Moving body detection system, wiring device, illumination device, moving body detection method, and program

Abstract

To provide a moving body detection system capable of improving the accuracy of motion detection using a reflection wave of a transmitted radio wave.SOLUTION: A moving body detection system 100 detects a moving object in an area by using a reception signal that is a signal based on a reflection wave of a radio wave transmitted from each of multiple frames. The moving body detection system 100 includes a processing unit 22 that executes processing based on the reception signal. The processing unit 22 calculates multiple time differences by performing a series of processing to calculate a plurality of time differences by performing processing for calculating a time difference which is a difference of two reception signals respectively received with two different frames in the multiple frames at multiple time intervals that are different from each other. The processing unit 22 detects a moving object corresponding to one or multiple moving points by acquiring the distribution of one or multiple moving points with a time difference greater than or equal to a threshold in the area from the multiple time differences.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a motion detection system, a wiring device, a lighting device, a motion detection method, and a program, and more specifically, to a motion detection system, a wiring device, a lighting device, a motion detection method, and a program that detect moving objects (moving objects) such as human bodies using a radio wave sensor. [Background technology]

[0002] Patent Document 1 describes an intrusion detection device that receives a reflected wave of a transmitted radio wave reflected by an object and detects an intrusion into a detection area based on the intensity of the received reflected wave (received wave intensity). This intrusion detection device detects a moving object (moving body), such as an intruder who is moving in some way, based on the change over time in the received wave intensity. The change over time in the received wave intensity is acquired using multiple frames of data that are consecutive in time. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2002-236171 A Summary of the Invention [Problem to be solved by the invention]

[0004] In the intrusion detection device described in Patent Document 1, it is difficult to detect an intruder that does not move. In the intrusion detection device, if the period for detecting the temporal change in the received wave intensity is shortened, it becomes possible to detect an intruder based on small movements such as slight respiratory movements and body movements, but on the other hand, it becomes difficult to detect an intruder based on large movements such as movement. In other words, in conventional moving object detection systems such as the intrusion detection device, it is difficult to detect a moving object depending on the type of movement (for example, long or short: long-term movements such as movement or body movements, and short-term movements such as slight respiratory movements, etc.), and as a result, the detection accuracy may decrease.

[0005] An object of the present disclosure is to provide a moving object detection system, a wiring device, a lighting device, a moving object detection method, and a program that can improve the accuracy of moving object detection using reflected waves of transmitted radio waves. [Means for solving the problem]

[0006] A moving object detection system according to one aspect of the present disclosure is a moving object detection system that detects a moving object within a region using a received signal. The received signal is a signal based on a reflected wave of an electric wave transmitted in each of a plurality of frames. The moving object detection system includes a processing unit that executes processing based on the received signal. The processing unit calculates a plurality of time differences by performing processing for calculating a time difference, which is a difference between two received signals received in two different frames among the plurality of frames, at a plurality of different time intervals. The processing unit then detects the moving object corresponding to the one or more moving points by acquiring a distribution of one or more moving points in the region where the time difference is equal to or greater than a threshold value from the plurality of time differences.

[0007] A wiring device according to one aspect of the present disclosure is a wiring device that detects a moving object within an area using a received signal. The received signal is a signal based on a reflected wave of an electric wave transmitted in each of a plurality of frames. The wiring device includes a processing unit that executes processing based on the received signal. The processing unit calculates a plurality of time differences by performing a process of calculating a time difference, which is a difference between two received signals received in two different frames among the plurality of frames, at a plurality of different time intervals. The processing unit then detects the moving object corresponding to the one or more moving points by acquiring a distribution of one or more moving points in the area where the time difference is equal to or greater than a threshold value from the plurality of time differences.

[0008] A lighting device according to one aspect of the present disclosure is a lighting device that detects a moving object within an area using a received signal. The received signal is a signal based on a reflected wave of a radio wave transmitted in each of a plurality of frames. The lighting device includes a processing unit that executes processing based on the received signal. The processing unit calculates a plurality of time differences by performing a process of calculating a time difference, which is a difference between two received signals received in two different frames among the plurality of frames, at a plurality of different time intervals. The processing unit then detects the moving object corresponding to the one or more moving points by obtaining a distribution of one or more moving points within the area where the time difference is equal to or greater than a threshold value from the plurality of time differences.

[0009] A moving object detection method according to one aspect of the present disclosure is a moving object detection method that detects a moving object within a region using a received signal. The received signal is a signal based on a reflected wave of an electric wave transmitted in each of a plurality of frames. The moving object detection method includes a processing step of executing a process based on the received signal. In the processing step, a process of calculating a time difference, which is a difference between two received signals received in two different frames among the plurality of frames, is performed at a plurality of different time intervals to calculate a plurality of the time differences. Then, in the processing step, a distribution of one or a plurality of moving points in the region where the time difference is equal to or greater than a threshold is obtained from the plurality of time differences, thereby detecting the moving object corresponding to the one or a plurality of moving points.

[0010] A program according to an embodiment of the present disclosure causes one or more processors to execute the moving object detection method. Effect of the Invention

[0011] The moving object detection system, wiring device, lighting device, moving object detection method, and program disclosed herein have the advantage of being able to improve the accuracy of moving object detection using reflected waves of transmitted radio waves. [Brief description of the drawings]

[0012] [Figure 1]FIG. 1 is a block diagram of a moving object detection system (device control system) according to the first embodiment of the present disclosure. [Diagram 2] FIG. 2 is a conceptual diagram of a room in which the above-mentioned device control system is used. [Diagram 3] FIG. 3 is a graph showing a change in frequency of a transmission wave transmitted by a radio wave sensor constituting the device control system. [Figure 4] FIG. 4 is a flowchart illustrating a part of the operation of the control device constituting the device control system. [Diagram 5] FIG. 5 is a flowchart illustrating another part of the operation of the above embodiment. [Figure 6] FIG. 6 is a flowchart illustrating a one-to-multiple frame difference acquisition process included in the above operation. [Figure 7] FIG. 7 is a flowchart illustrating a posture estimation process included in the above operation. [Figure 8] FIG. 8 is a waveform diagram for explaining an example of the inter-frame difference (one-to-one inter-frame difference). [Figure 9] FIG. 9A is a frequency spectrum diagram showing an FFT result in a current frame Fr0, FIG. 9B is a frequency spectrum diagram showing an FFT result in a subsequent frame Fr1, and FIG. 9C is a frequency spectrum diagram showing the difference between the FFT results. [Figure 10] FIG. 10 is a waveform diagram for explaining another example of the inter-frame difference (one-to-multiple inter-frame difference). [Figure 11] FIG. 11 is a conceptual diagram for explaining the characteristics of various movements of the human body. [Figure 12] FIG. 12 is a waveform diagram for explaining an example of obtaining one-to-multiple frame differences according to various movements in the above embodiment. [Figure 13] FIG. 13A is a distribution map showing an example of cluster distribution in an upright position, which is one of the postures of the human body. FIG. 13B is a distribution map showing an example of cluster distribution in a sitting position. FIG. 13C is a distribution map showing an example of cluster distribution in a lying position. [Figure 14]FIG. 14A is a conceptual diagram showing a three-dimensional figure surrounding a cluster group in a standing position, FIG. 14B is a conceptual diagram showing a three-dimensional figure surrounding a cluster group in a sitting position, and FIG. 14C is a conceptual diagram showing a three-dimensional figure surrounding a cluster group in a lying position. [Figure 15] FIG. 15 is a data structure diagram of the automatic control information group. [Figure 16] FIG. 16 is a data structure diagram of the control history information. [Figure 17] FIG. 17 is a block diagram of a modified example of the device control system. [Figure 18] FIG. 18 is a flowchart illustrating a part of the operation of the control device constituting the moving object detection system according to the second embodiment of the present disclosure. [Figure 19] FIG. 19 is a block diagram of a wiring accessory according to a first modified example of the moving object detection system of the above embodiment. [Figure 20] FIG. 20 is a block diagram of a lighting fixture according to a second modified example of the moving object detection system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] (First embodiment) Hereinafter, a first embodiment of the present disclosure will be described with reference to FIGS.

[0014] (1) Overview of the equipment control system In this embodiment, the moving object detection system of the present disclosure is a device control system 100 having a device control function in addition to a moving object detection function (for example, a human body estimation function, in particular a human body posture estimation function). As shown in Fig. 2, the device control system 100 of this embodiment detects a moving object (hereinafter, may be referred to as a "moving object"), in particular performs estimation regarding a human body 301, using a radio wave sensor 1.

[0015] The detection of a moving object includes, for example, judging whether the target object is a moving object (moving object) or a stationary object (still object). The estimation of the human body 301 includes, for example, estimating whether the target object is the human body 301 or a moving object other than the human body 301, and further estimating the posture of the human body 301.

[0016] The posture of the human body 301 is, for example, but not limited to, a standing position, a sitting position, a lying position (see Figs. 13A to 13C), etc. Then, the device control system 100 detects various movements of the human body 301, and further the behavior of the person, based on the estimated change in posture, and controls the device 200 according to the detection result.

[0017] The change in posture is, for example, a change between a standing position, a sitting position, and a lying position, but is not limited thereto. The change in posture also includes a continuation of no change in posture (for example, a case where a position does not change to a standing position or the like even after a predetermined period of time has passed since the position was changed to a sitting position). In addition, the behavior to be detected is, in particular, a non-operation behavior (described later), but may also be an operation behavior (described later).

[0018] (1-1) Radio wave sensor The radio wave sensor 1 transmits radio waves (transmission wave Tr) from an antenna, receives reflected waves Re of the transmission wave Tr reflected by an object, and outputs information capable of identifying the position of the object (hereinafter referred to as "position-identifying information").

[0019] (1-1-1) Location-identifiable information The position-identifiable information is information that can identify the position of an object. The position of the object is preferably a three-dimensional position, but may be a two-dimensional or one-dimensional position. The position-identifiable information is, for example, an FFT result group (information that can identify a three-dimensional position) described later, but may also be each of a plurality of (e.g., three) FFT results that constitute the FFT result group (information that can identify a one-dimensional position).

[0020] Also, for example, a signal indicating the time difference Δt from the transmission of the transmission wave Tr to the reception of the reflected wave Re, or a signal indicating the frequency difference Δf corresponding to the time difference Δt (for example, an IF signal indicating the frequency difference Δf between the transmission wave Tr modulated by the FMCW method and the reflected wave Re: see FIG. 3), etc. may also be considered as a type of location identification information. Furthermore, the distance calculated from the time difference Δt or the frequency difference Δf, etc., may itself be location identification information.

[0021] (1-1-2) Antenna The antenna used to transmit the transmission wave Tr and the antenna used to receive the reflected wave Re may be the same antenna (hereinafter, "shared antenna") or different antennas (hereinafter, "transmitting antenna" and "receiving antenna"). In other words, the transmission wave Tr may be transmitted from the shared antenna, and the reflected wave Re of the transmission wave Tr may be received by the shared antenna, or the transmission wave Tr may be transmitted from the transmitting antenna, and the reflected wave Re of the transmission wave Tr may be received by the receiving antenna.

[0022] Generally, to identify the three-dimensional position of an object (for example, to locate a point corresponding to a human body in a virtual three-dimensional space 500), the radio wave sensor 1 needs to have, for example, three or more shared antennas, or one transmitting antenna and three or more receiving antennas. Note that the radio wave sensor 1 having multiple antennas does not necessarily mean that the multiple antennas are stored in one housing, but also includes cases where the antennas are located in multiple locations separated from each other. In the latter case, each of the multiple antennas is connected to the sensor body so as to be able to communicate with it via wire or wirelessly.

[0023] The radio wave sensor 1 in this embodiment has one transmitting antenna and three or more (for example, three) receiving antennas. The positions of the one transmitting antenna and the three or more receiving antennas included in the radio wave sensor 1 are known, and four or more pieces of position information (hereinafter, "antenna position information group") corresponding to the four or more antennas are stored in advance in, for example, a memory of the control device 2.

[0024] However, the number of receiving antennas constituting the radio wave sensor 1 may be two or one (two antennas can be arranged on a plane, and one antenna can be arranged along a line).

[0025] (1-1-3) Transmission and reception operations The radio wave sensor 1 performs a transmission / reception operation. The transmission / reception operation is an operation of transmitting a transmission wave Tr toward a real space in which a group of objects may exist, receiving a reflected wave Re from the real space, and outputting a signal (e.g., an IF signal) based on the transmission wave Tr and the reflected wave Re. The real space in which a group of objects may exist is, for example, a space (indoor space) surrounded by the floor, ceiling, and side walls of a room 400, and may hereinafter be referred to as the "real space (400)". Note that the real space in which a group of objects may exist is not limited to an indoor space, but may also be an outdoor space such as a corridor or terrace.

[0026] An object group is a collection of one or more objects. In this embodiment, the object group includes one or more of a human body 301, a moving object other than a human body (for example, an electric blind 200c, etc., hereinafter, may be referred to as a "moving object other than a human body (200c)"), and a stationary object 302. In other words, each of the one or more objects constituting the object group is either a human body 301, a moving object other than a human body (200c), or a stationary object 302.

[0027] The radio wave sensor 1 performs, for example, the following transmission and reception operation at a predetermined cycle (for example, once every 20 ms). This transmission and reception operation is an operation in which one transmitting antenna transmits a transmission wave Tr to a real space (400) in which a group of objects may exist, and three or more receiving antennas receive reflected waves Re from the real space (400).

[0028] For example, when a human body 301, a moving object other than a human body (200c), and a stationary object 302 exist in the real space (400), reflected waves Re from the group of objects (301, 200c, 302) are received by each of three or more receiving antennas. Note that the stationary object 302 is, for example, a desk 302a and a bed 302b placed on the floor surface of the room 400 in the example of Fig. 2, but may be the floor surface.

[0029] The transmission wave Tr is a radio wave modulated by a predetermined method. The predetermined method is, for example, the FMCW (Frequency Modulated Continuous Wave) method, but is not limited to this. In this embodiment, the transmission wave Tr is a radio wave modulated by the FMCW method.

[0030] (1-1-4)FMCW method The FMCW method is a method in which the frequency f of a transmission wave Tr (transmission signal) having a predetermined time length (chirp length Tc of the chirp signal) is linearly increased (or decreased) from a starting frequency f0 at a predetermined slope S as time t passes, as shown in FIG. 3, for example.

[0031] The location identifiable information as described above does not have to be acquired for all of the transmission and reception operations that are repeatedly performed in a predetermined cycle. For example, as described later, when a predetermined time T (e.g., T=200 ms) is one frame (described later) and N transmission and reception operations (N is a natural number, e.g., 10) are performed in one frame, location identifiable information may be acquired for each of the N transmission and reception operations among the N transmission and reception operations that belong to one frame, or location identifiable information may be acquired for only one of the predetermined operations (e.g., the first transmission and reception operation).

[0032] The predetermined period is, for example, once every 20 ms, and in this embodiment, with one frame being 200 ms, the frequency is 10 times per frame (N=10). However, the predetermined period may be, for example, 20 times per frame (N=20), 5 times per frame (N=5), etc.

[0033] (1-1-5) IF signal, FFT result, and FFT result group Each time a transmission / reception operation is performed, the radio wave sensor 1 generates an IF signal for each of three or more receiving antennas, obtains FFT results by performing FFT (Fast Fourier Transform) on the IF signal, and outputs a group of FFT results. That is, each time a transmission / reception operation is performed, the radio wave sensor 1 outputs a group of FFT results consisting of three or more FFT results corresponding to the three or more receiving antennas. However, a Fourier transform other than FFT may be performed on the IF signal, in which case the radio wave sensor 1 outputs a group of Fourier transform results consisting of three or more Fourier transform results corresponding to the three or more receiving antennas.

[0034] The IF signal is a signal that indicates the frequency difference Δf between the transmitted wave Tr and the reflected wave Re, as shown in Fig. 3. The IF signal is a signal that indicates the difference between the frequency of the transmitted wave Tr and the frequency of the reflected wave Re at time t, and is a function of time t, Δf(t), but indicates a constant value when the group of objects (301, 302) is stationary.

[0035] The IF signal is generated by mixing the transmission wave Tr and the reflected wave Re. The IF signal is generated over the period during which the transmission wave Tr is being transmitted and the reflected wave Re is being received (that is, the period from the start of reception of the reflected wave Re to the end of transmission of the transmission wave Tr).

[0036] The FFT result is a result of performing FFT on an IF signal. The FFT result is information indicating a frequency spectrum (the relationship between frequency f and reflection intensity amp) as shown in, for example, Figures 9A and 9B.

