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Inertial Measurement Units in Sport—2nd Edition
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Inertial Measurement Units in Sport—2nd Edition

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 3081

Special Issue Editor

Dr. Robert Crowther

E-Mail Website
Guest Editor
1. School of Behavioural and Health Sciences, Australian Catholic University, Brisbane 4014, Australia
2. UniSA Allied Health & Human Performance, University of South Australia, Adelaide 5001, Australia
Interests: biomechanics; motor control; variability of movement; non-linear dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There is a growing demand for ecologically valid, non-lab-based research on human movement. To meet this need, portable and cost-effective technologies have become pivotal. Inertial Measurement Units (IMUs) offer the flexibility required for real-world applications.

This Special Issue aims to showcase innovative research utilizing IMUs, exploring their reliability and validity, and elucidating methods for IMU-captured data analysis. By bridging the gap between laboratory studies and real-world scenarios, the goal is to facilitate a seamless transition for consumers, researchers, and sport scientists.

The key themes for this issue are innovative IMU applications, the reliability and validity of IMUs, data analysis methods, and practical implications and transitions.

Call for papers: Researchers are invited to contribute original studies, reviews, and methodological papers addressing the themes outlined above. Join us in unravelling the potential of IMUs and shaping the future of ecologically valid human movement research.

Dr. Robert Crowther
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.

Keywords

  • inertial measurement units
  • smart sensors
  • sensor fusion
  • wearable technology
  • angular kinematics
  • sport
  • outside laboratory
  • load monitoring

Related Special Issue

Published Papers (4 papers)

