Patent Description:
Conventional monitoring devices and systems are, to some degree, intrusive on family lives or business activities. These conventional techniques may require a user to manage operational configurations to allay privacy concerns that may arise with conventional monitoring devices.

<CIT> discloses detecting motion of an object in a based on wireless signals communicated through the space by a wireless communication system that includes multiple wireless communication devices. Each wireless signal is transmitted and received by a respective pair of the wireless communication devices. Motion indicator values are computed for the respective wireless communication devices. The motion indicator value for each individual wireless communication device represents a degree of motion detected by the individual wireless communication device based on a subset of the wireless signals transmitted or received by the individual wireless communication device. A location of the detected motion in the space is determined based on the motion indicator values.

<CIT> discloses a diagnostic system for spatial diagnosis of a WLAN.

This summary is provided to introduce simplified concepts of motion detection using wireless local area networks. The simplified concepts are further described below in the Detailed Description. This summary is not intended to identify essential features of the claimed subject matter, nor is it intended for use in determining the scope of the claimed subject matter.

The details of one or more implementations are set forth in the accompanying drawings and the following description. Other features and advantages will be apparent from the description and drawings and from the claims. This summary is provided to introduce subject matter that is further described in the Detailed Description and Drawings. Accordingly, this summary should not be considered to describe essential features nor used to limit the scope of the claimed subject matter.

Aspects of motion detection using wireless local area networks are described with reference to the following drawings. The same numbers are used throughout the drawings to reference like features and components:.

This document describes techniques and devices to detect and classify human and animal presence and/or motion based on changes in the interaction of the human or animal with Wireless Local Area Network (WLAN) radio frequency (RF) signals within or about a building structure, such as a home or office. A first WLAN device transmits a sounding packet to a second WLAN device and receives an acknowledgement (ACK) of receiving the sounding packet by the second WLAN device. The first WLAN device uses the received ACK to determine Channel State Information (CSI) for an RF signal path between the first WLAN device and the second WLAN device, aggregates the determined CSI with additional CSI, and uses the aggregated CSI to determine a presence or a motion within the structure.

As the Intemet-of-Things (IoT) expands, smart devices continue to proliferate. Internet-connected thermostats, appliances, vehicles, phones, lights, and machines are found in all areas of life, including home, work, business, recreation, and school. Many of these Internet-connected devices communicate using wireless networks and wireless protocols. Although a primary purpose of such wireless communication is to transfer information between or among the various devices, the transmission signals themselves can provide additional information. In particular, changes or variations in the transmission signals can indicate presence or lack of presence of a person, animal, or object in the physical space of the wireless network. Changes or variations can also indicate motion, lack of motion, changes in motion, or cessation of motion in the physical space of the wireless network. Advantageously, the additional information can be derived from the existing wireless network without additional equipment, hardware, or other devices.

Wireless networks, including mesh networks, can not only transmit information among various network devices but can also monitor changes or variations in the transmission signals used to communicate the information. Monitoring changes or variations in transmission signals can indicate presence, motion, lack of motion, or other characteristics about the physical space in which the wireless network operates without the need for additional equipment or dedicated devices.

Wireless networks, such as Wi-Fi, Bluetooth, and mesh networks like Zigbee, Z-Wave, and others all involve wirelessly connected components. Some components include routers, switches, plugs, repeaters, lights, thermostats, appliances, and many other types of devices. The wireless components can also connect to and interact with one another. For example, a home theater system that is turning on could send a signal that instructs the lights in the room to dim after a certain amount of time. The components may also be connected to other devices via the internet or other network connections. In such cases, the wireless components can be accessed and manipulated using a web browser, an application on a mobile device, a local remote, or programing system (e.g., voice commands, home automation systems).

As these various devices communicate with one another, RF signals pass throughout the physical space or geography of the wireless network. As discussed below, when a person moves through the physical space, the propagation of the RF signals is affected by the presence of the person. Machine Learning (ML) techniques can be used to train an ML system to classify human activities based on the changes in the propagation of the RF signals.

Conventional ML techniques for this classification are based on image classification techniques and require relatively large amounts of input data and computing resources to perform the classifications. In aspects, motion detection using wireless local area networks performs feature extraction on Channel State Information (CSI) data to detect and classify presence and motion. The use of feature extraction from CSI data reduces the volume of data required as an input to classification, as well as reducing the computation resources required for presence and motion detection.

In other aspects, CSI data can be acquired using various WLAN-enabled devices, such as Access Points (APs) and/or WLAN client devices (e.g., WLAN Station (STA) devices). The WLAN-enabled devices can operate in a WLAN network using any suitable network topology such as a bus network, a star network, a ring network, a mesh network, a star-bus network, a tree, or hierarchical network, and the like.

For example, multiple WLAN (e.g., IEEE <NUM>, Wi-Fi) APs providing a mesh WLAN network can acquire the CSI data for transmissions between the WLAN APs. In another example, WLAN client devices being served by a single WLAN AP can acquire the CSI data based on transmissions between the WLAN client devices and/or transmissions between the WLAN client devices and the single WLAN AP.

In another aspect, a device local to the WLAN can process the CSI data to detect motion, or a cloud service can process the CSI data to detect motion. For example, a root AP in a mesh LAN may receive CSI data from other APs and perform the motion detection. In an alternative example, an AP or STA device can forward the CSI data to a cloud service where the motion detection is performed.

The collection of CSI data is based on standards-defined communication techniques. A first WLAN device can transmit a null data frame (NDF) to a second device. The second device responds with an acknowledgement (ACK) of the null data frame. The first wireless device calculates the CSI data based on the received acknowledgement. By using standards-defined communication for channel probing, any WLAN device can be updated (e.g., downloading an updated software image to the device) to add motion detection capabilities in upper layer(s) of the network stack of the WLAN device, without modification of the standards-based Media Access Control (MAC) and Physical (PHY) layers of the WLAN network stack and the WLAN radio hardware.

