Son Wonjoon, Choi Lynn
School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea.
Sensors (Basel). 2025 Jul 25;25(15):4609. doi: 10.3390/s25154609.
Modern electronic devices such as smartphones, wearable devices, and robots typically integrate three-dimensional sensors to track the device's movement in the 3D space. However, sensor measurements in three-dimensional vectors are highly sensitive to device orientation since a slight change in the device's tilt or heading can change the vector values. To avoid complications, applications using these sensors often use only the magnitude of the vector, as in geomagnetic-based indoor positioning, or assume fixed device holding postures such as holding a smartphone in portrait mode only. However, using only the magnitude of the vector loses the directional information, while ad hoc posture assumptions work under controlled laboratory conditions but often fail in real-world scenarios. To resolve these problems, we propose a universal vector calibration algorithm that enables consistent three-dimensional vector measurements for the same physical activity, regardless of device orientation. The algorithm works in two stages. First, it transforms vector values in local coordinates to those in global coordinates by calibrating device tilting using pitch and roll angles computed from the initial vector values. Second, it additionally transforms vector values from the global coordinate to a reference coordinate when the target coordinate is different from the global coordinate by correcting yaw rotation to align with application-specific reference coordinate systems. We evaluated our algorithm on geomagnetic field-based indoor positioning and bidirectional step detection. For indoor positioning, our vector calibration achieved an 83.6% reduction in mismatches between sampled magnetic vectors and magnetic field map vectors and reduced the LSTM-based positioning error from 31.14 m to 0.66 m. For bidirectional step detection, the proposed algorithm with vector calibration improved step detection accuracy from 67.63% to 99.25% and forward/backward classification from 65.54% to 100% across various device orientations.
诸如智能手机、可穿戴设备和机器人等现代电子设备通常集成三维传感器，以跟踪设备在三维空间中的运动。然而，三维向量中的传感器测量对设备方向高度敏感，因为设备倾斜或航向的轻微变化都可能改变向量值。为避免出现问题，使用这些传感器的应用程序通常仅使用向量的大小，如基于地磁场的室内定位，或者假设固定的设备握持姿势，如仅以纵向模式握持智能手机。然而，仅使用向量的大小会丢失方向信息，而临时的姿势假设在受控的实验室条件下有效，但在实际场景中往往会失败。为解决这些问题，我们提出了一种通用向量校准算法，该算法能够针对相同的身体活动进行一致的三维向量测量，而不管设备方向如何。该算法分两个阶段工作。首先，通过使用根据初始向量值计算出的俯仰角和横滚角校准设备倾斜，将局部坐标中的向量值转换为全局坐标中的向量值。其次，当目标坐标与全局坐标不同时，通过校正偏航旋转以与特定应用的参考坐标系对齐，将向量值从全局坐标额外转换为参考坐标。我们在基于地磁场的室内定位和双向步数检测中对我们的算法进行了评估。对于室内定位，我们的向量校准使采样磁向量与磁场地图向量之间的不匹配减少了83.6%，并将基于长短期记忆网络（LSTM）的定位误差从31.14米降低到0.66米。对于双向步数检测，所提出的带有向量校准的算法在各种设备方向上，将步数检测准确率从67.63%提高到99.25%，将向前/向后分类准确率从65.54%提高到100%。
