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

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 70066

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Special Issue Editors

Prof. Dr. Marco Di Rienzo

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Guest Editor

Department of Biomedical Technology, IRCCS Fondazione Don Carlo Gnocchi, Milano, Italy
Interests: textile sensors; smart garments; body sensor networks; 24-h and home cardiovascular monitoring; autonomic cardiac control; baroreflex modelling; seismocardiography; cardiac mechanics; hypertension; heart failure; space physiology, telemedicine, biosignal processing

Prof. Dr. Ramakrishna Mukkamala

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Guest Editor

Bioengineering, Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, PA, USA
Interests: cardiovascular system; computational physiology; medical devices; mHealth; patient monitoring; physiologic sensors; physiologic signal processing and identification; whole-animal, human, and patient studies

Special Issue Information

Dear Colleagues,

Biosensors and systems in the form of wearables and “nearables” (everyday sensorized objects with transmitting capabilities, e.g., smartphones) are rapidly evolving and used for the monitoring of health, exercise activity, and performance. Unlike conventional approaches, these devices enable convenient, continuous, and/or unobtrusive monitoring of a user’s vital signs during daily life. Examples of measurements include biopotentials, body motion, pressure, blood flow, temperature, and biochemical markers. These systems combine innovations in sensor design, electronics, data transmission, power management and signal processing. However, although much research has been carried out so far in this area, many technological aspects still remain to be optimized and represent open challenges for the scientific community.

This Special Issue invites original research papers and review articles aimed at proposing advancements in wearable and nearable biosensors and systems for healthcare. Exemplary topics of interest include biocompatible and stretchable materials, textile/flexible/tattoo/remote sensors and electronics, device miniaturization, low-power signal conditioning, multisensor wireless data transmission and synchronization, advanced algorithms for the analysis of sensor data, and extraction of biological features.

Prof. Marco Di Rienzo
Prof. Ramakrishna Mukkamala
Guest Editors

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Keywords

Biosensors
Low power systems
Mobile devices
Physiologic monitoring
Sensor miniaturization
Sensor signal processing
Stretchable and flexible sensors and electronics
Textile sensors
Flexible and tattoo sensors and electronics
Wearables
Nearables
Wireless data transmission

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Published Papers (13 papers)

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Editorial

Jump to: Research

3 pages, 179 KiB

Open AccessEditorial

Wearable and Nearable Biosensors and Systems for Healthcare

by Marco Di Rienzo and Ramakrishna Mukkamala

Sensors 2021, 21(4), 1291; https://doi.org/10.3390/s21041291 - 11 Feb 2021

Cited by 9 | Viewed by 2634

Abstract

Biosensors and systems in the form of wearables and “nearables” (i [...] Full article

(This article belongs to the Special Issue Wearable and Nearable Biosensors and Systems for Healthcare)

Research

Jump to: Editorial

22 pages, 10407 KiB

Open AccessArticle

Thin-Film Flexible Wireless Pressure Sensor for Continuous Pressure Monitoring in Medical Applications

by Muhammad Farooq, Talha Iqbal, Patricia Vazquez, Nazar Farid, Sudhin Thampi, William Wijns and Atif Shahzad

Sensors 2020, 20(22), 6653; https://doi.org/10.3390/s20226653 - 20 Nov 2020

Cited by 28 | Viewed by 8666

Abstract

Physiological pressure measurement is one of the most common applications of sensors in healthcare. Particularly, continuous pressure monitoring provides key information for early diagnosis, patient-specific treatment, and preventive healthcare. This paper presents a thin-film flexible wireless pressure sensor for continuous pressure measurement in a wide range of medical applications but mainly focused on interface pressure monitoring during compression therapy to treat venous insufficiency. The sensor is based on a pressure-dependent capacitor (

