Method and computing device for adapting a holding on time of a lighting device deployed in a space location

Abstract

A method of adapting a hold-on time of at least one lighting device deployed within a spatial location is disclosed. The method comprises the steps of: determining an occurrence of an event by detecting an activity of one or more objects involved in the event specifically associated with a spatial position based on sensing data of the spatial position acquired by a radar sensor; determining a type of the spatial location based on occurrence of an event specifically associated with the spatial location; and adapting a hold-on time of at least one lighting device deployed within the spatial location based on the determined type of the spatial location.

Description

Technical Field

[0001] The present disclosure generally relates to the field of lighting control, and more particularly to a method and a computing device for adapting the hold - on time of lighting devices deployed within a spatial location. Background Art

[0002] In the field of lighting control, it is known to support or render different lighting based on various conditions that can be detected by using sensing devices or configured by users. As an example, lighting devices can be turned on and off based on the presence of targets (such as people or vehicles) detected by presence sensors (such as motion sensors).

[0003] With the development of smart lighting technology, more and more consumers are bringing smart lighting into more home spaces such as kitchens and bathrooms. For a room equipped with lighting devices that support on - demand lighting functions, when someone enters the room, the lighting devices can be automatically turned on. In addition, the lighting devices can also be automatically turned off when a person leaves and the room remains vacant for a predefined duration. The period from when a person leaves until the lighting device turns off or enters the standby state is called the hold - on time of the lighting device.

[0004] It is well - known that for lighting devices deployed in different locations, the periods of hold - on time can be different. As an example, the hold - on time of lighting devices (such as panel lights) installed in the kitchen is different from that of panel lights installed in the bathroom.

[0005] This is because users in the kitchen may leave the kitchen and come back soon. A too - short hold - on time of the lighting devices in the kitchen may cause the lighting devices to turn on / off frequently, which may lead to a poor user experience.

[0006] On the contrary, once users in the bathroom or the shower leave, they will not return within a short period. As a result, the hold - on time of the lighting devices installed in the bathroom can be set as short as, for example, a few seconds. This conforms to the users' habits and / or expectations, since people usually turn off the lights before leaving the bathroom.

[0007] In a smart home scenario, the hold - on time of lighting devices in different locations or rooms can be configured via, for example, a mobile application during the commissioning of the lighting devices. However, in fact, not all consumers are willing or able to perform the commissioning.

[0008] In addition, some entry - level lighting products do not actually support commissioning after installation because these products are non - IoT products.

[0009] Considering the above - mentioned situation, it is desirable to have a method available for automatically adapting the hold - on time of lighting devices deployed within a spatial location. Summary of the Invention

[0010] In a first aspect of the present disclosure, a method for adapting the keep - on time of at least one lighting device deployed within a spatial location is proposed. The method includes the following steps:

[0011] Determining the occurrence of an event by detecting the activities of one or more objects involved in an event specifically associated with the spatial location based on sensing data of the spatial location obtained by a radar sensor;

[0012] Determining the type of the spatial location based on the occurrence of the event specifically associated with the spatial location; and

[0013] Adapting the keep - on time of at least one lighting device deployed within the spatial location based on the determined type of the spatial location.

[0014] The present disclosure is based on the recognition that the type of the spatial location in which the lighting device is deployed can be determined or judged by detecting the occurrence of an event specifically associated with the spatial location, which can be used to adapt the keep - on time of the lighting device installed in the spatial location.

[0015] As is well known, events involving the activities of both humans and objects can occur in a spatial location. Although human activities may be similar for different spatial locations, the activities of objects that occur during an event may be specifically associated with the spatial location, or even unique to the spatial location. Therefore, by using a radar sensor deployed in the spatial location to identify or detect the activities of one or more objects involved in an event, an event specifically associated with the spatial location can be detected.

[0016] Based on this profound idea, the sensing data of the spatial location captured or obtained by the radar sensor is used to determine the occurrence of an event specifically associated with the spatial location. Since the occurrence of the event is specifically associated with the spatial location, the determination of the occurrence of the event allows the type of the spatial location to be judged. Based on the type of the spatial location, the keep - on time of the lighting device deployed in the spatial location is correspondingly adapted, for example, by making it shorter or longer than the default value.

[0017] Therefore, the method of the present disclosure allows the automatic adaptation of the keep - on time of lighting devices deployed in different spatial locations without being interfered with by the user. It can provide a better user experience and can save the energy consumption of some spatial locations.

