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Slopecki M, Charbonneau M, Lavallière JM, Côté JN, Clément J. Validation of Automatically Quantified Swim Stroke Mechanics Using an Inertial Measurement Unit in Paralympic Athletes. Bioengineering (Basel) 2023; 11:15. [PMID: 38247892 PMCID: PMC10813451 DOI: 10.3390/bioengineering11010015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 12/14/2023] [Accepted: 12/20/2023] [Indexed: 01/23/2024] Open
Abstract
Biomechanics and training load monitoring are important for performance evaluation and injury prevention in elite swimming. Monitoring of performance and swim stroke parameters is possible with inertial measurement units (IMU) but has not been validated in para-swimmers. The purpose of this study was to validate a single IMU-based system to accurately estimate pool-swam lap time, stroke count (SC), stroke duration, instantaneous stroke rate (ISR), and distance per stroke (DPS). Eight Paralympic athletes completed 4 × 50 m swims with an IMU worn on the sacrum. Strokes cycles were identified using a zero-crossing algorithm on the medio-lateral (freestyle and backstroke) or forward-backward (butterfly and breaststroke) instantaneous velocity data. Video-derived metrics were estimated using Dartfish and Kinovea. Agreement analyses, including Bland-Altman and Intraclass Correlation Coefficient (ICC), were performed on all outcome variables. SC Bland-Altman bias was 0.13 strokes, and ICC was 0.97. ISR Bland-Altman biases were within 1.5 strokes/min, and ICCs ranged from 0.26 to 0.96. DPS Bland-Altman biases were within 0.20 m, and ICCs ranged from 0.39 to 0.93. A single-IMU system can provide highly valid performance and swim stroke monitoring data for elite para-swimmers for the majority of strokes, with the exception of backstroke. Future work should improve bilateral stroke detection algorithms in this population.
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Affiliation(s)
- Matthew Slopecki
- Department of Kinesiology and Physical Education, McGill University, Montréal, QC H2W 1S4, Canada; (J.N.C.); (J.C.)
- Institut National du Sport du Québec, Montréal, QC H1V 3N7, Canada;
| | | | | | - Julie N. Côté
- Department of Kinesiology and Physical Education, McGill University, Montréal, QC H2W 1S4, Canada; (J.N.C.); (J.C.)
| | - Julien Clément
- Department of Kinesiology and Physical Education, McGill University, Montréal, QC H2W 1S4, Canada; (J.N.C.); (J.C.)
- Institut National du Sport du Québec, Montréal, QC H1V 3N7, Canada;
- École de Technologie Supérieure, Montréal, QC H3C 1K3, Canada
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Delhaye E, Bouvet A, Nicolas G, Vilas-Boas JP, Bideau B, Bideau N. Automatic Swimming Activity Recognition and Lap Time Assessment Based on a Single IMU: A Deep Learning Approach. SENSORS 2022; 22:s22155786. [PMID: 35957347 PMCID: PMC9371205 DOI: 10.3390/s22155786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 07/27/2022] [Accepted: 07/29/2022] [Indexed: 12/10/2022]
Abstract
This study presents a deep learning model devoted to the analysis of swimming using a single Inertial Measurement Unit (IMU) attached to the sacrum. Gyroscope and accelerometer data were collected from 35 swimmers with various expertise levels during a protocol including the four swimming techniques. The proposed methodology took high inter- and intra-swimmer variability into account and was set up for the purpose of predicting eight swimming classes (the four swimming techniques, rest, wallpush, underwater, and turns) at four swimming velocities ranging from low to maximal. The overall F1-score of classification reached 0.96 with a temporal precision of 0.02 s. Lap times were directly computed from the classifier thanks to a high temporal precision and validated against a video gold standard. The mean absolute percentage error (MAPE) for this model against the video was 1.15%, 1%, and 4.07%, respectively, for starting lap times, middle lap times, and ending lap times. This model is a first step toward a powerful training assistant able to analyze swimmers with various levels of expertise in the context of in situ training monitoring.
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Affiliation(s)
- Erwan Delhaye
- M2S Laboratory (Movement, Sports & Health), University Rennes 2, ENS Rennes, 35170 Bruz, France; (A.B.); (G.N.); (B.B.); (N.B.)
- MIMETIC-Analysis-Synthesis Approach for Virtual Human Simulation, INRIA Rennes Bretagne Atlantique, Campus de Beaulieu, 263 Av. Général Leclerc, 35042 Rennes, France
- Correspondence:
| | - Antoine Bouvet
- M2S Laboratory (Movement, Sports & Health), University Rennes 2, ENS Rennes, 35170 Bruz, France; (A.B.); (G.N.); (B.B.); (N.B.)