C

) and printed inductive coil (

L

) that form an inductor-capacitor (LC) resonant circuit. A matched reader coil provides an excellent coupling at the fundamental resonance frequency of the sensor. Considering varying requirements of venous ulceration, two versions of the sensor, with different sizes, were finalized after design parameter optimization and fabricated using a cost-effective and simple etching method. A test setup consisting of a glass pressure chamber and a vacuum pump was developed to test and characterize the response of the sensors. Both sensors were tested for a narrow range (0–100 mmHg) and a wide range (0–300 mmHg) to cover most of the physiological pressure measurement applications. Both sensors showed good linearity with high sensitivity in the lower pressure range <100 mmHg, providing a wireless monitoring platform for compression therapy in venous ulceration. Full article

(This article belongs to the Special Issue Wearable and Nearable Biosensors and Systems for Healthcare)

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17 pages, 1458 KiB

Open AccessArticle

Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender

by Francesco Asci, Giovanni Costantini, Pietro Di Leo, Alessandro Zampogna, Giovanni Ruoppolo, Alfredo Berardelli, Giovanni Saggio and Antonio Suppa

Sensors 2020, 20(18), 5022; https://doi.org/10.3390/s20185022 - 4 Sep 2020

Cited by 39 | Viewed by 4326

Abstract

Background: Experimental studies using qualitative or quantitative analysis have demonstrated that the human voice progressively worsens with ageing. These studies, however, have mostly focused on specific voice features without examining their dynamic interaction. To examine the complexity of age-related changes in voice, more advanced techniques based on machine learning have been recently applied to voice recordings but only in a laboratory setting. We here recorded voice samples in a large sample of healthy subjects. To improve the ecological value of our analysis, we collected voice samples directly at home using smartphones. Methods: 138 younger adults (65 males and 73 females, age range: 15–30) and 123 older adults (47 males and 76 females, age range: 40–85) produced a sustained emission of a vowel and a sentence. The recorded voice samples underwent a machine learning analysis through a support vector machine algorithm. Results: The machine learning analysis of voice samples from both speech tasks discriminated between younger and older adults, and between males and females, with high statistical accuracy. Conclusions: By recording voice samples through smartphones in an ecological setting, we demonstrated the combined effect of age and gender on voice. Our machine learning analysis demonstrates the effect of ageing on voice. Full article

(This article belongs to the Special Issue Wearable and Nearable Biosensors and Systems for Healthcare)

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14 pages, 5159 KiB

Open AccessArticle

A Novel Adaptive Recursive Least Squares Filter to Remove the Motion Artifact in Seismocardiography

by Shuai Yu and Sheng Liu

Sensors 2020, 20(6), 1596; https://doi.org/10.3390/s20061596 - 13 Mar 2020

Cited by 20 | Viewed by 3670

Abstract

This paper presents a novel adaptive recursive least squares filter (ARLSF) for motion artifact removal in the field of seismocardiography (SCG). This algorithm was tested with a consumer-grade accelerometer. This accelerometer was placed on the chest wall of 16 subjects whose ages ranged from 24 to 35 years. We recorded the SCG signal and the standard electrocardiogram (ECG) lead I signal by placing one electrode on the right arm (RA) and another on the left arm (LA) of the subjects. These subjects were asked to perform standing and walking movements on a treadmill. ARLSF was developed in MATLAB to process the collected SCG and ECG signals simultaneously. The SCG peaks and heart rate signals were extracted from the output of ARLSF. The results indicate a heartbeat detection accuracy of up to 98%. The heart rates estimated from SCG and ECG are similar under both standing and walking conditions. This observation shows that the proposed ARLSF could be an effective method to remove motion artifact from recorded SCG signals. Full article

(This article belongs to the Special Issue Wearable and Nearable Biosensors and Systems for Healthcare)

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15 pages, 3180 KiB

Open AccessEditor’s ChoiceArticle

SeisMote: A Multi-Sensor Wireless Platform for Cardiovascular Monitoring in Laboratory, Daily Life, and Telemedicine

by Marco Di Rienzo, Giovannibattista Rizzo, Zeynep Melike Işilay and Prospero Lombardi

