Finger Tapping Processor¶

class finger_tapping_processor.FingerTappingProcessor(window=6)¶

This is the main Finger Tapping Processor class. Once the data is loaded it will be accessible at data_frame (pandas.DataFrame), where it looks like: data_frame.x, data_frame.y: components of tapping position. data_frame.x_target, data_frame.y_target their target.

These values are recommended by the author of the pilot study [KSR+15]. Check reference for more details.

window = 6 #seconds

Example

>>> import pdkit
>>> ftp = pdkit.FingerTappingProcessor()
>>> ts = pdkit.FingerTappingTimeSeries().load(path_to_data, 'ft_cloudupdrs')
>>> frequency = ftp.frequency(ts)

akinesia_times(data_frame, crop='no')¶

This method calculates akinesia times, mean dwell time on each key in milliseconds

Parameters: data_frame (pandas.DataFrame) – the data frame
Return at: akinesia times
Rtype at: float
Return duration: test duration (seconds)
Rtype duration: float

continuous_frequency(data_frame, crop='no')¶

This method returns continuous frequency

Parameters: data_frame (pandas.DataFrame) – the data frame
Return cont_freq: frequency
Rtype cont_freq: float

dysmetria_score(data_frame, crop='no')¶

This method calculates accuracy of target taps in pixels

Parameters: data_frame (pandas.DataFrame) – the data frame
Return ds: dysmetria score in pixels
Rtype ds: float

extract_features(data_frame, pre='', crop='no')¶

This method extracts all the features available to the Finger Tapping Processor class.

Parameters: data_frame (pandas.DataFrame) – the data frame
Returns: ‘frequency’, ‘moving_frequency’,’continuous_frequency’,’mean_moving_time’,’incoordination_score’, ‘mean_alnt_target_distance’,’kinesia_scores’, ‘akinesia_times’,’dysmetria_score’
Return type: list

frequency(data_frame, crop='no')¶

This method returns the number of #taps divided by the test duration

Parameters: data_frame (pandas.DataFrame) – the data frame
Return frequency: frequency
Rtype frequency: float

incoordination_score(data_frame, crop='no')¶

This method calculates the variance of the time interval in msec between taps

Parameters: data_frame (pandas.DataFrame) – the data frame
Return is: incoordination score
Rtype is: float

kinesia_scores(data_frame, crop='no')¶

This method calculates the number of key taps

Parameters: data_frame (pandas.DataFrame) – the data frame
Return ks: key taps
Rtype ks: float
Return duration: test duration (seconds)
Rtype duration: float

mean_alnt_target_distance(data_frame, crop='no')¶

This method calculates the distance (number of pixels) between alternate tapping

Parameters: data_frame (pandas.DataFrame) – the data frame
Return matd: the mean alternate target distance in pixels
Rtype matd: float

mean_moving_time(data_frame, crop='no')¶

This method calculates the mean time (ms) that the hand was moving from one target to the next

Parameters: data_frame (pandas.DataFrame) – the data frame
Return mmt: the mean moving time in ms
Rtype mmt: float

moving_frequency(data_frame, crop='no')¶

This method returns moving frequency

Parameters: data_frame (pandas.DataFrame) – the data frame
Return diff_mov_freq: frequency
Rtype diff_mov_freq: float

Finger Tapping Time Series¶

References

KSR+15: Panagiotis Kassavetis, Tabish A. Saifee, George Roussos, Loukas Drougkas, Maja Kojovic, John C. Rothwell, Mark J. Edwards, and Kailash P. Bhatia. Developing a tool for remote digital assessment of parkinson's disease. Movement Disorders Clinical Practice, 3(1):59–64, 2015.