Detection system, radio wave sensor, detection method, and program

Abstract

To provide a detection system capable of easily identifying an object when detecting the object using a reflection wave of a radio wave transmitted in a predetermined direction.SOLUTION: A detection system 100 detects an object in an area by using a reception signal. The reception signal is a signal based on a reflection wave of a radio wave transmitted in a predetermined direction. The detection system 100 includes a processing unit 22 that acquires information concerning a position of the object in the area by executing distance-measuring processing based on the reception signal. The processing unit 22 is configured to acquire multiple pieces of positional information respectively corresponding to the multiple parts constituting the object, and identify the object on the basis of a positional relation between both ends in the predetermined direction in the distribution of multiple points respectively corresponding to the multiple pieces of positional information.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a detection system, a radio wave sensor, a detection method, and a program, and more particularly to a detection system, a radio wave sensor, a detection method, and a program that detects an object such as a human body using a radio wave sensor. [Background technology]

[0002] Patent Document 1 describes an intrusion detection device that receives reflected waves from an object that is transmitted from different directions and detects an intrusion into a detection area based on the strength of the received reflected waves (received wave strength). This intrusion detection device detects a moving object (moving body), such as an intruder that is moving in some way, based on the change over time in the received wave strength. [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, depending on the direction of the radio waves, it can be difficult to identify the object, for example, the type of object, such as whether it is a person, a cleaning robot, or a pet, or to identify the movement of the object, such as whether the object is moving or stationary, and if moving, whether the whole object is moving or only part of it is moving.

[0005] An object of the present disclosure is to provide a detection system, a radio wave sensor, a detection method, and a program that can facilitate identification of an object when detecting the object using reflected waves of radio waves transmitted in a specified direction. [Means for solving the problem]

[0006] A detection system according to one aspect of the present disclosure is a detection system that detects an 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 a predetermined direction. The detection system includes a processing unit that acquires information regarding the position of the object within the area by performing a distance measurement process based on the received signal. The processing unit acquires a plurality of pieces of position information respectively corresponding to a plurality of parts constituting the object, and identifies the object based on the positional relationship between both ends in the predetermined direction of a distribution of a plurality of points respectively corresponding to the plurality of pieces of position information.

[0007] A radio wave sensor according to one aspect of the present disclosure is a radio wave sensor that transmits radio waves in a predetermined direction, receives reflected waves of the radio waves, and detects an object within an area using a received signal that is a signal based on the reflected waves. The radio wave sensor includes a processing unit that acquires information regarding the position of the object within the area by performing a distance measurement process based on the received signal. The processing unit acquires a plurality of pieces of position information respectively corresponding to a plurality of parts that constitute the object, and identifies the object based on the positional relationship between both ends in the predetermined direction of a distribution of a plurality of points that respectively correspond to the plurality of pieces of position information.

[0008] A detection method according to one aspect of the present disclosure is a detection method for detecting an 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 a predetermined direction. The detection method includes a processing step of acquiring information regarding the position of the object within the area by performing a distance measurement process based on the received signal. In the processing step, a plurality of pieces of position information corresponding to a plurality of parts constituting the object are acquired, and the object is identified based on the positional relationship between both ends in the predetermined direction of a distribution of a plurality of points corresponding to the plurality of pieces of position information.

[0009] A program according to one embodiment of the present disclosure causes one or more processors to execute the detection method. Effect of the Invention

[0010] The detection system, radio wave sensor, detection method, and program disclosed herein have the effect of making it easier to identify an object when detecting the object using reflected waves of radio waves transmitted in a specified direction. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram of a detection system (device control system) according to a 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 detection system according to the second embodiment of the present disclosure. [Figure 19] 19 is a flowchart illustrating a type specification process included in the above operation. [Figure 20] FIG. 20A is a distribution map showing an example of a cluster distribution corresponding to a cleaning robot, and FIG. 20B is a distribution map showing an example of a cluster distribution corresponding to an electric fan. [Figure 21] FIG. 21A is a distribution map showing an example of cluster distribution before the cat starts to move, and FIG. 21B is a distribution map showing an example of cluster distribution after the cat starts to move. [Figure 22] FIG. 22 is a flowchart illustrating an individual identification process that is further included in the above operation. [Diagram 23] FIG. 23A is a distribution map showing an example of a cluster distribution corresponding to the human body of AA, and FIG. 23B is a distribution map showing an example of a cluster distribution corresponding to the human body of BB. [Figure 24]FIG. 24 is a block diagram of a radio wave sensor according to a modified example of the detection system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] (First embodiment) Hereinafter, a first embodiment of the present disclosure will be described with reference to FIGS.

[0013] (1) Overview of the equipment control system In this embodiment, the detection system of the present disclosure is a device control system 100 that has a device control function in addition to a detection function for detecting an object (for example, a function for detecting an object and identifying the type of the detected object, a function for identifying the movement of the identified object, a human body estimation function for making an inference regarding an object identified as a human body, and in particular a human body posture estimation function for estimating the posture of the human body). As shown in Fig. 2, the device control system 100 of this embodiment detects moving objects (hereinafter may be referred to as "moving objects") among objects, and in particular makes an inference regarding a human body 301, using a radio wave sensor 1.

[0014] An object is an object to be detected by the detection system. The object is, for example, a human body 301, an animal body other than the human body 301 (such as a pet), a moving object other than an animal (such as the motorized blinds 200c), and a non-moving object (a stationary object 302). The detection of an object is, for example, the detection of an object that matches any one of a plurality of predetermined characteristics. The identification of an object is the identification of the type of the object based on the detected characteristics.

[0015] Identifying the movement of the object is, for example, determining whether the object is moving or not, and if the object is moving, determining which of a plurality of predetermined movements the object's movement corresponds to. Determining whether the object is moving or not may be, for example, detecting a moving object, i.e., a moving body.

[0016] The identification of the movement may include, for example, identifying the timing when the object starts to move and identifying the timing when the object stops moving. Furthermore, the identification of the movement may include, for example, determining whether the movement is periodic or non-periodic (in other words, whether the movement is regular or irregular).

[0017] The estimation regarding 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, and the like.

[0018] 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.

[0019] 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).

[0020] (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").

[0021] (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).

[0022] 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.

[0023] (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.

[0024] 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.

[0025] 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.

[0026] 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).

[0027] (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.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] (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.

[0033] 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).

[0034] 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.

[0035] (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.

[0036] 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.

[0037] 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).

[0038] 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.

[0039] 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.

[0040] 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.

[0041] (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.

[0042] (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 .

[0043] 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.

[0044] (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.

[0045] 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).

[0046] 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).

[0047] 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").

[0048] (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.

[0049] (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.

[0050] 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)

[0051] (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.

[0052] 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).

[0053] 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.

[0054] (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).

[0055] 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).

[0056] (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.

[0057] (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.

[0058] (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.

[0059] 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.

[0060] (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).

[0061] 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.

[0062] 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.

[0063] 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.

[0064] (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.

[0065] 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.

[0066] 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.

[0067] (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.

[0068] 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.

[0069] 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).

[0070] (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).

[0071] (2-3-2) Representative value of each frame when calculating the frame 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] (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).

[0077] 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.

[0078] 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.

[0079] 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).

[0080] 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.

