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Wearable/Wireless Body Sensor Networks for Healthcare Applications

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

Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 74142

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Special Issue Editors

Prof. Dr. Fernanda Irrera

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Guest Editor

Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, Rome, Italy
Interests: wearable electronics; More-than-Moore integration; nanoelectronics; CMOS device reliability; CMOS image sensors; innovative non-volatile memories
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Adrian Ionescu

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Guest Editor

Ecole Polytechnique Fédérale Lausanne, Lausanne, Switzerland
Interests: nanoelectronic devices; silicon nanotechnology; silicon on insulator; radio frequency MEMS and NEMS; small swing switches; emerging memories modeling and simulation of solid-state electronic devices

Prof. Dr. Marco Santonico

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Guest Editor

Department of Science and Technology for Humans and the Environment, Campus Bio-Medico University of Rome, 00128 Rome, Italy
Interests: electronics for sensor systems; interfaces and integration of sensor systems and networks and their utilization in medical, food, and industrial applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The combination of the Internet-of-Things and the trillion sensors movement exploits technological and design concepts such as zero-power; intelligent, autonomous systems featuring energy efficient sensing; computation; and communication. This Special Issue calls for research contributions on wearable platforms, integrating human physical and physiological parameter biosensors and sensor networks. Smart signal processing and machine learning methods, together with advanced communication strategies, enable the use of the remarkable amount of data gathered from convergent integrated sensors for lifestyle and healthcare applications. More recently, the capabilities of these technologies to generate data in the form of digital biomarkers, enabling both diagnostic and prognostic features in the course of a disease, have attracted increased interest (including for infectious diseases such as coronavirus). Technological advancements of energy-efficient, wireless interconnected systems offer unique solutions for new generations of non-invasive, ubiquitous, and continuous healthcare monitoring and for forthcoming smart apparel with embedded autonomous sensing. Such multifunctional wearable systems will beneficially track and interact with the end-user through appropriate feedback channels on a daily basis. The ultimate target is enabling personalized advice, remote assistance, and treatment; promoting a healthier lifestyle; and improving healthcare prevention, monitoring, and follow-up. In the long term, these platforms could also form the basis for new generations of data generators for human–machine interfaces.

The scientific advisory board of this Special Issue is composed of the following: Dr. Francesco Bellando, Dr. Ivan Mazzetta, and Dr. Alessandro Zompanti.

Prof. Dr. Fernanda Irrera

Prof. Dr. Adrian Ionescu

Prof. Dr. Marco Santonico

Guest Editors

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Keywords

wearable electronics
smart, autonomous sensing platform
multifunctional wireless-connected platform
body sensor network
physical and physiological parameter sensing
zero-power consumption
lifestyle and healthcare prevention, monitoring, and follow-up
smart signal processing
machine learning
advanced communication strategies

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Published Papers (14 papers)

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Research

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26 pages, 4449 KiB

Open AccessArticle

A Heuristic Approach for Optical Transceiver Placement to Optimize SNR and Illuminance Uniformities of an Optical Body Area Network

by Komal Masroor, Varun Jeoti, Micheal Drieberg, Sovuthy Cheab and Sujan Rajbhandari

Sensors 2021, 21(9), 2943; https://doi.org/10.3390/s21092943 - 22 Apr 2021

Cited by 1 | Viewed by 2756

Abstract

The bi-directional information transfer in optical body area networks (OBANs) is crucial at all the three tiers of communication, i.e., intra-, inter-, and beyond-BAN communication, which correspond to tier-I, tier-II, and tier-III, respectively. However, the provision of uninterrupted uplink (UL) and downlink (DL) connections at tier II (inter-BAN) are extremely critical, since these links serve as a bridge between tier-I (intra-BAN) and tier-III (beyond-BAN) communication. Any negligence at this level could be life-threatening; therefore, enabling quality-of-service (QoS) remains a fundamental design issue at tier-II. Consequently, to provide QoS, a key parameter is to ensure link reliability and communication quality by maintaining a nearly uniform signal-to-noise ratio (

S N R

) within the coverage area. Several studies have reported the effects of transceiver related parameters on OBAN link performance, nevertheless the implications of changing transmitter locations on the

