Method and system to count movements of persons from vibrations in a floor
First Claim
1. A system for counting persons comprising:
- (a) A sensor to produce signals from vibrations in a floor connecting a set of at least three areas each of which a person may use to at least one of enter the floor and exit the floor;
(b) a processor to evaluate a machine learning model wherein;
the model comprises a first layer wherein outputs of the layer are generated from an evaluation of the sensor signals on the basis of training to classify events based on timing and frequency determinations,the model comprises a second layer to evaluate outputs of the first layer to produce an output identifying a multiplicity of locations of origin of signals produced by a particular person on the basis of training from signals collected from the sensor,the processor produces an output of the model on the basis of the locations which signifies that a person has entered using a particular one of the areas of the set and the person has exited using a particular one of the areas of the set; and
(c) an output device for at least one of signaling a count produced on the basis of the output of persons which have entered by a particular one of the areas of the set and exited by a particular one of the areas of the set and of displaying a count of persons which have entered by a particular one of the areas of the set and exited by a particular one of the areas of the set.
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Abstract
A system and method for counting persons using passages entering or exiting an area by analyzing vibrations in the floor with sensors and a machine learning system. The machine learning system uses a model, usually implemented as a neural network on a processor. The network is trained in levels and implemented in layers. Different levels classify and analyze vibrations by timing and frequency, by movements of persons, and by identity of persons The same person is identified by patterns in the vibrations and the vibrations are correlated to determine when a person uses a combination of passages and is thereby counted. Location information for the person is used to identify persons in places and doing activities of interest. The model may be trained on one processor and then downloaded to another processor for evaluation. Additional sensors and levels of training may be implemented on the latter processor.
35 Citations
13 Claims
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1. A system for counting persons comprising:
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(a) A sensor to produce signals from vibrations in a floor connecting a set of at least three areas each of which a person may use to at least one of enter the floor and exit the floor; (b) a processor to evaluate a machine learning model wherein; the model comprises a first layer wherein outputs of the layer are generated from an evaluation of the sensor signals on the basis of training to classify events based on timing and frequency determinations, the model comprises a second layer to evaluate outputs of the first layer to produce an output identifying a multiplicity of locations of origin of signals produced by a particular person on the basis of training from signals collected from the sensor, the processor produces an output of the model on the basis of the locations which signifies that a person has entered using a particular one of the areas of the set and the person has exited using a particular one of the areas of the set; and (c) an output device for at least one of signaling a count produced on the basis of the output of persons which have entered by a particular one of the areas of the set and exited by a particular one of the areas of the set and of displaying a count of persons which have entered by a particular one of the areas of the set and exited by a particular one of the areas of the set. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A system for counting persons comprising:
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(a) A sensor to produce signals from vibrations in a floor connecting a set of at least two areas each of which a person may use to at least one of enter the floor and exit the floor; (b) a processor to evaluate a machine learning model wherein; the model comprises a first layer wherein outputs of the layer are generated from an evaluation of the sensor signals on the basis of training to classify events based on timing and frequency determinations, the model comprises a second layer to evaluate outputs of the first layer to produce an output identifying a multiplicity of locations of origin of signals produced by a particular person on the basis of training from signals collected from the sensor, and the processor produces an output of the model on the basis of the locations which signifies that a person has entered using a particular one of the areas of the set and the person has exited using a particular one of the areas of the set; and (c) an output device for at least one of signaling a count of a set of persons produced on the basis of the output wherein the set of persons consists of persons who have entered by a particular one of the areas of the set of areas and exited by a particular one of the areas of the set of areas and of displaying a count of persons which have entered by a particular one of the areas of the set of areas and exited by a particular one of the areas of the set of areas, and wherein the set of persons is limited to persons who have been identified as having been in a specific area of the floor in a specific time range. - View Dependent Claims (9, 10, 11, 12, 13)
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Specification