Sensors 2020, 20(3), 680; https://doi.org/10.3390/s20030680 - 26 Jan 2020

Cited by 32 | Viewed by 5532

Abstract

This article presents a new wearable platform, SeisMote, for the monitoring of cardiovascular function in controlled conditions and daily life. It consists of a wireless network of sensorized nodes providing simultaneous multiple measures of electrocardiogram (ECG), acceleration, rotational velocity, and photoplethysmogram (PPG) from different body areas. A custom low-power transmission protocol was developed to allow the concomitant real-time monitoring of 32 signals (16 bit @200 Hz) from up to 12 nodes with a jitter in the among-node time synchronization lower than 0.2 ms. The BluetoothLE protocol may be used when only a single node is needed. Data can also be collected in the off-line mode. Seismocardiogram and pulse transit times can be derived from the collected data to obtain additional information on cardiac mechanics and vascular characteristics. The employment of the system in the field showed recordings without data gaps caused by transmission errors, and the duration of each battery charge exceeded 16 h. The system is currently used to investigate strategies of hemodynamic regulation in different vascular districts (through a multisite assessment of ECG and PPG) and to study the propagation of precordial vibrations along the thorax. The single-node version is presently exploited to monitor cardiac patients during telerehabilitation. Full article

(This article belongs to the Special Issue Wearable and Nearable Biosensors and Systems for Healthcare)

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19 pages, 4562 KiB

Open AccessArticle

Sensors for Expert Grip Force Profiling: Towards Benchmarking Manual Control of a Robotic Device for Surgical Tool Movements

by Michel de Mathelin, Florent Nageotte, Philippe Zanne and Birgitta Dresp-Langley

Sensors 2019, 19(20), 4575; https://doi.org/10.3390/s19204575 - 21 Oct 2019

Cited by 12 | Viewed by 4275

Abstract

STRAS (Single access Transluminal Robotic Assistant for Surgeons) is a new robotic system based on the Anubis^® platform of Karl Storz for application to intra-luminal surgical procedures. Pre-clinical testing of STRAS has recently permitted to demonstrate major advantages of the system in comparison with classic procedures. Benchmark methods permitting to establish objective criteria for ‘expertise’ need to be worked out now to effectively train surgeons on this new system in the near future. STRAS consists of three cable-driven sub-systems, one endoscope serving as guide, and two flexible instruments. The flexible instruments have three degrees of freedom and can be teleoperated by a single user via two specially designed master interfaces. In this study, small force sensors sewn into a wearable glove to ergonomically fit the master handles of the robotic system were employed for monitoring the forces applied by an expert and a trainee (complete novice) during all the steps of surgical task execution in a simulator task (4-step-pick-and-drop). Analysis of grip-force profiles is performed sensor by sensor to bring to the fore specific differences in handgrip force profiles in specific sensor locations on anatomically relevant parts of the fingers and hand controlling the master/slave system. Full article

(This article belongs to the Special Issue Wearable and Nearable Biosensors and Systems for Healthcare)

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16 pages, 4605 KiB

Open AccessArticle

Assessment of Ultra-Short Heart Variability Indices Derived by Smartphone Accelerometers for Stress Detection

by Federica Landreani, Andrea Faini, Alba Martin-Yebra, Mattia Morri, Gianfranco Parati and Enrico Gianluca Caiani

