Method for selecting sensors from a set of sensors

Abstract

Method and device for selecting sensors The present invention relates to a method for selecting sensors from a set of sensors present in an environment and associated with one or more functions, one or more sensors being activated to enable detection of a behavior of a living being, the method comprising: - a modification of the selection of the activated sensors from the set of sensors as a function of a confidence score relating to said detection of said behavior, said modification being carried out one or more times as a function of the confidence score. Figure for abstract: Fig. 1.

Description

Title of the invention: Method for selecting sensors from a set of sensors Technical field

[0001] The present invention relates to the field of sensor selection in an environment. The present invention applies in particular, but not limited to, the detection of behavior of a living being in an environment, in order to be able to provide suitable services. Prior art

[0002] The invention is situated in the context of understanding human behavior, in particular the prediction and detection of activities and actions or the location and identification of an occupant of a house, a building, a factory, based on different types of sensors. There are currently methods for detecting human activities based on the combination of ambient sensors and behavior understanding systems based on sound and visual modalities placed in the user's room, combining the sensors or not. There are also behavior understanding systems based on modalities originating from equipment worn by the user, such as smartwatches and augmented reality headsets. Generally, the data captured by these different modalities are complementary for understanding human behavior.There is therefore work that seeks to combine different types of modalities to improve the performance of human behavior recognition. For example, video, audio, and physiological data (worn sensors) are used to identify human emotions. However, the applicant is interested on the one hand in improving the protection of users with regard to their personal data and on the other hand in reducing the carbon footprint of its products and services and the present invention aims to propose a solution that can improve the behavior of a living being, while taking these two parameters into account. Statement of the invention

[0003] The present invention relates, according to a first aspect, to a method for selecting sensors from a set of sensors present in an environment and associated with one or more functions, one or more sensors being activated to allow detection of a behavior of a living being, the method comprising: - a modification of the selection of the activated sensors among the set of sensors according to a confidence score relating to said detection of said behavior, said modification being carried out one or more times according to the confidence score trust.

[0004] According to a second aspect, the present invention relates to a method for recognizing a behavior of a living being in an environment comprising a plurality of sensors associated with one or more functions, one or more sensors being activated, said method comprising: a)- an execution of a model for detecting a behavior of said living being from data received from said activated sensors, b)- a modification of a selection of activated sensors, based on a confidence score obtained following execution of the model, the confidence score being relative to the relevance of said detection, c)-an execution of said model for detecting the behavior of said living being based on data from the sensors of said modified selection, steps b) and c) being iterated one or more times depending on said confidence score.

[0005] According to certain embodiments, said modification is carried out as long as said confidence score is lower than a determined threshold.

[0006] According to certain embodiments, some of the set of said sensors are associated with an intrusive character, said selection of activated sensors selecting in priority the least intrusive sensors.

[0007] According to certain embodiments, an energy consumption level is associated with at least some of the set of said sensors, said selection of activated sensors selecting in priority the least energy-consuming sensors.

[0008] According to some embodiments, said modification of the selection of activated sensors comprises an addition, removal or replacement of sensors based on a detected behavior and the function associated with each sensor.

[0009] According to certain embodiments, said modification of the selection of activated sensors comprises an addition, a removal or a replacement of sensors according to a detected behavior, and according to either a level associated with the intrusiveness of a sensor, or a level of energy consumption associated with a sensor.

[0010] According to certain embodiments, when said behavior is detected with a confidence score greater than a determined threshold, - deactivation of at least one sensor among the activated sensors if -the function associated with said deactivated sensor is not used to detect said behavior or

[0011] - the power consumption associated with the sensor is high, or - the level of intrusiveness of the sensor is high.

[0012] According to certain embodiments, said confidence score relating to the detection of said behavior is determined from one or more of: - a quality indicator of a behavior recognition model used during said behavior detection, - an evaluation of a history of detected behavior data - an evaluation of data relating to said environment, - reception of at least one piece of information from a living being present in said environment.

[0013] According to some embodiments, the method comprises - obtaining, for at least some of said sensors, a level relating to its intrusive nature, said obtaining being received from a user or determined from the function associated with said sensor.

[0014] The characteristics presented in isolation in the present application in connection with certain embodiments of the method of the present application can be combined with each other according to other embodiments of the present method.