<FIG> illustrates an example environment <NUM> that illustrates the disturbance of WLAN RF signals by human motion within a structure. A number of RF signal paths are illustrated between a first WLAN device <NUM> and a second WLAN device <NUM>. An RF signal <NUM> can propagate directly (e.g., line-of-sight (LOS) propagation) from the first WLAN device <NUM> to the second WLAN device <NUM>. Other RF signals between the first WLAN device <NUM> and the second WLAN device <NUM> are reflected signals. For example, an RF signal <NUM> is reflected off a wall <NUM> of a structure where the WLAN is deployed. The characteristics of the RF signals <NUM> and <NUM> received at the second WLAN device <NUM> will remain constant over time as long as the physical relationship of the first WLAN device <NUM>, the second WLAN device <NUM> and the surrounding structure are unchanged.

Other RF signals between the first WLAN device <NUM> and the second WLAN device <NUM> can vary based on the motion of humans (or animals) within the structure. For example, an RF signal <NUM> reflects off a person at position <NUM> instead of propagating until the RF signal <NUM> reaches an element of the structure. The alteration of the reflection, as compared to reflections in an empty structure, can be used to detect the presence of the person at position <NUM>. As the person moves to the position <NUM>, an RF signal <NUM> is reflected off the person. Changes in the characteristics of multiple RF signals over time can be used to determine motion, whether a human or an animal, such as a dog, is in motion and/or moving, and what type of motion is detected, such as walking, sitting, falling, or the like.

In addition to changing characteristics of reflected RF signals, LOS signals can also be affected (e.g., attenuated) by the presence of the human. For example, if the human continued along the path of motion to a point that intersects the RF signal <NUM> between the first WLAN device <NUM> and the second WLAN device <NUM> (not illustrated in <FIG> for the sake of clarity), the RF signal <NUM> would be attenuated due to blocking of the RF signal <NUM> by the human.

To acquire WLAN channel information, WLAN devices (initiators) are scheduled to periodically transmit sounding packets, namely unicast IEEE <NUM> Null Data Frames (NDFs), to another WLAN device. The WLAN device that receives the NDF responds to the initiator by transmitting an acknowledgement (ACK), namely an IEEE <NUM> ACK, to the initiator. The ACK is used as a sounding signal for the RF channel(s) between the initiator and the other WLAN device.

For example, in a mesh WLAN system with multiple APs, a root AP may schedule periodic NDF transmissions to other APs in the mesh network to acquire channel soundings between the root AP and each of the other APs. The APs are typically stationary devices which provide soundings between fixed locations in the structure where the WLAN is deployed. The root AP may also schedule other APs to transmit NDFs to acquire sounding information for the locations of the other APs. Alternatively or optionally, the APs receiving an NDF can use the received NDF as a sounding signal. In another alternative or option, other WLAN STA devices that are stationary (e.g., a WLAN-connected thermostat, a television streaming device, a WLAN-connected camera, or the like) can be included as initiators and/or recipients of NDFs for acquiring channel sounding data. Other example WLAN configurations and soundings in those configurations is described in further detail below.

The initiator(s) calculate a radio link signal spectrogram and channel state information (CSI) based on the ACK packet. Alternatively or optionally, the APs receiving an NDF can calculate a radio link signal spectrogram and CSI based on the received NDF. In a further alternative or option, non-null data frames received by a WLAN device can also be utilized to extract radio link signal spectrograms and CSI.

The radio link signal spectrograms and channel state information (CSI) acquired by WLAN devices in the WLAN are aggregated for recognition of presence, motion, and/or human behavior at a detection server. Both the aggregated radio signal spectrogram and CSI data from the WLAN links are fed as inputs to a Machine-Learning (ML) algorithm, executed at the detection server, to recognize human behavior in the proximity of WLAN devices. The detection server can be located on a device in the WLAN, such as the root AP of the WLAN, at a remote (cloud-based) server device, or both. Alternatively or optionally, portions of the recognition processing can be distributed in any suitable manner among devices in the WLAN and or in the cloud.

<FIG> illustrates an example system <NUM> in which various aspects and techniques of motion detection using wireless local area networks can be implemented. The system <NUM> includes WLAN devices <NUM> and the detection server <NUM> to illustrate processing radio signal spectrograms and channel state information (CSI) to detect and classify presence and motion behaviors.

As described above, the detection server <NUM> aggregates the radio signal spectrograms and CSI from the WLAN devices <NUM>. Optionally or additionally, the radio signal spectrograms and CSI data may be preprocessed before feature extraction. For example, the preprocessing may include noise reduction, dimension reduction, or the like. In order for an ML model to use the channel-state-information (CSI) to classify among different motion classes, such as no-motion, human motion, and pet motion, an effective and efficient feature vector must be extracted from a sequence of CSI samples over a time interval. The required CSI feature vector must capture the granular pattern differences introduced onto CSIs by different motion classes. Consequently, the feature vector serves as the input to the downstream ML model which learns the different CSI patterns captured through the feature vector on different motion classes.

A motion feature extractor <NUM> performs a bi-projection feature extraction. The motion feature extractor <NUM> buffers the CSI on a two-dimensional plane, where the x-axis of the plane is a CSI sample index (time), and the y-axis of the plane is a subcarrier index (RF frequency). By way of example and not limitation, each CSI sample CSI(t) that is buffered includes <NUM> subcarriers. Each subcarrier is represented as a complex number which is a channel-response-coefficient of the RF channel for the particular subcarrier between a WLAN transmitter in a first WLAN device and a WLAN receiver in a second WLAN device. By way of example and not limitation, a buffer interval of <NUM> sec in time with a <NUM> CSI sampling rate is used which results in a CSI buffer block of <NUM> subcarriers by <NUM> CSI samples in the frequency-time plane.

Conventional techniques that process CSI data using image-related feature extraction methods require larger input sets of CSI data and greater processing resources than techniques using statistics of CSI motion patterns, such as variance of CSI block in temporal and frequency domain, that directly correlate to the physical motion of human or object in space-time that introduce disturbances onto CSI. The correlation between the CSI temporal-frequency variance and the physical motion disturbing RF channel in space-time performed by the motion feature extractor <NUM> results in classifications using less data and fewer processing resources.

