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When you use sensors, you need to process the raw data into meaningful context. So, how do sensor hubs and sensor fusion work together to improve your product performance? Read on to find out.
Data can't exist in a bubble. It is rendered meaningless if it can't be interpreted to provide a clear picture of what it's actually measuring. As described in our webinar (timestamp 19:36), sensor fusion is the process of fusing the raw data from multiple sensors together via algorithms to create one, coherent picture.
A sensor hub utilizes sensor fusion to turn raw sensor data into meaningful context. Sensor hubs are an idea that evolved from mobile devices: that sensor fusion could be done on a secondary, lower-power microprocessor to save power on the main processor. This enables better power management without sacrificing performance by allowing you to free up processor bandwidth for other tasks.
Sensor fusion allows you to determine the state of your device by fusing data from multiple sensors. This is important because it allows you to cross-reference multiple sources of information, which improves the certainty of your data. For example, you can combine the long-term stability of an accelerometer and magnetometer with the short-term accuracy of a gyroscope to create a fluid estimate for orientation between static and dynamic motion. Sensor fusion enables more accurate, precise context by bringing multiple data sources together.
A sensor hub is a central processing unit (CPU) or microcontroller with the explicit purpose of processing data from different sensors. This CPU is generally a lower-power device so that it can work independently from the main processor. Because it performs a specialized task, the sensor hub can save the device power that your main, higher-power CPU would otherwise have to spend to process the sensor data. A sensor hub is a great option for when you want to process data in the background, which allows you to run “always-on” sensors that use the low power sensor data.
If you're curious about what you need to consider when using fused sensor data, check out the terminology you need to know in our post, Sensor Technology: Deciphering Your Choices. If you'd like to understand how we account for the most common sensor anomalies using sensor fusion algorithms, read our post, Universal Technical Challenges in IMUs, or check out our on-demand webinar for an overview of Using IMUs and Sensor Fusion to Unlock Smarter Motion Sensing.
We have more than 15 years of experience in developing sensors and sensor fusion, so if you have any questions or want to dig deeper into this topic, we are happy to help. Contact us!