Motion detection based on machine learning of wireless signal properties
First Claim
1. A motion detection method comprising:
- obtaining, at a neural network training system, multiple sets of tagged neural network input data, each set of tagged neural network input data based on a statistical analysis of a series of wireless signals transmitted through a space over a respective time period, each set of the tagged neural network input data comprising a tag indicating whether motion occurred in the space over the respective time period,wherein the tagged neural network input data comprises histogram data, and the statistical analysis comprises;
obtaining a frequency-domain representation of the wireless signals,computing statistical parameter values based on the frequency-domain representation of the wireless signals,populating the statistical parameter values into an initial matrix, andgenerating the histogram data based on the initial matrix, the histogram data comprising a set of bins and a quantity for each bin, each bin corresponding to a respective range for each of the statistical parameters; and
by operation of the neural network training system, processing the sets of tagged neural network input data to parameterize nodes of a neural network system;
detecting motion, using the neural network system comprising the parameterized nodes, based on untagged neural network input data; and
activating a security system or a physical device associated with the space where the motion was detected.
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Abstract
In a general aspect, motion in a space can be detected based on machine learning of wireless signal properties. In some aspects, sets of tagged neural network input data are obtained at a neural network training system. Each set of tagged neural network input data is based on a statistical analysis of a series of wireless signals transmitted through a space over a respective time period, and each set of the tagged neural network input data includes a tag indicating whether motion occurred in the space over the respective time period. The sets of tagged neural network input data are processed by the neural network training system to parameterize nodes of a neural network system. Parameterizing the nodes configures the neural network system to detect motion based on untagged neural network input data.
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Citations
24 Claims
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1. A motion detection method comprising:
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obtaining, at a neural network training system, multiple sets of tagged neural network input data, each set of tagged neural network input data based on a statistical analysis of a series of wireless signals transmitted through a space over a respective time period, each set of the tagged neural network input data comprising a tag indicating whether motion occurred in the space over the respective time period, wherein the tagged neural network input data comprises histogram data, and the statistical analysis comprises; obtaining a frequency-domain representation of the wireless signals, computing statistical parameter values based on the frequency-domain representation of the wireless signals, populating the statistical parameter values into an initial matrix, and generating the histogram data based on the initial matrix, the histogram data comprising a set of bins and a quantity for each bin, each bin corresponding to a respective range for each of the statistical parameters; and by operation of the neural network training system, processing the sets of tagged neural network input data to parameterize nodes of a neural network system; detecting motion, using the neural network system comprising the parameterized nodes, based on untagged neural network input data; and activating a security system or a physical device associated with the space where the motion was detected. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A neural network training system comprising:
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a data processing apparatus; and memory comprising instructions that are operable when executed by the data processing apparatus to perform operations comprising; obtaining multiple sets of tagged neural network input data, each set of tagged neural network input data based on a statistical analysis of a series of wireless signals transmitted through a space over a respective time period, each set of the tagged neural network input data comprising a tag indicating whether motion occurred in the space over the respective time period, wherein the tagged neural network input data comprises histogram data, and the statistical analysis comprises; obtaining a frequency-domain representation of the wireless signals; computing the statistical parameter values based on the frequency-domain representation of the wireless signals; populating the statistical parameter values into an initial matrix; and generating the histogram data based on the initial matrix, the histogram data comprising a set of bins and a quantity for each bin, each bin corresponding to a respective range for each of the statistical parameters; and processing the sets of tagged neural network input data to parameterize nodes of a neural network system; detecting motion, using the neural network system comprising the parameterized nodes, based on untagged neural network input data; and activating a security system or a physical device associated with the space where the motion was detected. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A motion detection method, comprising:
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obtaining, at a neural network system, multiple sets of neural network input data, each set of neural network input data based on a statistical analysis of a series of wireless signals transmitted through a space over a respective time period, wherein the neural network input data comprises histogram data, and the statistical analysis comprises; obtaining a frequency-domain representation of the wireless signals; computing statistical parameter values based on the frequency-domain representation of the wireless signals; populating the statistical parameter values into an initial matrix; and generating the histogram data based on the initial matrix, and by operation of the neural network system, processing the sets of neural network input data through a plurality of programmed neural network nodes; determining whether motion occurred in the space during the respective time period; and responsive to determining motion occurred in the space, activating a security system or a physical device associated with the space where the motion was detected. - View Dependent Claims (14, 15, 16, 17, 18)
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19. A neural network system comprising:
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a data processing apparatus; and memory comprising instructions that are operable when executed by the data processing apparatus to perform operations comprising; obtaining multiple sets of neural network input data, each set of neural network input data based on a statistical analysis of a series of wireless signals transmitted through a space over a respective time period, wherein the neural network input data comprises histogram data, and the statistical analysis comprises; obtaining a frequency-domain representation of the wireless signals; computing statistical parameter values based on the frequency-domain representation of the wireless signals; populating the statistical parameter values into an initial matrix; and generating the histogram data based on the initial matrix; and processing the sets of neural network input data through a plurality of programmed neural network nodes; determining whether motion occurred in the space during the respective time period; and responsive to determining motion occurred in the space, activating a security system or a physical device associated with the space where the motion was detected. - View Dependent Claims (20, 21, 22, 23, 24)
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Specification