
Important note: In section 3 (data collection) in our paper, the term running is mistakenly used for the jogging. So please only consider the jogging activity.

In the data collection experiments, we collected data for seven physical activities. These are walking, sitting, standing, jogging, biking, walking upstairs and walking downstairs, which are mainly used in the related studies and they are the basic motion activities in daily life. There were ten participants involved in our data collection experiment who performed each of these activities for 3-4 minutes. All ten participants were male, between the ages of 25 and 30. The experiments were carried out indoors in one of the university buildings, except biking. For walking, and jogging, the department corridor was used. For walking upstairs and downstairs, a 5-floor building with stairs was used. Each of these participants was equipped with five smartphones on five body positions: 
1.	One in their right jean’s pocket. 
2.	One in their left jean’s pocket.
3.	One on belt position towards the right leg using a belt clipper.
4.	One on the right upper arm. 
5.	One on the right wrist. 
The first three positions are commonly used by people carrying smartphones. The fourth position is usually used when activities like jogging are performed. However, we used this position for all activities to see its role on the performance.  A smart-watch was simulated with the fifth position as smart-watches are coming into the market these days.  For these experiments, we used Samsung Galaxy SII (i9100) smartphones. 
  The orientation of the smartphones was portrait for the upper arm, wrist, and two pockets, and landscape for the belt position. The data was recorded for all five positions at the same time for each activity and it was collected at a rate of 50 samples per second. This sampling rate (50 samples per second) is enough to recognize human physical activities, as we show in our previous study . Moreover, in the state of the art, frequencies lower than 50 samples per second have been shown to be sufficient for activity recognition.
For data collection, we adapted our own data collection app from our previous study by adding the linear acceleration sensor. The data was collected for an accelerometer, a gyroscope, a magnetometer, and a linear acceleration sensor. 

Each excel file contains data for each participant's seven physical activities for all five positions. 

Notation in these files: 

Accelerometer ( Ax = x-axis, Ay = y-axis, Az= Z-aixs)   
Linear Acceleration Sensor ( Lx = x-axis, Ly = y axis, Lz= Z-aixs) 
Gyroscope ( Gx = x-axis, Gy = y-axis, Gz= Z-aixs) 
Magnetometer ( Mx = x-axis, My = y-axis, Mz= Z-aixs) 