[0037] The FFT result group is information composed of three or more FFT results corresponding to three or more receiving antennas, which are acquired for one transmission / reception operation. Such FFT result group makes it possible to identify the three-dimensional position of the target. Furthermore, by taking the difference between the multiple FFT result groups, the frequency components corresponding to the stationary object 302 are removed, and only the frequency components corresponding to the moving target such as the human body 301 (the frequency f at which amp exceeds the threshold value, and the value of amp corresponding to the frequency f) are acquired (see Figs. 9A to 9C). Based on the frequency components acquired in this way, it becomes possible to acquire information regarding the three-dimensional position and movement of the moving target such as the human body 301.

[0038] More specifically, in this embodiment, the radio wave sensor 1 outputs a plurality of FFT result groups corresponding to a series of a plurality of transmission and reception operations, and the outputted plurality of FFT result groups are stored in a memory of the control device 2 in a chronological order. Meanwhile, the memory stores the antenna position information group as described above. Then, the control device 2 calculates a time difference (a time difference between the FFT results for each of the three receiving antennas) between the plurality of FFT result groups (for example, two adjacent FFT result groups) stored in a chronological order in the memory, and acquires a distance measurement result group (two distance measurement results corresponding to the three receiving antennas) based on the calculated difference. The control device 2 performs three-point positioning using the distance measurement result group thus acquired and the antenna position information group stored in advance. This makes it possible to acquire information specifying the three-dimensional position of the human body 301, for example, to calculate three-dimensional coordinates.

[0039] (1-2) Attitude estimation function of the equipment control system As shown in FIG. 1, the device control system 100 includes a distance measurement unit 221, a point placement unit 222, and an attitude estimation unit 223.

[0040] (1-2-1) Distance measurement unit: one-to-one or one-to-many differential The distance measuring unit 221 measures the distance from the radio wave sensor 1 to the human body 301 based on the group of FFT results output by the radio wave sensor 1 .

[0041] In detail, the distance measuring unit 221 holds the FFT result group output by the radio wave sensor 1 for a period equal to or longer than the predetermined period (preferably, a period equal to or longer than twice the predetermined period).The distance measuring unit 221 then performs distance measurement processing based on the difference between a reference FFT result group (in this embodiment, a current FFT result group), which is one FFT result group, among the multiple FFT result groups held by the distance measuring unit 221, and at least one target FFT result group (in this embodiment, at least one previous FFT result group), to obtain at least one distance measurement result group.

[0042] (1-2-1a) Standard FFT result group and target FFT result group The reference FFT result group is one FFT result group that serves as a starting point for obtaining a difference, among multiple FFT result groups held by the distance measuring unit 221. The target FFT result group is each of one or more FFT result groups that are targets for obtaining a difference between the reference FFT result group and the multiple FFT result groups held by the distance measuring unit 221. The one or more target FFT result groups are located temporally before or after (usually before) the reference FFT group.

[0043] In this embodiment, one FFT result group is a current FFT result group (described later), and the one or more target FFT result groups are one or more previous FFT result groups (described later).

[0044] By taking the difference between the reference FFT result group and at least one target FFT result group, the reflected component from the stationary object 302 is removed from the reflected wave Re (the reflection intensity, which is the received intensity of the reflected wave Re, becomes below a threshold), and only the reflected component from the human body 301 (moving object) remains (at least one pair, preferably two or more pairs, of a reflection intensity amp exceeding the threshold and a frequency f corresponding to that reflection intensity are detected).

[0045] Note that the number of "at least one" previous FFT result group, that is, previous FFT result groups from which differences with the current FFT result group are obtained, is preferably two or more (one-to-many interframe difference: one-to-five interframe difference in the illustrated example) in terms of improving resolution, as shown in Figures 10 and 12, but may be just one. That is, even when one-to-one differences (one-to-one interframe differences) are taken as shown in Figures 8 and 9A to 9C, attitude estimation is possible, and the resolution can be improved by increasing the number of receiving antennas (hereinafter, "number of antennas").

[0046] (1-2-1b) Current FFT result group and previous FFT result group The current FFT result group is the most recent FFT result group among the multiple FFT result groups that are stored. The preceding FFT result group is the FFT result group that precedes the current FFT result group among the multiple FFT result groups that are stored. By setting the reference FFT result group as the current FFT result group and the target FFT result group as the preceding FFT result group, posture estimation can be performed in real time.

[0047] (1-2-1c) Distance measurement processing The distance measurement process is a process of measuring (ranging) the distance to an object such as the human body 301 by using the radio wave sensor 1. In the distance measurement process in this embodiment, the distance from each of three or more receiving antennas (hereinafter, each antenna) constituting the radio wave sensor 1 to the human body 301 is measured, and three or more distance measurement results (ranging result group: described later) corresponding to the three or more receiving antennas are obtained.

[0048] In the distance measurement process, for example, based on various parameters shown in Figure 3, namely, the above-mentioned difference (frequency difference Δf) between the transmitted wave Tr and the reflected wave Re, the slope S of the linear change in the frequency f of the transmitted wave Tr, and the speed of light c, the distance d from each antenna to the human body 301 is calculated using the following equation 1. d=(c / 2S)×Δf...(Formula 1)

[0049] (1-2-1d) Range measurement results group The group of distance measurement results is information consisting of three or more distance measurement results corresponding to three or more receiving antennas, which are obtained by the above formula 1 for one transmission / reception operation.

[0050] The three or more distance measurement results corresponding to the three or more receiving antennas are, for example, the first to third three distance measurement results corresponding to the first to third three receiving antennas. The first distance measurement result corresponds to approximately half the propagation distance of the radio wave radiated from the transmitting antenna, reflected by the human body 301, to reach the first receiving antenna (i.e., the distance from the human body 301 to the first receiving antenna). The second distance measurement result corresponds to approximately half the propagation distance of the radio wave radiated from the transmitting antenna, reflected by the human body 301, to reach the second receiving antenna (i.e., the distance from the human body 301 to the second receiving antenna). The third distance measurement result corresponds to approximately half the propagation distance of the radio wave radiated from the transmitting antenna, reflected by the human body 301, to reach the third receiving antenna (i.e., the distance from the human body 301 to the third receiving antenna).

[0051] It should be noted that the number of "at least one" distance measurement result group, that is, the number of distance measurement result groups to be acquired, is preferably two or more, but may be one.

[0052] (1-2-2) Point arrangement section: Multi-point arrangement The point arrangement unit 222 performs a coordinate calculation process every time the distance measurement unit 221 performs a distance measurement process, and arranges at least one point corresponding to the current FFT result group in a three-dimensional space 500, for example, as shown in Fig. 13A. However, the arrangement destination of the point may be a two-dimensional space (plane) or a one-dimensional space (line).

[0053] It should be noted that the number of points "at least one", that is, the number of points arranged at one time corresponding to the current FFT result group, is preferably two or more (multiple-point arrangement), but may be one (single-point arrangement).

[0054] (1-2-2a) Three-dimensional space The three-dimensional space 500 is a virtual space corresponding to a real space (e.g., a room 400: see FIG. 2) in which the radio wave sensor 1 and the group of objects (301, 302) exist. Arrangement in the three-dimensional space 500 may be a virtual operation or an operation of simply storing three-dimensional coordinates.

[0055] (1-2-2b) Coordinate calculation process and point cloud The coordinate calculation process is a process for calculating three-dimensional coordinates of the human body 301 based on at least one group of acquired distance measurement results.

[0056] (1-2-3) Posture estimation section The posture estimation unit 223 estimates the posture of the human body 301 (whether the posture is standing, sitting, or lying down in this embodiment) based on a point cloud 501 as shown in Figures 13A to 13C. The point cloud 501 is a collection of one or more points arranged in a three-dimensional space 500 by the point arrangement unit 222.

[0057] In this way, according to this embodiment, it is possible to estimate the posture of the human body 301 by using the radio wave sensor 1 based on the FMCW method.

[0058] (2) Details of the distance measurement unit and point placement unit (2-1) One-to-many difference The distance measurement unit 221 preferably holds the FFT results output by the radio wave sensor 1 for a period of at least twice the predetermined period. The period of at least twice the predetermined period may be, for example, a period of at least twice one frame (2×T).

[0059] Then, the distance measurement unit 221 calculates the difference between the current FFT result group and each of the two or more previous FFT result groups among the three or more FFT result groups currently held, and obtains two or more distance measurement result groups by performing distance measurement processing based on each of the two or more calculated differences.

[0060] The point arrangement unit 222 performs coordinate calculation processing based on each of the two or more distance measurement result groups acquired by the distance measurement unit 221 in this manner, thereby arranging two or more points corresponding to the current FFT result group in the three-dimensional space 500.

[0061] In this way, by calculating the difference between the current FFT result group and each of two or more previous FFT result groups, it is possible to improve the accuracy of estimating the posture of the human body 301 without increasing the number of antennas of the radio wave sensor 1.

[0062] (2-2) One-to-many difference suitable for detecting various human body movements More preferably, the distance measuring unit 221 holds the FFT result group output by the radio wave sensor 1 for a period of three or more times the predetermined cycle. Then, the distance measuring unit 221 calculates the difference between the current FFT result group and each of the three or more previous FFT result groups among the four or more FFT result groups currently held, and selects two or more differences corresponding to various movements of the human body 301 (for example, body movement, slight respiratory movement, hand and foot movement, etc.) from the three or more calculated differences. The distance measuring unit 221 performs the distance measurement process based on each of the two or more differences thus selected, thereby acquiring two or more distance measurement result groups.

[0063] The point placement unit 222 performs the coordinate calculation process based on each of the two or more distance measurement result groups acquired by the distance measurement unit 221, thereby placing two or more points corresponding to the current FFT result group in the three-dimensional space 500.

[0064] In this way, by calculating the difference between the current FFT result and each of three or more previous FFT results and selecting two or more differences corresponding to various movements of the human body 301, it is possible to improve the estimation accuracy of the posture of the human body 301 without increasing the number of antennas and while suppressing the number of differences used in the ranging process.

[0065] (2-3) Frames and interframe differences A frame is a unit of time for repeatedly performing an operation for detecting a moving object. The above-mentioned transmission and reception operation is performed N times (N is an integer of 2 or more) in one frame, with a predetermined time being one frame. In this embodiment, the predetermined time is 200 ms, and N=10.

[0066] In the memory, N sets of FFT results per frame are stored across multiple frames. Specifically, the radio wave sensor 1 performs, for example, 10 transmission and reception operations per frame, and 10 sets of FFT results per frame are stored in the memory of the control device 2 across multiple frames (for example, 6 frames). The distance measurement unit 221 obtains inter-frame differences between the multiple frames stored in the memory.

[0067] An inter-frame difference is a difference between a group of FFT results belonging to one frame (e.g., the reference frame Fr0 shown in FIG. 8) and a group of FFT results belonging to one or more other frames (e.g., the subsequent frames Fr1,... shown in FIG. 8).

[0068] (2-3-1) One-to-one frame difference The inter-frame difference is, for example, a one-to-one inter-frame difference, which is a difference between a group of FFT results belonging to one frame (e.g., a reference frame Fr0) and a group of FFT results belonging to another frame (e.g., a subsequent frame Fr1).

[0069] (2-3-2) Representative value of each frame when calculating the interframe difference Each of the two FFT result groups corresponding to the two frames from which the difference is to be obtained (e.g., the FFT result group belonging to the reference frame Fr0 and the FFT result group belonging to the subsequent frame Fr1) is a representative value among the N (e.g., 10) FFT result groups in the frame to which it belongs.

[0070] The representative value is, for example, the average value of N FFT result groups (specifically, information consisting of the average value of 10 FFT results corresponding to the first receiving antenna, the average value of 10 FFT results corresponding to the second receiving antenna, the average value of 10 FFT results corresponding to the third receiving antenna, etc.) In this case, the inter-frame difference is the difference between the average value of the 10 FFT result group belonging to the reference frame Fr0 and the average value of the 10 FFT result group belonging to the subsequent frame Fr1.

[0071] Alternatively, the representative value may be one FFT result group (for example, the k-th FFT result group: k is an integer between 1 and N) determined according to a predetermined rule among N FFT result groups. In this case, the inter-frame difference is the difference between the k-th (for example, the first) FFT result group among the 10 FFT result groups belonging to the reference frame Fr0 and the k-th (for example, the first) FFT result group among the 10 FFT result groups belonging to the subsequent frame Fr1.

[0072] It should be noted that the above-mentioned matters regarding inter-frame differences are not limited to one-to-one inter-frame differences, but also apply to one-to-many inter-frame differences.

[0073] For example, to calculate a one-to-multiple frame difference (described later), the distance measurement unit 221 holds the FFT results output by the radio wave sensor 1 for a period of (K+1) frames (K is an integer equal to or greater than 2). In this embodiment, K=5, and the FFT results are held (for example, stored in the memory of the control device 2) for a period of (5+1) frames, that is, 6×200 ms=1200 ms.

[0074] (2-3-3) One-to-multiple frame difference The distance measurement unit 221 calculates a one-to-many frame difference by using, for example, a group of FFT results over a (K+1) frame period stored in a memory. The one-to-many frame difference is a difference (first time difference T, second time difference 2×T,...) between a reference frame Fr0 and each of a plurality of target frames (for example, subsequent frames Fr1, Fr2,... as shown in FIG. 10).

[0075] In Figure 10, the multiple target frames are shown as multiple subsequent frames Fr1, Fr2, ... following the reference frame Fr0, but the multiple target frames in this embodiment are, for example, multiple preceding frames Fr-1, Fr-2, ... preceding the reference frame Fr0, as shown in Figure 12.

[0076] The difference between the FFT result groups is, for example, as shown in Figure 8, the difference between the FFT result group corresponding to the first transmission wave Tr1 of the reference frame Fr0 and the FFT result group corresponding to the first transmission wave Tr1 of the target frame (the subsequent frame Fr1 in the example of Figure 8) having a time difference T with respect to the reference frame Fr0.

[0077] Alternatively, the difference between the FFT result groups may be, for example, the difference between the FFT result group corresponding to the second transmission wave Tr2 of the reference frame Fr0 and the FFT result group corresponding to the second transmission wave Tr2 of the target frame (subsequent frame Fr1), or the difference between the FFT result group corresponding to the Nth transmission wave TrN of the reference frame Fr0 and the FFT result group corresponding to the Nth transmission wave Tr2 of the target frame (subsequent frame Fr1).

[0078] Alternatively, the difference of the FFT result group may be the sum of the above N differences. In this embodiment, the difference of the FFT result group is the sum of the above N differences, that is, the sum of the differences for each of the N transmission waves Tr1 to TrN.

[0079] In this embodiment, the one-to-many frame difference is a set of inter-frame differences of the current frame FFT result group for each of K or more (e.g., 5) previous frame FFT result groups out of the (N×(K+1)) or more (e.g., 10×(5+1)=60, where N=10 and K=5) FFT result groups currently held.

[0080] (2-3-4) Current frame and current frame FFT results The current frame Fr0 is the frame Fr0 that includes the most recent FFT result group. The current frame FFT result group is a collection of N FFT result groups that belong to the current frame Fr0.

[0081] (2-3-5) Previous frame and previous frame FFT results The previous frames (Fr-1, Fr-2, Fr-K) are frames that precede the current frame Fr0. The previous frame FFT result group is a set of N FFT results that belong to the K previous frames (Fr-1, Fr-2, Fr-K).

[0082] (2-3-6) Parameter K It should be noted that the larger the value of the parameter K, the easier it is to detect various movements of the human body 301 (for example, body movement, slight respiratory movement, and hand and foot movement).

[0083] (2-3-6a) Body movement, respiratory movements, and limb movements Body movement is the movement of the entire body (trunk). Body movement is shown by MV1 in FIG. 11, and as shown in FIG. 12, is irregular movement with a long movement time and a large amount of movement. Hand and foot movement is the movement of the hands and feet. Hand and foot movement is shown by MV3 in FIG. 11, and as shown in FIG. 12, is irregular movement with a short movement time and a large amount of movement. Respiratory micro-movement is the movement of the torso that accompanies breathing. Respiratory micro-movement is shown by MV2 in FIG. 11, and as shown in FIG. 12, is periodic movement with a small amount of movement and a slightly short movement time.

[0084] (2-3-6b) Specific examples of parameter K In this embodiment, as shown in FIG. 12, K=5, and of the five consecutive preceding frames (first preceding frame Fr-1, second preceding frame Fr-2, ... fifth preceding frame Fr-5), three frames (the second, third and fifth preceding frames Fr-2, Fr-3 and Fr-5) that are suitable for detecting body movement, limb movement and slight respiratory movements are selected, thereby improving the detection accuracy while reducing the amount of processing required for calculating the difference, but all five preceding frames Fr-1 to Fr-5 may also be used.

