The following description relates to processing radio frequency wireless signals in a motion detection system.
Motion detection systems have been used to detect movement, for example, of objects in a room or an outdoor area. In some example motion detection systems, infrared or optical sensors are used to detect movement of objects in the sensor's field of view. Motion detection systems have been used in security systems, automated control systems, and other types of systems.
In some aspects of what is described here, a wireless sensing system can process wireless signals (e.g., radio frequency signals) transmitted through a space between wireless communication devices for wireless sensing applications. Example wireless sensing applications include detecting motion, which can include one or more of the following: detecting motion of objects in the space, motion tracking, localization of motion in a space, breathing detection, breathing monitoring, presence detection, gesture detection, gesture recognition, human detection (e.g., moving and stationary human detection), human tracking, fall detection, speed estimation, intrusion detection, walking detection, step counting, respiration rate detection, sleep pattern detection, sleep quality monitoring, apnea estimation, posture change detection, activity recognition, gait rate classification, gesture decoding, sign language recognition, hand tracking, heart rate estimation, breathing rate estimation, room occupancy detection, human dynamics monitoring, and other types of motion detection applications. Other examples of wireless sensing applications include object recognition, speech recognition, keystroke detection and recognition, tamper detection, touch detection, attack detection, user authentication, driver fatigue detection, traffic monitoring, smoking detection, school violence detection, human counting, metal detection, human recognition, bike localization, human queue estimation, Wi-Fi imaging, and other types of wireless sensing applications. For instance, the wireless sensing system may operate as a motion detection system to detect the existence and location of motion based on Wi-Fi signals or other types of wireless signals.
The examples described herein may be useful for home monitoring. In some instances, home monitoring using the wireless sensing systems described herein may provide several advantages, including full home coverage through walls and in darkness, discreet detection without cameras, higher accuracy and reduced false alerts (e.g., in comparison with sensors that do not use Wi-Fi signals to sense their environments), and adjustable sensitivity. By layering Wi-Fi motion detection capabilities into routers and gateways, a robust motion detection system may be provided.
The examples described herein may also be useful for wellness monitoring. Caregivers want to know their loved ones are safe, while seniors and people with special needs want to maintain their independence at home with dignity. In some instances, wellness monitoring using the wireless sensing systems described herein may provide a solution that uses wireless signals to detect motion without using cameras or infringing on privacy, generates alerts when unusual activity is detected, tracks sleep patterns, and generates preventative health data. For example, caregivers can monitor motion, visits from health care professionals, and unusual behavior such as staying in bed longer than normal. Furthermore, motion is monitored unobtrusively without the need for wearable devices, and the wireless sensing systems described herein offer a more affordable and convenient alternative to assisted living facilities and other security and health monitoring tools.
The examples described herein may also be useful for setting up a smart home. In some examples, the wireless sensing systems described herein use predictive analytics and artificial intelligence (AI), to learn motion patterns and trigger smart home functions accordingly. Examples of smart home functions that may be triggered include adjusting the thermostat when a person walks through the front door, turning other smart devices on or off based on preferences, automatically adjusting lighting, adjusting HVAC systems based on present occupants, etc.
In some aspects of what is described here, wireless signals are communicated through a space over a time period by a wireless communication network including a plurality of wireless communication devices. The space includes a plurality of locations. Channel information is obtained based on the wireless signals. A motion detection system includes a motion detection engine and a pattern extraction engine. The motion detection engine of the motion detection system generates motion data based on the channel information. The motion data may include motion indicator values and motion localization values. The pattern extraction engine of the motion detection system generates activity data and one or more notifications based on the motion data and user input data. In some instances, the activity data can include an actual value of a metric of interest and a benchmark value of the metric of interest. The metric of interest can be or can be related to, for example, amount of sleep, amount of activity, amount of non-activity, amount of activity in a location, or a combination of these and other types of metrics. The activity data and the one or more notifications may be provided for display, for example, on a user interface of a user device. In some examples, the activity data and the one or more notifications are displayed to a user on a mobile device (e.g., on a smartphone or tablet) using a graphical user interface.
In some instances, aspects of the systems and techniques described here provide technical improvements and advantages over existing approaches. For example, higher-order information can be extracted from the motion data, and such higher-order information may inform the user of the user's activity and motion over various timeframes and locations. Additionally, the pattern extraction engine facilitates generation of motion-related user alerts responsive to determinations of activity level, location of motion, duration of motion, or the timing of motion occurrence. The technical improvements and advantages achieved in examples where the wireless sensing system is used for motion detection may also be achieved in other examples where the wireless sensing system is used for other wireless sensing applications.
In some instances, a wireless sensing system can be implemented using a wireless communication network. Wireless signals received at one or more wireless communication devices in the wireless communication network may be analyzed to determine channel information for the different communication links (between respective pairs of wireless communication devices) in the network. The channel information may be representative of a physical medium that applies a transfer function to wireless signals that traverse a space. In some instances, the channel information includes a channel response. Channel responses can characterize a physical communication path, representing the combined effect of, for example, scattering, fading, and power decay within the space between the transmitter and receiver. In some instances, the channel information includes beamforming state information (e.g., a feedback matrix, a steering matrix, channel state information (CSI), etc.) provided by a beamforming system. Beamforming is a signal processing technique often used in multi antenna (multiple-input/multiple-output (MIMO)) radio systems for directional signal transmission or reception. Beamforming can be achieved by operating elements in an antenna array in such a way that signals at particular angles experience constructive interference while others experience destructive interference.
