Indication of the likelihood of presence being detected via multiple indications

Abstract

The method includes determining (101) a sensing input and determining (103) a likelihood of a human or animal being present based on the sensing input. The sensing input reflects a change in a radio frequency signal received by one or more devices. The method further includes controlling (105) a lighting device to render a light effect upon determining that the likelihood exceeds a presence detection threshold, and to continue rendering the light effect for a predetermined period thereafter, even if the likelihood changes by more than a predetermined value within the predetermined period. The method further includes indicating (107) the likelihood to a user via an indication selected from a plurality of indications, for example, by rendering an indication on the lighting device. A different indication from the plurality of indications is selected upon determining that the likelihood has changed by more than a predetermined value.

Description

[Technical Field]

[0001] The present invention relates to a system for determining the likelihood of a human or animal presence based on sensing input, the sensing input reflecting changes in radio frequency signals received by one or more devices.

[0002] The present invention further relates to a method of determining the likelihood of a human or animal presence based on sensing input, the sensing input reflecting changes in radio frequency signals received by one or more devices.

[0003] The invention also relates to a computer program product enabling a computer system to carry out such a method. [Background technology]

[0004] In smart homes and smart offices, presence detection is becoming increasingly important, for example, to automatically turn lights on and off and automatically control heating / air conditioning. PIR sensors or cameras are often used to perform presence detection, which are relatively easy to set up and commission. For example, US 2010 / 00185969 A1 discloses a lighting control system with a user interface for interactively changing the settings of a lighting system, in particular a user interface that allows easy and comfortable interactive modification of lighting scenes created by the lighting system. In one embodiment, a scene to be illuminated is graphically represented, and some locations are recolored based on motion sensor information.

[0005] In recent years, network-based presence sensing technologies have matured and appeared on the market. A prominent example is Ivani's "network presence sensing" technology. Applications of this technology range from detecting movement based on environmental changes to people counting and locating. The main idea behind this technology is to measure the behavior of wireless communications (e.g., between IoT devices). The location and number of people, their weight, movement direction, and other parameters affect this behavior, and based on detected changes (e.g., changes in signal strength or channel state information (CSI)), a person or group of people can be detected. The accuracy and versatility of the system depend on the number of communicating devices; typically, the more devices present, the better (the minimum number of devices is two, so that signals can be generated and received to evaluate their behavior).

[0006] To set up and commission network presence sensing, a user (with support from the system) typically needs to define detection areas, assign lights and light scenes, find optimal settings for detection sensitivity for each area, etc. Unlike traditional presence sensing devices (e.g., PIRs, cameras), the system does not have a well-defined field of view from which detection occurs and can be triggered through objects (e.g., walls or doors), so it can be difficult for users to set sensitivity and understand why the system behaves in a certain way. This can be confusing and affect users' acceptance of the system. Furthermore, RF sensing detection performance can change over time, for example, due to furniture being moved or doors being opened and closed.

[0007] US 2017 / 150578 discloses a lighting control method using active wireless active feedback monitoring of the behavioral activity of moving objects. The presence of moving objects (humans, pets / animals, vehicles, etc.) within the range of an established wireless network tends to modulate the wireless signal strength between wireless nodes. Using the monitored variations in the standard deviation of the wireless signal strength between network nodes, the behavioral activity of the moving objects can be used to directly control the ambient lighting conditions within an area of ​​interest. Summary of the Invention [Problem to be solved by the invention]

[0008] A primary object of the present invention is to provide a system that helps a user set up and commission a network presence sensing system.

[0009] A second object of the present invention is to provide a method to help a user set up and commission a network presence sensing system. [Means for solving the problem]

[0010] In a first aspect of the present invention, there is provided a system for determining a likelihood of a human or animal being present based on sensing input, the sensing input reflecting a change in a radio frequency signal received by one or more devices, the system including at least one input interface, at least one output interface, and at least one processor configured to: use the at least one input interface to determine the sensing input; determine the likelihood of the human or animal being present based on the sensing input; render a light effect upon determining that the likelihood exceeds a presence detection threshold; and use the at least one output interface to control a lighting device to continue rendering the light effect for a predetermined period thereafter, even if the likelihood changes by more than a predetermined value within the predetermined period; and use the at least one output interface to indicate the likelihood to a user via an indication selected from a plurality of indications, a different one of the plurality of indications being selected upon determining that the likelihood has changed by more than the predetermined value.

[0011] By allowing a user to walk around a room or building and see how well presence sensing covers their current location (including whether it is covered at all), the user can easily determine whether network presence sensing has been properly set up and commissioned. A lighting device light source that is turned on when presence is detected (i.e., when the probability begins to exceed the presence detection threshold) is not immediately turned off when the probability falls below the presence detection threshold, thus avoiding, among other things, light flickering. This is not suitable for checking presence sensing coverage. To check presence sensing coverage, different indications are provided as soon as the probability changes by more than a predetermined value, e.g., as soon as probability / confidence levels corresponding to different indications are determined. The lighting device may be one of the one or more devices.

[0012] The at least one processor may be configured to use the at least one output interface to indicate the possibility to the user by displaying the indication on a display, for example a display of a mobile device, a TV or a projector, which allows more information to be provided than if only the lighting device itself were used to provide the indication.

[0013] The at least one processor may be configured to: in a normal operation mode of the lighting device, use the at least one output interface to control the lighting device to render the light effect upon determining that the likelihood exceeds the presence detection threshold; and in a configuration mode of the lighting device, use the at least one output interface to provide the indication to the lighting device by rendering a further light effect of a plurality of light effects, a different light effect of the plurality of light effects being selected upon determining that the likelihood has changed by more than the predetermined value.

