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Wearable Electronics, Smart Textiles and Computing

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

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 68695

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Special Issue Editors

Prof. Dr. Wendong Xiao

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School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: wireless localization and tracking; energy harvesting based network resource management; distributed machine learning for big data; wireless sensor networks; internet of things
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Prof. Dr. Dongyi Chen

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School of Automation Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China

Prof. Dr. Ting Zhang

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Suzhou Institute of Nano-tech and Nano-bionics, Chinese Academy of Sciences, Suzhou, China
Interests: nanoelectronics; nanosensor; nanofabrication
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Prof. Dr. Li Liu

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School of Big Data & Software Engineering, Chongqing University, Chongqing 401331, China
Interests: big data analysis; machine learning
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Prof. Dr. Giancarlo Fortino

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Computer Engineering, DIMES-Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, 87036 Rende, Italy
Interests: body area networks; Internet of Things; agent-based computing
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Special Issue Information

Dear Colleagues,

Wearable technology has developed new computing paradigms and interactive methodologies that bring intelligence and services close to, around, and on people’s bodies. It stimulates new research directions such as electronics, sensors, intelligent perception, data analysis, service provision, human–computer interface, and platform construction, resulting in new discoveries that push the boundaries of science and technology forward, with broad applications in health care, medical treatment, clothing and fashion, entertainment, military affairs, intelligent transportation, public safety, and many other fields.

Smart textiles research represents a new model for generating creative and novel solutions for integrating electronics into unusual environments, requiring innovative developments and applications of smart fabric sensors, electronic textile technology, and wearable computing for detecting human physiological signals.

Flexible and stretchable electronics are an exciting frontier for the next generation of wearable and portable electronic devices. Recently rapid research progress has been achieved in nanomaterials based high-performance flexible and stretchable sensing electronics, and there are versatile application areas such as robotic sensory skins, wearable health monitoring systems, bio-integrated devices, and human-machine interfaces, etc.

Furthermore, the combination of wearable computing with the internet of things, cloud computing, big data processing, mobile internet, and artificial intelligence has a profound impact on research, development, and applications of wearable technology, with huge new challenges and opportunities.

The purpose of this Special Issue is to invite novel contributions on recent research results and development activities on wearable electronics, flexible electronics, smart textiles, and computing. Topics of interests include, but are not limited to the following:

Fabric electrode, smart flexible, and stretchable sensors
Flexible and stretchable energy harvesting and storage
Smart glasses, watches, bracelets, finger rings, and cloths
Wearable non-invasive contactless measurement
Wearable sensor signal processing and data fusion
Wearable computing for health monitoring, medical treatment; context awareness, activity recognition, behavior analysis, and emotional recognition
Wearable communication and network protocols
Wearable low power design and computing
Wearable computing enabled big-data analytics
Wearable computing merged with artificial intelligence (e.g., data mining and machine learning)
Integrating wearable computing with cloud computing
Wearable localization and tracking
Wearable robots and automation
Wearable computing platforms, systems and case studies (e.g., neurological disorders, cardiovascular diseases, diabetes and obesity, neuro-rehabilitation, interactive games, smart ageing, entertainment and fashion)

Prof. Dr. Wendong Xiao
Prof. Dr. Dongyi Chen
Prof. Dr. Ting Zhang
Prof. Dr. Li Liu
Prof. Dr. Giancarlo Fortino
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.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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.

Published Papers (11 papers)

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Research

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11 pages, 3257 KiB

Open AccessArticle

A Wavelet Adaptive Cancellation Algorithm Based on Multi-Inertial Sensors for the Reduction of Motion Artifacts in Ambulatory ECGs

by Fan Xiong, Dongyi Chen and Miao Huang

Sensors 2020, 20(4), 970; https://doi.org/10.3390/s20040970 - 11 Feb 2020

Cited by 10 | Viewed by 2482

Abstract

Wearable electrocardiogram (ECG) devices are universally used around the world for patients who have cardiovascular disease (CVD). At present, how to suppress motion artifacts is one of the most challenging issues in the field of physiological signal processing. In this paper, we propose an adaptive cancellation algorithm based on multi-inertial sensors to suppress motion artifacts in ambulatory ECGs. Firstly, this method collects information related to the electrode motion through multi-inertial sensors. Then, the part that is not related to the electrode motion is removed through wavelet transform, which improves the correlation of the reference input signal. In this way, the ability of the adaptive cancellation algorithm to remove motion artifacts is improved in the ambulatory ECG. Subsequent experimentation demonstrated that the wavelet adaptive cancellation algorithm based on multi-inertial sensors can effectively remove motion artifacts in ambulatory ECGs. Full article

