gaitmap.trajectory_reconstruction: Algorithms to estimate IMU sensor orientation and position

Methods to calculate the global orientation and position of an IMU.

This module provides simple methods to estimate the orientation and position on custom IMU data and a set of wrappers to make applying these methods to the default gaitmap datasets easier.

TrajectoryWrapperClasses

StrideLevelTrajectory(*[, ori_method, ...])	Estimate the trajectory over the duration of a stride by considering each stride individually.
RegionLevelTrajectory(*[, ori_method, ...])	Estimate the trajectory over the duration of an entire gait sequence or region of interest.

Trajectory Estimation Methods

Methods to calculate position and orientation of an IMU withing a single algorithm.

RtsKalman(*[, initial_orientation, ...])	An Error-State-Kalman-Filter (ESKF) with Rauch-Tung-Striebel (RTS) smoothing for trajectory estimation.
MadgwickRtsKalman(*[, initial_orientation, ...])	An extention of the RTS Kalman filter that uses the Madgwick filter for orientation estimation.

Orientation Estimation Methods

Methods to calculate the global orientation of an IMU.

SimpleGyroIntegration([initial_orientation, ...])	Integrate Gyro values without any drift correction.
MadgwickAHRS([beta, initial_orientation, ...])	The MadwickAHRS algorithm to estimate the orientation of an IMU.
MahonyAHRS([kp, ki, initial_orientation, ...])	The MahonyAHRS algorithm to estimate the orientation of an IMU.

Position Estimation Methods

Methods to calculate the global position of an IMU.

ForwardBackwardIntegration([turning_point, ...])	Use forward(-backward) integration of acc to estimate velocity and position.
PieceWiseLinearDedriftedIntegration([...])	Use a piecewise linear drift model based on zupts for integration of acc to estimate velocity and position.