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Special Issue "Analysis of Biomedical Signals and Physical Behavior Sensing in the Development of Systems for Monitoring, Training, Controlling, and Improving Quality of Life"

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

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Prof. Dr. Michał Strzelecki

Guest Editor
Institute of Electronics, Lodz University of Technology, Wolczanska 211/215, 90-924 Łódź, Poland
Interests: Medical imaging; analysis of biomedical images; pattern recognition
Special Issues and Collections in MDPI journals
Prof. Dr. Adam Wojciechowski

Guest Editor
Institute of Information Technology, Lodz University of Technology, Wolczanska 215, 90-924 Łódź, Poland
Interests: human-computer interaction, biomedical engineering, computer games, machine learning, computer graphics

Special Issue Information

Dear Colleague,

Biomedical signals sensors and physical behavior in remote sensing convey digital data for intelligent analysis and detection and classification of living organism states, behaviors or physiological processes describing their nature or activity. This Special Issue covers multidisciplinary works in the field of biomedical engineering, computer science, human–computer interaction, electronics, and partly also medicine, sport, and psychology, aiming at monitoring and improving living organisms’ quality of life. The Special Issue addresses the application of sensor data processing and analysis, with special interest in, but not limited to, the following list of aspects:

  • Application of artificial intelligence for biomedical signal analysis and classification;
  • Efficiency and efficacy in multimodal data processing, synchronization, and fusion;
  • The problem of a small amount of data in method and system profiling, teaching, adaptation, and calibration;
  • Signal pattern and behavioral pattern recognition in monitoring and diagnosis systems;
  • Human–computer interaction in therapy, training, control, activity monitoring, and rehabilitation systems;
  • Physical and mental health monitoring, assistive living, and wellbeing-oriented systems;
  • Sensing challenges for cognitive and physical aging diagnosis and treatment;
  • Sensing challenges for elderly people and people with disabilities.

Prof. Dr. Michał Strzelecki
Prof. Dr. Adam Wojciechowski
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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.

Published Papers (1 paper)

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Open AccessArticle
Fully Automatic Fall Risk Assessment Based on a Fast Mobility Test
Sensors 2021, 21(4), 1338; - 13 Feb 2021
This paper presents a fall risk assessment approach based on a fast mobility test, automatically evaluated using a low-cost, scalable system for the recording and analysis of body movement. This mobility test has never before been investigated as a sole source of data [...] Read more.
This paper presents a fall risk assessment approach based on a fast mobility test, automatically evaluated using a low-cost, scalable system for the recording and analysis of body movement. This mobility test has never before been investigated as a sole source of data for fall risk assessment. It can be performed in a very limited space and needs only minimal additional equipment, yet provides large amounts of information, as the presented system can obtain much more data than traditional observation by capturing minute details regarding body movement. The readings are provided wirelessly by one to seven low-cost micro-electro-mechanical inertial measurement units attached to the subject’s body segments. Combined with a body model, these allow segment rotations and translations to be computed and for body movements to be recreated in software. The subject can then be automatically classified by an artificial neural network based on selected values in the test, and those with an elevated risk of falls can be identified. Results obtained from a group of 40 subjects of various ages, both healthy volunteers and patients with vestibular system impairment, are presented to demonstrate the combined capabilities of the test and system. Labelling of subjects as fallers and non-fallers was performed using an objective and precise sensory organization test; it is an important novelty as this approach to subject labelling has never before been used in the design and evaluation of fall risk assessment systems. The findings show a true-positive ratio of 85% and true-negative ratio of 63% for classifying subjects as fallers or non-fallers using the introduced fast mobility test, which are noticeably better than those obtained for the long-established Timed Up and Go test. Full article
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