sensors-logo

Journal Browser

Journal Browser

Special Issue "Intelligent Sensing in Biomedical Applications"

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

Deadline for manuscript submissions: closed (31 May 2021).

Special Issue Editors

Dr. Oldřich Vyšata
E-Mail Website
Guest Editor
Charles University, Prague Praha, Czech Republic
Interests: neurophysiology; digital signal processing; biological systems modeling
Prof. Dr. Aleš Procházka
E-Mail Website
Guest Editor
University of Chemistry and Technology & Czech Technical University, Prague, Czech Republic
Interests: digital signal processing; machine learning; computational intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Rafael Doležal
E-Mail Website
Guest Editor
University of Hradec Kralove, Faculty of Sciences, Czech Republic
Interests: computer-aided drug design; high-performance computing; machine learning

Special Issue Information

Dear Colleagues,

At present, monitoring the course of the disease and the effect of therapy in clinical practice mostly depends on clinical scales and clinical impression. Such a description of the development of the patient’s condition is subject to intra-individual and inter-individual variability. In addition, such monitoring takes place only for a short time, mostly in the unnatural conditions of medical facilities. On the other hand, modern sensors enable increasingly accurate long-term monitoring of many important quantities. Reducing the variability of patient follow-up makes it possible to reduce the number of subjects in clinical trials and thus significantly reduce the cost of the studies. It also reduces the likelihood of false-negative results, thus saving the cost of developing new treatments. Smart sensor devices make it possible to acquire, process, and transmit data to users. Smart implants like orthopedic implants instrumented with strain gauges increase their lifespan. Retina implant systems using image sensors restore vision. Wearable body sensor networks comprising various types of sensors can monitor the course of vital variables for a long time, as well as the signal needed for therapeutic intervention. Biosensors enable the monitoring of physical activities. Results of machine learning methods contribute to the diagnosis of neurological disorders and the detection of tissue changes.

This Special Issue is addressed to all types of smart sensors designed for biomedical applications.

The topic of this Special Issue concerns the following areas of interest of the magazine: biosensors, sensor networks, smart/intelligent sensors, signal processing, data fusion, and deep learning in sensor systems.

Dr. Oldřich Vyšata
Prof. Dr. Aleš Procházka
Dr. Rafael Doležal
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 papers will be 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 2200 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

  • smart implants
  • smart biosensors
  • wearables
  • sensor fusion
  • biomedical
  • motion monitoring
  • machine learning
  • signal processing

Published Papers (3 papers)

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

Research

Communication
Classification of Ataxic Gait
Sensors 2021, 21(16), 5576; https://doi.org/10.3390/s21165576 - 19 Aug 2021
Viewed by 368
Abstract
Gait disorders accompany a number of neurological and musculoskeletal disorders that significantly reduce the quality of life. Motion sensors enable high-quality modelling of gait stereotypes. However, they produce large volumes of data, the evaluation of which is a challenge. In this publication, we [...] Read more.
Gait disorders accompany a number of neurological and musculoskeletal disorders that significantly reduce the quality of life. Motion sensors enable high-quality modelling of gait stereotypes. However, they produce large volumes of data, the evaluation of which is a challenge. In this publication, we compare different data reduction methods and classification of reduced data for use in clinical practice. The best accuracy achieved between a group of healthy individuals and patients with ataxic gait extracted from the records of 43 participants (23 ataxic, 20 healthy), forming 418 segments of straight gait pattern, is 98% by random forest classifier preprocessed by t-distributed stochastic neighbour embedding. Full article
(This article belongs to the Special Issue Intelligent Sensing in Biomedical Applications)
Show Figures

Figure 1

Article
Advanced Statistical Analysis of 3D Kinect Data: Mimetic Muscle Rehabilitation Following Head and Neck Surgeries Causing Facial Paresis
Sensors 2021, 21(1), 103; https://doi.org/10.3390/s21010103 - 26 Dec 2020
Cited by 2 | Viewed by 689
Abstract
An advanced statistical analysis of patients’ faces after specific surgical procedures that temporarily negatively affect the patient’s mimetic muscles is presented. For effective planning of rehabilitation, which typically lasts several months, it is crucial to correctly evaluate the improvement of the mimetic muscle [...] Read more.
An advanced statistical analysis of patients’ faces after specific surgical procedures that temporarily negatively affect the patient’s mimetic muscles is presented. For effective planning of rehabilitation, which typically lasts several months, it is crucial to correctly evaluate the improvement of the mimetic muscle function. The current way of describing the development of rehabilitation depends on the subjective opinion and expertise of the clinician and is not very precise concerning when the most common classification (House–Brackmann scale) is used. Our system is based on a stereovision Kinect camera and an advanced mathematical approach that objectively quantifies the mimetic muscle function independently of the clinician’s opinion. To effectively deal with the complexity of the 3D camera input data and uncertainty of the evaluation process, we designed a three-stage data-analytic procedure combining the calculation of indicators determined by clinicians with advanced statistical methods including functional data analysis and ordinal (multiple) logistic regression. We worked with a dataset of 93 distinct patients and 122 sets of measurements. In comparison to the classification with the House–Brackmann scale the developed system is able to automatically monitor reinnervation of mimetic muscles giving us opportunity to discriminate even small improvements during the course of rehabilitation. Full article
(This article belongs to the Special Issue Intelligent Sensing in Biomedical Applications)
Show Figures

Figure 1

Article
Individualised Responsible Artificial Intelligence for Home-Based Rehabilitation
Sensors 2021, 21(1), 2; https://doi.org/10.3390/s21010002 - 22 Dec 2020
Cited by 1 | Viewed by 1200
Abstract
Socioeconomic reasons post-COVID-19 demand unsupervised home-based rehabilitation and, specifically, artificial ambient intelligence with individualisation to support engagement and motivation. Artificial intelligence must also comply with accountability, responsibility, and transparency (ART) requirements for wider acceptability. This paper presents such a patient-centric individualised home-based rehabilitation [...] Read more.
Socioeconomic reasons post-COVID-19 demand unsupervised home-based rehabilitation and, specifically, artificial ambient intelligence with individualisation to support engagement and motivation. Artificial intelligence must also comply with accountability, responsibility, and transparency (ART) requirements for wider acceptability. This paper presents such a patient-centric individualised home-based rehabilitation support system. To this end, the Timed Up and Go (TUG) and Five Time Sit To Stand (FTSTS) tests evaluate daily living activity performance in the presence or development of comorbidities. We present a method for generating synthetic datasets complementing experimental observations and mitigating bias. We present an incremental hybrid machine learning algorithm combining ensemble learning and hybrid stacking using extreme gradient boosted decision trees and k-nearest neighbours to meet individualisation, interpretability, and ART design requirements while maintaining low computation footprint. The model reaches up to 100% accuracy for both FTSTS and TUG in predicting associated patient medical condition, and 100% or 83.13%, respectively, in predicting area of difficulty in the segments of the test. Our results show an improvement of 5% and 15% for FTSTS and TUG tests, respectively, over previous approaches that use intrusive means of monitoring such as cameras. Full article
(This article belongs to the Special Issue Intelligent Sensing in Biomedical Applications)
Show Figures

Figure 1

Back to TopTop