Detecting location within a network
Systems and methods for detecting the presence of a body in a network without fiducial elements, using signal absorption, and signal forward and reflected backscatter of RF waves caused by the presence of a biological mass in a communications network.
1. A presence detection system comprising:
means for obtaining multiple sets of input training data, each set of input training data based on a statistical analysis of characteristics of wireless signals transmitted through a detection area over a respective time period, each set of the input training data indicating whether a human was detected in the detection area over the respective time period,
wherein the input training data comprises machine learning data, and the statistical analysis includes:
obtaining frequencies and power levels of the wireless signals;
computing statistical parameter values based on the frequencies and power levels of the wireless signals;
populating the statistical parameter values into an initial signal characteristic profile; and
generating the machine learning data based on the initial signal characteristic profile; and
means for processing the sets of input training data to parameterize nodes of a machine learning system; and
means for detecting presence of a human in the detection area, using the machine learning system comprising the parameterized nodes, based on a newly obtained set of input data.
2. The system of claim 1 , wherein the statistical parameters comprise a mean and a standard deviation.
3. The system of claim 1 , wherein the input training data indicates a category of motion, and parameterizing the nodes configures the machine learning system to detect a category of motion based on the newly obtained set of input data.
4. The system of claim 1 , comprising:
means for obtaining additional sets of input training data, each additional set of input training data indicating whether interference was present in the detection area over the respective time period; and
means for processing the additional sets of input raining data to parameterize nodes of the machine learning system, wherein parameterizing the nodes configures the machine learning system to detect interference based on the newly obtained set of input data.
5. The system of claim 1 , wherein the machine learning system comprises a neural network.
6. A motion detection system, comprising:
means for obtaining multiple sets of input training data, each set of input training data based on a statistical analysis of a series of wireless signals transmitted through a detection area over a respective time period,
wherein the input training data comprises machine learning data, and the statistical analysis comprises:
obtaining frequencies and power levels of the wireless signals;
computing statistical parameter values based on the frequencies and power levels of the wireless signals;
populating the statistical parameter values into an initial signal profile; and
generating the machine learning data based on the initial signal profile, and
means for processing the sets of input training data through a plurality of programmed machine learning nodes; and
means for determining whether motion occurred in the detection area during the respective time period.
7. The system of claim 6 , wherein the statistical parameters comprise a mean and a standard deviation.
8. The system of claim 6 , wherein determining whether motion occurred in the detection area comprises generating an indication of motion by an object in the detection area, a category of motion that occurred in the detection area, interference present in the detection area, or an absence of motion in the detection area.
9. The system of claim 6 , wherein the machine learning system comprises a neural network.