- MIMETIC-Analysis-Synthesis Approach for Virtual Human Simulation, INRIA Rennes Bretagne Atlantique, Campus de Beaulieu, 263 Av. Général Leclerc, 35042 Rennes, France
| | - Guillaume Nicolas
- M2S Laboratory (Movement, Sports & Health), University Rennes 2, ENS Rennes, 35170 Bruz, France; (A.B.); (G.N.); (B.B.); (N.B.)
- MIMETIC-Analysis-Synthesis Approach for Virtual Human Simulation, INRIA Rennes Bretagne Atlantique, Campus de Beaulieu, 263 Av. Général Leclerc, 35042 Rennes, France
| | - João Paulo Vilas-Boas
- LABIOMEP Laboratory (Porto Biomechanics Laboratory), Faculty of Sport, CIFI2D, University of Porto, 4200-450 Porto, Portugal;
| | - Benoît Bideau
- M2S Laboratory (Movement, Sports & Health), University Rennes 2, ENS Rennes, 35170 Bruz, France; (A.B.); (G.N.); (B.B.); (N.B.)
- MIMETIC-Analysis-Synthesis Approach for Virtual Human Simulation, INRIA Rennes Bretagne Atlantique, Campus de Beaulieu, 263 Av. Général Leclerc, 35042 Rennes, France
| | - Nicolas Bideau
- M2S Laboratory (Movement, Sports & Health), University Rennes 2, ENS Rennes, 35170 Bruz, France; (A.B.); (G.N.); (B.B.); (N.B.)
- MIMETIC-Analysis-Synthesis Approach for Virtual Human Simulation, INRIA Rennes Bretagne Atlantique, Campus de Beaulieu, 263 Av. Général Leclerc, 35042 Rennes, France
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Morais JE, Oliveira JP, Sampaio T, Barbosa TM. Wearables in Swimming for Real-Time Feedback: A Systematic Review. SENSORS 2022; 22:s22103677. [PMID: 35632086 PMCID: PMC9147718 DOI: 10.3390/s22103677] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/06/2022] [Accepted: 05/09/2022] [Indexed: 01/17/2023]
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.
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Affiliation(s)
- Jorge E. Morais
- Department of Sport Sciences, Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal; (J.E.M.); (J.P.O.); (T.S.)
- Research Centre in Sports, Health, and Human Development (CIDESD), University of Beira Interior, 6201-001 Covilhã, Portugal
| | - João P. Oliveira
- Department of Sport Sciences, Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal; (J.E.M.); (J.P.O.); (T.S.)
- Research Centre in Sports, Health, and Human Development (CIDESD), University of Beira Interior, 6201-001 Covilhã, Portugal
| | - Tatiana Sampaio
- Department of Sport Sciences, Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal; (J.E.M.); (J.P.O.); (T.S.)
| | - Tiago M. Barbosa
- Department of Sport Sciences, Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal; (J.E.M.); (J.P.O.); (T.S.)
- Research Centre in Sports, Health, and Human Development (CIDESD), University of Beira Interior, 6201-001 Covilhã, Portugal
- Correspondence:
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Hamidi Rad M, Gremeaux V, Dadashi F, Aminian K. A Novel Macro-Micro Approach for Swimming Analysis in Main Swimming Techniques Using IMU Sensors. Front Bioeng Biotechnol 2021; 8:597738. [PMID: 33520955 PMCID: PMC7841373 DOI: 10.3389/fbioe.2020.597738] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 12/10/2020] [Indexed: 11/26/2022] Open
Abstract
Inertial measurement units (IMU) are proven as efficient tools for swimming analysis by overcoming the limits of video-based systems application in aquatic environments. However, coaches still believe in the lack of a reliable and easy-to-use analysis system for swimming. To provide a broad view of swimmers' performance, this paper describes a new macro-micro analysis approach, comprehensive enough to cover a full training session, regardless of the swimming technique. Seventeen national level swimmers (5 females, 12 males, 19.6 ± 2.1 yrs) were equipped with six IMUs and asked to swim 4 × 50 m trials in each swimming technique (i.e., frontcrawl, breaststroke, butterfly, and backstroke) in a 25 m pool, in front of five 2-D cameras (four under water and one over water) for validation. The proposed approach detects swimming bouts, laps, and swimming technique in macro level and swimming phases in micro level on all sensor locations for comparison. Swimming phases are the phases swimmers pass from wall to wall (wall push-off, glide, strokes preparation, swimming, and turn) and micro analysis detects the beginning of each phase. For macro analysis, an overall accuracy range of 0.83–0.98, 0.80–1.00, and 0.83–0.99 were achieved, respectively, for swimming bouts detection, laps detection and swimming technique identification on selected sensor locations, the highest being achieved with sacrum. For micro analysis, we obtained the lowest error mean and standard deviation on sacrum for the beginning of wall-push off, glide and turn (−20 ± 89 ms, 4 ± 100 ms, 23 ± 97 ms, respectively), on shank for the beginning of strokes preparation (0 ± 88 ms) and on wrist for the beginning of swimming (−42 ± 72 ms). Comparing the swimming techniques, sacrum sensor achieves the smallest range of error mean and standard deviation during micro analysis. By using the same macro-micro approach across different swimming techniques, this study shows its efficiency to detect the main events and phases of a training session. Moreover, comparing the results of both macro and micro analyses, sacrum has achieved relatively higher amounts of accuracy and lower mean and standard deviation of error in all swimming techniques.
