MEMS Sensor Processor Segmentation for Mobile Attitude Detection
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Solution Overview
Problem
Current sensor data acquisition techniques in mobile devices suffer from high update latency, slow response times, and limited accuracy, particularly indoors due to sensitivity to ambient magnetic fields and resource constraints.
Innovation Solution
A computer-implemented method with distinct application and sensor processors that register for attitude data, activate sensors, process raw readings, and transmit event notifications, allowing for high-frequency data acquisition and processing without increasing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If high polling rate is used for sensor data acquisition, then response time is improved, but processing resources are overwhelmed and update latency increases
Solution Approach 1:
The patent segments the processing workload by introducing a dedicated sensor processor separate from the application processor. The sensor processor handles high-frequency sensor data acquisition and preprocessing, while the application processor handles application logic. This segmentation allows high polling rates without overwhelming the application processor, thus improving response time without increasing update latency.
Solution Approach 2:
The sensor processor acts as an intermediary between the sensors and the application processor. It receives raw sensor data, performs initial processing and filtering, then transmits processed data to the application processor. This intermediary approach enables high-frequency sampling while reducing the processing burden on the application processor, resolving the contradiction between fast response and low latency.
2Adaptability or versatility
If magnetic field sensor is used for heading determination, then indoor navigation is enabled, but accuracy is reduced due to ambient magnetic fields
Solution Approach 1:
The patent merges data from multiple sensors (accelerometer, gyroscope, and magnetic field sensor) to determine device attitude and heading. By combining the strengths of different sensors and using sensor fusion algorithms, the system maintains indoor navigation capability while compensating for magnetic field interference, thus improving heading accuracy without losing adaptability.
Solution Approach 2:
The system uses feedback from multiple sensors to continuously refine heading determination. The gyroscope and accelerometer provide feedback on device orientation and motion, which is combined with magnetic field data to correct for ambient magnetic field effects. This feedback mechanism maintains accurate heading determination despite magnetic interference.
3Volume of moving object
If sensor size is miniaturized, then device compactness is improved, but sensor accuracy is reduced
Solution Approach 1:
The patent combines data from multiple miniaturized sensors to achieve accurate attitude determination. By fusing data from small accelerometers, gyroscopes, and magnetic field sensors, the system compensates for the reduced accuracy of individual miniaturized sensors, thus maintaining device compactness while preserving measurement precision.
Solution Approach 2:
The system uses feedback from multiple miniaturized sensors to compensate for individual sensor inaccuracies. The sensor fusion algorithm processes feedback from all sensors to produce accurate attitude and heading information, enabling the use of small sensors without sacrificing overall measurement precision.
Data Source
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AI summary
Systems and methods for detecting an attitude of a device are disclosed. The system includes a processing system including at least a sensor processor and an application processor, which are distinct. The system further includes a memory system including one or more computer-readable media. The computer-readable media contains instructions that, if executed by the processing system, cause the system to perform operations, The operations include executing an application using the application processor, and receiving raw sensor readings from one or more sensors, using the sensor processor. The operations also include determining, using the sensor processor, a processed sensor metric comprising attitude data, and transmitting the processed sensor metric to the application.