Influence of standing position on mechanical and energy costs in uphill cycling.

This study was designed to examine the influence of standing position (vs. seated) during uphill cycling on both mechanical cost (MC) and energy cost (EC) in elite cyclists. For the study, thirteen elite cyclists (VO2max: 71.4 ±â€¯8.0 ml·min-1·kg-1) performed, in a randomised order, three sets of exercises. Each set comprised 2 min of exercise, alternating every 30 s between seated and standing postures, using different slopes and intensity levels on a motorised treadmill. MC was calculated from the measurement of power output and speed, whereas EC was calculated from the measurement of oxygen consumption and speed. MC was significantly higher (+4.3%, p < 0.001) in standing position compared to seated position when all slopes and intensities were considered. However, EC was not significantly affected by the change in position. The standing position also induced a significant increase in rolling resistance power (p < 0.001), rolling resistance coefficient (p < 0.001) and lateral sways (p < 0.001). The significant increase in MC observed in standing position was due to a higher rolling resistance induced by bicycle sways and a shift forward of the centre of mass compared to seated position. This result should lead bicycle tire manufacturers to reduce the increase in rolling resistance between the two positions. Considering the relationship observed between the MC and bicycle sways, cyclists would be well advised to decrease the bicycle sways in order to reduce the MC of locomotion.

A Study about Kalman Filters Applied to Embedded Sensors.

Over the last decade, smart sensors have grown in complexity and can now handle multiple measurement sources. This work establishes a methodology to achieve better estimates of physical values by processing raw measurements within a sensor using multi-physical models and Kalman filters for data fusion. A driving constraint being production cost and power consumption, this methodology focuses on algorithmic complexity while meeting real-time constraints and improving both precision and reliability despite low power processors limitations. Consequently, processing time available for other tasks is maximized. The known problem of estimating a 2D orientation using an inertial measurement unit with automatic gyroscope bias compensation will be used to illustrate the proposed methodology applied to a low power STM32L053 microcontroller. This application shows promising results with a processing time of 1.18 ms at 32 MHz with a 3.8% CPU usage due to the computation at a 26 Hz measurement and estimation rate.