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Advanced Signal Processing in Wearable Sensors for Health Monitoring

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

Deadline for manuscript submissions: closed (10 February 2022) | Viewed by 73817

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Special Issue Editors

Department of Electronic and Electrical Engineering, College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, UB8 3PH, United Kingdom
Interests: artificial intelligence; modelling and control
Special Issues, Collections and Topics in MDPI journals
Department of Mechanical Engineering, Graduate School of Biotechnology and Bioengineering, Graduate Program in Biomedical Informatics, Yuan Ze University, Taoyuan, Chung-Li 32003, Taiwan
Interests: intelligent analysis and control in industrial processes; bio-signal processing; anaesthesia monitoring and control; pain model and control; medical automation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart wearable devices are becoming widely available on a miniature scale, typically on smartwatches and other connected devices. Measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnea, and motion detection are becoming more available and play a significant role in healthcare monitoring. The industry is pushing hard to make these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances.

In this context, many researchers have utilized recent technological advances in developments in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for smart wearable devices. Work in the field includes filtration, quality check, signal transformation and decompositions, feature extraction, and most recently, machine learning-based methods.

In this Issue, we invite researchers to contribute original research papers or comprehensive reviews to this Special Issue on “Advanced Signal Processing in Wearable Sensors for Health Monitoring”. Your contributions will help to improve and advance methodologies to process and analyze systems data that will produce new signal processing techniques to advance wearable sensor technologies.

Dr. Maysam Abbod
Prof. Dr. Jiann-Shing Shieh
Guest Editors

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Published Papers (11 papers)

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Editorial

Jump to: Research, Review

3 pages, 163 KiB  
Editorial
Special Issue “Advanced Signal Processing in Wearable Sensors for Health Monitoring”
by Maysam Abbod and Jiann-Shing Shieh
Sensors 2022, 22(6), 2189; https://doi.org/10.3390/s22062189 - 11 Mar 2022
Viewed by 1643
Abstract
Wearable sensors are becoming very popular recently due to their ease of use and flexibility in recording data from home [...] Full article
(This article belongs to the Special Issue Advanced Signal Processing in Wearable Sensors for Health Monitoring)

