Special Issue "Recent Advances in Motion Analysis"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editors

Dr. Francesco Di Nardo
E-Mail Website
Guest Editor
Department of Information Engineering,Università Politecnica delle Marche, 60131 Ancona, Italy
Interests: EMG signal processing (filtering, feature extraction, pattern recognition, time–frequency analysis) and interpretation (physiology, clinics, sport); gait analysis; static and perturbed posturography; machine learning applications in motion analysis
Prof. Dr. Sandro Fioretti
E-Mail Website
Guest Editor
Department of Information Engineering,Università Politecnica delle Marche, 60131 Ancona, Italy
Interests: stereophotogrammetry; linear and nonlinear filtering; joint kinematics; analysis and identification of postural control; static and perturbed posturography; gait analysis; dynamic electromyography; wearable devices for motion analysis

Special Issue Information

Dear Colleagues,

The advances in the technology and methodology for human movement capture and analysis have been remarkable over the last decade. Besides acknowledged approaches for kinematic, dynamic, and EMG analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Thus, the synergy of classic instrumentation and novel smart devices has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, recognition, characterization, and interpretation of motion metrics and behaviors from sensor data are representing a challenging problem, not only in laboratories, but also at home and in the community. 

This Special Issue is designed to comprehensively cover the open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application (clinics, sports, ergonomics, etc.). Computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, contributions in this field are also welcome. This Special Issue will consider original research, reviews, and applications in clinics, sports, and ergonomics. Areas of interest include, but are not limited to, the following: 

  • Recent advances in kinematic and dynamic analysis;
  • Recent advances in EMG signal processing;
  • Recent advances in static and dynamic posturography;
  • Recent advances in gait analysis;
  • Wearables and inertial measurement units in motion analysis;
  • Artificial neural networks for motion analysis.

Dr. Francesco Di Nardo
Prof. Dr. Sandro Fioretti
Guest Editors

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. Electronics is an international peer-reviewed open access monthly 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 1400 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.

Keywords

  • Motion analysis
  • Movement biomechanics
  • Electromyography (EMG)
  • Inertial measurement units (IMU)
  • Gait analysis
  • Static and perturbed posture
  • Machine learning applications in motion analysis

Published Papers (2 papers)

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Research

Open AccessFeature PaperArticle
A Systematic Review of Performance Analysis in Rowing Using Inertial Sensors
Electronics 2019, 8(11), 1304; https://doi.org/10.3390/electronics8111304 - 07 Nov 2019
Abstract
Sporting organizations such as professional clubs and national sport institutions are constantly seeking novel training methodologies in an attempt to give their athletes a cutting edge. The advent of microelectromechanical systems (MEMS) has facilitated the integration of small, unobtrusive wearable inertial sensors into [...] Read more.
Sporting organizations such as professional clubs and national sport institutions are constantly seeking novel training methodologies in an attempt to give their athletes a cutting edge. The advent of microelectromechanical systems (MEMS) has facilitated the integration of small, unobtrusive wearable inertial sensors into many coaches’ training regimes. There is an emerging trend to use inertial sensors for performance monitoring in rowing; however, the use and selection of the sensor used has not been appropriately reviewed. Previous literature assessed the sampling frequency, position, and fixing of the sensor; however, properties such as the sensor operating ranges, data processing algorithms, and validation technology are left unevaluated. To address this gap, a systematic literature review on rowing performance monitoring using inertial-magnetic sensors was conducted. A total of 36 records were included for review, demonstrating that inertial measurements were predominantly used for measuring stroke quality and the sensors were used to instrument equipment rather than the athlete. The methodology for both selecting and implementing technology appeared ad hoc, with no guidelines for appropriate analysis of the results. This review summarizes a framework of best practice for selecting and implementing inertial sensor technology for monitoring rowing performance. It is envisaged that this review will act as a guide for future research into applying technology to rowing. Full article
(This article belongs to the Special Issue Recent Advances in Motion Analysis)
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Open AccessFeature PaperArticle
Closing the Wearable Gap—Part III: Use of Stretch Sensors in Detecting Ankle Joint Kinematics During Unexpected and Expected Slip and Trip Perturbations
Electronics 2019, 8(10), 1083; https://doi.org/10.3390/electronics8101083 - 24 Sep 2019
Abstract
Background: An induced loss of balance resulting from a postural perturbation has been reported as the primary source for postural instability leading to falls. Hence; early detection of postural instability with novel wearable sensor-based measures may aid in reducing falls and fall-related injuries. [...] Read more.
Background: An induced loss of balance resulting from a postural perturbation has been reported as the primary source for postural instability leading to falls. Hence; early detection of postural instability with novel wearable sensor-based measures may aid in reducing falls and fall-related injuries. The purpose of the study was to validate the use of a stretchable soft robotic sensor (SRS) to detect ankle joint kinematics during both unexpected and expected slip and trip perturbations. Methods: Ten participants (age: 23.7 ± 3.13 years; height: 170.47 ± 8.21 cm; mass: 82.86 ± 23.4 kg) experienced a counterbalanced exposure of an unexpected slip, an unexpected trip, an expected slip, and an expected trip using treadmill perturbations. Ankle joint kinematics for dorsiflexion and plantarflexion were quantified using three-dimensional (3D) motion capture through changes in ankle joint range of motion and using the SRS through changes in capacitance when stretched due to ankle movements during the perturbations. Results: A greater R-squared and lower root mean square error in the linear regression model was observed in comparing ankle joint kinematics data from motion capture with stretch sensors. Conclusions: Results from the study demonstrated that 71.25% of the trials exhibited a minimal error of less than 4.0 degrees difference from the motion capture system and a greater than 0.60 R-squared value in the linear model; suggesting a moderate to high accuracy and minimal errors in comparing SRS to a motion capture system. Findings indicate that the stretch sensors could be a feasible option in detecting ankle joint kinematics during slips and trips. Full article
(This article belongs to the Special Issue Recent Advances in Motion Analysis)
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