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Hollaus B, Heyer Y, Steiner J, Strutzenberger G. Location Matters-Can a Smart Golf Club Detect Where the Club Face Hits the Ball? SENSORS (BASEL, SWITZERLAND) 2023; 23:9783. [PMID: 38139629 PMCID: PMC10748325 DOI: 10.3390/s23249783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 11/20/2023] [Accepted: 12/05/2023] [Indexed: 12/24/2023]
Abstract
In golf, the location of the impact, where the clubhead hits the ball, is of imperative nature for a successful ballflight. Direct feedback to the athlete where he/she hits the ball could improve a practice session. Currently, this information can be measured via, e.g., dual laser technology; however, this is a stationary and external method. A mobile measurement method would give athletes the freedom to gain the information of the impact location without the limitation to be stationary. Therefore, the aim of this study was to investigate whether it is possible to detect the impact location via a motion sensor mounted on the shaft of the golf club. To answer the question, an experiment was carried out. Within the experiment data were gathered from one athlete performing 282 golf swings with an 7 iron. The impact location was recorded and labeled during each swing with a Trackman providing the classes for a neural network. Simultaneously, the motion of the golf club was gathered with an IMU from the Noraxon Ultium Motion Series. In the next step, a neural network was designed and trained to estimate the impact location class based on the motion data. Based on the motion data, a classification accuracy of 93.8% could be achieved with a ResNet architecture.
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Affiliation(s)
- Bernhard Hollaus
- Department of Medical, Health & Sports Engineering, MCI, Maximilianstraße 2, 6020 Innsbruck, Austria;
| | - Yannic Heyer
- Department of Medical, Health & Sports Engineering, MCI, Maximilianstraße 2, 6020 Innsbruck, Austria;
| | - Johannes Steiner
- Johannes Steiner Golf, Robert-Fuchs-Str. 40, 8053 Graz, Austria;
| | - Gerda Strutzenberger
- Institute for Sports Medicine Alpine Medicine & Health Tourism (ISAG), UMIT TIROL—Private University for Health Sciences and Health Technology, Eduard-Wallnoefer-Zentrum 1, 6060 Hall in Tirol, Austria;
- MOTUM—Human Performance Center, Steinbockallee 31, 6063 Rum, Austria
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Vicente-Martínez JA, Márquez-Olivera M, García-Aliaga A, Hernández-Herrera V. Adaptation of YOLOv7 and YOLOv7_tiny for Soccer-Ball Multi-Detection with DeepSORT for Tracking by Semi-Supervised System. SENSORS (BASEL, SWITZERLAND) 2023; 23:8693. [PMID: 37960393 PMCID: PMC10650813 DOI: 10.3390/s23218693] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 10/06/2023] [Accepted: 10/07/2023] [Indexed: 11/15/2023]
Abstract
Object recognition and tracking have long been a challenge, drawing considerable attention from analysts and researchers, particularly in the realm of sports, where it plays a pivotal role in refining trajectory analysis. This study introduces a different approach, advancing the detection and tracking of soccer balls through the implementation of a semi-supervised network. Leveraging the YOLOv7 convolutional neural network, and incorporating the focal loss function, the proposed framework achieves a remarkable 95% accuracy in ball detection. This strategy outperforms previous methodologies researched in the bibliography. The integration of focal loss brings a distinctive edge to the model, improving the detection challenge for soccer balls on different fields. This pivotal modification, in tandem with the utilization of the YOLOv7 architecture, results in a marked improvement in accuracy. Following the attainment of this result, the implementation of DeepSORT enriches the study by enabling precise trajectory tracking. In the comparative analysis between versions, the efficacy of this approach is underscored, demonstrating its superiority over conventional methods with default loss function. In the Materials and Methods section, a meticulously curated dataset of soccer balls is assembled. Combining images sourced from freely available digital media with additional images from training sessions and amateur matches taken by ourselves, the dataset contains a total of 6331 images. This diverse dataset enables comprehensive testing, providing a solid foundation for evaluating the model's performance under varying conditions, which is divided by 5731 images for supervised system and the last 600 images for semi-supervised. The results are striking, with an accuracy increase to 95% with the focal loss function. The visual representations of real-world scenarios underscore the model's proficiency in both detection and classification tasks, further affirming its effectiveness, the impact, and the innovative approach. In the discussion, the hardware specifications employed are also touched on, any encountered errors are highlighted, and promising avenues for future research are outlined.