Research

Jump to: Editorial, Review

23 pages, 14270 KiB  
Article
EEG Signal Multichannel Frequency-Domain Ratio Indices for Drowsiness Detection Based on Multicriteria Optimization
by Igor Stancin, Nikolina Frid, Mario Cifrek and Alan Jovic
Sensors 2021, 21(20), 6932; https://doi.org/10.3390/s21206932 - 19 Oct 2021
Cited by 11 | Viewed by 2282
Abstract
Drowsiness is a risk to human lives in many occupations and activities where full awareness is essential for the safe operation of systems and vehicles, such as driving a car or flying an airplane. Although it is one of the main causes of [...] Read more.
Drowsiness is a risk to human lives in many occupations and activities where full awareness is essential for the safe operation of systems and vehicles, such as driving a car or flying an airplane. Although it is one of the main causes of many road accidents, there is still no reliable definition of drowsiness or a system to reliably detect it. Many researchers have observed correlations between frequency-domain features of the EEG signal and drowsiness, such as an increase in the spectral power of the theta band or a decrease in the spectral power of the beta band. In addition, features calculated as ratio indices between these frequency-domain features show further improvements in detecting drowsiness compared to frequency-domain features alone. This work aims to develop novel multichannel ratio indices that take advantage of the diversity of frequency-domain features from different brain regions. In contrast to the state-of-the-art, we use an evolutionary metaheuristic algorithm to find the nearly optimal set of features and channels from which the indices are calculated. Our results show that drowsiness is best described by the powers in delta and alpha bands. Compared to seven existing single-channel ratio indices, our two novel six-channel indices show improvements in (1) statistically significant differences observed between wakefulness and drowsiness segments, (2) precision of drowsiness detection and classification accuracy of the XGBoost algorithm and (3) model performance by saving time and memory during classification. Our work suggests that a more precise definition of drowsiness is needed, and that accurate early detection of drowsiness should be based on multichannel frequency-domain features. Full article
(This article belongs to the Special Issue Advanced Signal Processing in Wearable Sensors for Health Monitoring)
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12 pages, 3943 KiB  
Article
Fusion Method to Estimate Heart Rate from Facial Videos Based on RPPG and RBCG
by Hyunwoo Lee, Ayoung Cho and Mincheol Whang
Sensors 2021, 21(20), 6764; https://doi.org/10.3390/s21206764 - 12 Oct 2021
Cited by 5 | Viewed by 2646
Abstract
Remote sensing of vital signs has been developed to improve the measurement environment by using a camera without a skin-contact sensor. The camera-based method is based on two concepts, namely color and motion. The color-based method, remote photoplethysmography (RPPG), measures the color variation [...] Read more.
Remote sensing of vital signs has been developed to improve the measurement environment by using a camera without a skin-contact sensor. The camera-based method is based on two concepts, namely color and motion. The color-based method, remote photoplethysmography (RPPG), measures the color variation of the face generated by reflectance of blood, whereas the motion-based method, remote ballistocardiography (RBCG), measures the subtle motion of the head generated by heartbeat. The main challenge of remote sensing is overcoming the noise of illumination variance and motion artifacts. The studies on remote sensing have focused on the blind source separation (BSS) method for RGB colors or motions of multiple facial points to overcome the noise. However, they have still been limited in their real-world applications. This study hypothesized that BSS-based combining of colors and the motions can improve the accuracy and feasibility of remote sensing in daily life. Thus, this study proposed a fusion method to estimate heart rate based on RPPG and RBCG by the BSS methods such as ensemble averaging (EA), principal component analysis (PCA), and independent component analysis (ICA). The proposed method was verified by comparing it with previous RPPG and RBCG from three datasets according to illumination variance and motion artifacts. The three main contributions of this study are as follows: (1) the proposed method based on RPPG and RBCG improved the remote sensing with the benefits of each measurement; (2) the proposed method was demonstrated by comparing it with previous methods; and (3) the proposed method was tested in various measurement conditions for more practical applications. Full article
(This article belongs to the Special Issue Advanced Signal Processing in Wearable Sensors for Health Monitoring)
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20 pages, 4510 KiB  
Article
Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder
by Muammar Sadrawi, Yin-Tsong Lin, Chien-Hung Lin, Bhekumuzi Mathunjwa, Ho-Tsung Hsin, Shou-Zen Fan, Maysam F. Abbod and Jiann-Shing Shieh
Sensors 2021, 21(18), 6264; https://doi.org/10.3390/s21186264 - 18 Sep 2021
Cited by 5 | Viewed by 2395
Abstract
This study evaluates cardiovascular and cerebral hemodynamics systems by only using non-invasive electrocardiography (ECG) signals. The Massachusetts General Hospital/Marquette Foundation (MGH/MF) and Cerebral Hemodynamic Autoregulatory Information System Database (CHARIS DB) from the PhysioNet database are used for cardiovascular and cerebral hemodynamics, respectively. For [...] Read more.