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Research

12 pages, 1253 KiB  
Article
Application of Machine Learning Methods to Investigate Joint Load in Agility on the Football Field: Creating the Model, Part I
by Anne Benjaminse, Eline M. Nijmeijer, Alli Gokeler and Stefano Di Paolo
Sensors 2024, 24(11), 3652; https://doi.org/10.3390/s24113652 - 5 Jun 2024
Viewed by 563
Abstract
Laboratory studies have limitations in screening for anterior cruciate ligament (ACL) injury risk due to their lack of ecological validity. Machine learning (ML) methods coupled with wearable sensors are state-of-art approaches for joint load estimation outside the laboratory in athletic tasks. The aim [...] Read more.
Laboratory studies have limitations in screening for anterior cruciate ligament (ACL) injury risk due to their lack of ecological validity. Machine learning (ML) methods coupled with wearable sensors are state-of-art approaches for joint load estimation outside the laboratory in athletic tasks. The aim of this study was to investigate ML approaches in predicting knee joint loading during sport-specific agility tasks. We explored the possibility of predicting high and low knee abduction moments (KAMs) from kinematic data collected in a laboratory setting through wearable sensors and of predicting the actual KAM from kinematics. Xsens MVN Analyze and Vicon motion analysis, together with Bertec force plates, were used. Talented female football (soccer) players (n = 32, age 14.8 ± 1.0 y, height 167.9 ± 5.1 cm, mass 57.5 ± 8.0 kg) performed unanticipated sidestep cutting movements (number of trials analyzed = 1105). According to the findings of this technical note, classification models that aim to identify the players exhibiting high or low KAM are preferable to the ones that aim to predict the actual peak KAM magnitude. The possibility of classifying high versus low KAMs during agility with good approximation (AUC 0.81–0.85) represents a step towards testing in an ecologically valid environment. Full article
(This article belongs to the Special Issue Inertial Measurement Units in Sport—2nd Edition)
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17 pages, 4032 KiB  
Article
Validation of Step Detection and Distance Calculation Algorithms for Soccer Performance Monitoring
by Gabriele Santicchi, Susanna Stillavato, Marco Deriu, Aldo Comi, Pietro Cerveri, Fabio Esposito and Matteo Zago
Sensors 2024, 24(11), 3343; https://doi.org/10.3390/s24113343 - 23 May 2024
Viewed by 439
Abstract
This study focused on developing and evaluating a gyroscope-based step counter algorithm using inertial measurement unit (IMU) readings for precise athletic performance monitoring in soccer. The research aimed to provide reliable step detection and distance estimation tailored to soccer-specific movements, including various running [...] Read more.
This study focused on developing and evaluating a gyroscope-based step counter algorithm using inertial measurement unit (IMU) readings for precise athletic performance monitoring in soccer. The research aimed to provide reliable step detection and distance estimation tailored to soccer-specific movements, including various running speeds and directional changes. Real-time algorithms utilizing shank angular data from gyroscopes were created. Experiments were conducted on a specially designed soccer-specific testing circuit performed by 15 athletes, simulating a range of locomotion activities such as walking, jogging, and high-intensity actions. The algorithm outcome was compared with manually tagged data from a high-quality video camera-based system for validation, by assessing the agreement between the paired values using limits of agreement, concordance correlation coefficient, and further metrics. Results returned a step detection accuracy of 95.8% and a distance estimation Root Mean Square Error (RMSE) of 17.6 m over about 202 m of track. A sub-sample (N = 6) also wore two pairs of devices concurrently to evaluate inter-unit reliability. The performance analysis suggested that the algorithm was effective and reliable in tracking diverse soccer-specific movements. The proposed algorithm offered a robust and efficient solution for tracking step count and distance covered in soccer, particularly beneficial in indoor environments where global navigation satellite systems are not feasible. This advancement in sports technology widens the spectrum of tools for coaches and athletes in monitoring soccer performance. Full article
(This article belongs to the Special Issue Inertial Measurement Units in Sport—2nd Edition)
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25 pages, 41909 KiB  
Article
KARATECH: A Practice Support System Using an Accelerometer to Reduce the Preliminary Actions of Karate
by Kwangyun Kim, Shuhei Tsuchida, Tsutomu Terada and Masahiko Tsukamoto
Sensors 2024, 24(7), 2306; https://doi.org/10.3390/s24072306 - 5 Apr 2024
Viewed by 942
Abstract
Kumite is a karate sparring competition in which two players face off and perform offensive and defensive techniques. Depending on the players, there may be preliminary actions (hereinafter referred to as “pre-actions”), such as pulling the arms or legs, lowering the shoulders, etc., [...] Read more.
Kumite is a karate sparring competition in which two players face off and perform offensive and defensive techniques. Depending on the players, there may be preliminary actions (hereinafter referred to as “pre-actions”), such as pulling the arms or legs, lowering the shoulders, etc., just before a technique is performed. Since the presence of a pre-action allows the opponent to know the timing of the technique, it is important to reduce pre-actions in order to improve the kumite. However, it is difficult for beginners and intermediate players to accurately identify their pre-actions and to improve them through practice. Therefore, this study aims to construct a practice support system that enables beginners and intermediate players to understand their pre-actions. In this paper, we focus on the forefist punch, one of kumite’s punching techniques. We propose a method to estimate the presence or absence of a pre-action based on the similarity between the acceleration data of an arbitrary forefist punch and a previously prepared dataset consisting of acceleration data of the forefist punch without a pre-action. We found that the proposed method can estimate the presence or absence of a pre-action in an arbitrary forefist punch with an accuracy of 86%. We also developed KARATECH as a system to support the practice of reducing pre-actions using the proposed method. KARATECH shows the presence or absence of pre-actions through videos and graphs. The evaluation results confirmed that the group using KARATECH had a lower pre-action rate. Full article
(This article belongs to the Special Issue Inertial Measurement Units in Sport—2nd Edition)
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23 pages, 6546 KiB  
Article
Distributed IMU Sensors for In-Field Dynamic Measurements on an Alpine Ski
by Leopold G. Beuken, Joshua L. Priest, Travis Hainsworth and J. Sean Humbert
Sensors 2024, 24(6), 1805; https://doi.org/10.3390/s24061805 - 11 Mar 2024
Viewed by 772
Abstract
Modern ski design is an inherently time-consuming process that involves an iterative feedback loop comprised of design, manufacturing and in-field qualitative evaluations. Additionally consumers can only rely on qualitative evaluation for selecting the ideal ski, and due to the variation in skier styles [...] Read more.
Modern ski design is an inherently time-consuming process that involves an iterative feedback loop comprised of design, manufacturing and in-field qualitative evaluations. Additionally consumers can only rely on qualitative evaluation for selecting the ideal ski, and due to the variation in skier styles and ability levels, consumers can find it to be an inconsistent and expensive experience. We propose supplementing the design and evaluation process with data from in-field prototype testing, using a modular sensor array that can be ported to nearly any ski. This paper discusses a new distributed Inertial Measurement Unit (IMU) suite, including details regarding the design and operation, sensor validation experiments, and outdoor in-field testing results. Data are collected from a set of spatially distributed IMUs located on the upper surface of the ski. We demonstrate that this system and associated post-processing algorithms provide accurate data at a high rate (>700 Hz), enabling the measurement of both structural and rigid ski characteristics, and are robust to repetitive testing in outdoor winter conditions. Full article
(This article belongs to the Special Issue Inertial Measurement Units in Sport—2nd Edition)
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