Special Issue "Wearable Sensors for Biomechanical Gait Analysis"
Deadline for manuscript submissions: 28 May 2021.
Interests: Wearable Tech., Biomechanics, Machine learning/Artificial Intelligence, Biomedical Signal Processing, Physiological Signal (EMG, MMG)
Interests: wearable sensors; gait biomechanics; motor control and motor learning
Interests: data mining and machine learning; systems biology
Interests: optical fiber sensors; fiber Bragg gratings; Fabry-Perot interformetric sensors; eHealth applications; gait analysis; wearable sensing devices
Special Issues and Collections in MDPI journals
Special Issue in Journal of Sensor and Actuator Networks: 5G and Beyond towards Enhancing Our Future
Special Issue in Sensors: Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures
Special Issue in Photonics: Optical Fiber Interferometric Sensors: New Production Methodologies and Novel Applications
Recently, wearable sensors (like body-worn inertial sensors) are attracting substantial interest due to their potential to provide continuous, real-time functional information via dynamic, non-invasive measurements of biochemical markers in the gait cycle, such as kinetic and kinematic behaviors. These wearable sensors make it easy for researchers to collect gait biomechanics data in indoor (treadmill) and/or outdoor (natural, real-world) settings due to the many advantageous factors, such as small size, lightweight, easy to set up, portable, highly effective, and low cost. Using biomechanical gait data from multiple wearable wireless sensors (like fusing IMU and wrist-worn sensors) during various movement activities, paired with advanced signal processing and machine learning techniques can make clinical predictions and diagnostics tools to help track and treat musculoskeletal disorders, injuries, and performance assessment. This Special Issue of the Sensors journal entitled “Wearable Sensors for Biomechanical Gait Analysis” will focus on all aspects of research and development related to these areas. This Special Issue focuses on the development, validity, use, and applicability of wearable devices in biomechanical gait pattern identification. The broader aim is to collect high-quality papers from researchers around the world working in this area to make biomechanical gait monitoring more widespread and more effective using wearable technologies.
Dr. Nizam Uddin Ahamed
Prof. Dr. Chris Connaboy
Prof. Dr. Qi Mi
Dr. Maria de Fátima Domingues
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 2200 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.
- Wearable sensor data
- Fusing wearable sensors
- Sensor-based signal processing in gait biomechanics
- Wearable gait measurement
- Biomechanical movement
- Wearable and machine-learning approach
- Inertial measurement unit (IMU)
- Biomechanical gait analysis