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Sensors and Artificial Intelligence for Analyzing Human Behavior in Sports and Physical Activity

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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: 15 September 2025 | Viewed by 20937

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Special Issue Editors

Dr. John Komar

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Guest Editor

Physical Education and Sports Sciences, National Institute of Education, Nanyang Technological University, 1 Nanyang Walk, Singapore 637616, Singapore
Interests: complexity; coordination; movement variability; pedagogy; performance analysis; data analytics

Prof. Dr. Ludovic Seifert

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Guest Editor

CETAPS Lab., Faculty of Sport Sciences, University of Rouen, Boulevard Siegfried, 76821 Mont Saint Aignan CEDEX, France
Interests: ecological dynamics; complexity; skill acquisition; dynamical systems theory; performance analysis; expertise

Special Issue Information

Dear Colleagues,

The rapid development of motion sensors now allows the collection of data out of the laboratory during training but also during competitive events. Although the data availability and/or data accuracy may be lower compared to a laboratory-based analysis, the possibility to collect data live in the performance context appears key to obtaining relevant insights into the activity of the performers. As such, the benefits of an in situ analysis of the performers is the result of a constant balance between the raw data that can be captured in situ, the accuracy and reliability of those data, and the possible analysis to be performed to really access insights into the performance. For instance, in team sports, indoor and outdoor tracking technologies have helped to provide physical information on the team, but the absence of information on the opposition team as well as on the ball drastically limits the potential for technical and tactical analysis. To overcome this situation, machine learning could help to learn from a small sample of full data (i.e., both teams’ positional data) to predict where the opposition players will be. Similarly, automatic detection of technical actions using machine learning does not always require the collection of full body kinematics with the sensors. On the other hand, advancements in computer vision and body pose estimation also today allow the collection of positional and kinematics data on every player without the need to wear sensors.

This Special Issue intends to highlight research works wherein sensor technologies, coupled with advanced analysis, allow the collection of insights into sports performance in the actual performance environment. We welcome the submission of basic and applied research studies, tutorials, reviews, and position papers that address the use of motion sensors in physical activity and sport sciences. We will accept high-quality, original, unpublished papers that are not currently under review by any other journal or conference.

Dr. John Komar
Prof. Dr. Ludovic Seifert
Guest Editors

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Keywords

global positioning systems
inertial measurement units
indoor tracking
local positioning systems
performance analysis
motion analysis
computer vision
machine learning
positional data

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Published Papers (12 papers)

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Research

15 pages, 853 KiB

Open AccessArticle

Evaluation of WIMU Sensor Performance in Estimating Running Stride and Vertical Stiffness in Football Training Sessions: A Comparison with Smart Insoles

by Salvatore Pinelli, Mauro Mandorino, Mathieu Lacome and Silvia Fantozzi

Sensors 2024, 24(24), 8087; https://doi.org/10.3390/s24248087 - 18 Dec 2024

Viewed by 630

Abstract

Temporal parameters are crucial for understanding running performance, especially in elite sports environments. Traditional measurement methods are often labor-intensive and not suitable for field conditions. This study seeks to provide greater clarity in parameter estimation using a single device by comparing it to the gold standard. Specifically, this study aims to investigate how the temporal parameters and vertical stiffness (K_vert) of running stride exerted by IMU sensors are related to the parameters of the smart insole for outdoor acquisition. Ten healthy male subjects performed four 60-meter high-speed runs. Data were collected using the WIMU PRO™ device and smart insoles. Contact time (CT) and flight time (FT) were identified, and K_vert was calculated using Morin’s method. Statistical analyses assessed data normality, correlations, and reliability. WIMU measured longer CT, with differences ranging from 26.3% to 38.5%, and shorter FT, with differences ranging from 27.3% to 54.5%, compared to smart insoles, across different running speeds. K_vert values were lower with WIMU, with differences ranging from 23.96% to 45.01% depending on the running activity, indicating significant differences (p < 0.001). Using these results, a multiple linear regression model was developed for the correction of WIMU’s K_vert values, improving the accuracy. The improved accuracy of K_vert measurements has significant implications for athletic performance. It provides sports scientists with a more reliable metric to estimate player fatigue, potentially leading to more effective training regimens and injury prevention strategies. This advancement is particularly valuable in team sports settings, where easy-to-use and accurate biomechanical assessments of multiple athletes are essential. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence for Analyzing Human Behavior in Sports and Physical Activity)