This study evaluates cardiovascular and cerebral hemodynamics systems by only using non-invasive electrocardiography (ECG) signals. The Massachusetts General Hospital/Marquette Foundation (MGH/MF) and Cerebral Hemodynamic Autoregulatory Information System Database (CHARIS DB) from the PhysioNet database are used for cardiovascular and cerebral hemodynamics, respectively. For cardiovascular hemodynamics, the ECG is used for generating the arterial blood pressure (ABP), central venous pressure (CVP), and pulmonary arterial pressure (PAP). Meanwhile, for cerebral hemodynamics, the ECG is utilized for the intracranial pressure (ICP) generator. A deep convolutional autoencoder system is applied for this study. The cross-validation method with Pearson’s linear correlation (R), root mean squared error (RMSE), and mean absolute error (MAE) are measured for the evaluations. Initially, the ECG is used to generate the cardiovascular waveform. For the ABP system—the systolic blood pressure (SBP) and diastolic blood pressures (DBP)—the R evaluations are 0.894 ± 0.004 and 0.881 ± 0.005, respectively. The MAE evaluations for SBP and DBP are, respectively, 6.645 ± 0.353 mmHg and 3.210 ± 0.104 mmHg. Furthermore, for the PAP system—the systolic and diastolic pressures—the R evaluations are 0.864 ± 0.003 mmHg and 0.817 ± 0.006 mmHg, respectively. The MAE evaluations for systolic and diastolic pressures are, respectively, 3.847 ± 0.136 mmHg and 2.964 ± 0.181 mmHg. Meanwhile, the mean CVP evaluations are 0.916 ± 0.001, 2.220 ± 0.039 mmHg, and 1.329 ± 0.036 mmHg, respectively, for R, RMSE, and MAE. For the mean ICP evaluation in cerebral hemodynamics, the R and MAE evaluations are 0.914 ± 0.003 and 2.404 ± 0.043 mmHg, respectively. This study, as a proof of concept, concludes that the non-invasive cardiovascular and cerebral hemodynamics systems can be potentially investigated by only using the ECG signal. Full article
(This article belongs to the Special Issue Advanced Signal Processing in Wearable Sensors for Health Monitoring)
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18 pages, 2658 KiB  
Article
Automatic Classification of Myocardial Infarction Using Spline Representation of Single-Lead Derived Vectorcardiography
by Yu-Hung Chuang, Chia-Ling Huang, Wen-Whei Chang and Jen-Tzung Chien
Sensors 2020, 20(24), 7246; https://doi.org/10.3390/s20247246 - 17 Dec 2020
Cited by 12 | Viewed by 2699
Abstract
Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases worldwide and most patients suffer from MI without awareness. Therefore, early diagnosis and timely treatment are crucial to guarantee the life safety of MI patients. Most wearable monitoring devices only provide single-lead [...] Read more.
Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases worldwide and most patients suffer from MI without awareness. Therefore, early diagnosis and timely treatment are crucial to guarantee the life safety of MI patients. Most wearable monitoring devices only provide single-lead electrocardiography (ECG), which represents a major limitation for their applicability in diagnosis of MI. Incorporating the derived vectorcardiography (VCG) techniques can help monitor the three-dimensional electrical activities of human hearts. This study presents a patient-specific reconstruction method based on long short-term memory (LSTM) network to exploit both intra- and inter-lead correlations of ECG signals. MI-induced changes in the morphological and temporal wave features are extracted from the derived VCG using spline approximation. After the feature extraction, a classifier based on multilayer perceptron network is used for MI classification. Experiments on PTB diagnostic database demonstrate that the proposed system achieved satisfactory performance to differentiating MI patients from healthy subjects and to localizing the infarcted area. Full article
(This article belongs to the Special Issue Advanced Signal Processing in Wearable Sensors for Health Monitoring)
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18 pages, 9406 KiB  
Article
Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography
by Muammar Sadrawi, Yin-Tsong Lin, Chien-Hung Lin, Bhekumuzi Mathunjwa, Shou-Zen Fan, Maysam F. Abbod and Jiann-Shing Shieh
Sensors 2020, 20(14), 3829; https://doi.org/10.3390/s20143829 - 09 Jul 2020
Cited by 21 | Viewed by 3301
Abstract
Hypertension affects a huge number of people around the world. It also has a great contribution to cardiovascular- and renal-related diseases. This study investigates the ability of a deep convolutional autoencoder (DCAE) to generate continuous arterial blood pressure (ABP) by only utilizing photoplethysmography [...] Read more.
Hypertension affects a huge number of people around the world. It also has a great contribution to cardiovascular- and renal-related diseases. This study investigates the ability of a deep convolutional autoencoder (DCAE) to generate continuous arterial blood pressure (ABP) by only utilizing photoplethysmography (PPG). A total of 18 patients are utilized. LeNet-5- and U-Net-based DCAEs, respectively abbreviated LDCAE and UDCAE, are compared to the MP60 IntelliVue Patient Monitor, as the gold standard. Moreover, in order to investigate the data generalization, the cross-validation (CV) method is conducted. The results show that the UDCAE provides superior results in producing the systolic blood pressure (SBP) estimation. Meanwhile, the LDCAE gives a slightly better result for the diastolic blood pressure (DBP) prediction. Finally, the genetic algorithm-based optimization deep convolutional autoencoder (GDCAE) is further administered to optimize the ensemble of the CV models. The results reveal that the GDCAE is superior to either the LDCAE or UDCAE. In conclusion, this study exhibits that systolic blood pressure (SBP) and diastolic blood pressure (DBP) can also be accurately achieved by only utilizing a single PPG signal. Full article
(This article belongs to the Special Issue Advanced Signal Processing in Wearable Sensors for Health Monitoring)
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14 pages, 4032 KiB  
Article
Motion Artifact Reduction in Wearable Photoplethysmography Based on Multi-Channel Sensors with Multiple Wavelengths
by Jongshill Lee, Minseong Kim, Hoon-Ki Park and In Young Kim
Sensors 2020, 20(5), 1493; https://doi.org/10.3390/s20051493 - 09 Mar 2020
Cited by 71 | Viewed by 9385
Abstract
Photoplethysmography (PPG) is an easy and convenient method by which to measure heart rate (HR). However, PPG signals that optically measure volumetric changes in blood are not robust to motion artifacts. In this paper, we develop a PPG measuring system based on multi-channel [...] Read more.