[0081] 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.

[0082] (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.

[0083] (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).

[0084] (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).

[0085] (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.

[0086] (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.

[0087] (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.

[0088] 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.

[0089] 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.

[0090] (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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] (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).

[0099] 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.

[0100] 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.

[0101] 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").

[0102] 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.

[0103] (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.

[0104] 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).

[0105] 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).

[0106] 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.

[0107] 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).

[0108] (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).

[0109] 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.

[0110] 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.

[0111] 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."

[0112] 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."

[0113] 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.

[0114] 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).

[0115] (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.

[0116] 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."

[0117] 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."

[0118] 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.

[0119] (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.

[0120] (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 .

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] (2-4-1a) Non-operational behavior and operational behavior A non-operational behavior is a behavior that is different from a manipulative behavior.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] (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.

[0131] 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.

[0132] 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).

[0133] (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).

[0134] 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.

[0135] 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.

[0136] 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 paired with the acquired feature information.

[0137] Note that automatic control information (see FIG. 15) described later may be customized for each of a plurality of person identifiers.

[0138] The behavior detection unit 224 detects a non-operation behavior further based on the environmental information acquired by the information acquisition unit 226 .

[0139] In this way, by using environmental information in addition to posture changes, it is possible to improve the accuracy of detecting non-operation behavior.

[0140] (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."

[0141] 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).

[0142] 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".

[0143] (2-4-3b) Manual control Furthermore, 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.

[0144] (2-4-4) History accumulation and learning The device control system 100 further includes a history accumulation unit 227 and a learning unit 228.

[0145] (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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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)".

[0151] 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.

[0152] (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.

[0153] (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)."

[0154] 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.

[0155] 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)."

[0156] 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.)."

[0157] 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.

[0158] (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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The device control process controls the device 200 based on the behavior, for example, by using pre-stored automatic control information.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] (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.

[0178] (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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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). Note that the one-to-multiple frame difference acquisition processing will be described with reference to the flowchart of FIG.

[0183] 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).

[0184] 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).

[0185] 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).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] Next, the history accumulation unit 227 accumulates manual control history information related to the manual control performed in step S14 (step S15).

[0194] 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.

[0195] 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.

[0196] 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.

[0197] (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.

[0198] The processing unit 22 sets an initial value "1" to a variable i (step S41).

[0199] 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.

[0200] 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).

[0201] Next, the processing unit 22 increments the variable i (step S44), after which the process returns to step S42.

[0202] 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).

[0203] (4-3) Attitude estimation processing The posture estimation process in step S9 is executed, for example, according to the flowchart in FIG.

[0204] 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).

[0205] 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).

[0206] 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).

[0207] 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).

[0208] 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).

[0209] 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).

[0210] 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).

[0211] 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).

[0212] (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.

[0213] 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.

[0214] (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.

[0215] (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.

[0216] (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".

[0217] 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.

[0218] (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.

[0219] 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.

[0220] (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.

[0221] 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.

[0222] 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.

[0223] 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).

[0224] (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.

[0225] The transmission and reception operations are repeated at a predetermined cycle (that is, periodically) as in the embodiment, but may be performed irregularly.

[0226] 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).

[0227] (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.

[0228] (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.

[0229] (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.

[0230] 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.

[0231] 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.).

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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).

[0236] 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."

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] (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).

[0242] 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.

[0243] 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.

[0244] 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.

[0245] (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).

[0246] 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.

[0247] 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.

[0248] (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).

[0249] 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.

[0250] 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).

[0251] 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).

[0252] 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.

[0253] (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.

[0254] 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.

[0255] (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.

[0256] (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.

[0257] 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.

[0258] (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.

[0259] 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.

[0260] 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.

[0261] (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.

[0262] 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.

[0263] (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.

[0264] 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.

[0265] 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.

[0266] 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).

[0267] (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.

[0268] 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.

[0269] 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).

[0270] (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.

[0271] (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.

[0272] 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.

[0273] (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.

[0274] 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.

[0275] 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.

[0276] (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.

[0277] 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.

[0278] (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.

[0279] (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.

[0280] (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.

[0281] 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.

[0282] 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.

[0283] 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.

[0284] 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.

[0285] (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.

[0286] (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).

[0287] (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).

[0288] (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).

[0289] (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.

[0290] Second embodiment Next, a second embodiment and a modified example of the present disclosure will be described with reference to FIGS. 1 to 22 (particularly FIGS. 4, 5, and 18 to 22).

[0291] The second embodiment is similar to the first embodiment, except for the process of identifying an object using a distribution of a plurality of points (CL, CL1, CL2). In the following, the description of the already mentioned matters 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.

[0292] (1) Commonalities with the first embodiment The radio wave sensor 1 transmits radio waves (transmission waves Tr: hereinafter, may be referred to as "radio waves Tr") in a predetermined direction, receives reflected waves Re, and outputs a reception signal.

[0293] Note that transmitting in a predetermined direction means emitting the radio wave Tr in a certain range centered on the predetermined direction. In other words, pointing the radio wave sensor 1 in a predetermined direction results in the radio wave Tr propagating in a certain range centered on the predetermined direction.

[0294] The radio wave sensor 1 in the first or second embodiment is located above the region R1 and transmits radio waves Tr in the height direction of the region R1 (specifically, vertically downward, that is, in the downward direction of a vertical straight line). In other words, the predetermined direction is the height direction of the region R1 (see FIG. 2), which is the Z direction in the three-dimensional space 500 shown in FIG. 13A etc. Therefore, the radio wave sensor 1 transmits radio waves Tr in the height direction, receives reflected waves Re of the transmitted radio waves Tr, and outputs a received signal.

[0295] The processing unit 22 detects objects (301, 200c) using the reception signal output by the radio wave sensor 1. The objects are, for example, a human body 301, an electric blind 200c which is a moving object other than the human body 301, and a stationary object 302 such as a desk 302 (see FIG. 2). The human body 301 is a moving object that performs an action such as walking, in other words, a non-periodic (or irregular) movement. The electric blind 200c is a moving object that performs a periodic (or regular) movement such as opening and closing.

[0296] (2) Differences from the First Embodiment (2-1) Object Identification in the First Embodiment: Identification Based on the Dimension Ratio of Length, Width, and Height The processing unit 22 of the first embodiment identified objects such as a human body 301 and a stationary object 302 based on the dimensional ratios of the length D, width W and height H of a rectangular parallelepiped 502 that encloses the distribution of one or more points (i.e., a point cloud 501 which is a collection of one or more points, or one or more clusters CL, CL1, CL2 formed by the point cloud 501) in a three-dimensional space 500 (see Figures 13A to 14C) corresponding to region R1.

[0297] (2-2) Object Identification in the Second Embodiment 1: Identification based on the positional relationship between the ends in the height direction In contrast to this, the processing unit 22 in the second embodiment identifies the object based on the positional relationship between both ends in a distribution of one or more points (one or more clusters CL, CL1, CL2 or the point cloud 501).