[0014] While it may be less informative than indicating possibilities on a display, this use of lighting devices to indicate possibilities may be the simplest to use and implement, as no additional devices (e.g., mobile devices) are required and feedback is provided directly in the environment. The configuration mode may be activated for all lighting devices in a home or office, or only for a subset of these lighting devices. In the former case, the system itself may switch between a normal operation mode and a configuration mode, thereby causing all associated lighting devices to switch to the same mode. The at least one processor may be configured to use the at least one input interface to receive user input, and to switch between the normal operation mode and the configuration mode based on the user input.

[0015] The at least one processor may be configured to: determine a chromaticity for the further light effect based on the likelihood, the chromaticity being indicative of the likelihood; determine a brightness and / or light output level for the further light effect based on the likelihood, the brightness and / or light output level being indicative of the likelihood; and / or determine a level of dynamicity for the further light effect based on the likelihood, the dynamic level being indicative of the likelihood. The dynamic level may, for example, be a flashing speed.

[0016] The at least one processor may be configured to determine the further light effect based on the possibility and a capability of the lighting device, for example, if the lighting device has color capability, a chromaticity may be determined for the further light effect based on the possibility, and if the lighting device does not have color capability, a light output level may be determined for the further light effect based on the possibility.

[0017] The at least one processor may be configured to select a first light effect from the plurality of light effects when determining that the likelihood exceeds the presence detection threshold, and to select a second light effect from the plurality of light effects when determining that the likelihood falls below the presence detection threshold. This makes it easier for a user to verify whether they are properly detected in their current location. Rendering a light effect even when a user is not detected provides better feedback to the user. If a light effect is not rendered when a user is not detected, the user may not know for sure that there is another reason why the lighting device is not rendering a light effect, for example, the power switch is turned off.

[0018] The at least one processor may be configured to determine a color for the further light effect in a first color spectrum upon determining that the likelihood exceeds the presence-detection threshold, and to determine a color for the further light effect in a second color spectrum upon determining that the likelihood falls below the presence-detection threshold. The at least one processor may be configured to determine the color within the first color spectrum or the second color spectrum based on the said first likelihood, and the color may further indicate the likelihood. For example, if the likelihood exceeds the presence-detection threshold, a light effect having a color in the green spectrum may be rendered, and if the likelihood remains below the presence-detection threshold, a light effect having a color in the red spectrum or the orange-red spectrum may be rendered.

[0019] The at least one processor may be configured to determine a plurality of possibilities for the presence of a human or animal based on a plurality of sensing inputs, each of the plurality of sensing inputs corresponding to a respective spatial location of the human or animal, and to associate each of the plurality of possibilities with the respective spatial location in memory. This may identify and indicate areas where the presence of a human or animal may not be well detected. For example, the at least one processor may be configured to use the at least one output interface to display a spatial map showing the plurality of possibilities at the respective spatial locations. The sensing inputs need not each be based on information generated by all sensing nodes; different sensing inputs may be based on information generated by different sets of sensing nodes.

[0020] In a second aspect of the present invention, a method for determining a likelihood of human or animal presence based on sensing input, the sensing input reflecting a change in a radio frequency signal received by one or more devices, the method includes: determining the sensing input; determining the likelihood of the human or animal presence based on the sensing input; rendering a light effect upon determining that the likelihood exceeds a presence detection threshold; and controlling a lighting device to continue rendering the light effect for a predetermined period thereafter, even if the likelihood changes by more than a predetermined value within the predetermined period; and indicating the likelihood to a user via an indication selected from a plurality of indications, a different one of which is selected upon determining that the likelihood has changed by more than the predetermined value. The method may be performed by software executing on a programmable device. The software may be provided as a computer program product.

[0021] Further provided are computer programs for practicing the methods described herein, as well as non-transitory computer-readable storage media having stored thereon the computer programs, which may, for example, be downloaded by or uploaded to existing devices or stored during manufacture of these systems.

[0022] The non-transitory computer-readable storage medium stores at least one software code portion that, when executed or processed by a computer, is configured to perform an executable operation for determining a likelihood of a human or animal presence based on sensing input, the sensing input reflecting changes in radio frequency signals received by one or more devices.

[0023] The executable operations include determining the sensing input; determining the likelihood of the human or animal being present based on the sensing input; rendering a light effect upon determining that the likelihood exceeds a presence detection threshold; and controlling a lighting device to continue rendering the light effect for a predetermined period thereafter, even if the likelihood changes by more than a predetermined value within the predetermined period; and indicating the likelihood to a user via an indication selected from a plurality of indications, a different one of which is selected upon determining that the likelihood has changed by more than the predetermined value.

[0024] As will be appreciated by those skilled in the art, aspects of the present invention may be embodied as a device, method, or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, microcode, etc.), or an embodiment combining software and hardware aspects, all of which may be collectively referred to herein as a "circuit," "module," or "system." Functions described in this disclosure may be implemented as an algorithm executed by a computer processor / microprocessor. Furthermore, aspects of the present invention may take the form of a computer program product embodied as one or more computer-readable medium(s) having computer-readable program code embodied thereon, e.g., stored thereon.

[0025] Any combination of one or more computer-readable media may be utilized. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the above. More specific examples of computer-readable storage media include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the context of the present invention, a computer-readable storage medium may be any tangible medium that contains or is capable of storing a program for use by or in connection with an instruction execution system, apparatus, or device.

