 The humanactivity data set contains 24,075 observations of five different 
 physical human activities: Sitting, Standing, Walking, Running, and       
 Dancing. Each observation has 60 features extracted from acceleration     
 data measured by smartphone accelerometer sensors. The data set contains  
 the following variables:                                                  
                                                                           
 * class — class containing the activities Sitting, Standing, Walking, Running, and Dancing, respectively                                                     
 * Attributes :
'TotalAccXMean'
'TotalAccYMean'
'TotalAccZMean'
'BodyAccXRMS'
'BodyAccYRMS'
'BodyAccZRMS'
'BodyAccXCovZeroValue'
'BodyAccXCovFirstPos'
'BodyAccXCovFirstValue'
'BodyAccYCovZeroValue'
'BodyAccYCovFirstPos'
'BodyAccYCovFirstValue'
'BodyAccZCovZeroValue'
'BodyAccZCovFirstPos'
'BodyAccZCovFirstValue'
'BodyAccXSpectPos1'
'BodyAccXSpectPos2'
'BodyAccXSpectPos3'
'BodyAccXSpectPos4'
'BodyAccXSpectPos5'
'BodyAccXSpectPos6'
'BodyAccXSpectVal1'
'BodyAccXSpectVal2'
'BodyAccXSpectVal3'
'BodyAccXSpectVal4'
'BodyAccXSpectVal5'
'BodyAccXSpectVal6'
'BodyAccYSpectPos1'
'BodyAccYSpectPos2'
'BodyAccYSpectPos3'
'BodyAccYSpectPos4'
'BodyAccYSpectPos5'
'BodyAccYSpectPos6'
'BodyAccYSpectVal1'
'BodyAccYSpectVal2'
'BodyAccYSpectVal3'
'BodyAccYSpectVal4'
'BodyAccYSpectVal5'
'BodyAccYSpectVal6'
'BodyAccZSpectPos1'
'BodyAccZSpectPos2'
'BodyAccZSpectPos3'
'BodyAccZSpectPos4'
'BodyAccZSpectPos5'
'BodyAccZSpectPos6'
'BodyAccZSpectVal1'
'BodyAccZSpectVal2'
'BodyAccZSpectVal3'
'BodyAccZSpectVal4'
'BodyAccZSpectVal5'
'BodyAccZSpectVal6'
'BodyAccXPowerBand1'
'BodyAccXPowerBand2'
'BodyAccXPowerBand3'
'BodyAccYPowerBand1'
'BodyAccYPowerBand2'
'BodyAccYPowerBand3'
'BodyAccZPowerBand1'
'BodyAccZPowerBand2'
'BodyAccZPowerBand3'         
 * featlabels — Labels of the 60 features                                 

 The Sensor HAR (human activity recognition) App [1] was used to create    
 the humanactivity data set. When measuring the raw acceleration data with 
 this app, a person placed a smartphone in a pocket so that the smartphone 
 was upside down and the screen faced toward the person. The software then 
 calibrated the measured raw data accordingly and extracted the 60         
 features from the calibrated data. For details about the calibration and  
 feature extraction, see [2] and [3], respectively.                        
                                                                           
 [1] El Helou, A. Sensor HAR recognition App. MathWorks File Exchange      
 http://www.mathworks.com/matlabcentral/fileexchange/54138-sensor-har-recognition-app 
 [2] STMicroelectronics, AN4508 Application note. “Parameters and          
 calibration of a low-g 3-axis accelerometer.” 2014.                       
 [3] El Helou, A. Sensor Data Analytics. MathWorks File Exchange           
 https://www.mathworks.com/matlabcentral/fileexchange/54139-sensor-data-analytics--french-webinar-code- "
