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Sensor Technology for Enhancing Training and Performance in Sport

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

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 31945

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Dr. Pui Wah (Veni) Kong

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Guest Editor

National Institute of Education, Nanyang Technological University, Singapore 637616, Singapore
Interests: sports science; biomechanics; performance; rehabilitation; injury; foot; footwear; gait

Special Issue Information

Dear Colleagues,

Sensor technology opens up exciting opportunities for sport. For example, advancements in motion tracking devices allow monitoring athletes’ movement patterns indoors and outdoors. Wearable sensors offer insightful information on the demands of the sport during both training and competition. Such data are important for coaches and athletes to optimize their training plan, minimize the risk of injuries, and improve performance. This Special Issue focuses on innovative development and application of sensors for enhancing training and performance in sport. Examples of sensors include those measuring motion, acceleration, force, neuromuscular response, and physiological parameters. Original research articles and reviews on wearable or laboratory-based sensor technology are welcomed.

Dr. Pui Wah (Veni) Kong
Guest Editor

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Keywords

Wearable
Wireless
Technology
Feedback
Instrumentation
Data processing
Athletic performance
Biomechanics
Physiology
Strength and conditioning

Published Papers (11 papers)

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Editorial

Jump to: Research, Review

4 pages, 195 KiB

Open AccessEditorial

Editorial–Special Issue on “Sensor Technology for Enhancing Training and Performance in Sport”

by Pui Wah Kong

Sensors 2023, 23(5), 2847; https://doi.org/10.3390/s23052847 - 06 Mar 2023

Cited by 1 | Viewed by 1323

Abstract

Sensor technology opens up exciting opportunities for sports [...] Full article

(This article belongs to the Special Issue Sensor Technology for Enhancing Training and Performance in Sport)

Research

Jump to: Editorial, Review

13 pages, 1084 KiB

Open AccessArticle

Microdosing Sprint Distribution as an Alternative to Achieve Better Sprint Performance in Field Hockey Players

by Víctor Cuadrado-Peñafiel, Adrián Castaño-Zambudio, Luis Manuel Martínez-Aranda, Jorge Miguel González-Hernández, Rafael Martín-Acero and Pedro Jiménez-Reyes

Sensors 2023, 23(2), 650; https://doi.org/10.3390/s23020650 - 06 Jan 2023

Cited by 2 | Viewed by 3021

Abstract

Introduction: The implementation of optimal sprint training volume is a relevant component of team sport performance. This study aimed to compare the efficiency and effectiveness of two different configurations of within-season training load distribution on sprint performance over 6 weeks. Methods: Twenty male professional FH players participated in the study. Players were conveniently assigned to two groups: the experimental group (MG; n = 11; applying the microdosing training methodology) and the control group (TG; n = 9; traditional training, with players being selected by the national team). Sprint performance was evaluated through 20 m sprint time (T20) m and horizontal force–velocity profile (HFVP) tests before (Pre) and after (Post) intervention. Both measurements were separated by a period of 6 weeks. The specific sprint training program was performed for each group (for vs. two weekly sessions for MG and TG, respectively) attempting to influence the full spectrum of the F-V relationship. Results: Conditional demands analysis (matches and training sessions) showed no significant differences between the groups during the intervention period (p > 0.05). No significant between-group differences were found at Pre or Post for any sprint-related performance (p > 0.05). Nevertheless, intra-group analysis revealed significant differences in F0, Pmax, RFmean at 10 m and every achieved time for distances ranging from 5 to 25 m for MG (p < 0.05). Such changes in mechanical capabilities and sprint performance were characterized by an increase in stride length and a decrease in stride frequency during the maximal velocity phase (p < 0.05). Conclusion: Implementing strategies such as microdosed training load distribution appears to be an effective and efficient alternative for sprint training in team sports such as hockey. Full article

(This article belongs to the Special Issue Sensor Technology for Enhancing Training and Performance in Sport)