[0015] According to another aspect, the present invention also relates to a computer program comprising instructions for executing the steps of the method according to the invention, according to any one of its embodiments, when said program is executed by a computer.

[0016] According to another aspect, the present invention also relates to a computer-readable recording medium on which is recorded a computer program comprising instructions for executing the steps of the method according to the invention, according to any one of its embodiments.

[0017] According to another aspect, the present invention also relates to a device for selecting sensors for executing the steps of the method according to the invention, according to any one of its embodiments. Thus, the present invention relates to a device for selecting sensors from a set of sensors present in an environment and associated with one or more functions, one or more sensors being activated to allow detection of a behavior of a living being, the device comprising one or more processors configured to: - modifying the selection of activated sensors from among the set of sensors based on a confidence score relating to said detection of said behavior, said modification being carried out one or more times based on the confidence score.

[0018] According to another aspect, the present invention relates to a system for detecting the behavior of a living being comprising: - one or more connected sensors comprising at least a subset of activated sensors, - a communication device configured to communicate with said sensors, and comprising one or more processors configured to - execute a model for detecting the behavior of said living being from data received from said activated sensors, - modify a selection of activated sensors, based on a confidence score obtained following execution of the model, the confidence score relating to a relevance of said detection, - execute said detection model from the data from the sensors of said modified selection, according to one or more iterations, the number of iterations being a function of said confidence score obtained following the iteration, the selection of activated sensors being modified during each iteration.

[0019] Other characteristics and advantages of the present invention will emerge from the description given below, with reference to the accompanying drawings which illustrate an exemplary embodiment thereof without any limiting character. Brief description of the drawings

[0020] [Fig-1] [Fig.l] represents a system according to certain embodiments of the invention,

[0021] [Fig.2] [Fig.2] represents a method according to certain embodiments of the invention,

[0022] [Fig.3] [Fig.3] represents a device according to certain embodiments of the invention. Description of the embodiments

[0023] The present disclosure can be applied to the detection of behavior in an environment and in particular to human or animal behavior, more generally to the behavior of a living being.

[0024] By environment, we can mean a domestic environment, an industrial environment, a professional environment, an environment in a vehicle such as a car and more generally any type of environment where it may be relevant to detect human behavior.

[0025] Human behavior detection can find applications in many fields. These applications include, in particular, the provision of services based on behavior, assistance to a technician in the context of maintenance operations, by providing him with a real-time display of relevant information, for example, the reduction of energy consumption, the management of home automation, etc.

[0026] To do this, many devices such as sensors are now present in the environments mentioned above. Among these sensors, we can cite, but are not limited to: - ambient sensors such as door and window opening devices, volumetric sensors, surveillance cameras, microphones - sensors worn by the user, such as smartwatches, augmented reality headsets, medical monitoring devices such as alert systems (connected bracelets for example), movement or fall sensors.

[0027] The sensors considered in the present disclosure are connected sensors, that is to say whose state, operation, start-up, standby, stop, can be controlled remotely by another or more devices. Such control devices can for example be access gateways controlling a network to which these sensors are connected. The control of these connected sensors can also be carried out by software present at network operators for example or at service providers. In such a context, the gateway controlling the local network to which the sensors are connected can constitute an interface between the local network and an external network, of the WAN type (for "wide area network") such as a cellular network.

[0028] [Fig.l] represents an exemplary system implementing the present disclosure.

[0029] An access gateway PI controls at least one local area network RL. The local area network RI is an Ethernet-type network, for example, and may be a wired or wireless network. According to certain embodiments, certain sensors may be connected by a protocol other than the Ethernet protocol, in particular WIFI, and may, for example, be connected by Bluetooth, Zigbee, Z-Wave, LoRaWAN, NFC, Thread, Sigfox, or even by cellular technology such as 5G, 4G, or 3G. The choice of the network type may depend on the specific requirements of the application, such as range, energy consumption, bandwidth, and the availability of existing communication infrastructures. Combinations of several of these technologies are often used to meet the complex needs of connected smart sensor networks such as Internet of Things (IoT) sensors.

[0030] Sensors Ci, C2, C3 are connected to the gateway PI via the network RI, here a WIFI type network. Sensors C4 and C5 are connected to the gateway PI via a Bluetooth network. Sensors C6 and C7 are connected to the gateway PI via a Zigbee network.

