gaitmap.parameters.TemporalParameterCalculation

class gaitmap.parameters.TemporalParameterCalculation(expected_stride_type: Literal['min_vel', 'ic'] = 'min_vel')

Calculat temporal parameters of strides based on detected gait events.

For details on the individual parameters see the Notes section. Calculations are based on [1].

Parameters:

expected_stride_type

The expected stride type of the stride list. This changes how the temporal parameters are calculated. This can either be “min_vel” or “ic”. “min_vel” stride lists are the typical output from Gaitmap event detection methods. However, for other systems (e.g. mocap systems) strides might be defined from one ic to the next ic. In this case the expected_stride_type should be “ic”.

Other Parameters:

stride_event_list

Gait events for each stride obtained from Rampp event detection as type min_vel-stride list.

sampling_rate_hz

The sampling rate of the data signal.

Attributes:

parameters_

Data frame containing temporal parameters for each stride in case of single sensor or dictionary of data frames in multi sensors.

parameters_pretty_

Return parameters with column names indicating units.

See also

gaitmap.parameters.SpatialParameterCalculation

Calculate spatial parameters

Notes

stride_time [s]

The stride time is the duration of the stride calculated based on the ic events of the stride. For a min_vel-stride the stride time is calculated by subtracting “pre_ic” from “ic”. For a ic-stride the stride time is calculated by subtracting “start”/”ic” from “end”.

swing_time [s]

The swing time is the time from the tc to the next ic. For a min_vel-stride this is the time between “tc” and “ic” For a ic-stride this is the time between “tc” and “end”.

stance_time [s]

The stance time is the time the foot is on the ground. Hence, it is the time from a ic to the next tc. For both stride types this is calculated as stride_time - swing_time.

[1]

A. Rampp, J. Barth, S. Schuelein, K.-G. Gassmann, J. Klucken, and B. M. Eskofier, “Inertial Sensor-Based Stride Parameter Calculation From Gait Sequences in Geriatric Patients,” IEEE Transactions on Biomedical Engineering, vol. 62, no. 4, pp. 1089-1097, Apr. 2015. [Online]. Available: http://ieeexplore.ieee.org/document/6949634/

Examples

This method requires the output of a event detection method as input.

>>> stride_list = ...  #  from event detection
>>> temporal_paras = TemporalParameterCalculation()
>>> temporal_paras = temporal_paras.calculate(stride_event_list=stride_list, sampling_rate_hz=204.8)
>>> temporal_paras.parameters_
<Dataframe/dictionary with all the parameters>
>>> temporal_paras.parameters_pretty_
<Dataframe/dictionary with all the parameters with units included in column names>

Methods

calculate(stride_event_list, sampling_rate_hz)	Find temporal parameters of all strides after segmentation and detecting events for all sensors.
clone()	Create a new instance of the class with all parameters copied over.
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__(expected_stride_type: Literal['min_vel', 'ic'] = 'min_vel') → None

calculate(stride_event_list: DataFrame | dict[Union[collections.abc.Hashable, str], pandas.core.frame.DataFrame], sampling_rate_hz: float) → Self

Find temporal parameters of all strides after segmentation and detecting events for all sensors.

Parameters:

stride_event_list

Gait events for each stride obtained from event detection

sampling_rate_hz

The sampling rate of the data signal.

Returns:

self

The class instance with temporal parameters populated in self.parameters_, self.parameters_pretty_

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

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.parameters.TemporalParameterCalculation

MaD DiGait Pipeline

MaD DiGait Pipeline

Temporal parameters calculation

Temporal parameters calculation