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RunsData edited this page Dec 14, 2020 · 5 revisions

USER GUIDE for the Python Package wearablecompute.

wearablecompute is a package for Feature Engineering of Wearable Sensors.

Installation

wearablevar requires the pandas, numpy, and datetime packages.

Recommended: Install via pip:

$ pip install wearablecompute

Install via git:

$ pip install git+git://github.com/brinnaebent/wearablecompute.git
$ git clone

Metrics computed:

  • Mean Heart Rate Variability
  • Median Heart Rate Variability
  • Maximum Heart Rate Variability
  • Minimum Heart Rate Variability
  • SDNN (HRV)
  • RMSSD (HRV)
  • NNx (HRV)
  • pNNx (HRV)
  • HRV Frequency Domain Metrics:
  • PowerVLF
  • PowerLF
  • PowerHF
  • PowerTotal
  • LF/HF
  • PeakVLF
  • PeakLF
  • PeakHF
  • FractionLF
  • FractionHF
  • EDA Peaks
  • Activity Bouts
  • Interday Summary:
  • Interday Mean
  • Interday Median
  • Interday Maximum
  • Interday Minimum
  • Interday Quartile 1
  • Interday Quartile 3
  • Interday Standard Deviation
  • Interday Coefficient of Variation
  • Intraday Standard Deviation (mean, median, standard deviation)
  • Intraday Coefficient of Variation (mean, median, standard deviation)
  • Intraday Mean (mean, median, standard deviation)
  • Daily Mean
  • Intraday Summary:
  • Intraday Mean
  • Intraday Median
  • Intraday Minimum
  • Intraday Maximum
  • Intraday Quartile 1
  • Intraday Quartile 3
  • TIR (Time in Range of default 1 SD)
  • TOR (Time outside Range of default 1 SD)
  • POR (Percent outside Range of default 1 SD)
  • MASE (Mean Amplitude of Sensor Excursions, default 1 SD)
  • Hours from Midnight (circadian rhythm feature)
  • Minutes from Midnight (ciracadian rhythm feature)

Functions:

