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Inertial Measurement Units in Sport

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

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 26421

Special Issue 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,

Recently researchers and sport scientists have turned towards the use of inertial measurement units (IMUs) to break out of the laboratory. IMUs have become small, portable, and now low cost. Even sport equipment companies are integrating IMUs into consumer products to provide consumers with more information about their movements.

This Special Issue is intended to report recent advances in IMU use in sporting activities. Articles will address topics including original research using IMUs in-field, the reliability and validity of IMUs, and methods to analyze data captured from IMUs that help provide practical transition to consumers, researchers, and sport scientists. Additionally, the challenges and gaps that remain in the implementation and outcome of using IMUs in research and sport science will be discussed.

Dr. Robert Crowther
Guest Editor

Manuscript Submission Information

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Keywords

  • Inertial measurement units
  • Smart sensors
  • Sensor fusion
  • Wearable technology
  • Angular kinematics
  • Sport
  • Outside laboratory
  • Load monitoring

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Published Papers (12 papers)

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Research

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32 pages, 7786 KiB  
Article
Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial Sensors
by Alexander Hoelzemann, Julia Lee Romero, Marius Bock, Kristof Van Laerhoven and Qin Lv
Sensors 2023, 23(13), 5879; https://0-doi-org.brum.beds.ac.uk/10.3390/s23135879 - 25 Jun 2023
Cited by 4 | Viewed by 1927
Abstract
We present a benchmark dataset for evaluating physical human activity recognition methods from wrist-worn sensors, for the specific setting of basketball training, drills, and games. Basketball activities lend themselves well for measurement by wrist-worn inertial sensors, and systems that are able to detect [...] Read more.
We present a benchmark dataset for evaluating physical human activity recognition methods from wrist-worn sensors, for the specific setting of basketball training, drills, and games. Basketball activities lend themselves well for measurement by wrist-worn inertial sensors, and systems that are able to detect such sport-relevant activities could be used in applications of game analysis, guided training, and personal physical activity tracking. The dataset was recorded from two teams in separate countries (USA and Germany) with a total of 24 players who wore an inertial sensor on their wrist, during both a repetitive basketball training session and a game. Particular features of this dataset include an inherent variance through cultural differences in game rules and styles as the data was recorded in two countries, as well as different sport skill levels since the participants were heterogeneous in terms of prior basketball experience. We illustrate the dataset’s features in several time-series analyses and report on a baseline classification performance study with two state-of-the-art deep learning architectures. Full article
(This article belongs to the Special Issue Inertial Measurement Units in Sport)
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16 pages, 1030 KiB  
Article
Analysis of Movement Variability in Cycling: An Exploratory Study
by Lachlan Winter, Clint Bellenger, Paul Grimshaw and Robert George Crowther
Sensors 2023, 23(10), 4972; https://0-doi-org.brum.beds.ac.uk/10.3390/s23104972 - 22 May 2023
Viewed by 1586
Abstract
The purpose of this study was to determine the test-retest repeatability of Blue Trident inertial measurement units (IMUs) and VICON Nexus kinematic modelling in analysing the Lyapunov Exponent (LyE) during a maximal effort 4000 m cycling bout in different body segments/joints. An additional [...] Read more.
The purpose of this study was to determine the test-retest repeatability of Blue Trident inertial measurement units (IMUs) and VICON Nexus kinematic modelling in analysing the Lyapunov Exponent (LyE) during a maximal effort 4000 m cycling bout in different body segments/joints. An additional aim was to determine if changes in the LyE existed across a trial. Twelve novice cyclists completed four sessions of cycling; one was a familiarisation session to determine a bike fit and become better accustomed to the time trial position and pacing of a 4000 m effort. IMUs were attached to the head, thorax, pelvis and left and right shanks to analyse segment accelerations, respectively, and reflective markers were attached to the participant to analyse neck, thorax, pelvis, hip, knee and ankle segment/joint angular kinematics, respectively. Both the IMU and VICON Nexus test-retest repeatability ranged from poor to excellent at the different sites. In each session, the head and thorax IMU acceleration LyE increased across the bout, whilst pelvic and shank acceleration remained consistent. Differences across sessions were evident in VICON Nexus segment/joint angular kinematics, but no consistent trend existed. The improved reliability and the ability to identify a consistent trend in performance, combined with their improved portability and reduced cost, advocate for the use of IMUs in analysing movement variability in cycling. However, additional research is required to determine the applicability of analysing movement variability during cycling. Full article
