Ultrasonic Transducer Stationary Object Detection
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Solution Overview
Problem
Existing ultrasonic transducer systems struggle to distinguish between a user and stationary objects, particularly in environments with noise interference from factors like airflow and temperature changes, leading to high false positive and false negative rates.
Innovation Solution
A device equipped with an ultrasonic transducer that emits pulses and receives returned signals, using a processor to identify candidate echoes, compare signal characteristics over time, and determine if the echoes represent stationary objects, thereby distinguishing them from users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If ultrasonic transducers are used for presence detection, then power consumption and cost are reduced compared to optical sensors, but the ability to distinguish users from stationary objects deteriorates due to noise interference
Solution Approach 1:
The system dynamically adapts by tracking signal characteristics over time and adjusting detection thresholds based on learned environmental patterns. The machine learning model continuously updates its understanding of stationary object signatures versus user presence, enabling accurate distinction despite noise variations.
Solution Approach 2:
The system changes detection parameters adaptively by analyzing signal characteristics across multiple time points. It adjusts sensitivity thresholds and evaluation criteria based on the temporal patterns observed in returned signals, allowing reliable detection across varying environmental conditions.
2Measurement precision
If high sensitivity to small motion is used to detect user presence, then user detection accuracy is improved, but false positive rate increases due to environmental noise
Solution Approach 1:
The system performs preliminary characterization of the environment by analyzing returned signals from stationary objects before user detection begins. It builds a baseline profile of environmental noise and stationary object signatures, which is then used to filter and contextualize subsequent detections, reducing false positives while maintaining sensitivity.
Solution Approach 2:
The system implements feedback mechanisms where detection results and signal characteristics are continuously analyzed to adjust detection parameters. The machine learning model uses feedback from identified stationary objects to refine its understanding of environmental patterns, improving its ability to distinguish true user presence from noise over time.
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 solution effectively reduces false detections by accurately identifying stationary objects based on consistent signal characteristics, improving the reliability of user presence detection in noisy environments.
Implementation Method 1
Piezoelectric Micromachined Ultrasonic Transducers (PMUTs), which may be air-coupled, are one type of sonic transducer which operates in the ultrasonic range
Implementation Method 2
evaluate the returned signals to identify a first candidate echo indicating a first object within range of the ultrasonic transducer
Data Source
AI summary
Methods and systems are disclosed for employing an ultrasonic sensor to identify a stationary object. The ultrasonic transducer emits an ultrasonic pulse and receives returned signals corresponding to the emitted ultrasonic pulse. The returned signals are evaluated for candidate echoes of a first object. Characteristics of the received returned signals are compared to determine the first candidate echo represents a stationary object.