Recent analysis of elite-level half-pipe snowboard competition has revealed a number of sport specific key performance
variables (KPV's) that correlate well to score. Information on these variables is difficult to acquire and analyse, relying
on collection and labour intensive manual post processing of video data. This paper presents the use of inertial sensors
as a user-friendly alternative and subsequently implements signal processing routines to ultimately provide automated,
sport specific feedback to coaches and athletes. The author has recently shown that the key performance variables
(KPV's) of total air-time (TAT) and average degree of rotation (ADR) achieved during elite half-pipe snowboarding
competition show strong correlation with an athlete's subjectively judged score. Utilising Micro-Electrochemical
System (MEMS) sensors (tri-axial accelerometers) this paper demonstrates that air-time (AT) achieved during half-pipe
snowboarding can be detected and calculated accurately using basic signal processing techniques. Characterisation of the
variations in aerial acrobatic manoeuvres and the associated calculation of exact degree of rotation (DR) achieved is a
likely extension of this research. The technique developed used a two-pass method to detect locations of half-pipe
snowboard runs using power density in the frequency domain and subsequently utilises a threshold based search
algorithm in the time domain to calculate air-times associated with individual aerial acrobatic manoeuvres. This
technique correctly identified the air-times of 100 percent of aerial acrobatic manoeuvres within each half-pipe
snowboarding run (n = 92 aerial acrobatic manoeuvres from 4 subjects) and displayed a very strong correlation with a
video based reference standard for air-time calculation (r = 0.78 ± 0.08; p value < 0.0001; SEE = 0.08 ×/÷ 1.16; mean
bias = -0.03 ± 0.02s) (value ± or ×/÷ 95% CL).
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