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raw_data.zip → DiFronzo--Human_Activity_Recognition/text-train.parquet

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README.md

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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. 
-
-```bash
-'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](https://github.com/DiFronzo/LSTM-for-Human-Activity-Recognition-classification)