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 15 | 49
Modifications reserved | Data subject to change
without notice Document number: BST-BME688-AN001-00
2.5.3 Phase III: Duty Cycle Exploration – balancing power consumption and performance
A duty cycle exploration is important if you want to optimize power consumption of the BME688 for your application.
Therefore, the purpose of duty cycle exploration is to find the best duty cycle for your use case – it is the one that uses
a minimum amount of energy while still providing very good performance.
Try configuring multiple duty cycles
, while keeping the heater profile that was performing best and that you determined
within the heater profile exploration. While comparing different duty cycles, keep in mind that the ratio of scanning cycles
to sleeping cycles determines the average power consumption of the sensor. You find an estimated power consumption
within the
board configuration in your specimen collection.
Once you found your optimal duty cycle, you can export your BSEC configuration to be used with the BME library
(BSEC).
2.6 How can I use the results for my product development?
The BME AI-Studio with BME board and the BME library (BSEC) are built around the BME688 gas sensor. Together
they form an ecosystem to explore, validate and prototype gas sensor use cases. With the BME board, you can record
data to be used within the AI-Studio for training neural nets. These can be exported to be used with the BME library
(BSEC) and inside an embedded application.
For any trained algorithm, you can export a BSEC configuration file that can be used with the BME library (BSEC).
Please refer to the website of Bosch Sensortec Environmental Cluster (BSEC)
for further information.