Special Issue "Inertial Sensors"
Deadline for manuscript submissions: closed (31 December 2019).
Interests: methods for state estimation, sensor fusion, and learning control; inertial sensor networks; applications in rehabilitation, vehicle, and energy systems
Interests: probabilistic modelling; sensor fusion; signal processing; machine learning; inertial sensors; magnetometers; indoor localisation; inertial motion capture
Interests: signal processing; machine learning; biomechanics; computational dynamics; development of digital biomarkers, phenotypes, and therapeutics
Special Issues and Collections in MDPI journals
Inertial sensors have become a key enabling technology in a large variety of applications, and are currently found in almost every digital device and every intelligent vehicle. Inertial sensor information is, for instance, combined with absolute position measurements to facilitate localization, navigation, and mapping. Wearable inertial sensors are also frequently used in health care and sports applications for capturing movement patterns outside of typical laboratory and clinical environments. Many other application domains and examples can be found.
At the same time, inertial measurements are known to be corrupted by errors such as sensor bias, noise, and misalignment. Because of this, integration of the sensor signals to position and orientation results in integration drift. Inertial sensor measurements are therefore typically combined with additional information, such as from additional sensor measurements or additional models. Advanced sensor fusion and state estimation methods are being developed to combine this information and minimize the influence of the sensor errors on the motion states of interest. Statistical filters and state estimation techniques are commonly used. Machine learning methods have recently emerged as a promising new direction.
This Special Issue aims to gather novel developments in the use of inertial sensors, including both recent methodological developments and new results in applications of inertial sensors. Given the focus on methodological developments, we strongly encourage authors to deposit their source code in a public repository (e.g., GitHub) if possible. Topics include but are not limited to the following keywords.
Dr. Thomas Seel
Dr. Manon Kok
Dr. Ryan McGinnis
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.
- accelerometers, gyroscopes, magnetometers
- sensor fusion algorithms
- body area networks
- inertial motion tracking
- error modelling and calibration
- localization and mapping, SLAM
- machine learning applied to inertial sensor data
- inertial sensors in vehicle motion estimation and control
- inertial sensors in robotics and manufacturing
- inertial sensors in health care and sports engineering