Patent Description:
The invention further relates to a method of superimposing a detection zone over an image.

Smart lighting can be connected to sensors so the light triggers when some event in the environment is detected. For example, a Philips Hue lighting system can be connected to a motion sensor such that the light switches on when people enter the room. One major issue when considering purchasing such a sensor is whether it will work properly and one major issue when installing such a sensor is how to make it work properly.

<CIT> discloses a method in which a PIR detector equipped with a PIR sensor and a camera records camera images on which a range and signal strength of the PIR sensor is superimposed. This allows an installer of a security system to perform a walking test by walking through the area in front of the PIR sensor. Afterwards, the installer can playback the recorded video on a computer to determine how the PIR sensor thresholds need to be adjusted in order to cover the locations in which intrusions should be detected.

A drawback of this method is that it can only be used in a limited number of situations and only with detectors that are able to record camera images. Another relevant prior art document is seen in <CIT>), which allows to place a virtual sensor in different locations in a 3D model of a detection space.

It is a first object of the invention to provide an electronic device, which facilitates installation of a sensor in a wide range of situations. This is achieved by claim <NUM>.

It is a second object of the invention to provide a method, which facilitates installation of a sensor in a wide range of situations This is achieved by claim <NUM>.

In a first aspect of the invention, the electronic device comprises at least one processor configured to obtain an image captured with a camera, determine a location for a motion sensor, determine a detection zone of said sensor in relation to said image based on said location determined for said sensor, and display said image and virtual representations of said sensor and said detection zone superimposed over said image, wherein said at least one processor is configured to allow a user to specify or adapt at least one property for said sensor, said at least one property including said location. The electronic device may be a mobile phone, a tablet, augmented reality glasses or an augmented reality headset, for example.

Said at least one processor may be configured to obtain a further image captured with said camera, determine a further detection zone of said sensor in relation to said further image based on said at least one adapted property, and display said further image and virtual representations of said sensor and said further detection zone superimposed over said further image. A detection zone may correspond to a field of view and/or a detection range, for example. A detection zone may depend on a sensor setting, e.g. a sensitivity setting.

The inventors have recognized that by superimposing virtual representations of the sensor and its detection zone on camera images, in real-time or near-real-time, this augmented reality view can be used to determine whether the sensor is able to work properly and/or how the sensor can be made to work properly. The sensor may yet to be installed (and may even yet to be purchased) or the sensor may have already been installed, but possibly at an inappropriate or suboptimal location.

Since no real sensor needs to be present with the same location as the represented sensor, the invention can be used in a wide range of situations, e.g. before the sensor is purchased, before the sensor is installed or before the sensor is repositioned, and the sensor does not need to incorporate a camera that captures and transmits images. The electronic device user may allow a user to specify a potential location for a sensor that he wishes to install or the electronic device may determine a current or suggested location for the sensor and allow the user to adapt the location, for example.

Said at least one processor may be configured to simulate dynamic input to said sensor and display a dynamic virtual representation of said dynamic input superimposed over said image. This allows the user to test at what moment input triggers the sensor. The sensor may comprise a motion detector and/or a heat detector, for example.

Said sensor may comprise a presence sensor and said at least one processor may be configured to display an animation of a virtual character walking through the physical space captured by said image as said dynamic virtual representation. This allows the user to test at what moment input triggers the presence sensor.

Said at least one processor may be configured to simulate said dynamic input causing said sensor to generate sensor output and control a virtual lighting device and/or an installed lighting device based on said sensor output. This allows the user to see which lighting device is triggered in response to the input and at what moment.

Said at least one processor may be configured to simulate said dynamic input causing said sensor to generate sensor output, simulate a light effect generated by said virtual lighting device in response to said sensor output, said light effect being generated based on a location and an orientation of said virtual lighting device, and display said simulated light effect superimposed over said image. This allows the user to see which virtual lighting device is triggered in response to the input in the augmented reality view.

Said at least one property for said sensor may further include an orientation for said sensor. This is beneficial if the sensor is not an omnidirectional sensor. If the sensor is not an omnidirectional sensor, the detection zone is preferably determined in relation to the image based on both the location and the orientation determined for the sensor.

Said at least one property for said sensor may further include one or more settings for said sensor. By allowing the user to specify or adapt one or more settings for the sensor, e.g. its sensitivity, the user can check whether the desired coverage can be obtained with these one or more settings.