S N R

uniformity and communication quality have not been addressed. In this work, we undertake a DL scenario and analyze how the placement of light-emitting diode (LED) lamps can improve the

S N R

uniformity, regardless of the receiver position. Subsequently, we show that using the principle of reciprocity (POR) and with transmitter-receiver positions switched, the analysis is also applicable to UL, provided that the optical channel remains linear. Moreover, we propose a generalized optimal placement scheme along with a heuristic design formula to achieve uniform

S N R

and illuminance for DL using a fixed number of transmitters and compare it with an existing technique. The study reveals that the proposed placement technique reduces the fluctuations in

S N R

54 %

and improves the illuminance uniformity up to

102 %

as compared to the traditional approach. Finally, we show that, for very low luminous intensity, the

S N R

values remain sufficient to maintain a minimum bit error rate (BER) of

10^{- 9}

with on-off keying non-return-to-zero (OOK-NRZ) modulation format. Full article

(This article belongs to the Special Issue Wearable/Wireless Body Sensor Networks for Healthcare Applications)

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15 pages, 5373 KiB

Open AccessArticle

Development and Test of a Portable ECG Device with Dry Capacitive Electrodes and Driven Right Leg Circuit

by Alessandro Zompanti, Anna Sabatini, Simone Grasso, Giorgio Pennazza, Giuseppe Ferri, Gianluca Barile, Massimo Chello, Mario Lusini and Marco Santonico

Sensors 2021, 21(8), 2777; https://doi.org/10.3390/s21082777 - 15 Apr 2021

Cited by 20 | Viewed by 5650

Abstract

The use of wearable sensors for health monitoring is rapidly growing. Over the past decade, wearable technology has gained much attention from the tech industry for commercial reasons and the interest of researchers and clinicians for reasons related to its potential benefit on patients’ health. Wearable devices use advanced and specialized sensors able to monitor not only activity parameters, such as heart rate or step count, but also physiological parameters, such as heart electrical activity or blood pressure. Electrocardiogram (ECG) monitoring is becoming one of the most attractive health-related features of modern smartwatches, and, because cardiovascular disease (CVD) is one of the leading causes of death globally, the use of a smartwatch to monitor patients could greatly impact the disease outcomes on health care systems. Commercial wearable devices are able to record just single-lead ECG using a couple of metallic contact dry electrodes. This kind of measurement can be used only for arrhythmia diagnosis. For the diagnosis of other cardiac disorders, additional ECG leads are required. In this study, we characterized an electronic interface to be used with multiple contactless capacitive electrodes in order to develop a wearable ECG device able to perform several lead measurements. We verified the ability of the electronic interface to amplify differential biopotentials and to reject common-mode signals produced by electromagnetic interference (EMI). We developed a portable device based on the studied electronic interface that represents a prototype system for further developments. We evaluated the performances of the developed device. The signal-to-noise ratio of the output signal is favorable, and all the features needed for a clinical evaluation (P waves, QRS complexes and T waves) are clearly readable. Full article

(This article belongs to the Special Issue Wearable/Wireless Body Sensor Networks for Healthcare Applications)

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12 pages, 2697 KiB

Open AccessArticle

Electrophysiological Correlation Underlying the Effects of Music Preference on the Prefrontal Cortex Using a Brain–Computer Interface

by Kevin C. Tseng

Sensors 2021, 21(6), 2161; https://doi.org/10.3390/s21062161 - 19 Mar 2021

Cited by 4 | Viewed by 3248

Abstract

This study aims to research the task of recognising brain activities in the prefrontal cortex that correspond to music at different preference levels. Since task performance regarding the effects of the subjects’ favourite music can lead to better outcomes, we focus on the physical interpretation of electroencephalography (EEG) bands underlying the preference level for music. The experiment was implemented using a continuous response digital interface for the preference classification of three types of musical stimuli. The results showed that favourite songs more significantly evoked frontal theta than did the music of low and moderate preference levels. Additionally, correlations of frontal theta with cognitive state indicated that the frontal theta is associated not only with the cognitive state but also with emotional processing. These findings demonstrate that favourite songs can have more positive effects on listeners than less favourable music and suggest that theta and lower alpha in the frontal cortex are good indicators of both cognitive state and emotion. Full article

