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Special Issue "Wearable Sensors & Gait"

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

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editors

Prof. Felipe García-Pinillos
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Guest Editor
Department of Physical Education and Sport, Faculty of Sport Sciences, University of Granada, Granada, Spain.
Interests: training; sport biomechanics; sport technology; endurance performance; endurance running
Prof. Luis Enrique Roche-Seruendo
Website
Guest Editor
Universidad San Jorge, Campus Universitario, Autov A23 km 299, 50830, Villanueva de Gállego Zaragoza, Spain
Interests: sport biomechanics; sport technology; gait biomechanics; running biomechanics
Dr. Diego Jaén-Carrillo
Website
Guest Editor
Universidad San Jorge, Campus Universitario, Autov A23 km 299, 50830, Villanueva de Gállego Zaragoza, Spain
Interests: port biomechanics; endurance; performance; running; sport technology; training

Special Issue Information

Dear Colleagues,

Gait analysis has been traditionally conducted in laboratory settings and thereby has requested specific conditions and expensive equipment. The emergence of wearable sensors solves the lack of ecology for these measurements and offers a more economical and easy to use option to perform gait analysis. Lately, wearable sensors have allowed the quantification of performance and workload by providing mechanical and physiological parameters and their popularity has grown exponentially. In this context, more and more wearable sensors are commercially available and, when applied to gait analysis (either walking or running), these devices are able to provide both kinetic and kinematic variables improving consequently the feasibility and testing time of such assessments and, therefore, becoming a real alternative for clinicians, researchers and sport practitioners.

The incremental growth in big data, cloud computing and artificial intelligent make these sensors suitable to connect gait biomechanics with real life and real time analysis. All these benefits broaden the possibilities, among others, to provide real-time biofeedback while walking and running, or to integrate sensors with cloud platforms or mobile apps to improve health and/or performance.

This Special Issue encourages authors to submit their research and contributions about the use and application of wearable sensors for gait assessment and analysis.

The main topics for this issue include, but not limited to:

- Validity analysis of novel wearable sensors for human locomotion.

- Reliability analysis of wearable sensor.

- New applications and uses of metrics provided by wearable sensors in training, competition and injury management settings.

- Novel technologies applied to gait analysis.

- State of the art for wearable devices.

-Algorithms, integrations with other platforms or software, signal processing, and bigdata obtained by wearable sensors.

Prof. Felipe García-Pinillos
Prof. Luis Enrique Roche-Seruendo
Dr. Diego Jaén-Carrillo
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. 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.

Published Papers (4 papers)