(This article belongs to the Special Issue Inertial Measurement Units in Sport)
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11 pages, 2168 KiB  
Article
Using Wearable Inertial Sensors to Monitor Effectiveness of Different Types of Customized Orthoses during CrossFit® Training
by Lorenzo Brognara, Antonio Mazzotti, Federica Rossi, Francesca Lamia, Elena Artioli, Cesare Faldini and Francesco Traina
Sensors 2023, 23(3), 1636; https://0-doi-org.brum.beds.ac.uk/10.3390/s23031636 - 02 Feb 2023
Cited by 1 | Viewed by 1548
Abstract
Background: Dynamic balance plays a key role in high-impact sports, such as CrossFit, where athletes are required to maintain balance in various weightlifting exercises. The loss of balance in these sport-specific movements may not only affect athlete performance, but also increase the risk [...] Read more.
Background: Dynamic balance plays a key role in high-impact sports, such as CrossFit, where athletes are required to maintain balance in various weightlifting exercises. The loss of balance in these sport-specific movements may not only affect athlete performance, but also increase the risk of injuries. Objectives: The aim of the study is to achieve greater insight into the balance and athlete position during the CrossFit training by means of inertial sensors, with a particular focus on the role of different custom foot orthoses (CFOs) in order to detect correlations with the role of the cavus foot. Methods: A total of 42 CrossFit® athletes, aged 25 to 42 years, were enrolled in this study. One-way ANOVA tests with post-hoc analysis of variance were used to compare foot posture groups and effects of different types of customized foot orthoses. Results: When comparing the effects of CFOs with the respective balance basal level during the pistol squat exercise, we observed a significant (p = 0.0001) decrease in the sway area, antero-posterior displacement (APD) and medio-lateral displacement (MLD) compared to the basal using both types of CFOs. Conclusion: No significant positive effects of CFOs were observed in some static tests. On the contrary, positive effects of CFOs and, in particular, postural insoles, are relevant to dynamic balance. Full article
(This article belongs to the Special Issue Inertial Measurement Units in Sport)
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12 pages, 459 KiB  
Article
Prototype Machine Learning Algorithms from Wearable Technology to Detect Tennis Stroke and Movement Actions
by Thomas Perri, Machar Reid, Alistair Murphy, Kieran Howle and Rob Duffield
Sensors 2022, 22(22), 8868; https://0-doi-org.brum.beds.ac.uk/10.3390/s22228868 - 16 Nov 2022
Cited by 8 | Viewed by 1994
Abstract
This study evaluated the accuracy of tennis-specific stroke and movement event detection algorithms from a cervically mounted wearable sensor containing a triaxial accelerometer, gyroscope and magnetometer. Stroke and movement data from up to eight high-performance tennis players were captured in match-play and movement [...] Read more.
This study evaluated the accuracy of tennis-specific stroke and movement event detection algorithms from a cervically mounted wearable sensor containing a triaxial accelerometer, gyroscope and magnetometer. Stroke and movement data from up to eight high-performance tennis players were captured in match-play and movement drills. Prototype algorithms classified stroke (i.e., forehand, backhand, serve) and movement (i.e., “Alert”, “Dynamic”, “Running”, “Low Intensity”) events. Manual coding evaluated stroke actions in three classes (i.e., forehand, backhand and serve), with additional descriptors of spin (e.g., slice). Movement data was classified according to the specific locomotion performed (e.g., lateral shuffling). The algorithm output for strokes were analysed against manual coding via absolute (n) and relative (%) error rates. Coded movements were grouped according to their frequency within the algorithm’s four movement classifications. Highest stroke accuracy was evident for serves (98%), followed by groundstrokes (94%). Backhand slice events showed 74% accuracy, while volleys remained mostly undetected (41–44%). Tennis-specific footwork patterns were predominantly grouped as “Dynamic” (63% of total events), alongside successful linear “Running” classifications (74% of running events). Concurrent stroke and movement data from wearable sensors allows detailed and long-term monitoring of tennis training for coaches and players. Improvements in movement classification sensitivity using tennis-specific language appear warranted. Full article
(This article belongs to the Special Issue Inertial Measurement Units in Sport)
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14 pages, 7484 KiB  
Article
Alpine Skiing Activity Recognition Using Smartphone’s IMUs
by Behrooz Azadi, Michael Haslgrübler, Bernhard Anzengruber-Tanase, Stefan Grünberger and Alois Ferscha
Sensors 2022, 22(15), 5922; https://0-doi-org.brum.beds.ac.uk/10.3390/s22155922 - 08 Aug 2022
Cited by 5 | Viewed by 2000
Abstract
Many studies on alpine skiing are limited to a few gates or collected data in controlled conditions. In contrast, it is more functional to have a sensor setup and a fast algorithm that can work in any situation, collect data, and distinguish alpine [...] Read more.