[0298] In the present embodiment, the objects include, in addition to the moving objects (301, 200c) such as the human body 301 and the electric blind 200c shown in FIG. 2, a cleaning robot 303 shown in FIG. 20A, an electric fan 304 shown in FIG. 20B, and a cat 305 shown in FIG. 20A and FIG. 20B (hereinafter, they may be referred to as "objects 301 to 305"). The cleaning robot 303 is an example of a moving object that moves automatically (for example, moves autonomously). The electric fan 304 is an example of a moving object that moves partially (for example, the fins rotate, or the upper part including the fins makes a swinging motion). The cat 305 is an example of an animal (for example, a pet) other than the human body 301.

[0299] (2-2-1) Both ends of the distribution in the height direction The two ends in the height direction of the distribution (CL, CL1, CL2) are, for example, the upper cluster CL1 and the lower cluster CL2 in the distribution (point cloud 501 including two clusters CL1, CL2) shown in FIG. 13A.

[0300] The positional relationship between both ends in the height direction 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 difference between the height of the upper cluster CL1 based on the XY plane (corresponding to the floor surface of the area R1 shown in FIG. 2) shown in FIG. 13A and the height of the lower cluster CL2 based on the XY plane (i.e., the height difference DH between the upper cluster CL1 and the lower cluster CL2: see FIGS. 20A and 20B).

[0301] The height of cluster CL1 is the Z component of the position information corresponding to cluster CL1, and the height of cluster CL2 is the Z component of the position information corresponding to cluster CL2.

[0302] Alternatively, the two ends of the distribution (CL, CL1, CL2) in the height direction may be, for example, the distance between two horizontal planes passing through the uppermost and lowermost points of a cluster CL, in other words, the difference between the Z component of the coordinate of the uppermost point and the Z component of the coordinate of the lowermost point.

[0303] Furthermore, the positional relationship between both ends may further include a change in distance over time, that is, a movement, in addition to the distance such as the height difference DH. The movement includes various movements such as periodic or regular movement of the object (movement of the cleaning robot 303, swinging of the electric fan 304, etc.), non-periodic or irregular movement of the object (walking of the human body 301 or the cat 305, etc.), overall movement of the object (e.g., movement of the cleaning robot 303, walking of the human body 301, etc.), and partial movement of the object (e.g., swinging of the electric fan 304, movement of the hand of the human body 301, etc.).

[0304] In detail, the processing unit 22 acquires a plurality of pieces of position information corresponding to a plurality of parts constituting the object (301 to 305), and acquires a distribution (CL, CL1, CL2) of a plurality of points (point cloud 501) corresponding to the plurality of pieces of position information, respectively. Then, the processing unit 22 identifies the object (301 to 305) based on the positional relationship between both ends in a predetermined direction of the acquired distribution of point cloud 501.

[0305] In this embodiment, as described above, the predetermined direction is the height direction of region R1, that is, the Z direction of the three-dimensional space 500 corresponding to region R1 (see FIGS. 20A to 21B). In this embodiment, the positional relationship is the distance between both ends in the height direction (Z direction), and more specifically, the height difference DH between the upper cluster CL1 and the lower cluster CL2, or the height H of the rectangular parallelepiped 502.

[0306] The distance between both ends in the height direction is the difference in the Z component between the two pieces of position information corresponding to the two ends. For example, the processing unit 22 may calculate the difference in the Z component between the position information corresponding to the upper cluster CL1 and the position information corresponding to the lower cluster CL2 as the height difference DH.

[0307] The position information corresponding to the upper cluster CL1 may be, for example, the coordinates of a representative point of the cluster CL1. The representative point of the cluster CL1 is, for example, the top point of the cluster CL1, but may also be the center of gravity or the bottom point. The position information corresponding to the lower cluster CL2 may be, for example, the coordinates of a representative point of the cluster CL2. The representative point of the cluster CL2 is, for example, the bottom point of the cluster CL2, but may also be the center of gravity or the top point.

[0308] In this way, the processing unit 22 obtains a plurality of pieces of position information corresponding to a plurality of parts constituting the object (301-305) by using a received signal based on a reflected wave Re of a radio wave Tr transmitted in the height direction (Z direction) from the radio wave sensor 1 located above the region R1, and obtains a distribution (CL, CL1, CL2) of a plurality of points (501).Then, the object (301-305) is identified based on the positional relationship (DH, H) between both ends in the height direction of the distribution (CL, CL1, CL2) of the plurality of points (501), thereby facilitating the identification of the object (301-305).

[0309] Furthermore, by including the distance (height difference DH) between both ends of the distributions (CL, CL1, CL2) in the height direction in the positional relationship, it is possible to facilitate identification of the target objects (301 to 305) and improve the accuracy.

[0310] (2-3) Target Identification 2 in the Second Embodiment: Identification based on the spread of distribution in a plane perpendicular to the height direction The processing unit 22 specifies the target objects (301 to 305) based on the spread of the distribution (CL, CL1, CL2) of the point cloud 501 in a plane having the height direction (Z direction) as the normal direction.

[0311] The plane having the height direction (Z direction) as its normal direction is a horizontal plane, for example, the XY plane along the X and Y directions shown in FIG. 20A, or a plane parallel to the XY plane.

[0312] The spread in the horizontal plane is, for example, the distance DW between both ends in the lateral direction (X direction) as shown in Figures 20A to 21B. The distance DW corresponds to the width W of the rectangular parallelepiped 502 in the first embodiment.

[0313] The extent in the horizontal plane (XY plane) may include, for example, at least one of the extent in the horizontal direction (X direction), i.e., the positional relationship between both ends in the horizontal direction, and the extent in the vertical direction (Y direction), i.e., the positional relationship between both ends in the vertical direction.

[0314] In this way, the processing unit 22 identifies the object based on not only the positional relationship between the ends of the distribution (CL, CL1, CL2) of the point cloud 501 in a specified direction, but also the spread (DW, W) of the distribution (CL, CL1, CL2) of the point cloud 501 in a plane (horizontal plane) perpendicular to the height direction, thereby making it possible to further facilitate the identification of the object (301-305).

[0315] However, it is also possible to facilitate identification of the target object (301-305) by considering the spread (DW, W) of the distribution (CL, CL1, CL2) of the point cloud 501 in a plane (horizontal plane) perpendicular to the height direction, without considering the positional relationship between the ends of the distribution (CL, CL1, CL2) of the point cloud 501 in the height direction.

[0316] (2-4) Identifying an object based on the positional relationship, the change in the positional relationship over time, and the extent and the change in the extent over time More specifically, each of the one or more points described above is a moving point, i.e., a moving point. That is, in the first embodiment or the second embodiment, a "point" is a moving point, and hereinafter, may be referred to as a "moving point" as appropriate.

[0317] The positional relationship further includes a time change in distance (height difference DH), for example, a movement in the vertical direction of the object. The above-mentioned spread further includes a time change in spread, for example, an expansion and contraction of the object in a horizontal plane. In this way, the positional relationship includes a time change in distance and a time change in spread, which makes it easier to identify the object (301-305).

[0318] (2-4-1) Acquisition of time changes such as positional relationships based on multiple time differences The radio waves Tr are transmitted in each of a plurality of frames, and the processing unit 22 calculates a plurality of time differences by repeatedly performing a process of calculating a time difference, which is the difference between two received signals received in two different frames among the plurality of frames.