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Affiliation(s)
- Jorge Armando Vicente-Martínez
- Centro de Investigación e Innovación Tecnológica (CIITEC), Instituto Politécnico Nacional (IPN), Cerrada Cecati s/n Col. Sta. Catarina, Azcapotzalco, Mexico City 02250, Mexico;
| | - Moisés Márquez-Olivera
- Centro de Investigación e Innovación Tecnológica (CIITEC), Instituto Politécnico Nacional (IPN), Cerrada Cecati s/n Col. Sta. Catarina, Azcapotzalco, Mexico City 02250, Mexico;
| | - Abraham García-Aliaga
- Departamento de Deportes, Facultad de Ciencias, de la Actividad Física y del Deporte, INEF, Universidad Politécnica de Madrid, Calle Martín Fierro, 7, 28040 Madrid, Spain;
| | - Viridiana Hernández-Herrera
- Centro de Investigación e Innovación Tecnológica (CIITEC), Instituto Politécnico Nacional (IPN), Cerrada Cecati s/n Col. Sta. Catarina, Azcapotzalco, Mexico City 02250, Mexico;
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Hoelzemann A, Romero JL, Bock M, Laerhoven KV, Lv Q. Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:5879. [PMID: 37447730 DOI: 10.3390/s23135879] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/12/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023]
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 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.
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Affiliation(s)
| | - Julia Lee Romero
- Computer Science, University of Colorado Boulder, Boulder, CO 80302, USA
| | - Marius Bock
- Ubiquitous Computing, University of Siegen, 57076 Siegen, Germany
| | | | - Qin Lv
- Computer Science, University of Colorado Boulder, Boulder, CO 80302, USA
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Kim M, Park C, Yoon J. The Design of GNSS/IMU Loosely-Coupled Integration Filter for Wearable EPTS of Football Players. SENSORS (BASEL, SWITZERLAND) 2023; 23:1749. [PMID: 36850348 PMCID: PMC9965289 DOI: 10.3390/s23041749] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 06/18/2023]
Abstract
This study presents the filter design of GNSS/IMU integration for wearable EPTS (Electronic Performance and Tracking System) of football players. EPTS has been widely used in sports fields recently, and GNSS (Global Navigation Satellite System) and IMU (Inertial Measurement Unit) in wearable EPTS have been used to measure and provide players' athletic performance data. A sensor fusion technique can be used to provide high-quality analysis data of athletic performance. For this reason, the integration filter of GNSS data and IMU data is designed in this study. The loosely-coupled strategy is considered to integrate GNSS and IMU data considering the specification of the wearable EPTS product. Quaternion is used to estimate a player's attitude to avoid the gimbal lock singularity in this study. Experiment results validate the performance of the proposed GNSS/IMU loosely-coupled integration filter for wearable EPTS of football players.
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Affiliation(s)
- Mingu Kim
- Division of Mechanical and Electronics Engineering, Hansung University, Seoul 02876, Republic of Korea
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Rennie MJ, Kelly SJ, Bush S, Spurrs RW, Sheehan WB, Watsford ML. Phases of Match-Play in Professional Australian Football: Positional Demands and Match-Related Fatigue. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249887. [PMID: 36560253 PMCID: PMC9785180 DOI: 10.3390/s22249887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/29/2022] [Accepted: 12/12/2022] [Indexed: 05/27/2023]
Abstract
This study examined the influence of player position and match quarter on activity profiles during the phases of play in Australian Football. Global positioning satellite data was collected for one season from an Australian Football League team for nomadic, key position and ruck players (age: 24.8 ± 4.2 years, body mass: 88.3 ± 8.7 kg, height: 1.88 ± 0.8 m). Separate linear mixed models and effect sizes were used to analyse differences between positions and game quarter within each phase of play for values of distance, speed and metabolic power indices. There were clear differences between positions for low-speed running, high-speed running, total distance and average speed. Nomadic players generally recorded the highest match running outputs, followed by key position players and ruckmen. Within each position, offence and defence involved the highest intensities, followed by contested play and then stoppage periods. Across the four quarters, there were small to large reductions in average speed, high-speed running, high power and energy expenditure during offence, defence and contested play, but not during stoppages. Accordingly, conditioning staff should consider the intermittent intensities of the phases of match-play for each position to optimally prepare players for competition. Reductions in match intensities were evident during active periods of play providing implications for real-time monitoring to optimise the timing of rotations.
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Affiliation(s)
- Michael J. Rennie
- School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney, Moore Park, Sydney, NSW 2021, Australia
| | - Stephen J. Kelly
- School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney, Moore Park, Sydney, NSW 2021, Australia
- Sydney Swans Football Club, Moore Park, Sydney, NSW 2021, Australia
| | - Stephen Bush
- School of Mathematics and Physical Sciences, Faculty of Science, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Robert W. Spurrs
- School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney, Moore Park, Sydney, NSW 2021, Australia
| | - William B. Sheehan
- School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney, Moore Park, Sydney, NSW 2021, Australia
- Sydney Swans Football Club, Moore Park, Sydney, NSW 2021, Australia
| | - Mark L. Watsford
- School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney, Moore Park, Sydney, NSW 2021, Australia
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