TABLE 1.

Activity prediction features.

Feature	Description
lastSensorEventHours+*	Hour of day for current event
lastSensorEventSeconds+*	Seconds since the beginning of the day for the current event
windowDuration+*	Window duration (sec)
timeSinceLastSensorEvent+*	Seconds since previous event
prevDominantSensor1+*	Most frequent sensor in the previ- ous window
prevDominantSensor2+*	Most frequent sensor in the win- dow before that
lastSensorID+*	Current event sensor
lastLocation+*	Most recent location sensor
sensorCount+**	Number of events in the window for each sensor
sensorElTime+**	Time since each sensor fired
timeStamp+*	Normalized time since beginning of the day
laggedTimeStamps*	Previous event timeStamps
laggedPredictions***	Previous event predictions
timeSinceLastPrediction****	Time since previous predictions
maximumValue#	Maximum value of sensor
minimumValue#	Minimum value of sensor
sum#	Sum of sensor values
mean#	Mean of sensor values
meanAbsoluteDeviation#	Average difference from mean
medianAbsoluteDeviation#	Avg. difference from median
standardDeviation#	Value standard deviation
coeffVariation#	Coefficient of value variation
numZeroCrossings#	Number of median crossings
percentiles#	Number below which a percent- age of values fall
sqSumPercentile#	Sq. sum values < percentile
interQuartileRange#	Difference between 25th and 75th percentiles
binCount#	Values binned into 10 bins
skewness#	Symmetry of values
kurtosis#	Measure of value “peakedness”
signalEnergy#	Sum of squares of values
logSignalEnergy#	Sum of logs of squares
signalPower#	SignalEnergy average
peakToPeak#	Maximum - minimum
avgTimeBetweenPeaks#	Time between local maxima
numPeaks#	Number of peaks

⁺Used for activity recognition.

^*Local features Ψ_local, one of each.

^**Local features Ψ_local, one sensorCount and one sensorElTime for each sensor used.

^***Context features Ψ_context, one per activity per context spot.

^****Context feature Ψ_context, one per context spot.

^#Based on window of recent values for each sensor.