Said at least one processor may be configured to allow said user to configure said sensor with said one or more settings. If satisfactory settings are found, this allows the user to configure the sensor with these settings, either immediately if the sensor has already been installed or later (e.g. upon installation of the sensor) if not.

Said at least one processor may be configured to allow said user to specify and/or adapt said location for said sensor in said image. By allowing the user to specify and/or adapt the location in the image instead of in e.g. a model, the effect of specifying or adapting the location can be shown immediately.

Said at least one processor may be configured to allow said user to specify said location for said sensor in a model of at least part of a building, said image capturing at least a portion of said at least part of said building. By allowing the user to specify and/or adapt the location in such a model instead of in e.g. the image itself, it may be easier for the user to position the sensor in 3D space, i.e. to know which of the x, y and z coordinates of the virtual representation will change when moving the virtual representation of the sensor in a certain way.

Said at least one processor may be configured to determine said location for said sensor as a suggested location for said sensor based on information relating to at least a part of a building, said image capturing at least a portion of said at least part of said building. By automatically determining and suggesting a certain location for the sensor, it becomes easier and takes less time for the user to determine a satisfactory or optimal location.

Said at least one processor may be configured to determine one or more suggested settings for said sensor based on information relating to at least a part of a building, said image capturing at least a portion of said at least part of said building, and determine said detection zone of said sensor in relation to said image based on said one or more suggested settings. By automatically determining and suggesting certain settings for the sensor, it becomes easier and takes less time for the user to determine satisfactory or optimal settings.

In a second aspect of the invention, the method comprises obtaining an image captured with a camera, determining a location for a motion sensor, determining a detection zone of said sensor in relation to said image based on said location determined for said sensor, and displaying said image and virtual representations of said sensor and said detection zone superimposed over said image, wherein a user interface is provided for allowing a user to specify or adapt at least one property for said sensor, said at least one property including said location. The method may be implemented in hardware and/or software.

A non-transitory computer-readable storage medium stores at least one software code portion, the software code portion, when executed or processed by a computer, being configured to perform executable operations comprising: obtaining an image captured with a camera, determining a location and an orientation for a sensor, determining a detection zone of said sensor in relation to said image based on said location and said orientation determined for said sensor, and displaying said image and virtual representations of said sensor and said detection zone superimposed over said image, wherein a user interface is provided for allowing a user to specify or adapt at least one property for said sensor, said at least one property including at least one of said location and said orientation.

<FIG> shows an embodiment of the electronic device of the invention. A tablet <NUM> comprises a processor <NUM>, a camera <NUM>, a display <NUM>, a transceiver <NUM> and storage means <NUM>. The tablet <NUM> is connected to a wireless access point <NUM>. A bridge <NUM>, e.g. a Philips Hue bridge, is also connected to the wireless access point <NUM>, e.g. via an Ethernet link. Lights <NUM> and <NUM> (e.g. Philips Hue lights) a motion sensor <NUM> (e.g. Philips Hue motion sensor) communicate with the bridge <NUM>, e.g. via Zigbee.

The processor <NUM> is configured to obtain an image captured with the camera <NUM> and determine a location and an orientation for the sensor <NUM>. Sensor <NUM> may already have been installed or may yet to be installed at that point in time. The processor <NUM> is further configured to determine a detection zone of the sensor <NUM> in relation to the image based on the location and the orientation determined for the sensor <NUM> and display the image and virtual representations of the sensor and the detection zone superimposed over the image. The processor <NUM> is configured to allow a user to specify or adapt at least one property for the sensor <NUM>. The at least one property includes at least one of the location and the orientation.

The processor <NUM> is further configured to obtain a further image captured with the camera <NUM>, determine a further detection zone of the sensor <NUM> in relation to the further image based on the at least one adapted property, and display the further image and virtual representations of the sensor and the further detection zone superimposed over the further image. The operation of the tablet <NUM> is further explained in the description of <FIG>.

In the embodiment of the tablet <NUM> shown in <FIG>, the tablet <NUM> comprises one processor <NUM>. In an alternative embodiment, the tablet <NUM> comprises multiple processors. The processor <NUM> of the tablet <NUM> may be a general-purpose processor, e.g. from Qualcomm or ARM-based, or an application-specific processor. The processor <NUM> of the tablet <NUM> may run an Android or iOS operating system for example. The storage means <NUM> may comprise one or more memory units. The storage means <NUM> may comprise one or more hard disks and/or solid-state memory, for example. The storage means <NUM> may be used to store an operating system, applications and application data, for example.

