.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/pipeline/_03_preconfigured_mobilised_pipelines.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_pipeline__03_preconfigured_mobilised_pipelines.py: Preconfigured Mobilised Pipelines ================================= As part of the Mobilise-D project two separate pipelines have been developed depending on the patient characteristics. The first pipeline :class:`~mobgab.pipeline.MobilisedPipelineHealthy` (P1 in [1]_) is designed for people that likely still have a somewhat normal gait pattern. In Mobilise-D, this pipeline is used for healthy controls and patients with "COPD" and "CHF". The second pipeline :class:`~mobgab.pipeline.MobilisedPipelineImpaired` (P2 in [1]_) is designed for patients with likely significantly impaired gait patterns. In Mobilise-D, this pipeline is used for patients with "PD", "PFF" and "MS". In this example we will show how to use these preconfigured pipelines. If you want to understand the details of the pipelines, please refer to the `step-by-step example `_. .. [1] Kirk, C., Küderle, A., Micó-Amigo, M.E. et al. Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device. Sci Rep 14, 1754 (2024). https://doi.org/10.1038/s41598-024-51766-5 .. GENERATED FROM PYTHON SOURCE LINES 23-27 Data ---- For this example, we will use the provided example data. It contains data from Lab tests from MS patients and healthy controls. .. GENERATED FROM PYTHON SOURCE LINES 27-32 .. code-block:: default from mobgap.data import LabExampleDataset data_ha = LabExampleDataset().get_subset(cohort="HA") data_ha .. raw:: html

LabExampleDataset [6 groups/rows]

	cohort	participant_id	time_measure	test	trial
0	HA	001	TimeMeasure1	Test5	Trial1
1	HA	001	TimeMeasure1	Test5	Trial2
2	HA	001	TimeMeasure1	Test11	Trial1
3	HA	002	TimeMeasure1	Test5	Trial1
4	HA	002	TimeMeasure1	Test5	Trial2
5	HA	002	TimeMeasure1	Test11	Trial1

.. GENERATED FROM PYTHON SOURCE LINES 33-36 .. code-block:: default data_ms = LabExampleDataset().get_subset(cohort="MS") data_ms .. raw:: html

LabExampleDataset [3 groups/rows]

	cohort	participant_id	time_measure	test	trial
0	MS	001	TimeMeasure1	Test5	Trial1
1	MS	001	TimeMeasure1	Test5	Trial2
2	MS	001	TimeMeasure1	Test11	Trial1

.. GENERATED FROM PYTHON SOURCE LINES 37-39 Mobilised Pipeline Healthy -------------------------- .. GENERATED FROM PYTHON SOURCE LINES 39-43 .. code-block:: default from mobgap.pipeline import MobilisedPipelineHealthy, MobilisedPipelineUniversal pipeline_ha = MobilisedPipelineHealthy() .. GENERATED FROM PYTHON SOURCE LINES 44-45 We just apply the pipeline to the first long test in the data. .. GENERATED FROM PYTHON SOURCE LINES 45-49 .. code-block:: default long_test_ha = data_ha.get_subset(test="Test11")[0] pipeline_ha = pipeline_ha.safe_run(long_test_ha) .. GENERATED FROM PYTHON SOURCE LINES 50-58 Now we can access the results. Note, that the pipelines contain a large number of results. Not all of them are relevant for every use case. We only show the main outputs here: The main output are the aggregated parameters. By default, this is just a single output for each recording. It describes the overall statistics and aggregated parameters over all WBs. .. GENERATED FROM PYTHON SOURCE LINES 58-60 .. code-block:: default pipeline_ha.aggregated_parameters_ .. raw:: html

	wb_all__count	total_walking_duration_h	wb_all__duration_s__avg	wb_all__duration_s__max	wb_all__duration_s__var	wb_all__cadence_spm__avg	wb_all__stride_duration_s__avg	wb_all__cadence_spm__var	wb_all__stride_duration_s__var	wb_10_30__count	wb_10_30__walking_speed_mps__avg	wb_10_30__stride_length_m__avg	wb_10__count	wb_10__walking_speed_mps__max	wb_30__count	wb_30__walking_speed_mps__avg	wb_30__stride_length_m__avg	wb_30__cadence_spm__avg	wb_30__stride_duration_s__avg	wb_30__walking_speed_mps__max	wb_30__cadence_spm__max	wb_30__walking_speed_mps__var	wb_30__stride_length_m__var	wb_60__count
all_wbs	7	0.013853	6.38	10.828	0.440279	88.934451	1.293848	0.084865	0.135207	1	0.45935	0.699701	1	0.45935	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0

.. GENERATED FROM PYTHON SOURCE LINES 61-63 On level below, we have the WB parameters. This is a DataFrame with the parameters per WB. .. GENERATED FROM PYTHON SOURCE LINES 63-65 .. code-block:: default pipeline_ha.per_wb_parameters_ .. raw:: html

	start	end	n_strides	rule_name	rule_obj	duration_s	stride_duration_s	cadence_spm	stride_length_m	walking_speed_mps
wb_id
0	630	1112	7	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	4.82	1.012857	102.691989	1.089851	0.935974
1	2832	4138	14	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	13.06	1.305714	79.290506	0.699701	0.459350
2	4488	5198	7	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	7.10	1.604286	91.005498	1.073694	0.812935
3	7848	8782	11	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	9.34	1.253636	85.665068	0.876606	0.631880
4	9530	10168	9	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	6.38	1.231111	83.159936	0.866376	0.627456
5	11032	11465	5	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	4.33	1.306000	88.480662	0.814981	0.619937
6	13118	13602	6	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	4.84	1.343333	92.247498	1.042166	0.833501

