Method and device for motion sensing based on wireless LAN signal

Abstract

According to the present invention, a sensing device in a wireless LAN system includes a memory and a processor. The processor is connected to an access point (AP) through a plurality of wireless channels. The plurality of wireless channels use different frequency bands, and can detect the state of motion of an object on the basis of different sensing ranges corresponding to the plurality of wireless channels, respectively.

Description

Method and device for motion detection based on wireless LAN signals

[0001] The present invention relates to a method and device for detecting motion using a wireless LAN signal.

[0002] With the recent proliferation of mobile devices, wireless LAN (WLAN) technology, which can provide them with fast wireless Internet service, is attracting significant attention. WLAN technology utilizes short-range wireless communication technology to enable mobile devices such as smartphones, tablets, laptops, portable multimedia players, and embedded devices to wirelessly connect to the Internet at home, in businesses, or in specific service areas.

[0003] With the proliferation of wireless LAN, research is also underway to detect motion around access points (APs) and stations using the signals exchanged between them. Motion detection using WLAN signals has the advantage of minimizing unnecessary exposure of privacy, as it eliminates the need for direct recording of the user's appearance. However, further research is needed to improve the accuracy of motion detection and to avoid interfering with existing WLAN communications.

[0004] An embodiment of the present invention aims to provide a method and device for detecting motion using a wireless LAN signal.

[0005] In the wireless LAN system of the present specification, a sensing device includes a memory; and a processor, wherein the processor is connected to an access point (AP) through a plurality of wireless channels, wherein the plurality of wireless channels use different frequency bands, and the motion state of an object can be detected based on different sensing ranges corresponding to each of the plurality of wireless channels. In addition, in the present specification, the plurality of wireless channels may be two or three. In addition, in the present specification, when the plurality of wireless channels are two, the frequency bands used by the plurality of wireless channels may be 2.4 GHz, 5 GHz, or 2.4 GHz and 6 GHz, respectively, and when the plurality of wireless channels are three, the frequency bands used by the plurality of wireless channels may be 2.4 GHz, 5 GHz, and 6 GHz, respectively. In addition, in the present specification, the first sensing range corresponding to the frequency band of 2.4 GHz is wider than the second sensing range corresponding to the frequency band of 5 GHz, the second sensing range is wider than the third sensing range corresponding to the frequency band of 6 GHz, the first sensing range includes the second sensing range and the third sensing range, and the second sensing range may include the third sensing range. In addition, in the present specification, the processor may determine whether the activity level of the object is higher than a threshold value for each of the first sensing range, the second sensing range, and the third sensing range, so that whether the object exists may be detected for each sensing range. The movement state of the object may be determined based on whether the object exists, and the activity level may be determined based on a change amount of channel state information (CSI) of each of the plurality of wireless channels.In addition, in the present specification, when the object is detected as being present in all of the first sensing range, the second sensing range, and the third sensing range at a first time, and when the object is detected as being present in the first sensing range at a second time, and the object is detected as not being present in at least one of the second sensing range and the third sensing range, if the first time is a time before the second time, the movement state of the object may be a state moving away from the sensing device, and if the first time is a time after the second time, the movement state of the object may be a state moving closer to the sensing device.

[0006] In the wireless LAN system of the present disclosure, a sensing device includes a memory; and a processor, wherein the processor obtains a measurement value related to a channel state using a wireless LAN signal for a predetermined number of measurements or within a predetermined time interval, and determines whether movement of an object occurs or the movement state of the object within the wireless LAN system based on the measurement value. The processor may obtain a standard deviation of the magnitude of the measurement value, and obtain a feature value of a sensing algorithm based on the obtained standard deviation. The sensing algorithm may be an algorithm learned based on a correlation between a value obtained based on the magnitude of the measurement value and whether movement of an object occurs around devices exchanging the wireless LAN signal. The processor may determine whether movement of the object occurs based on the algorithm. The measurement value may include a value related to channel state information (CSI), a received signal strength indicator (RSSI), the number of antennas of the sensing device, and a sensing band to which the sensing device is connected. The algorithm may be learned through machine learning using a weight related to detection sensitivity as an input. The processor obtains a standard deviation related to the result of a short time Fourier transform (STFT) of the measurement value, and determines the motion state of the object based on the standard deviation, wherein the motion state of the object may be a periodic motion or a non-periodic motion. If the standard deviation has a repetitive pattern within a certain period, the motion state of the object may be a periodic motion. If the standard deviation is smaller than a threshold value, the motion state of the object may be a periodic motion.

[0007] In addition, in the present specification, a method for performing an operation of a customized Internet of Things (IoT) device for each user may include the steps of: determining whether the user is located within a certain space; and, if the user is located within the certain space, performing a preset operation by the IoT device. The preset operation may be an operation set by the user. The IoT devices may be classified into a first device that supports Wi-Fi and a second device that does not support Wi-Fi. The user's personal device is connected to a first access point (AP), and if the IoT device is the first device, the first device is connected to a second AP, and if the IoT device is the second device, the second device may be connected to a third AP via an IoT hub. If the first AP, the second AP, and the third AP are the same AP, the user's personal device may be located within the same space as the first device and the second device. The above personal device is one, the first device and the second device are each plural, and each of the plural devices can perform different preset operations.

[0008] One embodiment of the present invention provides a method and device for detecting motion using a wireless LAN signal.

[0009] One embodiment of the present invention provides a method and device for determining whether movement of an object has occurred or the state of movement of an object using a wireless LAN signal.

[0010] One embodiment of the present invention provides a method and device for performing the operation of a user-specific customized Internet of Things (IoT) device using a wireless LAN signal.

[0011] FIG. 1 illustrates a system for detecting a user's motion using a wireless LAN signal according to an embodiment of the present invention.

[0012] FIG. 2 shows an AP and a sensing node performing frame exchange according to an embodiment of the present invention.

[0013] FIG. 3 shows that channel status changes occur due to various events in a wireless channel in which an AP and a sensing node exchange signals according to an embodiment of the present invention.

[0014] FIG. 4 shows that channel status changes occur due to various events in a channel in which an AP having multiple antennas and a sensing node exchange signals according to an embodiment of the present invention.

[0015] FIG. 5 shows that a sensing device according to an embodiment of the present invention uses a sum of multiple measured values ​​measured immediately before the most recent measured value as a feature value of a sensing algorithm.

[0016] FIG. 6 illustrates a process for processing channel state information (CSI) according to an embodiment of the present invention.

[0017] FIG. 7 illustrates a method for selecting a CSI sample according to an embodiment of the present invention.

[0018] FIG. 8 illustrates a method for processing CSI through a shape-preserving filter according to an embodiment of the present invention.

[0019] FIG. 9 illustrates how CSI is processed through a size-preserving filter according to an embodiment of the present invention.

[0020] FIG. 10 illustrates an operation of a device according to an embodiment of the present invention to apply an optional subcarrier shift removal filter to a measurement value.

[0021] FIG. 11 shows a sensing device according to an embodiment of the present invention applying an offset removal filter to a measurement value.

[0022] FIG. 12 shows an operation of a sensing device according to an embodiment of the present invention performing outlier filtering.

[0023] FIG. 13 illustrates a method by which a sensing device according to an embodiment of the present invention reconstructs a CSI signal.

[0024] FIG. 14 illustrates a method for extracting features used for detecting motion of an object in CSI according to an embodiment of the present invention.

[0025] FIG. 15 illustrates a method for obtaining features by measuring the strength of a received wireless signal (Received Signal Strength Indication, RSSI) according to an embodiment of the present invention.

[0026] Figure 16 illustrates features used to detect movement of an object according to an embodiment of the present invention.

[0027] FIG. 17 illustrates a method for detecting motion probability using a machine learning model according to an embodiment of the present invention.

[0028] Figure 18 illustrates a method for obtaining a machine learning model for a profile.

[0029] Figure 19 illustrates a method for obtaining a motion probability according to the present invention.

[0030] Figure 20 illustrates a method for determining whether motion is detected based on probability according to an embodiment of the present invention.

[0031] FIG. 21 illustrates a method for detecting periodic motion according to an embodiment of the present invention.

[0032] FIG. 22 illustrates a process of converting reshaped CSI according to an embodiment of the present invention.

[0033] Figure 23 illustrates a method for obtaining features for determining periodic motion according to an embodiment of the present invention.

[0034] Figure 24 illustrates a method for determining periodic motion according to an embodiment of the present invention.

[0035] Figure 25 illustrates a method for determining a short motion according to an embodiment of the present invention.

[0036] Figure 26 illustrates a method for determining a short motion according to an embodiment of the present invention.

[0037] Figure 27 shows a transmission path of a wireless signal according to an embodiment of the present invention.

[0038] Figure 28 shows a wireless signal calculation model according to an embodiment of the present invention.

[0039] FIG. 29 illustrates a multi-link device according to an embodiment of the present invention.

[0040] FIG. 30 shows an OFDM (Orthogonal Frequency Division Multiplexing) waveform according to an embodiment of the present invention.

[0041] FIG. 31 illustrates wireless signals transmitted on different frequency bands according to an embodiment of the present invention.

[0042] FIG. 32 illustrates a configuration for detecting movement of an object in a device configured with a multi-link according to an embodiment of the present invention.

[0043] FIG. 33 illustrates a method for determining whether an object is moving based on an activity level according to an embodiment of the present invention.

[0044] Figure 34 illustrates a method for classifying the movement of an object according to an embodiment of the present invention.

[0045] FIG. 35 illustrates a configuration for detecting movement of an object in a device configured with a multi-link according to one embodiment of the present invention.

[0046] FIG. 36 illustrates a method for determining whether an object is moving based on an activity level according to an embodiment of the present invention.

[0047] Figure 37 illustrates a method for classifying the movement of an object according to an embodiment of the present invention.

[0048] Figure 38 shows the configuration of an Internet of Things (IoT) system via Wi-Fi according to an embodiment of the present invention.

[0049] Figure 39 shows a configuration in which a Wi-Fi sensing environment and an IoT environment are combined according to an embodiment of the present invention.

[0050] Figures 40 and 41 illustrate a processing process in inbound integration according to an embodiment of the present invention.

[0051] Figure 42 illustrates a method for transmitting a command to an IoT device in an inbound integrated system according to an embodiment of the present invention.

[0052] Figure 43 illustrates a process of transmitting a command to an IoT device based on motion detection according to an embodiment of the present invention.

[0053] Figure 44 illustrates a processing process in outbound integration according to an embodiment of the present invention.

[0054] Figure 45 illustrates a method for obtaining a sensing device according to an embodiment of the present invention.

[0055] Figure 46 illustrates a process for determining a motion detection level according to an embodiment of the present invention.