e4import()
Brings in an empatica compiled file **this is not raw empatica data**
         Args:
             filepath (String): path to file
             sensortype (Sting): Options: 'EDA', 'HR', 'ACC', 'TEMP', 'BVP'
             Starttime (String): (optional, default arg = 'NaN') format '%Y-%m-%d %H:%M:%S.%f', if you want to only look at data after a specific time
             Endtime (String): (optional, default arg = 'NaN') format '%Y-%m-%d %H:%M:%S.%f', if you want to only look at data before a specific time
             window (String): default '5min'; this is the window your data will be resampled on.
         Returns:
             (pd.DataFrame): dataframe of data with Time, Mean, Std columns
HRV()
Computes Heart Rate Variability metrics
        Args:
            time (pandas.DataFrame column or pandas series): time column
            IBI (pandas.DataFrame column or pandas series): column with inter beat intervals
            ibimultiplier (IntegerType): defualt = 1000; transforms IBI to milliseconds. If data is already in ms, set as 1
        Returns:
            maxHRV (FloatType): maximum HRV
            minHRV (FloatType): minimum HRV
            meanHRV (FloatType): mean HRV
            medianHRV(FloatType): median HRV
SDNN
Computes Heart Rate Variability metric SDNN
        Args:
            time (pandas.DataFrame column or pandas series): time column
            IBI (pandas.DataFrame column or pandas series): column with inter beat intervals
            ibimultiplier (IntegerType): defualt = 1000; transforms IBI to milliseconds. If data is already in ms, set as 1
        Returns:
            SDNN (FloatType): standard deviation of NN intervals
RMSSD
Computes Heart Rate Variability metric RMSSD
        Args:
            time (pandas.DataFrame column or pandas series): time column
            IBI (pandas.DataFrame column or pandas series): column with inter beat intervals
            ibimultiplier (IntegerType): defualt = 1000; transforms IBI to milliseconds. If data is already in ms, set as 1
        Returns:
            RMSSD (FloatType): root mean square of successive differences
NNx
Computes Heart Rate Variability metrics NNx and pNNx
        Args:
            time (pandas.DataFrame column or pandas series): time column
            IBI (pandas.DataFrame column or pandas series): column with inter beat intervals
            ibimultiplier (IntegerType): defualt = 1000; transforms IBI to milliseconds. If data is already in ms, set as 1
            x (IntegerType): default = 50; set the number of times successive heartbeat intervals exceed 'x' ms
        Returns:
            NNx (FloatType): the number of times successive heartbeat intervals exceed x ms
            pNNx (FloatType): the proportion of NNx divided by the total number of NN (R-R) intervals.
FrequencyHRV()
Computes Heart Rate Variability frequency domain metrics
        Args:
            IBI (pandas.DataFrame column or pandas series): column with inter beat intervals
            ibimultiplier (IntegerType): defualt = 1000; transforms IBI to milliseconds. If data is already in ms, set as 1
            fs (IntegerType): Optional sampling frequency for frequency interpolation (default=1)
        Returns:
            (dictionary): dictionary of frequency domain HRV metrics with keys:
                PowerVLF (FloatType): Power of the Very Low Frequency (VLF): 0-0.04Hz band
                PowerLF (FloatType): Power of the Low Frequency (LF): 0.04-0.15Hz band
                PowerHF (FloatType): Power of the High Frequency (HF): 0.15-0.4Hz band
                PowerTotal (FloatType):Total power over all frequency bands
                LF/HF (FloatType): Ratio of low and high power
                Peak VLF (FloatType): Peak of the Very Low Frequency (VLF): 0-0.04Hz band
                Peak LF (FloatType): Peak of the Low Frequency (LF): 0.04-0.15Hz band
                Peak HF (FloatType): Peak of the High Frequency (HF): 0.15-0.4Hz band
                FractionLF (FloatType): Fraction that is low frequency
                FractionHF (FloatType): Fraction that is high frequency
PeaksEDA()
Calculates peaks in the EDA signal
        Args:
            eda (pandas.DataFrame column or pandas series): eda column
            time (pandas.DataFrame column or pandas series): time column
        Returns:
            countpeaks (IntegerType): the number of peaks total 
            peakdf (pandas.DataFrame): a pandas dataframe with time and peaks to easily integrate with your data workflow
exercisepts()
Calculates activity bouts using accelerometry and heart rate
        Args:
            acc (pandas.DataFrame column or pandas series): accelerometry column
            hr (pandas.DataFrame column or pandas series): heart rate column
            time (pandas.DataFrame column or pandas series): time column
        Returns:
            countbouts (IntegerType): the number of acitvity bouts total
            returndf (pandas.DataFrame): a pandas dataframe with time and activity bouts (designated as a '1') to easily integrate with your data workflow
interdaycv()
Computes the interday coefficient of variation on pandas dataframe Sensor column
        Args:
            column (pandas.DataFrame column or pandas series): column that you want to calculate over
        Returns:
            cvx (IntegerType): interday coefficient of variation
interdaysd()
Computes the interday standard deviation of pandas dataframe Sensor column
        Args:
            column (pandas.DataFrame column or pandas series): column that you want to calculate over
        Returns:
            interdaysd (IntegerType): interday standard deviation
intradaycv()
Computes the intradaycv, returns the mean, median, and sd of intraday cv Sensor column in pandas dataframe
        Args:
             column (pandas.DataFrame column or pandas series): column that you want to calculate over
             time (pandas.DataFrame): time column
             timeformat (String): default = '%Y-%m-%d %H:%M:%S.%f'; format of timestamp in time column
        Returns:
            intradaycv_mean (IntegerType): Mean, Median, and SD of intraday coefficient of variation 
            intradaycv_median (IntegerType): Median of intraday coefficient of variation 
            intradaycv_sd (IntegerType): SD of intraday coefficient of variation 
        Requires:
            interdaycv() function
intradaysd()
Computes the intradaysd, returns the mean, median, and sd of intraday sd Sensor column in pandas dataframe
        Args:
             column (pandas.DataFrame column or pandas series): column that you want to calculate over
             time (pandas.DataFrame): time column
             timeformat (String): default = '%Y-%m-%d %H:%M:%S.%f'; format of timestamp in time column
        Returns:
            intradaysd_mean (IntegerType): Mean, Median, and SD of intraday standard deviation 
            intradaysd_median (IntegerType): Median of intraday standard deviation 
            intradaysd_sd (IntegerType): SD of intraday standard deviation
intradaymean()
Computes the intradaymean, returns the mean, median, and sd of the intraday mean of the Sensor data
        Args:
             column (pandas.DataFrame column or pandas series): column that you want to calculate over
             time (pandas.DataFrame): time column
             timeformat (String): default = '%Y-%m-%d %H:%M:%S.%f'; format of timestamp in time column
        Returns:
            intradaymean_mean (IntegerType): Mean, Median, and SD of intraday standard deviation 
            intradaymean_median (IntegerType): Median of intraday standard deviation 
            intradaymean_sd (IntegerType): SD of intraday standard deviation
dailymean()
Computes the mean of each day
        Args:
             column (pandas.DataFrame column or pandas series): column that you want to calculate over
             time (pandas.DataFrame): time column
             timeformat (String): default = '%Y-%m-%d %H:%M:%S.%f'; format of timestamp in time column
        Returns:
            pandas.DataFrame with days and means as columns
dailysummary()
Computes the summary of each day (mean, median, std, max, min, Q1G, Q3G)
        Args:
             column (pandas.DataFrame column or pandas series): column that you want to calculate over
             time (pandas.DataFrame): time column
             timeformat (String): default = '%Y-%m-%d %H:%M:%S.%f'; format of timestamp in time column
        Returns:
            pandas.DataFrame with days and summary metrics as columns
interdaysummary()
Computes interday mean, median, minimum and maximum, and first and third quartile over a column
        Args:
             column (pandas.DataFrame column or pandas series): column that you want to calculate over
             dataframe (True/False): default=True; whether you want a pandas DataFrame as an output or each of the summary metrics as IntegerTypes
        Returns:
            pandas.DataFrame with columns: Mean, Median, Standard Deviation, Minimum, Maximum, Quartile 1, Quartile 3
            