[0026] A computer-readable signal medium may include a propagated data signal having computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electromagnetic, optical, or any suitable combination thereof. A computer-readable signal medium is not a computer-readable storage medium but may be any computer-readable medium capable of communicating, propagating, or transmitting a program for use by or in connection with an instruction execution system, apparatus, or device.

[0027] The program code embodied on the computer readable medium may be transmitted using any suitable medium, including, but not limited to, wireless, wired, fiber optic, cable, RF, etc., or any suitable combination of the above. The computer program code for performing operations related to aspects of the present invention may be implemented in any suitable programming language, including Java™, Smalltalk (registered trademark) , object-oriented programming languages ​​such as C++, and traditional procedural programming languages ​​such as the "C" programming language or similar programming languages. This program code may run entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (e.g., over the Internet using an Internet Service Provider).

[0028] Aspects of the present invention are described below with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor, particularly a microprocessor or central processing unit (CPU), of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to create a machine, whereby the instructions, executed by the processor of the computer, other programmable data processing apparatus, or other device, create means for performing the functions / acts specified in the flowchart and / or block diagram blocks.

[0029] These computer program instructions may also be stored in a computer-readable medium that can direct a computer, other programmable data processing apparatus, or other device to function in a particular manner, thereby producing a product in which the instructions stored in the computer-readable medium include instructions that perform the functions / acts specified in the flowchart and / or block diagram blocks.

[0030] Computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause the computer, other programmable data processing apparatus, or other device to perform a series of operational steps to create a computer-implemented process, whereby the instructions executing on the computer or other programmable apparatus provide a process for performing the functions / acts specified in the flowchart and / or block diagram blocks.

[0031] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, including one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flowchart diagrams, and combinations of blocks in the block diagrams and / or flowchart diagrams, may be implemented by a dedicated hardware-based system that performs the specified functions or acts, or a combination of dedicated hardware and computer instructions. [Brief explanation of the drawings]

[0032] These and other aspects of the invention will be apparent from and further elucidated, by way of example, with reference to the following drawings. [Figure 1] FIG. 1 is a block diagram of a first embodiment of a system. [Figure 2] FIG. 2 is a block diagram of a second embodiment of the system. [Figure 3] FIG. 2 is a flow diagram of a first embodiment of a method. [Figure 4] Shows that the presence probability is shown on a lighting device in one room. [Figure 5] Presence possibilities are shown indicated by lighting devices in two rooms. [Figure 6] FIG. 4 is a flow diagram of a second embodiment of the method. [Figure 7] 1 illustrates a first example of a diagnostic user interface displayed at a first user position. [Figure 8] 8 illustrates the user interface of FIG. 7 displayed at a second user position. [Figure 9] 8 illustrates the user interface of FIG. 7 displayed in a third user position. [Figure 10] 10 illustrates a second example of a diagnostic user interface displayed at a third user position. [Figure 11] FIG. 10 is a flow diagram of a third embodiment of the method. [Figure 12] 1 shows a first example of a displayed spatial map representing detection coverage. [Figure 13] 10 shows a second example of a displayed spatial map illustrating detection coverage. [Figure 14] 1 is a block diagram of an exemplary data processing system for implementing the methods of the present invention; DETAILED DESCRIPTION OF THE INVENTION

[0033] Corresponding elements in the drawings are indicated by the same reference numerals.

[0034] 1 illustrates a first embodiment of a system for determining the likelihood of a human or animal presence based on sensing input. The sensing input reflects changes in radio frequency (RF) signals received by one or more devices. The sensing input may reflect, for example, changes in signal strength and / or channel state information (CSI) of the received RF signals. Alternatively or additionally, the sensing input may reflect, for example, changes in reception difference and / or phase shift and / or time of arrival in the case of multiple transmit or receive antennas.

[0035] 1, the presence sensing system includes lighting devices 31-36 and bridge 16. At least one of these devices transmits RF signals, and the others receive RF signals. For example, lighting devices 31-36 may be Hue lamps, and bridge 16 may be a Hue bridge. In an alternative example, bridge 16 is not part of the presence sensing system.

[0036] In the first embodiment, the system is a mobile device 1. For example, the mobile device 1 may run an app that allows a user to control lighting devices 31-36. The lighting devices 31-36 communicate with a bridge 16, for example using Zigbee technology. The mobile device 1 can control the lighting devices 31-36 via a wireless LAN access point 17 and the bridge 16. The wireless LAN access point 17 is connected to the Internet 11. An Internet server 13 is also connected to the Internet 11. The Internet server 13 may also be connected to a service provider, for example, Amazon. (registered trademark) Alexa (registered trademark) The lighting devices 31 to 36 may be controlled based on input from a voice assistant such as:

[0037] Mobile device 1 includes receiver 3, transmitter 4, processor 5, memory 7, camera 8, and display 9. Processor 5 is configured to use receiver 3 to determine a sensing input by combining data received from the device receiving the RF signal, determine a likelihood of a human or animal being present based on the sensing input, and use transmitter 4 to control one or more of lighting devices 31-36 to render a light effect upon determining that the likelihood exceeds a presence detection threshold, and to continue rendering the light effect for a predetermined period thereafter, even if the likelihood changes by more than a predetermined value within the predetermined period.

[0038] Processor 5 is further configured to indicate the likelihood to the user via an indication selected from a plurality of indications, where a different indication from the plurality of indications is selected upon determining that the likelihood has changed by more than a predetermined value. In the embodiment of Figure 1, processor 5 is configured to indicate the likelihood to the user by displaying an indication on display 9.