The channel information for each of the communication links may be analyzed by one or more motion detection algorithms (e.g., running on a hub device, a client device, or other device in the wireless communication network, or on a remote device communicably coupled to the network) to detect, for example, whether motion has occurred in the space, to determine a relative location of the detected motion, or both. In some aspects, the channel information for each of the communication links may be analyzed to detect whether an object is present or absent, e.g., when no motion is detected in the space.
In some instances, a motion detection system returns motion data. In some implementations, the motion data indicate a degree of motion in the space, the location of motion in the space, a time at which the motion occurred, or a combination thereof. In some instances, wireless signals may be communicated through a space over a time period by a wireless communication network, and the motion data include motion indicator values indicative of a degree of motion that occurred in the space for each time point in a series of time points within the time period. In some implementations, the respective motion indicator values represent the degree of motion detected from the wireless signals exchanged on the respective wireless communication links in the network. In some instances, the space (e.g., a house) includes multiple locations (e.g., rooms or areas within the house), and the motion data include motion localization values for the individual locations, with the motion localization value for each individual location representing a relative degree of motion detected at the individual location for each time point in the series of time points within the time period. In some instances, the motion data include a motion score, which may include, or may be, one or more of the following: a scalar quantity indicative of a level of signal perturbation in the environment accessed by the wireless signals; an indication of whether there is motion; an indication of whether there is an object present; or an indication or classification of a gesture performed in the environment accessed by the wireless signals.
In some implementations, the motion detection system can be implemented using one or more motion detection algorithms. Example motion detection algorithms that can be used to detect motion based on wireless signals include the techniques described in U.S. Pat. No. 9,523,760 entitled “Detecting Motion Based on Repeated Wireless Transmissions,” U.S. Pat. No. 9,584,974 entitled “Detecting Motion Based on Reference Signal Transmissions,” U.S. Pat. No. 10,051,414 entitled “Detecting Motion Based On Decompositions Of Channel Response Variations,” U.S. Pat. No. 10,048,350 entitled “Motion Detection Based on Groupings of Statistical Parameters of Wireless Signals,” U.S. Pat. No. 10,108,903 entitled “Motion Detection Based on Machine Learning of Wireless Signal Properties,” U.S. Pat. No. 10,109,167 entitled “Motion Localization in a Wireless Mesh Network Based on Motion Indicator Values,” U.S. Pat. No. 10,109,168 entitled “Motion Localization Based on Channel Response Characteristics,” U.S. Pat. No. 10,743,143 entitled “Determining a Motion Zone for a Location of Motion Detected by Wireless Signals,” U.S. Pat. No. 10,605,908 entitled “Motion Detection Based on Beamforming Dynamic Information from Wireless Standard Client Devices,” U.S. Pat. No. 10,605,907 entitled “Motion Detection by a Central Controller Using Beamforming Dynamic Information,” U.S. Pat. No. 10,600,314 entitled “Modifying Sensitivity Settings in a Motion Detection System,” U.S. Pat. No. 10,567,914 entitled “Initializing Probability Vectors for Determining a Location of Motion Detected from Wireless Signals,” U.S. Pat. No. 10,565,860 entitled “Offline Tuning System for Detecting New Motion Zones in a Motion Detection System,” U.S. Pat. No. 10,506,384 entitled “Determining a Location of Motion Detected from Wireless Signals Based on Prior Probability,” U.S. Pat. No. 10,499,364 entitled “Identifying Static Leaf Nodes in a Motion Detection System,” U.S. Pat. No. 10,498,467 entitled “Classifying Static Leaf Nodes in a Motion Detection System,” U.S. Pat. No. 10,460,581 entitled “Determining a Confidence for a Motion Zone Identified as a Location of Motion for Motion Detected by Wireless Signals,” U.S. Pat. No. 10,459,076 entitled “Motion Detection based on Beamforming Dynamic Information,” U.S. Pat. No. 10,459,074 entitled “Determining a Location of Motion Detected from Wireless Signals Based on Wireless Link Counting,” U.S. Pat. No. 10,438,468 entitled “Motion Localization in a Wireless Mesh Network Based on Motion Indicator Values,” U.S. Pat. No. 10,404,387 entitled “Determining Motion Zones in a Space Traversed by Wireless Signals,” U.S. Pat. No. 10,393,866 entitled “Detecting Presence Based on Wireless Signal Analysis,” U.S. Pat. No. 10,380,856 entitled “Motion Localization Based on Channel Response Characteristics,” U.S. Pat. No. 10,318,890 entitled “Training Data for a Motion Detection System using Data from a Sensor Device,” U.S. Pat. No. 10,264,405 entitled “Motion Detection in Mesh Networks,” U.S. Pat. No. 10,228,439 entitled “Motion Detection Based on Filtered Statistical Parameters of Wireless Signals,” U.S. Pat. No. 10,129,853 entitled “Operating a Motion Detection Channel in a Wireless Communication Network,” U.S. Pat. No. 10,111,228 entitled “Selecting Wireless Communication Channels Based on Signal Quality Metrics,” and other techniques.
The example wireless communication system 100 includes three wireless communication devices 102A, 102B, 102C. The example wireless communication system 100 may include additional wireless communication devices 102 and/or other components (e.g., one or more network servers, network routers, network switches, cables, or other communication links, etc.).
The example wireless communication devices 102A, 102B, 102C can operate in a wireless network, for example, according to a wireless network standard or another type of wireless communication protocol. For example, the wireless network may be configured to operate as a Wireless Local Area Network (WLAN), a Personal Area Network (PAN), a Metropolitan Area Network (MAN), or another type of wireless network. Examples of WLANs include networks configured to operate according to one or more of the 802.11 family of standards developed by IEEE (e.g., Wi-Fi networks), and others. Examples of PANs include networks that operate according to short-range communication standards (e.g., BLUETOOTH®, Near Field Communication (NFC), ZigBee), millimeter wave communications, and others.