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17 pages, 1712 KiB

Open AccessArticle

Contextualizing Physical Data in Professional Handball: Using Local Positioning Systems to Automatically Define Defensive Organizations

by Brice Guignard, Claude Karcher, Xavier Reche, Roger Font and John Komar

Sensors 2022, 22(15), 5692; https://doi.org/10.3390/s22155692 - 29 Jul 2022

Cited by 4 | Viewed by 2059

Abstract

In handball, the way the team organizes itself in defense can greatly impact the player’s activity and displacement during the play, therefore impacting the match demands. This paper aims (1) to develop an automatic tool to detect and classify the defensive organization of the team based on the local positioning system data and check its classification quality, and (2) to quantify the match demands per defensive organization, i.e., defining a somehow cost of specific defensive organizations. For this study, LPS positional data (X and Y location) of players from a team in the Spanish League were analyzed during 25 games. The algorithm quantified the physical demands of the game (distance stand, walk, jog, run and sprint) broken down by player role and by specific defensive organizations, which were automatically detected from the raw data. Results show that the different attacking and defending phases of a game can be automatically detected with high accuracy, the defensive organization can be classified between 1–5, 0–6, 2–4, and 3–3. Interestingly, due to the highly adaptive nature of handball, differences were found between what was the intended defensive organization at a start of a phase and the actual organization that can be observed during the full defensive phase, which consequently impacts the physical demands of the game. From there, quantifying for each player role the cost of each specific defensive organization is the first step into optimizing the use of the players in the team and their recovery time, but also at the team level, it allows to balance the cost (i.e., physical demand) and the benefit (i.e., the outcome of the defensive phase) of each type of defensive organization. Full article

(This article belongs to the Special Issue Sensor Technology for Enhancing Training and Performance in Sport)

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21 pages, 3659 KiB

Open AccessArticle

Estimation of Heart Rate and Energy Expenditure Using a Smart Bracelet during Different Exercise Intensities: A Reliability and Validity Study

by Yihui Cai, Zi Wang, Wanxia Zhang, Weiya Kong, Jiayao Jiang, Ruobing Zhao, Dongxue Wang, Leyi Feng and Guoxin Ni

Sensors 2022, 22(13), 4661; https://doi.org/10.3390/s22134661 - 21 Jun 2022

Cited by 2 | Viewed by 2499

Abstract

Background. With wrist-worn wearables becoming increasingly available, it is important to understand their reliability and validity in different conditions. The primary objective of this study was to examine the reliability and validity of the Lexin Mio smart bracelet in measuring heart rate (HR) and energy expenditure (EE) in people with different physical activity levels exercising at different intensities. Methods. A total of 65 participants completed one maximal oxygen uptake test and two running exercise tests wearing the Mio smart bracelet, the Polar H10 HR band, and a gas-analysis system. Results. In terms of HR measurement reliability, the Mio smart bracelet showed good reliability in a left versus right test and good test–retest reliability (p > 0.05; mean absolute percentage error (MAPE) < 10%; intraclass correlation coefficient (ICC) > 0.4). For EE measurement, the Mio smart bracelet showed good reliability in a left versus right test, good test–retest reliability on the right (p > 0.05; MAPE > 10%; ICC > 0.4), and low test–retest reliability on the left (p > 0.05; MAPE > 10%; ICC < 0.4). Regarding validity, the Mio smart bracelet showed good validity for HR measurement (p > 0.05; MAPE < 10%; ICC > 0.4) and low validity for EE measurement (p < 0.05; MAPE > 10%; ICC < 0.4). Conclusion. The Lexin Mio smart bracelet showed good reliability and validity for HR measurement among people with different physical activity levels exercising at various exercise intensities in a laboratory setting. However, the smart bracelet showed good reliability and low validity for the estimation of EE. Full article

(This article belongs to the Special Issue Sensor Technology for Enhancing Training and Performance in Sport)

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21 pages, 4735 KiB

Open AccessArticle

Real-Life Application of a Wearable Device towards Injury Prevention in Tennis: A Single-Case Study

by Iztok Kramberger, Aleš Filipčič, Aleš Germič and Marko Kos

Sensors 2022, 22(12), 4436; https://doi.org/10.3390/s22124436 - 11 Jun 2022

Cited by 5 | Viewed by 2663

Abstract

The purpose of this article is to present the use of a previously validated wearable sensor device, Armbeep, in a real-life application, to enhance a tennis player’s training by monitoring and analysis of the time, physiological, movement, and tennis-specific workload and recovery indicators, based on fused sensor data acquired by the wearable sensor—a miniature wearable sensor device, designed to be worn on a wrist, that can detect and record movement and biometric information, where the basic signal processing is performed directly on the device, while the more complex signal analysis is performed in the cloud. The inertial measurements and pulse-rate detection of the wearable device were validated previously, showing acceptability for monitoring workload and recovery during tennis practice and matches. This study is one of the first attempts to monitor the daily workload and recovery of tennis players under real conditions. Based on these data, we can instruct the coach and the player to adjust the daily workload. This optimizes the level of an athlete’s training load, increases the effectiveness of training, enables an individual approach, and reduces the possibility of overuse or injuries. This study is a practical example of the use of modern technology in the return of injured athletes to normal training and competition. This information will help tennis coaches and players to objectify their workloads during training and competitions, as this is usually only an intuitive assessment. Full article

