Feature extraction of performance variables in elite half-pipe snowboarding using body mounted inertial sensors
Author(s) -
Jeffrey Harding,
J. W. Small,
Daniel James
Publication year - 2007
Publication title -
proceedings of spie, the international society for optical engineering/proceedings of spie
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
eISSN - 1996-756X
pISSN - 0277-786X
DOI - 10.1117/12.759259
Subject(s) - computer science , feature extraction , inertial frame of reference , inertial measurement unit , accelerometer , extraction (chemistry) , feature (linguistics) , acceleration , artificial intelligence , computer vision , physics , linguistics , philosophy , quantum mechanics , operating system , chemistry , chromatography , classical mechanics
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 ᠰ.08; p value < 0.0001; SEE = 0.08 ׯ砱.16; mean bias = -0.03 ᠰ.02s) (value ᠯr ׯ砹5% CL).Griffith Business School, Department of Tourism, Sport and Hotel ManagementFull Tex
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