The transceiver <NUM> may use one or more wireless communication technologies to communicate with the Internet access point <NUM>, for example. In an alternative embodiment, multiple transceivers are used instead of a single transceiver. In the embodiment shown in <FIG>, a receiver and a transmitter have been combined into a transceiver <NUM>. In an alternative embodiment, one or more separate receiver components and one or more separate transmitter components are used. The display <NUM> may comprise an LCD or OLED panel, for example. The display <NUM> may be a touch screen. The camera <NUM> may comprise a CMOS sensor, for example. The tablet <NUM> may comprise other components typical for a tablet such as a battery and a power connector.

In the embodiment of <FIG>, the electronic device of the invention is a tablet. In a different embodiment, the electronic device of the invention may be a mobile phone, augmented reality glasses, or a PC, for example. In the embodiment of <FIG>, the camera <NUM> is part of the electronic device. In a different embodiment, the camera <NUM> may be external to the electronic device. For example, the camera <NUM> may be part of an (other) mobile or wearable device of the user, e.g. a smart phone or augmented reality glasses, or a stationary camera, e.g. the camera of a (stationary) smartTV, the camera of a smart assistant (e.g. Amazon Echo Show) or a security camera. Multiple cameras may be used instead of a single camera. In an alternative embodiment, the sensor may be another kind of sensor, e.g. an omnidirectional sensor like a microphone. If the sensor is an omnidirectional sensor, it may not be necessary to allow the user to specify and/or adapt the orientation. The invention may be implemented using a computer program running on one or more processors.

An embodiment of the method of the invention is shown in <FIG>. A step <NUM> comprises obtaining an image captured with a camera. A step <NUM> comprises determining a location and an orientation for a sensor. A step <NUM> comprises determining a detection zone of the sensor in relation to the image based on the location and the orientation determined for the sensor. A step <NUM> comprises displaying the image and virtual representations of the sensor and the detection zone superimposed over the image.

A step <NUM>, a step <NUM> or a step <NUM> may be performed after step <NUM>. Alternatively, step <NUM> may be repeated after step <NUM>. If step <NUM> is performed again, step <NUM> comprises obtaining a further image captured with the camera. Step <NUM> then comprises determining a further detection zone of the sensor in relation to the further image based on the at least one adapted property and step <NUM> then comprises displaying the further image and virtual representations of the sensor and the further detection zone superimposed over the further image. Steps <NUM>-<NUM> may be repeated as often as desired/needed.

Step <NUM> comprises receiving user input to adapt sensor configuration settings. Step <NUM> comprises simulating dynamic input to the sensor. Step <NUM> comprises storing adjusted sensor configuration settings. Step <NUM> may be repeated after steps <NUM>, <NUM> and <NUM>. A user interface is provided for allowing a user to specify or adapt at least one property for the sensor. The at least one property includes at least one of the location and the orientation. The location and the orientation for the sensor can be specified or adapted in step <NUM>. Steps <NUM>-<NUM> are now described in more detail.

The images captured in step <NUM> may be 2D images or 3D images. If the augmented reality user interface is realized on a display of a mobile phone or tablet, then information will typically be superimposed over 2D images. If the augmented reality user interface is realized on augmented reality glasses, then information will typically be superimposed over 3D images. To implement augmented reality, the captured images need to be displayed in real-time or near-real-time, i.e. shortly after they were captured. Even if images can only be displayed in 2D, capturing 3D images may still be beneficial. For example, a Building Information Model (BIM) may be constructed from these captured 3D images. A BIM may range from an empty room with only walls/doors/windows in the proper location to images that also contain more details like the furniture and installed lighting/sensors. This BIM may be used in later steps.

In step <NUM>, a location and an orientation is determined for an already installed sensor or for a yet to be installed sensor. If the sensor has already been installed, the location and orientation of this sensor may be determined automatically. Alternatively, the user may be allowed to specify the location and the orientation of the sensor or the method may involve suggesting a location and orientation for the sensor, which may be different than the actual location and/or actual orientation. If the sensor has not yet been installed, and perhaps not even been purchased yet, the user may be allowed to specify the location and the orientation for the sensor or the method may involve suggesting a location and orientation for the sensor. In both cases, the sensor is preferably added to a 3D model (e.g. BIM) of at least part of a building of which the image captures at least a portion.