(This article belongs to the Special Issue Wearable Electronics, Smart Textiles and Computing)

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16 pages, 4784 KiB

Open AccessArticle

A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection

by Minggang Shao, Zhuhuang Zhou, Guangyu Bin, Yanping Bai and Shuicai Wu

Sensors 2020, 20(3), 606; https://doi.org/10.3390/s20030606 - 22 Jan 2020

Cited by 29 | Viewed by 6982

Abstract

In this paper we proposed a wearable electrocardiogram (ECG) telemonitoring system for atrial fibrillation (AF) detection based on a smartphone and cloud computing. A wearable ECG patch was designed to collect ECG signals and send the signals to an Android smartphone via Bluetooth. An Android APP was developed to display the ECG waveforms in real time and transmit every 30 s ECG data to a remote cloud server. A machine learning (CatBoost)-based ECG classification method was proposed to detect AF in the cloud server. In case of detected AF, the cloud server pushed the ECG data and classification results to the web browser of a doctor. Finally, the Android APP displayed the doctor’s diagnosis for the ECG signals. Experimental results showed the proposed CatBoost classifier trained with 17 selected features achieved an overall F₁ score of 0.92 on the test set (n = 7270). The proposed wearable ECG monitoring system may potentially be useful for long-term ECG telemonitoring for AF detection. Full article

(This article belongs to the Special Issue Wearable Electronics, Smart Textiles and Computing)

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15 pages, 4460 KiB

Open AccessArticle

Closing the Wearable Gap—Part V: Development of a Pressure-Sensitive Sock Utilizing Soft Sensors

by Tony Luczak, Reuben F. Burch V, Brian K. Smith, Daniel W. Carruth, John Lamberth, Harish Chander, Adam Knight, J.E. Ball and R.K. Prabhu

Sensors 2020, 20(1), 208; https://doi.org/10.3390/s20010208 - 30 Dec 2019

Cited by 17 | Viewed by 3329

Abstract

The purpose of this study was to evaluate the use of compressible soft robotic sensors (C-SRS) in determining plantar pressure to infer vertical and shear forces in wearable technology: A ground reaction pressure sock (GRPS). To assess pressure relationships between C-SRS, pressure cells on a BodiTrak^TM Vector Plate, and Kistler^TM Force Plates, thirteen volunteers performed three repetitions of three different movements: squats, shifting center-of-pressure right to left foot, and shifting toes to heels with C-SRS in both anterior–posterior (A/P) and medial–lateral (M/L) sensor orientations. Pearson correlation coefficient of C-SRS to BodiTrak^TM Vector Plate resulted in an average R-value greater than 0.70 in 618/780 (79%) of sensor to cell comparisons. An average R-value greater than 0.90 was seen in C-SRS comparison to Kistler^TM Force Plates during shifting right to left. An autoregressive integrated moving average (ARIMA) was conducted to identify and estimate future C-SRS data. No significant differences were seen in sensor orientation. Sensors in the A/P orientation reported a mean R² value of 0.952 and 0.945 in the M/L sensor orientation, reducing the effectiveness to infer shear forces. Given the high R values, the use of C-SRSs to infer normal pressures appears to make the development of the GRPS feasible. Full article

(This article belongs to the Special Issue Wearable Electronics, Smart Textiles and Computing)

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17 pages, 8159 KiB

Open AccessArticle

Proposal of the Tactile Glove Device

by José C. V. S. Junior, Matheus F. Torquato, Daniel H. Noronha, Sérgio N. Silva and Marcelo A. C. Fernandes

Sensors 2019, 19(22), 5029; https://doi.org/10.3390/s19225029 - 18 Nov 2019

Cited by 12 | Viewed by 4981

Abstract

This project aims to develop a tactile glove device and a virtual environment inserted in the context of tactile internet. The tactile glove allows a human operator to interact remotely with objects from a 3D environment through tactile feedback or tactile sensation. In other words, the human operator is able to feel the contour and texture from virtual objects. Applications such as remote diagnostics, games, remote analysis of materials, and others in which objects could be virtualized can be significantly improved using this kind of device. These gloves have been an essential device in all research on the internet next generation called “Tactile Internet”, in which this project is inserted. Unlike the works presented in the literature, the novelty of this work is related to architecture, and tactile devices developed. They are within the 10 ms round trip latency limits required in a tactile internet environment. Details of hardware and software designs of a tactile glove, as well as the virtual environment, are described. Results and comparative analysis about round trip latency time in the tactile internet environment is developed. Full article