[0056] Figure 47 shows the configuration of a Wi-Fi sensing and IoT integration system according to an embodiment of the present invention.

[0057] Figure 48 illustrates entities essential for motion detection and IoT system integration according to an embodiment of the present invention.

[0058] Figure 49 shows the operation process of a system for motion detection according to an embodiment of the present invention.

[0059] Figure 50 illustrates a user interface according to an embodiment of the present invention.

[0060] Figure 51 illustrates a method for detecting the motion state of an object according to an embodiment of the present invention.

[0061] Figure 52 illustrates a method for determining whether movement of an object has occurred or the state of movement of an object according to an embodiment of the present invention.

[0062] FIG. 53 illustrates a method for performing the operation of a user-specific customized Internet of Things (IoT) device according to an embodiment of the present invention.

[0063] The terms used in this specification have been selected from widely used and current terms, taking into account the functions of the present invention. However, these terms may vary depending on the intentions of those skilled in the art, customs, or the emergence of new technologies. Furthermore, in certain cases, the applicant may arbitrarily select terms, in which case their meanings will be described in the description of the relevant invention. Therefore, it should be noted that the terms used in this specification should be interpreted based on their substantive meaning and the overall content of this specification, rather than simply their names.

[0064] Throughout the specification, when a component is said to be "connected" to another component, this includes not only the case where the component is "directly connected," but also the case where the component is "electrically connected" with another component intervening therebetween. Furthermore, when a component is said to "include" a particular component, this does not exclude the other component, but rather allows the inclusion of other components, unless specifically stated otherwise. Furthermore, the terms "more than" or "less than" with respect to a specific threshold may be appropriately replaced with "greater than" or "less than", respectively, depending on the embodiment.

[0065] FIG. 1 illustrates a system for detecting a user's motion using a wireless LAN signal according to an embodiment of the present invention.

[0066] An AP can be associated with various stations. The AP can detect movement around the AP and the station based on the results of measuring RF signals transmitted by the station or information about the results of measurements of signals transmitted by the station. Specifically, when movement occurs around the AP and the station, the signal measurement results change. Therefore, the AP can detect movement around the AP and the station by tracking changes in the signal measurement results of the station. In this case, the signal measurement results may be channel state information (CSI). In this specification, a station that performs signal measurement or transmits a signal used for signal measurement is referred to as a sensing node.

[0067] The AP calculates the detected motion level based on the received signal measurement results. This motion level is referred to as the activity level. The AP transmits the calculated activity level to the cloud server. At this time, the AP may also transmit wireless LAN information related to the signal measurement. The wireless LAN information may include at least one of the following: information about the RF signal used for signal measurement, information about the basic service set (BSS) operated by the AP, and information about the station connected to the AP.

[0068] The user application can display information received from the cloud server based on user input. Specifically, the user application can display activity levels calculated from signal measurement results received from sensing nodes. The user application can be a mobile application running on a smartphone or a web application.

[0069] The cloud server stores data received from the AP. Additionally, the cloud server processes user application requests and transmits responses to the user application requests. Furthermore, the cloud server can transmit commands to the AP to which the user application has sent the request. Users can check information about stations associated with the AP through the user application. The station information may include at least one of the station name and location.

[0070] In Figure 1, the device that detects motion based on measurement results is described as an AP. However, in another specific embodiment, a cloud server can detect motion based on measurement results. Hereinafter, in this specification, a device that detects motion based on measurement results is referred to as a sensing device.

[0071] FIG. 2 shows an AP and a sensing node performing frame exchange according to an embodiment of the present invention.

[0072] Channel state information (CSI) can indicate signal distortion as it propagates from a transmitter to a receiver. CSI can also reflect effects such as scattering, fading, and power attenuation over distance. Transmitters and receivers can respond to changes in channel conditions based on CSI. This allows them to maintain high reliability in high-data-rate, multi-antenna communications. Because CSI can indicate signal distortion as it propagates from a transmitter to a receiver, it must be measured at the receiver. The measurement process must be performed continuously and at appropriate times. Therefore, it is recommended that the measurement be performed by a device powered by a non-battery source.

[0073] In Figure 2, the AP transmits a null data packet to the sensing node, and the sensing node transmits an ACK frame to the AP. The AP measures the CSI for the signal containing the ACK frame. This allows the AP to minimize the impact on the battery power consumption of the sensing node. Furthermore, the CSI measured by the AP can indicate the current wireless channel status. This wireless channel status indicates signal propagation distortion during the wireless transmission of the ACK frame transmitted from the sensing node to the AP.

[0074] FIG. 3 shows that channel status changes occur due to various events in a wireless channel in which an AP and a sensing node exchange signals according to an embodiment of the present invention.

[0075] When there is no interference between the AP and the sensing node, the AP and the sensing node can exchange signals through the optimal path. The optimal path between the AP and the sensing node can be disrupted by human movement. This can cause both the AP and the sensing node to modify the signal path.

[0076] In Fig. 3, signal exchange between the AP and the sensing node is performed through multiple paths. At this time, the received signal is expressed as the sum of all signals that reached the receiver through multiple paths. If there is no object (obstacle) between the AP and the sensing node, the signal propagated through the line-of-sight path (t0) generally has the greatest influence. If an object, such as a person, passes on the optimal path (t0) between the AP and the sensing node, the AP and the sensing node may take a path (t) that is not the line-of-sight path. n ) can have the greatest impact on the received signal. Therefore, if there is no change in the environment around the AP and sensing nodes, the value of CSI can be stable. In addition, if there is a change in the environment around the AP and sensing nodes, the deviation of the CSI for the received signal composed of the sum of multiple paths can increase.

[0077] FIG. 4 shows that channel status changes occur due to various events in a channel in which an AP having multiple antennas and a sensing node exchange signals according to an embodiment of the present invention.

[0078] When an AP and a sensing node exchange signals via multiple antennas, the signals can be transmitted via multiple, different paths. Therefore, when an AP and a sensing node exchange signals via multiple antennas and multiple CSI values ​​are used, changes in the environment surrounding the AP and sensing node can be detected more accurately than when signals are exchanged via a single antenna. Furthermore, when an AP and a sensing node exchange signals via multiple antennas and multiple CSI values ​​are used, the sensing range can be expanded compared to when signals are exchanged via a single antenna.

[0079] In Fig. 4, when there is no object between the AP and the sensing node with two antennas, the propagation path (t 00- t 10 ) can have the greatest impact on the signal exchanged through the propagation path (t) between the AP and the sensing node. 00- t 10 ) when an object, such as a person, passes by, the radio signal propagation path changes (path (t) 0n- t 1n )) A change occurs in the CSI of the received signal. At this time, the AP obtains multiple CSI values ​​depending on the number of receiving antennas.

[0080] As described with reference to FIGS. 3 and 4, human movement can be detected based on changes in CSI values. However, not only human movement but also various environmental changes around the AP and sensing nodes can cause changes in CSI values. Therefore, it is necessary to exclude environmental changes that are not human movement. Environmental changes that are not human movement can cause rapid changes in CSI values. Therefore, a method for excluding rapid changes in CSI values ​​is required. In addition, by removing changes that exhibit characteristics different from those of changes in CSI values ​​caused by human movement, false detections caused by movement of animals or objects can be prevented. The following describes embodiments for preventing false detections.

[0081] Additionally, while the previous description used CSI as an example, the sensing device according to an embodiment of the present invention can perform motion detection using other values ​​representing channel status instead of CSI. For convenience of explanation, the values ​​representing channel status are referred to as measurement values ​​or measurement results.

[0082] FIG. 5 shows that a sensing device according to an embodiment of the present invention uses a sum of multiple measured values ​​measured immediately before the most recent measured value as a feature value of a sensing algorithm.

[0083] In general, noise can temporarily affect and then disappear. Therefore, the sensing device can use the value obtained based on the average of the most recent measurement value and the measurement values ​​measured before the most recent measurement value as the feature value of the sensing algorithm. Specifically, the sensing device can use the value obtained based on the sum of the N-1 measurement values ​​measured immediately before the most recent value and the most recent measurement value as the feature value of the sensing algorithm. Here, N is a natural number greater than 1. In addition, the value obtained based on the sum of the N-1 measurement values ​​and the most recent measurement value may be the average of the N-1 measurement values ​​and the most recent measurement value. In addition, the sensing device can obtain the measurement value by measuring the channel status at regular time intervals. For convenience of explanation, the time period during which N measurement values ​​are obtained is referred to as a measurement window. In addition, N may be a predetermined value or may be adjusted by the sensing device. Through these embodiments, the sensing device can remove noise that temporarily causes changes in the measurement value.

[0084] FIG. 6 illustrates a process for processing channel state information (CSI) according to an embodiment of the present invention.

[0085] A sensing device can acquire CSI by measuring it using window data. The acquired CSI can be grouped into window data, and the sensing device can select the bandwidth over which the ACK frame is transmitted. Furthermore, the sensing device can acquire the CSI magnitude and determine whether the selected bandwidth can be used to process the CSI sample. A sensing processing device can acquire the CSI sample and process the CSI through a shape-preserving filter and a magnitude-preserving filter. The shape-preserving filter can filter out random noise generated during the CSI processing process. The magnitude-preserving filter can be used to estimate the magnitude level of the undistorted CSI. The sensing device can reconstruct the CSI by merging the output values ​​passed through the shape-preserving filter and the magnitude-preserving filter, respectively. Furthermore, the reconstructed CSI can be used for channel state estimation.

[0086] FIG. 7 illustrates a method for selecting a CSI sample according to an embodiment of the present invention.

[0087] The CSI samples selected through the method of FIG. 7 may be CSI samples to be maintained in the window data described through FIG. 6. CSI may be acquired through ACK frames transmitted over different bandwidths. In this case, the CSI samples acquired through the ACK frames may have different window data lengths.

[0088] Referring to FIG. 7, a sensing device can identify the bandwidth over which CSI samples are transmitted. The sensing device can acquire CSI samples across all bandwidths and identify all bandwidths over which CSI samples are transmitted. The length of the measurement value may vary depending on the bandwidth. Generally, the longer the bandwidth, the longer the measurement value, and the shorter the bandwidth, the shorter the measurement value. It may be inappropriate to directly use window data with different CSI sample lengths. Therefore, to unify the sample lengths within the window data, the sensing device can calculate the bandwidth frequency of the samples stored in the window data. All CSI samples except for the majority of bandwidths can be deleted from the window data. If too many CSI samples are deleted, the motion feature values ​​calculated from the window data may be sensitively altered. To prevent this, the sensing device can calculate the percentage of the number of CSI samples ultimately stored in the window data. If the percentage is lower than a preset threshold through a keep-ratio check, the entire subsequent process can be omitted. Noise contained in CSI window data processed to be unified in length can be removed through a shape-preserving filter and a size-preserving filter.