[0039] In this way, the status of the presence detection system is visualized for the user by showing how likely the user is to be detected in a particular area. The visualization may be achieved, for example, via one or more lighting devices or via a floorplan view. Typically, the level of details provided depends on the particular visualization technique used. In the embodiment of FIG. 1, the display 9 of the mobile device 1 is used to provide the visualization. As the user moves around, the likelihood / confidence level that the user will be detected is displayed. Additionally, the identification or name of the detection area may be shown. If multiple detection areas have a chance of detecting the user at their current location, both the confidence level and the name of the detection area may be displayed.

[0040] In the embodiment of the mobile device 1 shown in Figure 1, the mobile device 1 includes one processor 5. In an alternative embodiment, the mobile device 1 includes multiple processors. The processor 5 of the mobile device 1 may be, for example, an ARM (registered trademark) or Qualcomm (registered trademark) The processor 5 of the mobile device 1 may be a general-purpose processor or an application-specific processor from, for example, Android (registered trademark)or the iOS operating system. Display 9 may comprise, for example, an LCD or OLED display panel. Display 9 may be, for example, a touchscreen. Processor 5 may use the touchscreen to provide a user interface, for example. Memory 7 may include one or more memory units. Memory 7 may include, for example, solid-state memory. Camera 8 may include, for example, a CMOS or CCD sensor. Camera 8 may be used to provide an augmented reality view, for example.

[0041] The receiver 3 and transmitter 4 may use one or more wireless communication technologies, such as Wi-Fi (IEEE 802.11), for example, to communicate with a wireless LAN access point 17. In alternative embodiments, instead of a single receiver and a single transmitter, multiple receivers and / or multiple transmitters are used. In the embodiment shown in FIG. 1, separate receivers and separate transmitters are used. In alternative embodiments, the receiver 3 and transmitter 4 are combined into a transceiver. The mobile device 1 may include other components typical of mobile devices, such as a battery and a power connector. The present invention may be implemented using a computer program executed on one or more processors.

[0042] In the embodiment of Figure 1, the lighting devices 31-36 are controlled by the mobile device 1 via the bridge 16. In an alternative embodiment, one or more of the lighting devices 31-36 are controlled by the mobile device 1 directly without using a bridge, for example via Bluetooth or WiFi.

[0043] 2 shows a second embodiment of a system for determining the likelihood of human or animal presence based on sensing input. In this second embodiment, the system is a bridge 41. A mobile device 35 can control lighting devices 31-36 via a wireless LAN access point 17 and the bridge 41.

[0044] Bridge 41 includes receiver 43, transmitter 44, processor 45, and memory 47. Processor 45 is configured to use receiver 43 to determine the sensing input and to determine a likelihood of a human or animal presence based on the sensing input. Processor 45 is further configured to use transmitter 44 to control one or more of lighting devices 31-36 in a normal operating mode to render a light effect upon determining that the likelihood exceeds a presence-detection threshold, and to continue rendering the light effect for a predetermined period thereafter, even if the likelihood changes by more than a predetermined value within the predetermined period.

[0045] The processor 45 is further configured to use the transmitter 44 to provide an indication of the likelihood by controlling one or more of the lighting devices 31-36 to render a further light effect of the plurality of light effects in a configuration mode of the lighting devices 31-36, wherein a different light effect of the plurality of light effects is selected upon determining that the likelihood has changed by more than a predetermined value.

[0046] As noted above, the level of detail provided typically depends on the particular visualization technique used. In the embodiment of Figure 2, one or more of lighting devices 31-36 are used to provide the visualization. Depending on user preferences and lighting device capabilities, different mappings of system states to lighting devices may be adopted.

[0047] One or more of the following light settings may be used to indicate the possibilities: Color chromaticity color brightness / lightness Light output level / dimming level Dynamic level (e.g., frequency, pattern).

[0048] For example, if a user only has a white bulb, a full brightness light may indicate a high confidence level that a person will be detected (e.g., >90%), a light off may indicate a low confidence level (e.g., <30%), and anything in between may be indicated by a light on at half brightness.

[0049] If the user has a color bulb, color may be used to indicate the confidence level (e.g., as a gradient between green indicating close to 100% and red indicating close to 0%). A different color spectrum may be used for the probability below the presence detection threshold than the probability above the presence detection threshold. Thus, a color is determined for the further light effect in a first color spectrum when the probability is determined to be above the presence detection threshold, and in a second color spectrum when the probability is determined to be below the presence detection threshold. The color is determined within the first or second color spectrum based on the probability, such that the color further indicates the probability.

[0050] For example, if a human or animal is detected as being present, a color in the green spectrum is determined, and if a human or animal is not detected as being present, a color in the orange-red spectrum is determined. If the probability is below the presence detection threshold but relatively close to the threshold, the orange spectrum may be used. If the probability is below the presence detection threshold but relatively far from the threshold, the red spectrum may be used.

[0051] In the embodiment of bridge 41 shown in Figure 2, bridge 41 includes one processor 45. In alternative embodiments, bridge 41 includes multiple processors. Processor 45 of bridge 41 may be a general-purpose processor, e.g., ARM-based, or an application-specific processor. Processor 45 of bridge 41 may be, e.g., a Unix (registered trademark) The memory 47 may include one or more memory units. The memory 47 may include, for example, a solid-state memory. The memory 47 may be used, for example, to store a table of connected lights.