In some implementations, the wireless communication devices 102A. 102B, 102C may be configured to communicate in a cellular network, for example, according to a cellular network standard. Examples of cellular networks include: networks configured according to 2G standards such as Global System for Mobile (GSM) and Enhanced Data rates for GSM Evolution (EDGE) or EGPRS; 3G standards such as Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Universal Mobile Telecommunications System (UMTS), and Time Division Synchronous Code Division Multiple Access (TD-SCDMA); 4G standards such as Long-Term Evolution (LTE) and LTE-Advanced (LTE-A); 5G standards, and others.
In some cases, one or more of the wireless communication devices 102 can be a Wi-Fi access point or another type of wireless access point (WAP). In some cases, one or more of the wireless communication devices 102 is an access point of a wireless mesh network, such as, for example, a commercially-available mesh network system (e.g., GOOGLE Wi-Fi, EERO mesh, etc.). In some instances, one or more of the wireless communication devices 102 can be implemented as wireless access points (APs) in a mesh network, while the other wireless communication device(s) 102 are implemented as leaf devices (e.g., mobile devices, smart devices, etc.) that access the mesh network through one of the APs. In some cases, one or more of the wireless communication devices 102 is a mobile device (e.g., a smartphone, a smart watch, a tablet, a laptop computer, etc.), a wireless-enabled device (e.g., a smart thermostat, a Wi-Fi enabled camera, a smart TV), or another type of device that communicates in a wireless network.
In the example shown in
In the example shown in
In the example shown in
In some examples, the wireless signals propagate through a structure (e.g., a wall) before or after interacting with a moving object, which may allow the object's motion to be detected without an optical line-of-sight between the moving object and the transmission or receiving hardware. In some instances, the motion detection system may communicate the motion detection event to another device or system, such as a security system or a control center.
In some cases, the wireless communication devices 102 themselves are configured to perform one or more operations of the motion detection system, for example, by executing computer-readable instructions (e.g., software or firmware) on the wireless communication devices. For example, each device may process received wireless signals to detect motion based on changes in the communication channel. In some cases, another device (e.g., a remote server, a cloud-based computer system, a network-attached device, etc.) is configured to perform one or more operations of the motion detection system. For example, each wireless communication device 102 may send channel information to a specified device, system, or service that performs operations of the motion detection system.
In an example aspect of operation, wireless communication devices 102A, 102B may broadcast wireless signals or address wireless signals to the other wireless communication device 102C, and the wireless communication device 102C (and potentially other devices) receives the wireless signals transmitted by the wireless communication devices 102A, 102B. The wireless communication device 102C (or another system or device) then processes the received wireless signals to detect motion of an object in a space accessed by the wireless signals (e.g., in the zones 110A, 11B). In some instances, the wireless communication device 102C (or another system or device) may perform one or more operations of a motion detection system.
In some cases, a combination of one or more of the wireless communication devices 204A, 204B, 204C can be part of, or may be used by, a motion detection system. The example wireless communication devices 204A, 204B, 204C can transmit wireless signals through a space 200. The example space 200 may be completely or partially enclosed or open at one or more boundaries of the space 200. The space 200 may be or may include an interior of a room, multiple rooms, a building, an indoor area, outdoor area, or the like. A first wall 202A, a second wall 202B, and a third wall 202C at least partially enclose the space 200 in the example shown.
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As shown, an object is in a first position 214A at an initial time (t0) in
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In
The example wireless signals shown in
The transmitted signal can have a number of frequency components in a frequency bandwidth, and the transmitted signal may include one or more bands within the frequency bandwidth. The transmitted signal may be transmitted from the first wireless communication device 204A in an omnidirectional manner, in a directional manner, or otherwise. In the example shown, the wireless signals traverse multiple respective paths in the space 200, and the signal along each path can become attenuated due to path losses, scattering, reflection, or the like and may have a phase or frequency offset.
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In the example shown in
In the example shown in
In the example shown in
When the client devices 232 seek to connect to and associate with their respective APs 226, 228, the client devices 232 may go through an authentication and association phase with their respective APs 226, 228. Among other things, the association phase assigns address information (e.g., an association ID or another type of unique identifier) to each of the client devices 232. For example, within the IEEE 802.11 family of standards for Wi-Fi, each of the client devices 232 can identify itself using a unique address (e.g., a 48-bit address, an example being the MAC address), although the client devices 232 may be identified using other types of identifiers embedded within one or more fields of a message. The address information (e.g., MAC address or another type of unique identifier) can be either hardcoded and fixed, or randomly generated according to the network address rules at the start of the association process. Once the client devices 232 have associated to their respective APs 226, 228, their respective address information may remain fixed. Subsequently, a transmission by the APs 226, 228 or the client devices 232 typically includes the address information (e.g., MAC address) of the transmitting wireless device and the address information (e.g., MAC address) of the receiving device.
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In the example shown in
The motion detection system, which may include one or more motion detection or localization processes running on one or more of the client devices 232 or on one or more of the APs 226, 228, may collect and process data (e.g., channel information) corresponding to local links that are participating in the operation of the wireless sensing system. The motion detection system can be installed as a software or firmware application on the client devices 232 or on the APs 226, 228, or may be part of the operating systems of the client devices 232 or the APs 226, 228.