Photoplethysmography (PPG) is an easy and convenient method by which to measure heart rate (HR). However, PPG signals that optically measure volumetric changes in blood are not robust to motion artifacts. In this paper, we develop a PPG measuring system based on multi-channel sensors with multiple wavelengths and propose a motion artifact reduction algorithm using independent component analysis (ICA). We also propose a truncated singular value decomposition for 12-channel PPG signals, which contain direction and depth information measured using the developed multi-channel PPG measurement system. The performance of the proposed method is evaluated against the R-peaks of an electrocardiogram in terms of sensitivity (Se), positive predictive value (PPV), and failed detection rate (FDR). The experimental results show that Se, PPV, and FDR were 99%, 99.55%, and 0.45% for walking, 96.28%, 99.24%, and 0.77% for fast walking, and 82.49%, 99.83%, and 0.17% for running, respectively. The evaluation shows that the proposed method is effective in reducing errors in HR estimation from PPG signals with motion artifacts in intensive motion situations such as fast walking and running. Full article
(This article belongs to the Special Issue Advanced Signal Processing in Wearable Sensors for Health Monitoring)
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13 pages, 2358 KiB  
Article
Determining the Online Measurable Input Variables in Human Joint Moment Intelligent Prediction Based on the Hill Muscle Model
by Baoping Xiong, Nianyin Zeng, Yurong Li, Min Du, Meilan Huang, Wuxiang Shi, Guojun Mao and Yuan Yang
Sensors 2020, 20(4), 1185; https://doi.org/10.3390/s20041185 - 21 Feb 2020
Cited by 12 | Viewed by 3244
Abstract
Introduction: Human joint moment is a critical parameter to rehabilitation assessment and human-robot interaction, which can be predicted using an artificial neural network (ANN) model. However, challenge remains as lack of an effective approach to determining the input variables for the ANN [...] Read more.
Introduction: Human joint moment is a critical parameter to rehabilitation assessment and human-robot interaction, which can be predicted using an artificial neural network (ANN) model. However, challenge remains as lack of an effective approach to determining the input variables for the ANN model in joint moment prediction, which determines the number of input sensors and the complexity of prediction. Methods: To address this research gap, this study develops a mathematical model based on the Hill muscle model to determining the online input variables of the ANN for the prediction of joint moments. In this method, the muscle activation, muscle-tendon moment velocity and length in the Hill muscle model and muscle-tendon moment arm are translated to the online measurable variables, i.e., muscle electromyography (EMG), joint angles and angular velocities of the muscle span. To test the predictive ability of these input variables, an ANN model is designed and trained to predict joint moments. The ANN model with the online measurable input variables is tested on the experimental data collected from ten healthy subjects running with the speeds of 2, 3, 4 and 5 m/s on a treadmill. The variance accounted for (VAF) between the predicted and inverse dynamics moment is used to evaluate the prediction accuracy. Results: The results suggested that the method can predict joint moments with a higher accuracy (mean VAF = 89.67±5.56 %) than those obtained by using other joint angles and angular velocities as inputs (mean VAF = 86.27±6.6%) evaluated by jack-knife cross-validation. Conclusions: The proposed method provides us with a powerful tool to predict joint moment based on online measurable variables, which establishes the theoretical basis for optimizing the input sensors and detection complexity of the prediction system. It may facilitate the research on exoskeleton robot control and real-time gait analysis in motor rehabilitation. Full article
(This article belongs to the Special Issue Advanced Signal Processing in Wearable Sensors for Health Monitoring)
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17 pages, 4466 KiB  
Article
Retrieval and Timing Performance of Chewing-Based Eating Event Detection in Wearable Sensors
by Rui Zhang and Oliver Amft
Sensors 2020, 20(2), 557; https://doi.org/10.3390/s20020557 - 20 Jan 2020
Cited by 14 | Viewed by 3712
Abstract
We present an eating detection algorithm for wearable sensors based on first detecting chewing cycles and subsequently estimating eating phases. We term the corresponding algorithm class as a bottom-up approach. We evaluated the algorithm using electromyographic (EMG) recordings from diet-monitoring eyeglasses in free-living [...] Read more.
We present an eating detection algorithm for wearable sensors based on first detecting chewing cycles and subsequently estimating eating phases. We term the corresponding algorithm class as a bottom-up approach. We evaluated the algorithm using electromyographic (EMG) recordings from diet-monitoring eyeglasses in free-living and compared the bottom-up approach against two top-down algorithms. We show that the F1 score was no longer the primary relevant evaluation metric when retrieval rates exceeded approx. 90%. Instead, detection timing errors provided more important insight into detection performance. In 122 hours of free-living EMG data from 10 participants, a total of 44 eating occasions were detected, with a maximum F1 score of 99.2%. Average detection timing errors of the bottom-up algorithm were 2.4 ± 0.4 s and 4.3 ± 0.4 s for the start and end of eating occasions, respectively. Our bottom-up algorithm has the potential to work with different wearable sensors that provide chewing cycle data. We suggest that the research community report timing errors (e.g., using the metrics described in this work). Full article
(This article belongs to the Special Issue Advanced Signal Processing in Wearable Sensors for Health Monitoring)
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Review

