McCamley L/R Classifier

The McCamley L/R classifier is a simple algorithm to detect the laterality of initial contacts based on the sign of the angular velocity signal. We use a modified version of the original McCamley algorithm, which includes a smoothing filter to reduce the influence of noise on the detection.

This example shows how to use the algorithm and compares the output to the reference labels on some example data.

from mobgap.data import LabExampleDataset

Loading some example data

Note

More infos about data loading can be found in the data loading example.

We load example data from the lab dataset together with the INDIP reference system. We will use the INDIP “InitialContact_Event” output as ground truth.

We only use the data from the “simulated daily living” activity test from a single participant.

Like most algorithms, the algorithm requires the data to be in body frame coordinates. As we know the sensor was well aligned, we can just use to_body_frame to transform the data.

from mobgap.utils.conversions import to_body_frame

example_data = LabExampleDataset(
    reference_system="INDIP", reference_para_level="wb"
)
single_test = example_data.get_subset(
    cohort="MS", participant_id="001", test="Test11", trial="Trial1"
)

imu_data = to_body_frame(single_test.data_ss)
reference_wbs = single_test.reference_parameters_.wb_list

sampling_rate_hz = single_test.sampling_rate_hz
ref_ics = single_test.reference_parameters_.ic_list
ref_ics_rel_to_gs = single_test.reference_parameters_relative_to_wb_.ic_list

reference_wbs

/home/docs/checkouts/readthedocs.org/user_builds/mobgap/checkouts/v0.9.0/mobgap/data/_mobilised_matlab_loader.py:1082: UserWarning: There were multiple ICs with the same index value, but different LR labels. This is likely an issue with the reference system you should further investigate. For now, we set the `lr_label` of the stride corresponding to this IC to Nan. However, both values still remain in the IC list.
  return parse_reference_parameters(
/home/docs/checkouts/readthedocs.org/user_builds/mobgap/checkouts/v0.9.0/mobgap/data/_mobilised_matlab_loader.py:1082: UserWarning: There were multiple ICs with the same index value, but different LR labels. This is likely an issue with the reference system you should further investigate. For now, we set the `lr_label` of the stride corresponding to this IC to Nan. However, both values still remain in the IC list.
  return parse_reference_parameters(
/home/docs/checkouts/readthedocs.org/user_builds/mobgap/checkouts/v0.9.0/mobgap/data/_mobilised_matlab_loader.py:1091: UserWarning: There were multiple ICs with the same index value, but different LR labels. This is likely an issue with the reference system you should further investigate. For now, we set the `lr_label` of the stride corresponding to this IC to Nan. However, both values still remain in the IC list.
  return parse_reference_parameters(

	start	end	n_strides	duration_s	length_m	avg_walking_speed_mps	avg_cadence_spm	avg_stride_length_m	termination_reason
wb_id
0	1019	1768	9	7.48	4.468932	0.847668	107.795850	0.942678	Pause
1	4534	5549	11	10.14	2.900453	0.365176	93.396106	0.483923	Pause
2	9665	10569	9	9.03	2.140232	0.294058	75.981133	0.506458	Pause
3	12337	14633	28	22.95	11.201110	0.634425	92.337768	0.803933	Pause
4	20151	20982	11	8.30	2.390709	0.371746	87.915774	0.507484	Pause
5	21378	22129	9	7.50	2.517558	0.492965	95.365740	0.599360	Pause

Applying the algorithm using reference ICs

We use the McCamley algorithm to detect the laterality of the initial contacts. For this we need the IMU data and the indices of the initial contacts per GS. To focus this example on the L/R detection, we use the reference ICs from the INDIP system as input. In a real application, we would use the output of the IC-detectors as input.

We will use the GsIterator to iterate over the gait sequences and apply the algorithm to each wb. Note, that we use the ic_list result key, as the output of all L/R detectors is identical to the output of the IC-detectors, but with an additional lr_label column.

from mobgap.laterality import LrcMcCamley
from mobgap.pipeline import GsIterator

iterator = GsIterator()
algo = LrcMcCamley()

for (gs, data), result in iterator.iterate(imu_data, reference_wbs):
    result.ic_list = algo.predict(
        data,
        ic_list=ref_ics_rel_to_gs.loc[gs.id].drop("lr_label", axis=1),
        sampling_rate_hz=sampling_rate_hz,
    ).ic_lr_list_

detected_ics = iterator.results_.ic_list
detected_ics.assign(ref_lr_label=ref_ics.lr_label)

		ic	lr_label	ref_lr_label
wb_id	step_id
0	0	1019	right	right
	1	1065	left	left
	2	1129	right	right
	3	1172	left	left
	4	1236	right	right
...	...	...	...	...
5	7	21896	right	right
	8	21951	right	left
	9	22042	left	right
	10	22089	right	left
	11	22128	right	right

93 rows × 3 columns

We can see that for most ICs we correctly identify the laterality. If you want to learn more about evaluating the algorithm output, you can check the evaluation example.

Estimated memory usage: 9 MB