.. GENERATED FROM PYTHON SOURCE LINES 66-68 Even more granular are the stride level parameters. They contain only the strides that are also part of a valid WB. .. GENERATED FROM PYTHON SOURCE LINES 68-70 .. code-block:: default pipeline_ha.per_stride_parameters_ .. raw:: html

		original_gs_id	start	end	lr_label	stride_duration_s	cadence_spm	stride_length_m	walking_speed_mps
wb_id	s_id
0	0_0	0	630	698	left	0.68	105.263158	1.233167	1.081726
	0_1	0	698	760	left	0.62	105.263158	1.242933	1.090292
	0_2	0	760	872	left	1.12	105.612483	1.214743	1.068913
	0_3	0	815	925	right	1.10	106.067663	1.164436	1.029147
	0_4	0	872	985	left	1.13	100.861834	1.085603	0.915503
	0_5	0	925	1042	right	1.17	96.368564	0.974691	0.781070
	0_6	0	985	1112	left	1.27	99.407066	0.713383	0.585167
1	1_0	1	2832	3055	left	2.23	86.956522	1.005251	0.728442
	1_1	1	2928	2988	right	0.60	86.956522	1.084947	0.786193
	1_2	1	2988	3110	right	1.22	86.956522	0.689254	0.499460
	1_3	1	3055	3260	left	2.05	85.935274	0.435267	0.311735
	1_4	1	3110	3178	right	0.68	86.116186	0.421630	0.302720
	1_5	1	3178	3330	right	1.52	84.554919	0.449900	0.316750
	1_6	1	3260	3452	left	1.92	82.061563	0.481249	0.328964
	1_8	1	3452	3618	left	1.66	69.292961	0.371887	0.209171
	1_9	1	3618	3755	left	1.37	74.017038	0.440231	0.271202
	1_10	1	3755	3972	left	2.17	75.743429	0.875586	0.555817
	1_11	1	3852	3912	right	0.60	82.758621	1.027553	0.708657
	1_12	1	3912	4038	right	1.26	66.269121	1.019332	0.563132
	1_14	1	4038	4082	right	0.44	63.157895	0.739692	0.389312
	1_15	1	4082	4138	right	0.56	NaN	0.754043	NaN
2	2_0	2	4488	4638	left	1.50	85.764810	1.280748	0.899606
	2_1	2	4552	4755	right	2.03	95.303530	1.194072	0.938281
	2_2	2	4638	4812	left	1.74	94.844301	1.172218	0.926098
	2_3	2	4755	5040	right	2.85	88.907556	0.954563	0.708107
	2_4	2	4812	4862	left	0.50	84.507042	0.943272	0.664276
	2_5	2	4862	4965	left	1.03	89.293040	0.793105	0.588134
	2_7	2	5040	5198	right	1.58	98.418209	1.177883	0.966043
3	3_0	3	7848	7985	right	1.37	67.424794	0.653690	0.364378
	3_1	3	7985	8228	right	2.43	83.315121	0.768227	0.533268
	3_2	3	8095	8165	left	0.70	87.410370	0.789055	0.572525
	3_3	3	8165	8280	left	1.15	104.699114	0.800069	0.695230
	3_4	3	8228	8335	right	1.07	100.002189	0.902816	0.750226
	3_5	3	8280	8398	left	1.18	95.795841	1.006931	0.801849
	3_6	3	8335	8460	right	1.25	91.912397	1.078579	0.825598
	3_7	3	8398	8540	left	1.42	85.391470	1.070190	0.762467
	3_8	3	8460	8608	right	1.48	80.544024	1.074073	0.720653
	3_10	3	8608	8680	right	0.72	75.232198	0.885580	0.563626
	3_11	3	8680	8782	right	1.02	70.588235	0.613453	0.360855
4	4_0	4	9530	9725	right	1.95	90.248060	1.149733	0.864578
	4_1	4	9592	9658	left	0.66	90.333457	1.147545	0.863752
	4_2	4	9658	9792	left	1.34	88.942906	1.137896	0.843573
	4_3	4	9725	9855	right	1.30	87.568974	1.062686	0.776630
	4_4	4	9792	9932	left	1.40	85.683181	0.938018	0.670926
	4_5	4	9855	10062	right	2.07	85.745647	0.750183	0.548923
	4_6	4	9932	10002	left	0.70	96.000000	0.763166	0.610533
	4_7	4	10002	10105	left	1.03	67.313427	0.504927	0.306283
	4_9	4	10105	10168	left	0.63	56.603774	0.343235	0.161903
5	5_0	5	11032	11295	left	2.63	80.968581	0.821773	0.555831
	5_1	5	11140	11218	right	0.78	80.000000	0.977717	0.651811
	5_2	5	11218	11360	right	1.42	90.158730	0.854732	0.634798
	5_3	5	11295	11408	left	1.13	102.795336	0.754391	0.637309
	5_5	5	11408	11465	left	0.57	NaN	0.666294	NaN
6	6_0	6	13118	13295	left	1.77	95.569545	1.093563	0.871466
	6_1	6	13170	13232	right	0.62	95.410138	1.021363	0.812453
	6_2	6	13232	13355	right	1.23	94.060430	1.219165	0.958232
	6_3	6	13295	13420	left	1.25	90.620860	1.097766	0.831089
	6_4	6	13355	13492	right	1.37	85.576517	0.970061	0.694266
	6_5	6	13420	13602	left	1.82	NaN	0.851080	NaN