Jump to: Editorial, Research

18 pages, 682 KiB  
Review
Pain and Stress Detection Using Wearable Sensors and Devices—A Review
by Jerry Chen, Maysam Abbod and Jiann-Shing Shieh
Sensors 2021, 21(4), 1030; https://doi.org/10.3390/s21041030 - 03 Feb 2021
Cited by 75 | Viewed by 22679
Abstract
Pain is a subjective feeling; it is a sensation that every human being must have experienced all their life. Yet, its mechanism and the way to immune to it is still a question to be answered. This review presents the mechanism and correlation [...] Read more.
Pain is a subjective feeling; it is a sensation that every human being must have experienced all their life. Yet, its mechanism and the way to immune to it is still a question to be answered. This review presents the mechanism and correlation of pain and stress, their assessment and detection approach with medical devices and wearable sensors. Various physiological signals (i.e., heart activity, brain activity, muscle activity, electrodermal activity, respiratory, blood volume pulse, skin temperature) and behavioral signals are organized for wearables sensors detection. By reviewing the wearable sensors used in the healthcare domain, we hope to find a way for wearable healthcare-monitoring system to be applied on pain and stress detection. Since pain leads to multiple consequences or symptoms such as muscle tension and depression that are stress related, there is a chance to find a new approach for chronic pain detection using daily life sensors or devices. Then by integrating modern computing techniques, there is a chance to handle pain and stress management issue. Full article
(This article belongs to the Special Issue Advanced Signal Processing in Wearable Sensors for Health Monitoring)
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33 pages, 1365 KiB  
Review
A Survey of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG)
by Hongzu Li and Pierre Boulanger
Sensors 2020, 20(5), 1461; https://doi.org/10.3390/s20051461 - 06 Mar 2020
Cited by 47 | Viewed by 17485
Abstract
Cardiovascular diseases (CVDs) are the number one cause of death globally. An estimated 17.9 million people die from CVDs each year, representing 31% of all global deaths. Most cardiac patients require early detection and treatment. Therefore, many products to monitor patient’s heart conditions [...] Read more.
Cardiovascular diseases (CVDs) are the number one cause of death globally. An estimated 17.9 million people die from CVDs each year, representing 31% of all global deaths. Most cardiac patients require early detection and treatment. Therefore, many products to monitor patient’s heart conditions have been introduced on the market. Most of these devices can record a patient’s bio-metric signals both in resting and in exercising situations. However, reading the massive amount of raw electrocardiogram (ECG) signals from the sensors is very time-consuming. Automatic anomaly detection for the ECG signals could act as an assistant for doctors to diagnose a cardiac condition. This paper reviews the current state-of-the-art of this technology discusses the pros and cons of the devices and algorithms found in the literature and the possible research directions to develop the next generation of ambulatory monitoring systems. Full article
(This article belongs to the Special Issue Advanced Signal Processing in Wearable Sensors for Health Monitoring)
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