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Intelligent Health Monitoring Systems Based on Sensor Processing

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 8404

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Special Issue Editors

Prof. Dr. Toshiharu Mukai

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Guest Editor

Advanced Science Institute RIKEN (The Institute of Physical and Chemical Research), Meijo University, Nagoya, Japan
Interests: tactile sensor; sensor information processing; health monitoring; sleep monitoring; nursing care robot

Prof. Dr. Shijie Guo

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Guest Editor

Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai, China
Interests: tactile sensor; wearable sensor system; health monitoring; sleep monitoring; human–robot interaction; nursing care robot; wearable robot

Prof. Dr. Kazuto Takashima

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Guest Editor

Department of Biological Functions Engineering, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Fukuoka 808-0196, Japan
Interests: tactile sensor; piezoelectric sensor; smart soft materials; medical sensor; soft actuator; surgical simulator; stiffness control

Special Issue Information

Dear Colleagues,

In current society, the demand for daily health monitoring is increasing owing to the increase in health consciousness, the prevalence of infectious diseases, and the growing elderly population. Recent advances in small sensor device technologies, batteries, communication technologies, and so on have enabled the development of sensor systems, thereby satisfying such demands. These systems can be used in daily life with minimal or no discomfort and can store measurement data. The sensors must be small and enable unconstrained measurements to avoid interference with daily activities. This is achieved, for example, through the production of wearable sensors, attaching sensors to chairs or beds, or installing cameras inconspicuously in rooms. Because the targets are humans in their natural environments, sensor information processing must be able to cope with various situations, such as various human body shapes and sizes and daily changes in physical conditions and environments.

In this Special Issue, we invite papers on algorithm development, experimental systems, and system integration in health monitoring systems to obtain a person’s health information in daily life.

Prof. Dr. Toshiharu Mukai
Prof. Dr. Shijie Guo
Prof. Dr. Kazuto Takashima
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.

Keywords

wearable sensors
smart watch
sensors attached to chairs or beds
health monitoring systems
sleep monitoring systems
inertial measurement units (IMUs)
accelerometer
handy electrocardiograph
photoplethysmograph (PPG) sensor
camera health monitoring

Published Papers (4 papers)

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Research

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19 pages, 2583 KiB

Open AccessArticle

Enhancement in Capacitance of Ionic Type of EAP-Based Strain Sensors

by Nitin Kumar Singh, Kazuto Takashima and Shyam S. Pandey

Sensors 2023, 23(23), 9400; https://doi.org/10.3390/s23239400 - 25 Nov 2023

Viewed by 1698

Abstract

This paper aims to enhance the capacitance of electroactive polymer (EAP)-based strain sensors. The enhancement in capacitance was achieved by using a free-standing stretchable polymer film while introducing conducting polymer to fabricate a hybrid dielectric film with controlled conductivity. In this work, styrene-ethylene-butylene-styrene (SEBS) rubber was used as the base material, and dodecyl benzene sulfonate anion (DBSA)-doped polyaniline (PANI) was used as filler to fabricate a hybrid composite conducting film. The maleic anhydride group of the SEBS Rubber and DBSA, the anion of the polyaniline dopant, make a very stable dispersion in Toluene and form a free-standing stretchable film by solution casting. DBSA-doped polyaniline increased the conductivity and dielectric constant of the dielectric film, resulting in a significant enhancement in the capacitance of the EAP-based strain sensor. The sensor presented in this article exhibits capacitance values ranging from 24.7 to 100 µF for strain levels ranging from 0 to 100%, and sensitivity was measured 3 at 100% strain level. Full article

(This article belongs to the Special Issue Intelligent Health Monitoring Systems Based on Sensor Processing)

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17 pages, 2917 KiB

Open AccessArticle

Heart Rate Estimation from Facial Image Sequences of a Dual-Modality RGB-NIR Camera