Training data <NUM> is provided to the detection server <NUM> to train the motion feature selector <NUM> and the motion machine learning (ML) classifier <NUM> to identify motion or presence that corresponds to one of the classifications <NUM>, such as no presence, human presence, animal presence, human sitting, human walking, human falling, or the like. For example, the feature vectors extracted from the sequence of CSI samples are processed through two fully connected layers to identify motion or presence that corresponds to one of the classifications <NUM>.

<FIG> illustrates an example system <NUM> in which various aspects and techniques of motion detection using wireless local area networks can be implemented, as generally related to motion sensing by a mesh WLAN deployed in a structure <NUM>. The mesh WLAN includes a root mesh access point <NUM>, a mesh access point <NUM>, and a mesh access point <NUM>. The root mesh AP <NUM> connects the mesh WLAN to a communication network <NUM> (e.g., the Internet) and, in turn, to one or more cloud services <NUM>, such as a cloud-based detection server <NUM>, a smart-home cloud service, or the like. Although the mesh WLAN is illustrated with three mesh APs, any suitable number of mesh APs may be included in the mesh WLAN.

The root mesh AP <NUM> and the mesh APs <NUM> and <NUM> are interconnected by backhaul (or backbone) links <NUM>, <NUM>, and <NUM> that carry network traffic for client devices served by the APs to other client devices in the mesh WLAN or to nodes or services connected to the communication network <NUM>. For example, the root mesh AP <NUM> can configure backhaul communications with and between the mesh APs <NUM> and <NUM> on a particular WLAN channel or in a particular WLAN RF frequency band (e.g., a <NUM> radio band or a <NUM> radio band). The root mesh AP <NUM> and the mesh APs <NUM> and <NUM> provide WLAN connectivity to WLAN client devices in the same radio band as the backhaul communications links, a different radio band than the backhaul communications links, or both.

The root mesh AP <NUM> schedules the periodic transmission of NDFs by the root mesh AP <NUM> and the mesh APs <NUM> and <NUM> across the backhaul links <NUM>, <NUM>, and <NUM> to acquire radio signal spectrogram and CSI data for motion classification. The root mesh AP <NUM> receives the acquired radio signal spectrogram and CSI data from the mesh APs <NUM> and <NUM>. In one aspect, the root mesh AP <NUM> includes the detection server <NUM>, aggregates the acquired radio signal spectrogram and CSI data, and performs the motion classification. The root mesh AP <NUM> sends classification result(s) to applications and services that consume the classification data, such as a smart-home cloud service, a security monitoring service, a security system hub in the structure <NUM>, or the like.

In another aspect, the root mesh AP <NUM> aggregates the acquired radio signal spectrogram and CSI data and forwards the aggregated data to a cloud-based detection server <NUM>. The cloud-based detection server <NUM> may be a service that performs the motion classification and provides the classification results to other applications, services, and/or devices, or the detection server <NUM> may be included as a component in another cloud-based service, such as a smart-home cloud service, a security monitoring service, or the like.

<FIG> illustrates an example system <NUM> in which various aspects and techniques of motion detection using wireless local area networks can be implemented, as generally related to motion sensing in a WLAN served by an access point without capabilities to configure NDF transmissions to acquire radio signal spectrograms and CSI and/or classify presence or motion from the acquired data. The WLAN is deployed in a structure <NUM> and includes a WLAN access point <NUM> that connects the WLAN to the communication network <NUM> and in turn to one or more cloud services <NUM>, such as a cloud-based detection server <NUM>, a smart-home cloud service, or the like.

The WLAN includes multiple WLAN client devices <NUM>, <NUM>, and <NUM> that are connected to WLAN by the WLAN AP <NUM>. WLAN client devices may be mobile or nomadic, such as a laptop computer or a smartphone, or stationary, such as a media streaming device, a camera, a thermostat, a smart-speaker, or the like. Any WLAN client device can include the capability to schedule NDF transmissions for channel sounding, to aggregate acquired channel data, and/or to perform motion classification. For example, the WLAN client device <NUM> is a stationary device, such as a media streaming device attached to a television. The WLAN client device <NUM> determines that WLAN client devices <NUM> and <NUM> are also stationary devices. The WLAN client device <NUM> may make this determination in any suitable manner, such as transmitting NDFs over a period of time and determining that the devices are stationary, by determining a device type or identifier that indicates the devices are stationary, by querying a cloud service, such as a smart-home service, that can identify devices installed at the structure <NUM>, or the like. The WLAN client device <NUM> schedules NDF transmissions to other stationary WLAN client devices, such as WLAN client devices <NUM> and <NUM> to acquire radio signal spectrograms and CSI.

Continuing with the example, the WLAN client device <NUM> may also determine that other WLAN client devices, such as WLAN client devices <NUM> and <NUM>, can schedule NDF transmissions and acquire radio signal spectrograms and CSI, as illustrated at <NUM>, <NUM>, and <NUM>. The WLAN client device <NUM> schedules NDF transmissions for the other client WLAN device and receives the radio signal spectrograms and CSI from the other WLAN client devices.

In one aspect, the WLAN client device <NUM> includes the detection server <NUM>, aggregates the acquired radio signal spectrogram and CSI data, and performs the motion classification. The WLAN client device <NUM> sends to classification result(s) to applications and services that consume the classification data, such as a smart-home cloud service, a security monitoring service, a security system hub in the structure <NUM>, or the like.

In another aspect, the WLAN client device <NUM> aggregates the acquired radio signal spectrogram and CSI data and forwards the aggregated data to a cloud-based detection server <NUM>. The cloud-based detection server <NUM> may be a service that performs the motion classification and provides the classification results to other applications, services, and/or devices, or the detection server <NUM> may be included as a component in another cloud-based service, such as a smart-home cloud service, a security monitoring service, or the like.

