Cr-feat window length for all empaticas sensors.
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parent
1c42347b9b
commit
74cf4ada1c
36
config.yaml
36
config.yaml
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@ -477,10 +477,10 @@ EMPATICA_ACCELEROMETER:
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CONTAINER: ACC
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PROVIDERS:
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DBDP:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
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SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py
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CF:
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CR:
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COMPUTE: True
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FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq features
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"meanLow", "areaLow", "totalAbsoluteAreaBand", "totalMagnitudeBand", "entropyBand", "skewnessBand", "kurtosisBand",
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@ -490,7 +490,10 @@ EMPATICA_ACCELEROMETER:
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"peaksDataLow", "sumPerComponentBand", "velocityBand", "meanKineticEnergyBand", "totalKineticEnergyBand", "squareSumOfComponent",
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"sumOfSquareComponents", "averageVectorLength", "averageVectorLengthPower", "rollAvgLow", "pitchAvgLow", "rollStdDevLow",
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"pitchStdDevLow", "rollMotionAmountLow", "rollMotionRegularityLow", "manipulationLow", "rollPeaks", "pitchPeaks", "rollPitchCorrelation"] # Acc features
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SRC_SCRIPT: src/features/empatica_accelerometer/cf/main.py
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WINDOWS:
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COMPUTE: False
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WINDOW_LENGTH: 10 # specify window length in seconds
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SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py
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# See https://www.rapids.science/latest/features/empatica-heartrate/
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@ -507,48 +510,57 @@ EMPATICA_TEMPERATURE:
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CONTAINER: TEMP
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PROVIDERS:
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DBDP:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"]
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SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py
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CF:
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CR:
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COMPUTE: True
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FEATURES: ["autocorrelations", "countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean",
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"longestStrikeBelowMean", "stdDev", "median", "meanChange", "numberOfZeroCrossings", "absEnergy", "linearTrendSlope",
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"ratioBeyondRSigma", "binnedEntropy", "numOfPeaksAutocorr", "numberOfZeroCrossingsAutocorr", "areaAutocorr",
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"calcMeanCrossingRateAutocorr", "countAboveMeanAutocorr", "sumPer", "sumSquared", "squareSumOfComponent",
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"sumOfSquareComponents"]
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SRC_SCRIPT: src/features/empatica_temperature/cf/main.py
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WINDOWS:
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COMPUTE: False
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WINDOW_LENGTH: 90 # specify window length in seconds
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SRC_SCRIPT: src/features/empatica_temperature/cr/main.py
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# See https://www.rapids.science/latest/features/empatica-electrodermal-activity/
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EMPATICA_ELECTRODERMAL_ACTIVITY:
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CONTAINER: EDA
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PROVIDERS:
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DBDP:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
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SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
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CF:
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CR:
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COMPUTE: True
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FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic',
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'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore',
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'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio',
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'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', 'maxPeakResponseSlopeAfter',
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'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease']
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SRC_SCRIPT: src/features/empatica_electrodermal_activity/cf/main.py
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WINDOWS:
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COMPUTE: False
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WINDOW_LENGTH: 80 # specify window length in seconds
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SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py
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# See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/
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EMPATICA_BLOOD_VOLUME_PULSE:
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CONTAINER: BVP
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PROVIDERS:
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DBDP:
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COMPUTE: True
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COMPUTE: False
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FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq features
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"maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] # HRV features
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SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py
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CF:
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CR:
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COMPUTE: True
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FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR']
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SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cf/main.py
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WINDOWS:
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COMPUTE: False
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WINDOW_LENGTH: 4 # specify window length in seconds
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SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py
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# See https://www.rapids.science/latest/features/empatica-inter-beat-interval/
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EMPATICA_INTER_BEAT_INTERVAL:
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@ -14,7 +14,7 @@ def getSampleRate(data):
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return 1000/timestamps_diff
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def extractAccFeaturesFromIntradayData(acc_intraday_data, features, time_segment, filter_data_by_segment):
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def extractAccFeaturesFromIntradayData(acc_intraday_data, features, window_length, time_segment, filter_data_by_segment):
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acc_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
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if not acc_intraday_data.empty:
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@ -27,12 +27,20 @@ def extractAccFeaturesFromIntradayData(acc_intraday_data, features, time_segment
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acc_intraday_features = pd.DataFrame()
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# apply methods from calculate features module