(This article belongs to the Special Issue Sensor Technology for Enhancing Training and Performance in Sport)

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14 pages, 2391 KiB

Open AccessArticle

The Relationship between Accelerometry, Global Navigation Satellite System, and Known Distance: A Correlational Design Study

by Abdulmalek K. Bursais, Caleb D. Bazyler, Andrew R. Dotterweich, Adam L. Sayers, Mohammed S. Alibrahim, Anwar A. Alnuaim, Majed M. Alhumaid, Abdulrahman I. Alaqil, Ghareeb O. Alshuwaier and Jeremy A. Gentles

Sensors 2022, 22(9), 3360; https://doi.org/10.3390/s22093360 - 27 Apr 2022

Cited by 3 | Viewed by 1813

Abstract

Background: Previous research has explored associations between accelerometry and Global Navigation Satellite System (GNSS) derived loads. However, to our knowledge, no study has investigated the relationship between these measures and a known distance. Thus, the current study aimed to assess and compare the ability of four accelerometry based metrics and GNSS to predict known distance completed using different movement constraints. Method: A correlational design study was used to evaluate the association between the dependent and independent variables. A total of 30 physically active college students participated. Participants were asked to walk two different known distances (DIST) around a 2 m diameter circle (small circle) and a different distance around an 8 m diameter circle (large circle). Each distance completed around the small circle by one participant was completed around the large circle by a different participant. The same 30 distances were completed around each circle and ranged from 12.57 to 376.99 m. Instrumentation: Acceleration data was collected via a tri-axial accelerometer sampling at 100 Hz. Accelerometry derived measures included the sum of the absolute values of acceleration (SUM), the square root of the sum of squared accelerations (MAG), Player Load (PL), and Impulse Load (IL). Distance (GNSSD) was measured from positional data collected using a triple GNSS unit sampling at 10 Hz. Results: Separate simple linear regression models were created to assess the ability of each independent variable to predict DIST. The results indicate that all regression models performed well (R = 0.960–0.999, R² = 0.922–0.999; RMSE = 0.047–0.242, p < 0.001), while GNSSD (small circle, R = 0.999, R² = 0.997, RMSE = 0.047 p < 0.001; large circle, R = 0.999, R² = 0.999, RMSE = 0.027, p < 0.001) and the accelerometry derived metric MAG (small circle, R = 0.992, R² = 0.983, RMSE = 0.112, p < 0.001; large circle, R = 0.997, R² = 0.995, RMSE = 0.064, p < 0.001) performed best among all models. Conclusions: This research illustrates that both GNSS and accelerometry may be used to indicate total distance completed while walking. Full article

(This article belongs to the Special Issue Sensor Technology for Enhancing Training and Performance in Sport)

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10 pages, 1047 KiB

Open AccessArticle

Inter-Unit Consistency and Validity of 10-Hz GNSS Units in Straight-Line Sprint Running

