MONSTER: Monash Scalable Time Series Evaluation Repository
Paper • 2502.15122 • Published • 4
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Part of MONSTER: https://arxiv.org/abs/2502.15122.
| WISDM2 | |
|---|---|
| Category | HAR |
| Num. Examples | 149,034 |
| Num. Channels | 3 |
| Length | 100 |
| Sampling Freq. | 20 Hz |
| Num. Classes | 6 |
| License | Other |
| Citations | [1] |
WISDM2 extends the original WISDM dataset by collecting data in real-world environments using the Actitracker system. This system was designed for public use and provides a more extensive collection of sensor readings from users performing the same six activities. The dataset contains 2,980,765 samples with three dimensions, and the data was recorded from a larger and more diverse set of participants in naturalistic settings, offering a valuable resource for real-world activity recognition [1]. The processed dataset has 149,034 time series, each with length 100 (again, representing 5 seconds of data at a sampling rate of 20 Hz). WISDM2 is split based on subjects.
[1] Gary Mitchell Weiss and Jeffrey Lockhart. (2012). The impact of personalization on smartphone-based activity recognition. In Workshops at the 26th AAAI Conference on Artificial Intelligence.
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