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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: closed (31 July 2021) | Viewed by 11774

Special Issue Editors


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Guest Editor
Signals and Images Laboratory, Institute of Information Science and Technologies "A. Faedo" (ISTI), National Research Council of Italy (CNR), Via Moruzzi, 1, 56124 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
Special Issues, Collections and Topics in MDPI journals
Department of Anesthesiology and Intensive Care Medicine, Hospitallers Brothers Hospital, Paracelsus Medical University, 5020 Salzburg, Austria
Interests: accidental hypothermia; anaesthesiology; extreme environments; emergency medicine; mountain medicine; intensive care medicine; public health
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Institute of Information Science and Technologies, National Research Council of Italy, Signals and Images Laboratory, Via Moruzzi, 1, 56124 Pisa, Italy
Interests: computational intelligence and intelligent systems; artificial intelligence; computer vision; multimedia information processing; signal processing; assistive technologies; interactive systems and augmented reality
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
1. Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague, Czech Republic
2. Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 166 36 Prague, Czech Republic
Interests: digital signal processing; machine learning; computational intelligence
Special Issues, Collections and Topics 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 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.

Published Papers (2 papers)

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17 pages, 40770 KiB  
Article
Hand Gesture Recognition Using EGaIn-Silicone Soft Sensors
by Sungtae Shin, Han Ul Yoon and Byungseok Yoo
Sensors 2021, 21(9), 3204; https://doi.org/10.3390/s21093204 - 05 May 2021
Cited by 16 | Viewed by 7066
Abstract
Exploiting hand gestures for non-verbal communication has extraordinary potential in HCI. A data glove is an apparatus widely used to recognize hand gestures. To improve the functionality of the data glove, a highly stretchable and reliable signal-to-noise ratio sensor is indispensable. To do [...] Read more.
Exploiting hand gestures for non-verbal communication has extraordinary potential in HCI. A data glove is an apparatus widely used to recognize hand gestures. To improve the functionality of the data glove, a highly stretchable and reliable signal-to-noise ratio sensor is indispensable. To do this, the study focused on the development of soft silicone microchannel sensors using a Eutectic Gallium-Indium (EGaIn) liquid metal alloy and a hand gesture recognition system via the proposed data glove using the soft sensor. The EGaIn-silicone sensor was uniquely designed to include two sensing channels to monitor the finger joint movements and to facilitate the EGaIn alloy injection into the meander-type microchannels. We recruited 15 participants to collect hand gesture dataset investigating 12 static hand gestures. The dataset was exploited to estimate the performance of the proposed data glove in hand gesture recognition. Additionally, six traditional classification algorithms were studied. From the results, a random forest shows the highest classification accuracy of 97.3% and a linear discriminant analysis shows the lowest accuracy of 87.4%. The non-linearity of the proposed sensor deteriorated the accuracy of LDA, however, the other classifiers adequately overcame it and performed high accuracies (>90%). Full article
(This article belongs to the Special Issue Intelligent Sensors for Monitoring Physical Activities)
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13 pages, 1334 KiB  
Article
Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis
by Hana Charvátová, Aleš Procházka and Oldřich Vyšata
Sensors 2020, 20(5), 1523; https://doi.org/10.3390/s20051523 - 10 Mar 2020
Cited by 8 | Viewed by 3254
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)
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