<FIG> illustrates an example system <NUM> in which various aspects and techniques of motion detection using wireless local area networks can be implemented, as generally related to motion sensing by a mesh WLAN and WLAN client devices deployed in a structure <NUM>. The mesh WLAN includes a root mesh access point <NUM>, a mesh access point <NUM>, and a mesh access point <NUM>. The root mesh AP <NUM> connects the mesh WLAN to the communication network <NUM> and in turn to one or more cloud services <NUM>, such as a cloud-based detection server <NUM>, a smart-home cloud service, or the like. Although the mesh WLAN is illustrated with three mesh APs, any suitable number of mesh APs may be included in the mesh WLAN.

The root mesh AP <NUM> and the mesh APs <NUM> and <NUM> may operate using backhaul links <NUM>, <NUM>, and <NUM> (as illustrated by the dashed lines in <FIG>) to acquire radio signal spectrogram and CSI data, as described with respect to <FIG>, above. However, as illustrated in <FIG>, the location of the root mesh AP <NUM> and the mesh APs <NUM> and <NUM> may provide limited coverage of the structure <NUM> either due to the placement of the mesh APs, the location, number, and composition of walls, floors, and ceilings in the structure <NUM>, link budget limitations due to noise and interference, or the like.

In one aspect, the root mesh AP <NUM> schedules NDF transmissions to client WLAN devices <NUM>, <NUM>, <NUM> from the mesh WLAN APs to gather additional radio signal spectrograms and CSI for additional RF signal paths within the structure <NUM>, as shown by the dash-dot lines at <NUM>, <NUM>, <NUM>, and <NUM>. Additionally or optionally, the root mesh AP <NUM> determines that one or more of the WLAN client devices, in this example the WLAN client device <NUM>, has the capability to perform scheduled NDF transmissions and acquire radio signal spectrograms and CSI. The root mesh AP <NUM> configures the WLAN client device <NUM> to periodically transmit NDFs to the client devices <NUM> and <NUM>, as shown by the dotted lines <NUM> and <NUM>, and acquires radio signal spectrograms and CSI.

By including additional RF signal paths between the additional WLAN devices and/or the APs, additional radio signal spectrogram and CSI data is available to improve coverage and accuracy of feature extraction and classification for presence and motion detection. The root mesh AP <NUM> can schedule the NDF transmissions in a suitable radio band, channel, or combination of radio bands and channels between the mesh WLAN APs and WLAN client devices. The root mesh AP <NUM> can schedule the NDF transmissions based on the capabilities of the mesh WLAN APs and WLAN client devices, RF signal propagation characteristics in the structure <NUM>, to improve link budgets for NDF transmissions, or the like.

In a further aspect, in system <NUM> or <NUM>, there may be WLAN client devices that are capable (e.g., devices that include compatible hardware and a compatible networking stack) of performing scheduled NDF transmissions and acquiring radio signal spectrograms and CSI but lack higher layer software to perform these operations. These WLAN client devices can be updated with the capabilities of performing scheduled NDF transmissions and acquiring radio signal spectrograms and CSI by upgrading the software of the device, such as by downloading an updated software image to the device.

The decision to update WLAN client devices for motion and presence detection may be based on a variety of factors. For example, the detection server <NUM> may determine that the success rate for classification is below a threshold. Based on the determination, the detection server <NUM> may provide a user notification (e.g., via an email, text message, in-app notification, or the like) to the user that indicates that the classification accuracy can be improved by adding additional, motion-detection-capable WLAN devices. An application, such a smart-home application on a user device, can be triggered to search for capable devices to upgrade, to trigger the root mesh WLAN AP to search for capable devices to upgrade, or to query a cloud-service, such as a smart-home cloud service that maintains a list of WLAN client devices installed in the structure. Based on receiving the query, the cloud-service determines which WLAN client devices can be upgraded and provides a notification to the user so that the user can initiate the upgrades or, alternatively, the cloud-based service can push the software upgrade to the target devices without further user intervention.

In an aspect, updating WLAN client devices for motion and presence detection may be triggered by adding a device to the WLAN. For example, adding a security hub device to a smart-home system in the structure may trigger a smart-home cloud service connected to the smart-home system to determine to push updated software images to other devices in the smart-home system to enable or improve motion and presence detection for use by the security functions in the security hub device and smart-home system.

In another aspect, adding a monitoring service, such as a security-monitoring service, to a smart-home system can trigger updating WLAN client devices for motion and presence detection. For example, adding the security-monitoring service to a smart-home system in the structure may trigger a smart-home cloud service, connected to the smart-home system, to determine to push updated software images to other devices in the smart-home system to enable or improve motion and presence detection for use by the security-monitoring service that uses the smart-home system.

Example methods <NUM> and <NUM> are described with reference to <FIG> and <FIG> in accordance with one or more aspects of motion detection using wireless local area networks. The order in which the method blocks are described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order or skipped to implement a method or an alternate method. Generally, any of the components, modules, methods, and operations described herein can be implemented using software, firmware, hardware (e.g., fixed logic circuitry), manual processing, or any combination thereof. Some operations of the example methods may be described in the general context of executable instructions stored on computer-readable storage memory that is local and/or remote to a computer processing system, and implementations can include software applications, programs, functions, and the like. Alternatively or in addition, any of the functionality described herein can be performed, at least in part, by one or more hardware logic components, such as, and without limitation, Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-chip systems (SoCs), Complex Programmable Logic Devices (CPLDs), and the like.

<FIG> illustrates example method(s) <NUM> of motion detection using wireless local area networks as generally related to a WLAN device in a WLAN network. At block <NUM>, a first WLAN device (e.g., WLAN device <NUM>) transmits a sounding packet to a second WLAN device (e.g., WLAN device <NUM>).

At block <NUM>, the first WLAN device receives, from the second WLAN device, an acknowledgement (ACK) of receiving the sounding packet by the second WLAN device.

