INTRODUCTION: In order to obtain valuable and accurate skiing performance characteristics (such as edge angle, symmetry, or turn length/duration), it is imperative to accurately define the moment when each turn begins and ends; this moment is called turn switch (TS) point. The comparative performance of the different methods and their usability is thus far unknown. Consequently, the main goal of this work was to provide a tool and guidelines to facilitate the selection of appropriate and accurate TS detections for use during alpine skiing. METHODS: Data was collected from 14 expert skiers on an indoor ski-treadmill. Each participant was equipped with instrumented boots, pressure insoles (PI) and portable force plates, six IMUs, an EMG sensor, and a full body marker-set. Participants performed two 30 s trials, high and low dynamic turns. Time differences between TS detected from each methodology and the reference [1] were used to assess the accuracy and precision of the detected TS. The mean and standard deviation (SD) of all participant medians were used to evaluate the accuracy of each method. The precision was calculated as the range between the mean upper and lower confidence interval (percentiles 2.5 and 97.5) across all participants per method and turn size. RESULTS: Ten methodologies showed an accuracy better than 0.05 s. However, the standard deviation showed different levels of variability, ranging from less than ± 0.04 s to almost ± 0.20 s. The results show that the best performing method is based on 2D video edge change 2; closely followed my methods using IMUs 3 and PIs 4. DISCUSSION/CONCLUSION: Clear recommendations can be supported following the insights gained from the comprehensive methodological in the current comparison. Firstly, although the method that performed best overall was based on 2D video 2, its functionality is limited due to the impractical camera placements and the manual and subjective nature of the TS selection. Among the methodologies with the potential to identify TS in quasi-/real-time, the use of position data is the least accurate, choosing an IMU or PI based method would elicit a higher performance. Because skiing measurements typically occur with unique measurement scenarios, limited sensor availability, and diverse technological abilities, the timeline provides comprehensive insights into the ideal selection of TS detection methodologies and sensors.
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