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acc_intraday_features = \
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acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \
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convertInputInto2d(x['double_values_0'], x.shape[0]), \
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convertInputInto2d(x['double_values_1'], x.shape[0]), \
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convertInputInto2d(x['double_values_2'], x.shape[0]), \
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fs=int(sample_rate), featureNames=features))
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if window_length is None:
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acc_intraday_features = \
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acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \
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convertInputInto2d(x['double_values_0'], x.shape[0]), \
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convertInputInto2d(x['double_values_1'], x.shape[0]), \
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convertInputInto2d(x['double_values_2'], x.shape[0]), \
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fs=int(sample_rate), featureNames=features))
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else:
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acc_intraday_features = \
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acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \
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convertInputInto2d(x['double_values_0'], window_length*int(sample_rate)), \
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convertInputInto2d(x['double_values_1'], window_length*int(sample_rate)), \
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convertInputInto2d(x['double_values_2'], window_length*int(sample_rate)), \
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fs=int(sample_rate), featureNames=features))
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acc_intraday_features.reset_index(inplace=True)
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@ -40,18 +48,23 @@ def extractAccFeaturesFromIntradayData(acc_intraday_data, features, time_segment
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def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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acc_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
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requested_intraday_features = provider["FEATURES"]
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if provider["WINDOWS"]["COMPUTE"]:
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requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
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else:
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requested_window_length = None
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# name of the features this function can compute
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base_intraday_features_names = accelerometerFeatureNames + frequencyFeatureNames
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# the subset of requested features this function can compute
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intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
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# extract features from intraday data
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acc_intraday_features = extractAccFeaturesFromIntradayData(acc_intraday_data,
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intraday_features_to_compute, time_segment,
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filter_data_by_segment)
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acc_intraday_features = extractAccFeaturesFromIntradayData(acc_intraday_data, intraday_features_to_compute,
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requested_window_length, time_segment, filter_data_by_segment)
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return acc_intraday_features
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@ -15,7 +15,7 @@ def getSampleRate(data):
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return 1000/timestamps_diff
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def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, time_segment, filter_data_by_segment):
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def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, window_length, time_segment, filter_data_by_segment):
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bvp_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
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if not bvp_intraday_data.empty:
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@ -28,27 +28,37 @@ def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, time_segment
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bvp_intraday_features = pd.DataFrame()
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# apply methods from calculate features module
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bvp_intraday_features = \
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bvp_intraday_data.groupby('local_segment').apply(\
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lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], x.shape[0]), fs=int(sample_rate), featureNames=features))
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if window_length is None:
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bvp_intraday_features = \
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bvp_intraday_data.groupby('local_segment').apply(\
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lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], x.shape[0]), fs=int(sample_rate), featureNames=features))
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else:
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bvp_intraday_features = \
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bvp_intraday_data.groupby('local_segment').apply(\
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lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], window_length*int(sample_rate)), fs=int(sample_rate), featureNames=features))
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bvp_intraday_features.reset_index(inplace=True)
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return bvp_intraday_features
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def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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bvp_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
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requested_intraday_features = provider["FEATURES"]
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if provider["WINDOWS"]["COMPUTE"]:
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requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
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else:
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requested_window_length = None
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# name of the features this function can compute
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base_intraday_features_names = hrvFeatureNames + frequencyFeatureNames
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# the subset of requested features this function can compute
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intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
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# extract features from intraday data
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bvp_intraday_features = extractBVPFeaturesFromIntradayData(bvp_intraday_data,
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intraday_features_to_compute, time_segment,
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filter_data_by_segment)
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bvp_intraday_features = extractBVPFeaturesFromIntradayData(bvp_intraday_data, intraday_features_to_compute,
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requested_window_length, time_segment, filter_data_by_segment)
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return bvp_intraday_features
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@ -13,7 +13,7 @@ def getSampleRate(data):
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return 1000/timestamps_diff
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def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment, filter_data_by_segment):
