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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: 31 July 2021.

Special Issue Editors

Dr. Massimo Martinelli
E-Mail Website
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
E-Mail Website
Guest Editor
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 and Collections in MDPI journals
Dr. Davide Moroni
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 and Collections in MDPI journals
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 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 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 (2 papers)

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Research

Article
Hand Gesture Recognition Using EGaIn-Silicone Soft Sensors
Sensors 2021, 21(9), 3204; https://doi.org/10.3390/s21093204 - 05 May 2021
Viewed by 423
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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Article
Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis
Sensors 2020, 20(5), 1523; https://doi.org/10.3390/s20051523 - 10 Mar 2020
Cited by 2 | Viewed by 1065
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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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Modern Development of Smart Wearable Sensors
Authors: Haoxi Luoa, Bingbing Gaoa* and Bingfang Hea*
Affiliation: School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing 211816, China.
Abstract: Wearable biosensors are garnering substantial interest in the clinical and biological medical communities due to their potential in empowering patients with real-time diagnostics tools and information via dynamic, noninvasive measurements of biochemical markers in biofluids, such as sweat, tears, saliva and interstitial fluid. These replace monitoring available only in a hospital setting with minimally invasive devices that can be worn and are capable of high-frequency or continuous sampling. Herein, critical perspectives of emerging wearable sensor toward the future of digital health monitoring are provided. The involved processing technologies and materials in wearable sensors in recent years are discussed along with their monitoring schemes and system-level integration technologies. Finally, their implications toward early disease detection and monitoring are discussed, concluding with a future perspective into the challenges and opportunities in emerging wearable sensor designs for the next few years.

Title: Activity Recognition Techniques for Overcoming Domain Shift
Authors: Stefan Kalabakov 1, 2, Simon Stankoski 1, 2, Nina Reščič 1, 2, Ivana Kiprijanovska 1, 2, Andrejaana Andova 1, 2, Vito Janko 1, 2, Martin Gjoreski 1, 2, Carlo Maria De Masi 1, Matjaž Gams 1, 2 and Mitj
Affiliation: 1 Department of Intelligent Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia 2 Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
Abstract: Every year, the Sussex-Huawei Locomotion-Transportation Recognition Challenge presents a different scenario in which the participants of the challenge are tasked with recognizing eight different modes of locomotion and transportation using sensor data from smartphones. In 2019, the main challenge was cross-location activity recognition, which meant that the training data was recorded with a smartphone placed in different locations than the test data. The following year, the challenge was cross-person activity recognition in which the training data was recorded by a different person than the test data. Additionally, the location of the smartphone in the test data was withheld. Our team participated in both challenges and placed 1st and 3rd respectively. To this end, this paper explores methods that can deal with test data differing from training data, and that otherwise improve the activity recognition performance. These include models specialized for certain activities, feature selection to select transferrable features, as well as smoothing with hidden Markov models, semi-supervised learning, and a combination of the two.

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