At block <NUM>, the first WLAN device uses the received ACK to determine Channel State Information (CSI) for a radio frequency (RF) signal path between the first WLAN device and the second WLAN device.

At block <NUM>, the first WLAN device aggregates the determined CSI with additional CSI.

At block <NUM>, the first WLAN device uses the aggregated CSI to determine a presence or a motion within a structure.

<FIG> illustrates example method(s) <NUM> of motion detection using wireless local area networks as generally related to a WLAN device in a WLAN network. At block <NUM>, the WLAN device (e.g., WLAN device <NUM>) determines that a second WLAN device can be updated to transmit sounding packets for motion classification.

At block <NUM>, the first WLAN device initiates an update request that is effective to cause a cloud-based server (e.g., the cloud service <NUM>) to update software in the second WLAN device.

At block <NUM>, the first WLAN device receives a first sounding packet from the second WLAN device after the update of the software of the second WLAN device.

At block <NUM>, the first WLAN device transmits a first acknowledgement (ACK) of receiving the first sounding packet to the second WLAN device, the transmitting being effective to cause the second WLAN device to determine Channel State Information (CSI) for a radio frequency (RF) signal path between the first WLAN device.

<FIG> illustrates an example environment <NUM> in which aspects of motion detection using wireless local area networks can be implemented. Generally, the environment <NUM> includes a WLAN <NUM>, implemented as part of a smart-home or other type of structure with any number of WLAN devices <NUM> that are configured for communication in a WLAN. For example, the WLAN devices can include a thermostat <NUM>, hazard detectors <NUM> (e.g., for smoke and/or carbon monoxide), cameras <NUM> (e.g., indoor and outdoor), lighting units <NUM> (e.g., indoor and outdoor), and any other types of WLAN devices <NUM> that are implemented inside and/or outside of a structure <NUM> (e.g., in a smart-home environment). In this example, the WLAN devices can also include any of the previously described devices.

In the environment <NUM>, any number of the WLAN devices <NUM> can be implemented for wireless interconnection to wirelessly communicate and interact with each other. The WLAN devices <NUM> are modular, intelligent, multi-sensing, network-connected devices that can integrate seamlessly with each other and/or with a central server or a cloud-computing system to provide any of a variety of useful smart-home objectives and implementations. An example of a WLAN device <NUM> that can be implemented as any of the devices described herein is shown and described with reference to <FIG>.

In implementations, the thermostat <NUM> may include a Nest® Learning Thermostat that detects ambient climate characteristics (e.g., temperature and/or humidity) and controls a HVAC system <NUM> in the smart-home environment. The learning thermostat <NUM> and other smart devices "learn" by capturing occupant settings to the devices. For example, the thermostat learns preferred temperature set-points for mornings and evenings, and when the occupants of the structure are asleep or awake, as well as when the occupants are typically away or at home.

A hazard detector <NUM> can be implemented to detect the presence of a hazardous substance or a substance indicative of a hazardous substance (e.g., smoke, fire, or carbon monoxide). In examples of wireless interconnection, a hazard detector <NUM> may detect the presence of smoke, indicating a fire in the structure, in which case the hazard detector that first detects the smoke can broadcast a low-power wake-up signal to all of the connected WLAN devices. The other hazard detectors <NUM> can then receive the broadcast wake-up signal and initiate a high-power state for hazard detection and to receive wireless communications of alert messages. Further, the lighting units <NUM> can receive the broadcast wake-up signal and activate in the region of the detected hazard to illuminate and identify the problem area. In another example, the lighting units <NUM> may activate in one illumination color to indicate a problem area or region in the structure, such as for a detected fire or break-in, and activate in a different illumination color to indicate safe regions and/or escape routes out of the structure.

In various configurations, the WLAN devices <NUM> can include an entryway interface device <NUM> that functions in coordination with a network-connected door lock system <NUM>, and that detects and responds to a person's approach to or departure from a location, such as an outer door of the structure <NUM>. The entryway interface device <NUM> can interact with the other WLAN devices based on whether someone has approached or entered the smart-home environment. An entryway interface device <NUM> can control doorbell functionality, announce the approach or departure of a person via audio or visual means, and control settings on a security system, such as to activate or deactivate the security system when occupants come and go. The WLAN devices <NUM> can also include other sensors and detectors, such as to detect ambient lighting conditions, detect room-occupancy states (e.g., with an occupancy sensor <NUM>), and control a power and/or dim state of one or more lights. In some instances, the sensors and/or detectors may also control a power state or speed of a fan, such as a ceiling fan <NUM>. Further, the sensors and/or detectors may detect occupancy in a room or enclosure and control the supply of power to electrical outlets or devices <NUM>, such as if a room or the structure is unoccupied.

The WLAN devices <NUM> may also include connected appliances and/or controlled systems <NUM>, such as refrigerators, stoves and ovens, washers, dryers, air conditioners, pool heaters <NUM>, irrigation systems <NUM>, security systems <NUM>, and so forth, as well as other electronic and computing devices, such as televisions, entertainment systems, computers, intercom systems, garage-door openers <NUM>, ceiling fans <NUM>, control panels <NUM>, and the like. When plugged in, an appliance, device, or system can announce itself to the WLAN as described above and can be automatically integrated with the controls and devices of the WLAN, such as in the smart-home. It should be noted that the WLAN devices <NUM> may include devices physically located outside of the structure, but within wireless communication range, such as a device controlling a swimming pool heater <NUM> or an irrigation system <NUM>.