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12 pages, 2384 KiB

Open AccessArticle

Developing a New Expected Goals Metric to Quantify Performance in a Virtual Reality Soccer Goalkeeping App Called CleanSheet

by Matthew Simpson and Cathy Craig

Sensors 2024, 24(23), 7527; https://doi.org/10.3390/s24237527 - 25 Nov 2024

Viewed by 639

Abstract

As virtual reality (VR) sports training apps start to become more mainstream, it is important that human performance is measured from VR gameplay interaction data in a more meaningful way. CleanSheet is a VR training app that is played by over 100,000 users around the world. Many of those players are aspiring goalkeepers who want to use the app as a new way to train and improve their general goalkeeping performance. Whilst the leaderboards display how many shots players saved, these data do not take into account the difficulty of the shot faced. This study presents a regression model developed from a combination of existing expected goals (xG) models, goalkeeper performance metrics, and psychological research to produce a new shot difficulty metric called CSxG. Utilizing user save rate data as the target variable, a model was developed that incorporated three input variables relating to ball flight and in-goal positioning. Our analysis showed that the required rate of closure (RROC), adapted from Tau theory, was the most significant predictor of the proportion of goals conceded. A validation process evaluated the new xG model for CleanSheet by comparing its difficulty predictions against user performance data across players of varying skill levels. CSxG effectively predicted shot difficulty at the extremes but showed less accuracy for mid-range scores (0.4 to 0.8). Additional variables influencing shot difficulty, such as build-up play and goalpost size, were identified for future model enhancements. This research contributes to the advancement of predictive modeling in sports performance analysis, highlighting the potential for improved goalkeeper training and strategy development using VR technology. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence for Analyzing Human Behavior in Sports and Physical Activity)

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16 pages, 1721 KiB

Open AccessArticle

Comparison of Velocity and Estimated One Repetition Maximum Measured with Different Measuring Tools in Bench Presses and Squats

by Roland van den Tillaar, Hallvard Nygaard Falch and Stian Larsen

Sensors 2024, 24(23), 7422; https://doi.org/10.3390/s24237422 - 21 Nov 2024

Viewed by 992

Abstract

The aim of this study was to compare barbell velocities at different intensities and estimated 1-RM with actual 1-RM measured with different measuring tools in bench presses and squats. Fourteen resistance-trained athletes (eight men, six women, age 28.1 ± 7.5 years, body mass 78.1 ± 12.2 kg, body height 1.73 ± 0.09 m) performed bench presses and squats at five loads varying from 45 to 85% of one repetition maximum (1-RM), together with 1-RM testing, while measuring mean, mean propulsive, and peak barbell velocity with six different commercially used inertial measurement units (IMUs) and linear encoder software systems attached to the barbell. The 1-RM was also estimated based upon the load–velocity regression, which was compared with the actual 1-RM in the bench press and squat exercises. The main findings were that GymAware revealed the highest reliability along with minimal bias, while Musclelab and Vmaxpro showed moderate reliability with some variability at higher loads. Speed4lifts and PUSH band indicated greater variability, specifically at higher intensities. Furthermore, in relation to the second aim of the study, significant discrepancies were found between actual and estimated 1-RM values, with Speed4lifts and Musclelab notably underestimating 1-RM. These findings underscore the importance of selecting reliable tools for accurate velocity-based training and load prescription. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence for Analyzing Human Behavior in Sports and Physical Activity)

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14 pages, 3372 KiB

Open AccessArticle

The Effect of Virtual Reality Technology in Table Tennis Teaching: A Multi-Center Controlled Study

by Tingyu Ma, Wenhao Du and Qiufen Zhang

Sensors 2024, 24(21), 7041; https://doi.org/10.3390/s24217041 - 31 Oct 2024

Viewed by 1272

Abstract

This study investigated the effectiveness of virtual reality (VR) technology in table tennis education compared to traditional training methods. A 12-week randomized controlled trial was conducted with 120 participants divided equally between VR and traditional training groups. Performance metrics, learning motivation, and satisfaction were assessed at regular intervals. Results demonstrated significant advantages of VR training, with the VR group showing superior improvements in serve accuracy (23.5% vs. 15.8%, p < 0.001), rally endurance (an increase of 8.2 vs. 5.7 shots, p < 0.01), and overall skill scores (18.7 vs. 13.2 points improvement, p < 0.001). The VR group also exhibited higher increases in learning motivation (23.5% vs. 12.8%, p < 0.001) and satisfaction (31.5% vs. 18.7%, p < 0.001). Subgroup analysis revealed particular benefits for novice players and younger participants. These findings suggest that VR technology offers a promising approach to enhance table tennis education, potentially revolutionizing sports training methodologies. Future research should focus on long-term skill retention and the optimization of VR training protocols. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence for Analyzing Human Behavior in Sports and Physical Activity)

