Device Disposition Detection Using Hysteresis Filtering
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Solution Overview
Problem
Traditional systems for determining the physical disposition of devices, such as tilt and being held, are prone to errors due to noise and mechanical resonances, leading to false positives and negatives, especially in environments with vibrations and internal sources of movement.
Innovation Solution
The system uses multi-axis sensors like accelerometers and gyroscopes to process sensor data, determining a reference pose and implementing a finite state machine with hysteresis bands and override techniques to accurately distinguish between 'no motion' and 'in motion' states, accounting for noise and intentional device movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional systems use simple sensors to determine device disposition, then device complexity is reduced, but measurement precision deteriorates due to noise and mechanical resonances
Solution Approach 1:
The patent combines multiple sensors (accelerometer, gyroscope, and optionally magnetometer) into a unified disposition determination system. The sensors work together to provide complementary information that compensates for individual sensor limitations, enabling accurate disposition detection while managing system complexity through integrated processing.
Solution Approach 2:
The patent introduces an intermediary processing layer that includes reference pose determination and hysteresis band logic. This intermediary layer filters raw sensor data through multiple processing stages, using reference poses and hysteresis thresholds to distinguish between genuine disposition changes and noise, thereby improving measurement precision without directly exposing the complexity of the processing algorithm.
2Measurement precision
If the system uses multiple sensors and complex processing to improve measurement precision, then disposition detection accuracy improves, but device complexity increases
Solution Approach 1:
The patent segments the disposition determination process into distinct functional modules: reference pose determination, sensor data processing with hysteresis bands, and disposition state classification. This segmentation allows each module to be optimized independently and simplifies the overall system architecture by breaking down the complex task into manageable components with clear responsibilities.
Solution Approach 2:
The patent utilizes parameter changes through hysteresis bands and threshold values to simplify decision-making in the disposition detection system. By implementing hysteresis bands that switch between different threshold criteria based on current disposition state, the system manages complexity through adaptive parameter adjustment rather than complex control algorithms.
3Productivity
If the system responds quickly to motion detection, then productivity improves, but reliability deteriorates due to false positives from vibrations and noise
Solution Approach 1:
The patent implements preliminary action through reference pose determination and hysteresis band setup before actual disposition detection occurs. The system pre-establishes reference poses and hysteresis thresholds that enable rapid response to genuine motion while filtering out false positives through the pre-configured filtering mechanism, thus achieving both speed and reliability.
Solution Approach 2:
The patent employs feedback mechanisms through hysteresis bands that adjust detection thresholds based on current disposition state. The system continuously monitors and adjusts its response criteria based on feedback from sensor data, allowing rapid response to genuine motion while suppressing false positives through adaptive threshold adjustment that responds to the current operational context.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system reliably determines device disposition with minimal computational resources, reducing false reports and maintaining accurate operation under various conditions, including vibrations and noise.
Implementation Method 1
multi-axis sensors like accelerometers and gyroscopes
Implementation Method 2
multi-axis sensors like accelerometers and gyroscopes
Data Source
AI summary
Operation of a device may be influenced based on whether the device is being tilted or has been picked up. Accelerometers gather accelerometer data that is processed to determine a reference pose. The reference pose is determined by analyzing the accelerometer data to determine “quiet” periods of accelerometer data. A moving average of the quiet periods is used to determine the reference pose. One or more override techniques may be used to compensate for noise in the accelerometer data, such as produced by the device playing music. The accelerometer data may be processed to compensate for other influences, such as intended movement of the device by one or more actuators. This processed data is compared to the reference pose. Responsive to the processed data exceeding a threshold value, the device may stop moving the one or more actuators, present output on a user interface, and so forth.