.. GENERATED FROM PYTHON SOURCE LINES 71-72 For other results, see the documentation of the pipeline class itself. .. GENERATED FROM PYTHON SOURCE LINES 74-76 Mobilised Pipeline Impaired --------------------------- .. GENERATED FROM PYTHON SOURCE LINES 76-80 .. code-block:: default from mobgap.pipeline import MobilisedPipelineImpaired pipeline_ms = MobilisedPipelineImpaired() .. GENERATED FROM PYTHON SOURCE LINES 81-82 We just apply the pipeline to the first long test in the data. .. GENERATED FROM PYTHON SOURCE LINES 82-86 .. code-block:: default long_test_ms = data_ms.get_subset(test="Test11")[0] pipeline_ms = pipeline_ms.safe_run(long_test_ms) .. GENERATED FROM PYTHON SOURCE LINES 87-88 Like before we can access the results. .. GENERATED FROM PYTHON SOURCE LINES 88-90 .. code-block:: default pipeline_ms.aggregated_parameters_ .. raw:: html

	wb_all__count	total_walking_duration_h	wb_all__duration_s__avg	wb_all__duration_s__max	wb_all__duration_s__var	wb_all__cadence_spm__avg	wb_all__stride_duration_s__avg	wb_all__cadence_spm__var	wb_all__stride_duration_s__var	wb_10_30__count	wb_10_30__walking_speed_mps__avg	wb_10_30__stride_length_m__avg	wb_10__count	wb_10__walking_speed_mps__max	wb_30__count	wb_30__walking_speed_mps__avg	wb_30__stride_length_m__avg	wb_30__cadence_spm__avg	wb_30__stride_duration_s__avg	wb_30__walking_speed_mps__max	wb_30__cadence_spm__max	wb_30__walking_speed_mps__var	wb_30__stride_length_m__var	wb_60__count
all_wbs	5	0.017686	9.15	19.672	0.48378	92.917277	1.132127	0.086623	0.088637	2	0.683967	0.891545	2	0.730898	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0

.. GENERATED FROM PYTHON SOURCE LINES 91-93 .. code-block:: default pipeline_ms.per_wb_parameters_ .. raw:: html

	start	end	n_strides	rule_name	rule_obj	duration_s	stride_duration_s	cadence_spm	stride_length_m	walking_speed_mps
wb_id
0	979	1835	12	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	8.56	1.259167	92.857232	0.923175	0.745904
1	5230	5982	8	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	7.52	1.175000	87.808553	0.914782	0.650693
2	9715	10630	12	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	9.15	0.985000	100.656484	0.676807	0.570474
3	12994	14690	27	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	16.96	1.106667	100.847040	0.879673	0.742630
4	20287	22435	25	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	21.48	1.134800	82.417073	0.903417	0.625303

.. GENERATED FROM PYTHON SOURCE LINES 94-96 .. code-block:: default pipeline_ms.per_stride_parameters_ .. raw:: html

		original_gs_id	start	end	lr_label	stride_duration_s	cadence_spm	stride_length_m	walking_speed_mps
wb_id	s_id
0	0_0	0	979	1062	left	0.83	105.263158	1.160621	1.018089
	0_1	0	1062	1172	left	1.10	106.852358	1.177377	1.048420
	0_2	0	1117	1227	right	1.10	107.564058	1.143444	1.024778
	0_3	0	1172	1282	left	1.10	107.944130	1.100256	0.989706
	0_4	0	1227	1339	right	1.12	106.093671	1.079993	0.955065
...	...	...	...	...	...	...	...	...	...
4	4_22	4	21943	21985	right	0.42	60.606061	0.910531	0.459864
	4_23	4	21985	22090	right	1.05	58.347189	0.971656	0.472396
	4_25	4	22090	22130	right	0.40	60.000000	0.910531	0.455266
	4_26	4	22130	22227	right	0.97	67.813348	0.859493	0.480974
	4_28	4	22353	22435	left	0.82	NaN	0.786764	NaN

84 rows × 8 columns

.. GENERATED FROM PYTHON SOURCE LINES 97-107 That's it. As you can see, it is super simple to run the preconfigured pipelines on your data, if they are structured as a valid gait dataset. However, when running a larger study, you might want to process all data at once. Then it become "inconvenient" to run the pipeline for each recording separately manually. Luckily, it is relatively easy to implement a loop that runs the pipeline for each recording. We can even use :class:`~mobgap.pipeline.MobilisedPipelineUniversal` to automatically process all MS participants with the impaired pipeline and all HA participants with the healthy pipeline. .. GENERATED FROM PYTHON SOURCE LINES 107-115 .. code-block:: default meta_pipeline = MobilisedPipelineUniversal( pipelines=[ ("healthy", MobilisedPipelineHealthy()), ("impaired", MobilisedPipelineImpaired()), ] ) .. GENERATED FROM PYTHON SOURCE LINES 116-118 The meta-pipeline uses the ``recommended_cohorts`` parameter of the respective pipeline to determine which pipeline to use. .. GENERATED FROM PYTHON SOURCE LINES 118-120 .. code-block:: default MobilisedPipelineHealthy().recommended_cohorts .. rst-class:: sphx-glr-script-out .. code-block:: none ('HA', 'COPD', 'CHF') .. GENERATED FROM PYTHON SOURCE LINES 121-123 .. code-block:: default MobilisedPipelineImpaired().recommended_cohorts .. rst-class:: sphx-glr-script-out .. code-block:: none ('PD', 'MS', 'PFF') .. GENERATED FROM PYTHON SOURCE LINES 124-130 So we can simply loop over all the data and run the meta-pipeline. We add a little bit of logic to deal with trials that for which we might not detect a valid WB. Then we aggregate the results. For the ``aggreate_parameters`` we modify the index, so that we have rows with NaNs for the trials that did not have any valid WBs. .. GENERATED FROM PYTHON SOURCE LINES 130-151 .. code-block:: default import pandas as pd from tqdm.auto import tqdm per_wb_paras = {} aggregated_paras = {} for trial in tqdm(LabExampleDataset()): pipe = meta_pipeline.clone().safe_run(trial) if not (per_wb := pipe.per_wb_parameters_).empty: per_wb_paras[trial.group_label] = per_wb if not (agg := pipe.aggregated_parameters_).empty: aggregated_paras[trial.group_label] = agg per_wb_paras = pd.concat(per_wb_paras) aggregated_paras = ( pd.concat(aggregated_paras) .reset_index(-1, drop=True) .rename_axis(LabExampleDataset().index.columns) .reindex(pd.MultiIndex.from_tuples(LabExampleDataset().group_labels)) ) .. rst-class:: sphx-glr-script-out .. code-block:: none 0%| | 0/9 [00:00

