Function reference
Chapter 2
Defines the functions employed in the Chapter 2: Materials & Methods.
| chapter2.data_loading | Provides functions to load the collected data and associated metadata files. |
| chapter2.exploration | Provides functions to compute statistics regarding subjects and collected data. |
| chapter2.visualization | Provides a function to plot the collected data. |
Chapter 3
Defines the functions employed in the Chapter 3: Multidimensional analysis of ML and DL on HAR.
Pipeline
Functions employed to carry out the experiments.
| chapter3.pipeline.01_data-processing | Data preprocessing script. |
| chapter3.pipeline.02_hyperparameter-optimization | Hyperparameters Grid Search script. |
| chapter3.pipeline.03_incremental-loso | Incremental Leaving-One-Subject-Out script. |
Analysis
Functions employed to analyse the results of the experiment.
| chapter3.analysis.data_loading | Provides functions to load the obtained results. |
| chapter3.analysis.model | Defines a model to manage the reports generated by the machine and deep learning models. |
| chapter3.analysis.statistical_tests | Provides functions to compute statistical tests to determine the significance of the obtained results. |
| chapter3.analysis.visualization | Provides functions to visualize the obtained results. |
Chapter 4
Defines the functions employed in the Chapter 4 - HAR in mHealth: TUG test using smartphones and smartwatches.
Pipeline
Functions employed to carry out the experiments.
| chapter4.pipeline.01_relabel | Data relabelling script. |
| chapter4.pipeline.02_splitting-evaluation | Splitting approach evaluation script |
Analysis
Functions employed to analyse the results of the experiments.
| chapter4.analysis.data_loading | Provides functions to load the obtained results. |
| chapter4.analysis.statistical_tests | Provides functions to compute statistical tests to determine the significance of the obtained results. |
| chapter4.analysis.tug_results_processing | Provides functions to process the raw results of the TUG test obtained by the developed system and the reference method. |
| chapter4.analysis.visualization | Provides functions to visualize the obtained results. |
| chapter4.analysis.battery | Provides functions to process the battery consumption results |
Chapter 5
Defines the functions employed in the Chapter 5 - Looking into the future: Wi-Fi CSI based HAR.
Pipeline
Functions employed to carry out the experiments.
| chapter5.pipeline.01_1_preliminar-dataset-processing | Data preprocessing script for preliminar dataset. |
| chapter5.pipeline.01_2_stanwifi-processing | Data preprocessing script for StanWiFi dataset. |
| chapter5.pipeline.01_3_multienvironment-processing | Data preprocessing script for Multi-environment dataset. |
| chapter5.pipeline.01_4_lodo-dataset-processing | Data preprocessing script for LODO dataset. |
| chapter5.pipeline.02_hyperparameter-optimization | Hyperparameters Grid Search script. |
| chapter5.pipeline.03_1_multiple-evaluations | Multiple evaluation script |
| chapter5.pipeline.03_2_cross-validation | Cross-validation script |
| chapter5.pipeline.03_3_lodo | Leaving-One-Day-Out validation script |
Analysis
Functions employed to analyse the results of the experiments.
| chapter5.analysis.reports | Provides functions to process the reports generated in the evaluation of classification models. |
| chapter5.analysis.visualization | Provides functions to visualize the obtained results. |