Many studies on alpine skiing are limited to a few gates or collected data in controlled conditions. In contrast, it is more functional to have a sensor setup and a fast algorithm that can work in any situation, collect data, and distinguish alpine skiing activities for further analysis. This study aims to detect alpine skiing activities via smartphone inertial measurement units (IMU) in an unsupervised manner that is feasible for daily use. Data of full skiing sessions from novice to expert skiers were collected in varied conditions using smartphone IMU. The recorded data is preprocessed and analyzed using unsupervised algorithms to distinguish skiing activities from the other possible activities during a day of skiing. We employed a windowing strategy to extract features from different combinations of window size and sliding rate. To reduce the dimensionality of extracted features, we used Principal Component Analysis. Three unsupervised techniques were examined and compared: KMeans, Ward’s methods, and Gaussian Mixture Model. The results show that unsupervised learning can detect alpine skiing activities accurately independent of skiers’ skill level in any condition. Among the studied methods and settings, the best model had 99.25% accuracy. Full article
(This article belongs to the Special Issue Inertial Measurement Units in Sport)
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9 pages, 500 KiB  
Article
Influence of the Shod Condition on Running Power Output: An Analysis in Recreationally Active Endurance Runners
by Diego Jaén-Carrillo, Luis E. Roche-Seruendo, Alejandro Molina-Molina, Silvia Cardiel-Sánchez, Antonio Cartón-Llorente and Felipe García-Pinillos
Sensors 2022, 22(13), 4828; https://0-doi-org.brum.beds.ac.uk/10.3390/s22134828 - 26 Jun 2022
Cited by 3 | Viewed by 2295
Abstract
Several studies have already analysed power output in running or the relation between VO2max and power production as factors related to running economy; however, there are no studies assessing the difference in power output between shod and barefoot running. This study aims to [...] Read more.
Several studies have already analysed power output in running or the relation between VO2max and power production as factors related to running economy; however, there are no studies assessing the difference in power output between shod and barefoot running. This study aims to identify the effect of footwear on the power output endurance runner. Forty-one endurance runners (16 female) were evaluated at shod and barefoot running over a one-session running protocol at their preferred comfortable velocity (11.71 ± 1.07 km·h−1). The mean power output (MPO) and normalized MPO (MPOnorm), form power, vertical oscillation, leg stiffness, running effectiveness and spatiotemporal parameters were obtained using the Stryd™ foot pod system. Additionally, footstrike patterns were measured using high-speed video at 240 Hz. No differences were noted in MPO (p = 0.582) and MPOnorm (p = 0.568), whereas significant differences were found in form power, in both absolute (p = 0.001) and relative values (p < 0.001), running effectiveness (p = 0.006), stiffness (p = 0.002) and vertical oscillation (p < 0.001). By running barefoot, lower values for contact time (p < 0.001) and step length (p = 0.003) were obtained with greater step frequency (p < 0.001), compared to shod running. The prevalence of footstrike pattern significantly differs between conditions, with 19.5% of runners showing a rearfoot strike, whereas no runners showed a rearfoot strike during barefoot running. Running barefoot showed greater running effectiveness in comparison with shod running, and was consistent with lower values in form power and lower vertical oscillation. From a practical perspective, the long-term effect of barefoot running drills might lead to increased running efficiency and leg stiffness in endurance runners, affecting running economy. Full article
(This article belongs to the Special Issue Inertial Measurement Units in Sport)
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13 pages, 553 KiB  
Article
Inertial Sensor Estimation of Initial and Terminal Contact during In-Field Running
by Yue Yang, Li Wang, Steven Su, Mark Watsford, Lauren Marie Wood and Rob Duffield
Sensors 2022, 22(13), 4812; https://0-doi-org.brum.beds.ac.uk/10.3390/s22134812 - 25 Jun 2022
Cited by 6 | Viewed by 1749
Abstract
Given the popularity of running-based sports and the rapid development of Micro-electromechanical systems (MEMS), portable wireless sensors can provide in-field monitoring and analysis of running gait parameters during exercise. This paper proposed an intelligent analysis system from wireless micro–Inertial Measurement Unit (IMU) data [...] Read more.
