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He J, Wei R, Ma X, Wu W, Pan X, Sun J, Tang J, Xu Z, Wang C, Pan C. Contactless User-Interactive Sensing Display for Human-Human and Human-Machine Interactions. Adv Mater 2024:e2401931. [PMID: 38573797 DOI: 10.1002/adma.202401931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 03/18/2024] [Indexed: 04/06/2024]
Abstract
Creating a large-scale contactless user-interactive sensing display (CUISD) with optimal features is challenging but crucial for efficient human-human or human-machine interactions. This study reports a CUISD based on dynamic alternating current electroluminescence (ACEL) that responds to humidity. Subsecond humidity-induced luminescence is achieved by integrating a highly responsive hydrogel into the ACEL layer. The patterned silver nanofiber electrode and luminescence layer, produced through electrospinning and microfabrication, result in a stretchable, large-scale, high-resolution, multicolor, and dynamic CUISD. The CUISD is implemented for the real-time control of a remote-controlled car, wherein the luminescence signals induced by touchless finger movements are distinguished and encoded to deliver specific commands. Moreover, the distinctive recognition of breathing facilitates the CUISD to serve as a visual signal transmitter for information interaction, which is particularly beneficial for individuals with disabilities. The paradigm shift depicts in this work is expected to reshape the way authors interact with each other and devices, discovering niche applications in virtual/augmented reality and the metaverse.
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Affiliation(s)
- Jiaqi He
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, China
- Institute of Atomic Manufacturing, Beihang University, Beijing, 100191, China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ruilai Wei
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, China
| | - Xiaole Ma
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, China
| | - Wenqiang Wu
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, China
| | - Xiaojun Pan
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, China
| | - Junlu Sun
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, China
| | - Jiaqi Tang
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhangsheng Xu
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chunfeng Wang
- Guangdong Research Center for Interfacial Engineering of Functional Materials, College of Materials Science and Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Caofeng Pan
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, China
- Institute of Atomic Manufacturing, Beihang University, Beijing, 100191, China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
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Signore MA, Rescio G, Francioso L, Casino F, Leone A. Aluminum Nitride Thin Film Piezoelectric Pressure Sensor for Respiratory Rate Detection. Sensors (Basel) 2024; 24:2071. [PMID: 38610281 PMCID: PMC11014281 DOI: 10.3390/s24072071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 03/15/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024]
Abstract
In this study, we propose a low-cost piezoelectric flexible pressure sensor fabricated on Kapton® (Kapton™ Dupont) substrate by using aluminum nitride (AlN) thin film, designed for the monitoring of the respiration rate for a fast detection of respiratory anomalies. The device was characterized in the range of 15-30 breaths per minute (bpm), to simulate moderate difficult breathing, borderline normal breathing, and normal spontaneous breathing. These three breathing typologies were artificially reproduced by setting the expiratory to inspiratory ratios (E:I) at 1:1, 2:1, 3:1. The prototype was able to accurately recognize the breath states with a low response time (~35 ms), excellent linearity (R2 = 0.997) and low hysteresis. The piezoelectric device was also characterized by placing it in an activated carbon filter mask to evaluate the pressure generated by exhaled air through breathing acts. The results indicate suitability also for the monitoring of very weak breath, exhibiting good linearity, accuracy, and reproducibility, in very low breath pressures, ranging from 0.09 to 0.16 kPa. These preliminary results are very promising for the future development of smart wearable devices able to monitor different patients breathing patterns, also related to breathing diseases, providing a suitable real-time diagnosis in a non-invasive and fast way.
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Affiliation(s)
| | - Gabriele Rescio
- The National Research Council, Institute for Microelectronics and Microsystems (CNR IMM), Via Monteroni, 73100 Lecce, Italy; (M.A.S.); (L.F.); (F.C.); (A.L.)