[0031] The gateway PI can also be connected to a network R2 and therefore serve as an interface between the sensors Ci or the other equipment connected to the gateway PI (not shown such as TVs, multimedia decoders, etc.), and an external network such as a cellular network. This is the case when the access gateway is an access gateway provided by a telecommunications operator, the gateway making it possible to connect the devices in the environment to the operator's IR network infrastructure and to access the Internet network, for example. The network R2 can be a cellular network.

[0032] According to certain embodiments, additional sensors, C;, not shown, are connected to the gateway PI via the network R2.

[0033] The sensors C; can be characterized by different parameters. They are notably associated with a function. By function, we can understand, in an illustrative and non-exhaustive manner: - image capture (or video capture), - sound capture (or audio capture), - motion capture (presence detector for example), - opening capture (door or window opening), - fall capture, - activity capture, - position capture, - capturing emotion.

[0034] The sensors may be sensors worn by a user, such as smartwatches or augmented reality headsets, ambient sensors, or from behavioral understanding systems based on sound and visual modalities placed in the room where the user is located.

[0035] One or more functions can be associated with the same sensor. A connected watch can for example be associated with position detection, activity detection or emotion detection.

[0036] The C; sensors can also be characterized by their intrusive nature. Thus, a level of intrusiveness can be associated with a sensor.

[0037] According to certain embodiments, this level is binary and can be equal to “1” if the sensor is considered to be intrusive (or if its function is intrusive) and equal to “0” if the sensor is considered to be non-intrusive.

[0038] According to some embodiments, this level is not binary but can be defined on a scale, for example from 0 to 10, "0" representing a non-intrusive level and "10" representing a very intrusive level.

[0039] For example, a camera may be considered to be very intrusive and therefore either have a level equal to "1" in a binary system or equal to "10" in a level system comprising a scale from 0 to 10 as mentioned above.

[0040] According to this same example, a door opening detector can be considered as having a non-intrusive character and therefore either have a level equal to “0” in a binary system or equal to “0” in a level system comprising a scale from 0 to 10 as mentioned above.

[0041] The intrusiveness of each sensor is a property that can be defined a priori by the model, or that can be indicated by the user via a configuration means. (e.g. a user interface on a network device or on the gateway), or which may be derived from a database, or which may be any combination of these different possibilities. In particular, it is conceivable that the user may indicate that a sensor is never used by the model (and therefore has infinite intrusiveness), or that a sensor is always used by the model (and therefore has zero intrusiveness). This indication from the user may be given prior to the implementation of the method, for example when connecting the sensors to the gateway or in real time during the initial or subsequent selection of the sensors, for example by a “dialogue” with the user on a mobile phone or in an augmented reality headset.

[0042] C sensors can also be characterized by their energy consumption. Indeed, the multiplicity of devices such as Q sensors contributes to the increase in the carbon footprint of the environments in which they are used and it is therefore desirable to reduce their energy consumption, while benefiting from their function.

[0043] The present disclosure helps to control the use of these sensors in a relevant manner, by reducing their intrusiveness and their energy consumption.

[0044] The present invention can thus be used to recognize the behavior of a living being, while using the available sensors in a relevant manner, that is to say while controlling the intrusive side provided by these sensors and the energy consumption of these sensors.

[0045] In the remainder of the description, we will take a human being as an example of a living being but the present application can also be applied to an animal.

[0046] The PI gateway can thus implement a method as described later in [Fig.2]. The method can, in other embodiments, be implemented remotely, in the infrastructures of a network operator or at a service provider, these being connected to the PI gateway via the network R2. The PI gateway can then transmit the data from the sensors C;, in real time, to the remote devices on the network R2.

[0047] The system of [Fig.l] relates to a system for detecting the behavior of a living being comprising: - one or more connected sensors comprising at least a subset of activated sensors, - a communication device configured to communicate with said sensors, and comprising one or more processors configured to - execute a model for detecting a behavior of said living being from data received from said activated sensors, - modify a selection of activated sensors, based on a confidence score obtained following execution of the model, the confidence score being relative to the relevance of said detection, - execute said detection model from the data from the sensors of said modified selection, according to one or more iterations, the number of iterations being a function of said confidence score obtained following the iteration, the selection of activated sensors being modified during each iteration.

[0048] [Fig.2] represents a method according to certain embodiments of the invention.