Given the popularity of running-based sports and the rapid development of Micro-electromechanical systems (MEMS), portable wireless sensors can provide in-field monitoring and analysis of running gait parameters during exercise. This paper proposed an intelligent analysis system from wireless micro–Inertial Measurement Unit (IMU) data to estimate contact time (CT) and flight time (FT) during running based on gyroscope and accelerometer sensors in a single location (ankle). Furthermore, a pre-processing system that detected the running period was introduced to analyse and enhance CT and FT detection accuracy and reduce noise. Results showed pre-processing successfully detected the designated running periods to remove noise of non-running periods. Furthermore, accelerometer and gyroscope algorithms showed good consistency within 95% confidence interval, and average absolute error of 31.53 ms and 24.77 ms, respectively. In turn, the combined system obtained a consistency of 84–100% agreement within tolerance values of 50 ms and 30 ms, respectively. Interestingly, both accuracy and consistency showed a decreasing trend as speed increased (36% at high-speed fore-foot strike). Successful CT and FT detection and output validation with consistency checking algorithms make in-field measurement of running gait possible using ankle-worn IMU sensors. Accordingly, accurate IMU-based gait analysis from gyroscope and accelerometer information can inform future research on in-field gait analysis. Full article
(This article belongs to the Special Issue Inertial Measurement Units in Sport)
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15 pages, 3593 KiB  
Article
Pilot Study of Embedded IMU Sensors and Machine Learning Algorithms for Automated Ice Hockey Stick Fitting
by Taylor Léger, Philippe J. Renaud, Shawn M. Robbins and David J. Pearsall
Sensors 2022, 22(9), 3419; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093419 - 29 Apr 2022
Cited by 1 | Viewed by 2834
Abstract
The aims of this study were to evaluate the feasibility of using IMU sensors and machine learning algorithms for the instantaneous fitting of ice hockey sticks. Ten experienced hockey players performed 80 shots using four sticks of differing constructions (i.e., each stick differed [...] Read more.
The aims of this study were to evaluate the feasibility of using IMU sensors and machine learning algorithms for the instantaneous fitting of ice hockey sticks. Ten experienced hockey players performed 80 shots using four sticks of differing constructions (i.e., each stick differed in stiffness, blade pattern, or kick point). Custom IMUs were embedded in a pair of hockey gloves to capture resultant linear acceleration and angular velocity of the hands during shooting while an 18-camera optical motion capture system and retroreflective markers were used to identify key shot events and measure puck speed, accuracy, and contact time with the stick blade. MATLAB R2020a’s Machine Learning Toolbox was used to build and evaluate the performance of machine learning algorithms using principal components of the resultant hand kinematic signals using principal components accounting for 95% of the variability and a five-fold cross validation. Fine k-nearest neighbors algorithms were found to be highly accurate, correctly classifying players by optimal stick flex, blade pattern, and kick point with 90–98% accuracy for slap shots and 93–97% accuracy for wrist shots in fractions of a second. Based on these findings, it appears promising that wearable sensors and machine learning algorithms can be used for reliable, rapid, and portable hockey stick fitting. Full article
(This article belongs to the Special Issue Inertial Measurement Units in Sport)
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17 pages, 3732 KiB  
Article
SmartSwim, a Novel IMU-Based Coaching Assistance
by Mahdi Hamidi Rad, Vincent Gremeaux, Fabien Massé, Farzin Dadashi and Kamiar Aminian
Sensors 2022, 22(9), 3356; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093356 - 27 Apr 2022
Cited by 6 | Viewed by 2373
Abstract
Swimming coaches provide regular timed and technical feedback to swimmers and guide them efficiently in training sessions. Due to the complexity of swimmers’ performance, which is not visible in qualitative observation, quantitative and objective performance evaluation can better assist the coach in this [...] Read more.
Swimming coaches provide regular timed and technical feedback to swimmers and guide them efficiently in training sessions. Due to the complexity of swimmers’ performance, which is not visible in qualitative observation, quantitative and objective performance evaluation can better assist the coach in this regard. Inertial measurement units (IMUs) are used in swimming for objective performance evaluation. In this study, we propose a new performance evaluation feedback (SmartSwim) using IMU and investigate its effects on the swimmer’s weekly progress. Measurements were conducted each week with 15 competitive swimmers for 10 weeks using a Sacrum IMU. The SmartSwim report included a comprehensive representation of performance based on goal metrics of each phase extracted from the IMU signals. The swimmers were divided into two groups: the experimental and control groups. The SmartSwim report for each swimmer in the experimental group was given to the coach, who used it to adjust the training accordingly. The results showed that the experimental group outperformed the control group when comparing each swimmer, each session and the whole sessions. At the level of each individual, more members of the experimental group showed significant downward trend of average lap time (Mann-Kendall trend test, 95% confidence level). While comparing the sessions, the experimental group showed significantly lower lap time than the control group from the sixth session onwards (p-value < 0.05 from t-test). Considering all sessions, the experimental group showed significantly higher progress, lower average lap time, and more consistent records (Mann-Whitney U test at 95% confidence level) than the control group. This study demonstrated that SmartSwim can assist coaching by quantitatively assessing swimmers’ performance, leading to more efficient training. Full article
