Our work focuses on metric learning between gesture sample signatures using Siamese Neural Networks (SNN), which aims at model-ing semantic relations between classes to extract discriminative features. Our contribution is the notion of polar sine which enables a redefini-tion of the angular problem. Our final proposal improves inertial gesture classification in two challenging test scenarios, with respective average classification rates of 0.934 ± 0.011 and 0.776 ± 0.025.