[0018] In an example of the present disclosure, the step of determining the occurrence of an event specifically associated with the spatial location includes the following steps:

[0019] Obtaining a spectrogram representation of the sensing data of the spatial location by applying a transformation algorithm to the sensing data;

[0020] Identify signal patterns in a spectrogram representation, e.g., an energy distribution pattern within a predetermined frequency range, which represents the activities of one or more objects involved in an event; and

[0021] Determine the existence of an event specifically associated with a spatial location.

[0022] A spectrogram representation is a visual representation of the time-varying spectrum of a signal. A spectrogram representation of sensed data of a spatial location captured by a radar sensor can be obtained via a transformation algorithm. Different spectral patterns can be identified from the obtained spectrogram representation, including spectral patterns representing the activities of one or more objects involved in an event specifically associated with a spatial location.

[0023] When such a signal pattern is identified, it can be determined that an event specifically associated with a spatial location has occurred or exists.

[0024] Algorithms for obtaining a spectrogram representation of sensed data are known and can be readily adopted by the present disclosure.

[0025] In an example of the present disclosure, the occurrence of an event specifically associated with a spatial location is also determined with reference to the following: detecting the presence of a person based on sensed data of the spatial location.

[0026] Those skilled in the art can readily understand that the occurrence of an event mostly also involves the presence of humans. Therefore, by considering the presence of humans, the occurrence of an event specifically associated with a spatial location can be determined in a more accurate manner.

[0027] It should be noted that the step of determining the type of spatial location based on the occurrence of an event specifically associated with the spatial location is actually an intermediate step.

[0028] The gist of the present invention is to set different hold-on times for lighting devices according to the occurrence of different activities detected by a radar sensor via identifying an energy distribution pattern within a predetermined frequency range.

[0029] A method for adapting the hold-on time of at least one lighting device may include the following steps:

[0030] Obtain a spectrogram representation of the sensed data by applying a transformation algorithm to the sensed data obtained by a radar sensor;

[0031] Identify an energy distribution pattern within a predetermined frequency range in the spectrogram representation, the energy distribution pattern representing the activities of one or more objects involved in an event; and

[0032] Adapt the hold-on time of at least one lighting device based on the identified energy distribution pattern.

[0033] In one example, the keep - on time of the lighting device can be determined based on two or more different energy patterns identified in the spectrogram representation of the sensed data.

[0034] The keep - on time of the lighting device can be referred to as the timeout value of the lighting device, which is the duration between detecting the absence of an occupant and the lighting device turning off or entering the standby mode or dimming.

[0035] In an example of the present disclosure, detecting the presence of a person based on sensed data of a spatial location is performed with reference to the following: the spectrogram representation of the sensed data.

[0036] The obtained spectrogram representation of the sensed data can also be conveniently used to detect the presence of a person or humans. The detection method is known to those skilled in the art and will not be elaborated here.

[0037] In an example of the present disclosure, the transformation algorithm is the Fast Fourier Transform (FFT).

[0038] FFT is a well - established algorithm for converting a signal from the time domain to the frequency domain. It can be easily used to obtain the spectrogram representation of the sensed data of a spatial location acquired by a radar sensor without further development work.

[0039] In an example of the present disclosure, events specifically associated with a spatial location include a person flushing a toilet. The steps of identifying a signal pattern representing the activity of one or more objects involved in the event include:

[0040] Identifying a second energy distribution pattern in a second time period after a first time period from the spectrogram representation. The second energy distribution pattern represents the movement of water in the toilet within a frequency range from zero hertz to a first threshold frequency. The first time period has a first energy distribution pattern representing the movement of a person. According to the operating frequency of the radar sensor, the first threshold frequency is in the range of one hertz to more than ten hertz.

[0041] The inventors recognized that when the event is a person flushing a toilet, a special energy distribution pattern generated by the refilling or inflow of water into the toilet after flushing will exist in the spectrogram representation of the sensed data acquired by the radar sensor. Therefore, the identification of this pattern can be used to determine the type of the spatial location.

[0042] For a radar sensor operating at 5.8 GHz, the special energy distribution pattern includes waveforms in the range from zero hertz to, for example, approximately ten hertz. Depending on the operating frequency of the radar sensor, it can be up to more than ten hertz.

[0043] This special energy distribution pattern, when combined with the first energy distribution pattern associated with human movement, results in an event that identifies or determines a specific association with a spatial location (in this case, the toilet). Thereafter, the holding-on time of the spatial location can be adapted based on this location.