(This article belongs to the Special Issue Wearable Electronics, Smart Textiles and Computing)

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16 pages, 7926 KiB

Open AccessArticle

Optimized Electrode Locations for Wearable Single-Lead ECG Monitoring Devices: A Case Study Using WFEES Modules Based on the LANS Method

by Huaiyu Zhu, Yun Pan, Fan Wu and Ruohong Huan

Sensors 2019, 19(20), 4458; https://doi.org/10.3390/s19204458 - 14 Oct 2019

Cited by 11 | Viewed by 3264

Abstract

Body surface potential mapping (BSPM) is a valuable tool for research regarding electrocardiograms (ECG). However, the BSPM system is limited by its large number of electrodes and wires, long installation time, and high computational complexity. In this paper, we designed a wearable four-electrode electrocardiogram-sensor (WFEES) module that measures six-channel ECGs simultaneously for ECG investigation. To reduce the testing lead number and the measurement complexity, we further proposed a method, the layered (A, N) square-based (LANS) method, to optimize the ECG acquisition and analysis process using WFEES modules for different applications. Moreover, we presented a case study of electrode location optimization for wearable single-lead ECG monitoring devices using WFEES modules with the LANS method. In this study, 102 sets of single-lead ECG data from 19 healthy subjects were analyzed. The signal-to-noise ratio of ECG, as well as the mean and coefficient of variation of QRS amplitude, was derived among different channels to determine the optimal electrode locations. The results showed that a single-lead electrode pair should be placed on the left chest above the electrode location of standard precordial leads V1 to V4. Additionally, the best orientation was the principal diagonal as the direction of the heart’s electrical axis. Full article

(This article belongs to the Special Issue Wearable Electronics, Smart Textiles and Computing)

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24 pages, 6621 KiB

Open AccessArticle

Field Research Cooperative Wearable Systems: Challenges in Requirements, Design and Validation

by Mateus C. Silva, Vicente J. P. Amorim, Sérvio P. Ribeiro and Ricardo A. R. Oliveira

Sensors 2019, 19(20), 4417; https://doi.org/10.3390/s19204417 - 12 Oct 2019

Cited by 10 | Viewed by 3630

Abstract

The widespread availability of wearable devices is evolving them into cooperative systems. Communication and distribution aspects such as the Internet of Things, Wireless Body Area Networks, and Local Wireless Networks provide the means to develop multi-device platforms. Nevertheless, the field research environment presents a specific feature set, which increases the difficulty in the adoption of this technology. In this text, we review the main aspects of Field Research Gears and Wearable Devices. This review is made aiming to understand how to create cooperative systems based on wearable devices directed to the Field Research Context. For a better understanding, we developed a case study in which we propose a cooperative system architecture and provide validation aspects. For this matter, we provide an overview of a previous device architecture and study an integration proposal. Full article

(This article belongs to the Special Issue Wearable Electronics, Smart Textiles and Computing)

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12 pages, 2495 KiB

Open AccessArticle

An Approach to Dynamic Sensing Data Fusion

by Yunfei Yin, Liufa Guan and Chengen Zheng

Sensors 2019, 19(17), 3668; https://doi.org/10.3390/s19173668 - 23 Aug 2019

Cited by 5 | Viewed by 2856

Abstract

For the research and development of sensor systems, the collection and fusion of sensing data is the core. In order to make sensor data acquisition change with the change in environment, a dynamic data acquisition and fusion method based on feedback control is proposed in this paper. According to the sensing data acquisition and fusion model, the optimal acquisition of sensor data is achieved through real-time dynamic judgment of the collected data, decision-making of the next acquisition time interval, and adjustment. This model enables the sensor system to adapt to different environments. An experimental study of the proposed model was carried out on an experimental platform, and the results show that the proposed model can not only reflect the change in sensing data but also improve the transmission efficiency. Full article

(This article belongs to the Special Issue Wearable Electronics, Smart Textiles and Computing)

