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 method for detecting human presence in an area, the method comprising:
providing a plurality of transceivers in an area, said plurality automatically exchanging wireless signals bi-directionally, said wireless signals including status data about characteristics of said wireless signals;
receiving at a first transceiver in said plurality of transceivers, from a second transceiver in said plurality of transceivers, a first set of wireless signals, said first set of wireless signals having first status data;
receiving at said second transceiver from said first transceiver, a second set of wireless signals, said second set of wireless signals having second status data;
comparing said first status data and said second status data against a baseline signal profile representing status data of wireless signals exchanged by said first transceiver and said second transceiver when no human is present in said area;
comparing said first status data and said second status data against another signal profile representing status data of wireless signals exchanged by said first transceiver and said second transceiver when a human is present in said area;
determining whether a human is present within said area based at least in part on whether said first status data and said second status data is closer in said comparisons to said baseline signal profile or said another signal profile.
2. The method of claim 1 , further comprising:
determining a number of humans present within said area, said determination based at least in part on a comparison of said first status data and said second status data to said baseline signal profile and said another signal profile.
3. The method of claim 1 , further comprising:
determining a location of one or more humans present within said area, said determination based at least in part on a comparison of said first status data and said second status data to said baseline signal profile and said another signal profile.
4. The method of claim 1 , further comprising:
only after determining a human is present in said area, operating a second system which alters an environmental variable in said area where said wireless signals are being transmitted.
5. The method of claim 4 , wherein said second system is selected from the group consisting of: an electrical system; a lighting system; a heating, venting, and cooling (HVAC) system; and a security system.
6. The method of claim 1 , wherein said exchanging data bi-directionally via wireless signals utilizes a wireless communication protocol selected from the group consisting of: Bluetooth™, Bluetooth™ Low Energy, ANT, ANT+, WiFi, Zigbee, and Z-Wave.
7. The method of claim 1 wherein said another signal profile is one of a plurality of signal profiles representing status data of wireless signals exchanged by said plurality of transceivers when a human is present in said area.
8. The method of claim 7 wherein said first status data and said second status data are compared against other signal profiles in said plurality of signal profiles.
9. The method of claim 8 wherein said comparison of said first status data and said second status data to said other signal profiles in said plurality of signal profiles is used to determine a number of humans present within said area.
10. The method of claim 9 wherein said number of humans within said area is separately verified and said verification is used to refine said comparison to said other signal profiles.
11. The method of claim 10 wherein said verification comprises determining said number via determining a number of fiducial elements carried by said humans present within said area.
12. The method of claim 10 wherein said refining said comparison is performed by a machine learning methodology.
13. The method of claim 8 wherein said comparison of said first status data and said second status data to said other signal profiles in said plurality of signal profiles is used to determine a location of said human within said area.
14. The method of claim 13 wherein said location of said human within said area is separately verified and said verification is used to refine said comparison to said other signal profiles.
15. The method of claim 14 wherein said verification comprises determining said location via determining a location of a fiducial element carried by said human within said area.
16. The method of claim 14 wherein said refining said comparison is performed by a machine learning methodology.