In some implementations, the APs 226, 228 do not contain motion detection software and are not otherwise configured to perform motion detection in the space 201. Instead, in such implementations, the operations of the motion detection system are executed on one or more of the client devices 232. In some implementations, the channel information may be obtained by the client devices 232 by receiving wireless signals from the APs 226, 228 (or possibly from other client devices 232) and processing the wireless signal to obtain the channel information. For example, the motion detection system running on the client devices 232 can have access to channel information provided by the client device's radio firmware (e.g., Wi-Fi radio firmware) so that channel information may be collected and processed.
In some implementations, the client devices 232 send a request to their corresponding AP 226, 228 to transmit wireless signals that can be used by the client device as motion probes to detect motion of objects in the space 201. The request sent to the corresponding AP 226, 228 may be a null data packet frame, a beamforming request, a ping, standard data traffic, or a combination thereof. In some implementations, the client devices 232 are stationary while performing motion detection in the space 201. In other examples, one or more of the client devices 232 can be mobile and may move within the space 201 while performing motion detection.
Mathematically, a signal f(t) transmitted from a wireless communication device (e.g., the wireless communication device 204A in
where ωn represents the frequency of nth frequency component of the transmitted signal, cn represents the complex coefficient of the nth frequency component, and t represents time. With the transmitted signal f(t) being transmitted, an output signal rk(t) from a path k may be described according to Equation (2):
where αn,k represents an attenuation factor (or channel response; e.g., due to scattering, reflection, and path losses) for the nth frequency component along path k, and ϕn,k represents the phase of the signal for nth frequency component along path k. Then, the received signal R at a wireless communication device can be described as the summation of all output signals rk(t) from all paths to the wireless communication device, which is shown in Equation (3):
Substituting Equation (2) into Equation (3) renders the following Equation (4):
The received signal R at a wireless communication device (e.g., the wireless communication devices 204B, 204C in
The complex value Yn for a given frequency component ωn indicates a relative magnitude and phase offset of the received signal at that frequency component ωn. The signal f(t) may be repeatedly transmitted within a time period, and the complex value Yn can be obtained for each transmitted signal f(t). When an object moves in the space, the complex value Yn changes over the time period due to the channel response αn,k of the space changing. Accordingly, a change detected in the channel response (and thus, the complex value Yn) can be indicative of motion of an object within the communication channel. Conversely, a stable channel response may indicate lack of motion. Thus, in some implementations, the complex values Yn for each of multiple devices in a wireless network can be processed to detect whether motion has occurred in a space traversed by the transmitted signals f(t). The channel response can be expressed in either the time-domain or frequency-domain, and the Fourier-Transform or Inverse-Fourier-Transform can be used to switch between the time-domain expression of the channel response and the frequency-domain expression of the channel response.
In another aspect of
In some implementations, for example, a steering matrix may be generated at a transmitter device (beamformer) based on a feedback matrix provided by a receiver device (beamformee) based on channel sounding. Because the steering and feedback matrices are related to propagation characteristics of the channel, these beamforming matrices change as objects move within the channel. Changes in the channel characteristics are accordingly reflected in these matrices, and by analyzing the matrices, motion can be detected, and different characteristics of the detected motion can be determined. In some implementations, a spatial map may be generated based on one or more beamforming matrices. The spatial map may indicate a general direction of an object in a space relative to a wireless communication device. In some cases, “modes” of a beamforming matrix (e.g., a feedback matrix or steering matrix) can be used to generate the spatial map. The spatial map may be used to detect the presence of motion in the space or to detect a location of the detected motion.
In some implementations, the output of the motion detection system may be provided as a notification for graphical display on a user interface of a user device.
The example user interface 300 shown in
The example user interface 300 shown in
In some implementations, the output of the motion detection system is provided in real-time (e.g., to an end user). Additionally or alternatively, the output of the motion detection system can be stored (e.g., locally on the wireless communication devices 204, client devices 232, the APs 226, 228, or on a cloud-based storage service) and analyzed to reveal statistical information over a time frame (e.g., hours, days, or months). An example where the output of the motion detection system may be stored and analyzed to reveal statistical information over a time frame is in health monitoring, vital sign monitoring, sleep monitoring, etc. In some implementations, an alert (e.g., a notification, an audio alert, or a video alert) is provided based on the output of the motion detection system. For example, a motion detection event may be communicated to another device or system (e.g., a security system or a control center), a designated caregiver, a professional monitoring center that receives the alert and reacts to it, or a designated emergency contact based on the output of the motion detection system.
The example interface 430 can communicate (receive, transmit, or both) wireless signals. For example, the interface 430 may be configured to communicate radio frequency (RF) signals formatted according to a wireless communication standard (e.g., Wi-Fi, 4G, 5G, Bluetooth, etc.). In some implementations, the example interface 430 includes a radio subsystem and a baseband subsystem. The radio subsystem may include, for example, one or more antennas and radio frequency circuitry. The radio subsystem can be configured to communicate radio frequency wireless signals on the wireless communication channels. As an example, the radio subsystem may include a radio chip, an RF front end, and one or more antennas. The baseband subsystem may include, for example, digital electronics configured to process digital baseband data. In some cases, the baseband subsystem includes a digital signal processor (DSP) device or another type of processor device. In some cases, the baseband system includes digital processing logic to operate the radio subsystem, to communicate wireless network traffic through the radio subsystem or to perform other types of processes.
The example processor 410 can execute instructions, for example, to generate output data based on data inputs. The instructions can include programs, codes, scripts, modules, or other types of data stored in memory 420. Additionally or alternatively, the instructions can be encoded as pre-programmed or re-programmable logic circuits, logic gates, or other types of hardware or firmware components or modules. The processor 410 may be or include a general-purpose microprocessor, as a specialized co-processor or another type of data processing apparatus. In some cases, the processor 410 performs high level operation of the wireless communication device 400. For example, the processor 410 may be configured to execute or interpret software, scripts, programs, functions, executables, or other instructions stored in the memory 420. In some implementations, the processor 410 is included in the interface 430 or another component of the wireless communication device 400.