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def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, window_length, time_segment, filter_data_by_segment):
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eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
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if not eda_intraday_data.empty:
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@ -26,27 +26,38 @@ def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment
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eda_intraday_features = pd.DataFrame()
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# apply methods from calculate features module
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eda_intraday_features = \
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eda_intraday_data.groupby('local_segment').apply(\
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lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=int(sample_rate), featureNames=features))
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if window_length is None:
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eda_intraday_features = \
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eda_intraday_data.groupby('local_segment').apply(\
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lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=int(sample_rate), featureNames=features))
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else:
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eda_intraday_features = \
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eda_intraday_data.groupby('local_segment').apply(\
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lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], window_length*int(sample_rate)), fs=int(sample_rate), featureNames=features))
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eda_intraday_features.reset_index(inplace=True)
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return eda_intraday_features
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def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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eda_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
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requested_intraday_features = provider["FEATURES"]
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if provider["WINDOWS"]["COMPUTE"]:
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requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
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else:
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requested_window_length = None
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# name of the features this function can compute
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base_intraday_features_names = gsrFeatureNames
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# the subset of requested features this function can compute
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intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
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# extract features from intraday data
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eda_intraday_features = extractEDAFeaturesFromIntradayData(eda_intraday_data,
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intraday_features_to_compute, time_segment,
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filter_data_by_segment)
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eda_intraday_features = extractEDAFeaturesFromIntradayData(eda_intraday_data, intraday_features_to_compute,
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requested_window_length, time_segment, filter_data_by_segment)
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return eda_intraday_features
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@ -4,6 +4,7 @@ from scipy.stats import entropy
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from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, genericFeatureNames
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from CalculatingFeatures.calculate_features import calculateFeatures
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import sys
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def getSampleRate(data):
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try:
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@ -13,7 +14,7 @@ def getSampleRate(data):
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return 1000/timestamps_diff
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def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, time_segment, filter_data_by_segment):
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def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, window_length, time_segment, filter_data_by_segment):
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temperature_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
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if not temperature_intraday_data.empty:
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@ -26,27 +27,35 @@ def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, tim
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temperature_intraday_features = pd.DataFrame()
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# apply methods from calculate features module
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temperature_intraday_features = \
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temperature_intraday_data.groupby('local_segment').apply(\
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lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=int(sample_rate), featureNames=features))
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if window_length is None:
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temperature_intraday_features = \
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temperature_intraday_data.groupby('local_segment').apply(\
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lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=int(sample_rate), featureNames=features))
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else:
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temperature_intraday_features = \
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temperature_intraday_data.groupby('local_segment').apply(\
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lambda x: calculateFeatures(convertInputInto2d(x['temperature'], window_length*int(sample_rate)), fs=int(sample_rate), featureNames=features))
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temperature_intraday_features.reset_index(inplace=True)
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return temperature_intraday_features
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def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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temperature_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
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requested_intraday_features = provider["FEATURES"]
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if provider["WINDOWS"]["COMPUTE"]:
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requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
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else:
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requested_window_length = None
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# name of the features this function can compute
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base_intraday_features_names = genericFeatureNames
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# the subset of requested features this function can compute
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intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
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# extract features from intraday data
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temperature_intraday_features = extractTempFeaturesFromIntradayData(temperature_intraday_data,
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intraday_features_to_compute, time_segment,
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filter_data_by_segment)
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temperature_intraday_features = extractTempFeaturesFromIntradayData(temperature_intraday_data, intraday_features_to_compute,
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requested_window_length, time_segment, filter_data_by_segment)
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return temperature_intraday_features
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