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---
tags:
- time series
- time series classification
- monster
- HAR
license: other
pretty_name: WIS
size_categories:
- 10K<n<100K
---
Part of MONSTER: <https://arxiv.org/abs/2502.15122>.
|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. |