# US 2021/0333351 A1 · Systems and methods for occupancy detection using wifi sensing technologies > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0333351 A1 - Document ID: US20210333351A1 - Status: Published application - Kind: A1 - Application number: 17/367,986 - Filed: 2021-07-06 - Published: 2021-10-28 - Earliest claimed: 2016-04-22 - Inventors: Azar ZANDIFAR; Negar GHOURCHIAN; Michel Allegue MARTINEZ - Web page: https://patentgrep.com/patent/US20210333351A1 ## CPC - G01S 5/021: {Calibration, monitoring or correction (G01S5/0252 takes precedence)} - G01S 5/02: using radio waves (using satellite radio beacon systems for determining position G01S19/00) - G01S 5/0221: {Receivers} - G01S 7/006: {using shared front-end circuitry, e.g. antennas (G01S13/765, G01S13/825 take precedence)} - G01S 7/415: {Identification of targets based on measurements of movement associated with the target} - G01S 13/003: {Bistatic radar systems; Multistatic radar systems} - G01S 13/04: Systems determining presence of a target (based on relative movement of target G01S13/56) - G01S 13/56: for presence detection {(presence detection using near field arrangements G01V3/00, e.g. G01V3/08, G01V3/12; burglar, theft or intruder alarms with electrical actuation G08B13/22 - G08B13/26)} - H04B 17/27: for locating or positioning the transmitter - H04W 64/00: Locating users or terminals {or network equipment} for network management purposes, e.g. mobility management - G01S 7/417: {involving the use of neural networks} - Y02D 30/70: in wireless communication networks - G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES - H04B: TRANSMISSION - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) - Y02D: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE ## Abstract Wireless device free “occupancy detection” in residential or small industrial properties is an essential function within the broader scope of smart environments. Applications include, monitoring a subject's, e.g. an elderly person, behaviour for health assessments or moving towards more efficient energy usage in smart homes. Changes and disruption of wireless signals transmitted and received by the plurality of wireless devices are collected and analyzed to infer the presence of a subject the sensing area. More particularly, CSI information over time analysed with one or models can estimate the presence of a subject within the sensing area whether the subject moves or stays still. ## Claims ### Claim 1 A system comprising: a plurality of wireless devices, each wireless device associated with a predetermined indoor region of a property and operating according to a common wireless standard; and a device comprising at least a processor and a memory for storing computer executable instructions which when executed by the processor configure the device to: receive and store a plurality of metrics extracted from wireless signals transmitted and received by the plurality of wireless enabled devices; process the extracted plurality of metrics; and establish an occupancy of the predetermined indoor region of the property. ### Claim 2 (depends on 1) The system according to claim 1, wherein the plurality of metrics extracted comprise one or more metrics extracted from wireless channel information. ### Claim 3 (depends on 1) The system according to claim 1, wherein the plurality of metrics extracted comprise one or more of: channel state information of a first wireless channel of a plurality of wireless channels employed by the plurality of wireless enabled devices, a frequency response of a second wireless channel of the plurality of wireless channels employed by the plurality of wireless enabled devices; a phase response of a third wireless channel of the plurality of wireless channels employed by the plurality of wireless enabled devices; an impulse response of a fourth wireless channel of the plurality of wireless channels employed by the plurality of wireless enabled devices; one or more interface metrics extracted from one or more network interface cards associated with the system. ### Claim 4 (depends on 1) The system according to claim 1, wherein each wireless device of the plurality of wireless devices is a standard commercial wireless devices operating according to a predetermined telecommunications standard. ### Claim 5 (depends on 4) The system according to claim 4, wherein the predetermined telecommunications standard is one of IEEE 802.11n, IEEE 802.11ac, IEEE 802.11ax, IEEE 802.11s. ### Claim 6 (depends on 1) The system according to claim 1, wherein the computer executable instructions when executed by the processor configure the device to process the extracted plurality of metrics with one or more models to determine the presence or an absence of a subject in the predetermined indoor region of the property; and each model of the one or models is one of a machine learning model of a plurality of machine learning models and an artificial intelligence model of a plurality of artificial intelligence models. ### Claim 7 (depends on 1) The system according to claim 1, wherein the computer executable instructions when executed by the processor configure the device to process the extracted plurality of metrics in dependence upon a machine learning structure; the machine learning structure is established in dependence upon historical data of the plurality of metrics for the predetermined indoor region of the property and associated occupancy data predetermined indoor region of the property; and the machine learning structure provides robust performance in a range of environmental settings. ### Claim 8 (depends on 1) The system according to claim 1, wherein the computer executable instructions when executed by the processor configure the device to process the extracted