[0049] As mentioned previously, the method as described below can be implemented by the PI access gateway or in remote infrastructures or servers connected to the PI gateway or in a combination of both.

[0050] The PI gateway implements a method for selecting sensors from a set of sensors present in an environment and associated with one or more functions, one or more sensors being activated to enable detection of the behavior of a living being, the method comprising: - a modification of the selection of the activated sensors among the set of sensors according to a confidence score relating to said detection of said behavior, said modification being carried out one or more times according to the confidence score.

[0051] The PI gateway can also implement a method for recognizing the behavior of a living being in an environment comprising a plurality of sensors associated with one or more functions, one or more sensors being activated, said method comprising: a)- an execution of a model for detecting a behavior of said living being from data received from said activated sensors, b)- a modification of a selection of activated sensors, based on a confidence score obtained following execution of the model, the confidence score being relative to the relevance of said detection, c)-an execution of said model for detecting the behavior of said living being based on data from the sensors of said modified selection, steps b) and c) being iterated one or more times depending on said confidence score.

[0052] In the environment of [Fig.l], a plurality of sensors C; are installed, or are in operational mode, in the environment of the gateway PI, that is to say they can communicate as mentioned previously with the gateway PI according to different communication protocols and are identified by the gateway PL

[0053] According to certain embodiments, during a step E1, the method as described can be triggered permanently, that is to say it can always be active.

[0054] According to some embodiments, the method can be activated on demand, for example at the request of an energy supplier, or any service provider, or at the request of the user or owner of the PI access gateway.

[0055] One of the objectives or one of the applications of implementing this method consists of recognizing the behavior of a user. Thus, according to certain embodiments, the present method can be triggered when a behavior recognition request is requested or activated. This request can be made by a service or energy provider or any other request.

[0056] A selection of activated sensors may have been previously carried out or may be carried out during a step E2. By selection of activated sensors, we mean more precisely an activation of a selection of sensors. By activation, we mean the switching on, the fact of making a sensor operational and / or in operation, so that the function associated with it is activated and can be detected by the PI access gateway.

[0057] This first selection of sensors, or initial selection of sensors, may comprise a subset SEj of the sensors Ci. The subset SEj may comprise all the sensors Ci but more particularly comprises a part of the sensors Ci. This selection of the sensors of the initial subset may be carried out in different ways: - by a selection by the user based on his preferences and the use he makes of the sensors in his environment. Indeed, the sensors C; may be used by the user of the access gateway for applications other than behavior recognition, and may therefore have been selected for these applications, - by an initial selection dedicated to behavior recognition and taking into account one or more parameters. Among these parameters, one can select a sensor by function (for example, a single camera, a single microphone, a single opening detector). One can also select a sensor according to its intrusiveness level, favoring sensors whose intrusiveness level is the lowest. For example, one can select sensors whose intrusiveness level is lower than a given level. One can also select sensors according to their energy consumption. For example, one can select sensors whose energy consumption is lower than a given level. Of course, one can combine these criteria.Sensors can also be selected according to their geographical location in the environment of the PL access gateway. In fact, the PI gateway can be located in an environment with several rooms (this is the case in a domestic or industrial environment), and thus the activated sensors. initially may be those of the room where a user is most frequently located.

[0058] A user interface, on the gateway PI, or on a control device such as a computer connected to the network RI, can for example allow the user to select the sensors to be activated, the gateway or the device then being able to activate the selected sensors. According to a variant, the user can also activate the sensors one by one, by actuating them manually.

[0059] According to some embodiments, a home automation system may also be present in the environment where the gateway PI is located and the home automation system may have selected a set of sensors from among the sensors Ci.

[0060] According to the present disclosure, an activated sensor is a sensor that is either active or on standby but if on standby it is triggered when it detects an action. For example, surveillance cameras may be on standby but are automatically activated upon an intrusion. Thus, they are active within the meaning of the present disclosure.

[0061] A non-activated sensor is a sensor that does not transmit data, in other words that is not configured to transmit data, that is for example switched off (in the electrical sense) or that is in standby but does not activate upon detection of data, or a variation of data, in a way that does not automatically switch from the standby state to the data capture state.

[0062] We therefore place ourselves in the context where a request for behavior detection is received. This request for behavior detection can be the triggering of the method as mentioned previously or can be received at the end of step E2. Step E2 advantageously makes it possible to benefit from an initial selection of activated sensors.

