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Smart Sensors and Transducers for Wearable and Healthcare
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Smart Sensors and Transducers for Wearable and Healthcare

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

Deadline for manuscript submissions: closed (25 June 2023) | Viewed by 5475

Special Issue Editor

Dr. Guo-Hua Feng

E-Mail Website
Guest Editor
Department of Power Mechanical Engineering, Institute of NanoEngineering and MicroSystems, National Tsing Hua University, Hsinchu, Taiwan
Interests: piezoelectric thin film; electroactive polymer; micromachining; sensors; microactuators; ultrasonic devices; intelligent machine

Special Issue Information

Dear Colleagues,

Wearable devices have received greater attention because of their friendly interaction with the human body, such as monitoring motion, heart rate, blood pressure. These devices can regularly and constantly give users physiological or biochemical messages. Moreover, the local environmental conditions, such as the concentration of air pollutants and ultraviolet radiation, can be detected to notify people of hazardous information. The wearable devices with wireless functions allow the acquired messages from the transducers to be sent to a central hub, such as a cell phone, and selected information can be transmitted to a medical center for emergency demand. To maximize the portable advantages of wearable devices, energy harvesting technology is also critical for replacing bulk batteries.

In general, wearable modules for health detection include sensors/transducers, wearable materials, power supply units, wireless communication components, and software to process the acquired data. Unlike traditional health-monitoring systems, wearable devices provide continuous, real-time, recordable data associated with versatile health conditions in a timely fashion.

This Special Issue will address the research on smart sensors and transducers for wearable and healthcare. Original research and review articles are encouraged. Topics include but are not limited to:

  • Types of Biochemistry:
    • Gas sensors;
    • Ion sensors;
    • Sweat sensors;
    • Saliva sensors;
    • Urine sensor;
    • Drug sensors.
  • Types of physiology:
    • Temperature sensors;
    • Pressure sensors;
    • Strain sensors;
    • Body motion sensors;
    • Respiration sensors;
    • Tactile sensors.

Dr. Guo-Hua Feng
Guest Editor

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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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

  • biochemical sensors
  • physiological sensors
  • wearable
  • healthcare
  • environmental sensors

Published Papers (3 papers)