[0044] In an example of the present disclosure, an event specifically associated with a spatial location includes a person taking a shower. The steps of identifying a signal pattern representing the activities of one or more objects involved in the event include:

[0045] Identifying a uniformly distributed energy pattern from the spectrogram representation. The uniformly distributed energy pattern represents the movement of water in the frequency range from zero hertz to a second threshold frequency during a shower. The uniformly distributed energy pattern is superimposed on the first energy distribution pattern representing human movement. The first energy distribution pattern exists in a first time period and a second time period after the first time period. The uniformly distributed energy pattern only exists in the second time period. The second threshold frequency depends on the operating frequency of the radar sensor and is in the range of several hundred hertz to one thousand hertz.

[0046] Similarly, for an event including a person taking a shower, a special energy distribution pattern can also be identified. When this pattern is identified in combination with the pattern associated with human movement, it can be determined that an event specifically associated with a spatial location (in this case, the bathroom) has occurred. Thereafter, the holding-on time of the spatial location can be adapted based on this location.

[0047] In another example of the present disclosure, an event specifically associated with a spatial location includes a ventilator being turned on. The steps of identifying a signal pattern representing the activities of one or more objects involved in the event include:

[0048] Identifying an energy distribution pattern representing the start-up of the ventilator from the spectrogram representation. This energy distribution pattern has multiple frequency components. The corresponding peak frequencies of the multiple frequency components linearly increase from zero hertz and become stable both in terms of the peak frequency and the energy at the peak frequency. The multiple frequency components are multiples of the fundamental frequency. The energy distribution pattern exists in a second time period after the first time period. The first time period has the first energy distribution pattern representing human movement. The fundamental frequency depends on the operating frequency of the radar sensor.

[0049] In another example of the present disclosure, an event specifically associated with a spatial location includes a ventilator being turned off. The steps of identifying a signal pattern representing the activities of one or more objects involved in the event include:

[0050] Identify, from the spectrogram representation, an energy distribution pattern representing the ventilation fan being off in a second time period after a first time period, the energy distribution pattern having a plurality of frequency components, the peak frequencies of the plurality of frequency components linearly decreasing and disappearing simultaneously, the plurality of frequency components being multiples of a fundamental frequency, the first time period having a first energy distribution pattern representing human movement, and the fundamental frequency depending on the operating frequency of the radar sensor.

[0051] The two events in the above examples are respectively related to the turning on and off of the ventilation fan. Each event is associated with a special energy distribution pattern in the spectrogram representation, and this pattern can be used to determine or identify the occurrence of the event. The occurrence of the event is translated into a determination of the type of the spatial location, and then the holding-on time of the lighting device deployed in the spatial location can be automatically adapted.

[0052] In an example of the present disclosure, the step of determination includes determining the type of the spatial location as a bathroom or a toilet, and the step of adaptation includes changing the holding-on time of at least one lighting device to a time period shorter than the original configured time period.

[0053] For the above example, the occurrence of different events allows the device implementing the method of the present disclosure to determine that the spatial location includes a toilet or a bathroom. Since such locations usually require a shorter holding-on time, the originally configured holding-on time period is adapted to a shorter period. This will provide a better user experience and achieve energy conservation.

[0054] In an example of the present disclosure, the method further includes the following steps before the step of determination:

[0055] Determine the occurrence frequency of an event specifically associated with the spatial location based on the sensing data of the spatial location;

[0056] The step of determination is also performed with reference to the following: the occurrence frequency of the activities of one or more determined objects.

[0057] Those skilled in the art can conceive that, depending on the type of the spatial location, the events specifically associated with the spatial location may occur more or less frequently. As an example, cooking may occur one to three times a day, while flushing the toilet may be more frequent during the day. Therefore, when determining the type of the spatial location, the occurrence frequency of the events specifically associated with the spatial location can also be considered to obtain more accurate results.

[0058] In an example of the present disclosure, the method further includes the following steps before the step of determination:

[0059] Based on the sensing data of the spatial location, determine the time series of a plurality of events specifically associated with the spatial location;

[0060] The determination step is also performed with reference to the following items: the time series of multiple activities of one or more identified objects.

[0061] Those skilled in the art can conceive that a number of events specifically associated with a spatial position can occur in a specific order. As an example, when a person uses the toilet, they can first turn on the ventilator, use and flush the toilet, and then turn off the ventilator. Therefore, when determining the type of spatial position, it can also be helpful to consider the time series of multiple events specifically associated with the spatial position.

[0062] In an example of the present disclosure, a radar sensor is integrated into at least one lighting device deployed within a spatial position.

[0063] Some lighting devices (such as panel lights) integrate a radar sensor therein to enable more advanced applications. Such a radar sensor can be used to conveniently measure the sensing data used in the method of the present disclosure.

