Update Readme.md

Readme.md

-# WASP AS 1 Assigment: Activity Recognition,
-# by Frida Heskebeck, Matthias Mayr, Momina Rizwan and Pontus Andersson
+# WASP AS 1 Assigment: Activity Recognition, by Frida Heskebeck, Matthias Mayr, Momina Rizwan and Pontus Andersson
 
 Our activity recognition is based on thresholds.
-The approach is to use the mean of the x,y and z accelerometer data.
+The approach is to use the mean of the x, y and z accelerometer data.
 Over a time interval of two seconds, we look at the difference of the max and min values of the mean.
 If the differences is lower than a set threshold, the person is predicted to be standing still.
 If the difference is greater than the still treshold but smaller than a set running threshold, the person is predicted to be walking.
@@ -12,13 +11,13 @@ This repository contains three scripts used to perform activity recognition and
 The data is stored in the `data` directory. To include your own data, add a subfolder to `data` named Data_YourData.
 We assume the scripts are called from the as1-activity-recognition folder.
 
-* `data_visualization.py` allows you to visualize data. Usage: `python data_visualization.py Name DataType`,
+* `src/data_visualization.py` allows you to visualize data. Usage: `python src/data_visualization.py Name DataType`,
 where `Name` is the name of the person whose data you wish to show, and `DataType` is either `ACC` or `GYR` for accelerometer and
 gyro data, respectively. Red bars are used to separate the data files.
 
-* `get_data.py` is a helper function for `activity_recognition.py`. It collects the data used in the latter script.
+* `src/get_data.py` is a helper function for `src/activity_recognition.py`. It collects the data used in the latter script.
 
-* `activity_recognition.py` allows you to do activity recognition. Usage: `python activity_recognition.py Name PresentationMode`,
-where `Name` is as in `data_visualization.py` and `PresentationMode` is either `Text` or `ConfusionMatrix`, with
+* `src/activity_recognition.py` allows you to do activity recognition. Usage: `python src/activity_recognition.py Name PresentationMode`,
+where `Name` is as in `src/data_visualization.py` and `PresentationMode` is either `Text` or `ConfusionMatrix`, with
 `Text` yielding console output of each prediction and corresponding ground truth, and `ConfusionMatrix` yielding a confusion matrix.
 For both options, we print the accuracy in the console.
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