by Wen-Nung Lie, Dao-Quang Le, Chun-Yu Lai and Yu-Shin Fang

Sensors 2023, 23(13), 6079; https://doi.org/10.3390/s23136079 - 1 Jul 2023

Cited by 1 | Viewed by 1914

Abstract

This paper presents an RGB-NIR (Near Infrared) dual-modality technique to analyze the remote photoplethysmogram (rPPG) signal and hence estimate the heart rate (in beats per minute), from a facial image sequence. Our main innovative contribution is the introduction of several denoising techniques such as Modified Amplitude Selective Filtering (MASF), Wavelet Decomposition (WD), and Robust Principal Component Analysis (RPCA), which take advantage of RGB and NIR band characteristics to uncover the rPPG signals effectively through this Independent Component Analysis (ICA)-based algorithm. Two datasets, of which one is the public PURE dataset and the other is the CCUHR dataset built with a popular Intel RealSense D435 RGB-D camera, are adopted in our experiments. Facial video sequences in the two datasets are diverse in nature with normal brightness, under-illumination (i.e., dark), and facial motion. Experimental results show that the proposed method has reached competitive accuracies among the state-of-the-art methods even at a shorter video length. For example, our method achieves MAE = 4.45 bpm (beats per minute) and RMSE = 6.18 bpm for RGB-NIR videos of 10 and 20 s in the CCUHR dataset and MAE = 3.24 bpm and RMSE = 4.1 bpm for RGB videos of 60-s in the PURE dataset. Our system has the advantages of accessible and affordable hardware, simple and fast computations, and wide realistic applications. Full article

(This article belongs to the Special Issue Intelligent Health Monitoring Systems Based on Sensor Processing)

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15 pages, 4787 KiB

Open AccessArticle

Variable-Sensitivity Force Sensor Based on Structural Modification

by Kazuto Takashima, Kengo Ota and Hiroki Cho

Sensors 2023, 23(4), 2077; https://doi.org/10.3390/s23042077 - 12 Feb 2023

Viewed by 1838

Abstract

Force sensors are used in a wide variety of fields. They require different measurement ranges and sensitivities depending on the operating environment because there is generally a trade-off between measurement range and sensitivity. In this study, we developed a variable-sensitivity, variable-measurement-range force sensor that utilizes structural modification, namely changes in the distance between the force application point and the detection area, and changes in the cross-sectional area. The use of shape-memory materials allows the sensor structure to be easily changed and fixed by controlling the temperature. First, we describe the theory of the proposed sensor. Then, we present prototypes and the experimental methods used to verify the performance of the sensor. We fabricated the prototypes by attaching two strain gauges to two sides of a shape-memory alloy and shape-memory polymer plates. Experiments on the prototypes show that the relationship between the applied force and the detected strain can be changed by bending the plate. This allows the sensitivity and measurement range of the sensor to be changed. Full article

(This article belongs to the Special Issue Intelligent Health Monitoring Systems Based on Sensor Processing)

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Other

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27 pages, 2295 KiB

Open AccessSystematic Review

Contactless Technologies, Sensors, and Systems for Cardiac and Respiratory Measurement during Sleep: A Systematic Review

by Andrei Boiko, Natividad Martínez Madrid and Ralf Seepold

Sensors 2023, 23(11), 5038; https://doi.org/10.3390/s23115038 - 24 May 2023

Cited by 8 | Viewed by 2172

Abstract

Sleep is essential to physical and mental health. However, the traditional approach to sleep analysis—polysomnography (PSG)—is intrusive and expensive. Therefore, there is great interest in the development of non-contact, non-invasive, and non-intrusive sleep monitoring systems and technologies that can reliably and accurately measure cardiorespiratory parameters with minimal impact on the patient. This has led to the development of other relevant approaches, which are characterised, for example, by the fact that they allow greater freedom of movement and do not require direct contact with the body, i.e., they are non-contact. This systematic review discusses the relevant methods and technologies for non-contact monitoring of cardiorespiratory activity during sleep. Taking into account the current state of the art in non-intrusive technologies, we can identify the methods of non-intrusive monitoring of cardiac and respiratory activity, the technologies and types of sensors used, and the possible physiological parameters available for analysis. To do this, we conducted a literature review and summarised current research on the use of non-contact technologies for non-intrusive monitoring of cardiac and respiratory activity. The inclusion and exclusion criteria for the selection of publications were established prior to the start of the search. Publications were assessed using one main question and several specific questions. We obtained 3774 unique articles from four literature databases (Web of Science, IEEE Xplore, PubMed, and Scopus) and checked them for relevance, resulting in 54 articles that were analysed in a structured way using terminology. The result was 15 different types of sensors and devices (e.g., radar, temperature sensors, motion sensors, cameras) that can be installed in hospital wards and departments or in the environment. The ability to detect heart rate, respiratory rate, and sleep disorders such as apnoea was among the characteristics examined to investigate the overall effectiveness of the systems and technologies considered for cardiorespiratory monitoring. In addition, the advantages and disadvantages of the considered systems and technologies were identified by answering the identified research questions. The results obtained allow us to determine the current trends and the vector of development of medical technologies in sleep medicine for future researchers and research. Full article

(This article belongs to the Special Issue Intelligent Health Monitoring Systems Based on Sensor Processing)

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