In this work a dynamic control allocation approach is presented for an automotive vehicle yaw stabilization scheme. The stabilization strategy consists of a high level module that deals with the vehicle motion control objective (yaw rate reference generation and tracking), a low level module that handles the braking control for each wheel (longitudinal slip control and maximal tire-road friction parameter estimation), and an intermediate level dynamic control allocation module that generates the longitudinal slip reference for the low level brake control module and commands front wheel steering angle corrections. The control allocation design is such that the actual torque about the yaw axis tends to the desired torque calculated form the high level module, with desirable distribution of control forces satisfying actuator constraints and minimal control effort objectives. Conditions for uniform asymptotic stability are given for the case when the control allocation includes adaptation of the tire-road maximal friction coefficients, and the scheme has been implemented in a realistic non linear multi body vehicle simulation environment. The simulation cases show that the yaw control allocation strategy stabilizes the vehicle in extreme maneuvers where the non linear vehicle yaw dynamics otherwise (without active braking or active steering) becomes unstable in the sense of over- or under steering. The control allocation implementation is efficient and suitable for low cost automotive electronic control units.

The automatic classification of sub-techniques in classical cross-country skiing provides unique possibilities for analyzing the biomechanical aspects of outdoor skiing. This is currently possible due to the miniaturization and flexibility of wearable inertial measurement units (IMUs) that allow researchers to bring the laboratory to the field. In this study, we aimed to optimize the accuracy of the automatic classification of classical cross-country skiing sub-techniques by using two IMUs attached to the skier’s arm and chest together with a machine learning algorithm. The novelty of our approach is the reliable detection of individual cycles using a gyroscope on the skier’s arm, while a neural network machine learning algorithm robustly classifies each cycle to a sub-technique using sensor data from an accelerometer on the chest. In this study, 24 datasets from 10 different participants were separated into the categories training-, validation- and test-data. Overall, we achieved a classification accuracy of 93.9% on the test-data. Furthermore, we illustrate how an accurate classification of sub-techniques can be combined with data from standard sports equipment including position, altitude, speed and heart rate measuring systems. Combining this information has the potential to provide novel insight into physiological and biomechanical aspects valuable to coaches, athletes and researchers.

We investigated sex-based differences in speed, sub-technique selection, and kinematic patterns during low- (LIT) and high-intensity training (HIT) for classical cross-country (XC) skiing across varying terrain. Six male and six female elite XC skiers with an approximately 15% differences in VO_2max (men: 68.9±2.9 mL·min^-1·kg^-1, women: 60.1±3.3 mL·min^-1·kg^-1) were monitored using a multi-sensor system to collect time-synchronised data of heart rate, speed, and multiple tri-axial inertial measurements units while XC skiing on a 5-km competition track. Men skied 21% faster than women during HIT (5.9±0.3 m·s^-1 vs. 4.9±0.2 m·s^-1, P < .001), with the greatest difference (26%) while skiing on flat terrain, whereas skiing speed did not significantly differ between men and women during LIT. At similar instructed intensity and rating of perceived effort, women exhibited significantly higher relative heart rate (85±2% vs. 71±3% of maximum) and blood lactate levels (4.0±1.3 vs. 1.2±0.2 mmol/L) during LIT (all P < .001) than men, whereas physiological responses did generally not differ between the sexes during HIT. During both intensities and among both sexes, double poling (DP) was the sub-technique most used relative to distance, followed by miscellaneous sub-techniques (MISC), diagonal stride (DIA), kick double poling (DK) and herringbone (HRB). In relation to distance women used DIA more than men during LIT (22% vs. 17%, P = .009) and HIT (23% vs. 12%, P = .001), whereas men used MISC, including tucking and turning, more than women during LIT (39% vs. 25%, P = .017) and HIT (41% vs. 30%, P = .064). In particular, men used DP more than women while skiing the uphill sections during both LIT (24% vs. 11%, P = .015) and HIT (39% vs. 13%, P = .002). Our findings provide novel insights into sex-based differences in speed, sub-technique selection, and kinematic patterns during LIT and HIT for classical skiing.