[0319] The processing unit 22 acquires a distribution (CL, CL1, CL2) of one or more moving points whose time differences are equal to or greater than a threshold value from the multiple time differences. Then, the processing unit 22 identifies the target object (301-305) based on at least one of the positional relationship (DH, H) and the time change in the positional relationship in a predetermined direction (height direction) of the distribution (CL, CL1, CL2) of one or more moving points, and the spread (DW, W) and the time change in the spread (DW, W) in a plane (XY plane) having the predetermined direction (height direction) as a normal direction.

[0320] In this way, the processing unit 22 identifies the object (301-305) based on at least one of the positional relationship (DH, H) between both ends in the height direction (Z direction) and the change in the positional relationship over time of the distribution of one or more moving points (CL, CL1, CL2) obtained from multiple time differences, and the spread (DW, W) in the horizontal plane (XY plane) and the change in the spread (DW, W) over time, thereby making it possible to further facilitate the identification of the object (301-305).

[0321] (2-4-2) Identifying movements based on the horizontal distribution The processing unit 22 identifies the motion of the target object (301 to 305) based on, for example, a time change in the spread (DW, W) in the horizontal plane of the distribution (CL, CL1, CL2) of one or more moving points.

[0322] In this way, the processing unit 22 identifies the objects (301-305) based on the change over time in the spread (DW, W) of the distribution of multiple moving points (CL, CL1, CL2) in the horizontal plane (XY plane), thereby making it even easier to identify the objects (301-305).

[0323] (2-4-3) Identifying movement based on the positional relationship between both ends of the distribution in the height direction Alternatively, the processing unit 22 may identify the motion of the target object (301 to 305) based on the change over time in the positional relationship (DH, H) in the height direction (Z direction) of the distribution of one or more moving points (CL, CL1, CL2).

[0324] In this way, the processing unit 22 identifies the objects (301-305) based on the change over time in the positional relationship (DH, H) in a specified direction (height direction) of the distribution of multiple moving points (CL, CL1, CL2), thereby making it possible to further facilitate the identification of the objects (301-305).

[0325] (2-4-4) Identifying movement based on the positional relationship between both ends of the distribution in the height direction The processing unit 22 may identify the object (301-305) and the motion of the identified object (301-305) based on the spread (DW, W) of one or more moving point distributions (CL, CL1, CL2) in a plane whose normal direction is the height direction, i.e., a horizontal plane (in the three-dimensional space 500, the XY plane or a plane parallel to the XY plane), and based on the change in spread (DW, W) over time.

[0326] However, the processing unit 22 may identify the objects (301 to 305) and their movements based on either the spread (DW, W) of the distribution (CL, CL1, CL2) in the horizontal plane or the change in the spread (DW, W) over time.

[0327] In addition, the processing unit 22 may identify either the object (301 to 305) or the motion based on the spread (DW, W) in the horizontal plane of the distribution (CL, CL1, CL2) and the change in the spread (DW, W) over time.

[0328] Furthermore, the processing unit 22 may identify either the object (301-305) or the movement based on either the spread (DW, W) in the horizontal plane of the distribution (CL, CL1, CL2) or the change in the spread (DW, W) over time.

[0329] In this way, the processing unit 22 identifies the objects (301-305) based on at least one of the spread (DW, W) in a plane perpendicular to the height direction of the distribution of multiple moving points (CL, CL1, CL2) and the change in the spread (DW, W) over time, thereby making it possible to further facilitate the identification of the objects (301-305).

[0330] (2-4-5) Identifying movements based on the positional relationship between both ends of the distribution in the height direction and the time change of the positional relationship The processing unit 22 may identify the object (301-305) and the motion of the identified object (301-305) based on the positional relationship (DH, H) in the height direction of the distribution of one or more moving points (CL, CL1, CL2) and the change over time of the positional relationship (DH, H).

[0331] However, the processing unit 22 may identify the objects (301-305) and their movements based on either the positional relationship (DH, H) of the distribution (CL, CL1, CL2) in a predetermined direction (height direction) or the change in the positional relationship (DH, H) over time.

[0332] In addition, the processing unit 22 may identify either the object (301-305) or the motion based on the positional relationship (DH, H) in a predetermined direction (height direction) of the distribution (CL, CL1, CL2) and the change in the positional relationship (DH, H) over time.

[0333] Furthermore, the processing unit 22 may identify either the object (301-305) or the motion of the object (301-305) based on either the positional relationship (DH, H) in a predetermined direction (height direction) of the distribution (CL, CL1, CL2) or the change over time in the positional relationship (DH, H).

[0334] In this way, the processing unit 22 identifies the objects (301-305) based on at least one of the positional relationship (DH, H) between the ends of the distribution of multiple moving points (CL, CL1, CL2) in a specified direction (height direction) and the change in the positional relationship over time, thereby making it possible to further facilitate the identification of the objects (301-305).

[0335] (2-4-6) Calculating time differences at multiple different time intervals The processing unit 22 performs the process of calculating the time difference at a plurality of different time intervals (T, 2×T, . . . 5×T: see FIG. 10) to obtain a plurality of different time differences (ΔA1, ΔA2, ΔA3, etc.).

[0336] This makes it possible to improve the accuracy of identifying the objects (301 to 305) and to further facilitate identifying the objects (301 to 305).

[0337] (2-5) 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 process shown in Figures 4 and 5, the process from step S8 to step S10, i.e., "the process of estimating the posture of the human body and detecting a non-operation behavior based on the estimation result", is replaced with a process including six steps S8a, S8b, S9a to S9c, and S10a as shown in Figure 18, i.e., "the process of identifying the type of object based on the height of the object".

[0338] (2-5-1) Processing to find the height of an object After step S7, the processing unit 22 judges whether or not four or more points have been placed (step S8a). If it is judged that four or more points have not yet been placed (No in step S8a), the processing returns to step S1 (see FIG. 4).

[0339] If it is determined in step S8a that four or more points have been arranged (Yes), processing unit 22 determines whether or not two or more cloud points have been formed (step S8b). If it is determined that two or more cloud points have not yet been formed (No in step S8ba), the process returns to step S1 (see FIG. 4).

[0340] If it is determined in step S8b that two or more cloud points are formed (Yes), the processing unit 22 determines the point cloud with the highest height among the two or more formed point clouds as the upper end of the object (step S9a). Next, the processing unit 22 determines the point cloud with the lowest height among the two or more formed point clouds as the lower end of the object (step S9b). Next, the processing unit 22 determines the height difference between the upper end and the lower end as the height of the object (step S9c).

[0341] Next, the processing unit 22 executes a type identification process to identify the type of the object based on the height of the object (step S10a), after which the process proceeds to step S11 (see FIG. 5).

[0342] (2-5-2) Type-specific processing The type identification process in step S10a is executed according to the flowchart of FIG. 19, for example.

[0343] In this example, the memory of the control device 2 stores in advance a first threshold value, a second threshold value (where the first threshold value<the second threshold value) and a plurality of pieces of feature information corresponding to a plurality of types of objects. The plurality of types of objects are, for example, a cleaning robot 303, an electric fan 304, a cat 305, and a human body 301. The human body 301 includes three types of bodies: an adult, a child, and a baby. The plurality of pieces of feature information are first feature information corresponding to the cleaning robot 303 as shown in FIG. 20A, second feature information corresponding to the electric fan 304 as shown in FIG. 20B, third feature information corresponding to the cat 305 as shown in FIG. 21A and FIG. 21B, fourth feature information corresponding to an adult as shown in FIG. 13A, fifth feature information corresponding to a child, and sixth feature information corresponding to a baby.

