Information
Table Of Contents
- A. Overview
- 1. Getting started
- 1.1 The BME688 Gas Sensor
- 1.2 Example: Coffee vs. Normal Air
- 1.3 A Few Things To Keep In Mind
- 1.4 Step 1: Record Normal Air
- 1.5 Step 2: Record Espresso Coffee
- 1.6 Step 3: Record Normal Air Again
- 1.7 Step 4: Record Filter Coffee
- 1.8 Step 5: Import & Label The Data
- 1.9 Step 6: Create New Algorithm and Classes
- 1.10 Step 7: Train And Evaluate The Algorithm
- 1.11 Step 8: Export The Algorithm
- 1.12 Conclusion
- 2. Introduction
- 2.1 What is it about? – An analogy
- 2.2 Why the BME688?
- 2.3 What is a use case for a gas sensor?
- 2.4 What is special about the BME688 gas sensor?
- 2.5 How can I evaluate BME688 performance for a specific use case?
- 2.6 How can I use the results for my product development?
- 3. Glossary
- 3.1 Sensor Board
- 3.2 Measurement Session
- 3.3 Algorithm
- B. Process Steps
- 1. Configure Board
- 1.1 Overview
- 1.2 Board Type
- 1.3 Board Mode
- 1.4 Heater Profile
- 1.5 Duty Cycle
- 1.6 Board Layout
- 2. Record Data
- 2.1 Overview
- 2.2 Start recording
- 2.3 During recording
- 2.4 End recording
- 3. Import Data
- 3.1 Overview
- 3.2 Data Overview
- 3.3 Board ID
- 3.4 Board Type
- 3.5 Board Mode
- 3.6 Session Name
- 3.7 Session Date
- 3.8 Specimen Data
- 4. Collect Specimens
- 4.1 Overview
- 4.2 Label
- 4.3 Comment
- 4.4 Session
- 4.5 Start & End Time
- 4.6 Duration
- 4.7 Cycles Total
- 4.8 Cycles Dropped
- 4.9 Remaining Cycles
- 4.10 Board Configuration
- 4.11 Board ID
- 4.12 Board Type
- 4.13 Board Mode
- 4.14 Show Configuration
- 5. Train Algorithms
- 5.1 Overview
- 5.2 Name
- 5.3 Created
- 5.4 Classes
- 5.5 Class Name & Color
- 5.6 Common Data
- 5.7 Data Balance
- 5.8 Data Channels
- 5.9 Neural Net
- 5.10 Training Method
- 5.11 Max. Training Rounds
- 5.12 Data Splitting
- 6. Evaluate Algorithms
- 6.1 Overview
- 6.2 Confusion Matrix
- 6.3 Accuracy
- 6.4 Macro-averaged F1 Score
- 6.5 Macro-averaged False Positive Rate
- 6.6 Training Data
- 6.7 Test Data
- 6.8 Additional Testing
- 2.1
Bosch Sensortec | BME AI-Studio Documentation 40 | 49
Modifications reserved | Data subject to change
without notice Document number: BST-BME688-AN001-00
5. Train Algorithms
5.1 Overview
Within the section My Algorithms you are able to use the data collected in your specimen collection to train (classification)
algorithms. You are able to create multiple algorithms with a different data basis as well as various settings. Once trained
you can compare the performance of the different algorithms and choose the one algorithm that fits best your use case
(see
Evaluate algorithms). An algorithm can be exported as file for the BME library (BSEC), which is a set of specific
settings for the sensor that enables it to distinguish different gas compositions, once provided with these settings.
Create new algorithm
First switch to My Algorithms, then select New Algorithm to create a new algorithm. On the left you will see all your
algorithms in a list view.
See algorithm details
Once you select an algorithm within that list of algorithms, detail information of the selected algorithm is shown on the
right hand side. Make sure you select Algorithm Settings. Here you can set up everything for the training of your
algorithm.
Train algorithm
After setting up your algorithm, you can click Train Algorithm to start the training. Depending on the amount of data,
the training can take from a few seconds up to several minutes. After the training, you can see the training results
under Training Results. Jump to Evaluate algorithms for further information.
Duplicate algorithm
Once you select an algorithm, you can duplicate that algorithm, if you want to repeat your training with slightly different
settings. Just click Duplicate Algorithm on the bottom of the screen or via the context menu by clicking ....
Delete algorithm
Once you select an algorithm, you can delete that algorithm by clicking Delete Algorithm on the bottom of the screen
or via the context menu by clicking ....
5.2 Name
Give your algorithm a name. Choose a name that might help you to identify and distinguish your algorithms later on.
5.3 Created
The date and time, you created that algorithm.
5.4 Classes
Classes are the most important part of your algorithm. Each class represents one of the different gas compositions you
want to distinguish in your use case. For example, if you want to distinguish apples and oranges, you would create two
classes – one class for apples, one class for oranges.