[0064] The second aspect of the present disclosure provides a processing system, which is arranged to execute the method according to the first aspect of the present disclosure.

[0065] The method can be executed by a computing device or a processing system (such as a server) that supports all smart home applications.

[0066] The third aspect of the present invention discloses a lighting system, which includes the processing system, the radar sensor, and the lighting device in the second aspect; wherein the keep-on time of the lighting device is adapted by the processing system based on the sensing data acquired by the radar sensor.

[0067] The fourth aspect of the present disclosure provides a computer program product, which includes a computer-readable storage medium storing instructions that, when executed on at least one processor, cause the at least one processor to execute the method according to the first aspect of the present disclosure.

[0068] Through the following description with reference to the accompanying drawings, the above and other features and advantages of the present disclosure will be best understood. In the drawings, the same reference numerals denote the same components or components that perform the same or equivalent functions or operations. Description of the Drawings

[0069] Figure 1 An exemplary spatial position in which a lighting device is deployed is schematically shown.

[0070] Figure 2 An embodiment of a method for adapting the keep-on time of a lighting device deployed in a spatial position according to the present disclosure is schematically shown in the form of a flowchart.

[0071] Figure 3 Exemplary steps for determining the occurrence of an event specifically associated with a spatial location according to the present disclosure are schematically shown in the form of a flowchart.

[0072] Figure 4 Sensed data of a toilet flushing event and its spectrogram representation are shown.

[0073] Figure 5 Sensed data of a user showering event and its spectrogram representation are shown.

[0074] Figure 6 Sensed data of a ventilator turning on event and its spectrogram representation are shown.

[0075] Figure 7 Sensed data of a ventilator turning off event and its spectrogram representation are shown.

[0076] Figure 8 Signals collected by a radar sensor for realizing the position perception of the radar sensor are schematically shown. Detailed Description of the Invention

[0077] Embodiments contemplated by the present disclosure will now be described in more detail with reference to the accompanying drawings. The disclosed subject matter should not be construed as limited to the embodiments described herein. Instead, the illustrated embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0078] The hold - on time of the lighting device used in the present disclosure refers to the duration between when a user or person leaves a spatial location and when the lighting device deployed in the spatial location turns off or enters a standby state. Those skilled in the art will understand that this time period may be differently named but refers to the same concept as the hold - on time used herein.

[0079] Figure 1 An exemplary spatial location in which a lighting device is deployed is schematically shown. The hold - on time of the lighting device can be adapted according to the method of the present disclosure.

[0080] Figure 1 Kitchen 110 and bathroom 120 are shown. An oven 111, a lighting device 112, and a radar sensor 113 are provided or deployed in kitchen 110. Bathroom 120 has a toilet 121, a bathtub 122, a ventilator 123, a lighting device 124, and a radar sensor 125 deployed therein.

[0081] The lighting device 112 and the radar sensor 113 in the kitchen 110 are shown as separate devices, while the lighting device 124 in the bathroom 120 is shown as integrated with the radar sensor 125. Those skilled in the art can understand that, depending on the specific lighting device used, the radar sensor may or may not be provided in the lighting device. The creative idea of the present disclosure is applicable to both cases. In addition, the lighting device used is not limited to the present disclosure.

[0082] The radar sensors 113 and 125 can be commercially available radar sensors, such as 5.8 GHz Doppler radar. The present disclosure is not limited to any specific type of radar sensor.

[0083] Both the radar sensors 113 and 125 are communicatively connected to the computing device 11 via, for example, the Internet 12. The computing device 11 can be deployed remotely, such as at the backend or in the cloud, or locally.

[0084] Each of the radar sensors 113 and 125 respectively monitors the spatial location in which it is installed, namely the kitchen 110 and the bathroom 120, and obtains sensing data regarding the movement of humans and objects in the spatial location. The obtained sensing data is transmitted or transferred to the computing device 11 via, for example, the Internet 12.

[0085] Hereinafter, a method for adapting the holding-on time of lighting devices (such as the lighting devices 112 and 124) will be described.

[0086] The present disclosure proposes using a radar sensor (such as a radar sensor integrated in a lighting device such as a panel light) to determine the type of the spatial location in which the lighting device is installed, and then adapting the holding-on time of the lighting device accordingly.

[0087] Figure 2 An embodiment of a method 20 for adapting the holding-on time of a lighting device deployed in a spatial location according to the present disclosure is schematically shown in the form of a flowchart.

[0088] In step 21, the occurrence of an event is determined by detecting the activities of one or more objects involved in an event specifically associated with the spatial location based on the sensing data of the spatial location obtained by the radar sensor.

