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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: 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, recording 9 channels of data, with a sampling rate of 50 Hz [1]. The processed dataset contains 10,299 multivariate time series each with length 50 (i.e., one second of data at a sampling rate of 50 Hz). 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)*. |