The example memory 420 may include computer-readable storage media, for example, a volatile memory device, a non-volatile memory device, or both. The memory 420 may include one or more read-only memory devices, random-access memory devices, buffer memory devices, or a combination of these and other types of memory devices. In some instances, one or more components of the memory can be integrated or otherwise associated with another component of the wireless communication device 400. The memory 420 may store instructions that are executable by the processor 410. For example, the instructions may include instructions to perform one or more of the operations in the example process 1000 shown in
The example power unit 440 provides power to the other components of the wireless communication device 400. For example, the other components may operate based on electrical power provided by the power unit 440 through a voltage bus or other connection. In some implementations, the power unit 440 includes a battery or a battery system, for example, a rechargeable battery. In some implementations, the power unit 440 includes an adapter (e.g., an AC adapter) that receives an external power signal (from an external source) and converts the external power signal to an internal power signal conditioned for a component of the wireless communication device 400. The power unit 420 may include other components or operate in another manner.
The example system 500 includes an interface 502 configured to communicate wireless signals (e.g., radio frequency (RF) signals), formatted according to a wireless communication standard (e.g., Wi-Fi, 4G, 5G, Bluetooth, etc.), through a space (e.g., the space 200 or 201). In some implementations, the interface 502 can be identified with the interface 430 shown in
The motion detection system 504 receives the channel information 512 from the interface 502. In some implementations, operation of the motion detection engine 506 may depend, at least in part, on input data provided by a user (e.g., shown in in
The motion data 514 may also include a motion localization vector {circumflex over (L)}t 518 for each time point t in the series of time points within the time period. The motion localization vector {circumflex over (L)}t 518 for the time point t may include entries of motion localization values [Lt,1 Lt,2 . . . Lt,N], where N is the number of locations in the space. In some instances, the motion localization vector {circumflex over (L)}t indicates the relative degree of motion detected at each of the N locations in the space at the time point t. Stated differently, the motion localization value Lt,n for each of the N individual locations may represent a relative degree of motion detected at the individual location for the time point t. As an example, in the illustration shown in
In some implementations, the degree of motion that occurred at each of the N locations in the space at the time point t can be determined based on the vector mt{circumflex over (L)}t=[mtLt,1 mtLt,2 . . . mtLt,N]. The pattern extraction engine 508 receives the motion data 514 from the motion detection engine 506 and generates activity data 520 and one or more notifications 522 based on the motion data 514, user input data 524, or both the motion data 514 and the user input data 524. In some instances, the activity data 520 and the one or more notifications 522 are provided for display (e.g., graphical display) on a user interface of a user device.
In some implementations, the activity data 520 may be an actual value for a metric of interest for the time period during which the wireless signals are communicated through the space. The actual value for the metric of interest may be identified based on the motion data 514 received from the motion detection engine 506. In some implementations, the activity data 520 may be a benchmark value for the metric of interest, and the benchmark value for the metric of interest may be identified based on the user input data 524. Various examples of metrics of interest (and examples of actual and benchmark values of such metrics of interest) are discussed in further detail below.
In some implementations, the relative degree of motion detected at an individual location at the time point t depends, at least in part, on the degree of motion detected by the wireless communication device(s) in the individual location at the time point t. For example, in the example of
In some implementations, the user input data 524 include a time interval [t0, tp] within a time period (e.g., the time period 304 shown in
In some implementations, the activity data 520 (e.g., the actual value for the metric of interest) can include the degree of motion that occurred at each of the N locations in the space within the time interval [t0, tp]. In some instances, the degree of motion that occurred at the nth location within the time interval [t0, tp] may be expressed as follows:
In some implementations, the activity data 520 (e.g., the actual value for the metric of interest) can include the average degree of motion that occurred at each of the N locations in the space within the time interval [t0, tp]. In some instances, the average degree of motion that occurred at the nth location within the time interval [t0, tp] may be expressed as follows:
In some implementations, the activity data 520 (e.g., the actual value for the metric of interest) can include a determination of which location, among the N locations in the space, experienced the largest degree of motion within the time interval [t0, tp]. In some instances, the location that experienced the largest degree of motion within the time interval [t0, tp] can be determined by determining which location, among the N locations in the space, generated the largest value Bn(t0, tp) or the largest value Cn(t0, tp).
In some implementations, the activity data 520 (e.g., the actual value for the metric of interest) can include a determination of the number of active minutes at each of the N locations within the time interval [t0, tp]. As discussed above, the degree of motion that occurred at each of the N locations in the space at the time point t can be determined based on the vector mt=[mtLt,1 mtLt,2 . . . mtLt,N]. In some instances, the vector mt for each time point within the time interval [t0, tp] can be used to determine the number of active minutes at each of the N locations within the time interval [t0, tp]. As an example, the vector mt0=[mt0Lt0,1 mt0Lt0,2 . . . mtLt0,N] may represent the degree of motion that occurred at each of the N locations in the space at the time point t0; the vector mt1=[mt1Lt1,1 mt1Lt1,2 . . . mt1Lt1,N] may represent the degree of motion that occurred at each of the N locations in the space at the time point t1; and so on. In some implementations, the entries of each of the vectors mt0 . . . , mtp may be grouped to the nearest minute, and a non-zero entry may be indicative of an active minute (e.g., a minute in which there is a non-zero degree of motion). For each location across the vectors mt0 mt1 . . . , mtp the number of active minutes at a given location within the time interval [t0, tp] can be determined by adding number of non-zero entries for that given location across the vectors mt0 mt1 . . . , mtp. In some instances, the number of active minutes may be expressed as a percentage (e.g., relative to the number of minutes in the time interval [t0, tp]). In some implementations, the activity data 520 can include a determination of the number of inactive minutes at each of the N locations within the time interval [t0, tp]. For example, the entries of each of the vectors mt0, mt1, . . . , mtp may be grouped to the nearest minute, and a zero entry may be indicative of an inactive minute (e.g., a minute in which there is no degree of motion detected). For each user location across the vectors mt0, mt1, . . . , mtp, the number of inactive minutes at a given location within the time interval [t0, tp] can be determined by adding the number of zero entries for that given location across the vectors mt0, mt1, . . . , mtp. In some instances, the number of inactive minutes may be expressed as a percentage (e.g., relative to the number of minutes in the time interval [t0, tp]).