[0063] During a step E3, a behavior detection of at least one user present in the environment of the PI gateway is carried out from the data provided by the sensors of the initial selection of sensors. A human behavior detection model can allow the recognition of human behavior, from the data from the activated sensors and algorithms.

[0064] The data provided by the sensors are heterogeneous and may depend on the function associated with the sensor. A door opening sensor may transmit binary information, indicating the status of the door, either open or closed. A sensor accelerometer worn by an individual transmits data relating to a three-dimensional acceleration vector "(x,y,z). A camera transmits an image stream with a format that depends on the camera, a microphone transmits an audio stream, etc.

[0065] The data may be pre-processed before being used by the behavior detection model. In particular, they may be normalized, temporally synchronized, so much, concatenated. By pre-processing, we can also understand the detection of the segmentation of an image from a camera or the extraction of words from a sound stream.

[0066] The behavior detection model used is configured to process data from sensors of different nature or is configured to process multimodal situations. Approaches based on "machine learning", neural networks or "transformer" can be used in such models. The document entitled "accommodating missing modalities in time-continuous multimodal emotion recognition" by Rodrigues, Lefebvre, Cumin and Crowley, published on November 16, 2023 (https: / / arxiv.org / pdf / 2311.10119.pdf) describes a method for recognizing a person's emotions from video, audio and physiological data.

[0067] During a step E4, the detected behavior is evaluated, or in other words, the quality of the detected behavior is evaluated. This evaluation can make it possible to determine whether or not the detected behavior is close to the actual behavior of the user whose behavior was performed.

[0068] This evaluation may for example result in the determination and assignment of a confidence score for the detected behavior.

[0069] When the sensors selected and used for detection do not provide reliable detection of the behavior, then the confidence score associated with the detection is low. Conversely, when the sensors selected and used for detection provide reliable detection of the behavior, then the confidence score associated with the detection is high.

[0070] The quality assessment, or the determination of a confidence score Sc, can be determined from one or more of: - a quality indicator of the behavior recognition model used during behavior detection, - an evaluation of a history of detected behavior data - an evaluation of environmental data. This data could, for example, be data relating to the room (or location in an industrial or other environment) where the user is located. If the behavior is compatible with the room in which the user is located, then the quality can be high. - receipt of at least one piece of information from a human being present in the environment. This information may, for example, confirm or deny that the detected behavior is indeed consistent with the user's actual behavior.

[0071] At the end of step E4, it is therefore possible to obtain information relating to the quality of the detection of the behavior.

[0072] For example, we can set a confidence score threshold Si: - if the confidence score is less than Si, then the detected behavior is not good quality, - if the confidence score is greater than or equal to the threshold Sb then the detected behavior is of good quality. For example, we can choose Si equal to 5 on a confidence score scale of 1 to 10.

[0073] Depending on the quality obtained, the set of sensors used to determine the behavior can be modified.

[0074] Thus, the selection of activated sensors from the set of sensors is modified according to a confidence score relating to the detection of the behavior, the modification being carried out one or more times according to the confidence score. These modification steps are carried out during steps E5 and E6 described below. These modifications may be an addition or a removal, or both, of activated sensors.

[0075] If, at the end of step E4, the behavior detection is evaluated as being of good quality, then we move on to step E5. During step E5, we modify the set of activated sensors so as to remove, from the set of sensors activated and used during the detection of the behavior by the model, at least one sensor as a function of one or the other or several of: - the function associated with the deleted sensor is not related to the detected behavior, - the power consumption associated with the sensor is high, - the level of intrusiveness of the sensor is high.

[0076] In other words, removing one or more sensors from the set of activated sensors consists of deactivating at least one sensor among the activated sensors.

[0077] Deactivation may include sending messages or commands to the removed sensors to deactivate them, for example, commands telling them to go to sleep, or commands to turn them off.

[0078] We can prioritize removing the most intrusive sensors, as long as the confidence score is higher than the threshold Sp. We can also prioritize removing the most energy-consuming sensors.

[0079] During step E5, one sensor can also be replaced by another sensor rather than deleting a sensor. Thus, one can keep the same number of sensors but replace a very intrusive sensor by a less intrusive sensor or replace a very energy-consuming sensor by a less energy-consuming sensor, while maintaining a confidence score higher than the threshold Si and possibly lower than the score obtained with the initial set of sensors or with the set of sensors of a following iteration.

