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

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.     

Dynamic implication of biomass energy consumption on economic growth in Sub-Saharan Africa: evidence from panel data analysis

The present article examines the dynamic linkages between biomass energy consumption, capital stock, human capital and economic growth across selected Sub-Saharan African countries based on dynamic heterogeneous panels of a mean group (MG) and pooled mean group (PMG) techniques. The finding based on PMG as the preferred method reveals that biomass energy consumption, capital stock and human capital are statistically significant, which means aforementioned variables have positive significant impact on economic growth in the countries studied. When an alternative panel estimation techniques of panel cointegration, dynamic OLS (DOLS) and fully modified OLS (FMOLS) are applied, the result based on panel cointegration technique reveals that biomass energy consumption, capital stock, human capital and economic growth are cointegrated as null hypothesis of most statistics are rejected at 1 % level of significance. The finding based on FMOLS shows that biomass energy consumption, capital stock and human capital positively influences economic growth at 1 % level and same result is obtained from panel OLS. The result based on DOLS however reveals that biomass energy consumption and capital stock are significant at 1 % on economic growth while human capital is insignificant. Considering its positive effect on economic growth with little or no environmental degradation when compared with fossil fuel uses, consumption of biomass energy is more preferable in these countries therefore is the best option to adopt by the policy makers of Sub-Saharan African countries.