pyCGM2.Model.modelDecorator.harringtonRegression

pyCGM2.Model.modelDecorator.harringtonRegression(mp_input: Dict[str, float], mp_computed: Dict[str, float], predictors: str, markerDiameter: float = 14.0, basePlate: float = 2.0, cgmReferential: bool = True) → Tuple[ndarray, ndarray]

Implements the Harrington et al. regression for hip joint center estimation.

Reference:

Harrington, M., Zavatsky, A., Lawson, S., Yuan, Z., & Theologis, T. (2007). Prediction of the hip joint centre in adults, children, and patients with cerebral palsy based on magnetic resonance imaging. Journal of Biomechanics, 40(3), 595–602

Sangeux, M. (2015). On the implementation of predictive methods to locate the hip joint centres. Gait and Posture, 42(3), 402–405.

Parameters:

• mp_input (Dict[str, float]) – Dictionary of measured anthropometric parameters.

• mp_computed (Dict[str, float]) – Dictionary of computed anthropometric parameters.

• predictors (str) – Predictor choice of the regression (full, PWonly, LLonly).

• markerDiameter (float, optional) – Diameter of the marker. Defaults to 14.0.

• basePlate (float, optional) – Thickness of the base plate. Defaults to 2.0.

• cgmReferential (bool, optional) – Flag indicating if HJC position will be expressed in CGM pelvis coordinate system. Defaults to True.

Returns:

Tuple[np.ndarray, np.ndarray] – Estimated positions of left and right hip joint centers.

Note

• Predictor choice allows using modified Harrington’s regression from Sangeux 2015

• pelvisDepth,`asisDistance` and meanlegLength are automaticaly computed from CGM calibration