sensors-logo

Journal Browser

Journal Browser

Advances in Biomedical Sensing, Instrumentation and Systems: 2nd Edition

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

Deadline for manuscript submissions: 15 December 2024 | Viewed by 1887

Special Issue Editor


E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona (AN), Italy
Interests: analog, digital and mixed signal circuit design and simulation; embedded systems design; wireless sensors and networks; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in electronics and computational capabilities are constantly facilitating the creation of portable, wearable, miniaturized, more power-efficient, and/or more accurate sensing devices and instruments, which can also incorporate enough intelligence to autonomously analyze captured signals and possibly react to them.

The aim of this Special Issue is to collect papers that deal with all aspects regarding challenges and solutions in the development of sensing devices, their hardware, their communication requirements, and how the data thus acquired are processed to provide useful information to the user of the system, be it the subject themself or a qualified physician or technician.

Therefore, we are seeking papers that describe innovative developments in the acquisition of biomedical-related signals, their enabling technologies, and the interpretation of the data through automated techniques like machine learning and artificial intelligence.

Review articles that provide readers with scholarly educational material about the current research trends on the matter are also welcome.

Submissions are encouraged which address topics that include, but are not limited to, the following:

  • Biosignal acquisition.
  • Wearable devices.
  • Portable sensors.
  • Wireless sensors.
  • Health tracking.
  • Health monitoring.
  • Sensor networks for biomedical signal acquisition.
  • Machine learning for biomedical signal analysis.
  • Automatic diagnosis and classification.

Prof. Dr. Giorgio Biagetti
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

  • biosignal acquisition
  • wearable devices
  • portable sensors
  • wireless sensors

Related Special Issue

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

14 pages, 5760 KiB  
Article
Rectified Latent Variable Model-Based EMG Factorization of Inhibitory Muscle Synergy Components Related to Aging, Expertise and Force–Tempo Variations
by Subing Huang, Xiaoyu Guo, Jodie J. Xie, Kelvin Y. S. Lau, Richard Liu, Arthur D. P. Mak, Vincent C. K. Cheung and Rosa H. M. Chan
Sensors 2024, 24(9), 2820; https://doi.org/10.3390/s24092820 - 28 Apr 2024
Viewed by 441
Abstract
Muscle synergy has been widely acknowledged as a possible strategy of neuromotor control, but current research has ignored the potential inhibitory components in muscle synergies. Our study aims to identify and characterize the inhibitory components within motor modules derived from electromyography (EMG), investigate [...] Read more.
Muscle synergy has been widely acknowledged as a possible strategy of neuromotor control, but current research has ignored the potential inhibitory components in muscle synergies. Our study aims to identify and characterize the inhibitory components within motor modules derived from electromyography (EMG), investigate the impact of aging and motor expertise on these components, and better understand the nervous system’s adaptions to varying task demands. We utilized a rectified latent variable model (RLVM) to factorize motor modules with inhibitory components from EMG signals recorded from ten expert pianists when they played scales and pieces at different tempo–force combinations. We found that older participants showed a higher proportion of inhibitory components compared with the younger group. Senior experts had a higher proportion of inhibitory components on the left hand, and most inhibitory components became less negative with increased tempo or decreased force. Our results demonstrated that the inhibitory components in muscle synergies could be shaped by aging and expertise, and also took part in motor control for adapting to different conditions in complex tasks. Full article
Show Figures

Figure 1

Review

Jump to: Research

30 pages, 14249 KiB  
Review
Intelligent, Flexible Artificial Throats with Sound Emitting, Detecting, and Recognizing Abilities
by Junxin Fu, Zhikang Deng, Chang Liu, Chuting Liu, Jinan Luo, Jingzhi Wu, Shiqi Peng, Lei Song, Xinyi Li, Minli Peng, Houfang Liu, Jianhua Zhou and Yancong Qiao
Sensors 2024, 24(5), 1493; https://doi.org/10.3390/s24051493 - 25 Feb 2024
Viewed by 1223
Abstract
In recent years, there has been a notable rise in the number of patients afflicted with laryngeal diseases, including cancer, trauma, and other ailments leading to voice loss. Currently, the market is witnessing a pressing demand for medical and healthcare products designed to [...] Read more.
In recent years, there has been a notable rise in the number of patients afflicted with laryngeal diseases, including cancer, trauma, and other ailments leading to voice loss. Currently, the market is witnessing a pressing demand for medical and healthcare products designed to assist individuals with voice defects, prompting the invention of the artificial throat (AT). This user-friendly device eliminates the need for complex procedures like phonation reconstruction surgery. Therefore, in this review, we will initially give a careful introduction to the intelligent AT, which can act not only as a sound sensor but also as a thin-film sound emitter. Then, the sensing principle to detect sound will be discussed carefully, including capacitive, piezoelectric, electromagnetic, and piezoresistive components employed in the realm of sound sensing. Following this, the development of thermoacoustic theory and different materials made of sound emitters will also be analyzed. After that, various algorithms utilized by the intelligent AT for speech pattern recognition will be reviewed, including some classical algorithms and neural network algorithms. Finally, the outlook, challenge, and conclusion of the intelligent AT will be stated. The intelligent AT presents clear advantages for patients with voice impairments, demonstrating significant social values. Full article
Show Figures

Figure 1

Back to TopTop