In some implementations, the user input data 524 can include a time interval [ts1, ts2] within a time period (e.g., the time period 304 shown in
In some implementations, the total duration of movement observed during the time interval [ts1, ts2] can be obtained by determining the number of active minutes at the sleeping location within the time interval [ts1, ts2] and, as discussed above, the number of active minutes at a given location (e.g., the sleeping location) within the time interval [ts1, ts2] can be determined by adding the number of non-zero entries for the sleeping location across the vectors mt
In some implementations, the degree of motion observed for each time point within the time interval [ts1, ts2] can be obtained based on the vector [mt
In some implementations, the sleep levels observed during the time interval [ts1, ts2] can include an indication of durations of restful sleep within the time interval [ts1, ts2]; an indication of durations of light sleep within the time interval [ts1, ts2]; and an indication of durations of disrupted sleep within the time interval [ts1, ts2].
As an illustration, the person may lie on a bed and place the wireless communication device 400 on a nightstand. The wireless communication device 400 may determine the degree of motion while the person is lying in bed (e.g., based on channel information obtained from wireless signals transmitted in the space in which the person is sleeping). In some implementations, a low degree of motion may be inferred when the degree of motion is less than a first threshold, and a high degree of motion may be inferred when the degree of motion is greater than a second threshold. As an example, turning or repositioning in the bed can produce a smaller degree of motion over a first duration of time (e.g., between 1 and 5 seconds) compared to instances when the person is walking, which may produce a greater degree of motion over a second (longer) duration of time. In some instances (e.g., the example shown in
Periods during which the degree of motion is less than the threshold 612 may indicate periods of restful sleep (e.g., deep sleep or REM sleep). The person may toss and turn while sleeping, and the wireless communication device 400 can detect the degree of motion of the person. Periods during which the degree of motion is greater than the threshold 612 may indicate either that the person has woken from sleep or that the person is having a period of disrupted, restless sleep or light sleep. Short bursts of motion occurring after sleep monitoring has commenced may indicate periods of disrupted, restless sleep or light sleep. In some implementations, periods of disrupted, restless sleep or light sleep are detected when the degree of motion is greater than the threshold 612 for a first predetermined duration of time (e.g., less than 5 seconds, or another duration). Conversely, prolonged bursts of motion occurring after sleep monitoring has commenced may indicate that the person has woken from sleep. In some implementations, the wireless communication device 400 determines that the person is awake when the degree of motion is greater than the threshold 612 for a second predetermined duration of time (e.g., more than 5 seconds, or another duration). In some implementations, the first and second predetermined durations of time may be functions of the degree of motion detected. For example, a longer duration of time may be associated with a low degree of motion, and a shorter duration of time may be associated with a high degree of motion to distinguish between the light (rapid eye movement) sleep state and the disrupted sleep (awake) state.
The plots 608 and 610 are one example of showing corresponding periods of disrupted, light, and restful sleep.
The sleeping behavior (e.g., sleep quality) can be determined based on the level of motion during the time interval [ts1, ts2]. For example, in some implementations, a metric indicative of sleep quality can be determined based on a ratio of a total duration of the periods of restful sleep to the total duration of sleep monitoring (e.g., obtained from the starting and ending times in the time interval [ts1, ts2]).
In some implementations, the total duration of sleep observed during the time interval [ts1, ts2] can be determined based on the sleep levels observed during the time interval [ts1, ts2]. For example the total duration of sleep observed during the time interval [ts1, ts2] can be based on the total duration of restful sleep within the time interval [ts1, ts2] or a sum of the durations of restful sleep and light sleep within the time interval [ts1, ts2], although other methods of determining the total duration of sleep observed during the time interval [ts1, ts2] may be used.
In some implementations, the user input data 524 include a time interval [ta1, ta2] within a time period (e.g., the time period 304 shown in
In some instances, the user input data 524 include a time interval [tn1, tn2] within a time period (e.g., the time period 304 shown in
In some instances, the user input data 524 include an indication of one or more locations within the space at which motion is not expected. For example, the user input data 524 may include an indication that motion is not expected in the kitchen area. In some instances, the pattern extraction engine 508 may determine, based on the user input data 524 and the motion data 514, that motion has occurred in at least one of the locations specified by the user input data 524. In such instances, the pattern extraction engine 508 may generate a notification 522 (e.g., for display on a user interface of a user device) that motion has occurred at one or more of the locations at which motion was not expected.
In some instances, the user input data 524 include notification times designated by a user. The notification times may be times at which the one or more notifications 522 may be generated by the pattern extraction engine 508. In an event that the current time is not one of notification times designated by the user, the pattern extraction engine 508 may forgo generating the one or more notifications 522. In some instances, the user input data 524 include an indication of motion events for which the user would like to receive notifications 522. In an instance where the motion event is not one of events designated by the user, the pattern extraction engine 508 may forgo generating the one or more notifications 522.