[0089] FIG. 8 illustrates a method for processing CSI through a shape-preserving filter according to an embodiment of the present invention.

[0090] The sensing device can normalize the windowed CSI using z-score normalization along the axis of the subcarrier. That is, the sensing device can adjust the data distribution of the CSI samples at each time point so that the mean is 0 and the standard deviation is 1. By adjusting the data distribution of the CSI samples, the magnitude offset between the CSI samples at each time point can be eliminated. In addition, the sensing device can obtain the final result by applying an offset removal filter and an outlier removal filter to the CSI samples with the adjusted data distribution. The offset removal filter and the outlier removal filter can be applied independently. The final result of the shape preservation filter can be a value that maintains the shape of the outlier-free samples by adjusting values ​​outside the normal range to a point-like range.

[0091] FIG. 9 shows a method for removing noise that occurs when a channel state is measured according to an embodiment of the present invention.

[0092] When measuring channel conditions, various types of noise, such as spike noise or step noise, may occur. Spike noise is also referred to as impulse noise. Spike noise can be determined by the presence of sharp and sudden disturbances. Spike noise occurs over a relatively short period of time. Step noise, like impulse noise, can be determined by the presence of sharp and sudden disturbances. However, step noise occurs over a relatively long period of time. Wireless LAN signals are transmitted using multiple subcarriers. Therefore, noise may occur on a subcarrier-by-subcarrier basis. To offset noise affecting individual subcarriers, the sensing device can apply noise reduction filters to each subcarrier. Additionally, certain noises may occur on all subcarriers. To reduce this type of noise, the sensing device can apply noise reduction filters to multiple subcarriers.

[0093] The sensing device may apply an optional subcarrier shift removal filter to the measured values. Furthermore, the sensing device may apply an offset removal filter to the measured values. Furthermore, the sensing device may apply an outlier removal filter to the measured values.

[0094] FIG. 9 illustrates an embodiment of a size-preserving filter that operates by applying an optional subcarrier shift removal filter, an offset removal filter, and an outlier removal filter to a measurement value of a sensing device. The specific operations of applying the optional subcarrier shift removal filter, the offset removal filter, and the outlier removal filter are described with reference to FIGS. 10 to 12, respectively.

[0095] FIG. 10 shows an operation of a sensing device according to an embodiment of the present invention applying an optional subcarrier shift removal filter to a measured value.

[0096] The sensing device can use the values ​​obtained based on the average and standard deviation of the measured values ​​for each subcarrier measured at the same time as feature values ​​of the sensing algorithm. Specifically, the sensing device obtains the average and standard deviation of the measured values ​​for each subcarrier measured at the same time. For convenience of explanation, the measurement results for each subcarrier measured at the same time are referred to as samples, and the average and standard deviation of the measured values ​​for each subcarrier measured at the same time are referred to as sample average and sample standard deviation, respectively.

[0097] The sensing device can adjust a sample having a sample average value that is significantly different from other values ​​among the sample averages within the measurement window. Specifically, the sensing device can adjust a sample among the samples within the measurement window whose sample average is outside a specific range, and use a value obtained based on the value within the window including the adjusted value as a feature value of the sensing algorithm. Specifically, the sensing device can adjust a sample average (μ) of a sample outside a specific range among the sample averages. s2) can be replaced with a substituted average. In this case, the substituted average is the average (μ) of the remaining sample average values ​​excluding values ​​outside a specific range among the sample average values ​​within the measurement window. us ) can be. Also, the minimum value of a specific range is the average of the sample averages within the measurement window minus the average of the sample averages within the measurement window (μ us ) is the standard deviation (σ) of the sample means us ) is the value minus the maximum value of a specific range, and the average of the sample averages within the measurement window (μ us ) is the standard deviation (σ) of the sample mean within the measurement window. us ) may be the added value. The sample standard deviation (σ) is the sample mean outside a certain range. s2 ) can also be replaced by an alternative standard deviation. In this case, the alternative standard deviation is the average (μ) of the remaining sample standard deviation values, excluding the sample standard deviation values ​​of the samples whose sample mean is outside a specific range, among the sample standard deviation values ​​within the measurement window. σs ) may be.

[0098] Afterwards, the sensing device can Z-score average the measurement window samples, that is, make the mean 0 and the standard deviation 1. Afterwards, the Z-score averaged data can be restored to the original signal through inverse Z-score averaging. At this time, when the sensing device restores samples whose sample mean value is outside a specific range, the sensing device calculates the average (μ) of the newly calculated sample mean instead of the sample mean of the sample. us ) and the sample standard deviation of the sample, instead of the mean (μ) of the sample standard deviation values σs ) using inverse Z-Score averaging (X filtered = X norm μ us + μ σs ) can be applied. In addition, the sensing device can omit Z-Score averaging for samples having a sample mean within a specific range.

[0099] Figure 10 is a flowchart illustrating the operations described above. Through these embodiments, the sensing device can eliminate noise affecting the entire subcarrier used for wireless LAN signal transmission using a selective subcarrier shift removal filter. In particular, through these embodiments, the sensing device can eliminate the spike noise described above.

[0100] FIG. 11 shows a sensing device according to an embodiment of the present invention applying an offset removal filter to a measurement value.

[0101] In addition, the sensing device can obtain the difference in measurement values ​​of adjacent samples within the measurement window and apply an outlier removal filter to the obtained difference values. The sensing device can use the obtained value based on the filtered difference values ​​as a characteristic value of the sensing algorithm. At this time, the measurement value of the sample may be the sample mean and sample standard deviation filtered through FIG. 10 above. In addition, the measurement value of the sample may be the sample mean and sample standard deviation that have not been filtered through FIG. 10. Specifically, the sensing device can obtain the difference in measurement values ​​of adjacent samples within the measurement window. At this time, the sensing device can apply an outlier removal filter to the obtained difference values. The outlier removal filter is a filter for smoothing the values ​​of samples that are outside a specific range among a plurality of values. The outlier removal filter is described in detail with reference to FIG. 12. The sensing device can restore the measurement value of the first sample within the measurement window to a form identical to the measurement value within the measurement window by adding the outlier removal filtered difference values ​​to the measurement value of the first sample. At this time, the first sample is the first sample measured within the measurement window. Figure 11 illustrates this operation of the sensing device.

[0102] FIG. 12 shows an operation of a sensing device according to an embodiment of the present invention performing outlier filtering.

[0103] An outlier removal filter calculates the average of the remaining values, excluding outliers, among multiple values, and then replaces the average of the outliers with the average of the normal range. The normal range is defined as within n standard deviations of the average of the time series. This allows for the removal of values ​​that fall outside the normal range.

[0104] FIG. 13 illustrates a method by which a sensing device according to an embodiment of the present invention reconstructs a CSI signal.

[0105] The sensing device outputs (X) with a size-preserving filter applied. mag ) can be used to calculate the mean (μ) and standard deviation (σ) for the time axis. The mean and standard deviation values ​​can be equal to the product of the number of subcarriers and the number of antennas. The output of the shape-preserving filter (X shape ) is inversely Z-Score transformed into X shape The mean and standard deviation values ​​of the estimated original values ​​(X reconstructed ) can be returned to. The inverse Z-Score transformation is X reconstructed =X shape *It can be performed through the mathematical formula μ+σ.

[0106] FIG. 14 illustrates a method for extracting features used for detecting motion of an object in CSI according to an embodiment of the present invention.

[0107] Object movement can interfere with wireless signals, so sensing devices can detect object movement using CSI. CSI can indicate the difference between a received wireless signal and a predefined signal and can be expressed as a complex number.

[0108] The sensing device can calculate a median value for all subcarriers used to transmit a wireless signal by extracting a standard deviation over time. If the sensing device receives a wireless signal using multiple antennas, different CSI values ​​may exist for each antenna. The sensing device can calculate the median value for all antennas based on the median value for all subcarriers. The sensing device can then output the CSI magnitude deviation as an output value. This output value can be used to detect changes in the wireless signal due to the movement of an object. Since the CSI magnitude is related to signal strength, it can be used to detect the movement of an object.

[0109] FIG. 15 illustrates a method for obtaining features by measuring the intensity of a received wireless signal according to an embodiment of the present invention.

[0110] Received Signal Strength Indicator (RSSI) can be measured for each wireless signal received by each antenna, and the RSSI data can be equal to the window size multiplied by the number of antennas.

[0111] The sensing device converts the RSSI value into a positive (+) range by adding 100 to the RSSI. pos can obtain RSSI pos can be converted and acquired for each RSSI of each antenna. The sensing device acquires the RSSI for each antenna along the time axis. pos The maximum and minimum values ​​among them can be checked and the difference (the difference) obtained by subtracting the minimum value from the maximum value can be obtained. That is, the difference can be obtained for each antenna, and the difference can be obtained as many times as the number of antennas. Then, the sensing device can select the largest value among the differences and set it as the RSSI delta. The sensing device can set the RSSI posThe data can be averaged and merged along the antenna axis, and the data size can be the same as the window size. Then, the sensing device can apply an outlier removal filter to the averaged and merged data to remove outliers and calculate the median value along the time axis. Then, the sensing device can set the calculated median value as the RSSI value. The set RSSI delta and RSSI value can be used to detect the movement of the object.

[0112] Figure 16 illustrates features used to detect movement of an object according to an embodiment of the present invention.

[0113] The number of receiving antennas of a sensing device and the frequency band (sensing band) at which it detects wireless signals can also be used to detect object movement. A preprocessing process or feature extraction process can merge values ​​from different antennas. In other words, a sensing device can detect object movement based on any one of the following: CSI magnitude deviation, RSSI delta, RSSI value, number of antennas, or sensing band.

[0114] FIG. 17 illustrates a method for detecting motion probability using a machine learning model according to an embodiment of the present invention.

[0115] Referring to Figure 17, all features acquired to detect object motion can be input into a machine learning model, and the motion probability can be detected using profiled class weights. The machine learning model can be a model that can operate as a binary classifier. The motion prediction sensitivity of the machine learning model can be determined based on the profiled class weights.

[0116] To prioritize false positives and reduce the sensitivity of a machine learning model, the IDLE class weights can be set higher than the BUSY class weights. To obtain the optimal weighted class, a machine learning model can be trained by combining multiple class weights. Weights can be selected based on the profile, and grid search can be used to obtain the optimal weighted class.

[0117] Figure 18 illustrates a method for obtaining a machine learning model for a profile.

