IWR6843AOPEVM: Fall detection

Tanguy Tanguy

Part Number: IWR6843AOPEVM
Other Parts Discussed in Thread: IWR6843

Hello,

I'm trying to implement the fall detection example on my EVM but I can't find the good configuration file (.cfg) only ODS and ISK are available.

It is possible to send me the configuation file for AOP or is there any manipulation to get this file ?

And what kind of data comes out of the chip via UART serial to the computer (format, frame)?

Regards,

over 3 years ago

0 Jackson Thomas over 3 years ago

TI__Genius 14415 points

Hello,

For the fall detection with the AOP device, you can use the standard AOP cfg file located in the people counting folder below. You just may need to adjust the boundary box to your room size.

"C:\ti\mmwave_industrial_toolbox_4_9_0\labs\people_counting\68xx_3D_people_counting\chirp_configs\AOP_6m_default.cfg"
3D_people_counting_tracker_layer_tuning_guide.pdf

You can see the data output format at the end of the 3D people counting user guide.
https://dev.ti.com/tirex/explore/node?a=VLyFKFf__4.9.0&node=AIQzhJRYjeBjkiRvsK8EVg__VLyFKFf__4.9.0

Regards,

Jackson

0 Tanguy Tanguy over 3 years ago in reply to Jackson Thomas

Prodigy 40 points

Hi Jackson,

Thanks you for your reply,

Small context:

In my project, I don't need GUI, I just want to detect a human fall to return a binary status (fall or not?) and coordonates of the fall.

Is it possible to make the fall detection algorithm in the chip instead of outside like in the demo Elderly Care - Fall Detection using IWR6843 ?

Why in the demo the algorithm isn't done in the chip? Problems with memory, calculation ou just for the GUI?

Regards,

0 Jackson Thomas over 3 years ago in reply to Tanguy Tanguy

TI__Genius 14415 points

Hello,

Yes this should be possible. On the MSS side there should be enough memory to store this data and process several frames. It was done in the GUI just as a demonstration of the capability. The simple algorithm should have no issues porting to the C code.

Regards,

Jackson

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IWR6843AOPEVM: Fall detection