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Aqueveque P, Díaz M, Gomez B, Osorio R, Pastene F, Radrigan L, Morales A. Embedded Electronic Sensor for Monitoring of Breathing Activity, Fitting and Filter Clogging in Reusable Industrial Respirators. Biosensors (Basel) 2022; 12:991. [PMID: 36354500 PMCID: PMC9688112 DOI: 10.3390/bios12110991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 10/18/2022] [Accepted: 11/01/2022] [Indexed: 06/16/2023]
Abstract
Millions of workers are required to wear reusable respirators in several industries worldwide. Reusable respirators include filters that protect workers against harmful dust, smoke, gases, and vapors. These hazards may cause cancer, lung impairment, and diseases. Respiratory protection is prone to failure or misuse, such as wearing respirators with filters out of service life and employees wearing respirators loosely. Currently, there are no commercial systems capable of reliably alerting of misuse of respiratory protective equipment during the workday shifts or provide early information about dangerous clogging levels of filters. This paper proposes a low energy and non-obtrusive functional building block with embedded electronics that enable breathing monitoring inside an industrial reusable respirator. The embedded electronic device collects multidimensional data from an integrated pressure, temperature, and relative humidity sensor inside a reusable industrial respirator in real time and sends it wirelessly to an external platform for further processing. Here, the calculation of instantaneous breathing rate and estimation of the filter's respirator fitting and clogging level is performed. The device was tested with ten healthy subjects in laboratory trials. The subjects were asked to wear industrial reusable respirator with the embedded electronic device attached inside. The signals measured with the system were compared with airflow signals measured with calibrated transducers for validation purposes. The correlation between the estimated breathing rates using pressure, temperature, and relative humidity with the reference signal (airflow) is 0.987, 0.988 and 0.989 respectively, showing that instantaneous breathing rate can be calculated accurately using the information from the embedded device. Moreover, respirator fitting (well-fitted or loose condition) and filter's clogging levels (≤60%, 80% and 100% clogging) also can be estimated using features extracted from absolute pressure measurements combined to statistical analysis ANOVA models. These experimental outputs represent promising results for further development of data-driven prediction models using machine learning techniques to determine filters end-of-service life. Furthermore, the proposed system would collect relevant data for real-time monitoring of workers' breathing conditions and respirator usage, helping to improve occupational safety and health in the workplace.
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Affiliation(s)
- Pablo Aqueveque
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, 219 Edmundo Larenas St., Concepción 4030000, Chile
| | - Macarena Díaz
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, 219 Edmundo Larenas St., Concepción 4030000, Chile
| | - Britam Gomez
- Department of Multidisciplinary Engineering, Faculty of Engineering, Universidad de Santiago de Chile, Libertador Bernardo O’Higgins Av., Santiago 9170022, Chile
| | - Rodrigo Osorio
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, 219 Edmundo Larenas St., Concepción 4030000, Chile
| | - Francisco Pastene
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, 219 Edmundo Larenas St., Concepción 4030000, Chile
| | - Luciano Radrigan
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, 219 Edmundo Larenas St., Concepción 4030000, Chile
| | - Anibal Morales
- Department of Electrical Engineering, Universidad Católica de la Santísima Concepción, Concepción 4090541, Chile
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Rehouma H, Noumeir R, Essouri S, Jouvet P. Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration. Sensors (Basel) 2020; 20:E7252. [PMID: 33348827 PMCID: PMC7766256 DOI: 10.3390/s20247252] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 12/09/2020] [Accepted: 12/15/2020] [Indexed: 01/22/2023]
Abstract
Assessment of respiratory function allows early detection of potential disorders in the respiratory system and provides useful information for medical management. There is a wide range of applications for breathing assessment, from measurement systems in a clinical environment to applications involving athletes. Many studies on pulmonary function testing systems and breath monitoring have been conducted over the past few decades, and their results have the potential to broadly impact clinical practice. However, most of these works require physical contact with the patient to produce accurate and reliable measures of the respiratory function. There is still a significant shortcoming of non-contact measuring systems in their ability to fit into the clinical environment. The purpose of this paper is to provide a review of the current advances and systems in respiratory function assessment, particularly camera-based systems. A classification of the applicable research works is presented according to their techniques and recorded/quantified respiration parameters. In addition, the current solutions are discussed with regards to their direct applicability in different settings, such as clinical or home settings, highlighting their specific strengths and limitations in the different environments.