[0089] The radar sensor is installed in the same spatial location as the lighting device and continuously monitors the spatial location to obtain or capture sensing data representing an event occurring in the spatial location.

[0090] As can be conceived by those skilled in the art, events involving activities of both humans and objects can occur in spatial locations. Although human activities may be similar for different spatial locations, the activities that objects undergo during an event may be specifically associated with a spatial location, or even unique to a spatial location.

[0091] For example, events specifically associated with a bathroom or toilet can include a person flushing the toilet, taking a shower, turning on or off a ventilator. For example, flushing the toilet always involves the activity of refilling an empty water tank after flushing the toilet.

[0092] Therefore, when the activity of refilling the water tank is detected, it is determined that the event of flushing the toilet has occurred.

[0093] Next, at step 24, based on the occurrence of an event specifically associated with a spatial location, the type of the spatial location is determined or judged.

[0094] For example, refilling the water tank is used to determine the event of flushing the toilet. Since the event of flushing the toilet is specifically associated with a bathroom or toilet, it can be determined that the spatial location is a bathroom or toilet.

[0095] Those skilled in the art can conceive that during a specific time period, events occurring in a spatial location may occur several times. As an example, cooking can occur one to three times a day, while flushing the toilet may be more frequent during the day. Therefore, at step 22, it is also optionally possible to determine the occurrence frequency of an event specifically associated with a spatial location. The occurrence frequency can be considered when determining the type of the spatial location.

[0096] In addition, several events occurring in a spatial location can occur in a specific order. As an example, when using the toilet, the user first enters the toilet, then turns on the ventilator, then flushes the toilet, and then turns off the ventilator. Therefore, at step 23, it is also possible to determine the time sequence of multiple events specifically associated with a spatial location, and then this time sequence can be considered when determining the type of the spatial location at step 24.

[0097] At step 25, based on the determined type of the spatial location, the keep - on time of the lighting device deployed within the spatial location is adapted.

[0098] This is achieved considering user habits and expectations. As an example, the keep - on time of the lighting device deployed in the toilet can be adapted to be shorter than the pre - configured keep - on time because users generally do not return to the toilet soon after using it.

[0099] As another example, the keep - on time of the lighting device deployed in the kitchen can be adapted to be longer than the pre - configured keep - on time because users may leave and then quickly return to the kitchen, for example, to get something needed for cooking.

[0100] The determination of the occurrence of an event specifically associated with a spatial location can be performed with reference to a spectrogram representation of the sensed data of the spatial location, which will be described in detail later.

[0101] Figure 3 Exemplary steps for determining the occurrence of an event 31 specifically associated with a spatial location according to the present disclosure are schematically shown in the form of a flowchart.

[0102] In step 32, a spectrogram representation of the sensed data of the spatial location is obtained by applying a transformation algorithm to the sensed data.

[0103] The transformation algorithm can be, for example, a fast Fourier transform, which can be conveniently used to transform the sensed data from the time domain to the frequency domain.

[0104] In step 33, signal patterns representing the activities of one or more objects involved in the event are identified in the spectrogram representation.

[0105] Referring to the example of the toilet flushing event above, a pattern specifically representing the activity of the water tank refilling can be identified, which will be described in detail below.

[0106] In step 34, it is determined that an event specifically associated with the spatial location has occurred or exists.

[0107] In the above example, when a pattern specifically representing the activity of the water tank refilling is identified, it can be determined that the event of flushing the toilet has occurred.

[0108] Several examples of determining the occurrence of an event specifically associated with a spatial location will be described below.

[0109] In the case of a bathroom or toilet, a radar sensor (such as a 5.8 GHz Doppler radar sensor) can be used to detect events specifically associated with that space, including, for example, flushing the toilet, taking a shower, turning on or off the ventilator.

[0110] In the case of a kitchen, a radar sensor can be used to detect events specifically associated with the kitchen, including, for example, baking in the oven, running the dishwasher, cooking food, etc.

[0111] Figure 4 The sensed data of the toilet flushing event and its spectrogram representation are shown.

[0112] Figure 4 The upper figure 410 is the raw data captured by a radar sensor installed in the toilet, which includes the toilet flushing event. Figure 4 The lower figure 420 is the FFT result or spectrogram representation of the raw data of figure 410.

[0113] In the raw data 410, the signal cluster indicated by the number 411 represents the activity of a person entering the toilet, and in the frequency domain, this activity is represented by the energy pattern 421.