[0344] It should be noted that the child is shorter in height (height in a standing position) than the adult shown in Fig. 13A. Also, the baby is in a lying position, so is even shorter than the child.

[0345] The first characteristic information corresponding to the cleaning robot 303 is information indicating that "the height (DH, H) is equal to or less than a first threshold, and the robot moves periodically." The second characteristic information corresponding to the electric fan 304 is information indicating that "the height is greater than the first threshold and equal to or less than a second threshold, and the robot moves periodically."

[0346] The third feature information corresponding to the cat is information that "the height is equal to or less than the first threshold, and the point cloud extends horizontally when it starts to move." The state of the point cloud before it starts to move is shown in FIG. 21A, and the state of the point cloud after it starts to move is shown in FIG. 21B. The horizontal (X direction) spread (DW, W) of the point cloud (CL1, CL2) in the state of FIG. 21B is relatively larger than the horizontal (X direction) spread (DW, W) of the point cloud (CL1, CL2) in the state of FIG. 21B.

[0347] The fourth feature information corresponding to an adult is information that "the height is greater than the second threshold and the body moves non-periodically". The fifth feature information corresponding to a child's body is information that "the height is greater than the first threshold and is equal to or less than the second threshold and the body moves non-periodically". The sixth feature information corresponding to a baby is information that "the height is less than the second threshold and the body moves non-periodically".

[0348] In the type identification process, first, the processing unit 22 judges whether the height of the object is equal to or less than the first threshold value (step S101). If it is judged that the height of the object is not equal to or less than the first threshold value (No in step S101: that is, less than the first threshold value), the process returns to the upper flowchart (see FIG. 18).

[0349] If it is determined in step S101 that the height of the object is equal to or less than the first threshold (Yes), the processing unit 22 determines whether the movement of at least one of the two or more formed point clouds is periodic (step S102). If it is determined that the movement of none of the point clouds is periodic (No in step S102), the process proceeds to step S104.

[0350] If it is determined in step S102 that the movement of at least one point cloud is periodic (Yes), the processing unit 22 sets the type information indicating the type of the object to "cleaning robot" based on the first feature information (step S103). After that, the process returns to the upper flowchart (see FIG. 18).

[0351] In step S104, the processing unit 22 determines whether the point cloud extends laterally (X direction) when it starts to move. If it is determined that the point cloud does not extend laterally (X direction) when it starts to move (No in step S104), the processing proceeds to step S106.

[0352] If it is determined in step S104 that the points extend laterally (in the X direction) when they start to move (Yes), the processing unit 22 sets the type information to "cat" based on the third feature information (step S105). After that, the processing returns to the upper-level flowchart (see FIG. 18).

[0353] In step S106, the processing unit 22 sets the type information to “baby” based on the sixth characteristic information. After that, the process returns to the upper-level flowchart (see FIG. 18).

[0354] In step S107, it is determined whether the height of the object is greater than the first threshold and equal to or less than the second threshold. If it is determined that the height of the object is greater than the first threshold and not equal to or less than the second threshold (No in step S107: greater than the second threshold), the process proceeds to step S111.

[0355] If it is determined in step S107 that the height of the object is greater than the first threshold and equal to or less than the second threshold (Yes), the processing unit 22 determines whether the movement of at least one of the two or more formed point clouds is periodic (step S108). If it is determined that the movement of none of the point clouds is periodic (No in step S108), the process proceeds to step S110.

[0356] If it is determined in step S108 that the movement of at least one point cloud is periodic (Yes), the processing unit 22 sets the type information to "electric fan" based on the second feature information (step S109). After that, the processing returns to the upper flowchart (see FIG. 18).

[0357] In step S110, the processing unit 22 sets the type information to “child” based on the fifth characteristic information. After that, the processing returns to the upper-level flowchart (see FIG. 18).

[0358] In step S111, the processing unit 22 sets the category information to "adult" based on the fourth characteristic information. After that, the processing returns to the upper-level flowchart (see FIG. 18).

[0359] (2-6) Detection method and program The detection method is a detection method for detecting objects (301-305) in the region R1 using a received signal, which is a signal based on a reflected wave Re of a radio wave Tr transmitted in a predetermined direction (height direction). The detection method includes a processing step for acquiring information about the positions of the objects (301-305) in the region R1 by performing a distance measurement process based on the received signal. The processing steps are steps S8-S10 in the first embodiment, and steps S8a-10a and S101-S111 in the second embodiment. In the processing steps (S8-S10; S8a-10a and S101-S111), a plurality of pieces of position information corresponding to a plurality of parts constituting the object (301-305) are acquired, and the object (301-305) is identified based on the positional relationship (height difference DH, height H of the rectangular parallelepiped 502) between both ends in a predetermined direction (height direction) of a distribution of a plurality of points (CL, CL1, CL2) corresponding to the plurality of pieces of position information.

[0360] The program is for causing one or more processors to carry out the detection method.

[0361] (3) Variations Next, a modified example of this embodiment will be described. Note that the description of the already mentioned items will be omitted or simplified, and only the differences will be described in detail.

[0362] (3-1) First modified example of detection system In the process of FIG. 18, following the type identification process of step S10a, an individual identification process shown in FIG. 22 may be further executed.

[0363] In this example, two pieces of personal information about two adults and two conditions corresponding to the two pieces of personal information are pre-stored in the memory of the control device 2. One of the two pieces of personal information is personal information about AA, and includes identification information "AA" that identifies AA, and personal information about AA, "H=180cm, W=40cm". The other of the two pieces of personal information is personal information about BB, and includes identification information "BB" that identifies BB, and personal information about BB, "H=160cm, W=50cm".

[0364] AA's personal information, "H=180cm, W=40cm," corresponds to the point distribution shown in FIG. 23A, and BB's personal information, "H=160cm, W=50cm," corresponds to the point distribution shown in FIG. 23B.

[0365] Of the two conditions, the first condition corresponding to the personal information "H=180cm, W=40cm" is, for example, "175≦H<185 and 35≦W<45", and the second condition corresponding to the personal information "H=160cm, W=50cm" is, for example, "155≦H<165 and 45≦W<55".

[0366] In the individual identification process of Fig. 22, first, the processing unit 22 judges whether the type information is "adult" or not (step S201). If it is judged that the type information is not "adult" (No in step S201), the process returns to the upper flowchart (see Fig. 18).

[0367] If it is determined in step S201 that the type information is "adult" (Yes), the processing unit 22 determines whether the height H and width W of the point cloud satisfy the first condition (step S202). If it is determined that the height H and width W of the point cloud do not satisfy the first condition (No in step S202), the processing proceeds to step S204.

[0368] If it is determined in step S202 that the height H and width W of the point cloud satisfy the first condition (Yes), the processing unit 22 sets "AA" to the identification information (step S203). After that, the process returns to the upper-level flowchart (see FIG. 18).

