# IMUs

These routines facilitate the calculation of 3d movement kinematics for recordings from inertial measurement units (IMUs).

## Functions

### General

- `imus.data_import()` ... Read in Rate and stored 3D parameters from XSens IMUs

### IMUs

- `imus.calc_QPos()` ... Calculate orientation and position, from angular velocity and linear acceleration
- `imus.kalman_quat()` ... Calculate orientation from IMU-data using an Extended Kalman Filter

## Class

`imus.IMU`([inFile, inData]) | Class for working with working with inertial measurement units (IMUs)

### Methods

`imus.IMU.calc_orientation`(R_initialOrientation) | Calculate the current orientation
`imus.IMU.calc_position`(initialPosition) | Calculate the position, assuming that the orientation is already known.
`imus.IMU.setData`(data) | Set the properties of an IMU-object.

## Class

`imus.MadgwickAHRS`([SamplePeriod, Beta, ...]) |

## Class

`imus.MahonyAHRS`([SamplePeriod, Kp, Ki, ...]) | Madgwick’s implementation of Mayhony’s AHRS algorithm

## Details

Utilities for movements recordins with inertial measurement units (IMUs)

class `imus.``IMU`(inFile=None, inData=None)

  Class for working with working with inertial measurement units (IMUs)

  An IMU object can be initialized
    - by giving a filename
    - by providing measurement data and a samplerate
    - without giving any parameter; in that case the user is prompted to select an infile

  Parameters: | inFile (string) – path- and file-name of infile, if you get the sound from a file. inData (dictionary) – The following fields are required: acc : (N x 3) array Linear acceleration [m/s^2], in the x-, y-, and z-direction omega : (N x 3) array Angular velocity [deg/s], about the x-, y-, and z-axis [mag] : (N x 3) array (optional) Local magnetic field, in the x-, y-, and z-direction rate: integer sample rate [Hz]

  Notes

  IMU-Properties:
    - source
    - acc
    - omega
    - mag
    - rate
    - totalSamples
    - duration
    - dataType

  Examples

      >>> myimu = IMU(r'.\Data\Walking_02.txt')
      >>>
      >>> initialOrientation = np.array([[1,0,0],
      >>>                                [0,0,-1],
      >>>                                [0,1,0]])
      >>> initialPosition = np.r_[0,0,0]
      >>>
      >>> myimu.calc_orientation(initialOrientation)
      >>> q_simple = myimu.quat[:,1:]
      >>>
      >>> calcType = 'Madgwick'
      >>> myimu.calc_orientation(initialOrientation, type=calcType)
      >>> q_Kalman = myimu.quat[:,1:]

  `calc_orientation`(R_initialOrientation, type='quatInt')

    Calculate the current orientation

    Parameters: | R_initialOrientation (3x3 array) – approximate alignment of sensor-CS with space-fixed CS type (string) – ‘quatInt’ ...... quaternion integration of angular velocity ‘Kalman’ ..... quaternion Kalman filter ‘Madgwick’ .. gradient descent method, efficient ‘Mahony’ ... Formula from Mahony, as implemented by Madgwick

  `calc_position`(initialPosition)

    Calculate the position, assuming that the orientation is already known.

  `setData`(data)

    Set the properties of an IMU-object.

class `imus.``MahonyAHRS`(SamplePeriod=0.00390625, Kp=1.0, Ki=0, Quaternion=[1, 0, 0, 0])

  Madgwick’s implementation of Mayhony’s AHRS algorithm

  `Update`(Gyroscope, Accelerometer, Magnetometer)

    Calculate the best quaternion to the given measurement values.

`imus.``calc_QPos`(R_initialOrientation, omega, initialPosition, accMeasured, rate)

  Reconstruct position and orientation, from angular velocity and linear acceleration. Assumes a start in a stationary position. No compensation for drift.

  Parameters: | omega (ndarray(N,3)) – Angular velocity, in [rad/s] accMeasured (ndarray(N,3)) – Linear acceleration, in [m/s^2] initialPosition (ndarray(3,)) – initial Position, in [m] R_initialOrientation (ndarray(3,3)) – Rotation matrix describing the initial orientation of the sensor, except a mis-orienation with respect to gravity rate (float) – sampling rate, in [Hz]
  Returns: | q (ndarray(N,3)) – Orientation, expressed as a quaternion vector pos (ndarray(N,3)) – Position in space [m]

  Example

      >>> q1, pos1 = calc_QPos(R_initialOrientation, omega, initialPosition, acc, rate)

`imus.``import_data`(inFile, type='XSens', paramList=[])

  Read in Rate and stored 3D parameters from XSens IMUs

  Parameters: | inFile (string) – Path and name of input file paramList (list of strings) – Starting names of stored parameters
  Returns: | List, containing rate (float) – Sampling rate [List of x/y/z Parameter Values]

  Examples

      >>> data = getXSensData(fullInFile, ['Acc', 'Gyr'])
      >>> rate = data[0]
      >>> accValues = data[1]
      >>> Omega = data[2]

`imus.``kalman_quat`(rate, acc, gyr, mag)

  Calclulate the orientation from IMU magnetometer data.

  Parameters: | rate (float) – sample rate [Hz] acc ((N,3) ndarray) – linear acceleration [m/sec^2] gyr ((N,3) ndarray) – angular velocity [rad/sec] mag ((N,3) ndarray) – magnetic field orientation
  Returns: | qOut – unit quaternion, describing the orientation relativ to the coordinate system spanned by the local magnetic field, and gravity
  Return type: | (N,4) ndarray

  Notes

  Based on “Design, Implementation, and Experimental Results of a Quaternion-Based Kalman Filter for Human Body Motion Tracking” Yun, X. and Bachman, E.R., IEEE TRANSACTIONS ON ROBOTICS, VOL. 22, 1216-1227 (2006)