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Research

Open AccessArticle
Estimating Functional Threshold Power in Endurance Running from Shorter Time Trials Using a 6-Axis Inertial Measurement Sensor
Sensors 2021, 21(2), 582; https://doi.org/10.3390/s21020582 - 15 Jan 2021
Abstract
Wearable technology has allowed for the real-time assessment of mechanical work employed in several sporting activities. Through novel power metrics, Functional Threshold Power have shown a reliable indicator of training intensities. This study aims to determine the relationship between mean power output (MPO) [...] Read more.
Wearable technology has allowed for the real-time assessment of mechanical work employed in several sporting activities. Through novel power metrics, Functional Threshold Power have shown a reliable indicator of training intensities. This study aims to determine the relationship between mean power output (MPO) values obtained during three submaximal running time trials (i.e., 10 min, 20 min, and 30 min) and the functional threshold power (FTP). Twenty-two recreationally trained male endurance runners completed four submaximal running time trials of 10, 20, 30, and 60 min, trying to cover the longest possible distance on a motorized treadmill. Absolute MPO (W), normalized MPO (W/kg) and standard deviation (SD) were calculated for each time trial with a power meter device attached to the shoelaces. All simplified FTP trials analyzed (i.e., FTP10, FTP20, and FTP30) showed a significant association with the calculated FTP (p < 0.001) for both MPO and normalized MPO, whereas stronger correlations were found with longer time trials. Individual correction factors (ICF% = FTP60/FTPn) of ~90% for FTP10, ~94% for FTP20, and ~96% for FTP30 were obtained. The present study procures important practical applications for coaches and athletes as it provides a more accurate estimation of FTP in endurance running through less fatiguing, reproducible tests. Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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Open AccessArticle
Foot Strike Angle Prediction and Pattern Classification Using LoadsolTM Wearable Sensors: A Comparison of Machine Learning Techniques
Sensors 2020, 20(23), 6737; https://doi.org/10.3390/s20236737 - 25 Nov 2020
Abstract
The foot strike pattern performed during running is an important variable for runners, performance practitioners, and industry specialists. Versatile, wearable sensors may provide foot strike information while encouraging the collection of diverse information during ecological running. The purpose of the current study was [...] Read more.
The foot strike pattern performed during running is an important variable for runners, performance practitioners, and industry specialists. Versatile, wearable sensors may provide foot strike information while encouraging the collection of diverse information during ecological running. The purpose of the current study was to predict foot strike angle and classify foot strike pattern from LoadsolTM wearable pressure insoles using three machine learning techniques (multiple linear regression―MR, conditional inference tree―TREE, and random forest―FRST). Model performance was assessed using three-dimensional kinematics as a ground-truth measure. The prediction-model accuracy was similar for the regression, inference tree, and random forest models (RMSE: MR = 5.16°, TREE = 4.85°, FRST = 3.65°; MAPE: MR = 0.32°, TREE = 0.45°, FRST = 0.33°), though the regression and random forest models boasted lower maximum precision (13.75° and 14.3°, respectively) than the inference tree (19.02°). The classification performance was above 90% for all models (MR = 90.4%, TREE = 93.9%, and FRST = 94.1%). There was an increased tendency to misclassify mid foot strike patterns in all models, which may be improved with the inclusion of more mid foot steps during model training. Ultimately, wearable pressure insoles in combination with simple machine learning techniques can be used to predict and classify a runner’s foot strike with sufficient accuracy. Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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Open AccessArticle
Wearable Sensors Detect Differences between the Sexes in Lower Limb Electromyographic Activity and Pelvis 3D Kinematics during Running
Sensors 2020, 20(22), 6478; https://doi.org/10.3390/s20226478 - 12 Nov 2020
Abstract
Each year, 50% of runners suffer from injuries. Consequently, more studies are being published about running biomechanics; these studies identify factors that can help prevent injuries. Scientific evidence suggests that recreational runners should use personalized biomechanical training plans, not only to improve their [...] Read more.
Each year, 50% of runners suffer from injuries. Consequently, more studies are being published about running biomechanics; these studies identify factors that can help prevent injuries. Scientific evidence suggests that recreational runners should use personalized biomechanical training plans, not only to improve their performance, but also to prevent injuries caused by the inability of amateur athletes to tolerate increased loads, and/or because of poor form. This study provides an overview of the different normative patterns of lower limb muscle activation and articular ranges of the pelvis during running, at self-selected speeds, in men and women. Methods: 38 healthy runners aged 18 to 49 years were included in this work. We examined eight muscles by applying two wearable superficial electromyography sensors and an inertial sensor for three-dimensional (3D) pelvis kinematics. Results: the largest differences were obtained for gluteus maximus activation in the first double float phase (p = 0.013) and second stance phase (p = 0.003), as well as in the gluteus medius in the second stance phase (p = 0.028). In both cases, the activation distribution was more homogeneous in men and presented significantly lower values than those obtained for women. In addition, there was a significantly higher percentage of total vastus medialis activation in women throughout the running cycle with the median (25th–75th percentile) for women being 12.50% (9.25–14) and 10% (9–12) for men. Women also had a greater range of pelvis rotation during running at self-selected speeds (p = 0.011). Conclusions: understanding the differences between men and women, in terms of muscle activation and pelvic kinematic values, could be especially useful to allow health professionals detect athletes who may be at risk of injury. Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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Open AccessArticle
A Random Forest Machine Learning Framework to Reduce Running Injuries in Young Triathletes
Sensors 2020, 20(21), 6388; https://doi.org/10.3390/s20216388 - 09 Nov 2020
Abstract
Background: The running segment of a triathlon produces 70% of the lower limb injuries. Previous research has shown a clear association between kinematic patterns and specific injuries during running. Methods: After completing a seven-month gait retraining program, a questionnaire was used to assess [...] Read more.
Background: The running segment of a triathlon produces 70% of the lower limb injuries. Previous research has shown a clear association between kinematic patterns and specific injuries during running. Methods: After completing a seven-month gait retraining program, a questionnaire was used to assess 19 triathletes for the incidence of injuries. They were also biomechanically analyzed at the beginning and end of the program while running at a speed of 90% of their maximum aerobic speed (MAS) using surface sensor dynamic electromyography and kinematic analysis. We used classification tree (random forest) techniques from the field of artificial intelligence to identify linear and non-linear relationships between different biomechanical patterns and injuries to identify which styles best prevent injuries. Results: Fewer injuries occurred after completing the program, with athletes showing less pelvic fall and greater activation in gluteus medius during the first phase of the float phase, with increased trunk extension, knee flexion, and decreased ankle dorsiflexion during the initial contact with the ground. Conclusions: The triathletes who had suffered the most injuries ran with increased pelvic drop and less activation in gluteus medius during the first phase of the float phase. Contralateral pelvic drop seems to be an important variable in the incidence of injuries in young triathletes. Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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