largest Lyapunov exponent (LyE) as a measure of variability; and the number of zero-crossings (N) as a measure of the tightness of neuromuscular control. The results show that the sex effects appear in the specific PMs. Specifically, in PM₁, resembling the swing-phase movement, females have greater LyE (p = 0.013) and N (p = 0.017) values than males. Moreover, in PM₃, representing the mid-stance-phase movement, females have smaller rVAR (p = 0.020) but greater N (p = 0.008) values than males. These empirical findings suggest that the inherent sex differences in walking stability should be considered in assessing and training locomotion. Full article

(This article belongs to the Special Issue Sensor Based Pattern Recognition and Signal Processing)

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16 pages, 5386 KiB

Open AccessArticle

Research on Emotion Recognition Method Based on Adaptive Window and Fine-Grained Features in MOOC Learning

by Xianhao Shen, Jindi Bao, Xiaomei Tao and Ze Li

Sensors 2022, 22(19), 7321; https://doi.org/10.3390/s22197321 - 27 Sep 2022

Cited by 1 | Viewed by 1008

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 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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12 pages, 1026 KiB

Open AccessArticle

Impact of Label Noise on the Learning Based Models for a Binary Classification of Physiological Signal

by Cheng Ding, Tania Pereira, Ran Xiao, Randall J. Lee and Xiao Hu

Sensors 2022, 22(19), 7166; https://doi.org/10.3390/s22197166 - 21 Sep 2022

Cited by 1 | Viewed by 1702

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 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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