[0052] The receiver 43 and transmitter 44 may use one or more wired or wireless communication technologies, such as, for example, Ethernet for communicating with the wireless LAN access point 17 and Zigbee for communicating with the lighting devices 31-36. In alternative embodiments, instead of a single receiver and a single transmitter, multiple receivers and / or multiple transmitters are used. In the embodiment shown in FIG. 2, separate receivers and separate transmitters are used. In alternative embodiments, the receiver 43 and transmitter 44 are combined into a transceiver. The bridge 41 may include other components typical of network devices, such as a power connector. The present invention may be implemented using a computer program executed on one or more processors.

[0053] In the embodiment of Figures 1 and 2, the system of the present invention includes a mobile device or a bridge. In alternative embodiments, the system of the present invention is a different device, such as a personal or server computer or a lighting device. In the embodiment of Figures 1 and 2, the system of the present invention includes a single device. In alternative embodiments, the system of the present invention includes multiple devices.

[0054] A first embodiment of determining the likelihood of a human or animal presence based on a sensing input is shown in Figure 3. The sensing input reflects changes in radio frequency signals received by one or more devices, e.g., signal strength or channel state information (CSI). Step 101 includes determining the sensing input. Step 103 includes determining the likelihood of a human or animal presence based on the sensing input. Step 105 includes rendering a light effect upon determining that the likelihood exceeds a presence detection threshold, and controlling the lighting device to continue rendering the light effect for a predetermined period thereafter, even if the likelihood changes by more than a predetermined amount within the predetermined period.

[0055] Step 107 includes indicating the likelihood to the user via an indication selected from a plurality of indications. A different one of the plurality of indications is selected upon determining that the likelihood has changed by more than a predetermined value. The likelihood may be indicated on the lighting device, for example, in a configuration mode of the presence detection system, or on a different device, for example, a mobile device. The likelihood may be indicated on multiple lighting devices, for example, multiple lighting devices in one room as shown in FIG. 4, or multiple lighting devices in multiple rooms as shown in FIG. 5.

[0056] A second embodiment for determining the likelihood of a human or animal being present based on sensing input is shown in Figure 6. In the embodiment of Figure 6, step 101 of Figure 3 is preceded by step 121, and steps 123 and 125 are performed after step 103 of Figure 3 and before steps 105 and 107 of Figure 3. Furthermore, step 105 of Figure 3 includes sub-steps 131-137, and step 107 of Figure 3 includes sub-steps 141-145.

[0057] Step 121 includes receiving a user input and switching between a normal operation mode and a configuration mode based on the user input. Step 101 includes determining a sensing input (e.g., from data received from one or more sensing devices and / or by determining changes in signal strength or CSI in received RF sensing signals). Step 103 includes determining a likelihood Lh of a human or animal presence based on the sensing input. Step 123 includes determining whether the likelihood Lh exceeds a presence detection threshold Pt. If so, a value of 1 is set to a presence indicator P k (where k represents the current iteration / time). Otherwise, the value 0 is assigned to the presence indication P k is assigned to.

[0058] Step 125 involves determining whether a normal operation mode or a configuration mode is active. If a normal operation mode is active, step 131 is executed. Step 131 determines whether the P determined in step 123 is active. k has the value 0 or the value 1. k If Lh has the value 1, i.e., if the likelihood Lh exceeds the presence detection threshold Pt, then step 135 is performed. Step 135 comprises causing one or more lighting devices to render a light effect, for example by sending a control command to the lighting device whose light source is turned off.

[0059] P k If Lh has the value 0, i.e., if the likelihood Lh does not exceed the presence detection threshold Pt, then step 133 is executed. k The last x values ​​of the presence indication P before P k-x From P k-1It is determined whether _i also has the value 0. If so, then step 137 is performed. If not, step 101 is repeated. Step 137 may involve preventing one or more lighting devices from rendering light effects, for example by sending control commands to lighting devices whose light sources are turned on. Step 101 is repeated after step 137.

[0060] If the configuration mode is active, step 141 is executed. Step 141 involves selecting an indication x that corresponds to the likelihood Lh determined in step 103, for example using a function called IND. For example, the likelihood range 0% to 30% may be associated with the value 1, the likelihood range 31% to 75% may be associated with the value 2, and the likelihood range 76% to 100% may be associated with the value 3.

[0061] Step 143 involves determining whether the value determined in step 141 is different from the value determined in a previous iteration of step 141. If the values ​​are not different, a different light effect need not be rendered and step 101 is repeated. If the values ​​are different, step 145 is performed. Step 145 involves controlling one or more lighting devices to render a light effect corresponding to the value determined in step 141. Light setting LS1 may be associated with a red light effect, light setting LS2 may be associated with a yellow light effect, and light setting LS3 may be associated with a green light effect. Step 101 is repeated after step 145.

[0062] 6 shows only step 101 being repeated after steps 133, 137, 143, or 145 are performed, step 121 may also be repeated to allow the user to switch modes. In the embodiment of FIG. 6, step 121 is performed before step 101. In alternative embodiments, step 121 is performed after or simultaneously with step 101.

[0063] In the embodiment of Figure 6, one or more lighting devices are associated with a room and turn on when presence is detected (in normal operation mode), and indication is provided only via these one or more lighting devices (in configuration mode). An example of this is shown in Figure 4. A user 19 is standing in a living room 51 where lighting devices 31-34 are installed. In the example of Figure 4, the presence of user 19 is detected with the help of sensors in lighting devices 31-34, and lighting devices 31-34 render a green light effect to indicate that the user's presence has been detected.

[0064] In the example of Figure 4, the lighting devices 35-36 in the kitchen 52 do not render light effects. In the example of Figure 5, the lighting devices 35-36 also render light effects, which the user 19 may be able to see through the open door. The lighting devices 35-36 indicate whether presence is detected with the help of their sensors. If not, the lighting devices 35-36 render a red light effect.