[0118] During the training process of a machine learning model, different weight combinations can be assigned to the IDLE and BUSY classes. Each time the training process is performed, the weight combinations and the performance of the machine learning model can be saved. For a machine learning model that produces results with the lowest sensitivity, the weight combination that produces the highest precision can be used. Conversely, for a machine learning model that produces results with the highest sensitivity, the weight combination that produces the highest recall can be used. In other words, the weight combination determined based on sensitivity can vary.

[0119] Figure 19 illustrates a method for obtaining a motion probability according to the present invention.

[0120] Both CSI and RSSI can be used as inputs to a machine learning model by passing through the preprocessing and feature extraction blocks. In other words, both CSI and RSSI can be used in the preprocessing and feature extraction stages. Furthermore, the number of antennas and sensing bands can be used as inputs to the machine learning model. As described above, the machine learning model can be trained based on the profiled class weights. Furthermore, the motion probability can be obtained by the machine learning model.

[0121] Figure 20 illustrates a method for determining whether motion is detected based on probability according to an embodiment of the present invention.

[0122] According to the embodiments described above, the output value (e.g., motion probability) of the sensing algorithm can be normalized. As the output value is normalized, a constant sensitivity can be maintained even when the sensing node is changed. Furthermore, the sensing device can determine movement based on whether the output value of the sensing algorithm is greater than a threshold value. At this time, the sensing device can adjust the sensitivity of the motion detection by adjusting the size of the threshold value. For example, the sensing device can reduce false detections by increasing the size of the threshold value. Furthermore, the sensing device can increase the sensitivity of the motion detection by decreasing the size of the threshold value. In the following description, the result determined by comparing the output value of the sensing algorithm with the threshold value is referred to as a detection result, and the detection result ultimately output by the sensing device is referred to as the final result. For example, referring to FIG. 20, a sensitivity threshold value for determining whether motion is detected can be defined. The sensing device can determine whether movement of an object is detected by comparing the acquired motion probability with the sensitivity threshold value. The motion probability can be expressed as a normalized value or a normalized level. If the normalized level is higher than the sensitivity threshold, the sensing device can determine that the object movement is detected and set the flag value to a value indicating activation (e.g., 1). Conversely, if the normalized level is not greater than the sensitivity threshold, the sensing device can determine that the object movement is not detected and set the flag value to a value indicating deactivation (e.g., 0). There can be multiple sensitivity thresholds, and thus the object movement can be expressed in more detail. In other words, various sensitivity levels can be set.

[0123] In addition, the detection results can be monitored for a certain period of time and a final detection result can be output. At this time, the certain period of time can be referred to as a voting period, and the length of the voting period can be referred to as a voting size. Specifically, if a plurality of detection results output during a certain period exceed a certain number and are output as motion detection, the sensing device can set the final output as motion detection. If a plurality of detection results output during a certain period are output as motion detection below a certain number, the sensing device can set the final output as motion not detected. At this time, the certain number can be half of the number of detection results determined during the certain period. In addition, the voting size can be adjusted by the sensing device. The sensing device can adjust the sensitivity of motion detection by adjusting the voting size. For example, the sensing device can increase the sensitivity of motion detection by reducing the voting size. However, the sensing device can reduce the probability of false detection by increasing the voting size. Through such an embodiment, the sensing device can reduce false detection due to noise.

[0124] When detecting object movement using a wireless communication system, the sensing device must distinguish between human and non-human movement. For example, the sensing device needs to distinguish whether the detected movement is caused by mechanical movement (e.g., a fan, air conditioner, etc.). In this case, the movement of non-human objects may be periodic. Therefore, the sensing device needs to detect such periodic movement and filter out the non-human movement.

[0125] FIG. 21 illustrates a method for detecting periodic motion according to an embodiment of the present invention.

[0126] A sensing device can use raw CSI data as input to detect periodic motion. The sensing device can convert the CSI data into a magnitude value. Then, the sensing device can Z-Score normalize the CSI data along the subcarrier axis. Since the mean value of each CSI sample becomes 0 and the standard deviation becomes 1 according to the normalization, the magnitude offset over time can be removed. Then, the sensing device can obtain the reshaped CSI by reshaping the normalized data. The magnitude (N) of the reshaped CSI stream ) is the number of sub-panels (N) subcarrier ) and the number of antennas (N antenna ) may be equal to the product of the two.

[0127] FIG. 22 illustrates a process of converting reshaped CSI according to an embodiment of the present invention.

[0128] Referring to Figure 22, the order of the reshaped CSI is N time ×N stream It could be. N time can be the window size, N stream may be the number of CSI data streams. The sensing device may define the amount of FFT data (FFT size) to which the Fast Fourier Transform (FFT) is to be applied. At this time, the data may be smaller than the window size. In addition, the sensing device may define the FFT size for the Short time Fourier transform (STFT) and initialize the starting point of the STFT to the starting point of the CSI window data. The sensing device may generate STFT window data for converting the reshaped CSI samples consisting of a time series into a frequency domain based on the FFT size. The size of the STFT window data may be the number of FFT samples (N FFT ) number of streams (N) stream) can be the same as the product of the STFT window. The sensing device can acquire data in the STFT window and apply the STFT process to each stream. The STFT process can be a process of converting values ​​into the frequency domain. Since periodic movements occur repeatedly within a certain period of time, it is expected to have a peak value at a specific frequency. In the result of the STFT process with symmetry, the size excluding the symmetrical area is N. FFT / 2 x N stream The result can be stored in an array. The sensing device can check whether the stored result is for the last sample of the CSI, and if not, adjust the starting point and perform the STFT processing process again. If the stored result is for the last sample of the CSI, the sensing device can preprocess the result and perform the RMS (root mean square) calculation along the stream axis. At this time, the size of the stored result is (N time -N FFT )×(N FFT / 2)×N stream can be. And the size of the final merged STFT result (transformed CSI) on which the RMS calculation is performed is (N time -N FFT )×(N FFT / 2) It could be.

[0129] Figure 23 illustrates a method for obtaining features for determining periodic motion according to an embodiment of the present invention.

[0130] The sensing device can identify the index at which the value is maximum for each time sample of the converted CSI acquired through FIG. 22. The first index may be excluded. This is because, if there is a DC offset in the magnitude of the CSI, the maximum value may be located at the first index (i.e., f = 0). The maximum value may indicate the period of the CSI value. If a periodic motion occurs, the CSI may have a similar pattern that repeats over time, and the STFT result may have a distinct peak value. The sensing device can calculate the standard deviation of the array. If a periodic motion occurs, a constant peak value may occur over time, and in this case, the standard deviation of the position of the peak value may be close to 0. However, if a non-periodic motion (e.g., human motion) occurs, the STFT result may not have a constant peak value. Therefore, the standard deviation of the position of the peak value can be used to determine whether a periodic motion has occurred. For example, if the standard deviation of the positions of the peak values ​​is close to 0, the sensing device can determine that a periodic movement has occurred, and if the standard deviation of the positions of the peak values ​​is large, the sensing device can determine that a non-periodic movement has occurred. Specifically, if the standard deviation is less than a specific value, the sensing device can determine that a periodic movement has occurred, and if the standard deviation is equal to or greater than the specific value, the sensing device can determine that a periodic movement has not occurred. In this case, the specific value may be a value within a certain range based on 0.

[0131] Figure 24 illustrates a method for determining periodic motion according to an embodiment of the present invention.

[0132] The sensing device can determine whether an object moves periodically using the acquired features. The sensing device can determine whether the object moves periodically by comparing the level value of the extracted feature with a threshold value for determining periodic movement. For example, if the level value of the extracted feature is greater than the threshold value for determining periodic movement, the sensing device can determine that the object moves periodically and set the flag value to 1. Conversely, if the level value of the extracted feature is equal to or less than the threshold value for determining periodic movement, the sensing device can determine that the object does not move periodically and set the flag value to 0. In addition, the sensing result can be monitored for a certain period of time and the final sensing result can be output. In this case, the certain period of time can be referred to as a voting period, and the length of the voting period can be referred to as a voting size. Specifically, if a plurality of detection results output during a certain period of time (i.e., a plurality of detection results that are judged to have periodic movement of an object (the value of the flag is 1)) exceed a certain number and are output as detected movement, the sensing device can set the final output as detecting periodic movement of the object. If a plurality of detection results output during a certain period of time are output as detecting periodic movement of an object below a certain number, the sensing device can set the final output as detecting no periodic movement. In this case, the certain number may be half of the number of detection results judged during a certain period of time. In addition, the voting size can be adjusted by the sensing device. The sensing device can adjust the sensitivity of motion detection by adjusting the voting size. For example, the sensing device can increase the sensitivity of periodic motion detection by reducing the voting size. However, the sensing device can decrease the probability of false detection by increasing the voting size. Through such an embodiment, the sensing device can reduce false detection due to noise.

[0133] Figure 25 illustrates a method for determining a short motion according to an embodiment of the present invention.

[0134] The movements of non-human objects can be short, spurious movements. For example, these movements could be those of a robot vacuum cleaner or a pet. Therefore, sensing devices need to filter out short, spurious movements.

[0135] Motion probabilities can be grouped into window data, and the sensing device can extract spikes occurring within the window data. A spike can refer to a phenomenon in which the probability of motion suddenly occurs (peaks) when the probability is 0, and then drops below a certain level. This can be expressed as a return-to-zero tolerance (ε), which can be 0. Additionally, a peak threshold can be set to determine which peaks can be detected as spikes. Probabilities lower than the peak threshold may not be determined as spikes.

[0136] When a sensing device extracts spikes, it needs to determine how many spikes occurred. Furthermore, it needs to determine the duration of each spike and average the number of spikes. Furthermore, it needs to obtain and average the spike amplitudes. To detect short-term movements, the number of spikes, average spike duration, and average spike amplitude can be used as features.

[0137] Figure 26 illustrates a method for determining a short motion according to an embodiment of the present invention.

[0138] The sensing device can determine whether a short-term movement exists by comparing features used to determine a short-term movement with a threshold value. At this time, there may be three features used to determine a short-term movement (number of spikes, average spike duration, and average spike size), each of which may have a corresponding threshold value.

