FR3085525A1 - MONITORING OF PEOPLE WITH ACCOUNT OF VISITS - Google Patents

Abstract

The invention relates to a method of quantifying an activity of a person operating in a defined environment in which are arranged several sensors connected to a central unit of a monitoring system, the central unit being able to detect the presence of a a second person within said defined environment, the method being executed by the central unit, and comprising a step of learning a personalized sequence of detection results capable of being received from said sensors, said personalized sequence characterizing l 'activity of the person evolving in the defined environment; a monitoring step comprising a sub-step of acquiring second detection results received from said sensors, a sub-step of searching for said personalized sequence among said second detection results; and, if said personalized sequence is not found during a predetermined time interval, a step of notifying an anomaly to a user; characterized in that when the central unit detects the presence of the second person within said delimited environment during the execution of the learning step or of the acquisition sub-step, the execution of the step in course is suspended for the duration of this presence.

Description

Monitoring of people with consideration of visits

The present invention relates to the field of monitoring the activity at residence of a person with a physical and / or psychological state of weakness.

Nowadays, the increase in life expectancy, as well as the evolution of outpatient-oriented medicine lead to an increase in the number of people whose follow-up is provided at home, as long as it is possible.

In order to provide this monitoring automatically, there are a large number of different devices. For example, there are portable connected objects, equipped with an alert button allowing the wearer, in case of difficulty, to notify the emergency services for intervention. Some of these devices include an accelerometer to detect a fall and send a signal to alert the emergency services.

A drawback of this type of device lies in the interaction between the person being monitored and the device they are wearing. In the event of loss of consciousness, in particular without falling, most of the known devices do not make it possible to issue an alert. In addition, this solution assumes that the person is able to remember to systematically wear the device, which can be a difficulty, given the profile of the people monitored.

Another known solution consists in installing an audio-video surveillance system. This solution is not well accepted by those under surveillance, because it is perceived as too intrusive.

Other solutions have been developed, by equipping with sensors a demarcated environment in which the person monitored evolves, for example their accommodation. The sensors used in this case are chosen to be perceived as less intrusive, and therefore better accepted. Such sensors can be, for example motion and activity detectors, as well as door opening detectors, sensors for detecting the use of household electrical appliances and more generally any sensor allowing directly or indirectly to retrieve a measurement linked to the environment or to the person, for example temperature, light, humidity sensors, etc.

Such a solution indeed allows non-intrusive surveillance, therefore better accepted, and more reliable, because the operation of the sensors does not require that they be worn by the person being monitored. Since this solution does not use audio / video sensors, the raw data from the sensors must be interpreted to analyze the activity of the person being monitored, and possibly detect an anomaly. Thus, by relying on the raw data, these monitoring systems operate firstly in "learning" mode of the person's lifestyle, and secondly, in "monitoring" mode.

Conventional systems implementing this solution have several drawbacks: in "learning" mode, the person's lifestyle is either deduced from the averages calculated from raw data from the sensors, or defined by preprogrammed scenarios, including the personalized parameters, adapted to the lifestyle of the person being monitored, must be entered manually when the system is put into service.

The systems implementing a learning phase based on detection results sent by the sensors do not make it possible to take into account the relevance of the data used for learning, in particular the relevance of the data obtained during the visits. In particular, the raw data, sent by the sensors during the visits and from which the averages will be calculated to deduce the lifestyle of the person in the monitored environment, are not representative of the activity of the person evolving alone in this environment.

Conventional systems also have a drawback when operating in "surveillance" mode. In particular, the raw data sent by the sensors during the visits is not representative of the activity of the person operating alone in the monitored environment. Consequently, large deviations from the expected values may appear, and false alarms may be generated.

Thus the solution proposed by the invention aims to determine the relevance of the data used by the monitoring system both in learning mode and in monitoring mode, providing a complete and reliable monitoring solution.

To resolve one or more of the drawbacks mentioned above, the invention proposes a method for quantifying an activity of a person operating in a defined environment, in which several sensors are arranged connected to a central unit of a surveillance system, the central unit being able to detect a presence of a second person within said delimited environment, the method being executed by the central unit, and comprising:

• a step of learning a personalized sequence of detection results capable of being received from said sensors, said personalized sequence characterizing the activity of the person operating in the defined environment;

• a monitoring step comprising:

o a sub-step for acquiring second detection results received from said sensors, o a sub-step for searching for said personalized sequence among said second detection results;

and, if said personalized sequence is not found during a predetermined time interval, • a step of notifying an anomaly to a user;

characterized in that when the central unit detects the presence of the second person within said delimited environment during the execution of the learning step or of the acquisition sub-step, the execution of the step in course is suspended for the duration of this presence.