The user may be allowed to specify the location and orientation for the sensor in the image, e.g. by tapping a position on the touch display that is rendering the image, or in a 2D representation (e.g. floor plan) or 3D representation of the 3D model. The location and orientation of other sensors and/or of lighting devices may be determined or specified in a similar manner, possibility using additional touch gestures like drag, flick, pinch, spread, and rotate. Both already installed and yet to be installed devices may be added to the 3D model of the building.

It may be possible to detect these devices and their locations (and possibly their orientations) automatically from the captured image(s) using object detection methods. This can be done in various ways, for instance, an app on a mobile device may use local processing for pattern recognition (or other techniques) to identify device classes (e.g. light strip, light Bulb or motion sensor). Another option is that the information on the location of sensors and lighting devices is retrieved from the lighting system. For example, automatic localization of these devices can be done through RF signal strength analysis (beacons) or using Visible Light Communication (VLC).

The orientation of the sensor(s) is often important, because a sensor usually has a limited viewing angle and the detection zone is therefore dependent on this orientation. The orientation from an already installed sensor can be retrieved by integrating a compass (orientation to Earth magnetic field) and/or accelerometer (orientation to the Earth gravity) in the sensor device and link this orientation to the axes of the sensor. Another option is that the orientation is derived from analysis of the captured image. The orientation is included in the 3D model and the user may be allowed to manipulate the orientation in the augmented reality view or in the 3D model (e.g. rotate gesture on the touch screen region where the sensor is depicted).

A location and orientation for the sensor may be suggested based on information relating to at least a part of the building. The captured (3D) image may be analyzed to determine a good location and orientation for the sensor. This requires capturing one or more (3D) images from which the 3D geometry of the environment is derived. The 3D geometry preferably indicates surfaces on which sensors can be placed and the dimensions of the room(s). In addition to the 3D spatial input, usage info may be retrieved to determine optimal sensor positions and configuration settings. Such usage info may come from explicit user inputs or may be derived from observed human behavior (e.g. frequently followed pathways or frequently used seats), from previously configured room type(s) (e.g. living room, hall or kitchen), from pre-defined usage templates, or from (analyzed) system usage data, for example. Human behavior may be observed using a stationary camera, for example. For instance, one or more motion sensors may be located such that they are directed towards typical user positions and/or that they can detect people entering the area.

In step <NUM>, information related to the sensor is retrieved, e.g. from a product database of the manufacturer of the sensor device and/or from the sensor device itself. As a first example, a detection zone at maximum sensitivity may be retrieved from a product database, a sensitivity may be retrieved from the sensor device and a detection zone at the selected (e.g. actual or recommended) sensitivity may be determined from this information. As a second example, a sensor range and a detection angle may be retrieved from a product database or the sensor device and a detection zone may be determined from this information. The determined detection zone may be added to the 3D model using on the location and orientation determined for the sensor.

In order to determine where and how to represent the detection zone, first the relation between the 3D model and the contents of the image needs to be determined. This may be implemented by determining which objects in the 3D model, e.g. fixed lighting devices, doors, windows, or other objects fixed in the building, are represented in the captured image. As soon as these objects have been identified, the location and angle of the camera can be determined in relation to the objects in the 3D model. , i.e. as if the camera were part of the 3D model.

A 2D or 3D view (representation) of the 3D modelled detection zone from the determined camera location in the 3D model and with the determined camera angle can then be determined. The creation of a 2D view of one or more objects in a 3D model/environment can be implemented using conventional techniques for representing 3D environments on 2D screens.

Step <NUM> further comprises determining the position at which the detection zone needs to be superimposed in the image. This can be implemented by determining where a sensor is located in the 3D model compared to the modelled objects that were identified in the image and using this information to determine where the sensor should be represented in the image compared to the detected locations of these objects in the image.

In step <NUM>, the captured image is displayed and the (virtual) representation of the detection zone determined in step <NUM> is displayed superimposed over the image (e.g. as a graphical overlay) at the position determined in step <NUM>. A (much smaller) virtual representation of the sensor is displayed superimposed over the image (e.g. as a graphical overlay) at the same position.