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Affiliation(s)
- Mahdi Hamidi Rad
- Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Vincent Gremeaux
- Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland.,Sport Medicine Unit, Division of Physical Medicine and Rehabilitation, Swiss Olympic Medical Center, Lausanne University Hospital, Lausanne, Switzerland
| | | | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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Cost-Effective eHealth System Based on a Multi-Sensor System-on-Chip Platform and Data Fusion in Cloud for Sport Activity Monitoring. ELECTRONICS 2018. [DOI: 10.3390/electronics7090183] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
eHealth systems provide medical support to users and contribute to the development of mobile and quality health care. They also provide results on the prevention and follow-up of diseases by monitoring health-status indicators and methodical data gathering in patients. Telematic management of health services by means of the Internet of Things provides immediate support and it is cheaper than conventional physical presence methods. Currently, wireless communications and sensor networks allow a person or group to be monitored remotely. The aim of this paper is to develop and assess a system for monitoring physiological parameters to be applied in different scenarios, such as health or sports. This system is based on a distributed architecture, where physiological data of a person are collected by several sensors; next, a Raspberry Pi joins the information and makes a standardization process; then, these data are sent to the Cloud to be processed. Our Cloud system stores the received data and makes a data fusion process in order to indicate the athlete’s fatigue status at every moment. This system has been tested in collaboration with a small group of voluntary tri-athletes. A network simulation has been performed to plan a monitoring network for a bigger group of athletes. Finally, we have found that this system is useful for medium-term monitoring of the sports activities.
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Guignard B, Rouard A, Chollet D, Seifert L. Behavioral Dynamics in Swimming: The Appropriate Use of Inertial Measurement Units. Front Psychol 2017; 8:383. [PMID: 28352243 PMCID: PMC5348530 DOI: 10.3389/fpsyg.2017.00383] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2016] [Accepted: 02/28/2017] [Indexed: 12/04/2022] Open
Abstract
Motor control in swimming can be analyzed using low- and high-order parameters of behavior. Low-order parameters generally refer to the superficial aspects of movement (i.e., position, velocity, acceleration), whereas high-order parameters capture the dynamics of movement coordination. To assess human aquatic behavior, both types have usually been investigated with multi-camera systems, as they offer high three-dimensional spatial accuracy. Research in ecological dynamics has shown that movement system variability can be viewed as a functional property of skilled performers, helping them adapt their movements to the surrounding constraints. Yet to determine the variability of swimming behavior, a large number of stroke cycles (i.e., inter-cyclic variability) has to be analyzed, which is impossible with camera-based systems as they simply record behaviors over restricted volumes of water. Inertial measurement units (IMUs) were designed to explore the parameters and variability of coordination dynamics. These light, transportable and easy-to-use devices offer new perspectives for swimming research because they can record low- to high-order behavioral parameters over long periods. We first review how the low-order behavioral parameters (i.e., speed, stroke length, stroke rate) of human aquatic locomotion and their variability can be assessed using IMUs. We then review the way high-order parameters are assessed and the adaptive role of movement and coordination variability in swimming. We give special focus to the circumstances in which determining the variability between stroke cycles provides insight into how behavior oscillates between stable and flexible states to functionally respond to environmental and task constraints. The last section of the review is dedicated to practical recommendations for coaches on using IMUs to monitor swimming performance. We therefore highlight the need for rigor in dealing with these sensors appropriately in water. We explain the fundamental and mandatory steps to follow for accurate results with IMUs, from data acquisition (e.g., waterproofing procedures) to interpretation (e.g., drift correction).