In particular embodiments, usable alone or in combination:

The quantification method further comprises:

• a step of verifying the personalized sequence and, in the event of variation greater than a predefined threshold, • a step of updating the personalized sequence, and when the central unit detects the presence of the second person within said delimited environment during the execution of the update stage, this execution is suspended for the duration of this presence.

An exclusion list can be associated with each sensor, composed of identifiers of sensors arranged in said delimited environment so that simultaneous or quasi-simultaneous detection results from a sensor and from a sensor belonging to its list of associated exclusion indicates the presence of the second person.

In one embodiment, an indicator is associated with said personalized sequence, said indicator being able to take a first predetermined value if said personalized sequence is recognized in the second detection results received from said sensors, the monitoring step further comprising a sub-step of assigning an effective value to said indicator, as a function of the result of the research sub-step; and:

• the quantification method further comprising a step of transmitting to a user the effective value of said indicator.

In one embodiment, the learning step comprises:

• a step of obtaining a generic sequence of results capable of being detected by said sensors, said generic sequence being defined by at least one parameter; and:

A step of determining, as a function of first detection results received from said sensors of a personalized value, of said at least one parameter defining said generic sequence for the person operating in the defined environment, said at least one parameter being designated as personalized parameter when it takes the personalized value, said personalized sequence being derived from said generic sequence, when it is defined by said at least one personalized parameter.

In one embodiment, the determination step further comprises a substep for identifying a time period during which the personalized sequence is found among the first detection results, with a number of occurrences greater than a first predefined threshold, called personalized time slot, and the predetermined time interval is equal to the personalized time slot.

In one embodiment, the first learning step is executed as long as a predetermined condition relating to the personalized sequence is not fulfilled.

The plurality of sensors includes:

• presence sensors;

• motion sensors;

• door opening contacts;

• accelerometers placed on the doors.

The plurality of sensors can further include:

• devices for measuring electrical consumption arranged on electrical outlets supplying a first group of predetermined household appliances;

• accelerometers placed on the doors of a second group of predetermined household appliances.

In one embodiment, the anomaly notification step comprises:

• the sending of an alert message if at least one sequence of detection results received from said sensors indicates a variation of the personalized sequence of a first predefined type;

• the sending of a prevention message if a plurality of sequences of detection results received from said sensors indicate a variation of the personalized sequence of a second predefined type.

Another aspect of the invention relates to a computer program, comprising instructions for implementing the steps of a method as defined above when the program is executed by a computer.

The invention also relates to a central unit for a system for monitoring the activity of a person operating in a defined environment, in which several sensors are arranged connected to said central unit, comprising:

• means for learning a personalized sequence of detection results capable of being received from said sensors, said personalized sequence characterizing the activity of the person operating in the defined environment;

• means for acquiring second detection results received from said sensors • means for searching for said personalized sequence among said second detection results;

• means of notification of anomalies to a user;

said central unit being capable of detecting the presence of a second person within said delimited environment, and when it detects the presence of the second person within said delimited environment during the execution of a learning step of the sequence personalized or of a sub-step of acquisition of second detection results received from said sensors, said central unit is configured to suspend the execution of the current step for the duration of this presence

The invention will be better understood on reading the description which follows, given solely by way of example and with reference to the accompanying drawings.

Brief description of the figures

FIG. 1 schematically represents an example of a monitoring system, according to an embodiment of the invention.

FIG. 2 schematically represents a central unit of the surveillance system, according to an embodiment of the invention.

Figures 3-5 and 7-9 schematically represent steps of a method for quantifying an activity of a person, according to different embodiments of the invention.

FIG. 6 represents an example of application of a step of the quantification method according to an embodiment of the invention.

In FIG. 1 is illustrated an environment delimited ENV, in which a person to be monitored evolves. In the environment delimited ENV are arranged several sensors C1, C2, ... Cj, ... Cm, Cn, connected to a central unit CPU of a SYS surveillance system, via a home automation box B located in said environment delimited ENV. The sensors C1, ... Cn, are, as in conventional solutions, motion and activity detectors, as well as door opening detectors, sensors for detecting the use of household appliances and more generally any environmental or worn sensor, allowing direct or indirect feedback of a measurement linked to the environment ENV or to the person P, for example sensors of temperature, light, humidity, accelerometers placed on doors or windows , or worn by the person, etc.

The home automation box B communicates with the central processing unit UC via an extended telecommunications network, for example the Internet. The central processing unit UC is connected to a SERV server hosting one or more databases. This arrangement allows on the one hand to share the resources of the central processing unit and the databases between several monitored environments, and on the other hand to enrich the databases with raw data coming from several environments. These data can then be analyzed to evolve the behavior models used by the surveillance system.

Alternatively, in a configuration not shown, the home automation box B and the central processing unit UC are located within the delimited environment ENV. The two entities can be implemented on the same device, or on two separate devices, communicating with each other by a local network. Only anomalies notifications will be sent via the extended telecommunications network to supervisors and / or assistance and rescue organizations. In this way, additional protection of private data is ensured.

