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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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***UCIActivity*** is a widely recognized benchmark for activity recognition research. It contains sensor readings from 30 participants performing six daily activities: walking, walking upstairs, walking downstairs, sitting, standing, and lying down. The data was collected using a Samsung Galaxy S2 smartphone mounted on the waist of each participant, with a sampling rate of 50 Hz [1]. To keep the evaluation fair, we perform subject-wise cross-validation.
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[1] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge Luis Reyes-Ortiz, et al. (2013). A public domain dataset for human activity recognition using smartphones. In *21<sup>st</sup> European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)*.
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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: UCIActivity
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---
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Part of MONSTER: <https://arxiv.org/abs/2502.15122>.
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|UCIActivity||
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|Category|HAR|
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|Num. Examples|10,299|
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|Num. Channels|9|
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|Length|128|
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|Sampling Freq.|50 Hz|
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|Num. Classes|6|
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|License|Other|
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|Citations|[1]|
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***UCIActivity*** is a widely recognized benchmark for activity recognition research. It contains sensor readings from 30 participants performing six daily activities: walking, walking upstairs, walking downstairs, sitting, standing, and lying down. The data was collected using a Samsung Galaxy S2 smartphone mounted on the waist of each participant, with a sampling rate of 50 Hz [1]. To keep the evaluation fair, we perform subject-wise cross-validation.
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[1] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge Luis Reyes-Ortiz, et al. (2013). A public domain dataset for human activity recognition using smartphones. In *21<sup>st</sup> European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)*.
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