[0369] In step S204, the processing unit 22 determines whether or not the height H and width W of the point cloud satisfy the second condition. If it is determined that the height H and width W of the point cloud do not satisfy the second condition (No in step S204), the processing proceeds to step S206.

[0370] If it is determined in step S204 that the height H and width W of the point cloud satisfy the second condition (Yes), the processing unit 22 sets "BB" to the identification information (step S205). After that, the process returns to the upper-level flowchart (see FIG. 18).

[0371] In step S206, the processing unit 22 sets the identification information to “unregistered person.” After that, the process returns to the upper-level flowchart (see FIG. 18).

[0372] When the process returns to the flowchart of Fig. 18, the process proceeds to step S11 (see Fig. 5). In the automatic control of step S11, if the type information on the detected object is "adult", control may be performed taking into consideration identification information for identifying an individual. For example, if the identification information is "AA", the television 200b may be turned on, whereas if the identification information is "BB", the television 200b may not be turned on.

[0373] In this modification, the height H may be the height difference DH, and the width W may be the distance DW. In addition, in this modification, when the object is a child, the individual may be identified in the same manner as in the case of an adult. Furthermore, the specific numerical values ​​of the first and second conditions are merely examples and may be changed as appropriate.

[0374] According to this modification, when the target object (301-305) is identified as the human body 301, it is possible to further identify the individual corresponding to the human body 301 based on pre-stored personal information, the height H (DH) and the width W (DW). As a result, for example, it is possible to enable different device control for each individual.

[0375] (3-2) Second Modification of Detection System The detection system 100 does not necessarily have to include the radio wave sensor 1. In this modification, the radio wave sensor 1 is disposed outside the detection system 100. Like the radio wave sensor 1 in the first or second embodiment, the radio wave sensor 1 transmits radio waves Tr in a predetermined direction (height direction), receives reflected waves Re, and outputs a received signal. However, the output in this modification is transmission of the received signal to the detection system 100. In the detection system 100, the reception unit 21 receives the received signal from the radio wave sensor 1, and the processing unit 22 performs processing based on the received signal received by the reception unit 21.

[0376] In this modified example, similar to the first or second embodiment, it is possible to facilitate identification of the target object (301 to 305).

[0377] (3-3) Third Modification of Detection System The detection system 100 may be a radio wave sensor 1. Similar to the radio wave sensor 1 in the first or second embodiment, the radio wave sensor 1 in this modification transmits radio waves Tr in a predetermined direction (height direction), receives reflected waves Re of the radio waves Tr, and detects objects (301-305) in an area R1 using a received signal that is based on the reflected waves Re. More specifically, the radio wave sensor 1 in this modification includes a control device 2, as shown in FIG.

[0378] In this modified example, similar to the first or second embodiment, it is possible to facilitate identification of the target object (301 to 305).

[0379] (3-4) Other Modifications of the Detection System The detection system 100 may be a wiring device including the control device 2. This wiring device may further include a radio wave sensor 1. The wiring device may be, for example, an outlet to which the lighting device 200a shown in FIG. 2 is connected.

[0380] The detection system 100 may be a lighting fixture including a control device 2 and a fixture body. The fixture body includes, for example, a light-emitting element such as an LED, a drive circuit for driving the light-emitting element, and a power source for supplying power to the drive circuit. This lighting fixture may further include a radio wave sensor 1. For example, the lighting fixture 200a shown in FIG. 2 may include the radio wave sensor 1 and the control device 2 built-in.

[0381] (3-5) Modifications of the arrangement of the radio wave sensor and the direction of radio wave transmission The radio wave sensor 1 does not necessarily have to be located above the region R1. In this modification, the radio wave sensor 1 is located to the side of the region R1. In this case, the transmission direction of the radio wave Tr, that is, the "predetermined direction", is the horizontal direction (sideways).

[0382] According to this modified example, the detection accuracy of the object may decrease compared to the case where the predetermined direction is the height direction (vertically downward). However, the detection accuracy can be improved by having the processing unit 22 specify the object (301-305) based on the positional relationship between the ends of the distribution (CL, CL1, CL2) in the horizontal direction (for example, the X direction, or the Y direction, or each of the X direction and the Y direction).

[0383] The radio wave sensor 1 may be located on either side of the region R1, as long as it transmits radio waves Tr in a predetermined direction relative to the region R1. In this way, the processing unit 22 identifies the target object (301-305) based on the positional relationship between the ends of the distributions (CL, CL1, CL2) in the predetermined direction and the change in that positional relationship over time, as well as the spread in a plane perpendicular to the predetermined direction and the change in that spread over time, and the like, thereby making it possible to improve the detection accuracy.

[0384] (3-6) Modifications of the detection method The detection process of the detection system 100 may be a process capable of detecting not only moving objects but also stationary objects. For example, such a process may be a process in which the calculation of the time difference of the received signals is omitted in the detection process in the second embodiment. Although the omission of the calculation of the time difference may make it difficult to identify the movement of the target object, it is possible to identify the target object based on the positional relationship between the ends of the distribution of the point cloud in a predetermined direction, the spread in a plane perpendicular to the predetermined direction, and the like.

[0385] (4) Summary The detection system (100) according to the first embodiment is a detection system (100) that detects objects (301 to 305) in an area (R1) using a received signal. The received signal is a signal based on a reflected wave (Re) of a radio wave (transmission wave Tr) transmitted in a predetermined direction (height (Z) direction). The detection system (100) includes a processing unit (22) that acquires information on the positions of the objects (301 to 305) in the area (R1) by performing a distance measurement process based on the received signal. The processing unit (22) acquires a plurality of pieces of position information corresponding to a plurality of parts constituting the objects (301 to 305), and identifies the objects (301 to 305) based on a positional relationship (height difference DH, height H of the rectangular parallelepiped 502) between both ends in a predetermined direction (height (Z) direction) of a distribution (CL, CL1, CL2) of a plurality of pieces of position information respectively corresponding to the plurality of pieces of position information.

[0386] According to this embodiment, when detecting the object (301-305) using the reflected wave (Re) of the radio wave (Tr) transmitted in a predetermined direction (height direction), the object (301-305) can be easily specified by considering the positional relationship (DH, H) between the ends of the distribution of multiple points (CL, CL1, CL2) in the predetermined direction (height direction). Note that, although the predetermined direction (height direction) is preferably the height direction, the object (301-305) can also be easily specified in a direction other than the height direction.

[0387] In the detection system (100) according to the second embodiment, in the first embodiment, the positional relationship (DH, H) includes the distance (DH, H) between both ends.

[0388] According to this aspect, by taking into consideration at least the distance (DH, H) between the ends of the distribution of multiple points (CL, CL1, CL2) in a predetermined direction (height direction), it is possible to facilitate and improve the accuracy of identifying the target object (301-305).

[0389] In the detection system (100) according to the third aspect, in the first or second aspect, the processing unit (22) identifies the objects (301 to 305) further based on the spread (the distance DW between the ends in the lateral direction, the width W of the rectangular solid 502) of the distributions (CL, CL1, CL2) in a plane (horizontal plane (XY plane, or a plane parallel to the XY plane)) having a normal direction in a predetermined direction (height direction).

[0390] According to this embodiment, by (further) taking into consideration the spread (DW, W) of the distribution of multiple points (CL, CL1, CL2) in a plane (horizontal plane) perpendicular to a predetermined direction (height direction), it is possible to facilitate identification of the target object (301-305).

