Accurate attitude for wearable devices and smartphones is needed for many applications. The major challenge is to cope with the acceleration resulting from the user or smartphone dynamics. To that end, a two-stage adaptive complementary filter for attitude estimation is proposed. Upon identifying the smartphone location on the user using a deep learning approach, the accelerometers weights in each axis are adjusted according to an optimized gain map. To evaluate the benefits of the proposed approach it is compared to commonly used algorithms both in simulation and experiments.

中文翻译：

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智能手机分类的行人导航姿态自适应估计

对于许多应用程序，需要可穿戴设备和智能手机的准确姿势。主要的挑战是应对由用户或智能手机动态产生的加速。为此，提出了一种用于姿态估计的两阶段自适应互补滤波器。在使用深度学习方法识别智能手机在用户身上的位置后，将根据优化的增益图调整每个轴上的加速度计权重。为了评估该方法的优势，将其与仿真和实验中常用的算法进行了比较。