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tags:
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- HAR
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---
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Part of MONSTER: <https://arxiv.org/abs/2502.15122>.
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***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]. Both WISDM and WISDM2 are split based on subjects.
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[1] Gary Mitchell Weiss and Jeffrey Lockhart. (2012). The impact of personalization on smartphone-based activity recognition. In Workshops at the *26<sup>th</sup> AAAI Conference on Artificial Intelligence*.
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---
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tags:
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- time series
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- time series classification
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- monster
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- HAR
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license: other
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pretty_name: WISDM2
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size_categories:
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- 100K<n<1M
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Part of MONSTER: <https://arxiv.org/abs/2502.15122>.
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|WISDM2||
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|Category|HAR|
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|Num. Examples|149,034|
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|Num. Channels|3|
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|Length|100|
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|Sampling Freq.|20 Hz|
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|Num. Classes|6|
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|License|Other|
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|Citations|[1]|
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***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]. Both WISDM and WISDM2 are split based on subjects.
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[1] Gary Mitchell Weiss and Jeffrey Lockhart. (2012). The impact of personalization on smartphone-based activity recognition. In Workshops at the *26<sup>th</sup> AAAI Conference on Artificial Intelligence*.
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