[0080] More specifically, and for example, when the initial selection includes two cameras, located in two different rooms, for example the living room and the kitchen, it may be useless to keep the two cameras active if the detected behavior of The user is cooking. The camera located in the living room can be disabled.

[0081] According to another example, when the initial selection includes a camera and a microphone, located in the same room, it may be unnecessary to keep the microphone active if the detected user behavior is working on a computer. The microphone can then be disabled and therefore removed from the list of active sensors.

[0082] According to another example, when two devices can detect the same behavior, for example if there are two cameras, the most energy-consuming camera can be removed from the list of active sensors.

[0083] According to another example, if the detected behavior is that the user is having a telephone conversation, then if the initial list of sensors included at least one camera and one microphone, the camera can be deactivated and the microphone kept, if the energy-consuming nature of the devices is taken into account, a camera consuming more than a microphone. According to another embodiment, if the non-intrusive nature is taken as a priority, the microphone can be deactivated (to prevent the telephone conversation from being heard) and the camera can be kept active. It can be noted that the intrusive nature can be related to the detected function.

[0084] The following three criteria can be ranked in order of priority: - the function associated with the deleted sensor is not related to the detected behavior, - the power consumption associated with the sensor is high, - the level of intrusiveness of the sensor is high.

[0085] This priority order can be used to select certain sensors over others.

[0086] Following the deletion, or replacement, of one or more sensors from the list of activated sensors, the behavior determination model may be executed again. One or more iterations of steps E3 to E5 may be necessary to refine the list of activated sensors giving a confidence score greater than or equal to the threshold Sp. In particular, one or more iterations may be implemented to modify the selection of sensors, either to reduce the intrusive level of the selected sensors, or to reduce the power consumption of the selected sensors, or to reduce the number of sensors. The above order of priority may be used to determine which sensor to prioritize or which type of sensor to prioritize. As illustrated in [Fig.2], following step E5, a measurement test of an overall intrusive level NivI of all the selected sensors and / or a measurement test of an overall level NivE of electrical consumption of the selected sensors can be carried out. Depending on the result of these tests, it can be determined whether to repeat steps E3 to E5.

[0087] The overall level NivE of electrical consumption or the overall intrusive level NivI can be calculated respectively by adding the electrical consumption levels of the selected sensors or by adding the intrusive levels of the selected sensors.

[0088] According to another variant, it may also be desired to carry out one or more iterations to reduce the number of selected sensors while maintaining an equivalent overall electrical consumption level or an equivalent overall intrusive level.

[0089] The modification of the selection of activated sensors can be carried out as long as the confidence score is higher than the determined threshold Sp. Thus, a lower confidence score can be obtained, but higher than Si during subsequent iterations with a reduced number of sensors, or a lower number of intrusive sensors or a higher number of less energy-consuming sensors.

[0090] Thus, it is possible to return to step E3 using real-time data. In order to avoid replaying the model on the real data observed during step E3, the data from the sensors and transmitted to (or recovered by) the model during step E3 can be recorded and reused during subsequent iterations of the model.

[0091] In other words, step E5 can be iterated with the set of updated sensors as long as the confidence score is greater than the threshold Si. When the confidence score reaches the threshold Si, according to certain embodiments, iteration can continue. - to reduce the intrusiveness of the selected sensors by replacing certain sensors with other less intrusive sensors or - to reduce the energy consumption of the selected sensors by replacing certain sensors with other less energy-consuming sensors.

[0092] Indeed, even if the selection of sensors, in order to remove them, during step E5, can be carried out from one iteration to the next by first selecting the most intrusive sensors then the less and less intrusive sensors, additional iterations can make it possible to reduce the energy footprint by keeping sensors having the same level of intrusiveness but a lower energy footprint.

[0093] In the same way, if the selection of sensors, to remove them during step E5, can be carried out from one iteration to another by first selecting the most energy-consuming sensors then the less and less energy-consuming sensors, additional iterations can make it possible to reduce the intrusive side by keeping sensors having the same energy footprint but a lower level of intrusiveness.

[0094] Similarly as mentioned previously as a variant, one may want to reduce the number of sensors involved.

