IMU Airtime Detection in Snowboard Halfpipe: U-Net Deep Learning Approach Outperforms Traditional Threshold Algorithms
Airtime is crucial for high-rotation tricks in snowboard halfpipe performance, significantly impacting trick difficulty, the primary judging criterion. This study aims to enhance the detection of take-off and landing events using inertial measurement unit (IMU) data in conjunction with machine learn...
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2024-10-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/21/6773 |
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| author | Tom Gorges Padraig Davidson Myriam Boeschen Andreas Hotho Christian Merz |
| author_facet | Tom Gorges Padraig Davidson Myriam Boeschen Andreas Hotho Christian Merz |
| author_sort | Tom Gorges |
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| description | Airtime is crucial for high-rotation tricks in snowboard halfpipe performance, significantly impacting trick difficulty, the primary judging criterion. This study aims to enhance the detection of take-off and landing events using inertial measurement unit (IMU) data in conjunction with machine learning algorithms since manual video-based methods are too time-consuming. Eight elite German National Team snowboarders performed 626 halfpipe tricks, recorded by two IMUs at the lateral lower legs and a video camera. The IMU data, synchronized with video, were labeled manually and segmented for analysis. Utilizing a 1D U-Net convolutional neural network (CNN), we achieved superior performance in all of our experiments, establishing new benchmarks for this binary segmentation task. In our extensive experiments, we achieved an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>80.34</mn></mrow></semantics></math></inline-formula>% lower mean Hausdorff distance for unseen runs compared with the threshold approach when placed solely on the left lower leg. Using both left and right IMUs further improved performance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>83.37</mn></mrow></semantics></math></inline-formula>% lower mean Hausdorff). For data from an algorithm-unknown athlete (Zero-Shot segmentation), the U-Net outperformed the threshold algorithm by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>67.58</mn></mrow></semantics></math></inline-formula>%, and fine-tuning on athlete-specific (Few-Shot segmentation) runs improved the lower mean Hausdorff to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>78.68</mn></mrow></semantics></math></inline-formula>%. The fine-tuned model detected takeoffs with median deviations of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.008</mn></mrow></semantics></math></inline-formula> s (IQR <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.030</mn></mrow></semantics></math></inline-formula> s), landing deviations of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.005</mn></mrow></semantics></math></inline-formula> s (IQR 0.020 s), and airtime deviations of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.000</mn></mrow></semantics></math></inline-formula> s (IQR <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.027</mn></mrow></semantics></math></inline-formula> s). These advancements facilitate real-time feedback and detailed biomechanical analysis, enhancing performance and trick execution, particularly during critical events, such as take-off and landing, where precise time-domain localization is crucial for providing accurate feedback to coaches and athletes. |
| format | Article |
| id | doaj-art-e08b61d13df340aa9715fa8a40b9d6a4 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e08b61d13df340aa9715fa8a40b9d6a42024-11-08T14:40:53ZengMDPI AGSensors1424-82202024-10-012421677310.3390/s24216773IMU Airtime Detection in Snowboard Halfpipe: U-Net Deep Learning Approach Outperforms Traditional Threshold AlgorithmsTom Gorges0Padraig Davidson1Myriam Boeschen2Andreas Hotho3Christian Merz4Research Group Snowboard, Department Strength, Power and Technical Sports, Institute for Applied Training Science, 04109 Leipzig, GermanyChair for Data Science, Center for Artificial Intelligence and Data Science, University of Würzburg, 97074 Wuerzburg, GermanyResearch Group Snowboard, Department Strength, Power and Technical Sports, Institute for Applied Training Science, 04109 Leipzig, GermanyChair for Data Science, Center for Artificial Intelligence and Data Science, University of Würzburg, 97074 Wuerzburg, GermanyResearch Group Snowboard, Department Strength, Power and Technical Sports, Institute for Applied Training Science, 04109 Leipzig, GermanyAirtime is crucial for high-rotation tricks in snowboard halfpipe performance, significantly impacting trick difficulty, the primary judging criterion. This study aims to enhance the detection of take-off and landing events using inertial measurement unit (IMU) data in conjunction with machine learning algorithms since manual video-based methods are too time-consuming. Eight elite German National Team snowboarders performed 626 halfpipe tricks, recorded by two IMUs at the lateral lower legs and a video camera. The IMU data, synchronized with video, were labeled manually and segmented for analysis. Utilizing a 1D U-Net convolutional neural network (CNN), we achieved superior performance in all of our experiments, establishing new benchmarks for this binary segmentation task. In our extensive experiments, we achieved an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>80.34</mn></mrow></semantics></math></inline-formula>% lower mean Hausdorff distance for unseen runs compared with the threshold approach when placed solely on the left lower leg. Using both left and right IMUs further improved performance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>83.37</mn></mrow></semantics></math></inline-formula>% lower mean Hausdorff). For data from an algorithm-unknown athlete (Zero-Shot segmentation), the U-Net outperformed the threshold algorithm by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>67.58</mn></mrow></semantics></math></inline-formula>%, and fine-tuning on athlete-specific (Few-Shot segmentation) runs improved the lower mean Hausdorff to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>78.68</mn></mrow></semantics></math></inline-formula>%. The fine-tuned model detected takeoffs with median deviations of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.008</mn></mrow></semantics></math></inline-formula> s (IQR <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.030</mn></mrow></semantics></math></inline-formula> s), landing deviations of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.005</mn></mrow></semantics></math></inline-formula> s (IQR 0.020 s), and airtime deviations of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.000</mn></mrow></semantics></math></inline-formula> s (IQR <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.027</mn></mrow></semantics></math></inline-formula> s). These advancements facilitate real-time feedback and detailed biomechanical analysis, enhancing performance and trick execution, particularly during critical events, such as take-off and landing, where precise time-domain localization is crucial for providing accurate feedback to coaches and athletes.https://www.mdpi.com/1424-8220/24/21/6773event detectionfreestyle sportsbinary segmentationairtimeconvolutional neural networkelite athletes |
| spellingShingle | Tom Gorges Padraig Davidson Myriam Boeschen Andreas Hotho Christian Merz IMU Airtime Detection in Snowboard Halfpipe: U-Net Deep Learning Approach Outperforms Traditional Threshold Algorithms Sensors event detection freestyle sports binary segmentation airtime convolutional neural network elite athletes |
| title | IMU Airtime Detection in Snowboard Halfpipe: U-Net Deep Learning Approach Outperforms Traditional Threshold Algorithms |
| title_full | IMU Airtime Detection in Snowboard Halfpipe: U-Net Deep Learning Approach Outperforms Traditional Threshold Algorithms |
| title_fullStr | IMU Airtime Detection in Snowboard Halfpipe: U-Net Deep Learning Approach Outperforms Traditional Threshold Algorithms |
| title_full_unstemmed | IMU Airtime Detection in Snowboard Halfpipe: U-Net Deep Learning Approach Outperforms Traditional Threshold Algorithms |
| title_short | IMU Airtime Detection in Snowboard Halfpipe: U-Net Deep Learning Approach Outperforms Traditional Threshold Algorithms |
| title_sort | imu airtime detection in snowboard halfpipe u net deep learning approach outperforms traditional threshold algorithms |
| topic | event detection freestyle sports binary segmentation airtime convolutional neural network elite athletes |
| url | https://www.mdpi.com/1424-8220/24/21/6773 |
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