						start	end	n_strides	rule_name	rule_obj	duration_s	stride_duration_s	cadence_spm	stride_length_m	walking_speed_mps
					wb_id
HA	001	TimeMeasure1	Test5	Trial1	0	498	1115	9	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	6.17	1.018889	97.899883	1.117766	0.913574
			Test5	Trial2	0	288	978	10	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	6.90	1.235000	98.030680	1.135156	0.940431
			Test11	Trial1	0	630	1112	7	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	4.82	1.012857	102.691989	1.089851	0.935974
					1	2832	4138	14	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	13.06	1.305714	79.290506	0.699701	0.459350
					2	4488	5198	7	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	7.10	1.604286	91.005498	1.073694	0.812935
					3	7848	8782	11	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	9.34	1.253636	85.665068	0.876606	0.631880
					4	9530	10168	9	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	6.38	1.231111	83.159936	0.866376	0.627456
					5	11032	11465	5	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	4.33	1.306000	88.480662	0.814981	0.619937
					6	13118	13602	6	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	4.84	1.343333	92.247498	1.042166	0.833501
	002	TimeMeasure1	Test5	Trial1	0	280	738	6	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	4.58	1.241667	91.804548	1.336648	1.086528
			Test5	Trial2	0	115	592	6	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	4.77	1.203333	89.651780	1.525990	1.140575
			Test11	Trial1	0	480	1138	7	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	6.58	1.501429	79.867370	0.876770	0.606076
					1	2458	3992	14	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	15.34	1.120714	79.699313	0.780370	0.531763
					2	5828	7165	14	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	13.37	1.531429	79.649525	0.907134	0.606389
					3	7422	7892	5	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	4.70	1.224000	68.876437	1.017318	0.574567
					4	15088	15520	4	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	4.32	1.212500	81.999762	0.605622	0.418481
MS	001	TimeMeasure1	Test5	Trial1	0	673	1185	8	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	5.12	1.127500	106.918436	1.125898	1.016039
			Test5	Trial2	0	415	920	8	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	5.05	1.043750	107.443125	1.069114	0.995062
			Test11	Trial1	0	979	1835	12	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	8.56	1.259167	92.857232	0.923175	0.745904
					1	5230	5982	8	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	7.52	1.175000	87.808553	0.914782	0.650693
					2	9715	10630	12	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	9.15	0.985000	100.656484	0.676807	0.570474
					3	12994	14690	27	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	16.96	1.106667	100.847040	0.879673	0.742630
					4	20287	22435	25	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	21.48	1.134800	82.417073	0.903417	0.625303