[0085] (2-3-7) Pose Estimation Based on One-to-Many Frame Differences The distance measurement unit 221 thus acquires a group of K or more distance measurement results corresponding to the K or more inter-frame differences constituting the one-to-multiple inter-frame differences calculated for the current frame Fr0.

[0086] The point placement unit 222 thus performs coordinate calculation processing based on each of the K or more distance measurement result groups acquired by the distance measurement unit 221 for the current frame Fr0, thereby placing K or more points corresponding to the current FFT result group in the three-dimensional space 500.

[0087] In this way, by calculating the one-to-many frame difference of the current FFT result group for each of two or more previous FFT result groups, it is possible to further improve the estimation accuracy of the posture of the human body 301 without increasing the number of antennas.

[0088] (2-3-8) Optimal value of parameter K The value of K is preferably an integer equal to or greater than 3. The distance measurement unit 221 selects two or more inter-frame differences corresponding to various movements of the human body 301 (e.g., body movement, slight respiratory movement, hand and foot movement, etc.) from among the three or more inter-frame differences constituting the one-to-multiple inter-frame difference calculated for the current frame Fr0, and obtains two or more distance measurement result groups corresponding to the selected two or more inter-frame differences.

[0089] The point placement unit 222 thus performs coordinate calculation processing based on each of the two or more distance measurement result groups acquired by the distance measurement unit 221 for the current frame Fr0, thereby placing two or more points corresponding to the current FFT result group in the three-dimensional space 500.

[0090] In this way, by calculating three or more inter-frame differences (one-to-many inter-frame differences) for each of three or more previous FFT result groups of the current FFT result group, and calculating two or more inter-frame differences among the three or more inter-frame differences according to various movements of the human body 301, it is possible to further improve the estimation accuracy of the posture of the human body 301 without increasing the number of antennas and while suppressing the number of inter-frame differences used in the ranging process.

[0091] A more preferable value of K is 5. The distance measurement unit 221 selects three inter-frame differences corresponding to the body movement, the micro-respiratory movement, and the limb movement of the human body 301 from the five inter-frame differences constituting the one-to-multiple inter-frame differences calculated for the current frame Fr0, and obtains three distance measurement result groups corresponding to the selected three inter-frame differences.

[0092] The three inter-frame differences corresponding to the body movement, micro-respiratory movement, and limb movement of the human body 301 are, for example, the second, third, and fifth inter-frame differences ΔA2, ΔA3, and ΔA5 among the first to fifth inter-frame differences ΔA1 to ΔA5, as shown in FIG. 12.

[0093] The point placement unit 222 performs coordinate calculation processing based on each of the three distance measurement result groups acquired by the distance measurement unit 221 for the current frame, thereby placing three points corresponding to the current FFT result group in the three-dimensional space 500.

[0094] In this way, by calculating five inter-frame differences (one-to-many inter-frame differences) for each of the five previous FFT result groups of the current FFT result group, and calculating three of the five inter-frame differences corresponding to the body movement, micro-respiratory movement, and limb movement of the human body 301, it is possible to further improve the estimation accuracy of the posture of the human body 301 without increasing the number of antennas and while suppressing the number of inter-frame differences used in the ranging process.

[0095] Note that the above is merely an example, and for example, the number of inter-frame differences to be calculated and the time difference can be appropriately changed so as to improve the estimation accuracy.

[0096] (2-3-9) Details of posture estimation part (2-3-9a) Clustering and distribution of cluster groups Pose estimation section 223, for example, performs clustering on point group 501 to obtain a cluster group (CL, CL1, CL2) which is a set of one or more clusters (CL, CL1, CL2).

[0097] The cluster group (CL, CL1, CL2) is, for example, a first cluster CL1 corresponding to the upper body (head, torso, and arms) of the human body 301 and a second cluster CL2 corresponding to the lower body (legs) as shown in Figures 13A and 13B. Alternatively, the cluster group (CL, CL1, CL2) may be, for example, a single cluster CL corresponding to the whole body (head, torso, arms, and legs) as shown in Figure 13C.

[0098] Furthermore, as the number of points constituting the point cloud 501 increases, it is expected that the cluster groups (CL, CL1, CL2) will have a resolution sufficient to distinguish the general shape (silhouette) of the human body 301 and, ultimately, each part constituting the human body 301.

[0099] Posture estimation section 223 estimates the posture of human body 301 based on the distribution in three-dimensional space 500 of the thus acquired cluster group (CL, CL1, CL2) (hereinafter simply referred to as "distribution").

[0100] The distribution is, for example, the number of acquired clusters (CL, CL1, CL2), the direction of spread of one cluster (CL, CL1, CL2), the distance between multiple clusters, and the like.

[0101] (2-3-9b) Posture Estimation Based on Distribution Conditions The posture estimation unit 223 estimates whether the posture of the human body 301 is lying down, sitting, or standing, based on a distribution condition that is a condition related to the distribution, for example.

[0102] The distribution condition includes, for example, a lying-down condition. A lying-down condition (first lying-down condition) constituting the distribution condition is, for example, that "only a single cluster CL is acquired, and the height of the single cluster CL from the floor surface is low enough to be lower than a threshold value" (see FIG. 13C).

[0103] The distribution condition further includes, for example, a locus condition. The locus condition (first locus condition) constituting the distribution condition is, for example, "first and second clusters CL1 and CL2 are obtained, and the distance between the two clusters CL1 and CL2 is close enough to be below a threshold" (see FIG. 13B).

[0104] The posture estimation unit 223 may, for example, determine that the posture of the human body 301 is lying down if the distribution satisfies the above-mentioned lying down condition (first lying down condition), determine that the posture is sitting if the distribution satisfies the above-mentioned sitting down condition (first sitting down condition), and determine that the posture is standing if the distribution satisfies neither the lying down condition nor the sitting down condition.

[0105] In this way, the posture of the human body 301 can be estimated with high accuracy based on the distribution of the cluster group (CL, CL1, CL2).

[0106] (2-3-9c) Posture Estimation Based on Dimension Ratio Conditions Alternatively, the posture estimation unit 223 may estimate the posture of the human body 301 based on dimensional ratio conditions regarding the ratios (hereinafter referred to as "dimension ratios") of the length D, width W, and height H of a three-dimensional figure 502 that encloses the cluster group (CL, CL1, CL2).

[0107] The three-dimensional figure 502 in this embodiment is, for example, a rectangular parallelepiped with length D, width W and height H as shown in FIGS. 14A to 14C, and the dimensional ratio is, for example, the ratio between length D or width W and height H.

[0108] However, the three-dimensional figure 502 may be, for example, an elliptical cylinder (not shown) etc. In an elliptical cylinder, the minor axis corresponds to the vertical axis D and the major axis corresponds to the horizontal axis W.

[0109] The size ratio condition includes a lying-down condition. A lying-down condition (second lying-down condition) constituting the size ratio condition may be, for example, "the length D or width W of the three-dimensional figure 502 is so large as to exceed a predetermined ratio (for example, three times) with respect to the height H."

[0110] The size ratio condition further includes, for example, an upright condition. The upright condition constituting the size ratio condition may be, for example, "the height H of the three-dimensional figure 502 is so large as to exceed a predetermined ratio (for example, three times) of the length D or width W."

[0111] The posture estimation unit 223 may, for example, determine that the posture of the human body 301 is lying down if the dimensional ratio satisfies the above-mentioned lying down condition (second lying down condition), determine that the posture is standing if the dimensional ratio satisfies the above-mentioned standing up condition, and determine that the posture is sitting if the dimensional ratio satisfies neither the lying down condition nor the standing up condition.

[0112] In this way, the posture of the human body 301 can be estimated with high accuracy based on the ratios of the length D, width W, and height H of the three-dimensional figure 502 surrounding the cluster group (CL, CL1, CL2).

[0113] (2-3-9d) Attitude Estimation Based on Distribution Size Ratio Condition The posture estimation unit 223 may estimate whether the posture of the human body 301 is lying down, sitting, or standing, based on a distribution size ratio condition, which is a condition related to the distribution and size ratio, for example.

[0114] The distribution size ratio condition includes a lying-down condition. A lying-down condition (third lying-down condition) constituting the distribution size ratio condition may be, for example, "only one cluster CL is acquired, and the length D or width W of the three-dimensional figure 502 is so large as to exceed a predetermined ratio (for example, three times) with respect to the height H."

[0115] The distribution size ratio condition further includes, for example, a locus condition. The locus condition (second locus condition) constituting the distribution size ratio condition may be, for example, "first and second clusters CL1 and CL2 are obtained, and the distance between the two clusters CL1 and CL2 is close enough to be less than a threshold value."

[0116] The posture estimation unit 223 may, for example, determine that the posture of the human body 301 is lying down if the distribution and size ratio satisfy the above lying down condition (third lying down condition), determine that the posture is sitting down if the distribution and size ratio satisfy the above sitting down condition (second sitting down condition), and determine that the posture is standing up if the distribution and size ratio satisfy neither the lying down condition nor the sitting down condition.

[0117] (2-4) Equipment control function of the equipment control system As shown in FIG. 1, the device control system 100 further includes a behavior detection unit 224 and a device control unit 225.

[0118] (2-4-1) Behavior detection unit The behavior detection unit 224 detects the behavior of the person corresponding to the human body 301 based on the estimation result of the posture estimation unit 223 with respect to the human body 301 .

[0119] The behavior detection section 224 in this embodiment detects a non-operation behavior based at least on a change in the estimation result of the posture estimation section 223 in particular.

[0120] In this embodiment, the change in the estimation result is a change between a standing position, a sitting position, and a lying position, and specifically, for example, a change from a standing position to a sitting position, a change from a sitting position to a standing position, a change from a standing position or a sitting position to a lying position, a change from a lying position to a standing position or a sitting position, etc.

[0121] Furthermore, the behavior detection unit 224 may detect a non-operation behavior when an unchanged state in which no change in the estimation result of the posture estimation unit 223 is detected continues for a predetermined time or more. The behavior detection unit 224 detects a non-operation behavior in response to, for example, an unchanged state continuing for a predetermined time or more after a change in the estimation result of the posture estimation unit 223 is detected.

[0122] Specifically, the behavior detection unit 224 detects a non-operation behavior of a continuation of a lying state (for example, falling asleep) in response to a state in which no change from the lying state to another posture is detected continuing for a predetermined time or more after the estimation result of the posture estimation unit 223 has changed from a standing or sitting position to a lying position. Also, the behavior detection unit 224 detects a non-operation behavior of a continuation of a lying state (for example, falling asleep) in response to a state in which no change from the sitting position to another posture is detected continuing for a predetermined time or more after the estimation result of the posture estimation unit 223 has changed from a standing or sitting position to a sitting position.

[0123] (2-4-1a) Non-operational behavior and operational behavior A non-operational behavior is a behavior that is different from a manipulative behavior.

[0124] An operation behavior is a behavior intended to operate device 200. Device 200 is, for example, a lighting device 200a attached to the ceiling of room 400, a television (TV) 200b placed on the floor, and an electric blind 200c attached to window 402, as shown in Fig. 2. An operation behavior is, for example, an on / off operation of lighting device 200a via a wall switch, an on / off operation or a channel switching operation of TV 200b via a remote control, an opening / closing operation of electric blind 200c via a remote control, and the like.

[0125] The non-operation behavior is, for example, a daily behavior, which is a behavior that does not intend to operate the device 200. The daily behavior is, for example, a behavior of entering the room 400 (entering the room 400 from the entrance / exit 401), a behavior of lying down on the bed 302b (going to sleep), a behavior of getting up from the bed 302b (getting up), a behavior of leaving the room 400 (leaving the room from the entrance / exit 401), etc. Furthermore, the daily behavior may be, for example, a behavior of sitting at the desk 302a (sitting) (desk work or relaxation), a behavior of leaving the desk 302a (leaving the desk), etc.

[0126] Note that sitting may be distinguished, for example, into sitting to work at the desk 302a (desk work) and sitting to relax while watching the TV 200b (relaxation). Desk work and relaxation may be determined, for example, based on the time period (daytime or nighttime) when sitting is detected.

[0127] The behavior detection unit 224 may detect the non-operation behavior by considering environmental information, history information, and the like (described later). For example, waking up may be classified into morning waking up and midnight waking up. Whether it is morning waking up or midnight waking up may be determined based on the time period when the user changes from a lying position to a sitting or standing position or the detected position.

[0128] (2-4-2) Equipment control section The device control unit 225 controls the device 200 based at least on the non-operation behavior detected by the behavior detection unit 224.

[0129] In addition to control (automatic control) according to a non-operation behavior, the device control unit 225 also performs control (manual control) according to an operation on the device 200. The operation is, for example, an operation of an operation device (touch panel, operation button, etc.) of the control device 2, but may be an operation of an operation device (wall switch, remote control, etc.) of the device 200, or a gesture for operating the device 200. The operation of the operation device of the device 200 is detected, for example, by intercepting a control signal from an operation device such as a wall switch to the device 200. The gesture is detected, for example, by analyzing an image of the human body 301 captured by a camera (not shown), but may be detected by the radio wave sensor 1 in distinction from a non-operation behavior.

[0130] In this way, by detecting the behavior (particularly non-operation behavior) of the human body 301 based on the estimated posture change and performing device control according to the detection result, it is possible to enable device control without operating the device 200 (operation-less device control).

[0131] (2-4-3) Information acquisition section 1, the device control system 100 further includes an information acquisition unit 226. The information acquisition unit 226 acquires various types of information. The various types of information include, for example, environmental information and a person identifier (both of which will be described later).

[0132] The information acquiring unit 226 acquires, for example, environmental information. The environmental information is information related to the environment in which the human body 301 exists. The environment is, for example, a time period (daytime, nighttime, early morning, late night, etc.), weather (sunny, rainy, etc.), temperature, humidity, etc., but may also be the type (bedroom, living room) or direction (south-facing, north-facing) of the room 400 in which the human body 301 exists.

[0133] In addition, when multiple people (two or more human bodies 301) may exist in the same environment, the information acquisition unit 226 may acquire a person identifier. In detail, for example, a set of group information (group information group) that is a pair of a person identifier that identifies a person and feature information that indicates the features of the person is stored in advance in a memory. The person identifier is, for example, an ID such as "1" or "2", but may be any information that can identify a person, such as a name or an email address. The feature information paired with the person identifier is, for example, height information that indicates the height of the person. However, the feature information may be speed information that indicates the walking speed of the person, or any information that indicates a feature that can distinguish the person from other people. The feature information itself may be used as the person identifier.

[0134] When the posture estimation section 223 detects the human body 301, the information acquisition section 226 may acquire feature information of the human body 301 from the posture estimation section 223, and read out from the memory a human identifier that pairs with the acquired feature information.

[0135] Note that automatic control information (see FIG. 15) described later may be customized for each of a plurality of person identifiers.

[0136] The behavior detection unit 224 detects a non-operation behavior further based on the environmental information acquired by the information acquisition unit 226 .

[0137] In this way, by using environmental information in addition to posture changes, it is possible to improve the accuracy of detecting non-operation behavior.

[0138] (2-4-3a) Automatic control and information for automatic control The device control unit 225 controls the device 200 based on the non-operation behavior, for example, by using one or more (six in this case) pieces of automatic control information (a group of automatic control information) as shown in Fig. 15. The group of automatic control information is stored in advance, for example, in the memory of the control device 2. In this embodiment, the control based on the group of automatic control information is referred to as "automatic control."

[0139] Each of the one or more pieces of automatic control information constituting the automatic control information group includes information on a status change, a posture change, an environment, a non-operational behavior, a control content, and a control ID, as shown in Fig. 15. Specifically, of the six pieces of automatic control information shown in Fig. 15, the first piece of automatic control information includes a status change of "absent → present", a posture change of "-", an environment of "-", a non-operational behavior of "entering", a control content of "lights normally on (brightness 5)", and a control ID of "1". Note that "-" is information indicating that the information is not included (same below).

[0140] Similarly, the second automatic control information includes a state change "-", a posture change "standing to sitting", an environment "daytime", a non-operation action "desk work", a control content "brighten the lights (+2)", and a control ID "2". The third automatic control information includes a state change "-", a posture change "standing to sitting", an environment "nighttime", a non-operation action "relaxing", a control content "TV on", and a control ID "3". The fourth automatic control information includes a state change "-", a posture change "standing or sitting to lying", an environment "nighttime", a non-operation action "sleeping", a control content "lights off", and a control ID "4". The fifth automatic control information includes a state change "-", a posture change "lying to standing or sitting", an environment "late at night", a non-operation action "awakening midway", a control content "lights dim (brightness 2)", and a control ID "5". The sixth automatic control information includes a state change "-", a posture change "lying down to standing or sitting", an environment "morning", a non-operational action "getting up", a control content "blinds open", and a control ID "6".