(This article belongs to the Special Issue Wearable/Wireless Body Sensor Networks for Healthcare Applications)

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18 pages, 9166 KiB

Open AccessArticle

Design and Fabrication of a New Wearable Pressure Sensor for Blood Pressure Monitoring

by Marian Ion, Silviu Dinulescu, Bogdan Firtat, Mihaela Savin, Octavian N. Ionescu and Carmen Moldovan

Sensors 2021, 21(6), 2075; https://doi.org/10.3390/s21062075 - 16 Mar 2021

Cited by 15 | Viewed by 5588

Abstract

In recent years, research into the field of materials for flexible sensors and fabrication techniques directed towards wearable devices has helped to raise awareness of the need for new sensors with healthcare applicability. Our goal was to create a wearable flexible pressure sensor that could be integrated into a clinically approved blood pressure monitoring device. The sensor is built from a microfluidic channel encapsulated between two polymer layers, one layer being covered by metal transducers and the other being a flexible membrane containing the microfluidic channel, which also acts as a sealant for the structure. The applied external pressure deforms the channel, causing changes in resistance to the microfluidic layer. Electrical characterization has been performed in 5 different configurations, using alternating current (AC) and (DC) direct current measurements. The AC measurements for the fabricated pressure sensor resulted in impedance values at tens of hundreds of kOhm. Our sensor proved to have a high sensitivity for pressure values between 0 and 150 mm Hg, being subjected to repeatable external forces. The novelty presented in our work consists in the unique technological flow for the fabrication of the flexible wearable pressure sensor. The proposed miniaturized pressure sensor will ensure flexibility, low production cost and ease of use. It is made of very sensitive microfluidic elements and biocompatible materials and can be integrated into a wearable cuffless device for continuous blood pressure monitoring. Full article

(This article belongs to the Special Issue Wearable/Wireless Body Sensor Networks for Healthcare Applications)

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16 pages, 8272 KiB

Open AccessArticle

ANTIGONE: A Programmable Energy-Efficient Current Digitizer for an ISFET Wearable Sweat Sensing System

by Evgenia Voulgari, François Krummenacher and Maher Kayal

Sensors 2021, 21(6), 2074; https://doi.org/10.3390/s21062074 - 16 Mar 2021

Cited by 4 | Viewed by 3072

Abstract

This article describes the design and the characterization of the ANTIGONE (ANalog To dIGital cONvErter) ASIC (Application Specific Integrated Circuit) built in AMS 0.35 m technology for low dc-current sensing. This energy-efficient ASIC was specifically designed to interface with multiple Ion-Sensitive Field-Effect Transistors [...] Read more.

{Na}^{+}

K^{+}

and

{Ca}^{2 +}

in human sweat. The ISFET-ASIC system can allow real-time noninvasive and continuous health monitoring. The ANTIGONE ASIC architecture is based on the current-to-frequency converter through the charge balancing principle. The same front-end can digitize multiple currents produced by four sweat ISFET sensors in time multiplexing. The front-end demonstrates good linearity over a dynamic range that spans from 1 pA up to 500 nA. The consumed energy per conversion is less than 1 J. The chip is programmable and works in eight different modes of operation. The system uses a standard Serial Peripheral Interface (SPI) to configure, control and read the digitally converted sensor data. The chip is controlled by a portable device over Bluetooth Low Energy (BLE) through a Microcontroller Unit (MCU). The sweat sensing system is part of a bigger wearable platform that exploits the convergence of multiparameter biosensors and environmental sensors for personalized and preventive healthcare. Full article

(This article belongs to the Special Issue Wearable/Wireless Body Sensor Networks for Healthcare Applications)

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16 pages, 11548 KiB

Open AccessArticle

Wearable Sensor Clothing for Body Movement Measurement during Physical Activities in Healthcare

by Armands Ancans, Modris Greitans, Ricards Cacurs, Beate Banga and Artis Rozentals

