--- tags: - time series - time series classification - monster - HAR license: other pretty_name: WIS size_categories: - 10K. |WISDM|| |-|-:| |Category|HAR| |Num. Examples|17,166| |Num. Channels|3| |Length|100| |Sampling Freq.|20 Hz| |Num. Classes|6| |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 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.