[0141] (2-4-3b) Manual control The device control unit 225 can also perform control (automatic control) using the automatic control information in response to an operation (hereinafter simply referred to as "operation") on the device 200. Control based on an operation is referred to as "manual control" in this embodiment.

[0142] (2-4-4) History accumulation and learning The device control system 100 further includes a history accumulation unit 227 and a learning unit 228.

[0143] (2-4-4a) History storage section The history accumulation unit 227 accumulates control history information. The control history information is information related to the control history of the device 200 performed by the device control unit 225. The control history information is, for example, automatic control history information, but may also be manual control history information. Alternatively, the control history information may include automatic control history information and manual control history information.

[0144] The automatic control history information is information on the control history of the device 200 according to an action (for example, a non-operational action, but may also be an operational action). The automatic control history information associates the control content of the action-based control with the time when the control was performed, environmental information, and a person identifier.

[0145] The manual control history information is information about the control history of the device 200 according to the operation. The manual control history information associates the control content of the device 200 based on the operation with one or more pieces of information including the time when the operation was performed, environmental information, and a person identifier.

[0146] When the device control unit 225 controls the device 200, the history storage unit 227 stores, for example, control history information as shown in Fig. 16 in the memory of the control device 2. Note that the control history information may be stored in an external memory.

[0147] For example, if device control unit 225 has executed three control operations on lighting device 200a as of 9:02, three pieces of control history information as shown in Fig. 16 are stored in the memory of control device 2. Each of the three pieces of control history information includes information regarding a history ID, a time, a control ID, and an operation.

[0148] 16, the first control history information includes a history ID "1", a time "9:00", a control ID "1", and an operation "-". The second control history information includes a history ID "2", a time "9:01", a control ID "2", and an operation "-". The third control history information includes a history ID "3", a time "9:02", a control ID "-", and an operation "operation to dim the lights slightly (-1)".

[0149] The aforementioned automatic control history information has operation information "-", which corresponds to the first and second control history information among the three control history information above. The aforementioned manual control history information has a control ID of "-", which corresponds to the third control history information among the three control history information above.

[0150] (2-4-5) Learning Department The learning unit 228 updates the automatic control information when the time and control content indicated by the automatic control history information and the time and operation indicated by the manual control history information satisfy a predetermined update condition.

[0151] (2-4-5a) Update conditions and update of automatic control information based on them An example of the update condition is that "after automatic control based on a non-operation behavior is performed on a device, an operation to cancel the control content of the automatic control is received within a predetermined time (e.g., two minutes)."

[0152] In the example of Figure 16, the time "9:01" and control content (i.e., the control content corresponding to control ID "2" in Figure 15) "brighten the lights (+2)" indicated by the automatic control information, which is the second control history information, and the time "9:03" and operation "dimm the lights slightly (-1)" indicated by the manual control information, which is the third control history information, satisfy the above update condition.

[0153] In other words, after the automatic control "brighten the lights (+2)" based on the non-operational behavior "desk work" was performed on one device "lighting device 200a," an operation "dimming the lights a little (-1)" that cancels the control content of the automatic control was received within two minutes, so the learning unit 228 determines that the update condition is satisfied. Then, the learning unit 228 updates the second automatic control information corresponding to control ID "2" of the six pieces of automatic update information in Fig. 15 to, for example, "dimming the lights a little (+1)."

[0154] Alternatively, the update condition may be, for example, "an operation different from the contents of automatic control has been received a predetermined number of times or more in a predetermined period (for example, "two or more times a day," "three or more times a week," etc.)."

[0155] In this way, by accumulating historical information regarding automatic control in response to non-operational behavior (automatic control historical information) and historical information regarding manual control in response to operation, and updating the information for automatic control based on these two pieces of historical information when the update conditions are met, a learning function can be realized.

[0156] (3) Examples of equipment control systems As shown in Fig. 1, the device control system 100 of this example includes a radio wave sensor 1 and a control device 2. The radio wave sensor 1 and the control device 2 each have a communication module, and are connected to each other so as to be able to communicate with each other via wire or wirelessly. The control device 2 further includes a processor and a memory. Programs and various information are stored in the memory, and the processor operates based on the programs and the like in the memory (and cooperates with the communication module), thereby realizing the operation of the control device 2 as described below. Note that the radio wave sensor 1 may also include a processor and a memory, and transmission and reception operations and communication with the control device 2 may be performed under the control of a program.

[0157] The radio wave sensor 1 is provided on the ceiling of a room 400 as shown in Fig. 2. The control device 2 is, for example, a multi-remote control device, and is provided on a side wall of the room 400 as shown in Fig. 2.

[0158] The control device 2 estimates the posture (standing, sitting, or lying) of the human body 301 present in the room 400 based on the output of the radio wave sensor 1. Then, the control device 2 detects non-operation behavior of the human body 301 based on a change in the estimated posture, and automatically controls the multiple devices 200 (lighting fixture 200a, TV 200b, and electric blinds 200c) according to the detection result. The control device 2 also has an operation device such as a touch panel, and can manually control the multiple devices 200 according to the operation.

[0159] 2, the control device 2 includes a reception unit 21, a processing unit 22, and an output unit 23. The processing unit 22 includes a distance measurement unit 221, a point placement unit 222, a posture estimation unit 223, a behavior detection unit 224, a device control unit 225, an information acquisition unit 226, a history accumulation unit 227, and a learning unit 228.

[0160] The reception unit 21 receives various types of information. The various types of information are, for example, operation information for the device 200. The reception unit 21 may receive information other than the operation information, for example, personal information such as the height of the human body 301.

[0161] The processing unit 22 performs various types of processing, such as processing by a distance measurement unit 221, processing by a point placement unit 222, processing by a posture estimation unit 223, processing by a behavior detection unit 224, processing by a device control unit 225, processing by an information acquisition unit 226, processing by a history accumulation unit 227, and processing by a learning unit 228. The processing unit 22 also performs various types of judgments that will be explained in the flowcharts.

[0162] In detail, the processing unit 22 may execute, for example, a holding process, an acquiring process, a position specifying process, a human body estimation process, and a placement process. The processing unit 22 executes a series of these processes, for example, every time position specifying information is output from the radio wave sensor 1, but may execute the processes every time a number of pieces of position specifying information corresponding to one frame are output.

[0163] The storage process is a process of storing the position identifiable information output by the radio wave sensor 1. The acquisition process is a process of acquiring the difference between the stored pieces of position identifiable information. The position identification process is a process of identifying the position of a moving object (301, 200c) among one or more objects (301, 302, 200c) constituting the object group (301, 302, 200c) based on the difference, and acquiring position information indicating the identified position. The human body detection process is a process of detecting the human body 301 using a signal received by the radio wave sensor. The human body estimation process is a process of estimating whether the acquired position information corresponds to the human body 301 or a moving object other than the human body 301 (for example, the electric blind 200c) based on at least a change in the difference. Note that the change in this embodiment is usually a change in value such as a difference over time, and may be called a "temporal change". However, the change may be, for example, a change in value according to a position in space (spatial change).

[0164] The placement process is a process of virtually placing a point corresponding to the acquired position information in space (three-dimensional space 500) when it is estimated that the acquired position information corresponds to the human body 301. The placement process is executed by the point placement unit 222 constituting the processing unit 22.

[0165] Particularly in this embodiment, the holding process holds three or more pieces of position identifiable information. The acquisition process acquires two or more differences with different time differences for the three or more pieces of position identifiable information held. The position identification process acquires two or more pieces of position information corresponding to the acquired two or more differences. The human body detection process, for example, estimates whether each of the acquired two or more pieces of position information corresponds to a human body 301 or a moving object (200c) other than the human body 301. The placement process virtually places in the space (500) a point corresponding to the position information estimated to correspond to the human body 301 among the acquired two or more pieces of position information.

[0166] In addition, the placement process may receive the estimation results of the human body detection process and display points corresponding to objects estimated to be human bodies 301 and points corresponding to objects estimated to be moving objects (200c) other than the human body 301 in a manner that makes them distinguishable from each other via the output unit 23 (for example, by displaying them in different colors, different sizes, etc.).

[0167] Moreover, the processing unit 22 in this embodiment further executes device control processing. The device control processing is processing for controlling the device 200 based at least on a point cloud 501, which is a collection of a plurality of points arranged in a space (500). The human body detection processing estimates the posture or posture change of the human body 301 based on the distribution of the point cloud 501.

[0168] The processing unit 22 may further execute a behavior detection process. The behavior detection process is a process for detecting a behavior of a person corresponding to the human body 301 based on an estimated posture or a change in posture. The device control process is, for example, for controlling the device 200 based on the detected behavior.

[0169] The processing unit 22 may further execute an information acquisition process. The information acquisition process is a process for acquiring environmental information related to the environment in which the human body 301 exists. The human body detection process detects behavior based on the environmental information acquired by the information acquisition process.

[0170] The device control process controls the device 200 based on the behavior, for example, by using pre-stored automatic control information.

[0171] The processing unit 22 may further execute a history accumulation process. The history accumulation process is a process of accumulating history information related to the control history of the device 200. The history information is, for example, automatic control history information. Alternatively, the history information may be manual control history information. The history accumulation process preferably accumulates both automatic control history information and manual control history information, for example, but may accumulate only one of them.

[0172] The processing unit 22 may further execute a learning process. The learning process is a process of updating the automatic control information when the time and operation indicated by the manual control history information satisfy a predetermined update condition. Alternatively, the learning process may be a process of updating the automatic control information when the time and control content indicated by the automatic control history information and the time and operation indicated by the manual control history information satisfy a predetermined update condition.

[0173] The radio wave sensor 1 may execute the conversion process described in "(10) Second Modification of Device Control System," and the processing unit 22 may execute the storage process, difference acquisition process, and moving object detection process described in "(10) Second Modification of Device Control System." In other words, the radio wave sensor 1 may include the conversion unit 11 shown in Fig. 17, and the processing unit 22 may include the conversion unit 11, storage unit 12, difference acquisition unit 13, and moving object detection unit 14 shown in Fig. 17.

[0174] The output unit 23 outputs various information. The various information is, for example, control information (for example, a remote control signal) to the device 200. The output here is usually a transmission to the device 200, but may also include display on a display.

[0175] (4) Example of device control system operation The control device 2 constituting the device control system 100 operates, for example, according to the flowcharts of FIGS.

[0176] (4-1) Overall processing The process in FIG. 4 is started in response to the start-up of the device control system 100, and is ended in response to the stop of operation.

[0177] First, the processing unit 22 constituting the control device 2 determines whether or not an FFT result group has been output from the radio wave sensor 1 (step S1). If it is determined that an FFT result group has not been output, the process proceeds to step S13.

[0178] If it is determined in step S1 that the FFT results have been output, the distance measurement unit 221 constituting the processing unit 22 holds the FFT results (step S2).

[0179] Next, the distance measuring unit 221 judges whether or not a group of FFT results for the (K+1) frame period or more has been stored (step S3). If it is judged that a group of FFT results for the (K+1) frame period or more has not been stored yet, the process returns to step S1.

[0180] If it is determined in step S3 that the FFT result group for the (K+1) frame period or more has been held, the distance measuring unit 221 performs one-to-multiple frame difference acquisition processing (step S4). The one-to-multiple frame difference acquisition processing will be described with reference to the flowchart of FIG.

[0181] Next, the distance measurement unit 221 acquires L (or K) distance measurement results (a group of distance measurement results) corresponding to the L (or K) differences (a group of differences) (step S5).

[0182] Next, the point arrangement unit 222 calculates L (or K) three-dimensional coordinates (a group of three-dimensional coordinates) based on the group of distance measurement results acquired in step S5 (step S6).

[0183] Next, the point arrangement unit 222 arranges L (or K) points (point group 501) in the three-dimensional space 500 based on the three-dimensional coordinate group acquired in step S6 (step S7).

[0184] It is determined whether or not a judgment start condition is satisfied (step S8). The judgment start condition is, for example, a condition that a predetermined number or more of points are arranged in the three-dimensional space 500. If it is determined that the judgment start condition is not yet satisfied, the process returns to step S1.

[0185] If it is determined in step S8 that the determination start condition is satisfied, posture estimation section 223 performs posture estimation processing (step S9). Note that the posture estimation processing will be described with reference to the flowchart of FIG.

[0186] Next, the behavior detection unit 224 detects a non-operation behavior based on changes in the estimation results in step S9 (for example, a state change, a posture change, or an environment) using the automatic control information (see FIG. 15) (step S10). For example, in response to a state change from "absent to present," a non-operation behavior "entering a room" is detected. In addition, in response to a posture change from "standing to sitting," it is determined whether the environment (current time zone) is daytime or nighttime, and in the case of daytime, a non-operation behavior "desk work" is detected, and in the case of nighttime, a non-operation behavior "relaxing" is detected.

[0187] Next, device control unit 225 performs device control (automatic control) in response to the detection results of the non-operation behavior, etc., in step S10 (step S11). For example, in response to the detection of the non-operation behavior “entering a room”, automatic control (control with control ID “1”) is performed to turn on lighting device 200a at normal brightness (brightness 5). In addition, in response to the detection of the non-operation behavior “desk work”, automatic control (control with control ID “2”) is performed to brighten lighting device 200a (brightness +2). In addition, in response to the detection of the non-operation behavior “relaxing”, automatic control (control with control ID “3”) is performed to turn on TV 200b.

[0188] Next, the history accumulation unit 227 accumulates automatic control history information related to the automatic control performed in step S11 (step S12), after which the process returns to step S1.

[0189] If it is determined in step S1 that the FFT results have not yet been output, the processing unit 22 determines whether the reception unit 21 has received an operation for the device 200 (step S13). If it is determined that the reception unit 21 has not yet received an operation for the device 200, the process returns to step S1.

[0190] If it is determined in step S13 that reception unit 21 has received an operation for device 200, device control unit 225 performs device control (manual control) in response to the operation (step S14). For example, if an operation is performed to slightly dim lighting device 200a (brightness -1), manual control is performed to slightly dim lighting device 200a.

[0191] Next, the history accumulation unit 227 accumulates manual control history information related to the manual control performed in step S14 (step S15).

[0192] Next, the learning unit 228 judges whether or not the update condition is satisfied (step S16). If it is judged that the update condition is not yet satisfied, the process returns to step S1.

[0193] If it is determined that the update condition is met, history accumulation unit 227 updates the automatic control information (step S17). For example, as shown in Fig. 16, one minute after the automatic control with control ID "2", manual control is performed to slightly dim lighting device 200a, so it is determined that the update condition is met and the control content of the automatic control with control ID "2" is updated to "brighten the lights (+1)". Processing then returns to step S1.

[0194] 4, the radio wave sensor 1 may output three or more IF signals (received signals) corresponding to three or more receiving antennas instead of the FFT result group (location identifiable information), and the processing unit 22 may acquire the FFT transformation result based on the output three or more IF signals. In that case, in the above step S1, the processing unit 22 may perform FFT transformation on each of the three or more IF signals and determine whether or not the FFT transformation result has been acquired.

[0195] (4-2) One-to-multiple frame difference acquisition process The one-to-multiple frame difference acquisition process in step S4 is executed, for example, according to the flowchart of FIG.

[0196] The processing unit 22 sets an initial value "1" to a variable i (step S41).

[0197] Next, the processing unit 22 judges whether or not the variable i is greater than K (step S42). If it is judged that the variable i is greater than K, the processing proceeds to step S45.

[0198] If it is determined in step S42 that the variable i is not greater than K (i.e., is less than or equal to K), the distance measurement unit 221 obtains the difference between the current FFT result group and the i-th preceding FFT result group that is the i-th time difference (= i × T seconds) before the current FFT result group, and sets the obtained difference to the variable “i-th difference” (step S43).

[0199] Next, the processing unit 22 increments the variable i (step S44), after which the process returns to step S42.

[0200] If it is determined in step S42 that the variable i is greater than K, the distance measuring unit 221 selects L differences (for example, three differences ΔA1, ΔA2, and ΔA3: see FIG. 12) corresponding to various movements of the human body 301 from the first to Kth K differences (for example, five differences ΔA1 to ΔA5: only a part of which is shown in FIG. 12) (step S45). After that, the process returns to the upper flowchart (see FIG. 4).

[0201] (4-3) Attitude estimation processing The posture estimation process in step S9 is executed, for example, according to the flowchart in FIG.