As an option, the home automation box B and / or the central processing unit UC can receive additional information from the extended telecommunications network, for example meteorological data, the calendar of holidays and public holidays, information concerning a programmed absence from the monitored person, entered by the supervisor or by another user, geolocation information of the monitored person in the event of an exit, etc.

Information on the presence of a second person in the circumscribed environment ENV, or the planned absence of the monitored person, for example for a vacation or for a hospitalization, can influence the relevance of the behavior model, as will be explained by the following.

During the installation of the monitoring system, operational data concerning the delimited environment ENV are transmitted to the central unit UC, for example the type of room where each sensor C1, C2 .. Cj .. is installed: kitchen, bedroom, living room, hallway, bathroom, garden, terrace, etc. One or more rooms can be designated as main living room (s). It is in this or these room (s) that the person spends the most time for their activities, and the sensors can be installed as a priority. Additional sensors of the same type or of a different type may also be installed. Thus, the system can analyze more closely the activity of the person being monitored, determining for example if they are watching television or if they are active, for example by cleaning. This differentiation allows a more precise quantification of the activity of the person, and in the event of a notable decrease in this activity, a slow deterioration in the state of the person could be detected. It should be noted that a purely reactive system would not trigger an alert in this case, since there are no urgent events.

The type of the various sensors is also informed, in addition to their distribution in the environment delimited ENV, thus giving a partial representation of the place of life of the person P.

The surveillance system can also use geolocation data of the monitored person, if he is equipped with a beacon, and / or of the environment delimited ENV, which makes it possible to correlate for example the meteorological data and the actions carried out by the person being monitored.

In FIG. 2 is schematically represented the central unit UC of the monitoring system SYS, according to an embodiment of the invention. The central unit includes a CPU processor, MEM memories to store data and computer programs, a Tempo time measurement unit, as well as RX communication interfaces to receive the detection results sent by the sensors Cj, TX for notify any anomalies to a user of the system, for example someone close to the person being monitored or an assistance and rescue organization, as well as interfaces (not shown) for communication with the home automation box B and the external SERV server.

FIGS. 3 and 4 schematically represent the steps of a method for quantifying an activity of a person, according to an embodiment of the invention.

The monitoring system according to the invention operates initially in “learning” mode of the person's lifestyle, during a learning step E000 of a personalized sequence SEQ_Pj of detection results capable of being received from said said sensors Cj, said personalized sequence SEQ_Pj characterizing the activity of the person P evolving in the delimited environment ENV. In a second step, the system operates in “monitoring” mode, during an E300 monitoring step, which will be detailed later. In the event of an anomaly, a notification will be sent during a step E400.

The quantification process is particularly suitable for monitoring a person living alone in the ENV environment. During social or medical visits, movement or presence sensors will send detection results regardless of the person who produces them, the person monitored and the visitor (s). This can happen both in learning mode and in detection mode. Thus, all the data received from the sensors is not relevant for monitoring person P.

The quantification method according to the invention makes it possible to select the relevant data from all the detection results received. To do this, the SYS monitoring system has the means to detect visits, and possibly means to receive information concerning the absences of the person to be monitored, which allows the central unit to first carry out an assessment. the relevance of the data sent by the sensors.

If information is obtained on a period of presence of a second person in the ENV environment, or on a period of programmed absence of the person being monitored, for example for holidays or for hospitalization, the data from sensors Cj are considered irrelevant during these periods and the learning step E000 is suspended for the duration of this presence. The methods of obtaining this information may vary depending on the learning and monitoring techniques used.

Thus, according to a first embodiment, each sensor Cj placed in the environment ENV is associated with an exclusion list LEj, composed of identifiers of sensors Ci, Ck, arranged in said environment delimited ENV, so that results simultaneous or quasi-simultaneous detection from a sensor Cj and a sensor Ci, Ck belonging to its associated exclusion list LEj indicate the presence of the second person P2.

According to variant embodiments, the exclusion lists LEj associated respectively with the sensors Cj can be predefined, for example during the installation of the sensors with respect to the rooms where the sensors are installed, or with respect to particular locations in the same room. In this variant, the sensors installed in other rooms than a given sensor Cj, will be part of the exclusion list associated with this sensor Cj. Similarly, sensors Ci, Ck, installed in the same room as the sensor Cj, but at a distance large enough that a single person cannot simultaneously or almost simultaneously generate presence detection results sent by the sensor Cj and by the sensors Ci, Ck. Of course, the distance depends on the types of sensors and the respective detection radii, specifications which are known during the installation of the system.