.. GENERATED FROM PYTHON SOURCE LINES 159-163 And the aggregated parameters. Note, that many values are NaN, because only a single WB was detected per trial. So we can not calculate the standard deviation or other statistics. To learn more about what the different aggregated values mean, check :class:`~mobgap.aggregation.MobilisedAggregator`. .. GENERATED FROM PYTHON SOURCE LINES 163-188 .. code-block:: default aggregated_paras # Modifying Parameters # ------------------- # Both pipelines are basically the same, but the algorithms used for certain steps are different. # Both just reimplement :class:`~mobgap.pipeline.BaseMobilisedPipeline` with the respective algorithms as default # parameters. # So we can easily modify the parameters of the pipeline either using the ``set_params`` method or by passing different # parameters/algorithms to the constructor. # # .. warning:: As part of Mobilise-D we only validated the pipelines with their default values in exactly the cohorts we # recommend them for. # If you change the parameters, or use them in a different cohort, we ask you to not call this approach # "the Mobilised Pipeline" anymore, when communicating your results. # # Starting simple, let's say we simply don't want to filter and aggregate the final DMOs. # We just set the respective parameters to None. from mobgap.pipeline import MobilisedPipelineHealthy pipe_no_agg = MobilisedPipelineHealthy( dmo_thresholds=None, dmo_aggregation=None ) pipe_no_agg.safe_run(long_test_ha) .. rst-class:: sphx-glr-script-out .. code-block:: none MobilisedPipelineHealthy(cadence_calculation=CadFromIcDetector(ic_detector=IcdShinImproved(axis='norm'), max_interpolation_gap_s=3, silence_ic_warning=True, step_time_smoothing=HampelFilter(half_window_size=2, n_sigmas=3.0)), dmo_aggregation=None, dmo_thresholds=None, gait_sequence_detection=GsdIluz(acc_v_standing_threshold=4.903325, allowed_acc_v_change_per_window=0.15, allowed_steps_per_s=(0.5, 3), mean_activity_threshold=-0.980665, min_gsd_duration_s=5, pre_filter=FirFilter(cutoff_freq_hz=(0.5, 3), filter_type='bandpass', order=200, window='hamming', zero_phase=True), sin_template_freq_hz=2, std_activity_threshold=0.0980665, step_detection_thresholds=(3.92266, 14.709975), window_length_s=3, window_overlap=0.5), initial_contact_detection=IcdIonescu(cwt_width=9.0, pre_filter=EpflDedriftedGaitFilter(zero_phase=True)), laterality_classification=LrcUllrich(clf_pipe=Pipeline(steps=[('scaler_old', MinMaxScaler()), ('clf_old', SVC(C=0.1, kernel='linear'))]), smoothing_filter=ButterworthFilter(cutoff_freq_hz=(0.5, 2), filter_type='bandpass', order=4, zero_phase=True)), recommended_cohorts=('HA', 'COPD', 'CHF'), stride_length_calculation=SlZijlstra(acc_smoothing=ButterworthFilter(cutoff_freq_hz=0.1, filter_type='highpass', order=4, zero_phase=True), max_interpolation_gap_s=3, orientation_method=None, speed_smoothing=ButterworthFilter(cutoff_freq_hz=1, filter_type='highpass', order=4, zero_phase=True), step_length_scaling_factor=1.14675, step_length_smoothing=HampelFilter(half_window_size=2, n_sigmas=3.0)), stride_selection=StrideSelection(incompatible_rules='warn', rules=[('stride_duration_thres', IntervalDurationCriteria(inclusive=(False, True), max_duration_s=3.0, min_duration_s=0.2)), ('stride_length_thres', IntervalParameterCriteria(inclusive=(False, True), lower_threshold=0.15, parameter='stride_length_m', upper_threshold=None))]), turn_detection=TdElGohary(allowed_turn_angle_deg=(45, inf), allowed_turn_duration_s=(0.5, 10), lower_threshold_velocity_dps=5, min_gap_between_turns_s=0.05, min_peak_angle_velocity_dps=15, orientation_estimation=None, smoothing_filter=ButterworthFilter(cutoff_freq_hz=0.5, filter_type='lowpass', order=4, zero_phase=True)), walking_speed_calculation=WsNaive(), wba=WbAssembly(rules=[('min_strides', NStridesCriteria(min_strides=4, min_strides_left=3, min_strides_right=3)), ('max_break', MaxBreakCriteria(consider_end_as_break=True, max_break_s=3, remove_last_ic=False))])) .. GENERATED FROM PYTHON SOURCE LINES 189-190 Now, the aggregated parameters are empty. .. GENERATED FROM PYTHON SOURCE LINES 190-192 .. code-block:: default pipe_no_agg.aggregated_parameters_ .. GENERATED FROM PYTHON SOURCE LINES 193-194 And the per WB parameters are still there. .. GENERATED FROM PYTHON SOURCE LINES 194-196 .. code-block:: default pipe_no_agg.per_wb_parameters_ .. raw:: html

	start	end	n_strides	rule_name	rule_obj	duration_s	stride_duration_s	cadence_spm	stride_length_m	walking_speed_mps
wb_id
0	630	1112	7	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	4.82	1.012857	102.691989	1.089851	0.935974
1	2832	4138	14	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	13.06	1.305714	79.290506	0.699701	0.459350
2	4488	5198	7	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	7.10	1.604286	91.005498	1.073694	0.812935
3	7848	8782	11	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	9.34	1.253636	85.665068	0.876606	0.631880
4	9530	10168	9	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	6.38	1.231111	83.159936	0.866376	0.627456
5	11032	11465	5	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	4.33	1.306000	88.480662	0.814981	0.619937
6	13118	13602	6	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	4.84	1.343333	92.247498	1.042166	0.833501

