| 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) |