[0139] For example, if the level value of a feature for determining a short motion is greater than a threshold value for determining a periodic motion, the sensing device can determine that a spike has occurred (i.e., a short motion has occurred) and set the flag value to 1. Conversely, if the level value of a feature for determining a short motion is equal to or less than a threshold value for determining a periodic motion, the sensing device can determine that a spike has not occurred (i.e., there is no short motion) and set the flag value to 0. At this time, if all three features are greater than the corresponding threshold values, it can be determined that a spike has occurred. In addition, the detection result can be monitored for a certain period of time and the final detection result can be output. At this time, the certain period of time can be referred to as a voting period, and the length of the voting period can be referred to as a voting size. Specifically, if a plurality of detection results output during a certain period of time (i.e., a spike is judged to exist (the flag value is 1)) exceeds a certain number and is output as detected motion, the sensing device can set the final output as detected motion. If a plurality of detection results output during a certain period of time is output as detected motion of an object less than a certain number, the sensing device can set the final output as detected motion of no spike. In this case, the certain number may be half of the number of detection results judged during a certain period of time. In addition, the voting size can be adjusted by the sensing device. The sensing device can adjust the sensitivity of motion detection by adjusting the voting size. For example, the sensing device can increase the sensitivity of periodic motion detection by reducing the voting size. However, the sensing device can decrease the probability of false detection by increasing the voting size. Through such an embodiment, the sensing device can reduce false detection due to noise.

[0140] Figure 27 shows a transmission path of a wireless signal according to an embodiment of the present invention.

[0141] Referring to FIG. 27, a wireless signal receiving device (e.g., STA) can receive a wireless signal transmitted from a wireless signal transmitting device (e.g., AP). At this time, there may be multiple transmission paths for the wireless signal.

[0142] Since a wireless signal can be transmitted through multiple transmission paths, the reception signal received by the receiving device can be represented as the sum of the signals transmitted through multiple travel paths. Fig. 27 l 11 , l 12 , l 21 , l 22 represents the movement path of the wireless signal. Fig. 27(a) shows a situation in which no object exists in the space where the wireless signal is transmitted. That is, this is an embodiment showing a situation in which there is no object on the transmission path of the wireless signal. Fig. 27(b) shows a situation in which an object exists in the space where the wireless signal is transmitted. That is, this is an embodiment showing a situation in which an object exists on the transmission path of the wireless signal. Referring to Fig. 27(a), when there is no object on the transmission path of the wireless signal and the transmitting device and the receiving device each transmit and receive wireless signals at a fixed location, the transmission path of the wireless signal can always be constant. Referring to Fig. 27(b), when an object exists on the transmission path of the wireless signal, the transmission path of the transmission signal can be changed by the object. That is, referring to Figs. 27(a) and (b), l among the transmission paths of the wireless signal 12 Wow l 22 Since there are no objects on the surface, the transmission path of the wireless signal does not change, but l among the transmission paths of the wireless signal 11 Wow l 21Because objects exist on the surface, the transmission path of a wireless signal can change. That is, the transmission path of a wireless signal can change depending on the object's position. In other words, if an object moves, the transmission path of the wireless signal can change due to reflection or absorption by the object.

[0143] Figure 28 shows a wireless signal calculation model according to an embodiment of the present invention.

[0144] In Fig. 28, y is a wireless signal received by a receiving device (i.e., a received signal), x is a wireless signal transmitted by a transmitting device (i.e., a transmitted signal), and H may be a value representing changes such as reflection, absorption, and attenuation that occur when a wireless signal is transmitted. z is noise included in wireless transmission. Therefore, the received signal y is expressed in the form of the transmitted signal x multiplied by H and z added. Since H is a value related to the environment experienced by a wireless signal while propagating, it may be expressed as the sum of all propagation paths. That is, a transmitted signal may reach the receiving antenna of a receiving device through multiple transmission paths, and the sum of the signals reaching the receiving antenna may be the received signal. The channel state of a wireless signal may vary depending on the transmission environment of the CSI (channel state information) wireless signal. Using the CSI, the receiving device can estimate the wireless channel state and restore a distorted signal. That is, since CSI can indicate distortions that occur during wireless signal transmission, a receiving device can determine whether an object exists within the space where the wireless signal is transmitted (e.g., whether the object is moving) based on changes in CSI. For example, if an object moves between the transmitting and receiving devices, the transmission path of the wireless signal may change, and thus the CSI may also change.

[0145] FIG. 29 illustrates a multi-link device according to an embodiment of the present invention.

[0146] Referring to FIG. 29, a multi-link device may have one or more STAs affiliated therewith. The one or more affiliated STAs may be represented as a single device, and in this case, the single device may be referred to as a multi-link device (MLD). Alternatively, the MLD may be referred to as a multi-band device, a multi-link logical entity (MLEE), or a multi-link entity (MLE). The MLD may be a logical concept. The MLD may have one MAC SAP (medium access control service access point) up to an LLC (logical link control). In addition, the MLD may have one MAC data service.

[0147] Likewise, a multi-link device may have one or more APs. One or more of these APs may also be referred to as MLDs. Referring to Figure 28, the APs may be referred to as AP MLDs, and the STAs may be referred to as Non-AP MLDs.

[0148] Additionally, STAs included in an MLD may operate on more than one link or channel. That is, STAs included in an MLD may operate on multiple different channels (Link 1, Link 2, and Link 3 in FIG. 28). For example, the multiple different channels may be channels that use different bands, such as 2.4 GHz, 5 GHz, and 6 GHz.

[0149] FIG. 30 shows an OFDM (Orthogonal Frequency Division Multiplexing) waveform according to an embodiment of the present invention.

[0150] OFDM is a modulation technique that divides a single piece of information into multiple carriers and adds orthogonality to minimize the gap between each divided carrier and multiplexes them. In a wireless communication system (e.g., Wi-Fi system), the basic bandwidth may be 20 MHz, and the radio signal can be modulated onto each subcarrier and transmitted. In this case, the subcarrier may be a carrier in which the 20 MHz bandwidth is divided into 64 parts. The higher the frequency band a radio signal is transmitted on, the lower the transmission power of the subcarrier. This is because the higher the frequency band, the wider the bandwidth the radio signal can be transmitted.

[0151] FIG. 31 illustrates wireless signals transmitted on different frequency bands according to an embodiment of the present invention.

[0152] Wireless signals transmitted on lower frequency bands can travel farther than those transmitted on higher frequency bands. This is because the subcarriers carrying the wireless signal have higher transmission power in lower frequency bands. Furthermore, lower frequencies may have longer wavelengths and higher diffraction than higher frequencies. For example, a wireless signal transmitted at 2.4 GHz can travel farther than a wireless signal transmitted at 6 GHz.

[0153] Referring to Fig. 31, S 11 , S 12 is an example of a transmission path of a wireless signal transmitted on a 2.4 GHz bandwidth transmitted from an STA, and S 21 , S 22 may be an example of a transmission path of a wireless signal transmitted on a 6 GHz bandwidth transmitted from an STA. Referring to FIG. 31, S 12 , S 22 There may be an object (e.g., an animal, a person, etc.) at position p on the top. In this case, S transmitted on the 2.4 GHz bandwidth 12The wireless signal can reach the AP's antenna, but it is transmitted over the 6GHz bandwidth. 22 Wireless signals transmitted over a lower frequency bandwidth may not reach the AP's antenna. This may be because wireless signals transmitted over a lower frequency bandwidth have higher transmission power (signal strength) and greater diffraction than wireless signals transmitted over a higher frequency bandwidth.

[0154] Referring to Figure 31, the AP transmits a 2.4 GHz radio signal to S 11 and S 12 All can be received on the top. That is, the AP can receive 2.4GHz radio signals (S) that are changed by objects. 12 (wireless signal) can be received. On the other hand, the AP can receive 6GHz wireless signal. 21 , S 22 Medium S 21 Only the wireless signals transmitted from above can be received. That is, the AP can receive the 6GHz wireless signal (S) that is changed by the object. 22 The AP may not be able to receive wireless signals from the STA. The AP can detect the surroundings of the AP and the STA based on changes in the measurement results of the wireless signals transmitted from the STA. For example, the AP can detect whether there are objects around the AP and the STA, the movement of the objects, etc. The range in which the AP can detect the surroundings can be determined based on the frequency band in which the wireless signal is transmitted.

[0155] FIG. 32 illustrates a configuration for detecting movement of an object in a device configured with a multi-link according to an embodiment of the present invention.

[0156] In this specification, multi-link may refer to a state in which multiple channels using different bands are linked between an STA and an AP. The number of linked channels may be determined based on the hardware and / or software configurations of the AP and sensing node.

[0157] The receiving device needs to measure CSI to determine the channel status. In other words, the receiving device needs to measure CSI to determine the distortion that occurs during the transmission of the wireless signal transmitted by the transmitting device.

[0158] Specifically, Fig. 32 detects a change (movement) of an object between an AP and a sensing node configured with three multi-links, and indicates an activity level according to the change detection. Fig. 32(a) shows a state in which an STA and an AP are connected with a multi-link. For example, an STA and an AP can be linked through three bands of 2.4 GHz, 5 GHZ, and 6 GHZ. At this time, the STA can be set as a sensing node. Parameters (e.g., CSI) related to each link may exist between devices configured with an MLD. A receiving device can measure the parameters related to each link. An AP can detect the movement of an object around the AP and STA based on the result of measuring the status of a wireless signal (e.g., RF signal) transmitted by an STA. A sensing device that detects the movement of an object may be an AP. Referring to Fig. 32, the range in which the movement of an object can be detected is widest at 2.4 GHz, followed by 5 GHz and 6 GHz.

[0159] Figures 32(b) and 32(c) illustrate activity levels according to the movement of an object. Specifically, they illustrate the direction in which the object moves (e.g., the direction in which a person is moving) and the degree of movement, respectively. The AP can periodically receive wireless signals from the STA. Furthermore, the AP can acquire parameters (e.g., CSI) related to the channel status of the received wireless signals. The AP can calculate an activity level based on the amount of change in the acquired CSI. At this time, the activity level can be normalized to an integer from 0 to 254. The activity level may be lower as the object moves less or the object's location is farther from the AP or sensing node. Conversely, the activity level may be higher as the object's movement is greater or the object's location is closer to the AP or sensing node.

[0160] Referring to Fig. 32(b), an object may gradually approach a sensing node or AP. At this time, the activity level through the 2.4 GHz band, which has the widest sensing range, may be detected first. After that, the activity level may be detected in the 5 GHz and 6 GHz bands in that order. Conversely, referring to Fig. 32(c), an object may move away from a sensing node or AP. At this time, after activity levels are detected in all 2.4 GHz, 5 GHz, and 6 GHz bands, the activity level in the 6 GHz band may decrease, followed by the activity level in the 5 GHz band, and finally the activity level in the 2.4 GHz band.

[0161] FIG. 33 illustrates a method for determining whether an object is moving based on an activity level according to an embodiment of the present invention.

