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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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***WISDM*** describes six daily activities collected in a controlled laboratory environment. The activities include *Walking*, *Jogging*, *Stairs*, *Sitting*, *Standing*, and *Lying Down*, recorded from 36 users using a cell phone placed in their pocket. The data is sampled at a rate of 20 Hz, resulting in a total of 1,098,207 samples across 3 dimensions [1].
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[1] Jeffrey W Lockhart, Tony Pulickal, and Gary M Weiss. (2012). Applications of mobile activity recognition. In *Conference on Ubiquitous Computing*, pages 1054–1058.
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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: WIS
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---
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Part of MONSTER: <https://arxiv.org/abs/2502.15122>.
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|WISDM||
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|Category|HAR|
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|Num. Examples|17,166|
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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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***WISDM*** describes six daily activities collected in a controlled laboratory environment. The activities include *Walking*, *Jogging*, *Stairs*, *Sitting*, *Standing*, and *Lying Down*, recorded from 36 users using a cell phone placed in their pocket. The data is sampled at a rate of 20 Hz, resulting in a total of 1,098,207 samples across 3 dimensions [1].
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[1] Jeffrey W Lockhart, Tony Pulickal, and Gary M Weiss. (2012). Applications of mobile activity recognition. In *Conference on Ubiquitous Computing*, pages 1054–1058.
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