Special Issue "Sensor Data Fusion Analysis for Broad Applications"
Deadline for manuscript submissions: 20 March 2023 | Viewed by 14870
Interests: sensor data fusion; industry applications; machine learning; data analysis algorithms
Special Issues, Collections and Topics in MDPI journals
Special Issue in Sensors: Sensors for Medical and Industrial Applications
Nowadays, most applications use many different sensors as it is very common to collect as much information as possible from our various systems. New technologies allow us to analyze these data and obtain relevant information from them. Analyzing these data is very important because it allows us to modify our industrial strategy to obtain higher productivity and more efficient operations.
There are some emerging areas that would greatly benefit from sensor data fusion analysis, such as Industrial Applications, Medical or Biomedical Applications, Robotics, Monitoring Systems, Transportation Systems, Information Systems or Control Processes. It is important to note that to analyze and understand these large volumes of data from different sensors, we need special mathematical methods, algorithms and techniques.
This Special Issue encourages authors, from academia and industry, to submit new research results from the analysis of data obtained from multiple sensors in different areas and types of applications. The Special Issue topics include, but are not limited to:
- Sensor data fusion analysis in Industrial Applications;
- Sensor data fusion analysis in Medical or Biomedical Applications;
- Sensor data fusion analysis in Robotics Applications;
- Data preparation techniques for sensor data fusion analysis;
- Mathematical algorithms for sensor data fusion analysis;
- Principles and techniques for sensor data fusion.
Dr. Natividad Duro Carralero
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 2400 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.
- sensor data fusion
- industrial applications
- medical or biomedical applications
- robotics applications
- mathematical algorithms and techniques for data fusion
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: Comparison of Data Fusion Techniques for Stress Level Evaluation using Wearable and Ambient Sensing
Authors: Alessandro Leone 1, Andrea Caroppo 1, Andrea Manni 1, Alessandra Papetti 2, Marianna Ciccarelli 2, Gabriele Rescio 1
Affiliation: 1. CNR—National Research Council of Italy, IMM—Institute for Microelectronics and Microsystems, 73100 Lecce, Italy
2. Polytechnique University of Marche, 60121 Ancona, Italy
Abstract: Stress can affect all aspects of the life, such as physical health, behaviors and thinking ability. Consequently, it is very important to detect high-level of stress at early stages to prevent its negative effects. With the emergence of smart technologies, both wearable and ambient, researchers have started detecting extreme stress of individuals with them during daily routines. This work describes a novel framework designed to provide a decision support tool for automatic stress detection system using physiological signals obtained from minimally invasive smart wearable device and video signals acquired through a commercial and low-cost vision sensor. A comparison of data fusion techniques was performed to the overall multi-modal feature set extracted from the heterogeneous sensing technologies.