[0162] Whether an object is moving can be determined based on whether the detected activity level is higher than a threshold value. The sensing node can determine that there is object movement if the detected activity level is higher than the threshold value. Conversely, the sensing node can determine that there is no object movement if the detected activity level is equal to or less than the threshold value. The presence of object movement can be determined for each frequency band. That is, the sensing node can determine whether there is object movement in each sensing range of the 2.4 GHz, 5 GHz, and 6 GHz frequency bands. The threshold value can be set differently for each frequency band. Additionally, the threshold value can be a value set by the user. Referring to FIG. 33, the sensing node can set a flag value to 1 if the detected activity level for each frequency band is greater than the threshold value, and can set the flag value to 0 if the activity level is not greater than the threshold value. In addition, the sensing node can generate an indication value by adding the flag values ​​corresponding to each band. The indication value can be a value from 0 to n, where n is the number of multi-links. For example, if there are three links between the AP and the sensing node (e.g., 2.4 GHz, 5 GHz, and 6 GHz), n can be 3. The sensing node can determine whether there is object movement and generate an indication value whenever an activity level is detected.

[0163] Figure 34 illustrates a method for classifying the movement of an object according to an embodiment of the present invention.

[0164] Specifically, FIG. 34 illustrates a method for classifying the movement of objects around an AP and a sensing node based on indication values.

[0165] Referring to Figure 34, first, the sensing node can generate an instruction value and determine whether the generated instruction value is the first generated instruction value. Then, if the instruction value is the first generated instruction value, the sensing node stores the buffer {id 3rd , id 2nd , id 1st} can be created and all values ​​in the buffer can be initialized to 0. The size of the buffer can be 3. Then, the sensing node stores the first generated instruction value as the id of the buffer. 1st You can enter it in .

[0166] The sensing node generates an instruction value that is not the first instruction value generated, and the generated instruction value is an id 1st can be compared to see if it is the same as the generated instruction value. And, the sensing node has an id 1st If it is not the same as the Id, the buffer can be updated. The sensing node has an Id 3rd Id 2nd Replace with , Id 2nd is Id 1st The buffer can be updated in the form of being replaced by Id 1st can be replaced with the most recently generated instruction value. Meanwhile, the sensing node generates an instruction value with id 1st If it is the same, the buffer may not be updated.

[0167] And, the sensing node can determine the movement state of the object by comparing the buffer with a preset code. The preset code can be {3, 2, 1}, {1, 2, 3}, {x, 3, 0}, {x, 2, 0}, {x, 1, 0}, {0, 0, 0} as illustrated in FIG. 33. At this time, x is a value from 0 to 3 and means an irrelevant value regardless of whether it is set to any value. For example, when the buffer is {3, 2, 1}, the sensing node can determine that the object is moving away from the AP or the sensing node. When the buffer is {1, 2, 3}, the sensing node can determine that the object is moving closer to the AP or the sensing node. When the buffer is {x, 3, 0} or {x, 2, 0}, the sensing node can determine that the object exists without movement around the AP or the sensing node. If the buffer is {x, 1, 0} or {0, 0, 0}, the sensing node can determine that the object has left the AP or the sensing node and is not within the sensing range. If the buffer does not match the preset code, the sensing node can determine that there is movement of the object between the AP and the sensing node.

[0168] FIG. 35 illustrates a configuration for detecting movement of an object in a device configured with a multi-link according to one embodiment of the present invention.

[0169] Specifically, Fig. 35 detects a change (movement) of an object between an AP and a sensing node composed of two multi-links, and indicates an activity level according to the change detection.

[0170] Figure 35(a) shows a state in which an STA and an AP are coupled in a multi-link manner. For example, an STA and an AP can be linked through two bands of 2.4 GHz and 5 GHz, and can be linked through two bands of 2.4 GHz and 6 GHz. At this time, the STA can be set as a sensing node. Parameters (e.g., CSI) related to each link can exist between devices configured as an MLD. A receiving device can measure the parameters related to each link. An AP can detect the movement of objects around the AP and the STA based on the result of measuring the status of a wireless signal (e.g., RF signal) transmitted by the STA. A sensing device that detects the movement of an object can be an AP. Referring to Figure 34, the range in which the movement of an object can be detected is widest in 2.4 GHz, followed by 5 GHz and 6 GHz.

[0171] Figures 35(b) and 35(c) illustrate activity levels according to the movement of an object. Specifically, they illustrate the direction in which the object moves (e.g., the direction in which a person is moving) and the degree of movement, respectively. The AP can periodically receive wireless signals from the STA. Furthermore, the AP can acquire parameters (e.g., CSI) related to the channel status of the received wireless signals. The AP can calculate an activity level based on the amount of change in the acquired CSI. At this time, the activity level can be normalized to an integer from 0 to 254. The activity level may be lower as the object moves less or the object's location is farther from the AP or sensing node. Conversely, the activity level may be higher as the object's movement is greater or the object's location is closer to the AP or sensing node.

[0172] Referring to Fig. 35(b), an object may gradually approach a sensing node or AP. At this time, the activity level through the 2.4 GHz band, which has the widest sensing range, may be detected first. Subsequently, the activity level through the 5 GHz or 6 GHz band may be detected. Conversely, referring to Fig. 35(c), an object may move away from a sensing node or AP. At this time, after activity levels are detected in all of the 2.4 GHz, 5 GHz, or 6 GHz bands, the activity level in the 5 GHz or 6 GHz band may decrease, and then the activity level in the 2.4 GHz band may decrease.

[0173] FIG. 36 illustrates a method for determining whether an object is moving based on an activity level according to an embodiment of the present invention.

[0174] Whether an object is moving can be determined based on whether the detected activity level is higher than a threshold value. The sensing node can determine that there is object movement if the detected activity level is higher than the threshold value. Conversely, the sensing node can determine that there is no object movement if the detected activity level is equal to or less than the threshold value. The presence of object movement can be determined for each frequency band. That is, the sensing node can determine whether there is object movement in each sensing range of the 2.4 GHz, 5 GHz, and 6 GHz frequency bands. The threshold value can be set differently for each frequency band. Additionally, the threshold value can be a value set by the user. Referring to Figure 36, the sensing node can set the flag value to 1 if the detected activity level for each frequency band is greater than the threshold value, and can set the flag value to 0 if the activity level is not greater than the threshold value. In addition, the sensing node can generate an indication value by adding the flag values ​​corresponding to each band. The indication value can be a value from 0 to n, where n is the number of multi-links. For example, if there are two links between the AP and the sensing node (e.g., 2.4 GHz, 5 GHz or 2.4 GHz, 6 GHz), n can be 2. The sensing node can determine whether there is object movement and generate an indication value whenever an activity level is detected.

[0175] The sensing range can be determined based on the frequency band used by the wireless channel, and the lower the frequency band, the wider the sensing range. The sensing range of the lower frequency band may include the sensing range of the higher frequency band. For example, the sensing range according to the 2.4 GHz band may include the sensing range of the 5 GHz and 6 GHz bands. The sensing range according to the 5 GHz band may include the sensing range according to the 6 GHz band.

[0176] Figure 37 illustrates a method for classifying the movement of an object according to an embodiment of the present invention.

[0177] Specifically, FIG. 37 illustrates a method for classifying the movement of objects around an AP and a sensing node based on indication values.

[0178] Referring to Figure 37, first, the sensing node can generate an instruction value and determine whether the generated instruction value is the first generated instruction value. Then, if the instruction value is the first generated instruction value, the sensing node stores the buffer {id 3rd , id 2nd , id 1st} can be created and all values ​​in the buffer can be initialized to 0. The size of the buffer can be 3. Then, the sensing node stores the first generated instruction value as the id of the buffer. 1st You can enter it in .

[0179] The sensing node generates an instruction value that is not the first instruction value generated, and the generated instruction value is an id 1st can be compared to see if it is the same as the generated instruction value. And, the sensing node has an id 1st If it is not the same as the Id, the buffer can be updated. The sensing node has an Id 3rd Id 2nd Replace with , Id 2nd is Id 1st The buffer can be updated in the form of being replaced by Id 1st can be replaced with the most recently generated instruction value. Meanwhile, the sensing node generates an instruction value with id 1st If it is the same, the buffer may not be updated.

[0180] And, the sensing node can determine the movement state of the object by comparing the buffer with a preset code. The preset code can be {2, 1, 0}, {0, 1, 2}, {x, 2, 0}, {x, 1, 0}, {0, 0, 0} as illustrated in FIG. 34. At this time, x is a value from 0 to 2 and means an irrelevant value regardless of whether it is set to any value. For example, when the buffer is {2, 1, 0}, the sensing node can determine that the object is moving away from the AP or the sensing node. When the buffer is {0, 1, 2}, the sensing node can determine that the object is moving closer to the AP or the sensing node. When the buffer is {x, 2, 0}, the sensing node can determine that the object exists without movement around the AP or the sensing node. If the buffer is {x, 1, 0} or {0, 0, 0}, the sensing node can determine that the object has left the AP or the sensing node and is not within the sensing range. If the buffer does not match the preset code, the sensing node can determine that there is movement of the object between the AP and the sensing node.

[0181] Figure 38 shows the configuration of an Internet of Things (IoT) system via Wi-Fi according to an embodiment of the present invention.

[0182] An IoT system may include a Wi-Fi sensing AP, a Wi-Fi sensing STA, an IoT hub, and IoT devices. The IoT devices may include Wi-Fi-enabled devices that establish a direct link with the AP, and devices that do not support Wi-Fi and are connected through the IoT hub (non-Wi-Fi devices). In this specification, a Wi-Fi sensing AP may be described as an AP, and a Wi-Fi sensing STA may be described as an STA. The AP may perform a function of detecting and capturing Wi-Fi signals around the AP. The AP may detect Wi-Fi signals through a link with the STA. The STA may be represented as a sensing node.

[0183] An IoT hub can act as a central orchestrator, integrating Wi-Fi and non-Wi-Fi devices into a single network. This means that non-Wi-Fi devices can connect to the Internet and receive commands from users through the IoT hub.

[0184] Figure 39 shows a configuration in which a Wi-Fi sensing environment and an IoT environment are combined according to an embodiment of the present invention.

[0185] A Wi-Fi sensing environment can refer to interactions between APs and STAs acting as sensing nodes via links. Sensing nodes can measure CSI and use this CSI to obtain movement activity information.

[0186] Movement activity information can be transmitted to the Sensing Cloud, which communicates via an API. The Sensing Cloud can receive, store, and manage movement activity information. Through the Sensing App (APP) and / or the Sensing Web, users can visually monitor the movement activity of objects within a user-specified area in real time. Furthermore, the Sensing App and / or the Web can detect unexpected movement activity of objects.

[0187] The IoT cloud can store IoT device status information and transmit user commands to operate IoT devices. IoT apps and / or websites can provide real-time status information about IoT devices, and users can control IoT devices through the IoT apps and / or websites.