[0065] In the embodiment of Figure 6, the likelihood indication is provided via one or more lighting devices. In an alternative embodiment, the likelihood indication is provided via another device, for example, a mobile device. Figures 7-10 show examples of diagnostic user interfaces for providing an indication via a mobile device display. Figure 7 shows a first example of a diagnostic user interface. If a user is standing at a first user position, it is determined that there is a 95% likelihood that a human or animal is present. This likelihood is represented by a label 65 on the display 9 of the mobile device 1.

[0066] In the examples of Figures 7-10, the probability range 0% to 30% is associated with value 1, the probability range 31% to 75% is associated with value 2, and the probability range 76% to 100% is associated with value 3. These ranges may be user-configurable. A happy smiley 61 is associated with value 3 and is therefore displayed on display 9. This user interface allows the user to walk around the room / building and check whether their presence is detected at all (relevant) user locations and diagnose problems with the current presence setup configuration.

[0067] In the example of Figure 8, if the user is standing at the second user position, it is determined that there is a 20% chance that a human or animal is present. Again, this chance is represented by label 65 on display 9. A sad smiley 62 is associated with the value 1 and is therefore displayed on display 9. In the example of Figure 9, if the user is standing at the third user position, it is determined that there is a 58% chance that a human or animal is present. Again, this chance is represented by label 65 on display 9. A neutral smiley 63 is associated with the value 2 and is therefore displayed on display 9.

[0068] In the above description of Figures 5-8, a person is described as walking around to configure presence sensing. However, the person configuring presence sensing is not necessarily the object to be detected. The presence to be detected may be that of another person or animal. For example, a chicken farmer may configure presence sensing in a barn to detect the presence of chickens, or a pet owner may configure presence sensing in their home to detect the presence of their pet.

[0069] 10 shows a second example of a diagnostic user interface that is displayed when the user is standing in the third user position. This second user interface displays the same neutral smile 63 as the first user interface when the user is in the third user position. However, in this second user interface, rooms 71 and 72 are graphically represented on the display 9, and the likelihood of the user being present in rooms 71 and 72 is represented by labels 67 and 66, respectively.

[0070] It has been determined that there is a 58% chance that a human or animal is present in living room 71, and a 30% chance that a human or animal is present in kitchen 72. In the example of Figure 10, the highest of the two possibilities is used to determine what indication to provide (in this case, neutral smile 63). In an alternative embodiment, a neutral smile 63 is shown in the representation of room 71, and a sad smile is shown in the representation of room 72.

[0071] A third embodiment for determining the likelihood of a human or animal being present based on sensing input is shown in Figure 11. In the embodiment of Figure 11, step 123 of Figure 6 is included in the embodiment of Figure 3, and steps 161-167 are performed after steps 105 and 107. Steps 105 and 107 may be performed as shown in Figure 6 or may be performed differently, for example, to provide an indication on a display of a mobile device.

[0072] Step 161 determines the spatial position (SP) of the user at their current location, for example using an RF beacon. k Step 163 includes determining in memory the likelihood P determined in step 123. k This spatial position (SP k ) and this possibility P k was determined based on the RF signals transmitted and received while the user was at the determined location by comparing the signal strength or CSI of these RF signals with the signal strength or CSI of previously received RF signals.

[0073] In step 165, it is determined whether step 101 should be repeated or whether step 167 should be performed next. For example, the method may begin when a user activates a configuration or diagnostic mode, launches an app, or presses a start button in an app, and step 167 may be performed when the user deactivates the configuration or diagnostic mode or presses a stop button in the app. Step 167 is typically performed after multiple possibilities for the presence of a human or animal have been determined (based on multiple sensing inputs). Thus, multiple possibilities are associated in memory with respective spatial locations.

[0074] Step 167 includes retrieving the plurality of possibilities and their respective spatial locations from memory, generating a spatial map showing the plurality of possibilities at each spatial location, and displaying the spatial map, for example, on a display of a mobile device. This spatial map may thus represent detection coverage. Step 101 is repeated after step 167.

[0075] The spatial map may be visualized, for example, using any of the following techniques: (1) Use of floorplan visualization: For example, the likelihood / confidence level of detection can be displayed as a floorplan with areas indicated using color (e.g., green: high confidence to be detected, red: high confidence not to be detected). (2) Using the augmented reality (AR) capabilities of a mobile device (e.g., a smartphone or AR glasses). For example, a user can view an area through a smart device (e.g., a smartphone) and directly see areas with high and low detection likelihood / confidence levels. Thus, a user may point the smart device toward an area to see the detectability / confidence levels overlaid on the camera view.

[0076] 12 shows an example of a spatial map visualized using floor plan visualization. Rooms 71 and 72 are graphically represented on the display 9 of the mobile device 1. The black circles indicate the locations of lighting devices, which are also nodes of the presence sensor system and transmit or receive RF signals. A disk 81 and two rings 82, 83 are superimposed on the room 71. A disk 84 and two rings 85, 86 are superimposed on the room 72.

[0077] Disks 81 and 84 indicate a high probability of detection and may be colored, for example, green. Rings 82 and 85 indicate a medium probability of detection and may be colored, for example, orange. Rings 83 and 86 indicate a low probability of detection and may be colored, for example, red. The area enclosed by disks 81, 84 and rings 82, 85 is the detection area.

