Capacitive Proximity Sensor Foot Detection Energy Saving Mode
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Solution Overview
Problem
Existing methods for detecting foot movement to trigger a tailgate actuation using capacitive proximity sensors in vehicles are slow and susceptible to interference, leading to energy inefficiency and delayed detection.
Innovation Solution
A method that switches between energy-saving and normal evaluation modes by adjusting the sampling rate and filter calculations, allowing for immediate processing of stored digital values and improved filtering, enabling faster detection of foot movement with reduced energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the sampling rate is increased to improve detection speed, then the detection speed improves, but the energy consumption increases
Solution Approach 1:
The system dynamically adjusts the evaluation mode between energy-saving and normal modes based on whether a foot movement is detected. In energy-saving mode, filter values are calculated at longer intervals with lower sampling rate to reduce energy consumption. When a foot movement is detected, the system switches to normal mode with higher sampling rate and more frequent filter calculations for faster detection, thus adapting the detection speed and energy consumption to the actual operational needs.
Solution Approach 2:
The system changes the sampling rate and filter calculation frequency as adjustable parameters. In energy-saving mode, the sampling interval is extended and filter calculations are performed less frequently. Upon detecting a foot movement, the system reduces the sampling interval and increases filter calculation frequency, thereby changing the operational parameters to balance detection speed and energy consumption based on the detected event.
2Measurement precision
If filter calculations are performed frequently to improve detection accuracy, then the detection accuracy improves, but the computational effort and energy consumption increase
Solution Approach 1:
The system performs filter calculations periodically at different intervals depending on the operational mode. In energy-saving mode, filter values are calculated only after a predetermined number of sensor readings have been stored, resulting in longer intervals between calculations. In normal mode, filter calculations are performed more frequently to improve detection accuracy. This periodic action with variable intervals allows the system to balance computational effort and detection accuracy.
3Speed
If the system switches to higher sampling rate upon threshold exceedance, then the detection speed improves, but a dead time occurs during which filter values cannot be calculated
Solution Approach 1:
The system continuously stores sensor readings in a buffer memory in advance, preparing the data for future filter calculations. When a foot movement is detected and the system switches from energy-saving to normal mode, the pre-stored readings are immediately available for filter calculation, eliminating the dead time that would otherwise occur while waiting to accumulate sufficient readings. This preliminary storage of data ensures immediate processing capability upon mode switching.
4Measurement precision
If digital measured values are stored in buffer memory for filtering, then the filtering accuracy improves, but the memory usage increases
Solution Approach 1:
The system uses different buffer memory sizes for different operational modes. In energy-saving mode, a smaller buffer is sufficient since filter calculations are performed less frequently. In normal mode, a larger buffer is allocated to improve filtering accuracy. This local quality adjustment optimizes memory usage according to the specific operational requirements of each mode, avoiding the need to maintain large buffer capacity during low-activity periods.
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
This approach allows for quicker recognition of foot movement with reduced interference and energy efficiency by maintaining constant sampling frequency, utilizing stored values for immediate analysis and adapting filtering to detect movement patterns effectively.
Implementation Method 1
capacitive proximity sensor arranged near the ground in the rear area of a motor vehicle... capacitance relative to a reference potential (e.g. ground) changes when an operator standing behind the motor vehicle puts a foot in the direction of the rear area
Data Source
Figure 1~2
AI summary
In a method for detecting and processing measurement values of at least one capacitive proximity sensor which is arranged in the rear region of a motor vehicle close to the ground and for initiating an operating function of a tailgate by a prespecified foot movement of an operator, a measurement value of the proximity sensor is periodically detected after in each case one scanning interval, and a digital measurement value which corresponds to the measurement value is stored in a FIFO memory. In a first, energy-saving evaluation mode, a filter value is calculated from the currently stored digital measurement value and a prespecified second number of previously stored digital measurement values, in each case after a prespecified first number of digital measurement values are stored, by each of said stored digital measurement values being multiplied by an associated first factor in each case and the results being added, the filter value being compared with the threshold value and a second evaluation mode being activated when the filter value exceeds the threshold value. In the second, normal evaluation mode, a filter value is calculated from the currently stored digital measurement value and a prespecified fourth number of previously stored digital measurement values, in each case after a prespecified third number of digital measurement values, which third number is smaller than the first number, are stored, by each of said digital measurement values being multiplied by an associated second factor in each case and the results being added, and the filter value being fed to an algorithm for identifying a signal profile which corresponds to the prespecified foot movement.