(This article belongs to the Special Issue Inertial Measurement Units in Sport)
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14 pages, 650 KiB  
Article
External Load Analysis in Beach Handball Using a Local Positioning System and Inertial Measurement Units
by Carsten Müller, Christina Willberg, Lukas Reichert and Karen Zentgraf
Sensors 2022, 22(8), 3011; https://0-doi-org.brum.beds.ac.uk/10.3390/s22083011 - 14 Apr 2022
Cited by 5 | Viewed by 1835
Abstract
Beach handball is a young discipline that is characterized by numerous high-intensity actions. By following up on previous work, the objective was to perform in-depth analyses evaluating external load (e.g., distance traveled, velocity, changes in direction, etc.) in beach handball players. In cross-sectional [...] Read more.
Beach handball is a young discipline that is characterized by numerous high-intensity actions. By following up on previous work, the objective was to perform in-depth analyses evaluating external load (e.g., distance traveled, velocity, changes in direction, etc.) in beach handball players. In cross-sectional analyses, data of 69 players belonging to the German national or prospective team were analyzed during official tournaments using a local positioning system (10 Hz) and inertial measurement units (100 Hz). Statistical analyses comprised the comparison of the first and second set and the effects of age and sex (female adolescents vs. male adolescents vs. male adults) and playing position (goalkeepers, defenders, wings, specialists, and pivots) on external load measures. We found evidence for reduced external workload during the second set of the matches (p = 0.005, ηp2 = 0.09), as indicated by a significantly lower player load per minute and number of changes in direction. Age/sex (p < 0.001, ηp2 = 0.22) and playing position (p < 0.001, ηp2 = 0.29) also had significant effects on external load. The present data comprehensively describe and analyze important external load measures in a sample of high-performing beach handball players, providing valuable information to practitioners and coaches aiming at improving athletic performance in this new sport. Full article
(This article belongs to the Special Issue Inertial Measurement Units in Sport)
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11 pages, 481 KiB  
Article
A Single Sacral-Mounted Inertial Measurement Unit to Estimate Peak Vertical Ground Reaction Force, Contact Time, and Flight Time in Running
by Aurélien Patoz, Thibault Lussiana, Bastiaan Breine, Cyrille Gindre and Davide Malatesta
Sensors 2022, 22(3), 784; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030784 - 20 Jan 2022
Cited by 8 | Viewed by 2605
Abstract
Peak vertical ground reaction force (Fz,max), contact time (tc), and flight time (tf) are key variables of running biomechanics. The gold standard method (GSM) to measure these variables is a force plate. [...] Read more.
Peak vertical ground reaction force (Fz,max), contact time (tc), and flight time (tf) are key variables of running biomechanics. The gold standard method (GSM) to measure these variables is a force plate. However, a force plate is not always at hand and not very portable overground. In such situation, the vertical acceleration signal recorded by an inertial measurement unit (IMU) might be used to estimate Fz,max, tc, and tf. Hence, the first purpose of this study was to propose a method that used data recorded by a single sacral-mounted IMU (IMU method: IMUM) to estimate Fz,max. The second aim of this study was to estimate tc and tf using the same IMU data. The vertical acceleration threshold of an already existing IMUM was modified to detect foot-strike and toe-off events instead of effective foot-strike and toe-off events. Thus, tc and tf estimations were obtained instead of effective contact and flight time estimations. One hundred runners ran at 9, 11, and 13 km/h. IMU data (208 Hz) and force data (200 Hz) were acquired by a sacral-mounted IMU and an instrumented treadmill, respectively. The errors obtained when comparing Fz,max, tc, and tf estimated using the IMUM to Fz,max, tc, and tf measured using the GSM were comparable to the errors obtained using previously published methods. In fact, a root mean square error (RMSE) of 0.15 BW (6%) was obtained for Fz,max while a RMSE of 20 ms was reported for both tc and tf (8% and 18%, respectively). Moreover, even though small systematic biases of 0.07 BW for Fz,max and 13 ms for tc and tf were reported, the RMSEs were smaller than the smallest real differences [Fz,max: 0.28 BW (11%), tc: 32.0 ms (13%), and tf: 32.0 ms (30%)], indicating no clinically important difference between the GSM and IMUM. Therefore, these results support the use of the IMUM to estimate Fz,max, tc, and tf for level treadmill runs at low running speeds, especially because an IMU has the advantage to be low-cost and portable and therefore seems very practical for coaches and healthcare professionals. Full article
(This article belongs to the Special Issue Inertial Measurement Units in Sport)
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Review