[0202] Pose estimation section 223 performs clustering on point group 501 arranged in three-dimensional space 500, and acquires cluster groups (CL, CL1, CL2: see FIGS. 13A to 13C) (step S91).

[0203] Next, posture estimation section 223 places, in three-dimensional space 500, a solid figure (rectangular parallelepiped) 502 (see FIGS. 14A to 14C) surrounding the group of clusters (CL, CL1, CL2) acquired in step S91 (step S92).

[0204] Next, the posture estimation unit 223 acquires the ratio (dimension ratio) of the length D, width W, and height K of the three-dimensional figure 502 arranged in step S92 (step S93).

[0205] Next, the posture estimation unit 223 determines whether or not the distribution (cluster distribution) of the cluster group (CL, CL1, CL2) acquired in step S91 and the size ratio acquired in step S93 satisfy the lying position condition (step S94).

[0206] If it is determined that the cluster distribution and the size ratio satisfy the lying-down condition, the posture estimation unit 223 sets the variable "posture" to the value "lying-down" (step S95). After that, the process returns to the upper-level flowchart (see FIG. 4).

[0207] When it is determined that the cluster distribution and the size ratio do not satisfy the lying position condition, the posture estimation unit 223 further determines whether or not the cluster distribution and the size ratio satisfy the sitting position condition (step S96).

[0208] If it is determined that the cluster distribution and the size ratio satisfy the sitting position condition, the posture estimation unit 223 sets the variable "posture" to the value "sitting position" (step S97). After that, the process returns to the upper flowchart (see FIG. 4).

[0209] If it is determined that the cluster distribution and the size ratio do not satisfy the sitting condition either, the posture estimation unit 223 sets the variable "posture" to the value "standing" (step S98). After that, the process returns to the upper flowchart (see FIG. 4).

[0210] (5) Human body posture estimation method, device control method, and program The human body posture estimation function of the present disclosure may be realized by a human body posture estimation method or a program. The human body posture estimation method includes at least steps S2 to S5 (distance measurement steps), steps S6 and S7 (placement steps), and step S9 (posture estimation step) among the various steps described above. The program is a program for causing one or more processors to execute the human body posture estimation method.

[0211] The device control function of the present disclosure may be realized by a device control method or a program. The device control method further includes step S10 (behavior detection step) and step S11 (device control step) in addition to the distance measurement step, the positioning step, and the attitude estimation step. The program is a program for causing one or more processors to execute the device control method.

[0212] (6) Variations of non-operational behavior The non-operation behavior may further include, in addition to going to bed and waking up, for example, falling asleep after going to bed and waking up before waking up. Falling asleep and waking up can be detected, for example, based on changes (stopping and starting) in body movement. Stopping body movement may be, for example, a state in which body movement exceeding a threshold value is not detected for a predetermined period of time or more. Starting body movement may be, for example, a state in which body movement exceeding a threshold value is detected for a predetermined period of time or more.

[0213] (7) Modifications to the layout of each part In this embodiment, the control device 2 includes a distance measurement unit 221, a point placement unit 222, a posture estimation unit 223, a behavior detection unit 224, a device control unit 225, an information acquisition unit 226, a history storage unit 227, and a learning unit 228, but all of these elements may be included in the radio wave sensor 1. Alternatively, the radio wave sensor 1 may include only the distance measurement unit 221 among these elements, and the other elements may be included in the control device 2. Alternatively, the control device 2 may be built into the radio wave sensor 1.

[0214] (8) First Modification of Equipment Control System: Human Body Posture Estimation System In this modification, the elements related to the device control function, that is, the behavior detection unit 224, the device control unit 225, the information acquisition unit 226, the history accumulation unit 227, and the learning unit 228, are omitted from the device control system 100 shown in Fig. 1. Such a device control system 100 includes at least a distance measurement unit 221, a point placement unit 222, and a posture estimation unit 223, and may be referred to as a "human body posture estimation system 100".

[0215] Furthermore, the human body posture estimation system 100 may further include a behavior detection unit 224, a device control unit 225, an information acquisition unit 226, a history accumulation unit 227, and a learning unit 228, in addition to the distance measurement unit 221, the point placement unit 222, and the posture estimation unit 223.

[0216] (9) Variation of difference acquisition: Intra-frame difference The radio wave sensor 1 may perform transmission and reception operations in a cycle of N times (N is an integer equal to or greater than 4) per frame, with a predetermined time being one frame, and output N pieces of location identifiable information per frame. The processing unit 22 holds at least N pieces of location identifiable information corresponding to one frame. Then, the processing unit 22 selects three or more pieces of location identifiable information (representative location identifiable information) representing a frame from one frame among the N or more pieces of location identifiable information corresponding to one or more frames held by the processing unit 22, and obtains two or more differences for the selected three or more pieces of representative location identifiable information. Then, the processing unit 22 may obtain two or more pieces of location information based on two or more differences obtained for the three or more pieces of representative location identifiable information representing one frame.

[0217] In this example, the processing unit 22 further holds N pieces of position identifiable information corresponding to the next frame after the first frame. When two or more pieces of position information acquired for three or more pieces of representative position identifiable information all correspond to a moving object (200c) other than the human body 301, the processing unit 22 sets the first frame and the next frame as a new frame. Then, the processing unit 22 selects three or more pieces of representative position identifiable information representing the new frame from the 2×N pieces of position identifiable information corresponding to the new frame, and acquires two or more differences for the selected three or more pieces of representative position identifiable information. The processing unit 22 may acquire two or more pieces of position information based on two or more differences acquired for the three or more pieces of representative position identifiable information representing such a new frame.

[0218] (10) Second Modification of Device Control System: Device Control System Including Motion Detection System In this modified example, explanations of matters already mentioned in the embodiment may be omitted or simplified.

[0219] As shown in Fig. 17, the device control system 100 in this modification includes a moving object detection system 100A and a device control device 2B. The moving object detection system 100A includes a radio wave sensor 1 and a moving object detection device 2A. The moving object detection device 2A includes a conversion unit 11, a storage unit 12, a difference acquisition unit 13, a moving object detection unit 14, and a point arrangement unit 222. The moving object detection unit 14 includes a distance measurement unit 221, a posture estimation unit 223, and a behavior detection unit 224. The device control device 2B includes a device control unit 225, an information acquisition unit 226, a history accumulation unit 227, and a learning unit 228.

[0220] 17, the moving object detection device 2A includes the conversion unit 11, but the conversion unit 11 may be built into the radio wave sensor 1 or may be interposed between the radio wave sensor 1 and the moving object detection device 2A. In the above-described embodiment, the conversion unit 11 is built into the radio wave sensor 1, although not shown in the drawings.

[0221] 17, the moving object detection device 2A and the equipment control device 2B are separate, but may be configured as an integrated device. Furthermore, although omitted in FIG 17, the moving object detection system 100A typically further includes a reception unit 21, a processing unit 22 (for example, a first processing unit on the moving object detection device 2A side and a second processing unit on the equipment control device 2B side), and an output unit 23 (see FIG 1).

[0222] (10-1) Radio wave sensor The radio wave sensor 1 that constitutes the moving object detection system 100A performs a transmission / reception operation of transmitting a transmission wave Tr, which is a radio wave modulated in a predetermined manner, toward a real space (for example, the inside of a room 400 shown in FIG. 2: hereinafter referred to as "the real space (400)"), receiving a reflected wave Re from the real space (400), and outputting a reception signal based on the transmission wave Tr and the reflected wave Re.

[0223] The transmission and reception operations are repeated at a predetermined cycle (that is, periodically) as in the embodiment, but may be performed irregularly.

[0224] Although the radio wave sensor 1 in the embodiment outputs position identifying information such as a group of FFT results, the radio wave sensor 1 in this modified example outputs a received signal such as an IF signal. The received signal is converted into position identifying information outside the radio wave sensor 1 (for example, by a conversion unit 11 constituting the moving object detection device 2A).

[0225] (10-1-1) Received signal For example, in the case of radio waves modulated by the FMCW method, the received signal is a signal (IF signal: see FIG. 3) indicating the frequency difference Δf between the transmitted wave Tr and the reflected wave Re at the same time. However, the received signal may be a signal indicating the time difference Δt between the transmitted wave Tr and the reflected wave Re, which is calculated from the frequency difference Δf. Note that, in the case of radio waves modulated by the pulse modulation method, the received signal may also be a signal indicating the time difference Δt between the transmitted wave Tr and the reflected wave Re.

[0226] (10-1-2) Modulation method The predetermined method, that is, the radio wave modulation method, is the FMCW method in this example, but may be, for example, a pulse modulation method, or any method that results in the acquisition of location identifiable information.

[0227] (10-2) Motion detection device The conversion unit 11 constituting the moving object detection device 2A performs a conversion process. The conversion process is a process of converting the received signal output by the radio wave sensor 1 into position identifiable information. The conversion process is, for example, a process of performing a Fourier transform on an IF signal, which is a type of received signal. The Fourier transform is, for example, an FFT, but may also be a short-time Fourier transform (STFT) or the like.

[0228] The position-identifiable information is information that can identify the position of one or more objects (human body 301, motorized blinds 200c, stationary object 302) that exist in the real space (400), such as, for example, the FFT result group described in the embodiment, but is not limited to this.

[0229] The storage unit 12 performs a storage process. The storage process in this example is a process of storing the position identifiable information converted by the conversion process. The position identifiable information is written, for example, into a memory included in the moving object detection device 2A, and is stored in the memory for a predetermined period (for example, one frame period, three frame periods, etc.).

[0230] The difference acquisition unit 13 performs a difference acquisition process, which is a process for acquiring differences between a plurality of pieces of position identifiable information held in the holding process.

[0231] The moving object detection unit 14 performs a moving object detection process. The moving object detection process is a process for detecting a moving object using a reception signal received by the radio wave sensor 1. The moving object detection process is a process for making an estimation regarding a moving object based on a difference acquired using a reception signal, for example. Specifically, the moving object detection process is a process for estimating whether each of one or more objects is a moving object or a non-moving object (a stationary object 302) based on the difference acquired by the difference acquisition process, for example, to identify the position of the object estimated to be a moving object (a human body 301, an electric blind 200c), and to acquire moving object position information indicating the identified position.

[0232] The point arrangement unit 222 performs a point arrangement process. The point arrangement process is a process of virtually arranging points corresponding to the moving object position information acquired by the moving object detection process in a virtual space (e.g., three-dimensional space 500 in the embodiment) corresponding to the real space (400). The point arrangement process may be, for example, a process such as multi-point arrangement described as the operation of the point arrangement unit 222 in the embodiment.

[0233] In this example, the difference acquisition process acquires two or more differences with different time differences for the three or more pieces of position identifiable information stored in the storage process. The moving object detection process acquires two or more pieces of moving object position information corresponding to the two or more differences acquired in the difference acquisition process. The point arrangement process virtually arranges two or more points corresponding to the two or more pieces of moving object position information acquired in the moving object detection process in a virtual space (500).

[0234] Here, "virtually placing two or more points in a virtual space (500)" includes the case where "all points are placed" and "only some points are placed" (in other words, at least one point is placed) of the two or more points corresponding to two or more pieces of moving object position information acquired by the moving object detection process, and may also include the case where "not a single point is placed."

[0235] However, cases in which "no points are placed" may be excluded, and such point placement processing virtually places "all or a part of" two or more points corresponding to two or more pieces of moving object position information acquired by the moving object detection processing in the virtual space (500). In other words, even if all of the two or more points are determined to be moving objects other than the human body 301, for example, one representative point of the two or more points (any one point, or one point corresponding to the average value of the two or more pieces of moving object position information corresponding to the two or more points) may be placed.

[0236] In addition, when all of the two or more points corresponding to the two or more pieces of moving object position information acquired by the moving object detection process are always positioned, it is not necessary to determine whether the moving object is a human body 301 or a moving object other than a human body 301 (whether the moving object position information is information corresponding to a human body 301 or information corresponding to a moving object other than a human body 301), as is done in this example.

[0237] In this example, the above-mentioned judgment is performed for each of the two or more pieces of acquired moving object position information, and if it is judged that the information corresponds to the human body 301, a point corresponding to that moving object position information is placed. Therefore, depending on the individual judgment results, there may be cases where all of the two or more points corresponding to the two or more pieces of moving object position information are placed, where only some of the points are placed (at least one point is placed), or where none of the points are placed.

[0238] In addition, for example, when the moving object position information to be judged is information that is not expected to obtain a valid judgment result, it may be excluded from the judgment target, and as a result, only some points may be placed, or none may be placed. In this example, for example, based on information such as the reception strength of the reflected wave Re corresponding to the moving object position information, it may be judged whether or not each of two or more moving object position information acquired by the moving object detection process is to be the target of the above judgment, and the above judgment may be performed only for the moving object position information judged to be the target.

[0239] (10-2-1) Point arrangement details In the point placement process, for example, only the points corresponding to the human body 301 among the moving bodies (i.e., some of the two or more points) may be placed. In other words, points corresponding to moving objects other than the human body 301 do not have to be placed. However, points corresponding to moving objects other than the human body 301 may also be placed (i.e., all of the two or more points).

[0240] In addition, the point placement process may place points corresponding to the human body 301 and living bodies (such as pets: not shown) other than the human body 301 among the moving bodies. In other words, points corresponding to moving bodies other than living bodies may be excluded from placement targets.

[0241] In this way, by acquiring two or more differences with different time differences for three or more pieces of position identifiable information, an increase in the number of points to be placed in the virtual space (500) (increasing the number of points in the point cloud 501) can be expected. As a result, it becomes possible to more accurately determine whether the point cloud 501 corresponds to a moving object such as a human body 301 or a stationary object 302, and the accuracy of moving object detection using reflected waves of transmitted radio waves, for example, the accuracy of estimation regarding a moving object using the radio wave sensor 1, can be improved.

[0242] Furthermore, as a result of improving the accuracy of estimation regarding a moving object in this way, the feasibility of controlling the device 200 using the radio wave sensor 1 (for example, the control accuracy of the device control device 2B described later) is improved.

[0243] (10-2-2) Estimation of moving object location information: Is the moving object a human body or a non-human moving object? The moving object is either the human body 301 or a moving object other than the human body 301. The moving object other than the human body 301 is, for example, the electric blind 200c that opens and closes electrically, but it may be any object that moves, such as a cleaning robot that cleans while moving. The moving object other than the human body 301 may also include the body of a living organism other than a human (for example, a pet kept by a person).

[0244] The moving object detection process further estimates whether each of the two or more pieces of moving object position information acquired corresponds to the human body 301 or a moving object (200c) other than the human body 301, based on at least the change in the difference. The point arrangement process virtually arranges, in the virtual space (500), points corresponding to the moving object position information that the moving object detection process estimates to correspond to the human body 301, among the two or more pieces of moving object position information acquired by the moving object detection process. In other words, points corresponding to the moving object position information that the human body detection process estimates to correspond to a moving object other than the human body 301 are excluded from the objects to be arranged.

[0245] In this way, in the moving object detection system 100A, when detecting the human body 301, for example, when making inferences regarding the human body 301, by obtaining two or more differences with different time differences for three or more pieces of position-identifying information, it becomes possible to detect various movements of the human body 301 (for example, body movement, slight respiratory movement, hand and foot movement, etc.: see Figure 12), thereby improving the detection accuracy of the human body 301, for example, improving the accuracy of inferences regarding the human body 301.

[0246] (10-2-3) Modification of estimation of moving object location information: Is a moving object other than a human body a living object or a non-living object? The moving object other than the human body 301 may be, for example, either a living body other than the human body 301 (for example, the body of a living thing such as a pet kept by a person: not shown) or a moving object other than a living body (such as the motorized blinds 200c).

[0247] In this case, the point placement process places points corresponding to a living body in the virtual space 500. In other words, points corresponding to moving objects other than a living body are excluded from the points to be placed.

[0248] In this example, the device control process (described later) may perform control such as turning on lighting fixtures or turning on an air conditioner when a point cloud 501 corresponding to a living body is detected (for example, when a person or pet enters a room).

[0249] Furthermore, the moving object detection process may distinguish multiple points in the virtual space (500) into a point group 501 corresponding to the human body 301 and a point group 501 corresponding to a non-human living body based on the distribution of multiple points in the virtual space (500) (such as the shape of the cluster CL).