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13 pages, 805 KiB

Open AccessArticle

Influence of Training Load on Muscle Contractile Properties in Semi-Professional Female Soccer Players Across a Competitive Microcycle: A Pilot Study

by Ezequiel Rey, María Lois-Abal, Alexis Padrón-Cabo, Miguel Lorenzo-Martínez and Pablo B. Costa

Sensors 2024, 24(21), 6996; https://doi.org/10.3390/s24216996 - 30 Oct 2024

Cited by 1 | Viewed by 785

Abstract

This study aimed to evaluate changes in muscle contractile properties during a training microcycle in semi-professional female football players and explore their relationship with training load variables. Nineteen players (age: 23.9 ± 3.9 years; body mass: 60.6 ± 6.9 kg; height: 164.5 ± 6.7 cm) underwent myotonometric assessments of the biceps femoris (BF) and rectus femoris (RF) before and after the following training sessions: MD1 (i.e., 1 day after the match), MD3, MD4, and MD5. Training loads were quantified for each session, revealing significant variations, with MD4 exhibiting the highest values for high-speed running distance, number of sprints, and accelerations. Notably, MD3 showed the highest perceived exertion (RPE), while MD5 recorded the lowest total distance run. Myotonometric assessments indicated significant differences in stiffness of the RF in MD3 and BF in MD5, as well as RF tone in MD5. The findings underscore a notable relationship between training load and myotometric variables, particularly in muscle stiffness and tone. These results emphasize the need for further research to clarify how training loads affect muscle properties in female athletes. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence for Analyzing Human Behavior in Sports and Physical Activity)

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12 pages, 2363 KiB

Open AccessArticle

The Determination of On-Water Rowing Stroke Kinematics Using an Undecimated Wavelet Transform of a Rowing Hull-Mounted Accelerometer Signal

by Daniel Geneau, Drew Commandeur, Ryan Brodie, Ming-Chang Tsai, Matt Jensen and Marc Klimstra

Sensors 2024, 24(18), 6085; https://doi.org/10.3390/s24186085 - 20 Sep 2024

Viewed by 800

Abstract

Boat acceleration profiles can provide valuable information for coaches and practitioners to make meaningful technical interventions and monitor the determinants of success in rowing. Previous studies have used simple feature detection methods to identify key phases within individual strokes, such as drive onset, drive time, drive offset and stroke time. However, based on skill level, technique or boat class, the hull acceleration profile can differ, making robust feature detection more challenging. The current study’s purpose is to employ the undecimated wavelet transform (UWT) technique to detect individual features in the stroke acceleration profile from a single rowing hull-mounted accelerometer. In this investigation, the temporal and kinematic values obtained using the AdMos^TM sensor in conjunction with the UWT processing approach were strongly correlated with the comparative measures of the Peach™ instrumented oarlock system. The measures for stroke time displayed very strong agreeability between the systems for all boat classes, with ICC values of 0.993, 0.963 and 0.954 for the W8+, W4− and W1x boats, respectively. Similarly, the drive time was also very consistent, with strong to very strong agreeability, producing ICC values of 0.937, 0.901 and 0.881 for the W8+, W4− and W1x boat classes. Further, a Bland–Altman analysis displayed little to no bias between the AdMos^TM-derived and Peach™ measures, indicating that there were no systematic discrepancies between signals. This single-sensor solution could form the basis for a simple, cost-effective and accessible alternative to multi-sensor instrumented systems for the determination of sub-stroke kinematic phases. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence for Analyzing Human Behavior in Sports and Physical Activity)

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22 pages, 594 KiB

Open AccessArticle

Engineering Features from Raw Sensor Data to Analyse Player Movements during Competition

by Valerio Antonini, Alessandra Mileo and Mark Roantree

Sensors 2024, 24(4), 1308; https://doi.org/10.3390/s24041308 - 18 Feb 2024

Cited by 2 | Viewed by 1498

Abstract

Research in field sports often involves analysis of running performance profiles of players during competitive games with individual, per-position, and time-related descriptive statistics. Data are acquired through wearable technologies, which generally capture simple data points, which in the case of many team-based sports are times, latitudes, and longitudes. While the data capture is simple and in relatively high volumes, the raw data are unsuited to any form of analysis or machine learning functions. The main goal of this research is to develop a multistep feature engineering framework that delivers the transformation of sequential data into feature sets more suited to machine learning applications. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence for Analyzing Human Behavior in Sports and Physical Activity)