.. GENERATED FROM PYTHON SOURCE LINES 197-203 If you want to change the algorithm used for a certain step, you can simply pass a different algorithm to the constructor. For example, let's say you want to use the Adaptive Ionescu GSD algorithm instead of the GSDIluz (which is the default for the healthy pipeline). For the sake of this example, we will also modify the default parameters of the algorithm. .. GENERATED FROM PYTHON SOURCE LINES 203-209 .. code-block:: default from mobgap.gait_sequences import GsdAdaptiveIonescu pipe_adaptive_gsd = MobilisedPipelineHealthy( gait_sequence_detection=GsdAdaptiveIonescu(min_n_steps=3) ) pipe_adaptive_gsd.safe_run(long_test_ha) .. rst-class:: sphx-glr-script-out .. code-block:: none MobilisedPipelineHealthy(cadence_calculation=CadFromIcDetector(ic_detector=IcdShinImproved(axis='norm'), max_interpolation_gap_s=3, silence_ic_warning=True, step_time_smoothing=HampelFilter(half_window_size=2, n_sigmas=3.0)), dmo_aggregation=MobilisedAggregator(groupby=None, unique_wb_id_column='wb_id', use_original_names=False), dmo_thresholds=condition free_living ... global threshold_type min max ... min max dmo cohort ... cadence_spm CHF 40.594770 167.942930 ... 40.000000 172.900 COPD 38.880484 151.558718 ... 40.000000 172.900 HA 35.751898 156.946955 ... 40.000000 172.900 MS 38.443211 155.394496 ... 40.000000 172.900 PD 40.957969 142.121947 ... 40.000000 172.900 PFF 42.191719 157.613665 ... 40.000000 172.900 walking_speed_mps CHF 0.112478 1.757103 ... 0.081515 2.220 COPD 0.090731 1.641773 ... 0.081515 2.220 HA 0.097413 1.965728 ... 0.081515 2.220 MS 0.085918 1.920262 ... 0.081515 2.220 PD 0.081515 1.735348 ... 0.081515 2.220 PFF 0.104547 1.553186 ... 0.081515 2.220 stride_length_m CHF 0.185308 2.166646 ... 0.150523 2.190 COPD 0.176403 1.711645 ... 0.150523 2.190 HA 0.155126 2.024694 ... 0.150523 2.190 MS 0.191099 1.940537 ... 0.150523 2.190 PD 0.150523 1.982926 ... 0.150523 2.190 PFF 0.206787 1.697251 ... 0.150523 2.190 stride_duration_s CHF 0.702000 3.030857 ... 0.460000 3.000 COPD 0.770000 3.000000 ... 0.460000 3.000 HA 0.735435 3.254400 ... 0.460000 3.000 MS 0.775597 3.057750 ... 0.460000 3.000 PD 0.836253 2.928000 ... 0.460000 3.000 PFF 0.744000 2.769000 ... 0.460000 3.000 step_duration_s CHF 0.376000 1.504500 ... 0.140000 2.124 COPD 0.390400 1.788000 ... 0.140000 2.124 HA 0.367972 1.730400 ... 0.140000 2.124 MS 0.388084 1.905000 ... 0.140000 2.124 PD 0.417216 1.605600 ... 0.140000 2.124 PFF 0.371429 2.124000 ... 0.140000 2.124 [30 rows x 8 columns], gait_sequence_detection=GsdAdaptiveIonescu(active_signal_fallback_threshold=1.4709975, max_gap_s=3.5, min_n_steps=3, min_step_margin_s=1.5, padding=0.75), initial_contact_detection=IcdIonescu(cwt_width=9.0, pre_filter=EpflDedriftedGaitFilter(zero_phase=True)), laterality_classification=LrcUllrich(clf_pipe=Pipeline(steps=[('scaler_old', MinMaxScaler()), ('clf_old', SVC(C=0.1, kernel='linear'))]), smoothing_filter=ButterworthFilter(cutoff_freq_hz=(0.5, 2), filter_type='bandpass', order=4, zero_phase=True)), recommended_cohorts=('HA', 'COPD', 'CHF'), stride_length_calculation=SlZijlstra(acc_smoothing=ButterworthFilter(cutoff_freq_hz=0.1, filter_type='highpass', order=4, zero_phase=True), max_interpolation_gap_s=3, orientation_method=None, speed_smoothing=ButterworthFilter(cutoff_freq_hz=1, filter_type='highpass', order=4, zero_phase=True), step_length_scaling_factor=1.14675, step_length_smoothing=HampelFilter(half_window_size=2, n_sigmas=3.0)), stride_selection=StrideSelection(incompatible_rules='warn', rules=[('stride_duration_thres', IntervalDurationCriteria(inclusive=(False, True), max_duration_s=3.0, min_duration_s=0.2)), ('stride_length_thres', IntervalParameterCriteria(inclusive=(False, True), lower_threshold=0.15, parameter='stride_length_m', upper_threshold=None))]), turn_detection=TdElGohary(allowed_turn_angle_deg=(45, inf), allowed_turn_duration_s=(0.5, 10), lower_threshold_velocity_dps=5, min_gap_between_turns_s=0.05, min_peak_angle_velocity_dps=15, orientation_estimation=None, smoothing_filter=ButterworthFilter(cutoff_freq_hz=0.5, filter_type='lowpass', order=4, zero_phase=True)), walking_speed_calculation=WsNaive(), wba=WbAssembly(rules=[('min_strides', NStridesCriteria(min_strides=4, min_strides_left=3, min_strides_right=3)), ('max_break', MaxBreakCriteria(consider_end_as_break=True, max_break_s=3, remove_last_ic=False))])) .. GENERATED FROM PYTHON SOURCE LINES 210-211 This works as before and all parameters of the pipeline are still available. .. GENERATED FROM PYTHON SOURCE LINES 211-213 .. code-block:: default pipe_adaptive_gsd.aggregated_parameters_ .. raw:: html

	wb_all__count	total_walking_duration_h	wb_all__duration_s__avg	wb_all__duration_s__max	wb_all__duration_s__var	wb_all__cadence_spm__avg	wb_all__stride_duration_s__avg	wb_all__cadence_spm__var	wb_all__stride_duration_s__var	wb_10_30__count	wb_10_30__walking_speed_mps__avg	wb_10_30__stride_length_m__avg	wb_10__count	wb_10__walking_speed_mps__max	wb_30__count	wb_30__walking_speed_mps__avg	wb_30__stride_length_m__avg	wb_30__cadence_spm__avg	wb_30__stride_duration_s__avg	wb_30__walking_speed_mps__max	wb_30__cadence_spm__max	wb_30__walking_speed_mps__var	wb_30__stride_length_m__var	wb_60__count
all_wbs	6	0.014753	7.245	15.335	0.615597	91.7224	1.143603	0.058541	0.150406	2	0.773952	1.057163	2	0.790726	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0

.. GENERATED FROM PYTHON SOURCE LINES 214-216 .. code-block:: default pipe_adaptive_gsd.per_wb_parameters_ .. raw:: html

	start	end	n_strides	rule_name	rule_obj	duration_s	stride_duration_s	cadence_spm	stride_length_m	walking_speed_mps
wb_id
0	533	1045	7	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	5.12	0.974286	98.374985	1.056063	0.890358
1	3853	5300	18	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	14.47	1.355000	86.077669	1.055774	0.752985
2	7740	8677	10	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	9.37	1.033000	91.138330	0.957432	0.720453
3	9532	10005	6	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	4.73	1.226667	88.473947	1.041943	0.770051
4	11142	11464	4	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	3.22	0.970000	98.290233	0.833365	0.663786
5	11982	13602	15	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	16.20	1.302667	87.979239	1.058551	0.794920