[0095] At the end of step E5, when a score is reached either of a correct overall intrusive level, or a correct overall energy consumption level, or a number of minimal sensors, we move on to step E7 in which we can record the list of sensors associated with the detected behavior. This can advantageously allow the activation of sensors corresponding to the detection of a specific desired behavior. Thus, we obtain a connection or correspondence of a set of activated sensors with a behavior, usable for example by an application of a service provider, or a telecommunications operator.

[0096] If, at the end of step E4, the behavior detection is evaluated as being of poor quality, then we move on to step E6. During step E6, we modify the set of activated sensors so as to add one or more sensors to increase the confidence score and reach a confidence score at least equal to the threshold Sp During step E6, we modify the set of activated sensors so as to add to the set of sensors activated and used during the detection of the behavior by the model, at least one sensor as a function of one or the other or more of: - the function associated with the sensor, - the power consumption associated with the sensor, - the level of intrusiveness of the sensor.

[0097] One or more sensors can be added simultaneously or one by one.

[0098] During step E6, in addition to adding sensors to improve the confidence score, one sensor can also be replaced by another sensor in order to improve the intrusiveness of all the sensors or in order to improve the energy performance of all the sensors. This replacement can be carried out following the addition of sensors or simultaneously. It is indeed possible, in a first step, to select the sensors to obtain a confidence score greater than or equal to Sb by taking for example less energy-consuming or less intrusive sensors first and in a second step, for example during subsequent iterations, to replace some of the selected sensors with sensors, having for example the same function, but a lower intrusiveness or a lower energy-consuming character.

[0099] More specifically, and for example, if the model detects a behavior corresponding to a person being in the kitchen, for example using activated volumetric sensors, the model may indicate a confidence score of 4 for a behavior that would correspond to a person eating. An additional sensor, for example a microphone, may then be activated to determine whether the person is actually eating or whether they are cooking. A microphone may be selected rather than a camera, if it is considered that less intrusive sensors are preferred and if a microphone is classified as a less intrusive sensor than a camera.

[0100] According to other embodiments, one or more added sensors may be added according to their energy consumption or according to their function and according to the detected behavior with a confidence score lower than Sp One aim being to improve the confidence score of the detected behavior, the added sensor(s) are selected according to the assumed behavior (assumed because the confidence score associated with the detected behavior is low), to allow above all to increase this score and among the sensors possibly allowing to increase this score, a selection of the least intrusive or least energy-consuming sensors is carried out.

[0101] Following the addition, or replacement, of one or more sensors from the list of activated sensors, during step E6, the behavior determination model can be executed again. Thus, it is possible to return to step E3 using real-time data. One or more iterations of steps E3 to E6 may be necessary to refine the list of activated sensors giving a confidence score greater than or equal to the threshold Si. The modification of the selection of activated sensors can be carried out as long as the confidence score is greater than the determined threshold Si.

[0102] In order to avoid replaying the model on the real data observed during step E3, the data from the sensors and transmitted to (or recovered by) the model during step E3, can be recorded and reused during subsequent iterations of the model.

[0103] Step E6 is iterated with the set of updated sensors as long as the confidence score is lower than the threshold Si. When the confidence score reaches the threshold Sb according to certain embodiments, iteration can continue. - to reduce the intrusiveness of the selected sensors by replacing certain sensors with other less intrusive sensors or - to reduce the energy consumption of the selected sensors by replacing certain sensors with other less energy-consuming sensors.

[0104] Indeed, even if the selection of the sensors, during step E6, can be carried out from one iteration to the next by first selecting the least intrusive sensors then the increasingly intrusive sensors, additional iterations can make it possible to reduce the energy footprint by keeping sensors having the same level of intrusiveness but a lower energy footprint.

[0105] In the same way, if the selection of the sensors, during step E6, can be carried out from one iteration to the next by first selecting the least energy-consuming sensors then the increasingly energy-consuming sensors, additional iterations can make it possible to reduce the intrusive side by keeping sensors having the same energy footprint but a lower intrusive level.

[0106] At the end of a plurality of iterations of the loop E3-E6, the confidence level reaches a confidence level greater than or equal to Si. We then move on to step E5 described above. cededly.

[0107] At the end of the method, all the sensors making it possible to obtain a detection of a behavior can be recorded, step E7. Thus, it is possible, for example, to associate, with a behavior to be detected, a set of sensors, in the environment considered, here in the environment of the PI gateway.