Sensors 2019, 19(17), 3729; https://doi.org/10.3390/s19173729 - 28 Aug 2019

Cited by 24 | Viewed by 6333

Abstract

Body acceleration due to heartbeat-induced reaction forces can be measured as mobile phone accelerometer (m-ACC) signals. Our aim was to test the feasibility of using m-ACC to detect changes induced by stress by ultra-short heart rate variability (USV) indices (standard deviation of normal-to-normal interval—SDNN and root mean square of successive differences—RMSSD). Sixteen healthy volunteers were recruited; m-ACC was recorded while in supine position, during spontaneous breathing at rest conditions (REST) and during one minute of mental stress (MS) induced by arithmetic serial subtraction task, simultaneous with conventional electrocardiogram (ECG). Beat occurrences were extracted from both ECG and m-ACC and used to compute USV indices using 60, 30 and 10 s durations, both for REST and MS. A feasibility of 93.8% in the beat-to-beat m-ACC heart rate series extraction was reached. In both ECG and m-ACC series, compared to REST, in MS the mean beat duration was reduced by 15% and RMSSD decreased by 38%. These results show that short term recordings (up to 10 s) of cardiac activity using smartphone’s accelerometers are able to capture the decrease in parasympathetic tone, in agreement with the induced stimulus. Full article

(This article belongs to the Special Issue Wearable and Nearable Biosensors and Systems for Healthcare)

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16 pages, 2460 KiB

Open AccessArticle

In-Ear Pulse Rate Measurement: A Valid Alternative to Heart Rate Derived from Electrocardiography?

by Stefanie Passler, Niklas Müller and Veit Senner

Sensors 2019, 19(17), 3641; https://doi.org/10.3390/s19173641 - 21 Aug 2019

Cited by 39 | Viewed by 7377

Abstract

Heart rate measurement has become one of the most widely used methods of monitoring the intensity of physical activity. The purpose of this study was to assess whether in-ear photoplethysmographic (PPG) pulse rate (PR) measurement devices represent a valid alternative to heart rate derived from electrocardiography (ECG), which is considered a gold standard. Twenty subjects (6 women, 14 men) completed one trial of graded cycling under laboratory conditions. In the trial, PR was recorded by two commercially available in-ear devices, the Dash Pro and the Cosinuss°One. They were compared to HR measured by a Bodyguard2 ECG. Validity of the in-ear PR measurement devices was tested by ANOVA, mean absolute percentage errors (MAPE), intra-class correlation coefficient (ICC), and Bland–Altman plots. Both devices achieved a MAPE ≤5%. Despite excellent to good levels of agreement, Bland–Altman plots showed that both in-ear devices tend to slightly underestimate the ECG’s HR values. It may be concluded that in-ear PPG PR measurement is a promising technique that shows accurate but imprecise results under controlled conditions. However, PPG PR measurement in the ear is sensitive to motion artefacts. Thus, accuracy and precision of the measured PR depend highly on measurement site, stress situation, and exercise. Full article

(This article belongs to the Special Issue Wearable and Nearable Biosensors and Systems for Healthcare)

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18 pages, 3012 KiB

Open AccessArticle

Unobtrusive Estimation of Cardiovascular Parameters with Limb Ballistocardiography

by Yang Yao, Sungtae Shin, Azin Mousavi, Chang-Sei Kim, Lisheng Xu, Ramakrishna Mukkamala and Jin-Oh Hahn

Sensors 2019, 19(13), 2922; https://doi.org/10.3390/s19132922 - 1 Jul 2019

Cited by 28 | Viewed by 5200

Abstract

This study investigates the potential of the limb ballistocardiogram (BCG) for unobtrusive estimation of cardiovascular (CV) parameters. In conjunction with the reference CV parameters (including diastolic, pulse, and systolic pressures, stroke volume, cardiac output, and total peripheral resistance), an upper-limb BCG based on an accelerometer embedded in a wearable armband and a lower-limb BCG based on a strain gauge embedded in a weighing scale were instrumented simultaneously with a finger photoplethysmogram (PPG). To standardize the analysis, the more convenient yet unconventional armband BCG was transformed into the more conventional weighing scale BCG (called the synthetic weighing scale BCG) using a signal processing procedure. The characteristic features were extracted from these BCG and PPG waveforms in the form of wave-to-wave time intervals, wave amplitudes, and wave-to-wave amplitudes. Then, the relationship between the characteristic features associated with (i) the weighing scale BCG-PPG pair and (ii) the synthetic weighing scale BCG-PPG pair versus the CV parameters, was analyzed using the multivariate linear regression analysis. The results indicated that each of the CV parameters of interest may be accurately estimated by a combination of as few as two characteristic features in the upper-limb or lower-limb BCG, and also that the characteristic features recruited for the CV parameters were to a large extent relevant according to the physiological mechanism underlying the BCG. Full article