In addition to the examples discussed above, the notification(s) 522 can include at least one of the following: one or more of the metrics of interest discussed above; an indication of an operating state of the motion detection system 500 (e.g., an indication that the motion detection system 500 was set to an Away or Home mode); an indication of a geofence event (e.g., an indication that a person has left the space or a location in the space); an activity alert (e.g., an indication that a person is not yet awake, an indication that no motion has been detected for a stated period of time, an indication of the number of times a person arose from sleep last night, etc.); or any other type of notification that conveys information about the motion detection system 500 or about motion that was detected in a space.
The system 700 includes a graphical generation engine 702 that generates a graphical display 704 based on the activity data 520 and the one or more notifications 522. As discussed above, in some instances, the activity data 520 may include one or more of the following: a total duration of sleep observed during the time interval [ts1, ts2]; a total duration of movement observed during the time interval [ts1, ts2]; a degree of motion observed for each time point within the time interval [ts1, ts2]; sleep levels observed during the time interval [ts1, ts2]; or the targeted duration of sleep during the time interval [ts1, ts2]. In such instances, the graphical display 704 that is generated by the graphical generation engine 702 may be a graphic that displays the total duration of sleep observed during the time interval [ts1, ts2] (e.g., relative to the targeted duration of sleep during the time interval [ts1, ts2]). Additionally or alternatively, the graphical display 704 that is generated by the graphical generation engine 702 may be a graphic that displays a total duration of movement observed during the time interval [ts1, ts2], a degree of motion observed for each time point within the time interval [ts1, ts2], the sleep levels observed during the time interval [ts1, ts2], or a combination thereof.
As discussed above, in some instances, the activity data 520 may include one or more of the following: a total duration of movement observed during the time interval [ta1, ta2]; a degree of motion observed at each location for each time point within the time interval [ta1, ta2]; the location exhibiting the highest degree of motion during the time interval [ta1, ta2]; or the targeted duration of movement during the time interval [ta1, ta2].
In such instances, the graphical display 704 that is generated by the graphical generation engine 702 may be a graphic that displays the total duration of movement observed during the time interval [ta1, ta2] (e.g., relative to the targeted duration of movement during the time interval [ta1, ta2]). Additionally or alternatively, the graphical display 704 that is generated by the graphical generation engine 702 may be a graphic that displays the degree of motion observed at each location for each time point within the time interval [ta1, ta2], the location exhibiting the highest degree of motion during the time interval [ta1, ta2], or a combination thereof.
The example graphical display 800 in
Each tile can be expanded to display further metrics of interest.
In some implementations, the graphical display 803 includes element 828 that displays numerical values for the total duration of movement observed for the day (e.g., indicated as 2.5 hours in the example of
In some implementations, the graphical display 803 includes element 830 that displays an average duration of sleep observed for the week (e.g., indicated as 5 hours in the example of
Each element 922 and 924 can be expanded to display further metrics of interest.
The timeframes indicated by example graphical displays shown in
The example process 1000 may include additional or different operations, and the operations may be performed in the order shown or in another order. In some cases, one or more of the operations shown in
At 1010, channel information is obtained based on wireless signals communicated through a space. The space (e.g., the space 201 shown in
At 1020, motion data is generated based on the channel information. As discussed above in reference to
At 1030, an actual value for a metric of interest for the time period is identified based on the motion data. As discussed above, the metric of interest can include one or more of the following: the degree of motion that occurred in the space within the time interval; the degree of motion that occurred at each of the N locations in the space within the time interval [t0, tp]; the average degree of motion that occurred at each of the N locations in the space within the time interval [t0, tp]; a determination of which location, among the N locations in the space, experienced the largest degree of motion within the time interval [t0, tp]; or a determination of the number of active minutes at each of the N locations within the time interval [t0, tp]. In some implementations, the metric of interest can include sleep data, and the actual value of the metric of interest can include one or more of the following: a total duration of sleep observed during a time interval [ts1, ts2]; a total duration of movement observed during the time interval [ts1, ts2]; a degree of motion observed for each time point within the time interval [ts1, ts2]; or sleep levels observed during the time interval [ts1, ts2]. In some implementations, the metric of interest can include movement data, and the actual value of the metric of interest can include one or more of the following: a total duration of movement observed during the time interval [ta1, ta2]; a degree of motion observed at each location for each time point within the time interval [ta1, ta2]; or the location exhibiting the highest degree of motion during the time interval [ta1, ta2].
At 1040, a benchmark value for the metric of interest is identified based on user input data (e.g., user input data 524 shown in
At 1050, the actual value for the metric of interest and the benchmark value for the metric of interest are provided for display on a user interface of a user device. For example, the values may be displayed as shown in
The example process 1100 may include additional or different operations, and the operations may be performed in the order shown or in another order. In some cases, one or more of the operations shown in
At 1110, the actual value for the metric of interest and the benchmark value for the metric of interest (e.g., that are provided at 1050 in
Some of the subject matter and operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Some of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, encoded on a computer storage medium for execution by, or to control the operation of, data-processing apparatus. A computer storage medium can be, or can be included in, a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of them. Moreover, while a computer storage medium is not a propagated signal, a computer storage medium can be a source or destination of computer program instructions encoded in an artificially generated propagated signal. The computer storage medium can also be, or be included in, one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices).
Some of the operations described in this specification can be implemented as operations performed by a data processing apparatus on data stored on one or more computer-readable storage devices or received from other sources.