Sensors 2021, 21(6), 2068; https://doi.org/10.3390/s21062068 - 16 Mar 2021

Cited by 24 | Viewed by 8155

Abstract

This paper presents a wearable wireless system for measuring human body activities, consisting of small inertial sensor nodes and the main hub for data transmission via Bluetooth for further analysis. Unlike optical and ultrasonic technologies, the proposed solution has no movement restrictions, such as the requirement to stay in the line of sight, and it provides information on the dynamics of the human body’s poses regardless of its location. The problem of the correct placement of sensors on the body is considered, a simplified architecture of the wearable clothing is described, an experimental set-up is developed and tests are performed. The system has been tested by performing several physical exercises and comparing the performance with the commercially available BTS Bioengineering SMART DX motion capture system. The results show that our solution is more suitable for complex exercises as the system based on digital cameras tends to lose some markers. The proposed wearable sensor clothing can be used as a multi-purpose data acquisition device for application-specific data analysis, thus providing an automated tool for scientists and doctors to measure patient’s body movements. Full article

(This article belongs to the Special Issue Wearable/Wireless Body Sensor Networks for Healthcare Applications)

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21 pages, 4140 KiB

Open AccessArticle

Modeling, Fabrication and Integration of Wearable Smart Sensors in a Monitoring Platform for Diabetic Patients

by Chiara De Pascali, Luca Francioso, Lucia Giampetruzzi, Gabriele Rescio, Maria Assunta Signore, Alessandro Leone and Pietro Siciliano

Sensors 2021, 21(5), 1847; https://doi.org/10.3390/s21051847 - 6 Mar 2021

Cited by 17 | Viewed by 4665

Abstract

The monitoring of some parameters, such as pressure loads, temperature, and glucose level in sweat on the plantar surface, is one of the most promising approaches for evaluating the health state of the diabetic foot and for preventing the onset of inflammatory events later degenerating in ulcerative lesions. This work presents the results of sensors microfabrication, experimental characterization and FEA-based thermal analysis of a 3D foot-insole model, aimed to advance in the development of a fully custom smart multisensory hardware–software monitoring platform for the diabetic foot. In this system, the simultaneous detection of temperature-, pressure- and sweat-based glucose level by means of full custom microfabricated sensors distributed on eight reading points of a smart insole will be possible, and the unit for data acquisition and wireless transmission will be fully integrated into the platform. Finite element analysis simulations, based on an accurate bioheat transfer model of the metabolic response of the foot tissue, demonstrated that subcutaneous inflamed lesions located up to the muscle layer, and ischemic damage located not below the reticular/fat layer, can be successfully detected. The microfabrication processes and preliminary results of functional characterization of flexible piezoelectric pressure sensors and glucose sensors are presented. Full custom pressure sensors generate an electric charge in the range 0–20 pC, proportional to the applied load in the range 0–4 N, with a figure of merit of 4.7 ± 1 GPa. The disposable glucose sensors exhibit a 0–6 mM (0–108 mg/dL) glucose concentration optimized linear response (for sweat-sensing), with a LOD of 3.27 µM (0.058 mg/dL) and a sensitivity of 21 µA/mM cm² in the PBS solution. The technical prerequisites and experimental sensing performances were assessed, as preliminary step before future integration into a second prototype, based on a full custom smart insole with enhanced sensing functionalities. Full article

(This article belongs to the Special Issue Wearable/Wireless Body Sensor Networks for Healthcare Applications)

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21 pages, 9142 KiB

Open AccessArticle

A Wearable Low-Power Sensing Platform for Environmental and Health Monitoring: The Convergence Project

by Elise Saoutieff, Tiziana Polichetti, Laurent Jouanet, Adrien Faucon, Audrey Vidal, Alexandre Pereira, Sébastien Boisseau, Thomas Ernst, Maria Lucia Miglietta, Brigida Alfano, Ettore Massera, Saverio De Vito, Do Hanh Ngan Bui, Philippe Benech, Tan-Phu Vuong, Carmen Moldovan, Yann Danlee, Thomas Walewyns, Sylvain Petre, Denis Flandre, Armands Ancans, Modris Greitans and Adrian M. Ionescu Show full author list Hide full author list