[0250] The device control process may perform different device control depending on whether the point cloud 501 corresponds to the human body 301 or corresponds to a living body other than the human body 301 (for example, a pet). In detail, the device control process may perform control such that, for example, the television is turned on when the point cloud 501 corresponding to the human body 301 is detected, but the television is not turned on even when the point cloud 501 corresponding to a living body other than the human body 301 (a pet) is detected. In this way, by performing different device control depending on whether the target object is the human body 301 or a living body other than the human body 301, it is possible to make the symbiotic environment between people and pets more comfortable, and further to achieve both comfort and energy saving.

[0251] (10-2-4) Estimation taking into account the received strength of reflected waves The moving object detection process estimates whether each of the two or more acquired moving object position information corresponds to a human body 301 or a moving object (200c) other than the human body 301 based on at least one of the reception intensity of the reflected wave Re corresponding to the moving object position information and the change in the reception intensity, in addition to the change in the difference.

[0252] In this way, by utilizing at least one of the reception strength and the change in reception strength of the reflected wave Re in addition to the change in the difference, the accuracy of estimation as to whether the moving object (301, 200c) is a human body 301 or a moving object (200c) other than the human body 301 is improved.

[0253] (10-2-5) Point Cloud Estimation The moving object detection process estimates whether the point cloud 501 as a whole corresponds to the human body 301 or a moving object (200c) other than the human body 301 based on at least one of the distribution and the change in distribution of the point cloud 501. In this way, by using at least one of the distribution and the change in distribution of the point cloud 501 arranged in the virtual space (500), it is possible to improve the accuracy of estimation as to whether the moving object (301, 200c) is the human body 301 or a moving object (200c) other than the human body 301.

[0254] (10-2-6) Frame Difference The radio wave sensor 1 performs transmission and reception operations in a cycle of N times (N is an integer equal to or greater than 1) per frame, with a predetermined time being one frame. As a result, N pieces of location identifiable information are output from the radio wave sensor 1 per frame. The storage process stores 3×N or more pieces of location identifiable information corresponding to three or more frames. The difference acquisition process selects one representative piece of location identifiable information representing a frame from each of the three or more frames among the 3×N or more pieces of location identifiable information corresponding to three or more frames stored in the storage process. Then, the difference acquisition process acquires two or more differences for the selected three or more pieces of representative location identifiable information. The moving object detection process acquires two or more pieces of moving object location information based on two or more differences acquired for the three or more pieces of representative location identifiable information each representing three or more frames.

[0255] This makes it possible to select three or more pieces of representative location identifiable information among three or more frames, and to obtain two or more differences for the selected three or more pieces of representative location identifiable information.

[0256] (10-2-7) Intraframe Difference The radio wave sensor 1 performs transmission and reception operations in a cycle of N times (N is an integer equal to or greater than 3) per frame, with a predetermined time being one frame, and outputs N or more received signals per frame. The conversion process converts each of the N or more received signals output in one frame into position identifying information. The storage process stores at least the N pieces of position identifying information corresponding to one frame.

[0257] The difference acquisition process selects three or more representative pieces of location identifiable information representing one frame from N or more pieces of location identifiable information corresponding to one or more frames held in the holding process.The difference acquisition process then acquires two or more differences for the three or more selected pieces of representative location identifiable information.The moving object detection process acquires two or more pieces of moving object location information based on the two or more differences acquired for the three or more pieces of representative location identifiable information representing one frame.

[0258] This makes it possible to select three or more pieces of representative location identifiable information within one frame, and to obtain two or more differences for the selected three or more pieces of representative location identifiable information.

[0259] (10-2-7a) Frame Combination The holding process further holds N pieces of position identifiable information corresponding to the next frame after the one frame. When two or more pieces of moving object position information acquired by the moving object detection process for three or more pieces of representative position identifiable information all correspond to a moving object (200c) other than the human body 301, the difference acquisition process sets the one frame and the next frame as a new frame. Then, the difference acquisition process selects three or more pieces of representative position identifiable information representative of the new frame from the 2×N pieces of position identifiable information corresponding to the new frame, and acquires two or more differences for the selected three or more pieces of representative position identifiable information. The moving object detection process acquires two or more pieces of moving object position information based on two or more differences acquired by the difference acquisition process for the three or more pieces of representative position identifiable information representative of the new frame.

[0260] As a result, when two or more differences cannot be obtained within one frame, a new frame is formed with the next frame, and two or more differences can be obtained within the new frame.

[0261] (10-2-8) Specific examples of motion detection (10-2-8a) One-dimensional position determination using a single antenna The radio wave sensor 1 has at least one antenna that receives a reflected wave Re. The received signal is an IF signal that is generated based on the transmitted wave Tr and the reflected wave Re received by one antenna for one transmission / reception operation. The IF signal is a signal that indicates the frequency difference between the transmitted wave Tr and the reflected wave Re, and is generated by mixing the transmitted wave Tr and the reflected wave Re.

[0262] The transformation process is a Fourier transform process. The Fourier transform process is a process of performing a Fourier transform on an IF signal. The location identifying information is a Fourier transform result of the Fourier transform process. The storage process stores a plurality of Fourier transform results corresponding to one antenna.

[0263] The moving object detection process includes a distance measurement process. The distance measurement process in this example is a process of measuring the distance from one antenna to an object estimated to be a moving object (301, 200c) based on the difference between multiple Fourier transform results corresponding to one antenna. The moving object position information is one-dimensional position information based on the distance measurement result of the distance measurement process.

[0264] This makes it possible to improve the accuracy of detecting a moving object, for example, when performing a process (moving object detection process) for detecting a moving object (301, 200c) by the radio wave sensor 1 using radio waves modulated by the FMCW method. Even if there is only one antenna for receiving the reflected wave Re, it is possible to at least improve the accuracy of measuring the distance to the moving object (301, 200c) (specifying the one-dimensional position).

[0265] (10-2-8b) Identifying two-dimensional or three-dimensional positions using multiple antennas The radio wave sensor 1 has a plurality of antennas that receive the reflected wave Re. A plurality of IF signals corresponding to the plurality of antennas are output from the radio wave sensor 1 for one transmission / reception operation. For each of the plurality of antennas, the conversion unit 11 performs a conversion process, the holding unit 12 performs a holding process, the difference acquisition unit 13 performs a difference acquisition process, and the moving object detection unit 14 performs a moving object detection process including a distance measurement process.

[0266] The location identifiable information is a Fourier transform result group consisting of multiple Fourier transform results corresponding to multiple antennas. The storage process stores the multiple Fourier transform result groups. The moving object detection process includes multiple ranging processes that measure the distance from each of the multiple antennas to an object estimated to be a moving object (301, 200c) based on the difference between the multiple Fourier transform result groups for each of the multiple antennas. The moving object position information is two-dimensional or three-dimensional position information based on multiple ranging results corresponding to the multiple ranging processes.

[0267] This makes it possible to improve the accuracy of the moving object detection process that identifies the two-dimensional position (e.g., two-dimensional coordinates in the two-dimensional virtual space 500, or a set of direction and distance, etc.) or three-dimensional position (e.g., three-dimensional coordinates in the three-dimensional virtual space 500, or a set of direction and distance, etc.) of the moving object (301, 200c).

[0268] (10-2-9) Motion detection method and program The moving object detection method in this example includes at least step S1 (conversion step), step S2 (storage step), step S4 (difference acquisition step), steps S5 and S6 (moving object detection step), and step S7 (point arrangement step) among the various steps described in the embodiment. The program causes one or more processors to execute this moving object detection method.

[0269] (10-3) Equipment control device The device control unit 225 constituting the device control device 2B performs device control processing to control the device 200 based at least on a point cloud 501, which is a collection of multiple points arranged in a virtual space (500). In this way, the radio wave sensor 1 acquires the point cloud 501 corresponding to the moving object (301, 200c), and the device 200 is controlled based on the acquired point cloud 501, thereby achieving control of the device 200 using the radio wave sensor 1.

[0270] In particular, by acquiring a point cloud 501 corresponding to the human body 301 and controlling the device 200 based on the acquired point cloud 501, it is possible to realize control of the device 200 based on the position and movement of the human body 301.

[0271] (10-3-1) Posture Estimation and Action Detection The moving object detection process by the moving object detection device 2A includes a posture estimation process. The posture estimation process is a process for estimating the posture of the human body 301. The posture estimation process estimates the posture of the human body 301 based on, for example, the distribution of the point cloud 501 (FIGS. 13A to 14C). Specifically, the posture estimation process may be, for example, the process described in the embodiment with reference to FIG. 7.

[0272] The behavior detection unit 224 executes a behavior detection process. The behavior detection process in this example is a process for detecting a behavior of a person corresponding to the human body 301 based on the posture or a change in posture estimated by the posture estimation process. The device control process in this example controls the device 200 based on the behavior detected by the behavior detection process.

[0273] In this way, the posture or posture change of the human body 301 is estimated based on the distribution of the point cloud 501, the human behavior corresponding to the human body 301 is detected based on the estimation result, and the device 200 is controlled based on the detection result, thereby realizing control of the device 200 based on the posture of the human body 301 and ultimately the human behavior.

[0274] (10-3-2) Acquisition of environmental information The information acquisition unit 226 executes the information acquisition process. As described above, the information acquisition process is a process for acquiring environmental information related to the environment in which the human body 301 exists. The moving object detection process detects behavior based on the environmental information acquired by the information acquisition process.

[0275] In this way, by using environmental information in addition to posture or posture changes, the accuracy of detecting behavior and therefore the accuracy of controlling the device 200 can be improved.

[0276] (10-3-3) Equipment control using automatic control information The device control process uses pre-stored automatic control information to control the device 200 based on the behavior, thereby making it possible to automatically control the device 200 based on the behavior using the automatic control information.

[0277] (10-3-4) Accumulation of automatic control history information The history accumulation unit 227 in this example executes a history accumulation process. The history accumulation process in this example accumulates automatic control history information. Accumulating the automatic control history information in this way enables a learning process based on the automatic control history information, and ultimately makes it possible to improve the control accuracy using the automatic control history information. As described above, the automatic control history information associates the control content of the device 200 based on behavior with one or more pieces of information among the time, environmental information, and a person identifier. This allows control appropriate to each individual person.

[0278] (10-3-5) Further accumulation of manual control history information The history accumulation process further accumulates manual control history information. The manual control history information is information on the control history of the device 200 based on operations on the device 200. The operations are, for example, operations to adjust the brightness of lighting as described above, and are accepted via an operating device such as a remote control. In this way, by further accumulating the manual control history information, it becomes possible to improve the control accuracy using two types of control history information, automatic and manual.

[0279] For example, based on the accumulated automatic control history information and manual control history information, if it is detected that no operation to cancel the automatic control (e.g., dim the lights) is performed within a certain time (e.g., within one minute) after automatic control to brighten the lights in response to the detection of sitting down is performed (hereinafter referred to as "tacit inaction") a specified number of times or more in a specified period (e.g., three or more times in a week), the system learns that the control content is appropriate, and by repeating this learning process, control accuracy can be improved.

[0280] However, the above-mentioned learning process can be realized by accumulating only the automatic control history information, without accumulating the manual control history information. For example, while accumulating the automatic control history information, a learning process may be repeated in which it is determined whether or not tacit inaction has been detected a predetermined number of times or more in a predetermined period (for example, three or more times in one week) based on real-time operation information, and if tacit inaction has been detected a predetermined number of times or more in a predetermined period, the control content is learned to be appropriate.

[0281] Alternatively, only manual control history information may be stored without storing automatic control history information.For example, based on the stored manual control history information, if it is detected that an operation to brighten the lights has been accepted within a certain time (e.g., within one minute) from the detection of sitting down, and this is detected a predetermined number of times or more in a predetermined period (e.g., three times or more in one week), the control content "brighten the lights in response to the detection of sitting down" is registered in the automatic control information.By repeating this process, it is possible to improve the control accuracy of automatic control of the brightness of the lights, diversify the content of automatic control, etc.

[0282] As described above, the manual control history information associates the control content of the device 200 based on the operation with one or more pieces of information among the time, the environmental information, and the person identifier. For example, the learning unit 228 executes a learning process for updating the automatic control information when the time and the operation indicated by the control history information satisfy a predetermined update condition. This learning process using the automatic control history information makes it possible to realize control suited to the behavior of each individual person.

[0283] (10-3-6) Updating automatic control information Alternatively, the learning unit 228 may execute a learning process for updating the automatic control information when the time and control content indicated by the information and the time and operation indicated by the manual control history information regarding the control history of the device 200 based on the operation on the device 200 satisfy a predetermined update condition. By such a learning process using two types of control history information, automatic and manual, it becomes possible to realize control suited to the behavior and operation of each person.

[0284] (10-3-7) Clustering The virtual space (500) in this example is a three-dimensional space 500, similar to that in the embodiment. In the moving object detection process, a cluster group (CL, CL1, CL2) which is a set of one or more clusters (CL, CL1, CL2) is obtained by performing clustering on the point cloud 501, and the posture of the human body 301 is estimated based on the distribution of the cluster group (CL, CL1, CL2) in the three-dimensional space 500. This makes it possible to accurately estimate the posture of the human body 301 based on the distribution of the cluster group (CL, CL1, CL2).

[0285] (10-3-8) Posture Estimation The moving object detection process estimates the posture of the human body 301 based on the ratios of the length, width, and height of the three-dimensional figure 502 surrounding the cluster group (CL, CL1, CL2). This makes it possible to accurately and easily estimate the posture of the human body 301 based on the ratios of the length D, width W, and height H of the three-dimensional figure 502 surrounding the cluster group (CL, CL1, CL2).

[0286] (10-3-9) Behavior Detection The behavior detection process detects a non-operation behavior, which is a behavior different from an operation of the human body 301 on the device 200, based at least on the posture or a change in posture estimated by the posture estimation process. The device control process controls the device 200 based at least on the detected non-operation behavior. In this way, by controlling the device 200 based on the non-operation behavior (for example, an action not intended to operate the device 200, such as a daily behavior), it is possible to enable device control without intentional operation of the device 200 (operation-less device control).

[0287] (10-3-10) Device control method and program The device control method detects a moving object and controls the device 200 based on the detection result. The device control method uses, for example, a radio wave sensor 1 to estimate a moving object (human body 301, motorized blinds 200c) and controls the device 200 based on the estimation result. The radio wave sensor 1 transmits a transmission wave Tr, which is a radio wave modulated in a predetermined manner, toward a real space (inside a room 400), receives a reflected wave Re from the real space (400), and performs a transmission / reception operation to output a reception signal based on the transmission wave Tr and the reflected wave Re. The device control method includes a conversion step (S1), a holding step (S2), a difference acquisition step (S4), a moving object detection step (S5, S6), a point arrangement step (S7), and a device control step (S11). The conversion step (S1) performs a conversion process of converting the received signal output by the radio wave sensor 1 into position-identifiable information capable of identifying the position of each of one or more objects (301, 200c, 302) existing in the real space (400). The storage step (S2) performs a storage process of storing the position-identifiable information converted by the conversion process. The difference acquisition step (S4) performs a difference acquisition process of acquiring a difference between the multiple pieces of position-identifiable information stored in the storage process. The moving object detection steps (S5, S6) detect a moving object (301, 200c) based on the difference acquired by the difference acquisition process. The moving object detection steps (S5, S6) perform a moving object detection process of, for example, estimating whether each of one or more objects is a moving object or a stationary object (302) based on the acquired difference, identifying the position of the object estimated to be a moving object (301, 200c), and acquiring moving object position information indicating the identified position. The point arrangement step (S7) performs a point arrangement process in which points corresponding to the moving object position information acquired by the moving object detection process are virtually arranged in a virtual space (500) corresponding to the real space (400). The device control step (S11) controls the device 200 based at least on a point cloud 501 which is a collection of a plurality of points arranged in the virtual space (500). The program causes one or more processors to execute this device control method.

[0288] Second embodiment Next, a second embodiment and a first and second modified examples of the present disclosure will be described with reference to FIGS. 1 to 20 (particularly FIGS. 4 and 18).

[0289] The second embodiment is similar to the first embodiment except for the process of calculating one-to-many frame differences, i.e., multiple differences of different lengths. In the following, the description of the already mentioned items will be omitted or simplified, and the points in common with the first embodiment and the points of difference from the first embodiment will be described in detail.

[0290] (1) Commonalities with the first embodiment (1-1) Motion detection using multiple time differences of different lengths The moving object detection system 100 of this embodiment is a moving object detection system that detects a moving object using multiple time differences of different lengths, similar to the moving object detection system 100 of the first embodiment. The moving object detection system 100 detects moving objects (a human body 301, an electric blind 200c: hereinafter simply referred to as "moving objects (301, 200c)") within an area R1 using a signal received by a radio wave sensor 1.

