We discovered that the stock firmware from ST (the latest FP-AI-SENSING1 package) is disabling USB CDC (Communication Device Class) device by default, thus causing the issue you described.
Instead, please use the “DataLogExtended.bin” firmware from this package of FP-SNS-STBOX1. If you flash this firmware, Virtual COM port will be recognized on Windows 10, and then you should be able to follow the rest of the installation instructions for Qeexo AutoML.
When uploading CSV files, you will be asked to create an Environment and select the sensors and their configurations. What you select here must match with what is described in the CSV file.
Please make sure the “Discovery” option is not enabled in the Bluetooth Settings: Start -> Bluetooth & Other Device Setting-> More Bluetooth options -> Options tab -> Select Discovery (Allow Bluetooth devices to find this PC).
If the training appears to be stuck on Calculating Latency, it is likely due to the target hardware being disconnected from your machine. Since we calculate the latency by flashing the ML model onto the device to run live inferences and taking the average time, the process cannot be completed if the device is disconnected. You can exist out of the pop-up screen and still see Model training results without the latency. Click on the “Error” icon under the Latency column to try again after reconnecting the target hardware.
If you are in a Single-class classification project, multiple labels must be grouped into one single group before training can continue. If you are in a Multi-class classification project, you are able to skip this step.
Please make sure that there are no other trainings in session.
The Notifications page will tell you how much more data is needed to start a training.
Make sure to select the ODR and the FSR for the selected sensors.
On the training page, different datasets with the same sensor configurations and same label names are grouped. Please click the drop-down icon to see all datasets.
1. Go to the Details Page by clicking the “Details” icon in the Models Page, check Learning Curve to see whether you need more data. 2. Go to the Sensitivity Analysis page by clicking the “Sensitivity Analysis” icon and try adjusting weights and re-compiling the library. 3. For more information, please refer to the Qeexo AutoML User Guide.
This could be caused by various situations. 1. Make sure your target hardware is connected and matches with the current project. 2. If your device in the Arduino Nano 33 BLE Sense, try pressing the reset button twice before starting to flash. 3. Try connecting the device via another port of your laptop. 4. Check if your computer is connected to the internet through a proxy. If so, temporarily disabling the proxy may fix the issue.
Ensure the latest Qeexo AutoML is installed. Visit https://automl.qeexo.com/resources to download and install the latest software package.
Ensure the latest Qeexo AutoML is running, Start the application by clicking on the Qeexo AutoML icon.
Ensure the device is connected to the computer.
If all above criteria are met and Arduino device is still unable to flash, please do a double press on the reset button before flashing; the orange LED should slowly fade in and out to show that the board is in flashing mode.
The typical current consumption for normal mode is 33mA and it goes down to 29mA when it is in flash mode. However, this current consumption is not always stable when we use a USB-hub device. Sometimes, the current doesn’t come back to 33mA even though device is in normal mode. This issue never happens when we use “USB-C to USB” converter instead of using USB-hub. Based on this, we think USB-hub may not be able to drive enough power for our target device operation.
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