.. GENERATED FROM PYTHON SOURCE LINES 217-227 When you are planning to modify many algorithms, we would recommend to not use the specific pipeline classes anymore, to avoid the association (is it really still the Healthy pipeline if you change all algorithms?). In this case, we recommend the un-configured :class:`~mobgap.pipeline.GenericMobilisedPipeline`. This class is also used as the base class for the preconfigured pipelines. It has no algorithms set by default, so you have to set all algorithms yourself. See the end of the `step-by-step example `_ for a demonstration. If you want to reuse some of the defaults of the preconfigured pipelines, you can still use the ``PreconfiguredParameters``. For example, we could get the same pipeline as before like this: .. GENERATED FROM PYTHON SOURCE LINES 227-236 .. code-block:: default from mobgap.pipeline import GenericMobilisedPipeline pipe_custom = GenericMobilisedPipeline( **dict( GenericMobilisedPipeline.PredefinedParameters.regular_walking, gait_sequence_detection=GsdAdaptiveIonescu(min_n_steps=3), ) ) pipe_adaptive_gsd.safe_run(long_test_ha) .. rst-class:: sphx-glr-script-out .. code-block:: none MobilisedPipelineHealthy(cadence_calculation=CadFromIcDetector(ic_detector=IcdShinImproved(axis='norm'), max_interpolation_gap_s=3, silence_ic_warning=True, step_time_smoothing=HampelFilter(half_window_size=2, n_sigmas=3.0)), dmo_aggregation=MobilisedAggregator(groupby=None, unique_wb_id_column='wb_id', use_original_names=False), dmo_thresholds=condition free_living ... global threshold_type min max ... min max dmo cohort ... cadence_spm CHF 40.594770 167.942930 ... 40.000000 172.900 COPD 38.880484 151.558718 ... 40.000000 172.900 HA 35.751898 156.946955 ... 40.000000 172.900 MS 38.443211 155.394496 ... 40.000000 172.900 PD 40.957969 142.121947 ... 40.000000 172.900 PFF 42.191719 157.613665 ... 40.000000 172.900 walking_speed_mps CHF 0.112478 1.757103 ... 0.081515 2.220 COPD 0.090731 1.641773 ... 0.081515 2.220 HA 0.097413 1.965728 ... 0.081515 2.220 MS 0.085918 1.920262 ... 0.081515 2.220 PD 0.081515 1.735348 ... 0.081515 2.220 PFF 0.104547 1.553186 ... 0.081515 2.220 stride_length_m CHF 0.185308 2.166646 ... 0.150523 2.190 COPD 0.176403 1.711645 ... 0.150523 2.190 HA 0.155126 2.024694 ... 0.150523 2.190 MS 0.191099 1.940537 ... 0.150523 2.190 PD 0.150523 1.982926 ... 0.150523 2.190 PFF 0.206787 1.697251 ... 0.150523 2.190 stride_duration_s CHF 0.702000 3.030857 ... 0.460000 3.000 COPD 0.770000 3.000000 ... 0.460000 3.000 HA 0.735435 3.254400 ... 0.460000 3.000 MS 0.775597 3.057750 ... 0.460000 3.000 PD 0.836253 2.928000 ... 0.460000 3.000 PFF 0.744000 2.769000 ... 0.460000 3.000 step_duration_s CHF 0.376000 1.504500 ... 0.140000 2.124 COPD 0.390400 1.788000 ... 0.140000 2.124 HA 0.367972 1.730400 ... 0.140000 2.124 MS 0.388084 1.905000 ... 0.140000 2.124 PD 0.417216 1.605600 ... 0.140000 2.124 PFF 0.371429 2.124000 ... 0.140000 2.124 [30 rows x 8 columns], gait_sequence_detection=GsdAdaptiveIonescu(active_signal_fallback_threshold=1.4709975, max_gap_s=3.5, min_n_steps=3, min_step_margin_s=1.5, padding=0.75), initial_contact_detection=IcdIonescu(cwt_width=9.0, pre_filter=EpflDedriftedGaitFilter(zero_phase=True)), laterality_classification=LrcUllrich(clf_pipe=Pipeline(steps=[('scaler_old', MinMaxScaler()), ('clf_old', SVC(C=0.1, kernel='linear'))]), smoothing_filter=ButterworthFilter(cutoff_freq_hz=(0.5, 2), filter_type='bandpass', order=4, zero_phase=True)), recommended_cohorts=('HA', 'COPD', 'CHF'), stride_length_calculation=SlZijlstra(acc_smoothing=ButterworthFilter(cutoff_freq_hz=0.1, filter_type='highpass', order=4, zero_phase=True), max_interpolation_gap_s=3, orientation_method=None, speed_smoothing=ButterworthFilter(cutoff_freq_hz=1, filter_type='highpass', order=4, zero_phase=True), step_length_scaling_factor=1.14675, step_length_smoothing=HampelFilter(half_window_size=2, n_sigmas=3.0)), stride_selection=StrideSelection(incompatible_rules='warn', rules=[('stride_duration_thres', IntervalDurationCriteria(inclusive=(False, True), max_duration_s=3.0, min_duration_s=0.2)), ('stride_length_thres', IntervalParameterCriteria(inclusive=(False, True), lower_threshold=0.15, parameter='stride_length_m', upper_threshold=None))]), turn_detection=TdElGohary(allowed_turn_angle_deg=(45, inf), allowed_turn_duration_s=(0.5, 10), lower_threshold_velocity_dps=5, min_gap_between_turns_s=0.05, min_peak_angle_velocity_dps=15, orientation_estimation=None, smoothing_filter=ButterworthFilter(cutoff_freq_hz=0.5, filter_type='lowpass', order=4, zero_phase=True)), walking_speed_calculation=WsNaive(), wba=WbAssembly(rules=[('min_strides', NStridesCriteria(min_strides=4, min_strides_left=3, min_strides_right=3)), ('max_break', MaxBreakCriteria(consider_end_as_break=True, max_break_s=3, remove_last_ic=False))])) .. GENERATED FROM PYTHON SOURCE LINES 237-239 .. code-block:: default pipe_adaptive_gsd.aggregated_parameters_ .. raw:: html