[0114] In the raw data 410, the signal cluster indicated by the number 412 represents the activity of a person leaving the toilet, and in the frequency domain, this activity is represented by the energy pattern 422.

[0115] Figure 4 An enlarged view 431 of a part represented by the spectrogram indicated by the dashed rectangle 430 is shown. The ripple indicated by the circle 413 in the raw data 410 represents the activity of refilling the water tank after flushing the toilet, which is represented by the energy pattern indicated by the number 423 in the enlarged view 431.

[0116] The original radar data or signal 410 and the spectrogram representation 420 of the raw data show the event of the user flushing the toilet. Referring to the above description, Figure 4 The activities of the user first entering the toilet 411, 412, the activities of the user leaving (after flushing the toilet) 412, 422, and the activity of the water tank being refilled 413, 423 are shown.

[0117] Water flushing and flowing into the water tank generates a very specific or even unique signal pattern. For a 5.8 GHz radar sensor, a very slow waveform with a frequency less than 1 Hz reflects the refilling well. When the radar operates at a higher frequency, the upper limit of the pattern reflecting the refilling process is higher.

[0118] Detecting the movement of the user (including entering (optionally also leaving)) and the movement of the water flow (i.e., water flowing rapidly from the water tank to the basin and flowing slowly to refill the water tank) confirms the event of flushing the toilet.

[0119] Figure 5 The sensed data of the user's shower event and its spectrogram representation are shown.

[0120] Figure 5 The upper figure 510 is the raw data captured by a radar sensor installed in the bathroom, which includes the event of the user taking a shower. Figure 5 The middle part figure 520 is the FFT result or spectrogram representation of the raw data of figure 510.

[0121] Figure 5 An enlarged view of the energy distribution within the frequency range indicated by the dashed rectangle 530 is shown in the lower figure 531.

[0122] At Figure 5 Both the raw data and the spectrogram representation before the time point indicated by the dashed line t1 represent an empty bathroom. Starting from the time point t1, the user enters the bathroom and starts to prepare for a shower until the time point indicated by the dashed line t2.

[0123] The activity of a user entering the bathroom can be detected by a radar sensor as a large motion represented by a signal cluster 511 in the time domain and an energy pattern 521 in the frequency domain.

[0124] The activity performed by the user during shower preparation indicated by signal 512 is represented in the frequency domain as being mainly focused in the low-frequency range, which can also be easily detected as a large motion 522.

[0125] When the preparation is complete and the user turns on the showerhead, water sprays out of the showerhead. In addition to the large movements of the user during showering, the FFT energy is evenly distributed over the entire frequency range from 0 Hz to above 150 Hz. Note that the radar sensor operates at 5.8 GHz.

[0126] The evenly distributed FFT energies 523, 533 are caused by the Doppler effect of the water spray. This diffusion pattern over a wide FFT frequency range is a very special signal pattern and can be detected by a sensing algorithm.

[0127] The motion of the user (including entry, preparation, showering (and leaving)) and the water flow motion (including water spraying from the showerhead during showering) are detected via the relevant energy patterns in the identified spectrogram representation to confirm the shower event.

[0128] Since the shower is specifically associated with the bathroom, the spatial location can be determined to be the bathroom.

[0129] The toilet in the bathroom is also often equipped with a ventilator. The activity of turning the ventilator on / off can be identified as another special or representative signal pattern in the frequency domain.

[0130] Figure 6 The sensed data of the event of turning on the ventilator and its spectrogram representation are shown.

[0131] Figure 6 The upper figure 610 is the raw data captured by a radar sensor installed in the bathroom, which includes the event of turning on the ventilator. Figure 6 The lower figure 620 is the FFT result or spectrogram representation of the raw data of figure 610.

[0132] In Figure 6 the radar sensor detects the activity of the user entering the bathroom as a large motion 611, which is reflected as an energy pattern 621 in the frequency domain.

[0133] When the user turns on the ventilator, there is a very special signal pattern in the spectrogram representation 620. When the ventilator starts to operate, as Figure 6As shown by the solid square 630, many frequency components appear, and the peak frequencies of these frequency components almost increase linearly. When the operation of the ventilator stabilizes after a period of time, as Figure 6 shown by the solid square 640 in

[0134] These frequency components also become stable in terms of both the peak frequency value and the energy of these frequency peaks. In addition, these additional frequency components are almost multiples of a certain fundamental frequency.

[0135] Figure 7 The identification of this special energy pattern in the spectrogram representation is translated into the presence of an event specifically associated with a spatial location (in this case, the bathroom).