by Amandeep Kaur Chahal, Jolene Ziyuan Lim, Jing-Wen Pan and Pui Wah Kong

Sensors 2022, 22(5), 1888; https://doi.org/10.3390/s22051888 - 28 Feb 2022

Cited by 4 | Viewed by 1585

Abstract

The present study aimed to investigate the inter-unit consistency and validity of multiple 10-Hz Catapult Global Navigation Satellite System (GNSS) units in measuring straight-line sprint distances and speeds. A total of 13 participants performed one 45.72-m linear sprint at maximum effort while wearing all eight GNSS units at once. Total run distance and peak speed recorded using GNSS units during the sprint duration were extracted for analysis. Sprint time and peak speed were also obtained from video recordings as reference values. Inter-unit consistency was assessed using intraclass correlation coefficients (ICC) and standard errors of measurements (SEM). For a validity test, one-sample t-tests were performed to compare each GNSS unit’s distance with the known distance. Additionally, Wilcoxon signed-rank tests were performed to compare each unit’s peak speed with the reference peak speed measured using video analysis. Results showed poor inter-unit consistency for both distance (ICC = 0.131; SEM = 8.8 m) and speed (ICC = 0.323; SEM 1.3 m/s) measurements. For validity, most units recorded a total distance (44.50 m to 52.69 m) greater than the known distance of 45.72 m and a lower peak speed (7.25 (0.51) m/s) than the video-based reference values (7.78 (0.90) m/s). The present findings demonstrate that there exist variations in distance and speed measurements among different units of the same GNSS system during straight-line sprint running. Practitioners should be aware of the window of errors associated with GNSS measurements and interpret the results with caution. When making comparisons over a season, players should wear the same unit every time if logistically possible. Full article

(This article belongs to the Special Issue Sensor Technology for Enhancing Training and Performance in Sport)

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12 pages, 2083 KiB

Open AccessArticle

The Validity and Reliability of a Tire Pressure-Based Power Meter for Indoor Cycling

by Nicholas J. Fiolo, Hai-Ying Lu, Chia-Hsiang Chen, Philip X. Fuchs, Wei-Han Chen and Tzyy-Yuang Shiang

Sensors 2021, 21(18), 6117; https://doi.org/10.3390/s21186117 - 12 Sep 2021

Cited by 2 | Viewed by 2190

Abstract

The purpose of this study was to evaluate the validity and reliability of a tire pressure sensor (TPS) cycling power meter against a gold standard (SRM) during indoor cycling. Twelve recreationally active participants completed eight trials of 90 s of cycling at different pedaling and gearing combinations on an indoor hybrid roller. Power output (PO) was simultaneously calculated via TPS and SRM. The analysis compared the paired 1 s PO and 1 min average PO per trial between devices. Agreement was assessed by correlation, linear regression, inferential statistics, effect size, and Bland–Altman LoA. Reliability was assessed by ICC and CV comparison. TPS showed near-perfect correlation with SRM in 1 s (r_s = 0.97, p < 0.001) and 1-min data (r_s = 0.99, p < 0.001). Differences in paired 1 s data were statistically significant (p = 0.04), but of a trivial magnitude (d = 0.05). There was no significant main effect for device (F(1,9) = 0.05, p = 0.83,

η_{p}^{2}

= 0.97) in 1 min data and no statistical differences between devices by trial in post hoc analysis (p < 0.01–0.98; d < 0.01–0.93). Bias and LoA were −0.21 ± 16.77 W for the 1 min data. Mean TPS bias ranged from 3.37% to 7.81% of the measured SRM mean PO per trial. Linear regression SEE was 7.55 W for 1 min TPS prediction of SRM. ICC_3,1 across trials was 0.96. No statistical difference (p = 0.09–0.11) in TPS CV (3.6–5.0%) and SRM CV (4.3–4.7%). The TPS is a valid and reliable power meter for estimating average indoor PO for time periods equal to or greater than 1 min and may have acceptable sensitivity to detect changes under less stringent criteria (±5%). Full article

(This article belongs to the Special Issue Sensor Technology for Enhancing Training and Performance in Sport)

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13 pages, 2114 KiB

Open AccessArticle

Sensor-Based Gait Retraining Lowers Knee Adduction Moment and Improves Symptoms in Patients with Knee Osteoarthritis: A Randomized Controlled Trial

by Sizhong Wang, Peter P. K. Chan, Ben M. F. Lam, Zoe Y. S. Chan, Janet H. W. Zhang, Chao Wang, Wing Kai Lam, Kevin Ki Wai Ho, Rosa H. M. Chan and Roy T. H. Cheung

