BaseIcDetector#

class mobgap.initial_contacts.base.BaseIcDetector[source]#

Base class for IC-detectors.

This base class should be used for all initial contacts detection algorithms. Algorithms should implement the detect method, which will perform all relevant processing steps. The method should then return the instance of the class, with the ic_list_ attribute set to the detected initial contacts. Further, the detect method should set self.data and self.sampling_rate_hz to the parameters passed to the method. We allow that subclasses specify further parameters for the detect methods (hence, this baseclass supports **kwargs). However, you should only use them, if you really need them and apply active checks, that they are passed correctly. In 99% of the time, you should add a new parameter to the algorithm itself, instead of adding a new parameter to the detect method.

Other Parameters:

data: The raw IMU data of the gait sequence in the body frame passed to the detect method.
sampling_rate_hz: The sampling rate of the IMU data in Hz passed to the detect method.

Attributes:

ic_list_: A pandas dataframe with the indices of the detected initial contacts in the input data. It only has one column, ic, which contains the indices of the detected initial contacts.

Notes

You can use the base_icd_docfiller decorator to fill common parts of the docstring for your subclass. See the source of this class for an example.

Methods

`clone`()	Create a new instance of the class with all parameters copied over.
`detect`(data, , sampling_rate_hz, *kwargs)	Detect Initial contacts in the passed data.
`get_params`([deep])	Get parameters for this algorithm.
`set_params`(**params)	Set the parameters of this Algorithm.

__init__(*args, **kwargs)#

clone() → Self[source]#

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

detect(

data: DataFrame,

*,

sampling_rate_hz: float,

**kwargs: Unpack[dict[str, Any]],

) → Self[source]#

Detect Initial contacts in the passed data.

Parameters:

data: The raw IMU in the body frame.
sampling_rate_hz: The sampling rate of the IMU data in Hz.

Returns:

self: The instance of the class with the icd_list_ attribute set to the detected initial contacts.

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

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: