gaitmap.event_detection.FilteredRamppEventDetection

class gaitmap.event_detection.FilteredRamppEventDetection(ic_search_region_ms: tuple[float, float] = (80, 50), min_vel_search_win_size_ms: float = 100, ic_lowpass_filter: BaseFilter = cf(ButterworthFilter(cutoff_freq_hz=15, filter_type='lowpass', order=2)), memory: Memory | None = None, enforce_consistency: bool = True, detect_only: tuple[str, ...] | None = None)

Detect gait events using RamppEventDetection with an additional low-pass filter for ic calculation.

Note

This algorithm is only available via the gaitmap_mad package and distributed under a AGPL3 licence. To use it, you need to explicitly install the gaitmap_mad package. Learn more about that here.

Rampp event detection with an additional low-pass Butterworth filter. This method is suggested to be used on data containing high frequency noise or artifacts which occur around the ic part of the strides. The tc and min velocity are calculated from the original signal.

Parameters:

ic_search_region_ms

The region to look for the initial contact in the acc_pa signal in ms given an ic candidate. According to [1], for the ic the algorithm first looks for a local minimum in the gyr_ml signal after the swing phase. The actual ic is then determined in the acc_pa signal in the ic_search_region_ms around that gyr_ml minimum. ic_search_region_ms[0] describes the start and ic_search_region_ms[1] the end of the region to check around the gyr_ml minimum. The values of ic_search_region_ms must be greater or equal than the sample time (1/sampling_rate_hz).

min_vel_search_win_size_ms

The size of the sliding window for finding the minimum gyroscope energy in ms.

ic_lowpass_filter

An instance of a Filter-transform (e.g. ButterworthFilter) that will be applied to the gyr_ml data before the IC is detected. While not enforced, this should be a lowpass filter to ensure that the results are as expected.

memory

An optional joblib.Memory object that can be provided to cache the detection of all events.

enforce_consistency

An optional bool that can be set to False if you wish to disable postprocessing (see Notes section for more information).

detect_only

An optional tuple of strings that can be used to only detect a subset of events. By default, all events (“ic”, “tc”, “min_vel”) are detected. If min_vel is not detected, the min_vel_event_list_ output will not be available. If “ic” is not detected, the pre_ic will also not be available in the output.

Other Parameters:

data

The data passed to the detect method.

sampling_rate_hz

The sampling rate of the data

stride_list

A list of strides provided by a stride segmentation method. The stride list is expected to have no gaps between subsequent strides. That means for subsequent strides the end sample of one stride should be the start sample of the next stride.

Attributes:

min_vel_event_list_A stride list or dictionary with such values

The result of the detect method holding all temporal gait events and start / end of all strides. The stride borders for the stride_events are aligned with the min_vel samples. Hence, the start sample of each stride corresponds to the min_vel sample of that stride and the end sample corresponds to the min_vel sample of the subsequent stride. Strides for which no valid events could be found are removed. Additional strides might have been removed due to the conversion from segmented to min_vel strides. The ‘s_id’ index is selected according to which segmented stride the pre-ic belongs to.

segmented_event_list_A stride list or dictionary with such values

The result of the detect method holding all temporal gait events and start / end of all strides. This version of the results has the same stride borders than the input stride_list and has additional columns for all the detected events. Strides for which no valid events could be found are removed.

Notes

Due to attachment methods used for foot-worn IMUs, the sensor might experience bounce and vibrate at the time of the heel strike (IC) which leads to high frequency artifacts in the gyr_ml signal. This can lead to an inaccurate IC detection, as it relies on the identification of extrema in the signal. To resolve this issue, this event detection method applies a low-pass filter to remove high frequency artifacts. Note, that the lowpass filter is only used for the IC detection and not the detection of other events. Other than that, the implementation is identical to the normal Rampp event detection ( RamppEventDetection).

[1]

Rampp, A., Barth, J., Schülein, S., Gaßmann, K. G., Klucken, J., & Eskofier, B. M. (2014). Inertial sensor-based stride parameter calculation from gait sequences in geriatric patients. IEEE transactions on biomedical engineering, 62(4), 1089-1097.. https://doi.org/10.1109/TBME.2014.2368211

Methods

clone()	Create a new instance of the class with all parameters copied over.
detect(data, stride_list, *, sampling_rate_hz)	Find gait events in data within strides provided by stride_list.
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__(ic_search_region_ms: tuple[float, float] = (80, 50), min_vel_search_win_size_ms: float = 100, ic_lowpass_filter: BaseFilter = cf(ButterworthFilter(cutoff_freq_hz=15, filter_type='lowpass', order=2)), memory: Memory | None = None, enforce_consistency: bool = True, detect_only: tuple[str, ...] | 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

detect(data: DataFrame | dict[Union[collections.abc.Hashable, str], pandas.core.frame.DataFrame], stride_list: DataFrame | dict[Union[collections.abc.Hashable, str], pandas.core.frame.DataFrame], *, sampling_rate_hz: float) → Self

Find gait events in data within strides provided by stride_list.

Parameters:

data

The data set holding the imu raw data

stride_list

A list of strides provided by a stride segmentation method

sampling_rate_hz

The sampling rate of the data

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.event_detection.FilteredRamppEventDetection

Rampp event detection

Rampp event detection