In another variant, the list is instantiated dynamically, for example by studying a simultaneity matrix, and on the assumption that the person is mainly alone in the ENV environment and knowing that simultaneous detections can be sent by the sensors for a single person in a dwelling.

In another embodiment, using preprogrammed scenarios, the periods of visits of the home helpers, as well as the outputs of the person being monitored, can be entered manually during the commissioning of the system, or during the updating operations. system day. The detection results sent by the sensors Cj during the periods thus filled in will be excluded from the detection results taken into account for learning the lifestyle of the person P, respectively for monitoring this person. These indications of presence calendar can also make it possible to refine the profile of simultaneity or quasi-simultaneity of the detection results for the constitution of the exclusion lists LEj, for example in a form of supervised learning of visits.

In a preferred embodiment of the invention, the inputs and outputs of people in the environment respectively can be identified by specific sequences of the detection results sent by sensors specially placed for this purpose, for example motion sensors near the entrance doors to the ENV environment, or accelerometers placed on these doors. Other detection means can be used, for example identifiers linked to visitor telecommunications equipment, such as smartphones, access badges or buttons to be activated during entry / exit.

Whatever the mode of detecting the presence of a second person in the environment ENV, during the execution of the learning step E000, in particular the acquisition of the detection results r1, r2, ..rk, from the sensors if the method implements a learning step based on these results, the current execution is suspended for the duration of this presence.

Once the learning step E000 has been completed, the monitoring system switches to the "monitoring" operating mode proper. To do this, the central processing unit UC of the monitoring system SYS performs a monitoring step E300.

For each behavior model "j" of the monitored person P, the central unit UC has learned during step E000 the personalized sequence SEQ_Pj associated with the behavior model of this person P. If the sequence SEQ_Pj is obtained according to statistical methods, a statistical distribution and a variance of values are also determined to analyze the behavior of the person being monitored P.

The central processing unit UC then seeks to evaluate whether the expected personalized sequence SEQ_Pj has actually been received from the sensors Cj, taking into account, where appropriate, the abovementioned statistical data.

In a preferred embodiment, the monitoring step E300 first comprises a sub-step of acquisition E310 of second detection results R1, R2, Rk received from said sensors Cj, and then a search sub-step E320 of said personalized sequence SEQ_Pj among the second detection results R1, R2, Rk.

Similar to operating in “learning” mode, in this monitoring system having means for detecting visits, or means for receiving information concerning the absences of the person to be monitored, the central unit UC first performs a evaluation of the relevance of the detection results R1, R2, Rk.

If these results are deemed irrelevant, since they are not representative of the person's behavior during visits, or in the event of a planned absence, the E300 monitoring step is interrupted. Otherwise, if said personalized sequence SEQ_Pj is not found during a predetermined time interval T_p, the quantification method then comprises a step E400 of anomaly notification to a user, by known means, such as electronic messages .

If a visit is detected, the detection results will therefore not be taken into account for learning the behavior patterns of the single person, or for monitoring corresponding to these behavior patterns. On the other hand, a detection of a visit received is a precious indicator of the activity of the person being monitored, which will be quantified according to another criterion, that of social ties.

To assess the social bond, the central processing unit UC can also perform a quantification of the person's exits from their accommodation. An output can be modeled, for example, by a sequence of door opening / closing detections, correlated with an absence of presence detection in the environment delimited ENV. An exit detected one day, which does not recur, will not be considered as an anomaly. On the other hand, if the person goes out less and less, this can show a drop in social ties.

Additional data can be taken into account for the evaluation of variations in the frequency of visits and / or outings, for example the day of the week, the calendar of public holidays, meteorology (rain, heat wave), etc. This data can be received by the central processing unit UC from the extended telecommunications network, according to known methods.

Variations in the frequency of visits and / or exits can thus be monitored by the central processing unit UC, and if a downward trend is identified, a preventive anomaly can be notified to a supervisor of the person P.

More generally, the method according to the invention makes it possible to detect not only events, for example by comparing expected values with measured values, but also trends in the evolution of the measured values, thus ensuring a predictive character to the monitoring system. .

A particular embodiment of the quantification method will now be described with reference to FIG. 5. This particular embodiment makes it possible to benefit from the behavior models studied beforehand. Such behavior patterns are defined for example for eating a meal, getting up, using the bathroom, cleaning, etc.

Each of the behaviors includes a series of movements of the person within the housing, and / or a series of actions exerted on the household appliances, and / or door openings / closings, which are detectable by the sensors Cj. Detection results, the order of detections, and time information for certain time-stamped results are analyzed to identify behavior.

A behavior model, capable of being identified and quantified by the monitoring system, is modeled by a sequence of results, in a certain order, the results being sent by certain sensors, for a certain duration.

A behavior model represents a general case, and a sequence SEQj of results capable of being detected by one or more sensors Cj, associated with this general behavior model, is a sequence SEQj called generic.