[0078] 13 shows a second example of a displayed spatial map representing detection coverage. In this second example, areas where detection results are expected to conflict between two detection areas are shown. Area 89 indicates that the overlap area between rooms 71 and 72 is most problematic, meaning that the likelihood / confidence level of detecting a user at that particular spot is similar in both detection areas.

[0079] This visualization can help resolve problems in multi-area situations, such as when a user is unable to determine which area they are in. Changing the location of the RF transmitter and / or receiver, changing the transmit power, and / or changing detection parameters such as area sensitivity can help resolve these problems. In the example of Figure 13, rings 83 and 86 are shown for ease of understanding the visualization. In alternative visualizations, rings 83 and 86 are omitted and / or one or more of disks 81 and 84 and rings 82 and 85 are shown.

[0080] FIG. 14 shows a block diagram illustrating an exemplary data processing system that may implement the methods as described with reference to FIGS.

[0081] 14, data processing system 300 may include at least one processor 302 coupled to memory elements 304 via a system bus 306. Thus, the data processing system may store program code in memory elements 304. Furthermore, processor 302 may execute program code accessed from memory elements 304 via system bus 306. In one aspect, the data processing system may be implemented as a computer suitable for storing and / or executing program code. However, it should be understood that data processing system 300 may be implemented in the form of any system including a processor and memory capable of performing the functions described herein.

[0082] The memory elements 304 may include one or more physical memory devices, such as, for example, a local memory 308 and one or more mass storage devices 310. Local memory may refer to random access memory or other non-persistent memory devices typically used during the actual execution of program code. The mass storage devices may be implemented as hard drives or other persistent data storage devices. The processing system 300 may also include one or more cache memories (not shown) that provide temporary storage of at least some of the program code to reduce the number of times the program code must be retrieved from the mass storage device 310 during execution. The processing system 300 may also be able to use memory elements of another processing system, for example, if the processing system 300 is part of a cloud computing platform.

[0083] Input / output (I / O) devices, shown as input devices 312 and output devices 314, may optionally be coupled to the data processing system. Examples of input devices include, but are not limited to, a keyboard, a pointing device such as a mouse, a microphone (e.g., for voice and / or speech recognition), etc. Examples of output devices include, but are not limited to, a monitor or display, speakers, etc. The input and / or output devices may be coupled to the data processing system directly or through intervening I / O controllers.

[0084] In one embodiment, the input and output devices may be implemented as a hybrid input / output device (illustrated in FIG. 14 by the dashed line surrounding input device 312 and output device 314). One example of such a hybrid device is a touch-sensitive display, sometimes referred to as a "touchscreen display" or simply a "touchscreen." In such an embodiment, input to the device may be provided by movement of a physical entity, such as a stylus or a user's finger, on or near the touchscreen display.

[0085] A network adapter 316 may also be coupled to the data processing system to enable the data processing system to be coupled to other systems, computer systems, remote network devices, and / or remote storage devices through intervening private or public networks. The network adapter may include a data receiver for receiving data transmitted to data processing system 300 by such systems, devices, and / or networks, and a data transmitter for transmitting data from data processing system 300 to such systems, devices, and / or networks. Modems, cable modems, and Ethernet cards are examples of various types of network adapters that may be used with data processing system 300.

[0086] As shown in Figure 14, memory element 304 may store application 318. In various embodiments, application 318 may be stored in local memory 308, one or more mass storage devices 310, or may be separate from the local memory and mass storage devices. It should be appreciated that data processing system 300 may also execute an operating system (not shown in Figure 14) that may facilitate the execution of application 318. Application 318 may be implemented in the form of executable program code and may be executed by data processing system 300, for example, by processor 302. In response to executing the application, data processing system 300 may be configured to perform one or more of the operations or method steps described herein.

[0087] 14 illustrates input device 312 and output device 314 as separate from network adapter 316. However, additionally or alternatively, input may be received and output may be sent via network adapter 316. For example, data processing system 300 may be a cloud server. In this case, input may be received from and output may be sent to user devices functioning as terminals.

[0088] Various embodiments of the present invention may be implemented as a program product for use with a computer system, the program of the program product defining the functions of the embodiments (including the methods described herein). In one embodiment, the program may be contained on various non-transitory computer-readable storage media; as used herein, the phrase "non-transitory computer-readable storage medium" includes all computer-readable media, with the sole exception of a transitory propagating signal. In another embodiment, the program may be contained on various transitory computer-readable storage media. Exemplary computer-readable storage media include, but are not limited to, (i) non-writable storage media in which information is permanently stored (e.g., a read-only memory device internal to a computer, such as a CD-ROM disk readable by a CD-ROM drive, a ROM chip, or any type of non-volatile solid-state semiconductor memory), and (ii) writable storage media in which changeable information is stored (e.g., a flash memory, a floppy disk inside a diskette drive or hard disk drive, or any type of random-access solid-state semiconductor memory). The computer program may be executed on the processor 302 described herein.

[0089] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It will be further understood that as used herein, the terms "comprise" and / or "comprising" specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0090] Corresponding structure, materials, acts, and equivalents of all means-plus-function or step-plus-function elements in the following claims are intended to include any structure, material, or acts for performing the function in combination with other claim elements as specifically claimed. The description of the embodiments of the present invention has been presented for illustrative purposes, but is not intended to be exhaustive or limited to the disclosed form of implementation. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the invention. The embodiments were chosen and described to best explain the principles and some practical applications of the invention, and to enable others skilled in the art to understand the invention in terms of various embodiments with various modifications as suited to the particular uses contemplated.

