Method of occupancy classification in a vehicle seat
First Claim
1. A method of recognizing and classifying a physical presence occupying a vehicle seat having a occupancy sensing system, said method including the steps of:
- sensing the output of an array of sensors that detect a physical presence in a seat;
applying the sensor array output as a vector representation to a neural net that was trained using a learning vector quantization algorithm; and
recognizing the sensor array output as falling within one of a group of predetermined classification patterns that represent a physical presence in the seat defined by size, weight, and physical orientation.
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Accused Products
Abstract
A method of recognizing and classifying the occupancy in a vehicle seat having an occupancy sensing system, including the steps of sensing the output of an array of sensors that detect a physical presence in a seat and applying the sensor array output as a vector representation to a neural net that was trained using a learning vector quantization algorithm. The method also includes the step of recognizing the sensor array output as falling within one of a group of predetermined classification patterns that represent a physical presence in the seat defined by size, weight, and physical orientation.
106 Citations
12 Claims
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1. A method of recognizing and classifying a physical presence occupying a vehicle seat having a occupancy sensing system, said method including the steps of:
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sensing the output of an array of sensors that detect a physical presence in a seat;
applying the sensor array output as a vector representation to a neural net that was trained using a learning vector quantization algorithm; and
recognizing the sensor array output as falling within one of a group of predetermined classification patterns that represent a physical presence in the seat defined by size, weight, and physical orientation. - View Dependent Claims (2, 3, 4)
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5. A method of recognizing and classifying a physical presence occupying a vehicle seat having a occupancy sensing system by training a neural network, said method including the steps of:
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determining the number of times to process a training set of input values through a neural network for a first learning vector quantization algorithm;
determining the number of times to process a training set of input values through a neural network for a second learning vector quantization algorithm;
processing the set of training samples through the neural network the determined number of times using said first learning vector quantization algorithm;
adjusting one of the output units of the neural net each time one of said training samples is processed using said first learning vector quantization algorithm;
processing the set of training samples through the neural network the determined number of times using said second learning vector quantization algorithm;
adjusting two of the output units of the neural net each time one of said training samples is processed using said second learning vector quantization algorithm; and
storing the adjusted output units as the final trained values for the neural net. - View Dependent Claims (6, 7, 8, 9, 10, 11)
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12. A method of recognizing and classifying a physical presence occupying a vehicle seat having a occupancy sensing system, said method including the steps of:
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sensing the analog output of an array of sensors that detect a physical presence in a seat;
converting the analog sensor array output into a digital vector expression;
applying the vector expression to a trained neural net having a predetermined learning vector quantization algorithm;
recognizing the output from the neural net as belonging to one of a variety of predetermined patterns representative of a physical presence in the seat;
determining which one of a predetermined series of classifications defined by size, weight, and physical orientation that the recognized pattern belongs to; and
continuously reinitiating the method steps to redetermine the classification.
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Specification