Data Analysis Tools
Over the years, we have developed a number of tools, many in [R]/Shiny that we use to analyze everything from RawData to our FootSteps Model.
Below are a few of the tools we’ve built to explore these datasets.
Over the years, we have developed a number of tools, many in [R]/Shiny that we use to analyze everything from RawData to our FootSteps Model.
Below are a few of the tools we’ve built to explore these datasets.
RawData is collected at 500Hz, resulting in file sizes that are far beyond what is typical for the consumer metrics. On-board flash memory will typically fill within 20min, and uploads can take upwards of an hour for ever 10min of collection.
Via Run Detail:
or via your Profile:
# Time in (1/4) msec
Motion.Time <<- as.numeric(CSV_MotionData[,5]) / 4.0
Motion.Accel.X <<- as.numeric(CSV_MotionData[,8]) / (256*8.0) ;
Motion.Accel.Y <<- as.numeric(CSV_MotionData[,11]) / (256*8.0) ;
Motion.Accel.Z <<- as.numeric(CSV_MotionData[,14]) / (256*8.0) ;
Motion.Gyro.X <<- as.numeric(CSV_MotionData[,17]) / 16.384;
Motion.Gyro.Y <<- as.numeric(CSV_MotionData[,20]) / 16.384;
Motion.Gyro.Z <<- as.numeric(CSV_MotionData[,23]) / 16.384;
Motion.Mag.X <<- as.numeric(CSV_MotionData[,26]) / 32;
Motion.Mag.Y <<- as.numeric(CSV_MotionData[,29]) / 32;
Motion.Mag.Z <<- as.numeric(CSV_MotionData[,32]) / 32;
Motion.Quat.Q0 <<- as.numeric(CSV_MotionData[,35]) / 16384.0 ;
Motion.Quat.Q1 <<- as.numeric(CSV_MotionData[,38]) / 16384.0 ;
Motion.Quat.Q2 <<- as.numeric(CSV_MotionData[,41]) / 16384.0 ;
Motion.Quat.Q3 <<- as.numeric(CSV_MotionData[,44]) / 16384.0 ;
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