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Human Activity Recognition (HAR) using smartphones dataset. Classifying the type of movement amongst five categories:
- WALKING,
- WALKING_UPSTAIRS,
- WALKING_DOWNSTAIRS,
- SITTING,
- STANDING
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.
'raw_data/labels.txt': include all the activity labels available for the dataset (1 per row).
Column 1: experiment number ID,
Column 2: user number ID,
Column 3: activity number ID
Column 4: Label start point (in number of signal log samples (recorded at 50Hz))
Column 5: Label end point (in number of signal log samples)
activity_type:
1 WALKING
2 WALKING_UPSTAIRS
3 WALKING_DOWNSTAIRS
4 SITTING
5 STANDING
Repository: DiFronzo/LSTM-for-Human-Activity-Recognition-classification
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