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Sensor Technologies for Gait Analysis: 2nd Edition
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Sensor Technologies for Gait Analysis: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 20 August 2024 | Viewed by 1736

Special Issue Editor

Dr. Miguel Velhote Correia

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Universidade do Porto, 4200-465 Porto, Portugal
Interests: sensors; electronics; biomedical instrumentation; computational vision; image and signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to highlight the most recent research regarding sensor technologies for gait analysis. This Special Issue focuses on the development, validity, use, and applicability of devices in gait pattern identification, assessment and recognition. The broader aim is to collect high-quality papers from researchers around the world working in this area to make gait monitoring more widespread and more effective using sensor technologies. Research articles and reviews are solicited that provide a comprehensive insight into the sensor technologies used for gait analysis on any aspect of novel sensor development and applications. Topics of interest include but are not limited to the following:

  • Gait analysis;
  • Gait measurement;
  • Gait recognition;
  • Impaired and modified gait analysis;
  • Neurological gait disorders assessment;
  • Machine Learning in Gait Analysis;
  • Balance/stability/posture;
  • Sports and sports performance;
  • Muscles/electromyography;
  • Rehabilitation;
  • Novel biomechanics;
  • Data and analysis methods,

Dr. Miguel Velhote Correia
Guest Editor

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. 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 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.

Published Papers (2 papers)

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Research

16 pages, 3983 KiB  
Article
Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System
by Madina Shayne, Leonardo A. Molina, Bin Hu and Taylor Chomiak
Sensors 2024, 24(8), 2649; https://doi.org/10.3390/s24082649 - 21 Apr 2024
Viewed by 313
Abstract
Smart algorithms for gait kinematic motion prediction in wearable assistive devices including prostheses, bionics, and exoskeletons can ensure safer and more effective device functionality. Although embedded systems can support the use of smart algorithms, there are important limitations associated with computational load. This [...] Read more.
Smart algorithms for gait kinematic motion prediction in wearable assistive devices including prostheses, bionics, and exoskeletons can ensure safer and more effective device functionality. Although embedded systems can support the use of smart algorithms, there are important limitations associated with computational load. This poses a tangible barrier for models with increased complexity that demand substantial computational resources for superior performance. Forecasting through Recurrent Topology (FReT) represents a computationally lightweight time-series data forecasting algorithm with the ability to update and adapt to the input data structure that can predict complex dynamics. Here, we deployed FReT on an embedded system and evaluated its accuracy, computational time, and precision to forecast gait kinematics from lower-limb motion sensor data from fifteen subjects. FReT was compared to pretrained hyperparameter-optimized NNET and deep-NNET (D-NNET) model architectures, both with static model weight parameters and iteratively updated model weight parameters to enable adaptability to evolving data structures. We found that FReT was not only more accurate than all the network models, reducing the normalized root-mean-square error by almost half on average, but that it also provided the best balance between accuracy, computational time, and precision when considering the combination of these performance variables. The proposed FReT framework on an embedded system, with its improved performance, represents an important step towards the development of new sensor-aided technologies for assistive ambulatory devices. Full article
(This article belongs to the Special Issue Sensor Technologies for Gait Analysis: 2nd Edition)
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25 pages, 4156 KiB  
Article
KeepRunning: A MoCap-Based Rapid Test to Prevent Musculoskeletal Running Injuries
by Javier Rodríguez, Javier Marín, Ana C. Royo, Luis Padrón, Manuel Pérez-Soto and José J. Marín
Sensors 2023, 23(23), 9336; https://doi.org/10.3390/s23239336 - 22 Nov 2023
Viewed by 865
Abstract
The worldwide popularisation of running as a sport and recreational practice has led to a high rate of musculoskeletal injuries, usually caused by a lack of knowledge about the most suitable running technique for each runner. This running technique is determined by a [...] Read more.
The worldwide popularisation of running as a sport and recreational practice has led to a high rate of musculoskeletal injuries, usually caused by a lack of knowledge about the most suitable running technique for each runner. This running technique is determined by a runner’s anthropometric body characteristics, dexterity and skill. Therefore, this study aims to develop a motion capture-based running analysis test on a treadmill called KeepRunning to obtain running patterns rapidly, which will aid coaches and clinicians in assessing changes in running technique considering changes in the study variables. Therefore, a review and proposal of the most representative events and variables of analysis in running was conducted to develop the KeepRunning test. Likewise, the minimal detectable change (MDC) in these variables was obtained using test–retest reliability to demonstrate the reproducibility and viability of the test, as well as the use of MDC as a threshold for future assessments. The test–retest consisted of 32 healthy volunteer athletes with a running training routine of at least 15 km per week repeating the test twice. In each test, clusters of markers were placed on the runners’ body segments using elastic bands and the volunteers’ movements were captured while running on a treadmill. In this study, reproducibility was defined by the intraclass correlation coefficient (ICC) and MDC, obtaining a mean value of ICC = 0.94 ± 0.05 for all variables and MDC = 2.73 ± 1.16° for the angular kinematic variables. The results obtained in the test–retest reveal that the reproducibility of the test was similar or better than that found in the literature. KeepRunning is a running analysis test that provides data from the involved body segments rapidly and easily interpretable. This data allows clinicians and coaches to objectively provide indications for runners to improve their running technique and avoid possible injury. The proposed test can be used in the future with inertial motion capture and other wearable technologies. Full article
(This article belongs to the Special Issue Sensor Technologies for Gait Analysis: 2nd Edition)
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