As described above, the WLAN includes a WLAN access point (e.g., AP <NUM>, <NUM>, or <NUM>) that interfaces for communication with an external network, outside the WLAN. The access point connects to the communication network <NUM>, such as the Internet. A cloud service <NUM>, which is connected via the communication network <NUM>, provides services related to and/or using the devices within the WLAN. By way of example, the cloud service <NUM> can include applications for connecting end user devices <NUM>, such as smart phones, tablets, and the like, to devices in the WLAN, processing and presenting data acquired in the WLAN to end users, linking devices in one or more WLANs to user accounts of the cloud service <NUM>, provisioning and updating devices in the WLAN, and so forth. For example, a user can control the thermostat <NUM> and other WLAN devices in the smart-home environment using a network-connected computer or portable device, such as a mobile phone or tablet device. Further, the WLAN devices can communicate information to any central server or cloud-computing system via the access point <NUM>. The data communications can be carried out using any of a variety of custom or standard wireless protocols (e.g., IEEE <NUM>, Wi-Fi, ZigBee, Z-Wave, 6LoWPAN, Thread, etc.) and/or by using any of a variety of custom or standard wired protocols (CAT6 Ethernet, HomePlug, etc.).

Any of the WLAN devices in the WLAN can serve as low-power and communication nodes to create the WLAN in the smart-home environment. Individual low-power nodes of the network can regularly send out messages regarding what they are sensing, and the other low-powered nodes in the environment - in addition to sending out their own messages - can repeat the messages, thereby communicating the messages from node to node (i.e., from device to device) throughout the WLAN. The WLAN devices can be implemented to conserve power, particularly when battery-powered, utilizing low-powered communication protocols to receive the messages, translate the messages to other communication protocols, and send the translated messages to other nodes and/or to a central server or cloud-computing system. For example, an occupancy and/or ambient light sensor can detect an occupant in a room as well as measure the ambient light, and activate the light source when the ambient light sensor <NUM> detects that the room is dark and when the occupancy sensor <NUM> detects that someone is in the room. Further, the sensor can include a low-power wireless communication chip (e.g., an IEEE <NUM> chip) that regularly sends out messages regarding the occupancy of the room and the amount of light in the room, including instantaneous messages coincident with the occupancy sensor detecting the presence of a person in the room. As mentioned above, these messages may be sent wirelessly, using the WLAN, from node to node (i.e., smart device to smart device) within the smart-home environment as well as over the Internet to a central server or cloud-computing system.

In other configurations, various ones of the WLAN devices can function as "tripwires" for an alarm system in the smart-home environment. For example, in the event a perpetrator circumvents detection by alarm sensors located at windows, doors, and other entry points of the structure or environment, the alarm could still be triggered by receiving an occupancy, motion, heat, sound, etc. message from one or more of the low-powered mesh nodes in the WLAN. In other implementations, the WLAN can be used to automatically turn on and off the lighting units <NUM> as a person transitions from room to room in the structure. For example, the WLAN devices can detect the person's movement through the structure and communicate corresponding messages via the nodes of the WLAN. Using the messages that indicate which rooms are occupied, other WLAN devices that receive the messages can activate and/or deactivate accordingly. As referred to above, the WLAN can also be utilized to provide exit lighting in the event of an emergency, such as by turning on the appropriate lighting units <NUM> that lead to a safe exit. The light units <NUM> may also be turned-on to indicate the direction along an exit route that a person should travel to safely exit the structure.

The various WLAN devices may also be implemented to integrate and communicate with wearable computing devices <NUM>, such as may be used to identify and locate an occupant of the structure, and adjust the temperature, lighting, sound system, and the like accordingly. In other implementations, RFID sensing (e.g., a person having an RFID bracelet, necklace, or key fob), synthetic vision techniques (e.g., video cameras and face recognition processors), audio techniques (e.g., voice, sound pattern, vibration pattern recognition), ultrasound sensing/imaging techniques, and infrared or near-field communication (NFC) techniques (e.g., a person wearing an infrared or NFC-capable smartphone), along with rules-based inference engines or artificial intelligence techniques that draw useful conclusions from the sensed information as to the location of an occupant in the structure or environment.

In other implementations, personal comfort-area networks, personal health-area networks, personal safety-area networks, and/or other such human-facing functionalities of service robots can be enhanced by logical integration with other WLAN devices and sensors in the environment according to rules-based inferencing techniques or artificial intelligence techniques for achieving better performance of these functionalities. In an example relating to a personal health-area, the system can detect whether a household pet is moving toward the current location of an occupant (e.g., using any of the WLAN devices and sensors), along with rules-based inferencing and artificial intelligence techniques. Similarly, a hazard detector service robot can be notified that the temperature and humidity levels are rising in a kitchen, and temporarily raise a hazard detection threshold, such as a smoke detection threshold, under an inference that any small increases in ambient smoke levels will most likely be due to cooking activity and not due to a genuinely hazardous condition. Any service robot that is configured for any type of monitoring, detecting, and/or servicing can be implemented as a mesh node device on the WLAN, conforming to the wireless interconnection protocols for communicating on the WLAN.

The WLAN devices <NUM> may also include a smart alarm clock <NUM> for each of the individual occupants of the structure in the smart-home environment. For example, an occupant can customize and set an alarm device for a wake time, such as for the next day or week. Artificial intelligence can be used to consider occupant responses to the alarms when they go off and make inferences about preferred sleep patterns over time. An individual occupant can then be tracked in the WLAN based on a unique signature of the person, which is determined based on data obtained from sensors located in the WLAN devices, such as sensors that include ultrasonic sensors, passive IR sensors, and the like. The unique signature of an occupant can be based on a combination of patterns of movement, voice, height, size, etc., as well as using facial recognition techniques.

In an example of wireless interconnection, the wake time for an individual can be associated with the thermostat <NUM> to control the HVAC system in an efficient manner so as to pre-heat or cool the structure to desired sleeping and awake temperature settings. The preferred settings can be learned over time, such as by capturing the temperatures set in the thermostat before the person goes to sleep and upon waking up. Collected data may also include biometric indications of a person, such as breathing patterns, heart rate, movement, etc., from which inferences are made based on this data in combination with data that indicates when the person actually wakes up. Other WLAN devices can use the data to provide other smart-home objectives, such as adjusting the thermostat <NUM> so as to pre-heat or cool the environment to a desired setting, and turning-on or turning-off the lights <NUM>.