	wb_all__count	total_walking_duration_h	wb_all__duration_s__avg	wb_all__duration_s__max	wb_all__duration_s__var	wb_all__cadence_spm__avg	wb_all__stride_duration_s__avg	wb_all__cadence_spm__var	wb_all__stride_duration_s__var	wb_10_30__count	wb_10_30__walking_speed_mps__avg	wb_10_30__stride_length_m__avg	wb_10__count	wb_10__walking_speed_mps__max	wb_30__count	wb_30__walking_speed_mps__avg	wb_30__stride_length_m__avg	wb_30__cadence_spm__avg	wb_30__stride_duration_s__avg	wb_30__walking_speed_mps__max	wb_30__cadence_spm__max	wb_30__walking_speed_mps__var	wb_30__stride_length_m__var	wb_60__count
all_wbs	6	0.014753	7.245	15.335	0.615597	91.7224	1.143603	0.058541	0.150406	2	0.773952	1.057163	2	0.790726	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0

.. GENERATED FROM PYTHON SOURCE LINES 240-242 .. code-block:: default pipe_adaptive_gsd.per_wb_parameters_ .. raw:: html

	start	end	n_strides	rule_name	rule_obj	duration_s	stride_duration_s	cadence_spm	stride_length_m	walking_speed_mps
wb_id
0	533	1045	7	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	5.12	0.974286	98.374985	1.056063	0.890358
1	3853	5300	18	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	14.47	1.355000	86.077669	1.055774	0.752985
2	7740	8677	10	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	9.37	1.033000	91.138330	0.957432	0.720453
3	9532	10005	6	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	4.73	1.226667	88.473947	1.041943	0.770051
4	11142	11464	4	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	3.22	0.970000	98.290233	0.833365	0.663786
5	11982	13602	15	max_break	MaxBreakCriteria(consider_end_as_break=True, m...	16.20	1.302667	87.979239	1.058551	0.794920

.. GENERATED FROM PYTHON SOURCE LINES 243-257 On the other end, if you are only planning to change a single sub-parameter of a pipeline, it might be easier to use the ``set_params`` method, instead of passing all parameters to the constructor. We show the extreme example of this here, by using the Universal-Pipeline as starting point and changing the filter order of the pre-processing filter of the GSD algorithm of the healthy pipeline used internally in the MetaPipeline. .. note:: The MobilisedPipelineUniversal is a special case, as it makes use of a tpcp feature called ``composite_params``. This allows us to target the ``pipelines__healthy`` parameters, even tough ``pipelines`` is not an object, but a list of tuples. Learn more about this feature in the `tpcp documentation `_. .. GENERATED FROM PYTHON SOURCE LINES 257-263 .. code-block:: default from mobgap.pipeline import MobilisedPipelineUniversal meta_pipeline_modified = MobilisedPipelineUniversal().set_params( pipelines__healthy__gait_sequence_detection__pre_filter__order=50 ) .. GENERATED FROM PYTHON SOURCE LINES 264-268 This parameter name is a bit long, but it demonstrates that it is possible to change even deeply nested parameters. This might be in particular useful, when you want to run approaches like GridSearch. The algorithm works as before (note we don't expect any change in output for this parameter change). .. GENERATED FROM PYTHON SOURCE LINES 268-270 .. code-block:: default meta_pipeline_modified.safe_run(long_test_ha) meta_pipeline_modified.aggregated_parameters_ .. raw:: html

	wb_all__count	total_walking_duration_h	wb_all__duration_s__avg	wb_all__duration_s__max	wb_all__duration_s__var	wb_all__cadence_spm__avg	wb_all__stride_duration_s__avg	wb_all__cadence_spm__var	wb_all__stride_duration_s__var	wb_10_30__count	wb_10_30__walking_speed_mps__avg	wb_10_30__stride_length_m__avg	wb_10__count	wb_10__walking_speed_mps__max	wb_30__count	wb_30__walking_speed_mps__avg	wb_30__stride_length_m__avg	wb_30__cadence_spm__avg	wb_30__stride_duration_s__avg	wb_30__walking_speed_mps__max	wb_30__cadence_spm__max	wb_30__walking_speed_mps__var	wb_30__stride_length_m__var	wb_60__count
all_wbs	7	0.013853	6.38	10.828	0.440279	88.934451	1.293848	0.084865	0.135207	1	0.45935	0.699701	1	0.45935	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0

.. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 14.841 seconds) **Estimated memory usage:** 16 MB .. _sphx_glr_download_auto_examples_pipeline__03_preconfigured_mobilised_pipelines.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: _03_preconfigured_mobilised_pipelines.py <_03_preconfigured_mobilised_pipelines.py>` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: _03_preconfigured_mobilised_pipelines.ipynb <_03_preconfigured_mobilised_pipelines.ipynb>` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_