The definition of the generic sequences is carried out prior to the implementation of the invention. This definition can be, for example, the result of behavioral studies carried out by universities or specialized laboratories. An advantage of the solution proposed by the present invention is precisely to make it possible to benefit from these studies with substantial human contribution, in an automated monitoring system.

Each generic sequence SEQj is defined as a function of one or more parameters mj, nj. Special values, vmj_p, vnj_p assigned to the parameters mj, nj, make it possible to obtain a particular instantiation SEQ_Pj of the generic sequence SEQj. This particular instantiation SEQ_Pj is thus representative of a particular instantiation of the behavior model, for a given monitored person P. In the remainder of this document, such a sequence is designated as a “personalized sequence” SEQ_Pj. The particular values vmj_p, vnj_p allow, when they are assigned to the parameters mj, nj, to obtain the personalized sequence SEQ_Pj, from the generic sequence SEQj. These parameters are designated in the remainder of this document as "personalized parameters" mj_p, nj_p.

For example, a person's sunrise can be modeled by a series of presence detections in one or more rooms in the ENV environment, for a certain period of time.

A meal is modeled by a presence detection in the kitchen, followed by a certain number of detections of the use of household appliances, signaled by devices for measuring electrical consumption arranged on electrical outlets supplying a first group. predetermined household appliances, and / or accelerometers arranged on the doors of a second group of predetermined household appliances, etc.

If we consider the survey modeled by a series of presence detections in one or more rooms of the ENV environment, for a certain duration, the generic sequence SEQj can be characterized by two parameters: a first parameter mj, linked to the number of detections presence, and a second parameter nj, linked to the duration during which the presence detection results are considered significant.

In one embodiment of the invention, the duration can be measured by counting a number of time intervals of predefined duration, for example ten minutes. In this example, the first parameter mj is the minimum number of presence detections during each time interval, and the second parameter nj is the number of successive intervals of predefined duration during which these detection results are expected.

For a first person P1, rising is manifested by a minimum of two presence detections per ten-minute interval, for at least one interval; for a second person P2, by a minimum of three presence detections per ten-minute interval, for at least two successive intervals.

Thus, for the first person P1, the generic sequence SEQj can be instantiated so as to obtain a personalized sequence SEQ_P1 representing the behavior of this person P1 if the first parameter mj takes the value vmj_1 equal to 2, and the second parameter nj takes the value vnj_1 equal to 1; for the second person P2, the generic sequence SEQj can be instantiated so as to obtain a personalized sequence SEQ_P2 representing the behavior of this person P2 if the first parameter mj takes the value vmj_2 equal to 3, and the second parameter nj takes the value vnj_2 equal to 2. The personalized parameters mj_p, nj_p respectively take the values (2, 1) for the first person P1 and (3, 2) for the second person P2.

The order of the parameters is important, so the values of the personalized parameters mj_p, ⁿ LP constitute vectors of dimension 2 in this example. A third dimension can be added, for example by introducing a third parameter representative of the time of day when the expected sequence is actually detected, or the seasonality of this sequence. More generally, N parameters can be grouped to constitute vectors of N-dimensional parameters.

The effective values of the personalized parameters mj_p, nj_p make it possible to adapt the generic sequence SEQj to each monitored person P, evolving in the environment ENV, in other words to customize the generic sequence SEQj to the person P and to the environment ENV.

An embodiment of the quantification method will now be described with reference to FIG. 5. The example described illustrates the learning and monitoring of a single behavior model of the monitored person, but of course, a different instance of the method according to the invention can be executed for each behavior mode and each associated generic sequence SEQi of results capable of being detected by the sensor or sensors installed in the environment delimited ENV.

The SYS monitoring system operates, after installation, in learning mode. The central processing unit UC first obtains, in an obtaining step E100, the generic sequence SEQj representative of the behavior mode considered. This sequence SEQj is defined beforehand, and stored beforehand either by the same central processing unit UC, or by a separate entity, for example a server dedicated to the SERV application. Depending on the case, the obtaining step E100 can take different forms, for example a step of reading from memory of the central processing unit UC, a step of receiving one or more messages from the SERV server via a network of telecommunications, an operator input step, etc.

Whatever the form of this step of obtaining E100, following its execution by the central unit UC, the latter is in possession of the generic sequence SEQj, of the parameters mj, nj defining it, including their type, as well as the sensor (s) Cj capable of reporting detection results relevant to the mode of behavior considered, and therefore for the associated generic sequence SEQj.

Then, the central unit performs a step E200 of determining one or more personalized value (s) vmj_p, vnj_p, of the parameter (s) mj, nj defining the generic sequence SEQj for the person P operating in the environment delimited ENV. This (s) parameter (s) are designated as custom parameter (s) mj_p, nj_p when it (s) takes / take the value (s) custom (s) vmj_p, vnj_p.