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18 pages, 3961 KiB

Open AccessArticle

Electrocardiogram Classification Based on Faster Regions with Convolutional Neural Network

by Yinsheng Ji, Sen Zhang and Wendong Xiao

Sensors 2019, 19(11), 2558; https://doi.org/10.3390/s19112558 - 05 Jun 2019

Cited by 73 | Viewed by 6001

Abstract

The classification of electrocardiograms (ECG) plays an important role in the clinical diagnosis of heart disease. This paper proposes an effective system development and implementation for ECG classification based on faster regions with a convolutional neural network (Faster R-CNN) algorithm. The original one-dimensional ECG signals contain the preprocessed patient ECG signals and some ECG recordings from the MIT-BIH database in this experiment. Each ECG beat of one-dimensional ECG signals was transformed into a two-dimensional image for experimental training sets and test sets. As a result, we classified the ECG beats into five categories with an average accuracy of 99.21%. In addition, we did a comparative experiment using the one versus rest support vector machine (OVR SVM) algorithm, and the classification accuracy of the proposed Faster R-CNN was shown to be 2.59% higher. Full article

(This article belongs to the Special Issue Wearable Electronics, Smart Textiles and Computing)

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15 pages, 4287 KiB

Open AccessArticle

A Highlight-Generation Method for Rendering Translucent Objects

by Hui Yu, Peter X. Liu and Lingyan Hu

Sensors 2019, 19(4), 860; https://doi.org/10.3390/s19040860 - 19 Feb 2019

Cited by 3 | Viewed by 3433

Abstract

The acquisition of translucent objects has become a very common task thanks to the progress of 3D scanning technology. Since the characteristic soft appearance of translucent objects is due to subsurface scattering, the details are naturally left out in this appearance. For objects that have complex shapes, this lack of detail is obviously more prominent. In this paper, we propose a method to preserve the details of surface geometry by adding highlight effects. In generating highlight effects, our method employs a local orthonormal frame and combines, in a novel way, the incoming and outgoing light in approximating the subsurface scattering process. When the incident illuminant direction changes from nearly overhead to nearly horizontal, our method effectively preserves complex surface geometry details in the appearance of translucent materials. Through experiments, we show that our method can store surface features as well as maintain the translucency of the original materials and even enhance the perception of translucency. By numerically comparing the generated highlight effects with those generated by the traditional Bidirectional Reflectance Distribution Function (BRDF) models with different bandwidth parameters, we demonstrate the validity of our proposed method. Full article

(This article belongs to the Special Issue Wearable Electronics, Smart Textiles and Computing)

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Review

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23 pages, 2315 KiB

Open AccessReview

Review on Smart Electro-Clothing Systems (SeCSs)

by Abu Sadat Muhammad Sayem, Siew Hon Teay, Hasan Shahariar, Paula Luise Fink and Alhussein Albarbar

Sensors 2020, 20(3), 587; https://doi.org/10.3390/s20030587 - 21 Jan 2020

Cited by 64 | Viewed by 24270

Abstract

This review paper presents an overview of the smart electro-clothing systems (SeCSs) targeted at health monitoring, sports benefits, fitness tracking, and social activities. Technical features of the available SeCSs, covering both textile and electronic components, are thoroughly discussed and their applications in the industry and research purposes are highlighted. In addition, it also presents the developments in the associated areas of wearable sensor systems and textile-based dry sensors. As became evident during the literature research, such a review on SeCSs covering all relevant issues has not been presented before. This paper will be particularly helpful for new generation researchers who are and will be investigating the design, development, function, and comforts of the sensor integrated clothing materials. Full article

(This article belongs to the Special Issue Wearable Electronics, Smart Textiles and Computing)

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35 pages, 466 KiB

Open AccessReview

Fabrics and Garments as Sensors: A Research Update

by Sophie Wilson and Raechel Laing

Sensors 2019, 19(16), 3570; https://doi.org/10.3390/s19163570 - 15 Aug 2019

Cited by 27 | Viewed by 6105

Abstract

Properties critical to the structure of apparel and apparel fabrics (thermal and moisture transfer, elasticity, and flexural rigidity), those related to performance (durability to abrasion, cleaning, and storage), and environmental effects have not been consistently addressed in the research on fabric sensors designed to interact with the human body. These fabric properties need to be acceptable for functionalized fabrics to be effectively used in apparel. Measures of performance such as electrical conductivity, impedance, and/or capacitance have been quantified. That the apparel/human body system involves continuous transient conditions needs to be taken into account when considering performance. This review highlights gaps concerning fabric-related aspects for functionalized apparel and includes information on increasing the inclusion of such aspects. A multidisciplinary approach including experts in chemistry, electronics, textiles, and standard test methods, and the intended end use is key to widespread development and adoption. Full article

(This article belongs to the Special Issue Wearable Electronics, Smart Textiles and Computing)

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