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README.md
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Column 1: experiment number ID,
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Column 2: user number ID,
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Column 3: activity number ID
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Column 5: Label end point (in number of signal log samples)
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activity_type:
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1 WALKING
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2 WALKING_UPSTAIRS
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3 WALKING_DOWNSTAIRS
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4 SITTING
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5 STANDING
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Human Activity Recognition (HAR) using smartphones dataset and an LSTM RNN. Classifying the type of movement amongst six categories:
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- WALKING,
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- WALKING_UPSTAIRS,
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- WALKING_DOWNSTAIRS,
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- SITTING,
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- STANDING
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The experiments have been carried out with a group of 16 volunteers within an age bracket of 19-26 years. Each person performed five activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING) wearing a smartphone (Samsung Galaxy S8) in the pucket. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually.
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```bash
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'raw_data/labels.txt': include all the activity labels available for the dataset (1 per row).
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Column 1: experiment number ID,
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Column 2: user number ID,
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Column 3: activity number ID
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Column 5: Label end point (in number of signal log samples)
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activity_type:
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1 WALKING
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2 WALKING_UPSTAIRS
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3 WALKING_DOWNSTAIRS
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4 SITTING
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5 STANDING
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```
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