Pressure Sensor Surface Type Calibration
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Solution Overview
Problem
Wearable technology sensors, such as those in smart shoes, face inconsistencies in data collection across various environments, leading to inaccurate and inconsistent sensor data due to differences in surface types.
Innovation Solution
A system that includes a sensor component, a surface estimation component, and a calibration component to receive and adjust sensor data based on the type of surface, ensuring accurate pressure readings by normalizing or calibrating output data according to the surface type, thereby improving data consistency across indoor and outdoor environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pressure sensor data is collected across various environments, then the versatility and applicability of the sensor system is improved, but the measurement precision and data consistency deteriorate due to surface type variations
Solution Approach 1:
The system changes the pressure threshold parameters dynamically based on the detected surface type. Different surface types (carpet, wood, tile, concrete) have different optimal pressure thresholds stored in a lookup table. When the surface type is identified, the corresponding threshold parameters are applied to normalize the sensor readings, allowing consistent measurement across diverse environments.
Solution Approach 2:
The system introduces an intermediary surface type classification layer between the raw pressure sensor data and the final interpretation. The pressure readings are first used to determine the surface type, which then serves as a mediator to select appropriate calibration parameters. This intermediary step enables the system to adapt to different surfaces while maintaining measurement consistency.
2Measurement precision
If surface-specific calibration is implemented, then the measurement precision is improved, but the device complexity increases due to additional calibration components and processing
Solution Approach 1:
The system performs self-calibration by automatically detecting the surface type and selecting appropriate parameters without requiring manual intervention. The microcontroller automatically compares pressure readings against stored surface profiles, identifies the current surface type, and applies the correct calibration parameters, making the calibration process self-service and transparent to the user.
Solution Approach 2:
The system performs preliminary calibration by pre-storing pressure threshold data for multiple surface types in a lookup table during manufacturing. This preliminary preparation allows the runtime system to simply query and apply the appropriate parameters based on detected surface type, avoiding complex real-time calibration algorithms and reducing computational complexity.
Data Source
AI summary
Technologies are generally described that relate to managing and/or generating sensor data. An example method may include receiving output data generated by a pressure sensor. The method may also include determining surface data, indicative of a surface type associated with a force applied to a surface of the pressure sensor, based on the output data. Furthermore, the method may include adjusting the output data based on the surface data to facilitate generating adjusted output data indicative of an adjusted pressure value generated by the pressure sensor.