In addition to allowing the user to specify or adapt the location and orientation for the sensor in step <NUM>, step <NUM> allows the user to adapt one or more configuration settings for the sensor to configure it to his needs. This may be done before and/or after the dynamic input to the sensor is simulated in step <NUM>. For example, if the motion sensor triggers too fast, users may adapt the sensitivity, detection time, detection range, or detection field of view of the motion sensor. The virtual representation of the detection zone displayed in step <NUM> helps the user determine how to adapt the configuration settings. In case no current configuration settings could be retrieved in step <NUM>, e.g. because the sensor has not yet been installed or does not allow access to these settings, default settings can be used.

In a handheld device embodiment, the adaption of these sensor configuration settings may be done via touch gestures on the augmented reality image that is displayed on a touch screen. In a wearable device embodiment, the user input may be done by gestures made in front of the wearable device, e.g. augmented reality glasses. In this case, the gestures can be detected by one or more sensors integrated in the wearable device.

Instead of or in addition to allowing the user to directly adjust sensor settings, one or more sensor settings may be suggested to the user based on automatically obtained information and/or on usage information input by the user. Step <NUM> may involve determining one or more suggested settings for the sensor based on information relating to at least a part of a building (of which the image captures at least a portion) and determining the detection zone of the sensor in relation to the image based on the one or more suggested settings.

For example, usage information may be used that identifies the various area types, e.g. indicating what the most frequently populated areas are, and indicates where the typical entrance and pathways are located. Sensor configuration settings can then be generated according to those identified areas. Other examples of usage information that may be used have been provided in relation to step <NUM>.

In step <NUM>, dynamic input to the sensor is simulated and a dynamic virtual representation of the dynamic input is displayed superimposed over the image to render the behavior of the light system that comprises the sensor. If the sensor comprises a presence sensor, the dynamic virtual representation may be an animation of a virtual character walking through the physical space captured by the image to visualize/simulate the behavior of the lighting system in reaction to the presence sensor. Each iteration of step <NUM>, a next frame of the animation may be displayed. The behavior of the character may be based on a general model of typical user behavior, or modelled behavior of the system's current user(s). The animation preferable has the same scale as the displayed image and the displayed virtual representation of the detection zone. The user may be able to configure the height of the character, e.g. set it to his own height or to his child's height.

Step <NUM> may further comprise simulating the dynamic input causing the sensor to generate sensor output and controlling a virtual lighting device (e.g. added by the user in step <NUM>) and/or an installed (i.e. physical) lighting device (if available) based on the sensor output. The (virtual/installed) lights react as if real objects are in the physical space. In case a virtual lighting device is controlled, a light effect generated by the virtual lighting device in response to the sensor output may be simulated. The light effect is generated based on a location and an orientation of the virtual lighting device. The simulated light effect is the displayed superimposed over the image.

A user may be able to start the visualization of pre-defined use cases by selecting a button on the display device and/or may be able to create his own behavior (e.g. dragging objects in or drawing a path). Optionally, also real objects can be sensed in the environment (e.g. real person walking in the room) and trigger the lights as if the represented sensor(s) were actually present at the represented location and with the represented orientation. In this case, the camera image can be used to detect these objects and their parameters (e.g. location, trajectory) to use this as input to the virtual sensor in the simulation.

In case an installed lighting device is controlled, the simulated sensor output is sent to the lighting control system. The installed lights are controlled based on this simulated sensor data to demonstrate the behavior of the system to the user. When an installed sensor is connected to an installed controller, it may be possible to use a "man in the middle" approach by replacing the data from the installed sensor with the simulated sensor data and forwarding this modified data to the installed controller.

In step <NUM>, the user is allowed to indicate whether he wants to store one or more suggested settings or the one or more settings resulting from an adaptation performed in step <NUM>. If the sensor has already been installed, these one or more settings may be stored in the sensor (device) itself, thereby configuring the sensor. If the sensor has not yet been installed, these one or more settings may be stored in another device, e.g. the device rendering the augmented reality visualization, and transferred to the sensor (device) after it has been installed. Alternatively, these one or setting may be stored in a control device that contains the logic to respond to the sensor output, thereby configuring the sensor.