(This article belongs to the Special Issue Wearable and Nearable Biosensors and Systems for Healthcare)

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28 pages, 3870 KiB

Open AccessFeature PaperArticle

Learning the Orientation of a Loosely-Fixed Wearable IMU Relative to the Body Improves the Recognition Rate of Human Postures and Activities

by Michael B. Del Rosario, Nigel H. Lovell and Stephen J. Redmond

Sensors 2019, 19(13), 2845; https://doi.org/10.3390/s19132845 - 26 Jun 2019

Cited by 6 | Viewed by 4817

Abstract

Features were developed which accounted for the changing orientation of the inertial measurement unit (IMU) relative to the body, and demonstrably improved the performance of models for human activity recognition (HAR). The method is proficient at separating periods of standing and sedentary activity (i.e., sitting and/or lying) using only one IMU, even if it is arbitrarily oriented or subsequently re-oriented relative to the body; since the body is upright during walking, learning the IMU orientation during walking provides a reference orientation against which sitting and/or lying can be inferred. Thus, the two activities can be identified (irrespective of the cohort) by analyzing the magnitude of the angle of shortest rotation which would be required to bring the upright direction into coincidence with the average orientation from the most recent 2.5 s of IMU data. Models for HAR were trained using data obtained from a cohort of 37 older adults (83.9 ± 3.4 years) or 20 younger adults (21.9 ± 1.7 years). Test data were generated from the training data by virtually re-orienting the IMU so that it is representative of carrying the phone in five different orientations (relative to the thigh). The overall performance of the model for HAR was consistent whether the model was trained with the data from the younger cohort, and tested with the data from the older cohort after it had been virtually re-oriented (Cohen’s Kappa 95% confidence interval [0.782, 0.793]; total class sensitivity 95% confidence interval [84.9%, 85.6%]), or the reciprocal scenario in which the model was trained with the data from the older cohort, and tested with the data from the younger cohort after it had been virtually re-oriented (Cohen’s Kappa 95% confidence interval [0.765, 0.784]; total class sensitivity 95% confidence interval [82.3%, 83.7%]). Full article

(This article belongs to the Special Issue Wearable and Nearable Biosensors and Systems for Healthcare)

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11 pages, 10897 KiB

Open AccessArticle

A Glove-Based Form Factor for Collecting Joint Acoustic Emissions: Design and Validation

by Nicholas B. Bolus, Hyeon Ki Jeong, Daniel C. Whittingslow and Omer T. Inan

Sensors 2019, 19(12), 2683; https://doi.org/10.3390/s19122683 - 13 Jun 2019

Cited by 15 | Viewed by 3411

Abstract

Sounds produced by the articulation of joints have been shown to contain information characteristic of underlying joint health, morphology, and loading. In this work, we explore the use of a novel form factor for non-invasively acquiring acoustic/vibrational signals from the knee joint: an instrumented glove with a fingertip-mounted accelerometer. We validated the glove-based approach by comparing it to conventional mounting techniques (tape and foam microphone pads) in an experimental framework previously shown to reliably alter healthy knee joint sounds (vertical leg press). Measurements from healthy subjects (N = 11) in this proof-of-concept study demonstrated a highly consistent, monotonic, and significant (p < 0.01) increase in low-frequency signal root-mean-squared (RMS) amplitude—a straightforward metric relating to joint grinding loudness—with increasing vertical load across all three techniques. This finding suggests that a glove-based approach is a suitable alternative for collecting joint sounds that eliminates the need for consumables like tape and the interface noise associated with them. Full article

