Special Issue "The Use of Motion Analysis for Diagnostics"
Deadline for manuscript submissions: 31 October 2023 | Viewed by 18902
Interests: machine learning; statistics; gait analysis; health technology assessment; lean six sigma; biomedical engineering
Special Issues, Collections and Topics in MDPI journals
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Special Issue in Bioengineering: Discovery of the "Neural World": Technological and Clinical Advances in Neural Engineering
Special Issue in Bioengineering: Machine Learning for Biomedical Applications, Volume II
Special Issue in Diagnostics: Wearable Sensors and Artificial Intelligence for Ergonomics—2nd Edition
Interests: mathematical modeling; signal processing; systems and synthetic biology
2. Lab of Biomedical Signal Processing, Scientific Clinical Institute Maugeri, Telese Terme (BN), Italy
Interests: biomedical engineering; biomedical signals; gait analysis; biomedical imaging; healthcare management
Gait analysis and, more generally, motion analysis have been at the center of scientific research in recent decades for several purposes and in several forms: gait analysis labs have been used to analyze gait patterns, inertial measurement units have been employed to perform analysis in non-hospital settings, and there are also researchers who have designed new wearable systems known as “e-textile”. Indeed, these types of measurements allow clinicians to obtain a quantitative evaluation of motion of patients, supporting them in so-called clinical decision making since there are things that cannot be assessed through medical visits and clinical scales. There is proof in literature that the data acquired by these systems can be used to study diagnosis/prognosis with the implementation of artificial-intelligence-based techniques. Therefore, this Special Issue welcomes all the papers and reviews of literature dealing with motion analysis and diagnosis/prognosis (with and without artificial intelligence solutions).
Dr. Carlo Ricciardi
Prof. Dr. Mario Cesarelli
Prof. Dr. Francesco Amato
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. Diagnostics 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.
- Motion analysis
- Gait analysis
- Wearable inertial systems
- Machine learning
- Deep learning
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: A systematic review on e-textile for medical applications
Authors: Carlo Ricciardi , et al
Affiliation: 1. Department of Electrical Engineering and Information Technology and Department of Chemical, Materials and Process Engineering, University of Naples "Federico II", Naples, Italy 2. Lab of Biomedical Signal Processing, Scientific Clinical Institute Maugeri, Telese Terme (BN), Italy
Title: Sleep position detection with a wireless audio-motion sensor – a validation study.
Authors: Młyńczak,et al
Affiliation: Warsaw University of Technology, Faculty of Mechatronics, Institute of Metrology and Biomedical Engineering, Warsaw, Poland
Abstract: It is well documented that body position significantly affects breathing indices during sleep in patients with obstructive sleep apnea. Those usually worsen while changing from non-supine to supine posture. Therefore, body posture should be an accurately measured and credible parameter in all types of sleep studies. The aim of this study was to specify the accuracy of neck-based monitoring device (Clebre, Warsaw, Poland) mounted in the suprasternal notch, in determining supine and non-supine sleeping position, as well as specific body postures during sleep, in comparison to polysomnography (PSG). A sleep study (full PSG along with neck-based audio-motion sensor) was performed on 92 consecutive patients. The accuracy in determining supine and non-supine position was 96.8% ± 3.97% and 97.0 ± 3.65%, respectively. For lateral positions the accuracy was 98.64% ± 2.0% and 97.31% ± 4.55% for right and left side, respectively. Prone position was detected with the accuracy of 97.34% ± 5.56%. The study showed a high accuracy in detecting supine, as well as other gross postures during sleep based on a sensor attached in the suprasternal notch, compared to full PSG study. We feel that the suprasternal notch is a promising area for placing wireless sleep study devices.
Title: Motion analysis can be used to pinpoint muscles for spasticity treatment with Botulinum toxin A
Authors: Opheim, et al
Affiliation: Head of research group Movement and function Sunnaas Rehabilitation Hospital, Nesoddtangen, Norway