[0291] The received signal is a signal based on a reflected wave Re of a radio wave (i.e., a transmitted wave Tr) transmitted from the radio wave sensor 1. However, the source of the transmitted wave Tr may be a transmitter or a transmitting antenna other than the radio wave sensor 1. The transmitted wave Tr is a transmitted wave by the FMCW method, but may be a radio wave of a method other than the FMCW method, such as a pulse method or a Doppler method. When the transmitted wave Tr is a radio wave of the FMCW method, the received signal is a result of decomposing the IF signal into a frequency spectrum by a Fourier transform such as FFT, but it may be an IF signal or other position-identifying information. Also, when the transmitted wave Tr is a radio wave of a method other than the FMCW method, it is sufficient that the signal contains some position-identifying information.

[0292] The moving object detection system 100 includes a processing unit 22. The processing unit 22 executes processing based on the received signal. The processing unit 22 calculates a plurality of time differences (ΔA1 to ΔA5) of different lengths of the received signal. The plurality of time differences of different lengths are, for example, five time differences corresponding to the first time difference (T) to the fifth time difference (5×T) between the reference frame and each of the series of frames Fr1 to Fr5 following the reference frame Fr0 shown in FIG. 10. Specifically, the plurality of time differences of different lengths are, for example, a time difference ΔA1 corresponding to the first time difference (T), a time difference ΔA2 corresponding to the second time difference (2×T), ... and a time difference ΔA5 corresponding to the fifth time difference (5×T).

[0293] Then, the processing unit 22 detects a moving object (301, 200c) by acquiring a distribution of each moving point whose time difference in the region R1 is equal to or greater than a threshold value from the calculated multiple time differences (ΔA1 to ΔA5).

[0294] Note that the calculation of a plurality of time differences having mutually different lengths may be performed simultaneously (first embodiment) or sequentially (second embodiment).

[0295] In addition, the moving points referred to here are points (i.e., moving points, each of a plurality of points constituting the surface of a moving object) corresponding to a frequency whose intensity is equal to or greater than a threshold value in the FFT result of FFTing the IF signal when the transmission wave Tr is an FMCW type radio wave. The distribution of each moving point is the point distribution when each point corresponding to each moving point is plotted in the three-dimensional virtual space 500.

[0296] Furthermore, the multiple time differences of different lengths are, for example, five inter-frame differences ΔA1 to ΔA5 (hereinafter, sometimes simply referred to as "ΔA1 to ΔA5"). By using the multiple time differences (ΔA1 to ΔA5) of different lengths, the difficulty of detecting a moving object due to the type of movement of the moving object (301, 200c) (for example, a movement over a long period of time such as moving, or a movement over a short period of time such as slight movement: hereinafter, simply referred to as "long and short") is reduced, and the detection accuracy of the moving object (301, 200c) can be improved.

[0297] (1-1-1) Detection of various human body movements The moving objects (301, 200c) to be detected by the moving object detection system 100 include a human body 301, and the multiple time differences (ΔA1 to ΔA5) include long-term time differences and short-term time differences shorter than the long-term time differences, corresponding to the movement of the human body 301 over a long period of time and movement over a period of time shorter than the long period of time.

[0298] The long-term movement of the human body 301 is, for example, movement such as walking, or body movement such as turning over in bed, etc. The short-term movement of the human body 301 is, for example, slight breathing movement, or movement of the limbs, etc.

[0299] Of the five inter-frame differences ΔA1 to ΔA5, the three inter-frame differences ΔA1 to ΔA3 shown in Fig. 12 are inter-frame differences suitable for detecting various movements of the human body 301. The inter-frame difference ΔA1 is a time difference corresponding to the fifth time difference (5×T: see Fig. 10) and is suitable for detecting body movements. The inter-frame difference ΔA2 is a time difference corresponding to the third time difference (3×T) and is suitable for detecting micro-respiratory movements. The inter-frame difference ΔA3 is a time difference corresponding to the second time difference (2×T) and is suitable for detecting movements of the limbs (hand and foot movements).

[0300] This makes it possible to reduce the difficulty in estimating a human body due to the type of human body movement (for example, walking, body movement, slight respiratory movement, etc.), and further improve the accuracy of human body detection.

[0301] (1-1-2) Detection of moving objects The processing unit 22 detects a moving object (301, 200c) moving within the region R1 based on the displacement of the distribution (e.g., cluster CL), that is, the position change of each moving point. The movement of the human body 301 is, for example, a behavior such as walking, and the movement of a moving object other than the human body 301 is, for example, the running of an automatic vacuum cleaner.

[0302] The moving object (301, 200c) is detected based on, for example, the displacement of the entire distribution, that is, the position change of each moving point along one direction. In this way, the moving object (301, 200c) can be detected based on the distribution of the cluster CL, etc., or the position change of each moving point constituting the distribution.

[0303] (1-1-3) Estimating human posture based on the height difference between the two ends of the distribution The processing unit 22 calculates the distance between both ends of the distribution in the height direction of the region R1 (see FIG. 2) (for example, the height H of the rectangular parallelepiped 502: see FIG. 14A), and estimates which of a plurality of postures including a standing posture, a sitting posture, and a lying posture, the posture of the human body 301 is in, based on the calculated distance (height H). Note that the lying posture is, for example, a lateral posture, a supine posture, a prone posture, etc., but may also include a fallen state.

[0304] The height direction of the region R1 is the direction from the floor surface side of the region R1 toward the ceiling R1b side, that is, the vertical direction (but upward). The height direction is the Z direction in the three-dimensional virtual space 500 shown in FIG. 13A and the like.

[0305] The two ends of the distribution are, for example, the upper cluster CL1 and the lower cluster CL2 in the distribution in the three-dimensional virtual space 500 shown in Fig. 14A. The distance between the two ends of the distribution is, for example, the distance between the upper cluster CL1 and the lower cluster CL2. The distance between the upper cluster CL1 and the lower cluster CL2 is, for example, the distance between the upper end of the upper cluster CL1 and the lower end of the lower cluster CL2, that is, the height H of the rectangular parallelepiped 502 that surrounds the distribution.

[0306] However, the distance between the upper cluster CL1 and the lower cluster CL2 may be the distance between the lower end of the upper cluster CL1 and the upper end of the lower cluster CL2. Also, the distance between both ends of the distribution may be the difference (i.e., the height difference) between the height of the upper end of the distribution (upper cluster CL1) relative to the floor surface and the height of the lower end of the distribution (lower cluster CL2) relative to the floor surface.

[0307] In this way, the posture of the human body 301 can be estimated by using the distance between both ends of the distribution in the height direction (height H, height difference).

[0308] (1-1-4) Placement of radio wave sensors The radio wave sensor 1 is provided on the ceiling R1a to improve the accuracy of posture estimation. However, the radio wave sensor 1 may be provided on the side wall R1a. The source of the radio waves is not limited to the radio wave sensor 1, and may be another transmitter or transmitting antenna. In this case, the source of the radio waves is located on the wall R1a side or the ceiling R1b side of the area R1. The radio wave sensor 1 is provided on the wall R1a or the ceiling R1b on the side where the source is located, receives the reflected wave Re, and outputs a received signal.

[0309] This makes it possible to improve the accuracy of detecting movement in the vertical or lateral direction.

[0310] The radio wave sensor 1 is provided on the ceiling R1b, transmits radio waves, receives reflected waves Re, and outputs a received signal.

[0311] This makes it possible to improve the accuracy of detecting movement in the height direction.

[0312] (2) Differences from the First Embodiment One of the differences between the present embodiment and the first embodiment is the method of calculating a plurality of time differences of different lengths.

[0313] (2-1) Difference calculation in the first embodiment: Concurrent calculation The processing unit 22 in the first embodiment calculates a plurality of time differences (ΔA1 to ΔA5) of different lengths at the same time. The same time means the same period, for example, simultaneously, and if the time difference between two calculation points is equal to or less than a threshold, it may be considered to be at the same time. A time difference that can be considered to be at the same time is, for example, a time difference corresponding to one frame period. Calculating at the same time means, for example, calculating a plurality of time differences (ΔA1 to ΔA5) within one frame period. This further improves the detection accuracy.

[0314] The one frame period is, for example, a period corresponding to the current frame. In the example of FIG. 10, the current frame is the fifth frame Fr5. Hereinafter, it may be referred to as "current frame (Fr5)". This makes it possible to suppress delays in the moving object detection process compared to when the one frame period in which the difference calculation is performed is a frame prior to the current frame.

[0315] In detail, the processing unit 22 of the first embodiment calculates the difference between each of a plurality of frames (e.g., four frames Fr0 to Fr4) from the reference frame Fr0 to the frame (e.g., the fourth frame Fr4) before the current frame (Fr5) and the current frame (Fr5) during the period of the current frame (Fr5). The reference frame Fr0 is a frame a predetermined time before the current frame (Fr5).

[0316] The predetermined time here is a frame period of a fixed length (for example, a five-frame period) corresponding to a number of frames (Fr1 to Fr5). As a result, a number of inter-frame differences (ΔA1 to ΔA5) are calculated during the current frame period.

[0317] According to the contemporaneous calculation of the first embodiment, multiple inter-frame differences (ΔA1 to ΔA5), which are multiple time differences (ΔA1 to ΔA5), are acquired during the current frame period, thereby making it possible to further improve detection accuracy while suppressing delays.

[0318] (2-2) Specific examples of calculations for the same period 4 from "START" to step S4, i.e., "processing for simultaneously calculating differences of different lengths", for example, the differences between each of a plurality of frames (Fr0-Fr4) from the reference frame Fr0 to the frame (e.g., frame Fr4) preceding the current frame (e.g., frame Fr5) and the current frame (Fr5) are calculated within the period of the current frame. The processing unit 22 may execute a plurality of calculations for simultaneously calculating a plurality of time differences, for example, simultaneously using a plurality of processors, or may execute the calculations sequentially using a single processor, and the order of execution of the plurality of calculations does not matter as long as the plurality of calculations are completed within the period corresponding to the current frame.

[0319] In detail, for example, when the current frame is frame Fr5, the processing unit 22 stores the four preceding frames Fr0 to Fr4 that precede the current frame Fr5, and calculates five inter-frame differences during the period corresponding to the current frame Fr5: an inter-frame difference ΔA5 between the current frame Fr5 and frame Fr0, which is five frames before the current frame Fr5; an inter-frame difference ΔA4 between the current frame Fr5 and frame Fr1, which is four frames before the current frame Fr5; an inter-frame difference ΔA3 between the current frame Fr5 and frame Fr2, which is three frames before the current frame Fr5; an inter-frame difference ΔA2 between the current frame Fr5 and frame Fr3, which is two frames before the current frame Fr5; and an inter-frame difference ΔA1 between the current frame Fr5 and frame Fr4, which is one frame before the current frame Fr5.

[0320] However, the subject of concurrency calculation is not limited to the difference between the current frame and each of the multiple preceding frames preceding the current frame, but may be, for example, the difference between a reference frame and each of a series of frames following the reference frame. That is, for example, when the current frame is frame Fr5, the processing unit 22 may store the reference frame Fr0 and a series of frames Fr1 to Fr5 following the reference frame Fr0, and calculate inter-frame differences between the reference frame Fr0 and each of the series of frames Fr1 to Fr5 during the period corresponding to the current frame Fr5.

[0321] (2-3) Difference calculation in this embodiment: sequential calculation The processing unit 22 in this embodiment sequentially calculates a plurality of time differences (ΔA1 to ΔA5) for a series of a plurality of periods. The series of a plurality of periods is, for example, a period corresponding to a series of a plurality of frames (Fr1, Fr2, ..., Fr5), specifically, a period corresponding to the first frame Fr1, a period corresponding to the second frame Fr2, ..., and a period corresponding to the fifth frame Fr5, etc. This makes it possible to improve the detection accuracy while reducing the amount of processing per period.

[0322] In detail, the processing unit 22 of the second embodiment calculates inter-frame differences (e.g., ΔA1, ΔA2, ... ΔA5) between a reference frame Fr0 and a current frame (e.g., each of Fr1, Fr2, ... Fr5: hereinafter referred to as "current frame (Fr1, Fr2, ... Fr5)") during the period of the current frame (Fr1, Fr2, ... Fr5), thereby obtaining multiple inter-frame differences (ΔA1 to ΔA5), which are multiple time differences (ΔA1 to ΔA5).

[0323] In this way, the current frame in the second embodiment is each of the series of frames (Fr1, Fr2, ... Fr5) that become the current frame following the reference frame Fr0. In other words, the reference frame Fr0 is a frame that precedes the current frame (Fr1, Fr2, ... Fr5) by a predetermined time (T, 2xT, ... 5xT).

[0324] Here, the predetermined time (T, 2×T, ... 5×T) is a frame period (1 frame period, 2 frame periods, ... 5 frame periods) having a length corresponding to the time difference (first time difference, second time difference, ... fifth time difference) of the current frame (Fr1, Fr2, ... Fr5) with respect to the reference frame Fr0. This allows multiple inter-frame differences (ΔA1 to ΔA5) to be calculated sequentially during the period in which each of the series of multiple frames (Fr1 to Fr5) (Fr1, Fr2, ... Fr5) is the current frame.

[0325] According to the sequential calculation of the present embodiment, by sequentially calculating a plurality of time differences, it is possible to improve detection accuracy while suppressing delays and reducing the amount of processing per frame period.

[0326] (2-4) Example of operation The control device 2 of this embodiment basically operates according to the flowcharts of Figures 4 to 7, similarly to the control device 2 of the first embodiment. However, in this embodiment, in the processes shown in Figures 4 and 5, the processes from "start" to step S4, i.e., "processing for simultaneously calculating differences between different lengths", are replaced with processes including eight steps S1a, S1, S2a, S2b, S4a to S4c, and S45, as shown in Figure 18, i.e., "processing for sequentially calculating differences between different lengths".

[0327] When the operation is started, the processing unit 22 sets the variable i to an initial value "0" (step S1a).

[0328] Next, the processing unit 22 determines whether or not an FFT result group has been output from the radio wave sensor 1 (step S1). If it is determined in step S1 that an FFT result group has not been output (No), the process proceeds to step S13 (see FIG. 4).

[0329] If it is determined in step S1 that the FFT result group has been output (Yes), the processing unit 22 determines whether the variable i is "0" (step S2a). If it is determined in step S2a that the variable i is "0", the processing unit 22 holds the FFT result group output in step S1 as a reference FFT result group (step S2b). Then, the process proceeds to step S4b.

[0330] If it is determined in step S2a that the variable i is not "0" (No: i.e., "i≧1"), the distance measurement unit 221 obtains the difference between the current FFT result group (i.e., the FFT result group most recently output in step S1) and the FFT result group held in step S2b (i.e., the reference FFT result group which is the FFT result group that is the i-th time difference (=i×T seconds) before the current FFT result group), and sets the obtained difference to the variable "i-th difference" (step S4a). Then, the process proceeds to step S4b.

[0331] In step S4b, the processing unit 22 judges whether or not the first to the Kth K differences have been acquired. If it is judged that the first to the Kth K differences have not yet been acquired (No in step S4b), the processing returns to step S1.

[0332] If it is determined in step S4b that the first to Kth K differences have been acquired (Yes), the distance measuring unit 221 selects L differences (for example, three differences ΔA1, ΔA2, and ΔA3: see FIG. 12) corresponding to various movements of the human body 301 from the K differences (for example, five differences ΔA1 to ΔA5: only a part of which is shown in FIG. 12) (step S45). After that, the process proceeds to step S5 (see FIG. 4).

[0333] (2-5) Specific examples Through the above-described processing by the processing unit 22, for example, five inter-frame differences ΔA1 to ΔA5 are acquired during a five-frame period corresponding to a series of frames Fr1 to Fr5 after the reference frame Fr0 is held. In detail, for example, during a period in which the current frame is frame Fr0, the processing unit 22 stores frame Fr0 as the reference frame Fr0.

[0334] Next, while the current frame is Fr1, the processing unit 22 calculates an inter-frame difference ΔA1 between the current frame Fr1 and the reference frame Fr0. Next, while the current frame is frame Fr2, the processing unit 22 calculates an inter-frame difference ΔA2 between the current frame Fr2 and the reference frame Fr0. Next, while the current frame is frame Fr3, the processing unit 22 calculates an inter-frame difference ΔA3 between the current frame Fr3 and the reference frame Fr0. Next, while the current frame is frame Fr4, the processing unit 22 calculates an inter-frame difference ΔA4 between the current frame Fr4 and the reference frame Fr0. Then, while the current frame is frame Fr5, the processing unit 22 calculates an inter-frame difference ΔA5 between the current frame Fr5 and the reference frame Fr0.