[0136] Figure 7 The upper graph 710 is the raw data captured by a radar sensor installed in the bathroom, which includes the event of turning off the ventilator. Figure 7 The lower graph 720 is the FFT result or spectrogram representation of the raw data in Figure 710.

[0137] Before turning off, the ventilator had been operating stably for some time. The presence of the user in the bathroom where the ventilator is installed and the activity of turning off the ventilator are detected as large movements indicated by the energy pattern 721 in the spectrogram representation.

[0138] The ventilator generates a very special energy pattern during power-off. When the ventilator is turned off, the rotation of the fan and the ceiling vibration caused by the fan take time to slow down. In the spectrogram representation, this is indicated by a pattern almost opposite to the pattern during the startup process.

[0139] In Figure 7 within the frame 730, all the frequency components caused by the ventilator and the peak frequencies of these frequency components almost linearly decrease to 0. After the ventilator completely stops, they finally disappear.

[0140] Figure 6 And Figure 7 The activities in both

[0141] correspond to the movements of the user (e.g., entering, leaving) and the movements of the ventilator (starting, stable operation, decelerating, and stopping), which confirm the event of turning the ventilator on / off.

[0142] Due to the specific association of events with spatial positions, in a sense, the existence of such events is without the participation of users, which helps to generate more accurate results.

[0143] When the spatial position is a bathroom or a toilet, throughout the learning period, the radar sensor will detect one or more of these unique events by combining the frequency of the events (e.g., how many times the toilet is flushed per day) and / or the sequence (e.g., turning on the ventilator after taking a shower), and confirm that the radar sensor and the lighting device are installed in the bathroom.

[0144] When determining the spatial position where the lighting device is installed, the holding-on time of the lighting device is adapted to a time period shorter than the originally configured time period. Depending on the determination of the type of the spatial position, the holding-on time of the lighting device can be maintained at the default value or be shorter / longer than the default value.

[0145] Figure 8 Schematically shows the signals collected by the radar sensor for realizing the position perception of the radar sensor.

[0146] Figure 8 The upper figure 810 is the raw data captured by the radar sensor installed in the bathroom, which includes various activities occurring in the bathroom. Figure 8 The lower figure 820 is the FFT result or spectrogram representation of the raw data of figure 810.

[0147] The method of the present disclosure can identify the following events from Figure 8 as follows,

[0148] 1) At t1 to t3, t4 to t5, turn on the shower head,

[0149] 2) At t2, turn on the ventilator,

[0150] 3) At t6, turn off the ventilator.

[0151] The detection of whether there is other movement can be used to assist in determining the type of the spatial position,

[0152] 1) Large movements from t2 to t7

[0153] 2) No movement before t1 and after t7.

[0154] Based on the above information, the method of the present disclosure can easily draw the conclusion that someone is taking a shower within the sensor detection area, and its installation is confirmed to be a bathroom rather than a kitchen.

[0155] The present disclosure is not limited to the above-described examples and can be modified and enhanced by those skilled in the art outside the scope of the present disclosure disclosed in the appended claims without the application of inventive skills and can be used in any data communication, data exchange, and data processing environment, system, or network.

Claims

1. A method for adapting the holding-on time of at least one lighting device deployed within a spatial location, comprising the following steps: Determining the occurrence of an event by detecting the activity of one or more objects involved in the event specifically associated with the spatial location based on the sensed data of the spatial location obtained by a radar sensor; Determining the type of the spatial location based on the occurrence of the event specifically associated with the spatial location; And Adapting the holding-on time of the at least one lighting device deployed within the spatial location based on the determined type of the spatial location, wherein the step of determining the occurrence of the event specifically associated with the spatial location comprises the following steps: Obtaining a spectrogram representation of the sensed data of the spatial location by applying a transformation algorithm to the sensed data; Identifying an energy distribution pattern within a predetermined frequency range in the spectrogram representation, the energy distribution pattern representing the activity of the one or more objects involved in the event; and Determining the existence of the event specifically associated with the spatial location.

2. The method according to claim 1, wherein the occurrence of the event specifically associated with the spatial location is further determined with reference to the following: Detecting the presence of a person based on the sensed data of the spatial location.

3. The method according to claim 2, wherein detecting the presence of a person based on the sensed data of the spatial location is performed with reference to the following: The spectrogram representation of the sensed data.

4. The method according to any one of the preceding claims 2 to 3, wherein the transformation algorithm is a fast Fourier transform.

