gaitmap.trajectory_reconstruction.SimpleGyroIntegration

class gaitmap.trajectory_reconstruction.SimpleGyroIntegration(initial_orientation: ndarray | Rotation = cf(array([0., 0., 0., 1.])), memory: Memory | None = None)

Integrate Gyro values without any drift correction.

Parameters:

initial_orientationRotation or (1 x 4) quaternion array

The initial orientation used. If you pass a array, remember that the order of elements must be x, y, z, w.

memory

An optional joblib.Memory object that can be provided to cache the results of the integration.

Other Parameters:

data

The data passed to the estimate method

sampling_rate_hz

The sampling rate of this data

Attributes:

orientation_

Orientations as pd.DataFrame.

orientation_object_

The orientations as a single scipy Rotation object

rotated_data_

Rotated data.

See also

gaitmap.trajectory_reconstruction

Other implemented algorithms for orientation and position estimation

gaitmap.trajectory_reconstruction.StrideLevelTrajectory

Apply the method for each stride of a stride list.

Notes

This class uses Numba as a just-in-time-compiler to achieve fast run times. In result, the first execution of the algorithm will take longer as the methods need to be compiled first.

Examples

Your data must be a pd.DataFrame with at least columns defined by SF_GYR.

>>> import pandas as pd
>>> from gaitmap.utils.consts import SF_GYR
>>> data = pd.DataFrame(..., columns=SF_GYR)
>>> sampling_rate_hz = 100
>>> # Create an algorithm instance
>>> sgi = SimpleGyroIntegration(initial_orientation=np.array([0, 0, 0, 1.0]))
>>> # Apply the algorithm
>>> sgi = sgi.estimate(data, sampling_rate_hz=sampling_rate_hz)
>>> # Inspect the results
>>> sgi.orientation_
<pd.Dataframe with resulting quaternions>
>>> sgi.orientation_object_
<scipy.Rotation object>

Methods

clone()	Create a new instance of the class with all parameters copied over.
estimate(data, *, sampling_rate_hz, **_)	Estimate the orientation of the sensor by simple integration of the Gyro data.
from_json(json_str)	Import an gaitmap object from its json representation.
get_params([deep])	Get parameters for this algorithm.
set_params(**params)	Set the parameters of this Algorithm.
to_json()	Export the current object parameters as json.

__init__(initial_orientation: ndarray | Rotation = cf(array([0., 0., 0., 1.])), memory: Memory | None = None) → None

clone() → Self

Create a new instance of the class with all parameters copied over.

This will create a new instance of the class itself and all nested objects

estimate(data: DataFrame, *, sampling_rate_hz: float, **_) → Self

Estimate the orientation of the sensor by simple integration of the Gyro data.

Parameters:

data

Continuous sensor data that includes at least a Gyro. The gyro data is expected to be in deg/s!

sampling_rate_hz

The sampling rate of the data in Hz

Returns:

self

The class instance with all result attributes populated

classmethod from_json(json_str: str) → Self

Import an gaitmap object from its json representation.

For details have a look at the this example.

You can use the to_json method of a class to export it as a compatible json string.

Parameters:

json_str

json formatted string

get_params(deep: bool = True) → dict[str, Any]

Get parameters for this algorithm.

Parameters:

deep

Only relevant if object contains nested algorithm objects. If this is the case and deep is True, the params of these nested objects are included in the output using a prefix like nested_object_name__ (Note the two “_” at the end)

Returns:

params

Parameter names mapped to their values.

set_params(**params: Any) → Self

Set the parameters of this Algorithm.

To set parameters of nested objects use nested_object_name__para_name=.

to_json() → str

Export the current object parameters as json.

For details have a look at the this example.

You can use the from_json method of any gaitmap algorithm to load the object again.

Warning

This will only export the Parameters of the instance, but not any results!

Examples using gaitmap.trajectory_reconstruction.SimpleGyroIntegration

Stride Level Trajectory reconstruction

Stride Level Trajectory reconstruction