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Affiliation(s)
- Haythem Rehouma
- École de Technologie Supérieure, Montreal, QC H3T 1C5, Canada;
| | - Rita Noumeir
- École de Technologie Supérieure, Montreal, QC H3T 1C5, Canada;
| | - Sandrine Essouri
- CHU Sainte-Justine, Montreal, QC H3T 1C5, Canada; (S.E.); (P.J.)
| | - Philippe Jouvet
- CHU Sainte-Justine, Montreal, QC H3T 1C5, Canada; (S.E.); (P.J.)
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Cesareo A, Nido SA, Biffi E, Gandossini S, D’Angelo MG, Aliverti A. A Wearable Device for Breathing Frequency Monitoring: A Pilot Study on Patients with Muscular Dystrophy. Sensors (Basel) 2020; 20:s20185346. [PMID: 32961986 PMCID: PMC7571149 DOI: 10.3390/s20185346] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 09/12/2020] [Accepted: 09/14/2020] [Indexed: 12/24/2022]
Abstract
Patients at risk of developing respiratory dysfunctions, such as patients with severe forms of muscular dystrophy, need a careful respiratory assessment, and periodic follow-up visits to monitor the progression of the disease. In these patients, at-home continuous monitoring of respiratory activity patterns could provide additional understanding about disease progression, allowing prompt clinical intervention. The core aim of the present study is thus to investigate the feasibility of using an innovative wearable device for respiratory monitoring, particularly breathing frequency variation assessment, in patients with muscular dystrophy. A comparison of measurements of breathing frequency with gold standard methods showed that the device based on the inertial measurement units (IMU-based device) provided optimal results in terms of accuracy errors, correlation, and agreement. Participants positively evaluated the device for ease of use, comfort, usability, and wearability. Moreover, preliminary results confirmed that breathing frequency is a valuable breathing parameter to monitor, at the clinic and at home, because it strongly correlates with the main indexes of respiratory function.
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Affiliation(s)
- Ambra Cesareo
- Scientific Institute, IRCCS “E. Medea”, Bioengineering Lab, Bosisio Parini, 23842 Lecco, Italy; (A.C.); (E.B.)
| | - Santa Aurelia Nido
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy;
| | - Emilia Biffi
- Scientific Institute, IRCCS “E. Medea”, Bioengineering Lab, Bosisio Parini, 23842 Lecco, Italy; (A.C.); (E.B.)
| | - Sandra Gandossini
- Scientific Institute, IRCCS “E. Medea”, Department of Neurorehabilitation, Neuromuscular Unit, Bosisio Parini, 23842 Lecco, Italy; (S.G.); (M.G.D.)
| | - Maria Grazia D’Angelo
- Scientific Institute, IRCCS “E. Medea”, Department of Neurorehabilitation, Neuromuscular Unit, Bosisio Parini, 23842 Lecco, Italy; (S.G.); (M.G.D.)
| | - Andrea Aliverti
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy;
- Correspondence:
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Massaroni C, Venanzi C, Silvatti AP, Lo Presti D, Saccomandi P, Formica D, Giurazza F, Caponero MA, Schena E. Smart textile for respiratory monitoring and thoraco-abdominal motion pattern evaluation. J Biophotonics 2018; 11:e201700263. [PMID: 29297202 DOI: 10.1002/jbio.201700263] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Accepted: 01/01/2018] [Indexed: 05/21/2023]
Abstract
The use of wearable systems for monitoring vital parameters has gained wide popularity in several medical fields. The focus of the present study is the experimental assessment of a smart textile based on 12 fiber Bragg grating sensors for breathing monitoring and thoraco-abdominal motion pattern analysis. The feasibility of the smart textile for monitoring several temporal respiratory parameters (ie, breath-by-breath respiratory period, breathing frequency, duration of inspiratory and expiratory phases), volume variations of the whole chest wall and of its compartments is performed on 8 healthy male volunteers. Values gathered by the textile are compared to the data obtained by a motion analysis system, used as the reference instrument. Good agreement between the 2 systems on both respiratory period (bias of 0.01 seconds), breathing frequency (bias of -0.02 breaths/min) and tidal volume (bias of 0.09 L) values is demonstrated. Smart textile shows good performance in the monitoring of thoraco-abdominal pattern and its variation, as well.