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Research

18 pages, 2608 KiB  
Article
Wearable-Sensor-Based Weakly Supervised Parkinson’s Disease Assessment with Data Augmentation
by Peng Yue, Ziheng Li, Menghui Zhou, Xulong Wang and Po Yang
Sensors 2024, 24(4), 1196; https://doi.org/10.3390/s24041196 - 12 Feb 2024
Viewed by 704
Abstract
Parkinson’s disease (PD) is the second most prevalent dementia in the world. Wearable technology has been useful in the computer-aided diagnosis and long-term monitoring of PD in recent years. The fundamental issue remains how to assess the severity of PD using wearable devices [...] Read more.
Parkinson’s disease (PD) is the second most prevalent dementia in the world. Wearable technology has been useful in the computer-aided diagnosis and long-term monitoring of PD in recent years. The fundamental issue remains how to assess the severity of PD using wearable devices in an efficient and accurate manner. However, in the real-world free-living environment, there are two difficult issues, poor annotation and class imbalance, both of which could potentially impede the automatic assessment of PD. To address these challenges, we propose a novel framework for assessing the severity of PD patient’s in a free-living environment. Specifically, we use clustering methods to learn latent categories from the same activities, while latent Dirichlet allocation (LDA) topic models are utilized to capture latent features from multiple activities. Then, to mitigate the impact of data imbalance, we augment bag-level data while retaining key instance prototypes. To comprehensively demonstrate the efficacy of our proposed framework, we collected a dataset containing wearable-sensor signals from 83 individuals in real-life free-living conditions. The experimental results show that our framework achieves an astounding 73.48% accuracy in the fine-grained (normal, mild, moderate, severe) classification of PD severity based on hand movements. Overall, this study contributes to more accurate PD self-diagnosis in the wild, allowing doctors to provide remote drug intervention guidance. Full article
(This article belongs to the Special Issue Smart Sensors and Transducers for Wearable and Healthcare)
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29 pages, 5448 KiB  
Article
A Differential Inertial Wearable Device for Breathing Parameter Detection: Hardware and Firmware Development, Experimental Characterization
by Roberto De Fazio, Maria Rosaria Greco, Massimo De Vittorio and Paolo Visconti
Sensors 2022, 22(24), 9953; https://doi.org/10.3390/s22249953 - 16 Dec 2022
Cited by 4 | Viewed by 2648
Abstract
Breathing monitoring is crucial for evaluating a patient’s health status. The technologies commonly used to monitor respiration are costly, bulky, obtrusive, and inaccurate, mainly when the user moves. Consequently, efforts have been devoted to providing new solutions and methodologies to overcome these limitations. [...] Read more.
Breathing monitoring is crucial for evaluating a patient’s health status. The technologies commonly used to monitor respiration are costly, bulky, obtrusive, and inaccurate, mainly when the user moves. Consequently, efforts have been devoted to providing new solutions and methodologies to overcome these limitations. These methods have several uses, including healthcare monitoring, measuring athletic performance, and aiding patients with respiratory diseases, such as COPD (chronic obtrusive pulmonary disease), sleep apnea, etc. Breathing-induced chest movements can be measured noninvasively and discreetly using inertial sensors. This research work presents the development and testing of an inertia-based chest band for breathing monitoring through a differential approach. The device comprises two IMUs (inertial measurement units) placed on the patient’s chest and back to determine the differential inertial signal, carrying out information detection about the breathing activity. The chest band includes a low-power microcontroller section to acquire inertial data from the two IMUs and process them to extract the breathing parameters (i.e., RR—respiration rate; TI/TE—inhalation/exhalation time; IER—inhalation-to-exhalation time; V—flow rate), using the back IMU as a reference. A BLE transceiver wirelessly transmits the acquired breathing parameters to a mobile application. Finally, the test results demonstrate the effectiveness of the used dual-inertia solution; correlation and Bland–Altman analyses were performed on the RR measurements from the chest band and the reference, demonstrating a high correlation (r¯ = 0.92) and low mean difference (MD¯ = −0.27 BrPM (breaths per minute)), limits of agreement (LoA¯ = +1.16/−1.75 BrPM), and mean absolute error (MAE¯ = 1.15%). Additionally, the experimental results demonstrated that the developed device correctly measured the other breathing parameters (TI, TE, IER, and V), keeping an MAE of ≤5%. The obtained results indicated that the developed chest band is a viable solution for long-term breathing monitoring, both in stationary and moving users. Full article
(This article belongs to the Special Issue Smart Sensors and Transducers for Wearable and Healthcare)
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17 pages, 5346 KiB  
Article
Robot-Based Calibration Procedure for Graphene Electronic Skin
by Jan Klimaszewski, Krzysztof Wildner, Anna Ostaszewska-Liżewska, Michał Władziński and Jakub Możaryn
Sensors 2022, 22(16), 6122; https://doi.org/10.3390/s22166122 - 16 Aug 2022
Cited by 1 | Viewed by 1494
Abstract
The paper describes the semi-automatised calibration procedure of an electronic skin comprising screen-printed graphene-based sensors intended to be used for robotic applications. The variability of sensitivity and load characteristics among sensors makes the practical use of the e-skin extremely difficult. As the number [...] Read more.
The paper describes the semi-automatised calibration procedure of an electronic skin comprising screen-printed graphene-based sensors intended to be used for robotic applications. The variability of sensitivity and load characteristics among sensors makes the practical use of the e-skin extremely difficult. As the number of active elements forming the e-skin increases, this problem becomes more significant. The article describes the calibration procedure of multiple e-skin array sensors whose parameters are not homogeneous. We describe how an industrial robot equipped with a reference force sensor can be used to automatise the e-skin calibration procedure. The proposed methodology facilitates, speeds up, and increases the repeatability of the e-skin calibration. Finally, for the chosen example of a nonhomogeneous sensor matrix, we provide details of the data preprocessing, the sensor modelling process, and a discussion of the obtained results. Full article
(This article belongs to the Special Issue Smart Sensors and Transducers for Wearable and Healthcare)
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