Update README.md

README.md

@@ -22,6 +22,6 @@ Part of MONSTER: <https://arxiv.org/abs/2502.15122>.
 |License|Other|
 |Citations|[1]|
 
-***WISDM*** describes six daily activities—*Walking*, *Jogging*, *Stairs*, *Sitting*, *Standing*, and *Lying Down*—collected in a controlled laboratory environment. Data were recorded from 36 participants using a smartphone's built-in tri-axial accelerometer, with the device placed in the user's front pants pocket. The accelerometer captures acceleration along the x, y, and z axes, providing a comprehensive view of the user's movements. The data is sampled at a rate of 20 Hz, resulting in a total of 1,098,207 samples across 3 dimensions [1].  The processed dataset contains 17,166 multivariate time series with a length of 100 (representing 5 seconds of data at 20 Hz).
+***WISDM*** describes six daily activities—*Walking*, *Jogging*, *Stairs*, *Sitting*, *Standing*, and *Lying Down*—collected in a controlled laboratory environment. Data were recorded from 36 participants using a smartphone's built-in tri-axial accelerometer, with the device placed in the user's front pants pocket. The accelerometer captures acceleration along the x, y, and z axes, providing a comprehensive view of the user's movements. The data is sampled at a rate of 20 Hz, resulting in a total of 1,098,207 samples across 3 dimensions [1].  The processed dataset contains 17,166 multivariate time series with a length of 100 (representing 5 seconds of data at 20 Hz).  WISDM is split based on subjects.
 
 [1] Jeffrey W Lockhart, Tony Pulickal, and Gary M Weiss. (2012). Applications of mobile activity recognition. In *Conference on Ubiquitous Computing*, pages 1054–1058.