In implementations, the WLAN devices can also be utilized for sound, vibration, and/or motion sensing such as to detect running water and determine inferences about water usage in a smart-home environment based on algorithms and mapping of the water usage and consumption. This can be used to determine a signature or fingerprint of each water source in the home, and is also referred to as "audio fingerprinting water usage. " Similarly, the WLAN devices can be utilized to detect the subtle sound, vibration, and/or motion of unwanted pests, such as mice and other rodents, as well as by termites, cockroaches, and other insects. The system can then notify an occupant of the suspected pests in the environment, such as with warning messages to help facilitate early detection and prevention.

The environment <NUM> may include one or more WLAN devices that function as a hub <NUM>. The hub <NUM> may be a general-purpose home automation hub, or an application-specific hub, such as a security hub, an energy management hub, an HVAC hub, and so forth. The functionality of a hub <NUM> may also be integrated into any WLAN device, such as a smart thermostat device or a smart speaker <NUM>. Hosting functionality on the hub <NUM> in the structure <NUM> can improve reliability when the user's internet connection is unreliable, can reduce latency of operations that would normally have to connect to the cloud service <NUM>, and can satisfy system and regulatory constraints around local access between WLAN devices.

Additionally, the example environment <NUM> includes the smart-speaker <NUM>. The smart-speaker <NUM> provides voice assistant services that include providing voice control of smart-home devices. The functions of the hub <NUM> may be hosted in the smart-speaker <NUM>. The smart-speaker <NUM> can be configured to communicate via the WLAN, ZigBee, Z-Wave, Thread, or any combination thereof.

<FIG> illustrates an example WLAN device <NUM> that can be implemented as any of the WLAN devices in a WLAN in accordance with one or more aspects of motion detection using wireless local area networks as described herein. The device <NUM> can be integrated with electronic circuitry, microprocessors, memory, input output (I/O) logic control, communication interfaces and components, as well as other hardware, firmware, and/or software to implement the device in a WLAN. Further, the WLAN device <NUM> can be implemented with various components, such as with any number and combination of different components as further described with reference to the example device shown in <FIG>.

In this example, the WLAN device <NUM> includes a low-power microprocessor <NUM> and a high-power microprocessor <NUM> (e.g., microcontrollers or digital signal processors) that process executable instructions. The device also includes an input-output (I/O) logic control <NUM> (e.g., to include electronic circuitry). The microprocessors can include components of an integrated circuit, programmable logic device, a logic device formed using one or more semiconductors, and other implementations in silicon and/or hardware, such as a processor and memory system implemented as a system-on-chip (SoC). Alternatively or in addition, the device can be implemented with any one or combination of software, hardware, firmware, or fixed logic circuitry that may be implemented with processing and control circuits. The low-power microprocessor <NUM> and the high-power microprocessor <NUM> can also support one or more different device functionalities of the device. For example, the high-power microprocessor <NUM> may execute computationally intensive operations, whereas the low-power microprocessor <NUM> may manage less complex processes such as detecting a hazard or temperature from one or more sensors <NUM>. The low-power processor <NUM> may also wake or initialize the high-power processor <NUM> for computationally intensive processes.

The one or more sensors <NUM> can be implemented to detect various properties such as acceleration, temperature, humidity, water, supplied power, proximity, external motion, device motion, sound signals, ultrasound signals, light signals, fire, smoke, carbon monoxide, global-positioning-satellite (GPS) signals, radio frequency (RF), other electromagnetic signals or fields, or the like. As such, the sensors <NUM> may include any one or a combination of temperature sensors, humidity sensors, hazard-related sensors, other environmental sensors, accelerometers, microphones, optical sensors up to and including cameras (e.g., charged coupled-device or video cameras, active or passive radiation sensors, GPS receivers, and radio frequency identification detectors. In implementations, the WLAN device <NUM> may include one or more primary sensors, as well as one or more secondary sensors, such as primary sensors that sense data central to the core operation of the device (e.g., sensing a temperature in a thermostat or sensing smoke in a smoke detector), while the secondary sensors may sense other types of data (e.g., motion, light or sound), which can be used for energy-efficiency objectives or smart-operation objectives.

The WLAN device <NUM> includes a memory device controller <NUM> and a memory device <NUM>, such as any type of a nonvolatile memory and/or other suitable electronic data storage device. The WLAN device <NUM> can also include various firmware and/or software, such as an operating system <NUM> that is maintained as computer executable instructions by the memory and executed by a microprocessor. The device software may also include a motion sensing manager application <NUM> that implements aspects of motion detection using wireless local area networks. The WLAN device <NUM> also includes a device interface <NUM> to interface with another device or peripheral component, and includes an integrated data bus <NUM> that couples the various components of the WLAN device for data communication between the components. The data bus in the WLAN device may also be implemented as any one or a combination of different bus structures and/or bus architectures.

The device interface <NUM> may receive input from a user and/or provide information to the user (e.g., as a user interface), and a received input can be used to determine a setting. The device interface <NUM> may also include mechanical or virtual components that respond to a user input. For example, the user can mechanically move a sliding or rotatable component, or the motion along a touchpad may be detected, and such motions may correspond to a setting adjustment of the device. Physical and virtual movable user-interface components can allow the user to set a setting along a portion of an apparent continuum. The device interface <NUM> may also receive inputs from any number of peripherals, such as buttons, a keypad, a switch, a microphone, and an imager (e.g., a camera device).

The WLAN device <NUM> can include network interfaces <NUM>, such as a WLAN interface for communication with other WLAN devices in a WLAN network, and an external network interface for network communication, such as via the Internet. The WLAN device <NUM> also includes wireless radio systems <NUM> for wireless communication with other WLAN devices via the WLAN interface and for multiple, different wireless communications systems. The wireless radio systems <NUM> may include IEEE <NUM>, Wi-Fi, ZigBee, Z-Wave, Thread, Bluetooth™, Mobile Broadband, BLE, and/or IEEE <NUM>. Each of the different radio systems can include a radio device, antenna, and chipset that is implemented for a particular wireless communications technology. The WLAN device <NUM> also includes a power source <NUM>, such as a battery and/or to connect the device to line voltage. An AC power source may also be used to charge the battery of the device.

