Any advices on optimizing realtime loT applications with Edge Impulse on my Raspberry Pi 4
Hey guys I'm seeking expert advice on optimizing realtime loT applications with Edge Impulse on my Raspberry Pi 4. Specifically, I'm using Edge Impulse library version 2.14.0 to run a machine learning model for environmental monitoring and predictive maintenance. The model performs anomaly detection on accelerometer and gyroscope data from an MPU6050 sensor, processing 100 Hz streams. However, I'm struggling with synchronizing the real-time sensor data with the model's processing, leading to inconsistent performance. Any advice on optimizing data stream handling, such as buffering strategies or timing adjustments.
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Hi @Daniel kalu One approach to optimize your real-time data stream is to implement a double-buffering technique. This can help ensure that you are continuously collecting data without any gaps while the model is processing the previous batch. You maintain two buffers: one for reading sensor data and one for processing it. While one buffer is being processed, the other buffer collects new data.
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Hi @Daniel kalu One approach to optimize your real-time data stream is to implement a double-buffering technique. This can help ensure that you are continuously collecting data without any gaps while the model is processing the previous batch. You maintain two buffers: one for reading sensor data and one for processing it. While one buffer is being processed, the other buffer collects new data.