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18 pages, 6045 KiB

Open AccessArticle

Automated Service Height Fault Detection Using Computer Vision and Machine Learning for Badminton Matches

by Guo Liang Goh, Guo Dong Goh, Jing Wen Pan, Phillis Soek Po Teng and Pui Wah Kong

Sensors 2023, 23(24), 9759; https://doi.org/10.3390/s23249759 - 11 Dec 2023

Cited by 18 | Viewed by 4357

Abstract

In badminton, accurate service height detection is critical for ensuring fairness. We developed an automated service fault detection system that employed computer vision and machine learning, specifically utilizing the YOLOv5 object detection model. Comprising two cameras and a workstation, our system identifies elements, such as shuttlecocks, rackets, players, and players’ shoes. We developed an algorithm that can pinpoint the shuttlecock hitting event to capture its height information. To assess the accuracy of the new system, we benchmarked the results against a high sample-rate motion capture system and conducted a comparative analysis with eight human judges that used a fixed height service tool in a backhand low service situation. Our findings revealed a substantial enhancement in accuracy compared with human judgement; the system outperformed human judges by 3.5 times, achieving a 58% accuracy rate for detecting service heights between 1.150 and 1.155 m, as opposed to a 16% accuracy rate for humans. The system we have developed offers a highly reliable solution, substantially enhancing the consistency and accuracy of service judgement calls in badminton matches and ensuring fairness in the sport. The system’s development signifies a meaningful step towards leveraging technology for precision and integrity in sports officiation. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence for Analyzing Human Behavior in Sports and Physical Activity)

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10 pages, 797 KiB

Open AccessArticle

Physical Demands in the Worst-Case Scenarios of Elite Futsal Referees Using a Local Positioning System

by Gemma Martinez-Torremocha, Javier Sanchez-Sanchez, Antonio Alonso-Callejo, Maria Luisa Martin-Sanchez, Carlos Serrano, Leonor Gallardo, Jorge Garcia-Unanue and Jose Luis Felipe

Sensors 2023, 23(21), 8662; https://doi.org/10.3390/s23218662 - 24 Oct 2023

Cited by 1 | Viewed by 1207

Abstract

The aim of this study is to analyze the worst-case scenarios of professional futsal referees during the first and second half of official matches in the Spanish Futsal Cup using a Local Positioning System (LPS) for monitoring their movement patterns. Eight professional futsal referees (40 ± 3.43 years; 1.80 ± 0.03 m; 72.84 ± 4.01 kg) participated in the study. The external load (total distance, high-speed running distance and efforts, sprint distance and efforts, and accelerations and decelerations distances) of the referees was monitored and collected using an LPS. The results revealed significant differences in the worst-case scenarios of the futsal referees during the match according to the time window analyzed (p < 0.05). The longest time windows (120 s, 180 s, and 300 s) showed lower relative total distances in the worst-case scenarios (p < 0.05). The high-speed running distances were significatively higher in the first half for the 120 s (+2.65 m·min⁻¹; ES: 1.25), 180 s (+1.55 m·min⁻¹; ES: 1.28), and 300 s (+0.95 m·min⁻¹; ES: 1.14) time windows (p < 0.05). No differences were found between the first and second half for the high-intensity deceleration distance (p > 0.05). These results will serve to prepare the referees in the best conditions for the competition and adapt the training plans to the worst-case scenarios. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence for Analyzing Human Behavior in Sports and Physical Activity)

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11 pages, 2131 KiB

Open AccessArticle

Assessment of a New Change of Direction Detection Algorithm Based on Inertial Data