(This article belongs to the Special Issue Wearable and Nearable Biosensors and Systems for Healthcare)

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17 pages, 1994 KiB

Open AccessArticle

Evaluation of a Commercial Ballistocardiography Sensor for Sleep Apnea Screening and Sleep Monitoring

by Dorien Huysmans, Pascal Borzée, Dries Testelmans, Bertien Buyse, Tim Willemen, Sabine Van Huffel and Carolina Varon

Sensors 2019, 19(9), 2133; https://doi.org/10.3390/s19092133 - 8 May 2019

Cited by 28 | Viewed by 5761

Abstract

There exists a technological momentum towards the development of unobtrusive, simple, and reliable systems for long-term sleep monitoring. An off-the-shelf commercial pressure sensor meeting these requirements is the Emfit QS. First, the potential for sleep apnea screening was investigated by revealing clusters of contaminated and clean segments. A relationship between the irregularity of the data and the sleep apnea severity class was observed, which was valuable for screening (sensitivity 0.72, specificity 0.70), although the linear relation was limited (

R^{2}

of 0.16). Secondly, the study explored the suitability of this commercial sensor to be merged with gold standard polysomnography data for future sleep monitoring. As polysomnography (PSG) and Emfit signals originate from different types of sensor modalities, they cannot be regarded as strictly coupled. Therefore, an automated synchronization procedure based on artefact patterns was developed. Additionally, the optimal position of the Emfit for capturing respiratory and cardiac information similar to the PSG was identified, resulting in a position as close as possible to the thorax. The proposed approach demonstrated the potential for unobtrusive screening of sleep apnea patients at home. Furthermore, the synchronization framework enabled supervised analysis of the commercial Emfit sensor for future sleep monitoring, which can be extended to other multi-modal systems that record movements during sleep. Full article

(This article belongs to the Special Issue Wearable and Nearable Biosensors and Systems for Healthcare)

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13 pages, 2287 KiB

Open AccessArticle

Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods

by Ikhtiyor Majidov and Taegkeun Whangbo

Sensors 2019, 19(7), 1736; https://doi.org/10.3390/s19071736 - 11 Apr 2019

Cited by 77 | Viewed by 6610

Abstract

Single-trial motor imagery classification is a crucial aspect of brain–computer applications. Therefore, it is necessary to extract and discriminate signal features involving motor imagery movements. Riemannian geometry-based feature extraction methods are effective when designing these types of motor-imagery-based brain–computer interface applications. In the field of information theory, Riemannian geometry is mainly used with covariance matrices. Accordingly, investigations showed that if the method is used after the execution of the filterbank approach, the covariance matrix preserves the frequency and spatial information of the signal. Deep-learning methods are superior when the data availability is abundant and while there is a large number of features. The purpose of this study is to a) show how to use a single deep-learning-based classifier in conjunction with BCI (brain–computer interface) applications with the CSP (common spatial features) and the Riemannian geometry feature extraction methods in BCI applications and to b) describe one of the wrapper feature-selection algorithms, referred to as the particle swarm optimization, in combination with a decision tree algorithm. In this work, the CSP method was used for a multiclass case by using only one classifier. Additionally, a combination of power spectrum density features with covariance matrices mapped onto the tangent space of a Riemannian manifold was used. Furthermore, the particle swarm optimization method was implied to ease the training by penalizing bad features, and the moving windows method was used for augmentation. After empirical study, the convolutional neural network was adopted to classify the pre-processed data. Our proposed method improved the classification accuracy for several subjects that comprised the well-known BCI competition IV 2a dataset. Full article

(This article belongs to the Special Issue Wearable and Nearable Biosensors and Systems for Healthcare)

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