fixed typos and added clickable links to doc files

project_report.md

@@ -47,7 +47,7 @@ Behavior:
 - Produces training-ready feature tables = dataset
 - Parameter ```MIN_DUR_BLINKS``` can be adjusted, although this value needs to make sense in combination with your sampling frequency
 - With low videostream rates, consider to reevaluate the meaningfulness of some eye-tracking features, especially the fixations
-- running the script requires a manual installation of pygaze Analyser library from [github](https://github.com/esdalmaijer/PyGazeAnalyser.git)
+- running the script requires a manual installation of [pygaze Analyser library](https://github.com/esdalmaijer/PyGazeAnalyser.git) from github
 
 ### 2.3 Online Camera + Eye + AU Feature Extraction
 
@@ -71,13 +71,13 @@ Operational note:
 ## 3) EDA
 The directory EDA provides several files to get insights into both the raw data from AdaBase and your own dataset.
 
-- `EDA.ipynb` - main EDA notebook: recreates the plot from AdaBase documentation, lists all experiments and in general serves as a playground for you to get to know the files. 
+- `EDA.ipynb` - Main EDA notebook: recreates the plot from AdaBase documentation, lists all experiments and in general serves as a playground for you to get to know the files. 
 - `distribution_plots.ipynb` - This notebook aimes to visualize the data distributions for each experiment - the goal is the find out, whether the split of experiments into high and low cognitive load is clearer if some experiments are dropped.
 - `histogramms.ipynb` - Histogram analysis of low load vs high load per feature. Additionaly, scatter plots per feature are available.
 - `researchOnSubjectPerformance.ipynb` - This noteboooks aims to see how the performance values range for the 30 subjects. The code creates and saves a table in csv-format, which will later be used as the foundation of the performance based split in ```model_training/tools/performance_based_split```
 - `owncloud_file_access.ipynb` - Get access to the files via owncloud and safe them as .h5 files, in correspondence to the parquet file creation script 
-- `login.yaml` - used to store URL and password to access files from owncloud, used in previous notebook
-- `calculate_replacement_values.ipynb` - fallback / median computation notebook for deployment, creation of yaml syntax embedding
+- `login.yaml` -Used to store URL and password to access files from owncloud, used in previous notebook
+- `calculate_replacement_values.ipynb` -Fallback / median computation notebook for deployment, creation of yaml syntax embedding
 
 General information: 
 - Due to their size, its absolutely recommended to download and save the dataset files once in the beginning
@@ -93,7 +93,7 @@ Included model families:
 - OCSVM*
 - DeepSVDD*
 
-\* These trainings are unsupervised, which means only low cognitive load samples are used for training. Validation then also considers high low samples.  
+\* These training strategies are unsupervised, which means only low cognitive load samples are used for training. Validation then also considers high low samples.  
 
 
 Supporting utilities in ```model_training/tools```:
@@ -244,7 +244,7 @@ Role:
 - Run inference repeatedly without manual execution
 - Timer/service configuration can be customized
 
-More information on how to use and interact with the system service and timer can be found in `predict_service_timer_documentation.md`
+More information on how to use and interact with the system service and timer can be found in [predict_service_timer_documentation.md](/predict_pipeline/predict_service_timer_documentation.md)
 
 ## 5.2 Runtime Configuration
 

readme.md

@@ -2,7 +2,7 @@
 
 Short overview: this repository contains the data, feature, training, and inference pipeline for multimodal driver-state analysis using facial AUs and eye-tracking signals.
 
-For full documentation, see `project_report.md`.
+For full documentation, see [project_report.md](project_report.md).
 
 ## Quickstart
 
@@ -51,4 +51,4 @@ python dataset_creation/camera_handling/camera_stream_AU_and_ET_new.py
 python predict_pipeline/predict_sample.py
 ```
 
-3. Use  `predict_service_timer_documentation.md` to see how to use the service and timer for automation. On Ohm-UX driving simulator's jetson board, the service runs starts automatically when the device is booting. 
+3. Use  [predict_service_timer_documentation.md](/predict_pipeline/predict_service_timer_documentation.md) to see how to use the service and timer for automation. On Ohm-UX driving simulator's jetson board, the service runs starts automatically when the device is booting.