sensors-logo

Journal Browser

Journal Browser

Special Issue "Intelligent Sensors for Monitoring Physical Activities"

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

Deadline for manuscript submissions: 15 March 2021.

Special Issue Editors

Dr. Massimo Martinelli
Website SciProfiles
Guest Editor
Signals and Images Laboratory, Institute of Information Science and Technologies "A. Faedo" (ISTI), National Research Council of Italy (CNR), Via Moruzzi, 1 - Pisa, Italy
Interests: computational intelligence and intelligent systems; deep learning; artificial intelligence; decision support systems; advanced web technologies; multimedia information processing, signal processing, wearable sensors, biomedical sensors, physiological signal processing; assistive technologies; interactive systems and augmented reality
Dr. Peter Paal
Website SciProfiles
Guest Editor
Anaesthesiology and Intensive Care Medicine, Krankenhaus Barmherzige Brüder Salzburg, Austria
Interests: mountain medicine; critical care medicine; intensive care medicine; airway management; resuscitation; mechanical ventilation; emergency management; CPR; anesthesiology; ventilation
Dr. Davide Moroni
Website SciProfiles
Guest Editor
Signals and Images Laboratory, Institute of Information Science and Technologies "A. Faedo" (ISTI), National Research Council of Italy (CNR), Via Moruzzi, 1 - Pisa, Italy
Interests: computational intelligence and intelligent systems; artificial intelligence; multimedia information processing; signal processing; wearable sensors; biomedical sensors; physiological signal processing; assistive technologies; interactive systems and augmented reality
Special Issues and Collections in MDPI journals
Prof. Dr. Aleš Procházka
Website
Guest Editor
University of Chemistry and Technology & Czech Technical University, Prague, Czech Republic
Interests: digital signal processing; machine learning; computational intelligence
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Human physical activities are increasingly pushed to the limit in extreme environments, such as in the high mountains, in the depths of the sea, or in sports, both at a professional and amateur level.

The analysis and evaluation of biophysical responses in people who face severe conditions and efforts require complex and often multidisciplinary theoretical and practical skills.

The rapid development of biotechnological, computer, and engineering sciences and the increasingly sophisticated applications are greatly affecting research in this field. Consequently, the approach of monitoring human physical activities is changing significantly, also fostering the appearance of new professional figures with non-traditional skills.

In particular, the effective analysis of biometric parameters now requires big data approaches, capable of exploiting intelligent computational models that deal with multimedia information obtained from different types of sensors, often in real-time, for evaluating performance, adaptive planning, rehabilitation, prevention, or simulation.

This Special Issue, titled "Intelligent Sensors for Monitoring Physical Activities", intends to explore the scientific–technological frontier that underlies the optimal solution of the abovementioned problems, while, at the same time, involving the development and use of innovative sensors and smart methods for the interpretation of data and scenarios.

The main topics of this Special Issue include, but are not limited to, the following:

  • biological signals and sensors;
  • computational intelligence;
  • digital signals and images processing;
  • human physiology;
  • machine learning;
  • motion analysis;
  • multimedia data analysis;
  • neurological disorders;
  • physical activities;
  • positioning and depth sensors, sports, rural and mountain areas activities, and rehabilitation.
Dr. Massimo Martinelli
Dr. Peter Paal
Dr. Davide Moroni
Prof. Dr. Ales Procházka
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 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 2000 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)

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

Research

Open AccessArticle
Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis
Sensors 2020, 20(5), 1523; https://doi.org/10.3390/s20051523 - 10 Mar 2020
Abstract
Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart [...] Read more.
Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart rate during cycling, under different body loads. Acquired data include 1293 signal segments recorded by the mobile phone and the Garmin device for uphill and downhill cycling. The proposed method is based upon digital processing of the heart rate and the mean power in different frequency bands of accelerometric data. The classification of the resulting features was performed by the support vector machine, Bayesian methods, k-nearest neighbor method, and neural networks. The proposed criterion is then used to find the best positions for the sensors with the highest discrimination abilities. The results suggest the sensors be positioned on the spine for the classification of uphill and downhill cycling, yielding an accuracy of 96.5% and a cross-validation error of 0.04 evaluated by a two-layer neural network system for features based on the mean power in the frequency bands 3 , 8 and 8 , 15 Hz. This paper shows the possibility of increasing this accuracy to 98.3% by the use of more features and the influence of appropriate sensor positioning for motion monitoring and classification. Full article
(This article belongs to the Special Issue Intelligent Sensors for Monitoring Physical Activities)
Show Figures

Graphical abstract

Back to TopTop