[0391] In the detection system (100) according to the fourth aspect, in the third aspect, the positional relationship includes the distance (DH, H) between both ends and the spread (DW, W) in the plane (horizontal plane) of the distribution (CL, CL1, CL2). The processing unit (22) identifies the type of the target (301-305) based on the distance (DH, H) and the spread (DW, W).

[0392] According to this embodiment, the type of the object (301-305) can be identified by considering the distance (DH, H) between the ends of the distribution (CL, CL1, CL2) in a predetermined direction (height direction) and the spread (DW, W) in a plane (horizontal plane) perpendicular to the predetermined direction (height direction).

[0393] In the detection system (100) according to the fifth aspect, in the fourth aspect, personal information including information on the dimensions of the individual's body is stored in advance for each of the multiple individuals. When the processing unit (22) identifies the type as a human body (301), it further identifies the individual among the multiple individuals corresponding to the human body (301) based on each of the multiple personal information corresponding to the multiple individuals, the distance (DH, H) and the spread (DW, W).

[0394] According to this embodiment, when the type of the object (301-305) is identified as a human body (301), the individual corresponding to the human body (301) can be further identified by taking into account the distance (DH, H) and spread (DW, W).

[0395] In the detection system (100) according to the sixth aspect, in the first aspect, the radio wave (Tr) is transmitted in each of a plurality of frames. The processing unit (22) calculates a plurality of time differences by repeatedly performing a process of calculating a time difference, which is a difference between two received signals respectively received in two different frames among the plurality of frames. The processing unit (22) acquires a distribution (CL, CL1, CL2) of one or more moving points, which are points whose time difference is equal to or greater than a threshold, from the plurality of time differences. The processing unit (22) identifies the target (301 to 305) based on at least one of the positional relationship (DH, H) and the time change in the positional relationship in a predetermined direction (height direction) of the distribution (CL, CL1, CL2) of the one or more moving points, and the spread (DW, W) and the time change of the spread (DW, W) in a plane (XY plane or a plane parallel to the XY plane) having the predetermined direction (height direction) as a normal direction.

[0396] According to this aspect, by taking into consideration at least one of the positional relationship (DH, H) and the change in the positional relationship over time of the distribution of multiple moving points (CL, CL1, CL2) obtained from multiple time differences, and the spread (DW, W) within a plane (horizontal plane) and the change in the spread (DW, W) over time, it is possible to further facilitate identification of the target object (301-305).

[0397] In the detection system (100) according to the seventh aspect, in the sixth aspect, the processing unit (22) identifies the motion of the target object (301-305) based on the time change in the spread (DW, W) in a plane (horizontal plane) of the distribution (CL, CL1, CL2) of one or more moving points.

[0398] According to this embodiment, by taking into consideration the time change in the spread (DW, W) of the distribution (CL, CL1, CL2) of multiple moving points within a plane (horizontal plane), it is possible to further facilitate identification of the target object (301-305).

[0399] In the detection system (100) according to the eighth aspect, in the sixth or seventh aspect, the processing unit (22) identifies the motion of the target object (301-305) based on the time change in the positional relationship (DH, H) in a predetermined direction (height direction) of the distribution (CL, CL1, CL2) of one or more moving points.

[0400] According to this embodiment, by taking into consideration the change over time in the positional relationship (DH, H) in a specific direction (height direction) of the distribution of multiple moving points (CL, CL1, CL2), it is possible to further facilitate identification of the target object (301-305).

[0401] In the detection system (100) according to the ninth aspect, in the sixth aspect, the processing unit (22) identifies the object (301-305) or identifies the motion of the object (301-305) based on at least one of the spread (DW, W) of one or more moving point distributions (CL, CL1, CL2) in a plane (XY plane or a plane parallel to the XY plane) having a normal direction in a predetermined direction (height direction) and the change in the spread (DW, W) over time.

[0402] According to this embodiment, by taking into consideration at least one of the spread (DW, W) of the distribution of multiple moving points (CL, CL1, CL2) in a plane perpendicular to a predetermined direction (height direction) and the change in the spread (DW, W) over time, it is possible to further facilitate identification of the target object (301-305).

[0403] In the detection system (100) according to the tenth aspect, in the sixth or ninth aspect, the processing unit (22) identifies the object (301-305) or identifies the motion of the object (301-305) based on at least one of the positional relationship (DH, H) in a predetermined direction (height direction) of the distribution of one or more moving points (CL, CL1, CL2) and the change over time of the positional relationship (DH, H).

[0404] According to this aspect, by taking into consideration at least one of the positional relationship (DH, H) between the ends of the distribution of multiple moving points (CL, CL1, CL2) in a predetermined direction (height direction) and the change in the positional relationship over time, it is possible to further facilitate identification of the target object (301-305).

[0405] In the detection system (100) according to an eleventh aspect, in any one of the sixth to tenth aspects, the processing unit (22) performs the process of calculating the time difference at a plurality of time intervals different from each other.

[0406] According to this aspect, it is possible to improve the accuracy of identifying the objects (301 to 305) and to further facilitate identifying the objects (301 to 305).

[0407] A detection system (100) according to a twelfth aspect is any one of the first to eleventh aspects, further including a radio wave sensor (1). The radio wave sensor (1) transmits radio waves (Tr) in a predetermined direction (height direction), receives reflected waves (Re), and outputs a received signal.

[0408] According to this embodiment, when a radio wave (Tr) is transmitted in a predetermined direction (height direction) and a target object (301-305) is detected using a reflected wave (Re) of the transmitted radio wave (Tr), the target object (301-305) can be easily identified by taking into consideration the positional relationship (DH, H) between the ends of the distribution of multiple points (CL, CL1, CL2) in the predetermined direction (height direction).

[0409] In the detection system (100) according to the thirteenth aspect, in the twelfth aspect, the radio wave sensor (1) is located above the region (R1). The predetermined direction (height direction) is the height direction of the region (R1).

[0410] According to this embodiment, when the radio wave sensor (1) transmits radio waves (Tr) from above the region (R1) and detects the target object (301-305) using the reflected wave (Re) of the transmitted radio wave (Tr), the target object (301-305) can be easily identified by taking into consideration the positional relationship (DH, H) between the ends of the distribution of multiple points (CL, CL1, CL2) in the height direction of the region (R1).

[0411] The radio wave sensor (1) according to the fourteenth aspect is a radio wave sensor (1) that transmits radio waves (transmission waves Tr) in a predetermined direction (height direction), receives reflected waves (Re) of the radio waves (Tr), and detects objects (301-305) in an area (R1) using a received signal that is a signal based on the reflected waves (Re). The radio wave sensor (1) includes a processing unit (22) that acquires information regarding the positions of the objects (301-305) in the area (R1) by performing a distance measurement process based on the received signal. The processing unit (22) acquires a plurality of pieces of position information corresponding to a plurality of parts that constitute the objects (301-305), and identifies the objects (301-305) based on the positional relationship (height difference DH, height H of the rectangular parallelepiped 502) between both ends in a predetermined direction (height direction) of a distribution (CL, CL1, CL2) of a plurality of points that correspond to the plurality of pieces of position information.