An example in which the method of the invention is used with a motion sensor is illustrated with the help of <FIG>. In the example, a user is interested in buying a motion sensor for his kitchen, but wants to try out how this will work. He takes his tablet, starts the Philips hue app, and selects the menu for in-app purchasing. This will start an augmented reality function in which the user can point his smartphone camera to the region of his kitchen where he wants to install a motion sensor. <FIG> shows the user capturing the kitchen where he wants to install the motion sensor with the camera of his tablet. From analysis of the camera image, the kitchen is detected and information from the Hue lights <NUM> and <NUM> and their locations are retrieved from the lighting system.

The detection range of the motion sensor is visualized in augmented reality. <FIG> shows a virtual representation <NUM> of a sensor that has been placed at the shown location by a user selecting the sensor and then tapping this location on the touch screen. A virtual representation <NUM> of the detection zone of the motion sensor is displayed superimposed over the image. In the example of <FIG>, the detection zone representation consists of a single area. However, in practice, the probability of an object being detected is lower near the edges of the detection zone. The detection zone representation could be divided into a plurality of (e.g. differently colored) areas with a similar probability of an object being detected in this area. In other words, different areas represent different detection accuracies.

<FIG> shows the reproduction of an animation <NUM> of a person walking in the kitchen. Since the existing Hue lights <NUM> and <NUM> in the kitchen are not switched on, the user repositions the virtual representation <NUM> of the sensor to the right. The new location for the sensor is shown in <FIG>. In the example of <FIG>, the sensor is yet to be installed. If the sensor had already been installed, the actual sensor could be blurred or otherwise removed in the image of <FIG>.

Since the detection zone of the sensor is not wide enough, the user widens the detection zone with a pinch gesture. The widened detection zone is shown in <FIG> in which the virtual representation <NUM> of the detection zone has been modified to reflect the adaptation. <FIG> shows the animation <NUM> of the person walking into the kitchen being reproduced again and as soon as the virtual character enters the detection zone of the motion sensor, the existing Hue lights <NUM> and <NUM> in the kitchen are switched on.

In this example, after a first placement of the virtual motion sensor, the lights did not turn on as the virtual character entered the kitchen. The user then dragged and dropped the motion sensor to a different location to have a better coverage of the region of interest. Furthermore, he widened the detection zone of the sensor with a touch gesture on the touch screen.

Optionally, the app may suggest a type and location of a sensor (or multiple sensors when appropriate), based on the behavior requested by the user (e.g. automatic switching on of the lights when someone enters the kitchen). This suggestion can be visualized in augmented reality as described above, indicating the detection zone and showing the system behavior when a virtual character enters and walks around the room. The user can then further optimize/modify the proposed solution and see the implications on the (simulated) behavior.

When satisfied with the virtual sensor, its settings and the resulting lighting system behavior, the user may immediately order the virtual sensor and installed it in his home, e.g., as sensor <NUM> of <FIG>. Preferably the location, orientation and settings of each purchased sensor is stored, so the user can recall them at the time of installation (e.g. in a form of a personalized installation guide within the lighting control app). After installation any settings can be automatically applied.

<FIG> depicts a block diagram illustrating an exemplary data processing system that may perform the method as described with reference to <FIG>.

Claim 1:
An electronic device (<NUM>) comprising at least one processor (<NUM>) configured to:
- obtain an image captured with a camera (<NUM>),
- determine a location for a sensor (<NUM>) to be installed, wherein said sensor (<NUM>) is a motion sensor,
- determine a detection zone of said sensor (<NUM>) in relation to said image based on said location determined for said sensor (<NUM>), and
- display, in real-time or near-real-time, said image and virtual representations (<NUM>,<NUM>) of said sensor (<NUM>) and said detection zone superimposed over said image,
wherein said at least one processor (<NUM>) is configured to allow a user to specify or adapt at least one property for said to be installed sensor (<NUM>), said at least one property including at least said location,
wherein said at least one processor (<NUM>) is configured to:
- simulate dynamic input to said motion sensor (<NUM>) to generate sensor output,
- display a dynamic virtual representation (<NUM>) of said dynamic input superimposed over said image by displaying an animation of a virtual character (<NUM>) walking through the physical space captured by said image, and
- control a virtual lighting device and/or an installed lighting device (<NUM>, <NUM>) based on said sensor output.