calibration-cor-terms module

This module implements the correction-terms calibration process. The approach uses relative sensor values and therefore needs a neutral dataset.

It mainly considers a linear relationship between relative sensor-values and the corresponding angles. Thus, a gain (g) is required for each sensor to fulfill the linear function theta = g * s where theta is the angle and s is the relative sensor value. There is no offset because the measurement for 0° always corresponds to a relative sensor value of 0. Most gains are calculated by measuring two poses with predefined angles for each sensor.

Additionally, this calibration method adds correction-terms for the abduction of the digits and two correction terms for the thumb flexion and abduction.

Furthermore, this approach uses the flexion-hand-model, for which the thumb CMC joint has two degrees of freedom: flexion and abduction.

For more information about the approach, please refer to the Bachelor’s thesis by Johannes Michaelis.

Note

This calibration ignores the wrist abduction as it is broken on the CyberGlove III used at LTD.

class cyberglove.calibration_cor_terms.CROSSCOUPLING(value)

Bases: enum.Enum

Enumeration holding information about the cross-coupling effects of the abduction sensors. The enum-objects hold the sensor-value of the corresponding neighboring MCP joint sensors.

class cyberglove.calibration_cor_terms.CalibrationCorTerms(s_id='', use_latest_timestamp=False)

The class implementing the correction-terms calibration process.

__init__(s_id='', use_latest_timestamp=False)

The __init__()-method calls the parent-constructor (__init__()) which is taking care of asking for subject-information as well as creating and handling folder structure. It also creates a neutral_dataset, which is required to calculate relative sensor values. The __init__()-method() itself creates 22 gains and 5 correction-terms-constants for each sensor (although not every joint uses all 5 constants), it defines the visualization model to be of type flexion and sensor-names are set to the corresponding sensor-index number.

Parameters

s_id (string, optional) – subject ID if already known. if not specified, the parent-class will ask for it
use_latest_timestamp (bool, optional) – if set to True, the last timestamp-folder of the subject will be used

calibrate(glove)

Runs the complete correction-terms calibration process. It calls the functions measure() and postprocess().

Parameters: glove (cyberglove.glove.Glove) – connected glove object
Returns: None

export_json()

Exports all gains and constants to the json-file cor_terms.json in the filepath-folder.

Returns: None
Raises: FileNotFoundError – when file can’t be opened

import_json()

Reads all gains and constants from the json-file cor_terms.json in the filepath-folder.

Returns: None
Raises: FileNotFoundError – when file can’t be opened

measure(glove)

Performs all measurements required for the correction-terms calibration process. It tests for existing files in the corresponding folder and calls the private functions _measure_static_postures() and _measure_abduction_postures().

Parameters: glove (cyberglove.glove.Glove) – connected glove object
Returns: the folderpath where the files with captured data are saved
Return type: str

postprocess(folderpath)

Post-processes the captured data to get a calibration. It calls the functions _postprocess_linear(), _postprocess_abd_correction_terms() and _postprocess_thumb_correction_terms(). This post-processing method assumes that the static_postures.txt-file and the static_abduction_postures.txt-file are valid and hold all required information and it does not perform error checking/handling.

Parameters: folderpath (str) – folderpath of parent-directory of the static_postures.txt-file and the static_abduction_postures.txt-file
Returns: None

raw_to_angles(dataset)

Calculates angles from (absolute) raw sensor data with the gains, the correction-terms and the neutral dataset set as class-variables.

Parameters: dataset (numpy.ndarray) – 1-dimensional array of 22 raw-values
Returns: 1-dimensional array of 22 corresponding angles
Return type: numpy.ndarray