Sensors 2021, 21(5), 1802; https://doi.org/10.3390/s21051802 - 5 Mar 2021

Cited by 16 | Viewed by 4958

Abstract

The low-power sensing platform proposed by the Convergence project is foreseen as a wireless, low-power and multifunctional wearable system empowered by energy-efficient technologies. This will allow meeting the strict demands of life-style and healthcare applications in terms of autonomy for quasi-continuous collection of data for early-detection strategies. The system is compatible with different kinds of sensors, able to monitor not only health indicators of individual person (physical activity, core body temperature and biomarkers) but also the environment with chemical composition of the ambient air (NO_x, CO_x, NH_x particles) returning meaningful information on his/her exposure to dangerous (safety) or pollutant agents. In this article, we introduce the specifications and the design of the low-power sensing platform and the different sensors developed in the project, with a particular focus on pollutant sensing capabilities and specifically on NO₂ sensor based on graphene and CO sensor based on polyaniline ink. Full article

(This article belongs to the Special Issue Wearable/Wireless Body Sensor Networks for Healthcare Applications)

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22 pages, 4957 KiB

Open AccessArticle

A New Hybrid Sensitive PANI/SWCNT/Ferrocene-Based Layer for a Wearable CO Sensor

by Mihaela Savin, Carmen-Marinela Mihailescu, Viorel Avramescu, Silviu Dinulescu, Bogdan Firtat, Gabriel Craciun, Costin Brasoveanu, Cristina Pachiu, Cosmin Romanitan, Andreea-Bianca Serban, Alina Catrinel Ion and Carmen Moldovan

Sensors 2021, 21(5), 1801; https://doi.org/10.3390/s21051801 - 5 Mar 2021

Cited by 9 | Viewed by 3074

Abstract

Developing a sensing layer with high electroactive properties is an important aspect for proper functionality of a wearable sensor. The polymeric nanocomposite material obtained by a simple electropolymerization on gold interdigitated electrodes (IDEs) can be optimized to have suitable conductive properties to be used with direct current (DC) measurements. A new layer based on polyaniline:poly(4-styrenesulfonate) (PANI:PSS)/single-walled carbon nanotubes (SWCNT)/ferrocene (Fc) was electrosynthesized and deposed on interdigital transducers (IDT) and was characterized in detail using electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), scanning electron microscopy (SEM), Raman spectroscopy, X-ray photoemission spectroscopy (XPS), and X-ray diffraction (XRD). The sensor characteristics of the material towards carbon monoxide (CO) in the concentration range of 10–300 ppm were examined, showing a minimal relative humidity interference of only 1% and an increase of sensitivity with the increase of CO concentration. Humidity interference could be controlled by the number of CV cycles when a compact layer was formed and the addition of Fc played an important role in the decrease of humidity. The results for CO detection can be substantially improved by optimizing the number of deposition cycles and enhancing the Fc concentration. The material was developed for selective detection of CO in real environmental conditions and shows good potential for use in a wearable sensor. Full article

(This article belongs to the Special Issue Wearable/Wireless Body Sensor Networks for Healthcare Applications)

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20 pages, 6253 KiB

Open AccessEditor’s ChoiceArticle

Sensitivity, Noise and Resolution in a BEOL-Modified Foundry-Made ISFET with Miniaturized Reference Electrode for Wearable Point-of-Care Applications

by Francesco Bellando, Leandro Julian Mele, Pierpaolo Palestri, Junrui Zhang, Adrian Mihai Ionescu and Luca Selmi

