Vehicle Gesture Sensor Neural Network Segmentation
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Solution Overview
Problem
Current sensor systems for recognizing actuation gestures in motor vehicles face challenges in accuracy, particularly in distinguishing intended actions from unintended events and adapting to changing environmental conditions, leading to potential errors.
Innovation Solution
A sensor device utilizing a combination of capacitive sensors with overlapping detection areas and a processing unit equipped with a neural network to evaluate temporal signal curves, incorporating the sequence and characteristics of gestures, thereby enhancing recognition accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single capacitive sensor is used for detecting actuation gestures, then the device complexity is low, but the recognition accuracy is insufficient and false positives occur
Solution Approach 1:
The detection space is divided into multiple spatial zones (first spatial area, second spatial area, third spatial area) with each zone monitored by specific sensors. This segmentation allows the system to distinguish between gestures performed by different body parts (hand vs. foot) based on which sensor zones are activated, thereby improving recognition accuracy while maintaining a relatively simple sensor configuration.
Solution Approach 2:
The patent introduces a temporal dimension to gesture detection by evaluating the time course of sensor signals. Instead of relying solely on spatial information from single sensors, the system analyzes signal patterns over time, including the sequence and duration of sensor activations. This temporal analysis significantly improves gesture recognition accuracy by distinguishing intentional gestures from accidental movements.
2Measurement precision
If temporal signal curves are evaluated with multiple sensors, then the recognition accuracy is significantly improved, but the processing complexity increases
Solution Approach 1:
The control unit is pre-programmed with specific evaluation criteria and decision rules for interpreting temporal signal patterns from multiple sensors. Instead of implementing complex real-time analysis algorithms, the system uses predetermined evaluation rules that assess whether sensor activations follow expected temporal patterns for valid gestures. This approach improves processing efficiency while maintaining high recognition accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms where the evaluation of temporal signal curves from multiple sensors provides information about gesture validity. The control unit continuously monitors sensor signals and compares them against expected gesture patterns, adjusting its evaluation based on the temporal sequence and consistency of activations across different sensor zones. This feedback-driven approach enhances recognition accuracy without requiring overly complex processing.
3Difficulty of detecting and measuring
If the sensor system is made more sensitive to detect gestures, then the gesture detection capability is improved, but the susceptibility to false positives from everyday events increases
Solution Approach 1:
By dividing the detection space into multiple segmented zones and requiring specific patterns of activation across these zones, the system can distinguish between intentional gestures and random movements. A single sensor activation may be ignored, but a coordinated pattern across multiple segmented zones indicates a deliberate gesture, reducing false positives while maintaining sensitivity.
Solution Approach 2:
The system adds the temporal dimension to gesture detection by analyzing the time course of sensor activations. Intentional gestures produce characteristic temporal patterns (specific duration, sequence, and rhythm) that differ from random everyday movements. By evaluating signals over time rather than relying solely on instantaneous sensor readings, the system maintains high sensitivity while filtering out false positives from accidental movements.
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 significantly improves recognition accuracy by leveraging the temporal sequence of gestures and environmental factors, reducing false positives and adapting to user-specific movements, ensuring safer and more reliable actuation gesture detection.
Implementation Method 1
a first capacitive sensor (2) for detecting objects in a first spatial area and at least one further capacitive sensor (3) for detecting objects in a further spatial area
Data Source
Figure 1a~1b
Figure 2
Figure 3a~4
AI summary
The invention relates to a sensor device for detecting actuation gestures performed by a user of a motor vehicle to open the luggage compartment, for example. A first sensor is provided for detecting objects in a first three-dimensional zone, while at least one additional sensor is provided for detecting objects in another three-dimensional zone. The sensors have outputs for picking up time-related signal shapes, said outputs of the sensors being coupled to a processing unit. The processing unit implements a neural network to which the time-related signal shapes or patterns derived from the time-related signal shapes are fed. The processing unit includes a signal output which is coupled to the output units of the neural network and which can be queried in order to query an actuation signal. A signal is applied to said signal output in accordance with the result of the run through the neural network.