[0335] In this way, five inter-frame differences ΔA1 to ΔA5 are obtained during a five-frame period corresponding to the series of frames Fr1 to Fr5.

[0336] Similarly, during the period when the current frame is frame Fr0, frame Fr0 is stored as the reference frame Fr0.

[0337] Similarly, five inter-frame differences ΔA6 to ΔA10 are obtained during a five-frame period corresponding to a series of frames Fr7 to Fr11 after the reference frame Fr6 is held. In detail, for example, during a period in which the current frame is frame Fr6, frame Fr6 is stored as the reference frame Fr6. Next, during a period in which the current frame is Fr7, an inter-frame difference ΔA6 between the current frame Fr7 and the reference frame Fr6 is calculated, and during a period in which the current frame is frame Fr8, an inter-frame difference ΔA7 between the current frame Fr8 and the reference frame Fr6 is calculated. Next, during the period when the current frame is frame Fr9, the inter-frame difference ΔA9 between the current frame Fr9 and the reference frame Fr6 is calculated, during the period when the current frame is frame Fr10, the inter-frame difference ΔA9 between the current frame Fr10 and the reference frame Fr6 is calculated, and during the period when the current frame is frame Fr11, the inter-frame difference ΔA10 between the current frame Fr11 and the reference frame Fr6 is calculated.

[0338] In this way, five inter-frame differences ΔA6 to ΔA11 are obtained during a five-frame period corresponding to the series of frames Fr6 to Fr11.

[0339] (2-6) First modified example of the motion detection system: Wiring device A first modified example of the moving object detection system is, for example, a wiring device 100 as shown in Fig. 19. The wiring device 100 includes a radio wave sensor 1 and a control device 2, and a control signal based on a processing result of a processing unit 22 constituting the control device 2 is transmitted to a lighting device 200a by an output unit 23. The lighting device 200a performs, for example, turning on, dimming, or turning off the lighting device in response to the control signal.

[0340] (2-7) Second variant of motion detection system: lighting fixture A second modified example of the moving object detection system is, for example, a lighting fixture 100 as shown in Fig. 20. The lighting fixture 100 includes a radio wave sensor 1, a control device 2, and a fixture body 200A. The fixture body 200A includes, for example, a light-emitting element such as an LED, a drive circuit for driving the light-emitting element, and a power supply for supplying power to the drive circuit. In the lighting fixture 100, a control signal based on a processing result of a processing unit 22 constituting the control device 2 is output by an output unit 23 to the fixture body 200A, and the fixture body 200A performs, for example, turning on, dimming, or turning off the light in response to the control signal.

[0341] (2-8) Motion detection method and program The moving object detection method is a moving object detection method that detects a moving object (301, 200c) in an area R1 using a received signal. The received signal is a signal based on a reflected wave Re of a transmission wave Tr, which is a transmitted radio wave. The moving object detection method includes processing steps (steps S1 to S12 in the first embodiment; S1a to S45 and S5 to S12 in the second embodiment). In the processing steps, processing based on the received signal is executed. In the processing steps (S1 to S12; S1a to S45 and S5 to S12), multiple time differences (five inter-frame differences ΔA1 to ΔA5) of different lengths of the received signal are calculated, and a distribution of each moving point in the area R1 whose time difference is equal to or greater than a threshold is obtained from the multiple time differences (ΔA1 to ΔA5), thereby detecting the moving object (301, 200c). The program causes one or more processors to execute the moving object detection method.

[0342] (3) Summary The moving object detection system (100) according to the first aspect is a moving object detection system (100) that detects moving objects (human body 301, motorized blinds 200c) in an area (R1) using a received signal. The received signal is a signal based on a reflected wave (Re) of a radio wave (transmitted wave Tr) transmitted in each of a plurality of frames (Fr0 to Fr5). The moving object detection system (100) includes a processing unit (22) that executes processing based on the received signal. The processing unit (22) calculates multiple time differences (ΔA1 to ΔA5), which are the difference between two received signals received in two different frames out of multiple frames (Fr0 to Fr5), at multiple different time intervals (T, 2×T, ... 5×T), to calculate multiple time differences (ΔA1 to ΔA5), and detects moving objects (301, 200c) corresponding to the one or more moving points by obtaining a distribution of one or more moving points (point distribution when points corresponding to each moving point are plotted in three-dimensional virtual space 500) whose time differences within a region (R1) are greater than or equal to a threshold value from the multiple time differences (ΔA1 to ΔA5).

[0343] According to this aspect, by using a plurality of time differences (ΔA1 to ΔA5) of different lengths, the difficulty in detecting a moving object due to the type of motion (for example, long or short) of the moving object is reduced, and as a result, the accuracy of detecting a moving object can be improved.

[0344] In the moving object detection system (100) according to the second aspect, in the first aspect, the processing unit (22) sequentially calculates a plurality of time differences (ΔA1 to ΔA5) over a plurality of frames (Fr0 to Fr5).

[0345] According to this embodiment, by sequentially calculating multiple time differences (ΔA1 to ΔA5) for multiple frames (Fr0 to Fr5), i.e., a series of multiple periods, it is possible to reduce the amount of processing per frame (period) while improving detection accuracy.

[0346] In the moving object detection system (100) according to the third aspect, in the second aspect, the processing unit (22) holds a reception signal in a reference frame (Fr0), which is one frame among a plurality of frames (Fr0-Fr5), when the reference frame (Fr0) is a current frame. Then, when each of two or more frames (Fr1-Fr5) after the reference frame (Fr0) among the plurality of frames (Fr0-Fr5) (Fr1, Fr2, ..., Fr5) is the current frame, the processing unit (22) acquires a plurality of time differences by calculating differences between the reception signal in the reference frame (Fr0) and the reception signal in the current frame (Fr1, Fr2, ..., Fr5).

[0347] According to this aspect, by sequentially calculating multiple inter-frame differences (ΔA1 to ΔA5), which are the time differences between the received signal in the reference frame (Fr0) and the received signal in each of a series of multiple frames (Fr1 to Fr5) following the reference frame (Fr0), during the period in which each of the series of multiple frames (Fr1 to Fr5) (Fr1, Fr2, ... Fr5) is the current frame, it is possible to improve detection accuracy while suppressing delays and reducing the amount of processing per frame period.

[0348] In the moving object detection system (100) according to the fourth aspect, in the first aspect, the processing unit (22) calculates a plurality of time differences (ΔA1 to ΔA5) for each of a plurality of frames (Fr1 to Fr5).

[0349] According to this embodiment, by simultaneously calculating a plurality of time differences (ΔA1 to ΔA5), it is possible to further improve the detection accuracy.

[0350] In the moving object detection system (100) according to the fifth aspect, in the fourth aspect, the processing unit (22) acquires the multiple time differences by calculating, when each of a plurality of frames (Fr1 to Fr5) is a current frame, the difference between the received signal in the current frame and each of two or more frames prior to the current frame among the plurality of frames.

[0351] According to this embodiment, by calculating a plurality of inter-frame differences (ΔA1 to ΔA5) within the period of the current frame (Fr5), it is possible to suppress delays and further improve detection accuracy.

[0352] In the moving object detection system (100) according to the sixth aspect, in any one of the first to fifth aspects, the processing unit (22) detects a moving object (301, 200c) based on a displacement of the distribution.

[0353] According to this embodiment, a moving object (301, 200c) can be detected based on the change in distribution.

[0354] In a moving object detection system (100) according to a seventh aspect, in any one of the first to sixth aspects, the moving object (301, 200c) includes a human body (301). A processing unit (22) calculates a distance between both ends of a distribution in a height (Z) direction of a region (R1), and estimates, based on the distance, which of a plurality of postures the human body (301) is in, including a standing posture, a sitting posture, and a lying posture.

[0355] According to this aspect, the posture of the human body can be estimated by using the distance between both ends in the height direction of the distribution (the height H of the rectangular parallelepiped in the first embodiment, and the height difference between both ends in this embodiment).

[0356] A moving object detection system (100) according to an eighth aspect is any one of the first to seventh aspects, and further includes a radio wave sensor (1). The radio wave sensor (1) is located on the wall (R1a) side or the ceiling (R1b) side of the area (R1). The radio wave sensor (1) transmits radio waves (transmission waves Tr), receives reflected waves (Re), and outputs a reception signal.

[0357] According to this aspect, it is possible to improve the accuracy of detecting movement in the height direction or the lateral direction.

[0358] A wiring device (100) according to a ninth aspect is a wiring device (100) that detects a moving object (a human body 301, an electric blind 200c) in an area (R1) by using a received signal. The received signal is a signal based on a reflected wave (Re) of a radio wave (transmitted wave Tr) transmitted in each of a plurality of frames (Fr0 to Fr5). The wiring device (100) includes a processing unit (22) that executes processing based on the received signal. The processing unit (22) calculates multiple time differences (ΔA1 to ΔA5), which are the difference between two received signals received in two different frames out of multiple frames (Fr0 to Fr5), at multiple different time intervals (T, 2×T, ... 5×T), to calculate multiple time differences (ΔA1 to ΔA5), and detects moving objects (301, 200c) corresponding to the one or more moving points by obtaining a distribution of one or more moving points (point distribution when points corresponding to each moving point are plotted in three-dimensional virtual space 500) whose time differences within a region (R1) are greater than or equal to a threshold value from the multiple time differences (ΔA1 to ΔA5).

[0359] According to this aspect, by using a plurality of time differences (ΔA1 to ΔA5) having mutually different lengths, the difficulty in detecting a moving object due to the type of movement of the moving object is alleviated, and as a result, the accuracy of detecting a moving object can be improved.

[0360] A lighting device (100) according to a tenth aspect is a lighting device (100) that detects moving objects (human body 301, motorized blinds 200c) within an area (R1) using a received signal. The received signal is a signal based on a reflected wave (Re) of a radio wave (transmitted wave Tr) transmitted in each of a plurality of frames (Fr0 to Fr5). The lighting device (100) includes a processing unit (22) that executes processing based on the received signal. The processing unit (22) calculates multiple time differences (ΔA1 to ΔA5), which are the difference between two received signals received in two different frames out of multiple frames (Fr0 to Fr5), at multiple different time intervals (T, 2×T, ... 5×T), to calculate multiple time differences (ΔA1 to ΔA5), and detects moving objects (301, 200c) corresponding to the one or more moving points by obtaining a distribution of one or more moving points (point distribution when points corresponding to each moving point are plotted in three-dimensional virtual space 500) whose time differences within a region (R1) are greater than or equal to a threshold value from the multiple time differences (ΔA1 to ΔA5).

[0361] According to this aspect, by using a plurality of time differences (ΔA1 to ΔA5) having mutually different lengths, the difficulty in detecting a moving object due to the type of movement of the moving object is alleviated, and as a result, the accuracy of detecting a moving object can be improved.

[0362] The moving object detection method according to the eleventh aspect is a moving object detection method for detecting a moving object (human body 301, motorized blinds 200c) in an area (R1) using a received signal. The received signal is a signal based on a reflected wave (Re) of a radio wave (transmitted wave Tr) transmitted in each of a plurality of frames (Fr0 to Fr5). The moving object detection method includes processing steps (steps S1 to S12 in the first embodiment; S1a to S45 and S5 to S12 in the second embodiment) for executing processing based on the received signal. The processing steps (S1 to S12; S1a to S45 and S5 to S12) calculate a plurality of time differences (ΔA1 to ΔA5) by performing a process of calculating a time difference (ΔA1 to ΔA5) that is a difference between two received signals received in two different frames out of a plurality of frames (Fr0 to Fr5) at a plurality of different time intervals (T, 2×T, ... 5×T). Then, the processing steps (S1 to S12; S1a to S45 and S5 to S12) detect a moving object (301, 200c) corresponding to one or more moving points by obtaining a distribution (point distribution when points corresponding to each moving point are plotted in three-dimensional virtual space 500) of one or more moving points whose time differences within region (R1) are equal to or greater than a threshold value from the multiple time differences (ΔA1 to ΔA5).

[0363] According to this aspect, by using a plurality of time differences (ΔA1 to ΔA5) of different lengths, the difficulty in detecting a moving object due to the type of motion (for example, long or short) of the moving object is reduced, and as a result, the accuracy of detecting a moving object can be improved.

[0364] A program according to a twelfth aspect is a program for causing one or more processors to execute the moving object detection method according to the eleventh aspect.

[0365] According to this aspect, by using a plurality of time differences (ΔA1 to ΔA5) of different lengths, the difficulty in detecting a moving object due to the type of motion (for example, long or short) of the moving object is reduced, and as a result, the accuracy of detecting a moving object can be improved. [Explanation of symbols]

[0366] 100,100A Motion detection system (equipment control system, wiring device, lighting device) 1 Radio wave sensor 22 Processing section 200c Dynamic (electric blinds) 301 Moving body (human body) 302 Stationary objects CL, CL1, CL2 cluster group (distribution) Tr Transmission wave (radio wave) Re reflected wave R1 area R1a Wall R1b Ceiling ΔA1~ΔA5 Time difference Frames Fr1~Fr5

Claims

1. A moving object detection system that detects a moving object within an area using a received signal that is a signal based on a reflected wave of a radio wave transmitted in each of a plurality of frames, a processing unit that performs processing based on the received signal, The processing unit includes: calculating a time difference between two of the received signals received in two different frames among the plurality of frames at a plurality of different time intervals to calculate a plurality of the time differences; a distribution of one or a plurality of moving points in which the time difference is equal to or greater than a threshold value within the region is obtained from the plurality of time differences, thereby detecting the moving object corresponding to the one or a plurality of moving points; Motion detection system.

2. The processing unit sequentially calculates the plurality of time differences across the plurality of frames. The motion detection system according to claim 1 .

3. The processing unit includes: When a reference frame, which is one of the plurality of frames, is a current frame, holding the received signal at the reference frame; When each of two or more frames subsequent to the reference frame among the plurality of frames is the current frame, a difference between the received signal in the reference frame and the received signal in the current frame is calculated to obtain the plurality of time differences. The moving object detection system according to claim 2 .

4. The processing unit calculates the plurality of time differences for each of the plurality of frames. The motion detection system according to claim 1 .

5. the processing unit, when each of the plurality of frames is a current frame, calculates a difference between the received signal in the current frame and each of the received signals in two or more frames prior to the current frame among the plurality of frames, thereby acquiring the plurality of time differences. The moving object detection system according to claim 4 .

6. The processing unit detects the moving object based on the displacement of the distribution. The moving object detection system according to any one of claims 1 to 5.

7. The moving object includes a human body, The processing unit includes: Calculating the distance between both ends of the distribution in a height direction of the region; estimating whether the posture of the human body is one of a plurality of postures including a standing posture, a sitting posture, and a lying posture based on the distance; The moving object detection system according to any one of claims 1 to 5.

8. Further comprising a radio wave sensor located on a wall or ceiling of the area; The radio wave sensor transmits the radio wave, receives the reflected wave, and outputs the received signal. The moving object detection system according to any one of claims 1 to 5.

9. A wiring device that detects a moving object within an area using a received signal that is a signal based on a reflected wave of a radio wave transmitted in each of a plurality of frames, a processing unit that performs processing based on the received signal, The processing unit includes: calculating a time difference between two of the received signals received in two different frames among the plurality of frames at a plurality of different time intervals to calculate a plurality of the time differences; a distribution of one or a plurality of moving points in which the time difference is equal to or greater than a threshold value within the region is obtained from the plurality of time differences, thereby detecting the moving object corresponding to the one or a plurality of moving points; Wiring equipment.

10. A lighting device that detects a moving object within an area by using a received signal that is a signal based on a reflected wave of a radio wave transmitted in each of a plurality of frames, a processing unit that performs processing based on the received signal, The processing unit includes: calculating a time difference between two of the received signals received in two different frames among the plurality of frames at a plurality of different time intervals to calculate a plurality of the time differences; a distribution of one or a plurality of moving points in which the time difference is equal to or greater than a threshold value within the region is obtained from the plurality of time differences, thereby detecting the moving object corresponding to the one or a plurality of moving points; Lighting fixtures.

11. A moving object detection method for detecting a moving object within an area using a received signal that is a signal based on a reflected wave of a radio wave transmitted in each of a plurality of frames, comprising: A processing step of performing processing based on the received signal, In the processing step, calculating a time difference between two of the received signals received in two different frames among the plurality of frames at a plurality of different time intervals to calculate a plurality of the time differences; a distribution of one or a plurality of moving points in which the time difference is equal to or greater than a threshold value within the region is obtained from the plurality of time differences, thereby detecting the moving object corresponding to the one or a plurality of moving points; Motion detection methods.

12. A method for causing one or more processors to execute the method of claim 11. program.

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