Jump to: Research

14 pages, 1929 KiB  
Review
Using Wearable Sensors to Estimate Mechanical Power Output in Cyclical Sports Other than Cycling—A Review
by Vera G. de Vette, DirkJan (H. E. J.) Veeger and Marit P. van Dijk
Sensors 2023, 23(1), 50; https://0-doi-org.brum.beds.ac.uk/10.3390/s23010050 - 21 Dec 2022
Cited by 2 | Viewed by 1518
Abstract
More insight into in-field mechanical power in cyclical sports is useful for coaches, sport scientists, and athletes for various reasons. To estimate in-field mechanical power, the use of wearable sensors can be a convenient solution. However, as many model options and approaches for [...] Read more.
More insight into in-field mechanical power in cyclical sports is useful for coaches, sport scientists, and athletes for various reasons. To estimate in-field mechanical power, the use of wearable sensors can be a convenient solution. However, as many model options and approaches for mechanical power estimation using wearable sensors exist, and the optimal combination differs between sports and depends on the intended aim, determining the best setup for a given sport can be challenging. This review aims to provide an overview and discussion of the present methods to estimate in-field mechanical power in different cyclical sports. Overall, in-field mechanical power estimation can be complex, such that methods are often simplified to improve feasibility. For example, for some sports, power meters exist that use the main propulsive force for mechanical power estimation. Another non-invasive method usable for in-field mechanical power estimation is the use of inertial measurement units (IMUs). These wearable sensors can either be used as stand-alone approach or in combination with force sensors. However, every method has consequences for interpretation of power values. Based on the findings of this review, recommendations for mechanical power measurement and interpretation in kayaking, rowing, wheelchair propulsion, speed skating, and cross-country skiing are done. Full article
(This article belongs to the Special Issue Inertial Measurement Units in Sport)
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