Sensor Data Threshold Selection for Automatic Device State Detection
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Solution Overview
Problem
Separating signal from noise in sensor data is challenging due to environmental, operational, and inherent noise sources, making it difficult to accurately and consistently identify noise components over time and across various sensors.
Innovation Solution
The system employs a module to analyze the distribution of sensor data, determine if data clipping is necessary, and set a threshold based on variance between data classes, using a constrained data set to differentiate between signal and noise components, enabling adaptive event or state detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is processed to remove noise components, then measurement precision is improved, but device complexity increases due to the need for sophisticated signal processing algorithms
Solution Approach 1:
The system performs self-calibration by automatically determining calibration factors using sensor data collected during normal operation. The processor identifies calibration opportunities when sensors are exposed to known conditions (e.g., darkness for light sensors, zero-g for accelerometers) and autonomously adjusts calibration parameters without external intervention, enabling the system to self-correct noise and drift issues
Solution Approach 2:
The system dynamically changes processing parameters based on operating conditions. Calibration factors are adjusted in real-time based on environmental context, and the system switches between different noise filtering strategies depending on the sensor type, operating mode, and detected signal characteristics, allowing adaptive optimization without fixed complex algorithms
2Measurement precision
If manual calibration is performed to improve measurement accuracy, then measurement precision is improved, but loss of time increases due to manual intervention requirements
Solution Approach 1:
The system performs self-calibration by automatically determining calibration factors using sensor data collected during normal operation. The processor identifies calibration opportunities when sensors are exposed to known conditions (e.g., darkness for light sensors, zero-g for accelerometers) and autonomously adjusts calibration parameters without external intervention, enabling the system to self-correct noise and drift issues
Solution Approach 2:
The system performs preliminary calibration actions during idle periods or transitional states before actual measurement tasks begin. By calibrating sensors during manufacturing, shipping, or initial power-up phases when the device is not yet in use, the system prepares accurate baseline parameters in advance, eliminating the need for time-consuming manual calibration during operational periods
3Measurement precision
If sensor data is collected continuously to improve detection accuracy, then measurement precision is improved, but use of energy increases due to constant data acquisition and processing
Solution Approach 1:
The system uses periodic sampling with variable intervals based on detected conditions. During stable states, sampling occurs at lower frequencies to conserve energy, while during transitional or event-prone periods, sampling frequency increases automatically. The processor monitors for changes in signal characteristics and adjusts the sampling rate dynamically, maintaining detection accuracy while minimizing unnecessary continuous data acquisition
Solution Approach 2:
The system dynamically adjusts data collection frequency based on operational context. Sampling rates are modified in real-time according to device state, environmental conditions, and detected signal patterns. During normal operation, reduced sampling maintains accuracy for steady-state measurements, while automatic rate increases occur when events are detected or anticipated, optimizing the balance between detection precision and energy consumption
Data Source
AI summary
Apparatus and method to facilitate automatic detection of a device state are disclosed herein. Selectively constraining a sensor based data set associated with one or more states of a device, wherein selectively constraining the sensor based data set includes analyzing a distribution of the sensor based data set to determine whether to constrain the sensor based data set, the sensor based data set including a first class and a second class of data values. Determining a threshold associated with the sensor based data set by selecting the threshold based on a variance between the first and second classes of the sensor based data set, wherein selecting the threshold includes using a constrained sensor based data set when the sensor based data set is determined to be constrained, and wherein the threshold indicates the data values associated with the first and second classes.