Sensors 2021, 21(16), 5596; https://doi.org/10.3390/s21165596 - 19 Aug 2021

Cited by 11 | Viewed by 4843

Abstract

The present study compared the effect between walking exercise and a newly developed sensor-based gait retraining on the peaks of knee adduction moment (KAM), knee adduction angular impulse (KAAI), knee flexion moment (KFM) and symptoms and functions in patients with early medial knee osteoarthritis (OA). Eligible participants (n = 71) with early medial knee OA (Kellgren-Lawrence grade I or II) were randomized to either walking exercise or gait retraining group. Knee loading-related parameters including KAM, KAAI and KFM were measured before and after 6-week gait retraining. We also examined clinical outcomes including visual analog pain scale (VAS_P) and Knee Injury and Osteoarthritis Outcome Score (KOOS) at each time point. After gait retraining, KAM₁ and VAS_P were significantly reduced (both Ps < 0.001) and KOOS significantly improved (p = 0.004) in the gait retraining group, while these parameters remained similar in the walking exercise group (Ps ≥ 0.448). However, KAM₂, KAAI and KFM did not change in both groups across time (Ps ≥ 0.120). A six-week sensor-based gait retraining, compared with walking exercise, was an effective intervention to lower medial knee loading, relieve knee pain and improve symptoms for patients with early medial knee OA. Full article

(This article belongs to the Special Issue Sensor Technology for Enhancing Training and Performance in Sport)

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13 pages, 1871 KiB

Open AccessArticle

Comparison between Accelerometer and Gyroscope in Predicting Level-Ground Running Kinematics by Treadmill Running Kinematics Using a Single Wearable Sensor

by Daniel Hung Kay Chow, Luc Tremblay, Chor Yin Lam, Adrian Wai Yin Yeung, Wilson Ho Wu Cheng and Peter Tin Wah Tse

Sensors 2021, 21(14), 4633; https://doi.org/10.3390/s21144633 - 06 Jul 2021

Cited by 11 | Viewed by 3300

Abstract

Wearable sensors facilitate running kinematics analysis of joint kinematics in real running environments. The use of a few sensors or, ideally, a single inertial measurement unit (IMU) is preferable for accurate gait analysis. This study aimed to use a convolutional neural network (CNN) to predict level-ground running kinematics (measured by four IMUs on the lower extremities) by using treadmill running kinematics training data measured using a single IMU on the anteromedial side of the right tibia and to compare the performance of level-ground running kinematics predictions between raw accelerometer and gyroscope data. The CNN model performed regression for intraparticipant and interparticipant scenarios and predicted running kinematics. Ten recreational runners were recruited. Accelerometer and gyroscope data were collected. Intraparticipant and interparticipant R² values of actual and predicted running kinematics ranged from 0.85 to 0.96 and from 0.7 to 0.92, respectively. Normalized root mean squared error values of actual and predicted running kinematics ranged from 3.6% to 10.8% and from 7.4% to 10.8% in intraparticipant and interparticipant tests, respectively. Kinematics predictions in the sagittal plane were found to be better for the knee joint than for the hip joint, and predictions using the gyroscope as the regressor were demonstrated to be significantly better than those using the accelerometer as the regressor. Full article

(This article belongs to the Special Issue Sensor Technology for Enhancing Training and Performance in Sport)

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Review

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14 pages, 691 KiB

Open AccessReview

Wearables in Swimming for Real-Time Feedback: A Systematic Review

by Jorge E. Morais, João P. Oliveira, Tatiana Sampaio and Tiago M. Barbosa

Sensors 2022, 22(10), 3677; https://doi.org/10.3390/s22103677 - 12 May 2022

Cited by 6 | Viewed by 4281

Abstract

Nowadays, wearables are a must-have tool for athletes and coaches. Wearables can provide real-time feedback to athletes on their athletic performance and other training details as training load, for example. The aim of this study was to systematically review studies that assessed the accuracy of wearables providing real-time feedback in swimming. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were selected to identify relevant studies. After screening, 283 articles were analyzed and 18 related to the assessment of the accuracy of wearables providing real-time feedback in swimming were retained for qualitative synthesis. The quality index was 12.44 ± 2.71 in a range from 0 (lowest quality) to 16 (highest quality). Most articles assessed in-house built (n = 15; 83.3%) wearables in front-crawl stroke (n = 8; 44.4%), eleven articles (61.1%) analyzed the accuracy of measuring swimming kinematics, eight (44.4%) were placed on the lower back, and seven were placed on the head (38.9%). A limited number of studies analyzed wearables that are commercially available (n = 3, 16.7%). Eleven articles (61.1%) reported on the accuracy, measurement error, or consistency. From those eleven, nine (81.8%) noted that wearables are accurate. Full article

(This article belongs to the Special Issue Sensor Technology for Enhancing Training and Performance in Sport)

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