[0108] This information linking the selection of sensors allowing detection of behavior and minimizing the intrusive nature or the energy-consuming nature can be applied in the field of the smart home, understanding the behavior of occupants and can make it possible to provide adapted services in real time, such as, for example, redirecting a call to messaging, optimizing the energy consumption of home appliances, dynamic management of lighting and shutters, displaying information on a screen, etc.

[0109] In the field of augmented technicians, understanding behavior can make it possible to display in real time in augmented reality the information they need: the user guide for a tool, an assembly procedure, a route to a particular area of ​​a factory.

[0110] In these different fields, the confidentiality criterion is important and the adaptive aspect of the present disclosure can make it possible to comply with the expectations of users by leaving them in control and thus giving them increased confidence in the system.

[0111] [Fig.3] represents an example of at least one part of hardware architecture 30 of the PI gateway, allowing the implementation of a method according to the present invention and as represented for example in [Fig.2]. This hardware architecture is that of a computer. Other hardware architecture elements are present in the PI gateway and not represented here.

[0112] The hardware architecture 30 comprises one or more processors 31 (only one is shown in [Fig. 2]) implementing a method according to the present disclosure, a read-only memory 32 (of the “ROM” type), a rewritable non-volatile memory 33 (of the “EEPROM” or “NAND Flash” type for example), a rewritable volatile memory 34 (of the “RAM” type), a communication interface 35 with the gateway 2. The read-only memory 32 constitutes a recording medium in accordance with an exemplary embodiment of the invention, readable by the processor or processors 31 and on which is recorded a computer program Prog in accordance with an exemplary embodiment of the invention comprising instructions for executing steps of the sensor selection method according to the invention. Alternatively, the computer program Prog is stored in the rewritable non-volatile memory 33.

[0113] The computer program Prog may enable the gateway PI to implement at least a part of the method in accordance with the present disclosure and such as illustrated for example in [Fig.2].

[0114] This computer program Prog can thus define functional and software modules, configured to implement the steps of a selection method in accordance with an exemplary embodiment of the invention, or at least part of these steps. These functional modules rely on or control the hardware elements 31, 32, 33, 34, 35 of the PI access gateway cited above.

Claims

Claims

1. Method for selecting sensors from a set of sensors present in an environment and associated with one or more functions, one or more sensors being activated to enable detection of a behavior of a living being, the method comprising: - a modification of the selection of the activated sensors from the set of sensors as a function of a confidence score relating to said detection of said behavior, said modification being carried out one or more times as a function of the confidence score.

2. Method according to claim 1 wherein said modification is carried out as long as said confidence score is below a determined threshold.

3. A method according to claim 1 wherein some of the set of said sensors are associated with an intrusive character, said selection of activated sensors selecting in priority the least intrusive sensors.

4. Method according to claim 1 in which an energy consumption level is associated with at least some of the set of said sensors, said selection of activated sensors selecting in priority the least energy-consuming sensors.

5. The method of claim 1 wherein said modifying the selection of activated sensors comprises adding, removing or replacing sensors based on detected behavior and the function associated with each sensor.

6. A method according to claims 3 and 4 wherein said modifying the selection of activated sensors comprises adding, removing or replacing sensors based on detected behavior, and based on either a level associated with the intrusiveness of a sensor, or a level of energy consumption associated with a sensor.

7. Method according to claim 1 comprising, when said behavior is detected with a confidence score greater than a determined threshold, - a deactivation of at least one sensor among the activated sensors if - the function associated with said deactivated sensor is not used to detect said behavior or - the power consumption associated with the sensor is high, or - the level of intrusiveness of the sensor is high.

8. Method according to one of the preceding claims in which said confidence score relating to the detection of said behavior is determined from one or more of: - a quality indicator of a behavior recognition model used during said behavior detection, - an evaluation of a history of detected behavior data - an evaluation of data relating to said environment, - reception of at least one piece of information from a living being present in said environment.

9. Method according to one of the preceding claims comprising - obtaining, for at least some of said sensors, a level relating to its intrusive nature, said obtaining being received from a user or determined from the function associated with said sensor.

10. Device for selecting sensors from a set of sensors present in an environment and associated with one or more functions, one or more sensors being activated to enable detection of a behavior of a living being, the device comprising one or more processors configured to: - modifying the selection of activated sensors from among the set of sensors based on a confidence score relating to said detection of said behavior, said modification being carried out one or more times based on the confidence score.

Images