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Affiliation(s)
- Brice Guignard
- Centre d'Etudes des Transformations des Activités Physiques et Sportives (CETAPS EA3832), Faculty of Sport Sciences, University of Rouen Normandy, Mont Saint AignanFrance; Laboratoire Interuniversitaire de Biologie de la Motricité (LIBM), Department Sciences and Mountain (SceM), University Savoie Mont Blanc, Le Bourget-du-LacFrance
| | - Annie Rouard
- Laboratoire Interuniversitaire de Biologie de la Motricité (LIBM), Department Sciences and Mountain (SceM), University Savoie Mont Blanc, Le Bourget-du-Lac France
| | - Didier Chollet
- Centre d'Etudes des Transformations des Activités Physiques et Sportives (CETAPS EA3832), Faculty of Sport Sciences, University of Rouen Normandy, Mont Saint AignanFrance; Laboratoire Interuniversitaire de Biologie de la Motricité (LIBM), Department Sciences and Mountain (SceM), University Savoie Mont Blanc, Le Bourget-du-LacFrance
| | - Ludovic Seifert
- Centre d'Etudes des Transformations des Activités Physiques et Sportives (CETAPS EA3832), Faculty of Sport Sciences, University of Rouen Normandy, Mont Saint Aignan France
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Mooney R, Corley G, Godfrey A, Quinlan LR, ÓLaighin G. Inertial Sensor Technology for Elite Swimming Performance Analysis: A Systematic Review. SENSORS 2015; 16:s16010018. [PMID: 26712760 PMCID: PMC4732051 DOI: 10.3390/s16010018] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Revised: 12/01/2015] [Accepted: 12/02/2015] [Indexed: 11/16/2022]
Abstract
Technical evaluation of swimming performance is an essential factor of elite athletic preparation. Novel methods of analysis, incorporating body worn inertial sensors (i.e., Microelectromechanical systems, or MEMS, accelerometers and gyroscopes), have received much attention recently from both research and commercial communities as an alternative to video-based approaches. This technology may allow for improved analysis of stroke mechanics, race performance and energy expenditure, as well as real-time feedback to the coach, potentially enabling more efficient, competitive and quantitative coaching. The aim of this paper is to provide a systematic review of the literature related to the use of inertial sensors for the technical analysis of swimming performance. This paper focuses on providing an evaluation of the accuracy of different feature detection algorithms described in the literature for the analysis of different phases of swimming, specifically starts, turns and free-swimming. The consequences associated with different sensor attachment locations are also considered for both single and multiple sensor configurations. Additional information such as this should help practitioners to select the most appropriate systems and methods for extracting the key performance related parameters that are important to them for analysing their swimmers' performance and may serve to inform both applied and research practices.
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Affiliation(s)
- Robert Mooney
- Electrical & Electronic Engineering, School of Engineering & Informatics, NUI Galway, University Road, Galway, Ireland.
- Bioelectronics Research Cluster, National Centre for Biomedical Engineering Science, NUI Galway, University Road, Galway, Ireland.
| | - Gavin Corley
- Electrical & Electronic Engineering, School of Engineering & Informatics, NUI Galway, University Road, Galway, Ireland.
- Bioelectronics Research Cluster, National Centre for Biomedical Engineering Science, NUI Galway, University Road, Galway, Ireland.
| | - Alan Godfrey
- Institute for Neuroscience, Newcastle University, Newcastle upon Tyne, Tyne and Wear NE1 7RU, UK.
| | - Leo R Quinlan
- Physiology, School of Medicine, NUI Galway, University Road, Galway, Ireland.
- CÚRAM (SFI Centre for Research in Medical Devices), NUI Galway, University Road, Galway, Ireland.
| | - Gearóid ÓLaighin
- Electrical & Electronic Engineering, School of Engineering & Informatics, NUI Galway, University Road, Galway, Ireland.
- Bioelectronics Research Cluster, National Centre for Biomedical Engineering Science, NUI Galway, University Road, Galway, Ireland.
- CÚRAM (SFI Centre for Research in Medical Devices), NUI Galway, University Road, Galway, Ireland.
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Magalhaes FAD, Vannozzi G, Gatta G, Fantozzi S. Wearable inertial sensors in swimming motion analysis: a systematic review. J Sports Sci 2014; 33:732-45. [DOI: 10.1080/02640414.2014.962574] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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