Special Issue "State-of-the-Art Sensors Technologies in Italy 2016"
Deadline for manuscript submissions: closed (28 February 2017)
Prof. Dr. Vittorio Ferrari
Department of Information Engineering (DII), University of Brescia, Via Branze 38, I25133 Brescia, Italy
Website | E-Mail
Interests: piezoelectric sensors and transducers; resonant and acoustic-wave sensors; energy harvesting for sensors; sensor interface electronics; MEMS and microsensors for physical quantities
This Special Issue is intended to provide an up-to-date and comprehensive view on the state-of-the-art of sensors science and technology in Italy.
The covered topics will span from sensing devices and principles (including chemical, physical, biological, and optical sensors), to sensor technologies (including micro and nano fabrication, film and printed technologies), to sensor systems (including sensor electronics, energy harvesting, sensor networks, and internet of things), to applications in different scenarios (including industrial, automotive, environmental, food and agricolture, biomedical, and other fields).
High-quality research and review articles on any relevant aspect related to sensors in Italy are solicited and will be considered for publication in the Special Issue.
Potential topics include, but are not limited to:
- Chemical Sensors and Microsystems
- Physical Sensors and Microsystems
- Biological Sensors - Biomedical Devices and Systems
- Micro- and Nano-Fabrication for Sensors and Actuators
- MEMS, MOEMS, NEMS
- Materials and Technology
- Theory, modelling, design and simulation
- Micro-Fluidic and Micro Analytical Sensors and Systems
- Sensor Electronics and Signal Processing
- Energy Harvesting and Micro-Power Generation
- Wireless Sensor Networks
- Sensor Systems and Applications
- Electronic Noses
- Actuators and Micromachines
- Packaging and Assembly Technology
Prof. Dr. Giorgio Sberveglieri
Prof. Dr. Vittorio Ferrari
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 monthly 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 1800 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.
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: Marker-less Motion Capture System to Track Spontaneous Movements in Infants
Authors: Angela Grassi1*† and Alessandro Manzi 1†, Francesca Cecchi 1, Marchi Viviana 2, Cinzia Esposito 1, Michele Coluccini 2, Andrea Guzzetta 2, Filippo Cavallo 1 and Cecilia Laschi 1
Affiliations: 1The BioRobotics Institute, Scuola Superiore Sant'Anna, Polo Sant'Anna Valdera, Viale Rinaldo Piaggio 34, Pontedera 56025, PI, Italy
2 Department of Developmental Neuroscience, Stella Maris Scientific Institute, Pisa, Italy;
† These authors contributed equally to this work.
Abstract: Each year more than 15 million babies worldwide are born preterm (before 37 weeks gestational age). These infants are at higher risk of developing motor impairment than infants born at term. The spontaneous movements (SMs) assessment has a high predictive value for movement impairments in high risk infants. However, this assessment is qualitative and based on experiences and subjective observations of the clinicians. An objective and technology-based assessment is required. Available marker-based motion capture systems are invasive and cumbersome and they require a high set-up time due to the calibration procedure. Therefore, the challenge is to overcome all these factors with the use of marker-less systems.
The objectives of this paper is twofold. First, to present a new unobtrusive method to monitor infants’ SMs; second, to validate the proposed marker-less system with respect to the gold standard system for motion analysis.
Our innovative method is based on 3D video recording acquired with the RGB-Depth sensor (Asus Xtion Pro Live) and a tracking algorithm. The tracking algorithm is based on color filters. It is divided into two modules: the first one to segment the infant’s body from the background and the second one to identify five clusters, corresponding to infant’s body extremities. The trajectories of these five clusters are extracted.
In order to evaluate the accuracy of our method, a validation study was conducted. Thirteen infants (nine males and four females, mean age 106.8± 31.7 days) were involved. Infants’ movement data were acquired simultaneously by both systems: the RGB-Depth sensor (Asus Xtion Pro Live) and the optoelectronic system (BTS SMART DX400). The medians of RMSEs calculated for each trajectories are lower than 1.7 cm. Some errors occurred when there is a contact between two body extremities. However, results are encouraging and in future studies the system will be applied for the assessment of infant’s spontaneous movement in clinical routine.
Keywords: Biomedical engineering, biomedical signal processing, depth sensor, infants, motion tracking.