<FIG> illustrates an example system <NUM> that includes an example device <NUM>, which can be implemented as any of the WLAN devices that implement aspects of motion detection using wireless local area networks as described with reference to the previous <FIG>. The example device <NUM> may be any type of computing device, client device, mobile phone, tablet, communication, entertainment, gaming, media playback, and/or other type of device. Further, the example device <NUM> may be implemented as any other type of WLAN device that is configured for communication on a WLAN, such as a thermostat, hazard detector, camera, light unit, commissioning device, router, border router, joiner router, joining device, end device, leader, access point, and/or other WLAN devices.

The device <NUM> includes communication devices <NUM> that enable wired and/or wireless communication of device data <NUM>, such as data that is communicated between the devices in a WLAN, data that is being received, data scheduled for broadcast, data packets of the data, data that is synched between the devices, etc. The device data can include any type of communication data, as well as audio, video, and/or image data that is generated by applications executing on the device. The communication devices <NUM> can also include transceivers for cellular phone communication and/or for network data communication.

The device <NUM> also includes input / output (I/O) interfaces <NUM>, such as data network interfaces that provide connection and/or communication links between the device, data networks (e.g., a mesh network, external network, etc.), and other devices. The I/O interfaces can be used to couple the device to any type of components, peripherals, and/or accessory devices. The I/O interfaces also include data input ports via which any type of data, media content, and/or inputs can be received, such as user inputs to the device, as well as any type of communication data, as well as audio, video, and/or image data received from any content and/or data source.

The device <NUM> includes a processing system <NUM> that may be implemented at least partially in hardware, such as with any type of microprocessors, controllers, and the like that process executable instructions. The processing system can include components of an integrated circuit, programmable logic device, a logic device formed using one or more semiconductors, and other implementations in silicon and/or hardware, such as a processor and memory system implemented as a system-on-chip (SoC). Alternatively or in addition, the device can be implemented with any one or combination of software, hardware, firmware, or fixed logic circuitry that may be implemented with processing and control circuits. The device <NUM> may further include any type of a system bus or other data and command transfer system that couples the various components within the device. A system bus can include any one or combination of different bus structures and architectures, as well as control and data lines.

The device <NUM> also includes computer-readable storage memory <NUM>, such as data storage devices that can be accessed by a computing device, and that provide persistent storage of data and executable instructions (e.g., software applications, modules, programs, functions, and the like). The computer-readable storage memory described herein excludes propagating signals. Examples of computer-readable storage memory include volatile memory and non-volatile memory, fixed and removable media devices, and any suitable memory device or electronic data storage that maintains data for computing device access. The computer-readable storage memory can include various implementations of random access memory (RAM), read-only memory (ROM), flash memory, and other types of storage memory in various memory device configurations.

The computer-readable storage memory <NUM> provides storage of the device data <NUM> and various device applications <NUM>, such as an operating system that is maintained as a software application with the computer-readable storage memory and executed by the processing system <NUM>. The device applications may also include a device manager, such as any form of a control application, software application, signal processing and control module, code that is native to a particular device, a hardware abstraction layer for a particular device, and so on. In this example, the device applications also include a motion sensing manager application <NUM> that implements aspects of motion detection using wireless local area networks, such as when the example device <NUM> is implemented as any of the WLAN devices described herein.

The device <NUM> also includes an audio and/or video system <NUM> that generates audio data for an audio device <NUM> and/or generates display data for a display device <NUM>. The audio device and/or the display device include any devices that process, display, and/or otherwise render audio, video, display, and/or image data, such as the image content of a digital photo. In implementations, the audio device and/or the display device are integrated components of the example device <NUM>. Alternatively, the audio device and/or the display device are external, peripheral components to the example device. In aspects, at least part of the techniques described for motion detection using wireless local area networks may be implemented in a distributed system, such as over a "cloud" <NUM> in a platform <NUM>. The cloud <NUM> includes and/or is representative of the platform <NUM> for services <NUM> and/or resources <NUM>.

Claim 1:
A wireless local area network, WLAN, device (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>) comprising:
a WLAN radio transceiver (<NUM>, <NUM>, <NUM>);
a processor (<NUM>, <NUM>, <NUM>) and memory (<NUM>, <NUM>, <NUM>) system to implement a motion sensing manager application (<NUM>, <NUM>) configured to:
determine (<NUM>) that another WLAN device (<NUM>, <NUM>, <NUM>, <NUM>) can be updated to transmit sounding packets for motion classification, wherein determining that the other WLAN device (<NUM>, <NUM>, <NUM>, <NUM>) can be updated to transmit sounding packets for motion classification comprises sending a request to a cloud-based service (<NUM>, <NUM>, <NUM>) to identify WLAN devices installed at a structure that can be updated to support motion classification;
initiate (<NUM>) an update request that is effective to cause a cloud-based server (<NUM>, <NUM>, <NUM>) to update software in the other WLAN device (<NUM>, <NUM>, <NUM>, <NUM>);
receive (<NUM>) a first sounding packet from the other WLAN device (<NUM>, <NUM>, <NUM>, <NUM>) after the update of the software of the other WLAN device (<NUM>, <NUM>, <NUM>, <NUM>), wherein the sounding packet is an IEEE <NUM> Null Data Frame, NDF; and
transmit (<NUM>), to the other WLAN device (<NUM>, <NUM>, <NUM>, <NUM>), a first acknowledgement, ACK, of receiving the first sounding packet, wherein the ACK is an IEEE <NUM> ACK, the transmitting being effective to cause the other WLAN device (<NUM>, <NUM>, <NUM>, <NUM>) to:
determine Channel State Information, CSI, for a radio frequency, RF, signal path between the WLAN device (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>) and the other WLAN device (<NUM>, <NUM>, <NUM>, <NUM>); and
transmit the determined CSI to the WLAN device (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>).