Sensors 2021, 21(5), 1779; https://doi.org/10.3390/s21051779 - 4 Mar 2021

Cited by 19 | Viewed by 3880

Abstract

Ion-sensitive field-effect transistors (ISFETs) form a high sensitivity and scalable class of sensors, compatible with advanced complementary metal-oxide semiconductor (CMOS) processes. Despite many previous demonstrations about their merits as low-power integrated sensors, very little is known about their noise characterization when being operated in a liquid gate configuration. The noise characteristics in various regimes of their operation are important to select the most suitable conditions for signal-to-noise ratio (SNR) and power consumption. This work reports systematic DC, transient, and noise characterizations and models of a back-end of line (BEOL)-modified foundry-made ISFET used as pH sensor. The aim is to determine the sensor sensitivity and resolution to pH changes and to calibrate numerical and lumped element models, capable of supporting the interpretation of the experimental findings. The experimental sensitivity is approximately 40 mV/pH with a normalized resolution of 5 mpH per µm², in agreement with the literature state of the art. Differences in the drain current noise spectra between the ISFET and MOSFET configurations of the same device at low currents (weak inversion) suggest that the chemical noise produced by the random binding/unbinding of the H⁺ ions on the sensor surface is likely the dominant noise contribution in this regime. In contrast, at high currents (strong inversion), the two configurations provide similar drain noise levels suggesting that the noise originates in the underlying FET rather than in the sensing region. Full article

(This article belongs to the Special Issue Wearable/Wireless Body Sensor Networks for Healthcare Applications)

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19 pages, 779 KiB

Open AccessEditor’s ChoiceArticle

Prediction of Freezing of Gait in Parkinson’s Disease Using Wearables and Machine Learning

by Luigi Borzì, Ivan Mazzetta, Alessandro Zampogna, Antonio Suppa, Gabriella Olmo and Fernanda Irrera

Sensors 2021, 21(2), 614; https://doi.org/10.3390/s21020614 - 17 Jan 2021

Cited by 77 | Viewed by 7788

Abstract

Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson’s disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published in the direction of its prediction. Such predictions may be used for the administration of cues, in order to prevent the occurrence of gait freezing. The aim of the present study was to propose a wearable system able to catch the typical degradation of the walking pattern preceding FOG episodes, to achieve reliable FOG prediction using machine learning algorithms and verify whether dopaminergic therapy affects the ability of our system to detect and predict FOG. Methods: A cohort of 11 Parkinson’s disease patients receiving (on) and not receiving (off) dopaminergic therapy was equipped with two inertial sensors placed on each shin, and asked to perform a timed up and go test. We performed a step-to-step segmentation of the angular velocity signals and subsequent feature extraction from both time and frequency domains. We employed a wrapper approach for feature selection and optimized different machine learning classifiers in order to catch FOG and pre-FOG episodes. Results: The implemented FOG detection algorithm achieved excellent performance in a leave-one-subject-out validation, in patients both on and off therapy. As for pre-FOG detection, the implemented classification algorithm achieved 84.1% (85.5%) sensitivity, 85.9% (86.3%) specificity and 85.5% (86.1%) accuracy in leave-one-subject-out validation, in patients on (off) therapy. When the classification model was trained with data from patients on (off) and tested on patients off (on), we found 84.0% (56.6%) sensitivity, 88.3% (92.5%) specificity and 87.4% (86.3%) accuracy. Conclusions: Machine learning models are capable of predicting FOG before its actual occurrence with adequate accuracy. The dopaminergic therapy affects pre-FOG gait patterns, thereby influencing the algorithm’s effectiveness. Full article

(This article belongs to the Special Issue Wearable/Wireless Body Sensor Networks for Healthcare Applications)

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26 pages, 7690 KiB

Open AccessArticle

A New Wearable System for Home Sleep Apnea Testing, Screening, and Classification

by Alessandro Manoni, Federico Loreti, Valeria Radicioni, Daniela Pellegrino, Luigi Della Torre, Alessandro Gumiero, Damian Halicki, Paolo Palange and Fernanda Irrera