The term “data-processing apparatus” encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, a system on a chip, or multiple ones, or combinations, of the foregoing. The apparatus can include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). The apparatus can also include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or a combination of one or more of them.
A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
Some of the processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).
To provide for interaction with a user, operations can be implemented on a computer having a display device (e.g., a monitor, or another type of display device) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse, a trackball, a tablet, a touch sensitive screen, or another type of pointing device) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user's client device in response to requests received from the web browser.
In a general aspect, metrics of interest are generated based on motion data and displayed (e.g., on a user interface).
In a first example, a method includes obtaining channel information based on wireless signals communicated through a space over a time period by a wireless communication network. The wireless communication network includes a plurality of wireless communication devices, and the space includes a plurality of locations. The method includes generating motion data based on the channel information. The motion data includes motion indicator values and motion localization values for the plurality of locations. The motion indicator values may be indicative of a degree of motion that occurred in the space for each time point in a series of time points within the time period. The motion localization value for each individual location may represent a relative degree of motion detected at the individual location for each time point in the series of time points within the time period. The method further includes identifying, based on the motion data, an actual value for a metric of interest for the time period; identifying, based on user input data, a benchmark value for the metric of interest for the time period; and providing, for display on a user interface of a user device, the actual value for the metric of interest and the benchmark value for the metric of interest.
Implementations of the first example may include one or more of the following features. The user input data may include a first time interval within the time period, the first time interval indicative of a time interval during which a person expects to be asleep; and a targeted duration of sleep during the first time interval. The actual value of the metric of interest may include at least one of: a total duration of sleep observed during the first time interval; a total duration of movement observed during the first time interval; a degree of motion observed for each time point within the first time interval; or sleep levels observed during the first time interval. The sleep levels observed during the first time interval may include: durations of restful sleep within the first time interval; durations of light sleep within the first time interval; and durations of disrupted sleep within the first time interval. The user input data may include a second time interval within the time period, the second time interval indicative of times during which a person expects to be awake; and a targeted duration of movement during the second time interval. The actual value of the metric of interest may include at least one of: a total duration of movement observed during the second time interval; a degree of motion observed at each location for each time point within the second time interval; or the location exhibiting the highest degree of motion during the second time interval. The user input data may include an indication of a time duration within the time period during which motion is not expected, and the method may further include: determining, based on the user input data and the motion data, that motion has occurred during the time duration; and providing, for display on the user interface of the user device, a notification that motion has occurred within the time duration during which motion is not expected. The user input data may include an indication of one or more locations at which motion is not expected, and the method may further include: determining, based on the user input data and the motion data, that motion has occurred at the one or more locations; and providing, for display on the user interface of the user device, a notification that motion has occurred at one or more of the locations at which motion is not expected. Each wireless communication device may be located in a respective location of the plurality of locations. The wireless signals communicated through the space may include wireless signals exchanged on wireless communication links in the wireless communication network, and each motion indicator value represents the degree of motion detected from the wireless signals exchanged on a respective one of the wireless communication links.
In a second example, a method may include receiving an actual value for a metric of interest for a time period. The actual value for the metric of interest may be identified based on motion data, and the motion data may be generated based on channel information. The channel information may be obtained based on wireless signals communicated through a space over the time period by a wireless communication network. The wireless communication network may include a plurality of wireless communication devices, and the space may include a plurality of locations. The motion data includes motion indicator values and motion localization values for the plurality of locations. The motion indicator values may be indicative of a degree of motion that occurred in the space for each time point in a series of time points within the time period. The motion localization value for each individual location may represent a relative degree of motion detected at the individual location for each time point in the series of time points within the time period. The method further includes receiving a benchmark value for the metric of interest for the time period. The benchmark value for the metric of interest may be identified based on user input data. The method additionally includes displaying, on a user interface of a user device, the actual value for the metric of interest relative to the benchmark value for the metric of interest.
Implementations of the first example may include one or more of the following features. The method may additionally include generating a notification in response to the actual value of the metric of interest being greater than or equal to the benchmark value of the metric of interest.
In a third example, a non-transitory computer-readable medium stores instructions that are operable when executed by data processing apparatus to perform one or more operations of the first or second examples. In a fourth example, a system includes a plurality of wireless communication devices, and a computer device configured to perform one or more operations of the first or second examples.
Implementations of the fourth example may include one or more of the following features. One of the wireless communication devices can be or include the computer device. The computer device can be located remote from the wireless communication devices.
While this specification contains many details, these should not be understood as limitations on the scope of what may be claimed, but rather as descriptions of features specific to particular examples. Certain features that are described in this specification or shown in the drawings in the context of separate implementations can also be combined. Conversely, various features that are described or shown in the context of a single implementation can also be implemented in multiple embodiments separately or in any suitable subcombination.
Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the implementations described above should not be understood as requiring such separation in all implementations, and it should be understood that the described program components and systems can generally be integrated together in a single product or packaged into multiple products.
A number of embodiments have been described. Nevertheless, it will be understood that various modifications can be made. Accordingly, other embodiments are within the scope of the following claims.
This application is a continuation-in-part of International Patent Application No. PCT/CA2022/050228, filed on Feb. 17, 2022. International Patent Application No. PCT/CA2022/050228 claims priority to U.S. Non-provisional application Ser. No. 17/201,724 filed on Mar. 15, 2021. International Patent Application No. PCT/CA2022/050228 and U.S. patent application Ser. No. 17/201,724 are each hereby incorporated by reference.
Number | Date | Country | |
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Parent | 17201724 | Mar 2021 | US |
Child | PCT/CA2022/050228 | US |
Number | Date | Country | |
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Parent | PCT/CA2022/050228 | Feb 2022 | US |
Child | 18468457 | US |