plurality of metrics in dependence upon a machine learning structure; the machine learning structure is established in dependence upon an offline training method which establishes an initial robust model for occupancy detection using training data obtained over a range of different input settings. ### Claim 9 (depends on 1) The system according to claim 1, wherein the computer executable instructions when executed by the processor configure the device to process the extracted plurality of metrics in dependence upon a machine learning structure; stability of the machine learning structure is enhanced through the use of environmental state change configuration data extracted from the plurality of wireless signals. ### Claim 10 (depends on 1) The system according to claim 1, wherein the computer executable instructions when executed by the processor configure the device to process the extracted plurality of metrics in dependence upon a machine learning structure; stability of the machine learning structure is enhanced by augmenting a training set employed to generate the machine learning structure improve its representation of a sensing environment associated with the predetermined indoor region of the property. ### Claim 11 A system comprising: a device comprising at least a processor and a memory for storing computer executable instructions which when executed by the processor configure the device to: receive and store a plurality of metrics extracted from wireless signals transmitted and received by a plurality of wireless enabled devices; process the extracted plurality of metrics; and establish an occupancy of the predetermined indoor region of the property; wherein the plurality of wireless devices are associated with a predetermined indoor region of a property and operate according to a common wireless standard. ### Claim 12 (depends on 11) The system according to claim 11, wherein the plurality of metrics extracted comprise one or more metrics extracted from wireless channel information. ### Claim 13 (depends on 11) The system according to claim 11, wherein the plurality of metrics extracted comprise one or more of: channel state information of a first wireless channel of a plurality of wireless channels employed by the plurality of wireless enabled devices, a frequency response of a second wireless channel of the plurality of wireless channels employed by the plurality of wireless enabled devices; a phase response of a third wireless channel of the plurality of wireless channels employed by the plurality of wireless enabled devices; an impulse response of a fourth wireless channel of the plurality of wireless channels employed by the plurality of wireless enabled devices; one or more interface metrics extracted from one or more network interface cards associated with the system. ### Claim 14 (depends on 11) The system according to claim 11, wherein each wireless device of the plurality of wireless devices is a standard commercial wireless devices operating according to a predetermined telecommunications standard. ### Claim 15 (depends on 14) The system according to claim 14, wherein the predetermined telecommunications standard is one of IEEE 802.11n, IEEE 802.11ac, IEEE 802.11ax, IEEE 802.11s. ### Claim 16 (depends on 11) The system according to claim 11, wherein the computer executable instructions when executed by the processor configure the device to process the extracted plurality of metrics with one or more models to determine the presence or an absence of a subject in the predetermined indoor region of the property; and each model of the one or models is one of a machine learning model of a plurality of machine learning models and an artificial intelligence model of a plurality of artificial intelligence models. ### Claim 17 (depends on 11) The system according to claim 11, wherein the computer executable instructions when executed by the processor configure the device to process the extracted plurality of metrics in dependence upon a machine learning structure; the machine learning structure is established in dependence upon historical data of the plurality of metrics for the predetermined indoor region of the property and associated occupancy data predetermined indoor region of the property; and the machine learning structure provides robust performance in a range of environmental settings. ### Claim 18 (depends on 11) The system according to claim 11, wherein the computer executable instructions when executed by the processor configure the device to process the extracted plurality of metrics in dependence upon a machine learning structure; the machine learning structure is established in dependence upon an offline training method which establishes an initial robust model for occupancy detection using training data obtained over a range of different input settings. ### Claim 19 (depends on 11) The system according to claim 11, wherein the computer executable instructions when executed by the processor configure the device to process the extracted plurality of metrics in dependence upon a machine learning structure; stability of the machine learning structure is enhanced through the use of environmental state change configuration data extracted from the plurality of wireless signals. ### Claim 20 (depends on 11) The system according to claim 11, wherein the computer executable instructions when executed by the processor configure the device to process the extracted plurality of metrics in dependence upon a machine learning structure; stability of the machine learning structure is enhanced by augmenting a training set employed to generate the machine learning structure improve its representation of a sensing environment associated with the predetermined indoor region of the property.