Sensors 2020, 20(24), 7014; https://doi.org/10.3390/s20247014 - 8 Dec 2020

Cited by 45 | Viewed by 7003

Abstract

We propose an unobtrusive, wearable, and wireless system for the pre-screening and follow-up in the domestic environment of specific sleep-related breathing disorders. This group of diseases manifests with episodes of apnea and hypopnea of central or obstructive origin, and it can be disabling, with several drawbacks that interfere in the daily patient life. The gold standard for their diagnosis and grading is polysomnography, which is a time-consuming, scarcely available test with many wired electrodes disseminated on the body, requiring hospitalization and long waiting times. It is limited by the night-by-night variability of sleep disorders, while inevitably causing sleep alteration and fragmentation itself. For these reasons, only a small percentage of patients achieve a definitive diagnosis and are followed-up. Our device integrates photoplethysmography, an accelerometer, a microcontroller, and a bluetooth transmission unit. It acquires data during the whole night and transmits to a PC for off-line processing. It is positioned on the nasal septum and detects apnea episodes using the modulation of the photoplethysmography signal during the breath. In those time intervals where the photoplethysmography is detecting an apnea, the accelerometer discriminates obstructive from central type thanks to its excellent sensitivity to thoraco-abdominal movements. Tests were performed on a hospitalized patient wearing our integrated system and the type III home sleep apnea testing recommended by The American Academy of Sleep Medicine. Results are encouraging: sensitivity and precision around 90% were achieved in detecting more than 500 apnea episodes. Least thoraco-abdominal movements and body position were successfully classified in lying down control subjects, paving the way toward apnea type classification. Full article

(This article belongs to the Special Issue Wearable/Wireless Body Sensor Networks for Healthcare Applications)

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19 pages, 6730 KiB

Open AccessArticle

Comparison between the Airgo™ Device and a Metabolic Cart during Rest and Exercise

by Andrea Antonelli, Dario Guilizzoni, Alessandra Angelucci, Giulio Melloni, Federico Mazza, Alessia Stanzi, Massimiliano Venturino, David Kuller and Andrea Aliverti

Sensors 2020, 20(14), 3943; https://doi.org/10.3390/s20143943 - 15 Jul 2020

Cited by 25 | Viewed by 4774

Abstract

The aim of this study is to compare the accuracy of Airgo™, a non-invasive wearable device that records breath, with respect to a gold standard. In 21 healthy subjects (10 males, 11 females), four parameters were recorded for four min at rest and in different positions simultaneously by Airgo™ and SensorMedics 2900 metabolic cart. Then, a cardio-pulmonary exercise test was performed using the Erg 800S cycle ergometer in order to test Airgo™’s accuracy during physical effort. The results reveal that the relative error median percentage of respiratory rate was of 0% for all positions at rest and for different exercise intensities, with interquartile ranges between 3.5 (standing position) and 22.4 (low-intensity exercise) breaths per minute. During exercise, normalized amplitude and ventilation relative error medians highlighted the presence of an error proportional to the volume to be estimated. For increasing intensity levels of exercise, Airgo™’s estimate tended to underestimate the values of the gold standard instrument. In conclusion, the Airgo™ device provides good accuracy and precision in the estimate of respiratory rate (especially at rest), an acceptable estimate of tidal volume and minute ventilation at rest and an underestimation for increasing volumes. Full article

(This article belongs to the Special Issue Wearable/Wireless Body Sensor Networks for Healthcare Applications)

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23 pages, 2113 KiB

Open AccessReview

Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction

by Yun-Hsuan Chen and Mohamad Sawan

Sensors 2021, 21(2), 460; https://doi.org/10.3390/s21020460 - 11 Jan 2021

Cited by 23 | Viewed by 7812

Abstract

We review in this paper the wearable-based technologies intended for real-time monitoring of stroke-related physiological parameters. These measurements are undertaken to prevent death and disability due to stroke. We compare the various characteristics, such as weight, accessibility, frequency of use, data continuity, and response time of these wearables. It was found that the most user-friendly wearables can have limitations in reporting high-precision prediction outcomes. Therefore, we report also the trend of integrating these wearables into the internet of things (IoT) and combining electronic health records (EHRs) and machine learning (ML) algorithms to establish a stroke risk prediction system. Due to different characteristics, such as accessibility, time, and spatial resolution of various wearable-based technologies, strategies of applying different types of wearables to maximize the efficacy of stroke risk prediction are also reported. In addition, based on the various applications of multimodal electroencephalography–functional near-infrared spectroscopy (EEG–fNIRS) on stroke patients, the perspective of using this technique to improve the prediction performance is elaborated. Expected prediction has to be dynamically delivered with high-precision outcomes. There is a need for stroke risk stratification and management to reduce the resulting social and economic burden. Full article

(This article belongs to the Special Issue Wearable/Wireless Body Sensor Networks for Healthcare Applications)

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