Special Issue "Wearable Sensors for Gait and Motion Analysis 2018"
Deadline for manuscript submissions: 25 June 2019
Prof. Dr. Shigeru Tadano
National Institute of Technology, Hakodate College, Hakodatate, Japan and Hokkaido University, Sapporo, Japan
Fax: +81 11 706 6405
Interests: biomechanical engineering; musculo-skeletal and orthopaedic biomechanics; bone mechanics; medical and healthcare engineering
Wearable sensors are increasingly used to perform human gait and motion measurements. Some key issues of this success are their features of unobtrusiveness, light-weight, possibility to be used out of the lab, low costs and ease of use.
Wearable sensors were initially employed as diagnostic and monitoring tools for gait analysis, both to assess spatio-temporal gait parameters and joint kinematics. Nowadays, their main applications are still in the healthcare field, but new potential applications are emerging: Sport activities, e-health, tele-rehabilitation, elderly monitoring and wellness. More in general, all the activities that directly or indirectly involve motion might benefit from wearable sensors systems.
Wearable sensor-based systems can measure kinematic variables of a single or multiple body segments of the subject during motion. Although many researches have been reported on this topic, some issues associated to the reconstruction and analysis of the kinematics during motion are still an open challenge for the scientific community, especially in those fields that require high accuracy. Robust protocols and data post-processing are still work in progress, especially in cases in which there can be a high variability of motion patterns.
We invite original research papers and review articles aimed at proposing new kinds of wearable gait sensor systems, new methods for sensor signal processing, reports on applications in healthcare field, innovative and non-traditional motion analysis applications.
Contributions may include, but are not limited to:
- characterization of systems, techniques and methods for motion and gait analysis
- clinical reports using wearable sensors
- wearable sensors, methods and/or techniques for physiological monitoring
- wearable sensors, methods and/or techniques for medical decision making
- wearable sensors, methods and/or techniques for telemedicine applications
- wearable sensors, methods and/or techniques for activities modelling
- wearable sensor for motion analysis
- innovative applications of wearable sensor systems
Prof. Dr. Shigeru Tadano
Prof. Dr. Laura Gastaldi
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 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.
- Gait analysis
- Motion analysis
- Diagnostic tool
- Health monitoring
- Aged activity monitoring
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.
Daily in-home gait evaluation based on biometric recognition by deep Learning
University of Missouri
Gait is the most important sign of health in elderly. Moreover, gait speed in particular reflects the overall health of adults over 65. Our group has developed a gait evaluation system based on a depth camera. One of the challenge of the current gait detection system is to differentiate the resident(s) of the apartment from visitors and update the individual gait model(s) accordingly. While many laboratory tested solution have been proposed, we will test our deep learning methodology on gait systems currently running in 100 apartments in Columbia, MO, including TigerPlace