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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: 10 June 2025 | Viewed by 4976

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.

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Published Papers (3 papers)

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Research

22 pages, 3966 KiB  
Article
Are Gait Patterns during In-Lab Running Representative of Gait Patterns during Real-World Training? An Experimental Study
by John J. Davis IV, Stacey A. Meardon, Andrew W. Brown, John S. Raglin, Jaroslaw Harezlak and Allison H. Gruber
Sensors 2024, 24(9), 2892; https://doi.org/10.3390/s24092892 - 1 May 2024
Cited by 1 | Viewed by 1570
Abstract
Biomechanical assessments of running typically take place inside motion capture laboratories. However, it is unclear whether data from these in-lab gait assessments are representative of gait during real-world running. This study sought to test how well real-world gait patterns are represented by in-lab [...] Read more.
Biomechanical assessments of running typically take place inside motion capture laboratories. However, it is unclear whether data from these in-lab gait assessments are representative of gait during real-world running. This study sought to test how well real-world gait patterns are represented by in-lab gait data in two cohorts of runners equipped with consumer-grade wearable sensors measuring speed, step length, vertical oscillation, stance time, and leg stiffness. Cohort 1 (N = 49) completed an in-lab treadmill run plus five real-world runs of self-selected distances on self-selected courses. Cohort 2 (N = 19) completed a 2.4 km outdoor run on a known course plus five real-world runs of self-selected distances on self-selected courses. The degree to which in-lab gait reflected real-world gait was quantified using univariate overlap and multivariate depth overlap statistics, both for all real-world running and for real-world running on flat, straight segments only. When comparing in-lab and real-world data from the same subject, univariate overlap ranged from 65.7% (leg stiffness) to 95.2% (speed). When considering all gait metrics together, only 32.5% of real-world data were well-represented by in-lab data from the same subject. Pooling in-lab gait data across multiple subjects led to greater distributional overlap between in-lab and real-world data (depth overlap 89.3–90.3%) due to the broader variability in gait seen across (as opposed to within) subjects. Stratifying real-world running to only include flat, straight segments did not meaningfully increase the overlap between in-lab and real-world running (changes of <1%). Individual gait patterns during real-world running, as characterized by consumer-grade wearable sensors, are not well-represented by the same runner’s in-lab data. Researchers and clinicians should consider “borrowing” information from a pool of many runners to predict individual gait behavior when using biomechanical data to make clinical or sports performance decisions. Full article
(This article belongs to the Special Issue Sensor Technologies for Gait Analysis: 2nd Edition)
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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 1207
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
Cited by 1 | Viewed by 1385
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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