Gait Sequence Detection (GSD)#

Algorithms to detect gait sequences within raw IMU data.

Algorithms#

`GsdIluz`(*[, pre_filter, window_length_s, ...])	Implementation of the GSD algorithm by Iluz et al. (2014) [Rea5df3d1a481-1].
`GsdIonescu`(*[, min_n_steps, ...])	Implementation of the GSD algorithm developed by Paraschiv-Ionescu et al. (2014) [R58e9f9eaa522-1].
`GsdAdaptiveIonescu`(*[, min_n_steps, ...])	Implementation of the GSD algorithm by Paraschiv-Ionescu et al. (2019) [1, 2]_ with adaptive threshold.

Run a GSD algorithm in isolation on a Gait Dataset.

Base class for GSD detectors.

Base class for GS-detectors.

Decorator to fill common parts of the docstring for subclasses of BaseGsdDetector.

`calculate_matched_gsd_performance_metrics`(...)	Calculate commonly known performance metrics for based on the matched overlap with the reference.
`calculate_unmatched_gsd_performance_metrics`(*, ...)	Calculate general performance metrics that don't rely on matching the detected and reference gait sequences.
`categorize_intervals_per_sample`(*, ...[, ...])	Evaluate detected gait sequence intervals against a reference on a sample-wise level.
`categorize_intervals`(*, gsd_list_detected, ...)	Evaluate a gait sequence list against a reference sequence-by-sequence with a minimum overlap threshold.
`plot_categorized_intervals`(...)	Plot the categorized intervals together with the detected and reference intervals.
`get_matching_intervals`(*, metrics_detected, ...)	Extract the detected and reference gait sequences that are considered as matches sequence-by-sequence.

These scores are expected to be used in combination with Evaluation and EvaluationCV or directly with cross_validation and validation.

Scorer for GSD algorithms.

`gsd_per_datapoint_score`(pipeline, datapoint)	Evaluate the performance of a GSD algorithm on a single datapoint.
`gsd_final_agg`(agg_results, single_results, ...)	Aggregate the performance metrics of a GSD algorithm over multiple datapoints.