[0188] The sensing cloud and the IoT cloud can be linked and integrated through communication. This integration can include inbound and outbound integration. Inbound integration can refer to obtaining data from the IoT cloud through APIs and access tokens provided by third-party IoT entities. Outbound integration can refer to providing sensing data (e.g., movement activity information) to the IoT cloud. In outbound integration, the API and access token can be provided by the Wi-Fi sensing solution entity.

[0189] Figures 40 and 41 illustrate a processing process in inbound integration according to an embodiment of the present invention.

[0190] Referring to FIG. 40, processing may be performed between a sensing app (SENS App), a sensing cloud, and an IoT cloud. The sensing app may provide an interface that requests IoT account authentication from a user and may receive authentication input. When a user requests authentication through the interface of the sensing app, the sensing app may generate a user authentication request and transmit it to the sensing cloud. Furthermore, the sensing cloud may transmit the user authentication request received from the sensing app to the IoT cloud. At this time, the sensing cloud may verify whether the key, including the registered API key, is registered and transmit it to the IoT cloud for approval.

[0191] When the IoT cloud receives an authentication request, it can determine whether the authentication succeeds or fails through an authentication process. If authentication succeeds, the IoT cloud can generate an API access token specific to the IoT account and a successful authentication response. The IoT cloud can then transmit the successful authentication response to the sensing cloud. Upon receiving the successful authentication response, the sensing cloud can store the API access token in a database and establish a connection with the sensing user account. The sensing cloud can then transmit the successful authentication response to the sensing app, which can then display the successful authentication response to the user via an interface. If authentication fails, the IoT cloud can then transmit a failed authentication response to the sensing cloud. The sensing cloud can then transmit the received failed authentication response to the sensing app, which can then display the failed authentication response to the user via an interface.

[0192] Referring to Figure 41, in an inbound integration scenario, a list of IoT devices can be provided to the user. The user can request the list of IoT devices through the sensing app. The sensing app can request an API for the list of IoT devices from the sensing cloud. The sensing app can include authenticated user ID information when requesting the API for the list of IoT devices. When the sensing cloud receives an API request for the list of devices, it can check whether a list of devices corresponding to the user ID has already been stored. At this time, the sensing cloud can perform a cache check to determine whether the list of devices has been stored.

[0193] If the sensing cloud cannot find the device list in the cache, the sensing cloud can request the device list from the IoT cloud. At this time, the sensing cloud can send an authenticated user ID and an access token API to the IoT cloud. The IoT cloud can verify the device list request, query the IoT devices corresponding to the user ID, and send the IoT device list to the sensing cloud. The sensing cloud can store the received IoT device list in its cache. Then, the sensing cloud can send the device list to the sensing app. On the other hand, if the sensing cloud finds the device list in the cache, the sensing cloud may not request the device list from the IoT cloud. The sensing cloud can send the device list stored in the cache to the sensing app.

[0194] When a sensing app receives a list of devices, it can display the list of devices to the user through a user interface.

[0195] Figure 42 illustrates a method for transmitting a command to an IoT device in an inbound integrated system according to an embodiment of the present invention.

[0196] IoT devices can receive commands through interactions among a sensing app, a sensing cloud, an IoT cloud, and IoT devices. The sensing app can provide an interface for users to control IoT devices. That is, users can send commands to control IoT devices through separate inputs on the interface. Based on the user's input, the sensing app can generate an API request for IoT commands and send it to the sensing cloud. At this time, the sensing app can send information about the IoT device ID and a new state (e.g., a state requested by the command) to the sensing cloud. The sensing cloud can verify the API request for the command received from the sensing app and compare the new state with the current state of the IoT device. The sensing cloud can request an IoT device status update by sending a payload containing the IoT device ID, the new state, and an access token to the IoT cloud. The IoT cloud can verify the payload and request a device status update from the IoT device corresponding to the IoT device ID. If the IoT device successfully updates the device status, the updated new state can be sent to the IoT cloud. The IoT cloud can send the updated new state of the IoT device to the sensing cloud. The sensing cloud can store the updated status of IoT devices in a database. The sensing cloud can transmit the updated status of IoT devices to the sensing app, and the sensing app can then provide the updated status of IoT devices to the user through an interface.

[0197] Figure 43 illustrates a process of transmitting a command to an IoT device based on motion detection according to an embodiment of the present invention.

[0198] Referring to FIG. 43, the AP can measure CSI with the sensing node. Based on the CSI, the AP can determine whether the aforementioned object movement exists. The AP can generate a POST request encapsulating the movement level and sensing node information and transmit it to the sensing cloud. The sensing cloud can receive the POST request and initiate a query to retrieve a list of IoT devices for sensing node information. The sensing cloud can generate a payload containing IoT device commands and IoT cloud API requests and transmit it to the IoT cloud. At this time, the sensing cloud can transmit an access token and a list paired with the IoT device ID and new status to the IoT cloud. The IoT cloud can generate a status update request for the IoT device according to the IoT device command included in the payload and transmit it to the IoT device. The IoT device can receive the update request, update its status, and transmit the updated status to the IoT cloud. The IoT cloud can transmit the updated status to the sensing cloud.

[0199] Figure 44 illustrates a processing process in outbound integration according to an embodiment of the present invention.

[0200] The IoT app can provide an interface for the user to enter credentials for a Wi-Fi sensing account. The IoT app can generate an authentication request encapsulating the credentials entered by the user and send it to the IoT cloud. The IoT cloud can then send the received authentication request to the sensing cloud. The IoT cloud can include a registered API key in the authentication request and send it to the sensing cloud. The sensing cloud can verify the registered API key to determine whether to approve (authenticate) the request. If the sensing cloud successfully approves the request, it can generate an API access token. The sensing cloud can send the generated API access token and a response indicating successful authorization to the IoT cloud. The IoT cloud can store the API access token in a database. The IoT cloud can send the response to the IoT app. The IoT app can display successful authentication to the user through the interface. If the sensing cloud fails to approve the request, the sensing cloud can send a response indicating the request failure to the IoT cloud. The IoT cloud can send the failure response to the IoT app. IoT apps can display to the user through their interface that authentication has failed.

[0201] Figure 45 illustrates a method for obtaining a sensing device according to an embodiment of the present invention.

[0202] An IoT app can send a request for a list of sensing devices to the IoT cloud and encapsulate a user ID in the payload. The AP can periodically update the list of active sensing devices and send the list of sensing devices to the sensing cloud. When the sensing cloud receives the list of sensing devices from the AP, it can update its database with the received list of sensing devices. The IoT cloud can send the request for the list of sensing devices sent by the IoT app and a payload containing the user ID and an access token to the sensing cloud. The sensing cloud can verify the access token and query the list of sensing nodes corresponding to the user ID. The sensing cloud can send the list of sensing nodes to the IoT cloud. The IoT cloud can then send the received list of sensing nodes to the IoT app. The IoT app can display the list of sensing nodes to the user through an interface.

[0203] Figure 46 illustrates a process for determining a motion detection level according to an embodiment of the present invention.

[0204] The IoT app can generate a request for the motion detection level (activity level) of the sensing node and transmit it to the IoT cloud, encapsulating the sensing device ID as a payload and transmitting it to the IoT cloud. The AP can periodically update the activity level of the sensing device and transmit it to the sensing cloud. The sensing cloud can update the activity level of the sensing device and store it in a database. The IoT cloud can transmit the request for the activity level received from the IoT app, along with the sensing device ID and access token, to the sensing cloud in the payload. The sensing cloud can verify the access token and query the activity level corresponding to the sensing device ID. The sensing cloud can transmit the activity level of the sensing device to the IoT cloud. The IoT cloud can transmit the received activity level of the sensing device to the sensing app. The sensing app can display the activity level of the sensing device to the user through an interface.

[0205] Figure 47 shows the configuration of a Wi-Fi sensing and IoT integration system according to an embodiment of the present invention.

[0206] The integrated system may include sensing devices (e.g., APs), user apps and / or web, sensing clouds, and IoT clouds.

[0207] The sensing device can act as an AP that measures Wi-Fi CSI. That is, it can measure and obtain channel status information of the wireless link between the AP and the STA. A user app and / or web application can be a device that interacts with the user and provide a user interface. The user interface (UI) can include a personalized IoT UI, a UI for the sensing device and IoT devices, and a UI for a list of connected sensing devices.

[0208] A personalized IoT UI can be an interface that allows users to individually configure the behavior of IoT devices. For example, a user can perform user inputs, such as personal device settings, IoT device settings, and individual IoT commands, through the personalized IoT UI. The personalized IoT UI can generate API requests and transmit them to the sensing cloud to store personalized IoT settings.

[0209] The UI for sensing devices and IoT devices may be an interface for users to connect specific sensing devices with IoT devices, and may generate API requests to store connected sensing devices and IoT device configurations and transmit them to the sensing cloud.

[0210] The UI for the list of connected sensing devices may be an interface that allows a user to check the list of connected sensing devices and transmit a request for the list of sensing devices to the sensing cloud.

[0211] The sensing cloud can act as an intermediary between the sensing system and the IoT system. The sensing cloud can receive activity levels from the AP and user-related settings from user apps and / or the web. The sensing cloud can communicate directly with the IoT cloud and check the status of IoT devices through commands.

[0212] The IoT cloud can serve as a sensing system that accesses IoT devices and transmits commands.

[0213] Figure 48 illustrates entities essential for motion detection and IoT system integration according to an embodiment of the present invention.

[0214] Referring to FIG. 48, entities essential for motion detection and IoT system integration may include a user entity, a personal device (PID) entity, an IoT device entity, a sensing device entity, and a sensing AP entity. The user entity may store user information regarding motion detection in a Wi-Fi environment, including user credentials. A one-to-many relationship may exist between users, PIDs, and sensing APs in the system. That is, a single user may own multiple personal devices, but each personal device may only be associated with one user. Similarly, a single user may be associated with multiple sensing APs, but each sensing AP may only be associated with one user. The PID entity may store information about the user's personal devices. User personal devices may include mobile phones, smartwatches, tablets, etc. The IoT device entity may store information related to the IoT device, which may include information such as the current status, capabilities, and device type of the IoT device. An IoT device can be connected to multiple sensing devices, but each sensing device can only be connected to one IoT device. A sensing AP entity can contain information related to the AP. This information can include a Service Set Identifier (SSID), a Basic Service Set Identifier (BSSID), and a Media Access Control Address (MAC) address. A sensing AP can be connected to multiple sensing devices, but each sensing device can only be connected to one sensing AP.

[0215] Figure 49 shows the operation process of a system for motion detection according to an embodiment of the present invention.