Claims

1. 1. A system for determining the likelihood of a human or animal presence based on a sensing input, the sensing input reflecting changes in radio frequency signals received by one or more devices, the system comprising: at least one input interface; at least one output interface; using the at least one input interface to determine the sensing input; determining the likelihood of the human or animal being present based on the sensing input; and using the at least one output interface to control the lighting device to, in a normal operating mode of the lighting device, render a light effect upon determining that the determined likelihood exceeds a presence detection threshold, and to continue rendering the light effect for a predetermined period of time after determining the exceedance, even if the likelihood changes by more than a predetermined value within the predetermined period of time. using, in a configuration mode of the lighting device, the at least one output interface to indicate the determined possibilities to a user via the lighting device by rendering a further light effect of a plurality of light effects, the further light effect being selected based on the determined possibilities. at least one processor configured to: Including, the processor is configured in the configuration mode to determine a value of a plurality of values ​​corresponding to the determined possibilities, each value of the plurality of values ​​being associated with a respective range of possibilities and a corresponding further light effect; The system is configured, in the configuration mode, to select a further light effect from the plurality of light effects that is different from the selected further light effect when the processor determines that a probability determined after the determined probability corresponds to a value that is different from the determined value.

2. 2. The system of claim 1, wherein the at least one processor is configured to use the at least one input interface to receive user input and to switch between the normal operation mode and the configuration mode based on the user input.

3. 3. The system of claim 1 or 2, wherein the at least one processor is configured to determine a chromaticity, brightness and / or light output level for the further light effect based on the likelihood, the chromaticity, brightness and / or light output level being indicative of the likelihood.

4. The system of claim 1 or 2, wherein the at least one processor is configured to determine the further light effect based on the likelihood and a capability of the lighting device.

5. 3. The system of claim 1 or 2, wherein the at least one processor is configured, in the configuration mode, to select a first light effect from the plurality of light effects when it determines that the likelihood exceeds the presence detection threshold, and to select a second light effect from the plurality of light effects when it determines that the likelihood falls below the presence detection threshold.

6. 6. The system of claim 5, wherein the at least one processor is configured, in the configuration mode, to determine a color for the further light effect in a first color spectrum when it determines that the likelihood exceeds the presence detection threshold, and to determine a color for the further light effect in a second color spectrum when it determines that the likelihood falls below the presence detection threshold.

7. 7. The system of claim 6, wherein the at least one processor is configured, in the configuration mode, to determine the color within the first color spectrum or the second color spectrum based on the likelihood, the color further indicating the likelihood.

8. The system of claim 1 or 2, wherein the at least one processor is configured to determine a dynamics level for the further light effect based on the likelihood, the dynamics level being indicative of the likelihood.

9. 1. A system for determining the likelihood of a human or animal presence based on a sensing input, the sensing input reflecting changes in radio frequency signals received by one or more devices, the system comprising: at least one input interface; at least one output interface; using the at least one input interface to determine the sensing input; determining the likelihood of the human or animal being present based on the sensing input; and using the at least one output interface to control the lighting device to, in a normal operating mode of the lighting device, render a light effect upon determining that the determined likelihood exceeds a presence detection threshold, and to continue rendering the light effect for a predetermined period of time after determining the exceedance, even if the likelihood changes by more than a predetermined value within the predetermined period of time. indicating, in a configuration mode of the lighting device, the determined possibility to a user via an indication selected from a plurality of indications, by sending the indication to a remote display and using the at least one output interface to indicate the possibility to the user by displaying the indication on the remote display, or, if the system includes a display, by using the display to indicate the possibility to the user by displaying the indication on the display. at least one processor configured to: Including, the at least one processor is configured, in the configuration mode, to determine an indication of the plurality of indications that corresponds to the determined likelihood, each indication of the plurality of indications being associated with a respective likelihood or range of likelihood; The system wherein the indication is represented on the remote display or the display numerically, graphically, or a combination thereof.

10. 2. The system of claim 1, wherein the at least one processor is configured to determine a plurality of possibilities of a human or animal being present based on a plurality of sensing inputs, each of the plurality of sensing inputs corresponding to a respective spatial location of the human or animal, and to associate each of the plurality of possibilities with the respective spatial location in memory.

11. 11. The system of claim 10, wherein the system includes a display, and the at least one processor is configured to use the display to display a spatial map showing the plurality of possibilities at the respective spatial locations.

12. The system of claim 1 , wherein the lighting device is one of the one or more devices.

13. 1. A method for determining the likelihood of a human or animal presence based on a sensing input, the sensing input reflecting changes in radio frequency signals received by one or more devices, the method comprising: determining the sensing input; determining the likelihood of the human or animal being present based on the sensing input; controlling the lighting device in a normal operating mode to render a light effect upon determining that the determined likelihood exceeds a presence detection threshold, and to continue rendering the light effect for a predetermined period of time after determining the exceedance, even if the likelihood changes by more than a predetermined value within the predetermined period of time; In a configuration mode of the lighting device: indicating the determined possibility to a user via the lighting device by rendering a further light effect of a plurality of light effects; selecting the further light effect based on the determined likelihood; determining a value of a plurality of values ​​corresponding to the determined possibilities, each value of the plurality of values ​​being associated with a respective range of possibilities and a corresponding further light effect; and selecting a further light effect from the plurality of light effects different from the selected further light effect upon determining that the determined probability after the determined probability corresponds to a value different from the determined value; A method comprising:

14. 14. A computer program or suite of computer programs or a computer readable storage medium storing said computer program or suite of computer programs comprising at least one software code portion, said software code portion being configured to enable the method of claim 13 to be performed when said computer program or suite of computer programs is executed on a computer system.

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