To do this, the central processing unit UC receives, as and when the determination step E200 of the quantification process is executed, first detection results r1, r2, ..rk, coming from the sensors Cj installed in the environment ENV.

These first results r1, r2, ..rk, are used by the monitoring system still operating in “learning” mode. In this embodiment of the invention, the exploitation of the first results r1, r2, ..rk, is done according to statistical methods.

The results r1, r2, ..rk are obtained by a selection of relevant results from all the results received, thanks to the ability of the central processing unit UC to detect the presence of a second person within the housing, and possibly to receive information on the absence of person P from the ENV environment. Within the meaning of the invention, a relevant result is that which represents the behavior of a person operating alone in the environment delimited ENV, excluding visits.

The implementation of the quantification process is particularly advantageous if the personalized values of the parameters and the personalized sequence SEQ_Pj are determined by statistical or machine learning methods. Indeed, the detection results being selected according to their relevance, thanks to the detection of visits, the modeling of the behavior of the monitored person is more precise.

The central processing unit UC first accumulates a certain number of first results r1, r2, ..rk, which are relevant, for a predefined period or until a sufficient number of results is obtained in order to be able to apply statistical methods, and then performs successive tests, assigning to the parameters mj, nj, several sets of particular values, (vmj_1, vnj_1), (vmj_2, vnj_2), ... (vmj_k, vnj_k), ... (vmj_p, vnj_p),, .. (vmj_q, vnj_q), etc.

Each set of particular values, (vmj_k, vnj_k) is applied according to the definition of the generic sequence SEQ j, and a maximum likelihood is sought among the representations obtained.

FIGS. 6a to 6d illustrate representations obtained for the example of modeling the rising of the two people P1, P2, in two respective environments ENV1, ENV2, the first parameter mj1, mj2, being the minimum number of presence detections during each interval time, and the second parameter nj 1, nj2, being the number of successive intervals of predefined duration during which these detection results are expected. The curves of FIGS. 6a and 6c represent the interpretation of the first results r1, r2, ... rk, on the assumption that the parameters (mj1, nj1), respectively (mj2, nj2) take the personalized values from 1 to 6 for the parameter mj1, respectively mj2 and from 1 to 4 for the parameter nj 1, respectively nj2, for a given learning period.

The representations satisfying the criterion of maximum likelihood are those corresponding to the values (2,1) for figure 6a, respectively to the values (3,2) for figure 6c. Thus, these values will be assigned to the parameters (mj1, nj1), respectively (mj2, nj2), to obtain the personalized parameters mj_p1, nj_p1 and mj_p2, nj_p2, characterizing the personalized sequences associated with the lifting of people P1, P2, evolving in the two environments considered ENV1, ENV2.

Thanks to the personalized parameters (mj_p1, nj_p1), (mj_p2, nj_p2) thus determined, are then defined, from the generic sequence SEQj, the personalized sequences SEQ_Pj1, SEQ_Pj2 modeling the rising of the first person P1, respectively of the second person P2.

In this example, the sequence SEQ_Pj1 is thus defined as a minimum of two presence detections per ten-minute interval, for at least one interval; and the sequence SEQ_Pj2, as a minimum of three presence detections per ten-minute interval, for at least two successive intervals.

The personalized sequences SEQ_Pj1, SEQ_Pj2 are sought among the first results r1, r2, ... rk. A sequence SEQ_Pj1, SEQ_Pj2 found, means that the person monitored P1, P2 has actually stood up. The surveys of people P1, P2 can be time-stamped. Figures 6b and 6d represent a determination of the hours of lifting people P1, P2, evolving in the environments ENV1, ENV2, over 31 days, thanks to the use of personalized parameters (mj_p1, nj_p1), (mj_p2, nj_p2) and custom sequences SEQ_Pj1, SEQ_Pj2.

Other techniques which analyze data sets and give predictions of statistical distribution can be used, for example machine learning algorithms, as described for example in:

- Ryszard S. Michalski: The Vision and Evolution of Machine Learning;

- Machine Learning - An Artificial Intelligence Approach - Editors: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (Eds.);

- Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of statistical learning, Springer 2009; and

- Larry, Wasserman, All of Statistics. A Concise Course in Statistical Inference, Springer 2004.

Once the sequence SEQ_Pj with the maximum likelihood has been found, a search is carried out among the sets of particular values, (vmj_1, vnj_1), (vmj_2, vnj_2), ... (vmj_k, vnj_k), ... (vmj_p, vnj_p ),, .. (vmj_q, vnj_q), to identify the set applied to obtain this sequence SEQ_Pj. The values (vmj_p, vnj_p) constituting this set are designated as "custom values". The parameters mj, nj, taking respectively the personalized values vmj_p, vnj_p, are designated as “personalized parameters >> mj_p, nj_p, and the sequence SEQ_Pj obtained according to the definition of the generic sequence SEQj, when it is defined by the personalized parameters mj_p, nj_p, is designated as "custom sequence".

