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by Denise SANFILIPPO
Figure 16: Wearable applications and asset tracking examples
17.png
Figure 14: Sensor Evaluation tab - Quick Setup
Figure 15: Sensor Evaluation - Features Demo
Figure 18: Setup used for this hands-on
Figure 2: Pedometer - Debug tab
Figure 2: Pedometer - Debug tab
Figure 2: Pedometer - Debug tab
Figure 1: An industrial robotic arm for a pick and place use case
Figure 2: Hardware setup: STWIN.box and STEVAL-MKI245KA
Figure 3: STWIN.box DFU mode is enabled
Figure 4: Loading of STWIN.box firmware
Figure 5: HSDPython_SDK installation
Figure 6: High Speed Datalog GUI – Connect button
Figure 7: High Speed Datalog GUI – Enabling ISM330BX
Figure 8: High Speed Datalog GUI – Setting ODR and FS and Start/Stop logging
Figure 9: STWIN.box and ISM330BX mounted on a robotic arm for anomaly detection
Figure 10: MLC configuration in ISM330BX and STWIN.box
Figure 11: Robotic arm – Detection of stationary state
Figure 12: Robotic arm – Detection of pick and place movement
Figure 13: Robotic arm – when the anomaly is detected, the robotic arm stops moving
Figure 6: High Speed Datalog GUI – Connect button
Figure 9: STWIN.box and ISM330BX mounted on a robotic arm for anomaly detection
Figure 1: The datalog we have previously acquired using High Speed Datalog is converted to a compatible format to be imported in MEMS-Studio
Figure 2: The collected data is imported in MEMS-Studio
Figure 3: The collected data is labeled and divided into three files, one per class (stationary, pick and place movement and detected anomaly)
Figure 4: In the MLC tool of MEMS-Studio, the three files generated can be imported with their label
Figure 5: In the MLC tool of MEMS-Studio, the manual configuration of the machine learning core parameters like inputs, window features and so on is performed
Figure 6: In the MLC tool of MEMS-Studio, the decision tree can be generated and the user can also visualize its info
Figure 7: In the MLC tool of MEMS-Studio, the .ucf file can be generated
Picture9.png
Figure 8: The .ucf file generated by MEMS-Studio can be imported in the High Speed Datalog GUI to test it with real time data
Figure 9: We can see the result at 0 when there is no movement: this is the stationary class
Figure 10: We can see the result at 4 when there is pick and place movement: this is the pick and place class
Figure 11: We can see the result at 8 when there is anomaly detection: this is the anomaly class
Figure 12: MEMS-Studio can be used also for evaluation with the compatible boards, for instance the Professional MEMS tool board
Figure 13: In the Quick Setup section of MEMS-Studio, ISM330BX can be easily configured and the evaluation mode can be changed to sensor fusion
Figure 14: In the 3D model section of MEMS-Studio, the sensor fusion low power running on ISM330BX can be evaluated
Figure 15: In FFT section of MEMS-Studio, the Fast Fourier transform of the data coming from the accelerometer and gyroscope of ISM330BX can be evaluated
Figure 16: Ecosystem of the IMU ISM330BX
Figure 4: In the MLC tool of MEMS-Studio, the three files generated can be imported with their label
Figure 1: The datalog we have previously acquired using High Speed Datalog is converted to a compatible format to be imported in MEMS-Studio
Figure 4: In the MLC tool of MEMS-Studio, the three files generated can be imported with their label
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Figure 5: In the MLC tool of MEMS-Studio, the manual configuration of the machine learning core parameters like inputs, window features and so on is performed
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Added ‎2024-08-23 6:09 AM
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Figure 5: In the MLC tool of MEMS-Studio, the manual configuration of the machine learning core parameters like inputs, window features and so on is performed
Posted in: How to augment your anomaly detection application with ISM330BX machine learning core - ( ‎2024-09-04 5:00 AM )

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