5. The method according to any one of the preceding claims 2 to 4, wherein the event specifically associated with the spatial location includes a person flushing a toilet, and the step of identifying a signal pattern representing the activity of the one or more objects involved in the event comprises: Identifying a second energy distribution pattern in a second time period after a first time period from the spectrogram representation, the second energy distribution pattern representing the movement of water within the toilet within a frequency range between zero hertz and a first threshold frequency, the first time period having a first energy distribution pattern representing the movement of the person, the first threshold frequency depending on the operating frequency of the radar sensor and being in the range of one hertz to more than a dozen hertz.

6. The method according to any one of the preceding claims 2 to 4, wherein the event specifically associated with the spatial location includes a person taking a shower, and the step of identifying a signal pattern representing the activity of the one or more objects involved in the event comprises: Identify a uniformly distributed energy pattern from the spectrogram representation, the uniformly distributed energy pattern representing the movement of water in a frequency range from zero hertz to a second threshold frequency during a shower, the uniformly distributed energy pattern being superimposed on a first energy distribution pattern representing the movement of the person, the first energy distribution pattern existing in a first time period and a second time period after the first time period, the uniformly distributed energy pattern existing only in the second time period, the second threshold frequency depending on the operating frequency of the radar sensor and being in the range of several hundred hertz to one thousand hertz.

7. The method according to any one of the preceding claims 2 to 4, wherein the event specifically associated with the spatial position includes a ventilator being turned on, and the step of identifying a signal pattern representing the activity of the one or more objects involved in the event includes: Identify an energy distribution pattern representing the start of the ventilator from the spectrogram representation, the energy distribution pattern having a plurality of frequency components, the corresponding peak frequencies of the plurality of frequency components increasing linearly from zero hertz and becoming stable both in terms of the peak frequency and the energy at the peak frequency, the plurality of frequency components being multiples of a fundamental frequency, the energy distribution pattern existing in a second time period after a first time period, the first time period having a first energy distribution pattern representing the movement of a person, the fundamental frequency depending on the operating frequency of the radar sensor.

8. The method according to any one of the preceding claims 2 to 4, wherein the event specifically associated with the spatial position includes a ventilator being turned off, and the step of identifying a signal pattern representing the activity of the one or more objects involved in the event includes: Identify an energy distribution pattern representing the turning off of the ventilator in a second time period after a first time period from the spectrogram representation, the energy distribution pattern having a plurality of frequency components, the peak frequencies of the plurality of frequency components decreasing linearly and disappearing simultaneously, the plurality of frequency components being multiples of a fundamental frequency, the first time period having a first energy distribution pattern representing the movement of a person, the fundamental frequency depending on the operating frequency of the radar sensor.

9. The method according to any one of the preceding claims 5 to 8, wherein the step of determination includes determining the type of the spatial position as a bathroom, and the step of adaptation includes changing the keep-on time of the at least one lighting device to a time period shorter than the original configured time period.

10. The method according to any one of the preceding claims, further comprising the following steps before the step of determination: Based on the sensed data of the spatial position, determine the occurrence frequency of the event specifically associated with the spatial position; The step of determination is further performed with reference to the following: the determined occurrence frequency of the activity of the one or more objects.

11. The method according to any one of the preceding claims, wherein the method further comprises the following steps before the step of determination: Based on the sensed data of the spatial position, determine a time series of a plurality of events specifically associated with the spatial position; The determination step is also performed with reference to the following: the determined time series of the plurality of activities of the one or more objects.

12. The method according to any one of the preceding claims, wherein the radar sensor is integrated into at least one lighting device deployed within the spatial location.

13. A processing system for adapting the hold-on time of at least one lighting device deployed within a spatial location, the processing system being configured to: Determine the occurrence of the event by detecting the activities of one or more objects involved in the event specifically associated with the spatial location based on the sensed data of the spatial location obtained by the radar sensor; Determine the type of the spatial location based on the occurrence of the event specifically associated with the spatial location; And Based on the determined type of the spatial location, adapt the hold-on time of the at least one lighting device deployed within the spatial location, Wherein the processing system is configured to: Obtain a spectrogram representation of the sensed data of the spatial location by applying a transformation algorithm to the sensed data; Identify an energy distribution pattern within a predetermined frequency range in the spectrogram representation, the energy distribution pattern representing the activities of the one or more objects involved in the event; And Determine the existence of the event specifically associated with the spatial location.

14. A lighting system, comprising: The processing system according to claim 13; A radar sensor; And A lighting device; Wherein the hold-on time of the lighting device is adapted by the processing system based on the sensed data obtained by the radar sensor.

15. A computer program product, comprising a computer-readable storage medium storing instructions that, when executed on at least one processor, cause the at least one processor to perform the method according to any one of the preceding claims 1 to 12.

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