Automatic Recognition of Motor Skills in Triathlon: A Novel Tool for Measuring Movement Cadence and Cycling Tasks
<b>Background/Objectives</b>: The purpose of this research was to create a peak detection algorithm and machine learning model for use in triathlon. The algorithm and model aimed to automatically measure movement cadence in all three disciplines of a triathlon using data from a single in...
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MDPI AG
2024-12-01
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| Series: | Journal of Functional Morphology and Kinesiology |
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| Online Access: | https://www.mdpi.com/2411-5142/9/4/269 |
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| author | Stuart M. Chesher Carlo Martinotti Dale W. Chapman Simon M. Rosalie Paula C. Charlton Kevin J. Netto |
| author_facet | Stuart M. Chesher Carlo Martinotti Dale W. Chapman Simon M. Rosalie Paula C. Charlton Kevin J. Netto |
| author_sort | Stuart M. Chesher |
| collection | DOAJ |
| description | <b>Background/Objectives</b>: The purpose of this research was to create a peak detection algorithm and machine learning model for use in triathlon. The algorithm and model aimed to automatically measure movement cadence in all three disciplines of a triathlon using data from a single inertial measurement unit and to recognise the occurrence and duration of cycling task changes. <b>Methods</b>: Six triathletes were recruited to participate in a triathlon while wearing a single trunk-mounted measurement unit and were filmed throughout. Following an initial analysis, a further six triathletes were recruited to collect additional cycling data to train the machine learning model to more effectively recognise cycling task changes. <b>Results</b>: The peak-counting algorithm successfully detected 98.7% of swimming strokes, with a root mean square error of 2.7 swimming strokes. It detected 97.8% of cycling pedal strokes with a root mean square error of 9.1 pedal strokes, and 99.4% of running strides with a root mean square error of 1.2 running strides. Additionally, the machine learning model was 94% (±5%) accurate at distinguishing between ‘in-saddle’ and ‘out-of-saddle’ riding, but it was unable to distinguish between ‘in-saddle’ riding and ‘coasting’ based on tri-axial acceleration and angular velocity. However, it displayed poor sensitivity to detect ‘out-of-saddle’ efforts in uncontrolled conditions which improved when conditions were further controlled. <b>Conclusions</b>: A custom peak detection algorithm and machine learning model are effective tools to automatically analyse triathlon performance. |
| format | Article |
| id | doaj-art-33b5ad9604ae4e02a5274c0e3869c7f6 |
| institution | DOAJ |
| issn | 2411-5142 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Journal of Functional Morphology and Kinesiology |
| spelling | doaj-art-33b5ad9604ae4e02a5274c0e3869c7f62025-08-20T02:53:30ZengMDPI AGJournal of Functional Morphology and Kinesiology2411-51422024-12-019426910.3390/jfmk9040269Automatic Recognition of Motor Skills in Triathlon: A Novel Tool for Measuring Movement Cadence and Cycling TasksStuart M. Chesher0Carlo Martinotti1Dale W. Chapman2Simon M. Rosalie3Paula C. Charlton4Kevin J. Netto5Curtin School of Allied Health, Curtin University, Kent Street, Bentley, Perth, WA 6102, AustraliaCurtin Institute for Computation, Curtin University, Kent Street, Bentley, Perth, WA 6102, AustraliaCurtin School of Allied Health, Curtin University, Kent Street, Bentley, Perth, WA 6102, AustraliaSR Performance, Gairloch Drive, Frankston, Melbourne, VIC 3199, AustraliaAustralian Institute of Sport, Leverrier Street, Bruce, Canberra, ACT 2617, AustraliaCurtin School of Allied Health, Curtin University, Kent Street, Bentley, Perth, WA 6102, Australia<b>Background/Objectives</b>: The purpose of this research was to create a peak detection algorithm and machine learning model for use in triathlon. The algorithm and model aimed to automatically measure movement cadence in all three disciplines of a triathlon using data from a single inertial measurement unit and to recognise the occurrence and duration of cycling task changes. <b>Methods</b>: Six triathletes were recruited to participate in a triathlon while wearing a single trunk-mounted measurement unit and were filmed throughout. Following an initial analysis, a further six triathletes were recruited to collect additional cycling data to train the machine learning model to more effectively recognise cycling task changes. <b>Results</b>: The peak-counting algorithm successfully detected 98.7% of swimming strokes, with a root mean square error of 2.7 swimming strokes. It detected 97.8% of cycling pedal strokes with a root mean square error of 9.1 pedal strokes, and 99.4% of running strides with a root mean square error of 1.2 running strides. Additionally, the machine learning model was 94% (±5%) accurate at distinguishing between ‘in-saddle’ and ‘out-of-saddle’ riding, but it was unable to distinguish between ‘in-saddle’ riding and ‘coasting’ based on tri-axial acceleration and angular velocity. However, it displayed poor sensitivity to detect ‘out-of-saddle’ efforts in uncontrolled conditions which improved when conditions were further controlled. <b>Conclusions</b>: A custom peak detection algorithm and machine learning model are effective tools to automatically analyse triathlon performance.https://www.mdpi.com/2411-5142/9/4/269machine learningpeak detectioncycling taskcadencemotor performanceinertial measurement |
| spellingShingle | Stuart M. Chesher Carlo Martinotti Dale W. Chapman Simon M. Rosalie Paula C. Charlton Kevin J. Netto Automatic Recognition of Motor Skills in Triathlon: A Novel Tool for Measuring Movement Cadence and Cycling Tasks Journal of Functional Morphology and Kinesiology machine learning peak detection cycling task cadence motor performance inertial measurement |
| title | Automatic Recognition of Motor Skills in Triathlon: A Novel Tool for Measuring Movement Cadence and Cycling Tasks |
| title_full | Automatic Recognition of Motor Skills in Triathlon: A Novel Tool for Measuring Movement Cadence and Cycling Tasks |
| title_fullStr | Automatic Recognition of Motor Skills in Triathlon: A Novel Tool for Measuring Movement Cadence and Cycling Tasks |
| title_full_unstemmed | Automatic Recognition of Motor Skills in Triathlon: A Novel Tool for Measuring Movement Cadence and Cycling Tasks |
| title_short | Automatic Recognition of Motor Skills in Triathlon: A Novel Tool for Measuring Movement Cadence and Cycling Tasks |
| title_sort | automatic recognition of motor skills in triathlon a novel tool for measuring movement cadence and cycling tasks |
| topic | machine learning peak detection cycling task cadence motor performance inertial measurement |
| url | https://www.mdpi.com/2411-5142/9/4/269 |
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