by Roberto Avilés, Diego Brito Souza, José Pino-Ortega and Julen Castellano

Sensors 2023, 23(6), 3095; https://doi.org/10.3390/s23063095 - 14 Mar 2023

Cited by 2 | Viewed by 2003

Abstract

The purpose of this study was to study the validity and reproducibility of an algorithm capable of combining information from Inertial and Magnetic Measurement Units (IMMUs) to detect changes of direction (COD). Five participants wore three devices at the same time to perform five CODs in three different conditions: angle (45°, 90°, 135° and 180°), direction (left and right), and running speed (13 and 18 km/h). For the testing, the combination of different % of smoothing applied to the signal (20%, 30% and 40%) and minimum intensity peak (PmI) for each event (0.8 G, 0.9 G, and 1.0 G) was applied. The values recorded with the sensors were contrasted with observation and coding from video. At 13 km/h, the combination of 30% smoothing and 0.9 G PmI was the one that showed the most accurate values (IMMU1: Cohen’s d (d) = −0.29;%Diff = −4%; IMMU2: d = 0.04 %Diff = 0%, IMMU3: d = −0.27, %Diff = 13%). At 18 km/h, the 40% and 0.9 G combination was the most accurate (IMMU1: d = −0.28; %Diff = −4%; IMMU2 = d = −0.16; %Diff = −1%; IMMU3 = d = −0.26; %Diff = −2%). The results suggest the need to apply specific filters to the algorithm based on speed, in order to accurately detect COD. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence for Analyzing Human Behavior in Sports and Physical Activity)

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9 pages, 528 KiB

Open AccessArticle

Evaluating Physical and Tactical Performance and Their Connection during Female Soccer Matches Using Global Positioning Systems

by Ibai Errekagorri, Ibon Echeazarra, Aratz Olaizola and Julen Castellano

Sensors 2023, 23(1), 69; https://doi.org/10.3390/s23010069 - 21 Dec 2022

Cited by 4 | Viewed by 2460

Abstract

The objective of the present study was to evaluate the tactical and physical performance during official matches of a women’s soccer league and to correlate both dimensions in periods of 15 min. To do this, eight official matches of a semi-professional soccer team belonging to the Women’s Second Division of Spain (Reto Iberdrola) were analysed during the 2020–2021 season. The variables recorded were classified into two dimensions: tactical variables (i.e., Width, Length, Height and Surface Area) and physical variables (i.e., Total Distance Covered (TD), Total Distance Covered in High-Speed Running (HSR) and Total Distance Covered in Sprint). The main results were: (1) there were no differences between the periods in any of the tactical dimension variables; (2) in the physical dimension, a significant decrease in TD and HSR was described at the end of the match (period 60–75); and (3) some positive correlations were found among some variables of the tactical and physical dimension at the beginning and at the end of the match (periods 0–15, 60–75 and 75–90). The findings of the study suggest that connecting the tactical and physical dimension in the interpretation of team performance would allow for a better understanding of player and team performance and during competition. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence for Analyzing Human Behavior in Sports and Physical Activity)

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10 pages, 1737 KiB

Open AccessArticle

Reliability of Repeated Nordic Hamstring Strength in Rugby Players Using a Load Cell Device

by Christian Chavarro-Nieto, Martyn Beaven, Nicholas Gill and Kim Hébert-Losier

Sensors 2022, 22(24), 9756; https://doi.org/10.3390/s22249756 - 13 Dec 2022

Cited by 3 | Viewed by 2960

Abstract

Hamstring strain injuries are one of the most common injuries in Rugby Union players, representing up to 15% of all sustained injuries. The Nordic eccentric hamstring test assesses the maximal hamstring eccentric strength and imbalances between limbs. Asymmetries and deficits in hamstring strength between legs are commonly assessed and used as screening methods to prevent injuries which can only be proven effective if hamstring strength measures are reliable over time. We conducted a repeated-measures reliability study with 25 male Rugby Union players. Nordic eccentric strength and bilateral strength balance was assessed. Three testing sessions were undertaken over three consecutive weeks. Intrasession and intersession reliabilities were assessed using typical errors (TE), coefficient of variations (CV), and intraclass correlation coefficients (ICC). Our results showed good intrasession reliability (ICC = 0.79–0.90, TE = 26.8 N to 28.9 N, CV = 5.5% to 6.7%), whilst intersession reliability was fair for mean and the max (ICC = 0.52–0.64, TE = 44.1 N to 55.9 N, CV from 7.4% to 12.5%). Regarding the bilateral strength balance ratios, our results showed good intrasession reliability (ICC = 0.62–0.89, TE = 0.5, CV = 4.4% to 7.2%), whilst the intersession reliability for mean and max values was fair (ICC = 0.52–0.54) with a good absolute intersession reliability CV ranging from 8.2% to 9.6%. Assessing the Nordic eccentric hamstring strength and the bilateral strength balance in Rugby players using a load cell device is a feasible method to test, and demonstrated good intrasession and fair intersession reliability. Nordic eccentric strength assessment is a more practical and functional test than isokinetic; we provide data from Rugby Union players to inform clinicians, and to establish normative values in this cohort. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence for Analyzing Human Behavior in Sports and Physical Activity)

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