You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Currently, when using the pretrained RNN model with the app, the FPS is greatly dependent on how fast the accelerometer is streaming data. This means that if we are able to generate 50 samples in a second, and the AI only takes 50 samples, the AI will only use 1 second of history in the predictions.
This is of course suboptimal. Quite often models are also trained using a setup where data is streamed with a specific FPS. Hence, we should be able to cap the maximum FPS to a specified limit such that the AI is given sufficient history to solve the task.
This can be easily solved by adding a mechanism to only fetch data after a specific duration.
The text was updated successfully, but these errors were encountered:
Currently, when using the pretrained RNN model with the app, the FPS is greatly dependent on how fast the accelerometer is streaming data. This means that if we are able to generate 50 samples in a second, and the AI only takes 50 samples, the AI will only use 1 second of history in the predictions.
This is of course suboptimal. Quite often models are also trained using a setup where data is streamed with a specific FPS. Hence, we should be able to cap the maximum FPS to a specified limit such that the AI is given sufficient history to solve the task.
This can be easily solved by adding a mechanism to only fetch data after a specific duration.
The text was updated successfully, but these errors were encountered: