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- W2770645213 abstract "Human Activity Recognition (HAR) research on feet posture and movement information has seen an intense growth during the last five years, drawing attention of fields such as healthcare systems and context inference. In this work, we tested our 6-activity classes machine learning HAR classifier using a foot-based wearable device in an experiment involving 11 volunteers. The classifier uses a Random Forest algorithm with Leave-one-out Cross Validation, achieving an average of 93.34% accuracy. Targeting at a replicable research, we provide full hardware information, system source code and a public domain dataset consisting of 800,000 samples." @default.
- W2770645213 created "2017-12-04" @default.
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- W2770645213 date "2017-09-21" @default.
- W2770645213 modified "2023-09-27" @default.
- W2770645213 title "Bottom-up Investigation" @default.
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- W2770645213 doi "https://doi.org/10.1145/3134230.3134240" @default.
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