[0412] According to this embodiment, when detecting an object (301-305) using a reflected wave (Re) of a radio wave (Tr) transmitted in a predetermined direction (height direction), the object (301-305) can be easily identified by taking into consideration the positional relationship (DH, H) between the ends of the distribution of multiple points (CL, CL1, CL2) in the predetermined direction (height direction).

[0413] The radio wave sensor (1) according to the fifteenth aspect is located above the region (R1) in the fourteenth aspect. The predetermined direction (height direction) is the height direction of the region (R1).

[0414] According to this embodiment, when radio waves (Tr) are transmitted from above the region (R1) and the target objects (301-305) are detected using the reflected waves (Re) of the transmitted radio waves (Tr), the target objects (301-305) can be easily identified by taking into consideration the positional relationship (DH, H) between the ends of the distribution of multiple points (CL, CL1, CL2) in the height direction of the region (R1).

[0415] The detection method according to the sixteenth aspect is a detection method for detecting an object (301-305) in an area (R1) using a received signal, which is a signal based on a reflected wave (Re) of a radio wave (transmission wave Tr) transmitted in a predetermined direction (height direction). The detection method includes a processing step (steps S8-S10 in the first embodiment; steps S8a-10a and steps S101-S111 in the second embodiment) for acquiring information on the position of the object (301-305) in the area (R1) by performing a distance measurement process based on the received signal. In the processing steps (S8-S10; S8a-10a and S101-S111), a plurality of pieces of position information corresponding to a plurality of parts constituting the object (301-305) are acquired, and the object (301-305) is identified based on the positional relationship (height difference DH, height H of the rectangular parallelepiped 502) between both ends in a predetermined direction (height direction) of a distribution (CL, CL1, CL2) of a plurality of pieces of position information corresponding to each of the plurality of pieces of position information.

[0416] According to this embodiment, when detecting an object (301-305) using a reflected wave (Re) of a radio wave (Tr) transmitted in a predetermined direction (height direction), the object (301-305) can be easily identified by taking into consideration the positional relationship (DH, H) between the ends of the distribution of multiple points (CL, CL1, CL2) in the predetermined direction (height direction).

[0417] A program according to a seventeenth aspect is for causing one or more processors to execute the detection method according to the sixteenth aspect.

[0418] According to this embodiment, when detecting an object (301-305) using a reflected wave (Re) of a radio wave (Tr) transmitted in a predetermined direction (height direction), the object (301-305) can be easily identified by taking into consideration the positional relationship (DH, H) between the ends of the distribution of multiple points (CL, CL1, CL2) in the predetermined direction (height direction). [Explanation of symbols]

[0419] 100,100A Detection System (Instrument Control System) 1 Radio wave sensor 22 Processing section 200c Dynamic (electric blinds) 301 Moving body (human body) 302 Stationary objects 303 Moving object (cleaning robot) 304 Moving object (electric fan) 305 Moving object (cat) 502 point cloud (distribution) CL, CL1, CL2 cluster group (distribution) Tr Transmission wave (radio wave) Re reflected wave R1 area R1a Wall R1b Ceiling ΔA1~ΔA5 Interframe difference (time difference) Frames Fr1~Fr5

Claims

1. A detection system for detecting an object within an area using a received signal that is a signal based on a reflected wave of a radio wave transmitted in a predetermined direction, comprising: a processing unit that acquires information about a position of the object within the area by performing a distance measurement process based on the received signal; The processing unit includes: acquiring a plurality of pieces of position information corresponding to a plurality of parts constituting the object, identifying the object based on a positional relationship between both ends in the predetermined direction of a distribution of a plurality of points corresponding to the plurality of pieces of position information respectively; Detection system.

2. The positional relationship includes a distance between the two ends. The detection system of claim 1 .

3. The processing unit further identifies the object based on a spread of the distribution in a plane having a normal direction in the predetermined direction. The detection system of claim 1 .

4. The positional relationship is as follows: The distance between the ends; and the spread in the plane of the distribution; The processing unit identifies a type of the object based on the distance and the spread. The detection system of claim 3 .

5. personal information including information regarding the individual's body dimensions is stored in advance for each of a plurality of individuals; When the processing unit identifies that the type is the human body, the processing unit further identifies an individual among the plurality of individuals corresponding to the human body based on each of the plurality of personal information corresponding to the plurality of individuals, the distance, and the spread. The detection system of claim 4.

6. The radio wave is transmitted in each of a plurality of frames; The processing unit includes: calculating a plurality of time differences by repeatedly performing a process of calculating a time difference between two of the received signals received in two different frames among the plurality of frames; obtaining the distribution of one or more moving points, which are the points whose time differences are equal to or greater than a threshold, from the plurality of time differences; Identifying the object based on at least one of the positional relationship and the time change of the positional relationship in the predetermined direction of the distribution of the one or more moving points, and the spread in a plane having the predetermined direction as a normal direction and the time change of the spread. The detection system of claim 1 .

7. The processing unit includes: determining a motion of the object based on the time change of the extent of the distribution of the one or more moving points in the plane; The detection system of claim 6.

8. The processing unit includes: Identifying a motion of the object based on the time change of the positional relationship of the distribution of the one or more moving points in the predetermined direction.

8. A detection system according to claim 6 or 7.

9. The processing unit includes: Identifying the object or identifying a motion of the object based on at least one of the extent of the distribution of the one or more moving points in a plane having the predetermined direction as a normal direction and a time change in the extent. The detection system of claim 6.

10. The processing unit includes: Identifying the object or identifying a motion of the object based on at least one of the positional relationship and the time change of the positional relationship in the predetermined direction of the distribution of the one or more moving points.

10. A detection system according to claim 6 or 9.

11. The processing unit performs the process of calculating the time difference at a plurality of time intervals different from each other. The detection system of claim 6.

12. a radio wave sensor that transmits the radio wave in the predetermined direction, receives the reflected wave, and outputs the received signal; The detection system of claim 1 .

13. the radio wave sensor is located above the area; The predetermined direction is a height direction of the region. The detection system of claim 12.

14. A radio wave sensor that transmits radio waves in a predetermined direction, receives reflected waves of the radio waves, and detects an object within an area using a received signal that is a signal based on the reflected waves, a processing unit that acquires information about a position of the object within the area by performing a distance measurement process based on the received signal; The processing unit includes: acquiring a plurality of pieces of position information corresponding to a plurality of parts constituting the object, identifying the object based on a positional relationship between both ends in the predetermined direction of a distribution of a plurality of points corresponding to the plurality of pieces of position information respectively; Radio wave sensor.

15. Located above the region, The predetermined direction is a height direction of the region. The radio wave sensor according to claim 14.

16. A detection method for detecting an object within an area using a received signal that is a signal based on a reflected wave of a radio wave transmitted in a predetermined direction, comprising: A processing step of acquiring information regarding a position of the object within the area by performing a distance measurement process based on the received signal, In the processing step, acquiring a plurality of pieces of position information corresponding to a plurality of parts constituting the object, identifying the object based on a positional relationship between both ends in the predetermined direction of a distribution of a plurality of points corresponding to the plurality of pieces of position information respectively; Detection methods.

17. A method for causing one or more processors to execute the detection method according to claim 16. program.

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