Time-Series-Analysis-of-Human-Activity-Monitoring-Data

Explored walking, running, climbing down, climbing up data of 15 subjects to extract time series features. Developed Scatter Plots for all activities applying Natural Visibility Graph (NVG) and Horizontal Visibility Graph (HVG) methods

Dataset: The following link below provides human activity data for15 subjects. Click on each subject to access the time series data. For this project consider accelerometer data for all the 15 subjects for walking, running, climbing up and climbing down

Link - https://www.uni-mannheim.de/dws/research/projects/activity-recognition/#dataset_realworld

1. Applied Natural Visibility Graph (NVG) and Horizontal Visibility Graph (HVG) to the aforementioned data
2. Computed average degree, network diameter, and average path length
3. Generated scatter plots: average degree vs network diameter and color the points according to walking and running (for each accelerometer signal and each method (HVH and NVG))
4. Generated scatter plots: average degree vs network diameter and color the points according to climbing up and climbing down (for each accelerometer signal and each method (HVH and NVG))
5. Computed permutation entropy and complexity for the aforementioned data. Considered the accelerometer data in all three directions
6. Generated scatter plots: permutation entropy vs complexity and color the points according to walking and running (for signal length =4096, embedded delay = 1, and embedded dimension = 3, 4, 5, 6, and all three accelerometer directions)
7. Generated scatter plots: permutation entropy vs complexity and color the points according to climbing up and climbing down (for signal length =4096, embedded delay = 1, and embedded dimension = 3, 4, 5, 6, all three accelerometer directions)