In this article, the results of a piloted flight simulator campaign aimed at measuring biomechanical performance indicators of a helicopter pilot performing complex, realistic tasks are presented. The upper limbs' motion and the activation of the main muscle groups of the left arm were measured during ship-deck landings, performed flying several helicopter configurations with sea conditions of variable intensity. The analysis of the results shows an increase in the muscle activity relative to the increase in task difficulty, in agreement with subjective ratings (Bedford workload scale). The study provides useful indications to improve the corresponding biomechanical simulations, as well as to characterize pilot's performance during specific tasks.

Supervised classification techniques use training samples to find classification rules with small expected 0-1 loss. Conventional methods achieve efficient learning and out-of-sample generalization by minimizing surrogate losses over specific families of rules. This paper presents minimax risk classifiers (MRCs) that do not rely on a choice of surrogate loss and family of rules. MRCs achieve efficient learning and out-of-sample generalization by minimizing worst-case expected 0-1 loss w.r.t. uncertainty sets that are defined by linear constraints and include the true underlying distribution. In addition, MRCs' learning stage provides performance guarantees as lower and upper tight bounds for expected 0-1 loss. We also present MRCs' finite-sample generalization bounds in terms of training size and smallest minimax risk, and show their competitive classification performance w.r.t. state-of-the-art techniques using benchmark datasets.