In this embodiment, the personalized parameters mj_p, nj_p, and the sequence SEQ_Pj are determined according to the so-called maximum likelihood method, a statistical method known in the state of the art.

Other statistical methods can also be applied, for example the so-called regression method. It is a statistical analysis which describes the variations of an endogenous variable associated with the variations of several exogenous variables. For example, a regression analysis can reveal a positive relationship between certain days of the week and the variables corresponding to the activity such as cleaning or social contact (visit of a home help). Thus, the generic sequence SEQj can find several instantiations SEP_Pj1, SEQ_Pj2, ... SEQ_Pj7 different, depending on the days of the week. The learning mode of the monitoring system ensures the determination of all these instantiations, as well as the additional parameter which contributes to their differentiation.

For certain behavior models, the quantification method can also comprise a sub-step for identifying a time slot during which the personalized sequence SEQ_Pj is found among the first detection results r1, r2, rk, with a number d 'occurrences higher than a first predefined threshold, called personalized time slot T_Pj. This time slot can be determined according to known statistical methods, for example by the so-called “quantile” method; in this case, the threshold is defined according to the chosen quantile values.

The identification of the personalized time slot is particularly useful for the evaluation of slow degradations, during the operation of the system in "monitoring" mode, as will be explained below.

The determination step E200 of the personalized sequence SEQ_Pj is executed by iteratively applying a statistical method, as a function of the first detection results r1, r2, ..rk, received by the central unit UC from the sensor (s) Cj . According to the embodiments, the duration of this stage can have a predefined maximum duration, for example three months, but a variable effective duration, because during the visits the execution of this stage is suspended, as explained previously.

The determination step can, in another embodiment, be executed as long as a predetermined condition relating to the personalized sequence (SEQ_Pj) is not fulfilled. The latter case has the advantage of optimizing the learning time. The central processing unit UC evaluates the convergence of the results, the length of the learning period can vary greatly from one person to another or from one behavioral model to another.

In the particular case where a personalized time slot T_Pj has been determined during the identification step E250, the predetermined time interval T_p, while the personalized sequence SEQ_Pj is sought, is equal to this personalized time slot T_Pj.

If the personalized sequence SEQ_Pj is not found during the personalized time slot T_Pj, which means for example that the person has not risen according to his habits, the central unit UC generates an alarm message to notify the anomaly detected. The monitoring system operates in reactive mode, in this case.

Behavior will be considered normal if the personalized sequence SEQ_Pj is actually found among the detection results received during the personalized time slot T_Pj. There will therefore be no anomaly notification to be sent as a reaction to an event.

The precise moment of detection of the personalized sequence SEQ_Pj in this personalized time slot T_Pj can be evaluated over several days, weeks or months. If a drift of this moment of detection within the personalized time slot T_Pj is observed, for example a rising which takes place later and later, the central unit UC can deduce a slow deterioration of the general state of the person being monitored, and notify a supervisor of person P as a precaution. In these cases, the monitoring system operates in predictive mode, and sends a prevention message MP.

More generally, if several sequences of detection results received from said sensors (Cj) indicate a slow variation of the personalized sequence (SEQ_Pj) called variation of second type, the monitoring system operates in predictive mode, and sends a prevention message MP . Such slow variations can be deduced for example if the duration and / or the frequency of visits received and / or outings of person P decreases from one week to another, or from one month to another. Another slow deterioration can be deduced from the decrease or increase in the number of meals, the segmentation of night rest, a change in the distribution of activity in the accommodation, etc.

It should be noted that in this operation in predictive mode, the personalized sequences corresponding to the monitored behavior are found, so there is no urgency to react to a detected event. We consider a first person, who receives two visits per week on a regular basis, and a second person, who receives during a first period daily visits, and then a decreasing number of visits. The activity quantification method can send a prevention message MP, if the number of visits received by the second person reaches a certain threshold, predefined, for example three visits per week, or determined by learning the regularity of the visit of the person being monitored. Thus, for the first person considered, a PM prevention message is not sent, because the number of visits received by this first person, although lower than that of the second person considered, is almost constant. On the other hand, a PM prevention message will be sent to a supervisor of the second person, to signify a slow deterioration in the behavior of this person.

The method according to the invention thus makes it possible to detect two types of anomalies, a first type of event character requiring immediate intervention, and a second type of anomaly, indicating a slow deterioration in the behavior of the person. The quantification method according to the invention thus provides a complete monitoring solution, unlike conventional methods.

