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New jump trajectory determination method using low-cost MEMS sensor fusion and augmented observations for GPS/INS integration

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Abstract

Objective information on athletic maneuvers for performance evaluation has become highly desired in sports such as skiing, snowboarding, and mountain biking. Body-mounted devices, incorporating low-cost microelectromechanical, inertial navigation units, and global positioning system (GPS) receivers, to calculate sport-specific key performance variables (KPVs) and provide real-time feedback, are now commercially available. However, algorithms implemented for such purposes still lack accuracy and power efficiency. A new GPS/INS (inertial navigation system) integration algorithm is proposed to determine the trajectory of an athlete executing jumps while skiing, snowboarding, mountain biking etc. KPVs, such as jump horizontal distance, vertical height, and drop, are calculated from the trajectory. A new sensor error compensation scheme is developed using sensor fusion and linear Kalman filters (LKF). The LKF parameters are varied to address the fluctuating dynamics of the athlete during a jump. The extended Kalman filter used for GPS/INS integration has an observation vector augmented with sensor error measurements derived from sensor fusion. The performance of the proposed algorithm is evaluated through experimental field tests. For the determination of jump horizontal distance, height, and drop, the proposed algorithm has errors of 14.3 cm (5.5 %), 1.6 cm (38 %), and 6.7 cm (9.4 %), respectively. Errors in KPVs for a set of jumps were first determined with respect to the true KPVs, and then the errors for all the jumps were averaged to calculate the absolute and percentage errors. The accuracy achieved is deemed to fulfill the expectations of both recreational and professional athletes.

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Authors and Affiliations

  1. EME3213, The University of British Columbia, 3333 University Way, Kelowna, BC, V1V 1V7, Canada

    Fazle Sadi

  2. EME4261, The University of British Columbia, 3333 University Way, Kelowna, BC, V1V 1V7, Canada

    Richard Klukas

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  1. Fazle Sadi
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  2. Richard Klukas
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Correspondence to Fazle Sadi.

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Sadi, F., Klukas, R. New jump trajectory determination method using low-cost MEMS sensor fusion and augmented observations for GPS/INS integration. GPS Solut 17, 139–152 (2013). https://doi.org/10.1007/s10291-012-0267-7

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  • DOI: https://doi.org/10.1007/s10291-012-0267-7

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