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Current Research and Future Development for Wearable Measurement Sensors
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Current Research and Future Development for Wearable Measurement Sensors

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 2198

Special Issue Editors

Prof. Dr. Silvia Sangiovanni

E-Mail Website
Guest Editor
Department of Astronautics, Electrical and Energy Engineering, Sapienza University of Rome, 00184 Rome, Italy
Interests: wearable devices; impedance, power and energy measurement in sinusoidal and non-sinusoidal condition; power quality; data acquisition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marco Laracca

E-Mail Website
Guest Editor
Department of Astronautics, Electrical and Energy Engineering, Sapienza University of Rome, 00184 Rome, Italy
Interests: non destructive testing via eddy current; sensor design; realization and characterization; wearable devices; impedance, power, and energy measurement in sinusoidal and non-sinusoidal condition; non contact current measurement; power quality; calibration of vehicle speed meters
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable and flexible health sensing devices are currently crucial, and the development of advanced technologies for non-invasive motion detection and continuous long-term biophysical signal monitoring is of increasing interest.

The continuous advances in wearable sensors will progressively change the healthcare landscape by enabling individual management and continuous monitoring of a patient's health status, as they can measure vital parameters and perform motion analysis.

The application fields of wearable sensors and technologies is very broad, touching fitness and wellness and rehabilitation (where wearable devices can be used to monitor user performance in time, and if necessary, intervene to correct harmful behavior, e.g., sedentary lifestyle), sport science (used to improve the performance of the athletes), remote healthcare, long term monitoring, etc.

Advances in wearable sensors technologies are linked to the development of new multi-sensing wearable textiles, allowing the best compromise between the measurement performance and the usability (flexibility, stretchability, washability, reusability, etc.), to increase the number of physiological quantities to be measured using wearable devices, to improve the quality and reliability of the measurement, to adopt new data processing and data fusion techniques, etc.

In this Special Issue, we invite original research papers and review articles aimed at promoting advances in measurements made using wearable sensors and technologies across multiple application fields.

Prof. Dr. Silvia Sangiovanni
Prof. Dr. Marco Laracca
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.

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

  • wearables sensors
  • smart tissue
  • wearability
  • physiological measurement
  • healthcare
  • movement analysis
  • rehabilitation
  • measurement in sport science
  • wearables devices, healthcare and IoT

Published Papers (1 paper)

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Research

14 pages, 2139 KiB  
Article
Real-Time Risk Assessment Detection for Weak People by Parallel Training Logical Execution of a Supervised Learning System Based on an IoT Wearable MEMS Accelerometer
by Minh Long Hoang, Armel Asongu Nkembi and Phuong Ly Pham
Sensors 2023, 23(3), 1516; https://0-doi-org.brum.beds.ac.uk/10.3390/s23031516 - 30 Jan 2023
Cited by 4 | Viewed by 1839
Abstract
Activity monitoring has become a necessary demand for weak people to guarantee their safety. The paper proposed a Parallel Training Logical Execution (PTLE) system using machine learning (ML) models on a microelectromechanical system (MEMS) accelerometer to detect coughs, falls, and other normal activities. [...] Read more.
Activity monitoring has become a necessary demand for weak people to guarantee their safety. The paper proposed a Parallel Training Logical Execution (PTLE) system using machine learning (ML) models on a microelectromechanical system (MEMS) accelerometer to detect coughs, falls, and other normal activities. When there are many categories, the ML prediction can be confused between these activities with each other. The PTLE system trains several models in parallel with more specific activity classes in each dataset. The shared tasks between parallel models relieve the complexity for a single one. There are six additional parameters for accelerometer characteristics, which were calculated from three axes accelerations as input features to improve the ML’s consciousness. Once all models were trained, the system was ready to receive the input accelerations and activated the logical flow to manage link operation between these ML models for output predictions. Random Forest (RF) had the highest potential among the ML classification algorithms after the validation. In the experiment, the comparison between the PTLE model and the regular ML model were carried out with real-time data from an M5stickC wearable device on the user’s chest to the trained models on PC. The result showed the advancement of the proposed method in term of precision, recall, F1-score with an overall accuracy of 98% in the real-time test. The accelerations from the wearable device were sent to ML models via Wi-Fi with Message Queue Telemetry Transport (MQTT) broker, and the activity predictions were transferred to the cloud for the family members or doctor care based on Internet of Things (IoT) communication. Full article
(This article belongs to the Special Issue Current Research and Future Development for Wearable Measurement Sensors)
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