[0216] Referring to Figure 49, the AP can detect motion by measuring channel conditions. If no motion is detected, the system can loop back to collect CSI and continue the motion detection process. If motion is detected, the system can query the database for a list of IoT devices based on the device that detected the motion. The system can then identify the personal device (PID) associated with the identified IoT device. Each device (PID, IoT device, sensing device, AP, etc.) that constitutes the system can be connected to a network. The system can identify the number of PIDs currently connected to the network. If there is one PID, the IoT device can perform personalized settings individually set for the specific device. If the number of PIDs is 0 or greater than 1, the connected IoT device can perform a preset operation.

[0217] Figure 50 illustrates a user interface according to an embodiment of the present invention.

[0218] Figure 50(a) illustrates a user interface that displays all IoT devices associated with a sensing device. The displayed IoT devices may include both Wi-Fi-connected devices and non-Wi-Fi-connected devices. Figure 50(b) illustrates a user interface that provides settings for each IoT device. The gear-shaped button in Figure 50(a) allows the user to access the user interface in Figure 50(b). Figure 50(c) illustrates a user interface that allows the user to input a PID and provides settings for customized operations corresponding to the PID. Referring to Figure 50(c), the user can input a PID and an IoT device. The PID and IoT device can be input in a drop-down form. The user can then configure the operation of the IoT device according to the input PID. For example, the user can configure the IoT device as a lamp in the living room and set the brightness or color of the lamp, etc.

[0219] Figure 51 illustrates a method for detecting the motion state of an object according to an embodiment of the present invention.

[0220] The sensing device can be connected to an access point (AP) via multiple wireless channels (S5110).

[0221] The above multiple wireless channels may use different frequency bands.

[0222] The sensing device can detect the movement state of an object based on different sensing ranges corresponding to each of the plurality of wireless channels (S5120).

[0223] The above multiple wireless channels may be two or three.

[0224] When there are two of the above-mentioned multiple wireless channels, the frequency bands used by the above-mentioned multiple wireless channels may be 2.4 GHz and 5 GHz, or 2.4 GHz and 6 GHz, respectively.

[0225] When there are three of the above wireless channels, the frequency bands used by the above wireless channels may be 2.4 GHz, 5 GHz, and 6 GHz, respectively.

[0226] The first sensing range corresponding to the frequency band of 2.4 GHz may be wider than the second sensing range corresponding to the frequency band of 5 GHz, and the second sensing range may be wider than the third sensing range corresponding to the frequency band of 6 GHz.

[0227] The first sensing range may include the second sensing range and the third sensing range, and the second sensing range may include the third sensing range.

[0228] The sensing device can detect whether the object exists in each sensing range by checking whether the activity level of the object is higher than a threshold value in each of the first sensing range, the second sensing range, and the third sensing range.

[0229] The motion state of the object can be determined based on whether the object exists.

[0230] The above activity level can be determined based on the amount of change in channel state information (CSI) of each of the plurality of wireless channels.

[0231] At a first time, when the object is detected as being present in all of the first sensing range, the second sensing range, and the third sensing range, and at a second time, when the object is detected as being present in the first sensing range, and when the object is detected as not being present in at least one of the second sensing range and the third sensing range, if the first time is a time earlier than the second time, the movement state of the object may be a state of moving away from the sensing device, and if the first time is a time later than the second time, the movement state of the object may be a state of moving closer to the sensing device.

[0232] Figure 52 illustrates a method for determining whether movement of an object has occurred or the state of movement of an object according to an embodiment of the present invention.

[0233] The sensing device can obtain measurement values ​​related to channel conditions using a wireless LAN signal for a predetermined number of measurements or within a predetermined time interval (S5210).

[0234] The sensing device can determine whether movement of an object has occurred or the state of movement of an object within the wireless LAN system based on the measurement value (S5220).

[0235] The sensing device can obtain a standard deviation of the size of the above measurement value, and obtain a feature value of the sensing algorithm based on the obtained standard deviation.

[0236] The above sensing algorithm may be an algorithm learned based on a correlation between a value obtained based on the size of the measured value and whether movement of an object occurs around devices exchanging the wireless LAN signal.

[0237] The sensing device can determine whether movement of the object occurs based on the above algorithm.

[0238] The above measurement values ​​may include values ​​related to channel state information (CSI), received signal strength indicator (RSSI), the number of antennas of the sensing device, and the sensing band to which the sensing device is connected.

[0239] The above algorithm can be trained through machine learning by inputting weights related to detection sensitivity.

[0240] The sensing device can obtain a standard deviation associated with the result of a short time Fourier transform (STFT) of the above measurement value.

[0241] The sensing device can determine the movement state of the object based on the standard deviation.

[0242] The motion state of the above object may be periodic motion or non-periodic motion.

[0243] If the above standard deviation has a repetitive pattern within a certain period, the motion state of the object may be a periodic motion.

[0244] If the above standard deviation is less than the threshold value, the motion state of the object may be a periodic motion.

[0245] FIG. 53 illustrates a method for performing the operation of a user-specific customized Internet of Things (IoT) device according to an embodiment of the present invention.

[0246] The above method may include a step of checking whether the user is located within a certain space (S5310).

[0247] The above method may include a step in which, when the user is located within the predetermined space, the IoT device performs a preset operation (S5320).

[0248] The above preset action may be an action set by the user.

[0249] The above IoT devices can be classified into a first device that supports Wi-Fi and a second device that does not support Wi-Fi.

[0250] The user's personal device is connected to a first access point (AP), and if the IoT device is the first device, the first device is connected to a second AP, and if the IoT device is the second device, the second device can be connected to a third AP through the IoT hub.

[0251] If the first AP, the second AP, and the third AP are the same AP, the user's personal device may be located within the same space as the first device and the second device.

[0252] The above personal device is one, the first device and the second device are each plural, and each of the plural devices can perform different preset operations.

[0253] Devices operating in the wireless LAN system of this specification may be configured to include memory and a processor. In addition, devices operating in the wireless LAN system may perform the methods described in this specification.

[0254] While the present invention has been described using wireless LAN communication as an example, it is not limited thereto and can be equally applied to other communication systems, such as cellular communication. Furthermore, while the methods, devices, and systems of the present invention have been described with reference to specific embodiments, some or all of the components and operations of the present invention can be implemented using a computer system with a general-purpose hardware architecture.

[0255] The features, structures, effects, etc. described in the embodiments above are included in at least one embodiment of the present invention, and are not necessarily limited to just one embodiment. Furthermore, the features, structures, effects, etc. exemplified in each embodiment can be combined or modified in other embodiments by those skilled in the art to which the embodiments pertain. Therefore, the contents related to such combinations and modifications should be construed as being included within the scope of the present invention.

[0256] Although the above has been described focusing on embodiments, these are merely examples and do not limit the present invention. Those skilled in the art to which the present invention pertains will appreciate that various modifications and applications not exemplified above are possible without departing from the essential characteristics of the present embodiment. For example, each component specifically shown in the embodiments can be modified and implemented. In addition, differences related to such modifications and applications should be interpreted as being included within the scope of the present invention defined in the appended claims.

Claims

1. In a wireless LAN system, the sensing device is: memory; and Contains a processor, The above processor Connects to an access point (AP) through multiple wireless channels, The above multiple wireless channels use different frequency bands, A sensing device that detects the movement state of an object based on different sensing ranges corresponding to each of the plurality of wireless channels.

2. In paragraph 1, The sensing device has two or three wireless channels.

3. In paragraph 2, When the plurality of wireless channels are two, the frequency bands used by the plurality of wireless channels are 2.4 GHz and 5 GHz, or 2.4 GHz and 6 GHz, respectively. A sensing device in which, when the plurality of wireless channels are three, the frequency bands used by the plurality of wireless channels are 2.4 GHz, 5 GHz, and 6 GHz, respectively.

4. In paragraph 3, The first sensing range corresponding to the above 2.4 GHz frequency band is wider than the second sensing range corresponding to the above 5 GHz frequency band, The second sensing range is wider than the third sensing range corresponding to the frequency band of 6 GHz, The first sensing range includes the second sensing range and the third sensing range, A sensing channel, wherein the second sensing range includes the third sensing range.

5. In paragraph 4, The above processor, It is determined whether the activity level of the object is higher than a threshold value in each of the first sensing range, the second sensing range, and the third sensing range, and whether the object exists in each sensing range is detected. The movement state of the above object is determined based on whether the object exists or not, A sensing channel, wherein the activity level is determined based on the amount of change in channel state information (CSI) of each of the plurality of wireless channels.

6. In paragraph 5, At the first time, the object is detected to be present in all of the first sensing range, the second sensing range and the third sensing range, In the second time, if the object is detected as existing in the first sensing range and the object is detected as not existing in at least one of the second sensing range and the third sensing range, If the above first time is earlier than the above second time, the state of the object's movement is moving away from the sensing device, A sensing device, wherein if the first time is later than the second time, the movement state of the object is a state of approaching the sensing device.

7. In a wireless LAN system, a method for detecting the movement status of an object is as follows: A step for connecting to an access point (AP) through multiple wireless channels; The above multiple wireless channels use different frequency bands; A method comprising a step of detecting a movement state of an object based on different sensing ranges corresponding to each of the plurality of wireless channels.

8. In paragraph 7, A method wherein the above plurality of wireless channels are two or three.

9. In paragraph 8, When the plurality of wireless channels are two, the frequency bands used by the plurality of wireless channels are 2.4 GHz and 5 GHz, or 2.4 GHz and 6 GHz, respectively. A method in which, when the plurality of wireless channels are three, the frequency bands used by the plurality of wireless channels are 2.4 GHz, 5 GHz, and 6 GHz, respectively.

10. In paragraph 9, The first sensing range corresponding to the above 2.4 GHz frequency band is wider than the second sensing range corresponding to the above 5 GHz frequency band, The second sensing range is wider than the third sensing range corresponding to the frequency band of 6 GHz, The first sensing range includes the second sensing range and the third sensing range, A method wherein the second sensing range includes the third sensing range.

11. In paragraph 10, The above method, Further comprising a step of detecting whether the object exists in each sensing range by checking whether the activity level of the object is higher than a threshold value in each of the first sensing range, the second sensing range and the third sensing range, The movement state of the above object is determined based on whether the object exists or not, A method wherein the activity level is determined based on a change in channel state information (CSI) of each of the plurality of wireless channels.

12. In paragraph 11, At the first time, the object is detected to be present in all of the first sensing range, the second sensing range and the third sensing range, In the second time, if the object is detected as existing in the first sensing range and the object is detected as not existing in at least one of the second sensing range and the third sensing range, If the above first time is earlier than the above second time, the state of the object's movement is moving away from the sensing device, A method wherein if the first time is later than the second time, the movement state of the object is a state of approaching the sensing device.

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