Using micro-sensor data to quantify macro kinematics of classical cross-country skiing during on-snow training |
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| Authors: | Finn Marsland Colin Mackintosh Judith Anson Keith Lyons Gordon Waddington Dale W Chapman |
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| Institution: | 1. UC Research Institute for Sport and Exercise, University of Canberra, Canberra, Australia;2. Australian Institute of Sport, Canberra, Australiau3058169@uni.canberra.edu.au;4. Australian Institute of Sport, Canberra, Australia;5. Australian Institute of Sport, Canberra, Australia |
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| Abstract: | AbstractMicro-sensors were used to quantify macro kinematics of classical cross-country skiing techniques and measure cycle rates and cycle lengths during on-snow training. Data were collected from seven national level participants skiing at two submaximal intensities while wearing a micro-sensor unit (MinimaxX?). Algorithms were developed identifying double poling (DP), diagonal striding (DS), kick-double poling (KDP), tucking (Tuck), and turning (Turn). Technique duration (T-time), cycle rates, and cycle counts were compared to video-derived data to assess system accuracy. There was good reliability between micro-sensor and video calculated cycle rates for DP, DS, and KDP, with small mean differences (Mdiff% = ?0.2 ± 3.2, ?1.5 ± 2.2 and ?1.4 ± 6.2) and trivial to small effect sizes (ES = 0.20, 0.30 and 0.13). Very strong correlations were observed for DP, DS, and KDP for T-time (r = 0.87–0.99) and cycle count (r = 0.87–0.99), while mean values were under-reported by the micro-sensor. Incorrect Turn detection was a major factor in technique cycle misclassification. Data presented highlight the potential of automated ski technique classification in cross-country skiing research. With further refinement, this approach will allow many applied questions associated with pacing, fatigue, technique selection and power output during training and competition to be answered. |
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| Keywords: | Accelerometers performance analysis cycle rates cycle lengths technique detection |
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