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Affiliation(s)
- Carlo Massaroni
- Unit of Measurements and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Cecilia Venanzi
- Unit of Measurements and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Amanda P Silvatti
- Department of Physical Education, Universidade Federal de Viçosa Minas Gerais, Brazil
| | - Daniela Lo Presti
- Unit of Measurements and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
| | | | - Domenico Formica
- Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Francesco Giurazza
- Unit of Measurements and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Michele A Caponero
- Photonics Micro- and Nanostructures Laboratory, Research Centre of Frascati, Rome, Italy
| | - Emiliano Schena
- Unit of Measurements and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
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Kupper M, Sprengel W, Winkler P, Zurl B. A method for improved 4D-computed tomography data acquisition. Z Med Phys 2016; 27:31-38. [PMID: 27265776 DOI: 10.1016/j.zemedi.2016.05.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2015] [Revised: 05/11/2016] [Accepted: 05/12/2016] [Indexed: 12/25/2022]
Abstract
In four-dimensional time-dependent computed tomography (4D-CT) of the lungs, irregularities in breathing movements can cause errors in data acquisition, or even data loss. We present a method based on sending a synthetic, regular breathing signal to the CT instead of the real signal, which ensures 4D-CT data sets without data loss. Subsequent correction of the signal based on the real breathing curve enables an accurate reconstruction of the size and movement of the target volume. This makes it possible to plan radiation treatment based on the obtained data. The method was tested with dynamic thorax phantom measurements using synthetic and real breathing patterns.
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Affiliation(s)
- Martin Kupper
- Institut für Materialphysik, Technische Universität Graz, Austria.
| | | | - Peter Winkler
- Universitätsklinik für Strahlentherapie-Radioonkologie, Comprehensive Cancer Center Graz, Medizinische Universität Graz, Austria.
| | - Brigitte Zurl
- Universitätsklinik für Strahlentherapie-Radioonkologie, Comprehensive Cancer Center Graz, Medizinische Universität Graz, Austria.
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Chen G, Imtiaz SA, Aguilar-Pelaez E, Rodriguez-Villegas E. Algorithm for heart rate extraction in a novel wearable acoustic sensor. Healthc Technol Lett 2015; 2:28-33. [PMID: 26609401 PMCID: PMC4613720 DOI: 10.1049/htl.2014.0095] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Revised: 01/19/2015] [Accepted: 01/20/2015] [Indexed: 11/19/2022] Open
Abstract
Phonocardiography is a widely used method of listening to the heart sounds and indicating the presence of cardiac abnormalities. Each heart cycle consists of two major sounds – S1 and S2 – that can be used to determine the heart rate. The conventional method of acoustic signal acquisition involves placing the sound sensor at the chest where this sound is most audible. Presented is a novel algorithm for the detection of S1 and S2 heart sounds and the use of them to extract the heart rate from signals acquired by a small sensor placed at the neck. This algorithm achieves an accuracy of 90.73 and 90.69%, with respect to heart rate value provided by two commercial devices, evaluated on more than 38 h of data acquired from ten different subjects during sleep in a pilot clinical study. This is the largest dataset for acoustic heart sound classification and heart rate extraction in the literature to date. The algorithm in this study used signals from a sensor designed to monitor breathing. This shows that the same sensor and signal can be used to monitor both breathing and heart rate, making it highly useful for long-term wearable vital signs monitoring.
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Affiliation(s)
- Guangwei Chen
- Department of Electrical and Electronic Engineering , Imperial College London , London SW7 2AZ , UK
| | - Syed Anas Imtiaz
- Department of Electrical and Electronic Engineering , Imperial College London , London SW7 2AZ , UK
| | - Eduardo Aguilar-Pelaez
- Department of Electrical and Electronic Engineering , Imperial College London , London SW7 2AZ , UK
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