            or
            
            interdaymean (FloatType): mean 
            interdaymedian (FloatType): median 
            interdaysd (FloatType) : standard deviation
            interdaymin (FloatType): minimum 
            interdaymax (FloatType): maximum 
            interdayQ1 (FloatType): first quartile 
            interdayQ3 (FloatType): third quartile
TIR()
Computes time in the range of (default=1 sd from the mean)column in pandas dataframe
        Args:
             df (pandas.DataFrame):
             column (pandas.DataFrame column or pandas series): column that you want to calculate over
             sd (IntegerType): standard deviation from mean for range calculation (default = 1 SD)
             sr (IntegerType): sampling rate of sensor
        Returns:
            TIR (IntegerType): Time in Range set by sd, *Note time is relative to your SR
TOR()
Computes time outside the range of (default=1 sd from the mean) column in pandas dataframe
        Args:
             df (pandas.DataFrame):
             column (pandas.DataFrame column or pandas series): column that you want to calculate over
             sd (IntegerType): standard deviation from mean for range calculation (default = 1 SD)
             sr (IntegerType): sampling rate of sensor
        Returns:
            TOR (IntegerType): Time outside of range set by sd, *Note time is relative to your SR
POR()
Computes percent time outside the range of (default=1 sd from the mean) column in pandas dataframe
        Args:
             df (pandas.DataFrame):
             column (pandas.DataFrame column or pandas series): column that you want to calculate over
             sd (IntegerType): standard deviation from mean for range calculation (default = 1 SD)
             sr (IntegerType): 
        Returns:
            POR (IntegerType): percent of time spent outside range set by sd
MASE()
Computes the mean amplitude of sensor excursions (default = 1 sd from the mean)
        Args:
             df (pandas.DataFrame):
             column (pandas.DataFrame column or pandas series): column that you want to calculate over
             sd (IntegerType): standard deviation from mean to set as a sensor excursion (default = 1 SD)
        Returns:
           MASE (IntegerType): Mean Amplitude of sensor excursions
crhythm()
Computes 'minutes from midnight' and 'hours from midnight'- these features will allow you to account for circaidan rhythm effects
        Args:
             time (pandas.DataFrame): time column
             timeformat (String): default = '%Y-%m-%d %H:%M:%S.%f'; format of timestamp in time column
        Returns:
            hourfrommid (ListType): Hours from midnight, the same length as your time column
            minfrommid (ListType): Minutes from midnight, the same length as your time column
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