In the examples described above, the monitoring step E300 is executed by comparing the second results actually received R1, R2, ... Rk, during the acquisition sub-step E310, with the personalized sequence of results SEQ_Pj. The personalized parameters mj_p, nj_p defining this personalized sequence were determined during the determination step E200, on the basis of the first detection results r1, r2, ..rk. If there is a change in the behavior of the person being monitored P, these parameters may become obsolete, and therefore the monitoring system may lose precision and efficiency.

To solve this problem, the quantification method comprises, in particular embodiments represented in FIGS. 7 and 8, a first step of verification E600 of the personalized parameters mj_p, nj_p.

In the event of a variation of the parameters, greater than a second predefined threshold, the quantification method further comprises a first step of updating E700 of the personalized parameters mj_p, nj_p and of the personalized sequence SEQ_Pj.

If the quantification method uses a personalized time slot T_Pj, as illustrated in FIG. 8, the latter is also verified, during a second verification step E610 and, in the event of variation of the personalized time slot

T_Pj greater than a third predefined threshold, the method further comprises a second step of updating E710 of the personalized time slot T_Pj.

In a preferred embodiment, the first and / or the second updating step 700, 710 are executed as a function of a predefined number of detection results from the most recent second detection results R1, R2, Rk. In other words, the method again applies statistical methods to "relearn" the person's lifestyle, based on the latest detection results. This method ensures an almost continuous updating of the parameters, based on a sliding learning "window".

The system having visit detection means, the first and / or the second update step 700, 710 are interrupted during these visits, similar to the suspension of the learning step E000, and the sub- acquisition step E310, as illustrated in FIG. 9.

In a preferred embodiment, in which the central processing unit UC has means for receiving information relating to a hospitalization of the person, the quantification method can detect a significant variation in the lifestyle of this person on his return. In this case, the anomaly notification will not be sent, and the personalized parameters will not be updated following the detection of significant variation. On the other hand, the system will monitor the difference in behavior detected with the old behavioral mode, and only if the difference observed remains present for more than a predefined duration, for example a convalescence duration estimated beforehand, the unit central unit UC will trigger the execution of update steps E700, E710. In this way, the execution of these updating steps is avoided if the evolution of the detected behavior indicates a return to the behavior mode initially modeled by the personalized sequence SEQ_Pj. The operation of the system is thus optimized.

As already mentioned, the quantification method performs a step E400 of notification of anomaly to a user, if the expected personalized sequence SEQ_Pj is not actually received during the monitoring step E300.

Result sequences can be complex, depending on the behavior modeled, and passing them as such to a user, who is not a specialist in the field of home monitoring, can cause problems of understanding. To avoid this complexity, the quantification process can use an indicator INDj associated with the personalized sequence SEQ_Pj, and consequently with the associated behavior model. The indicator INDj can take a first predetermined value V1 if the personalized sequence SEQ_Pj is recognized in the second detection results R1, R2, Rk received from the sensors Cj.

In these embodiments, the monitoring step E300 further comprises

- a sub-step of attributing E330 to said indicator INDj with an effective value Vej, according to the result of the sub-step of research E320; and

a step of transmission E500 to a user of the effective value Vej of said indicator.

For example, in the case of monitoring the person's sunrise, a first indicator "LEVER >>" can be set up, and the value V1, for example "TRUE >> logical, will be assigned to the indicator" LEVER> > if the corresponding SEQJ personalized sequence was detected during the scheduled time slot. In this way, the user interface is simplified, ensuring a better user experience for the surveillance system.

With reference to FIGS. 6b and 6d, described above, when the personalized sequence SEQ_Pj1, SEQ_Pj2 representative of the lifting of the person has been identified among the detection results as explained previously, the central processing unit UC can perform a time-stamped detection of the personalized sequence SEQ_Pj, and a timestamped assignment of the value "TRUE" to the indicator "LEVER".

According to the modeled behavior modes, effective value Vej can take the value V1, thus indicating that the behavior of the monitored person does not present any anomaly, or any other value in the event of an anomaly, depending on the identified anomaly. This embodiment also has the advantage of minimizing message transmissions via telecommunications networks.

Claims (11)

1. Method for quantifying an activity of a person (P) evolving in a delimited environment (ENV), in which are arranged several sensors (Cj) connected to a central unit (UC) of a monitoring system (SYS ), the central unit (UC) being able to detect a presence of a second person (P2) within said delimited environment (ENV), the method being executed by the central unit (UC), and comprising: “A learning step (E000) of a personalized sequence (SEQ„ Pj) of detection results capable of being received from said sensors (Cj), said personalized sequence (SEQ_Pj) characterizing the activity of the person (P ) operating in the demarcated environment (ENV); • a monitoring step (E300) comprising o an acquisition sub-step (E310) of second detection results (R1, R2, Rk) received from said sensors (Cj), o a search sub-step (E320) said personalized sequence (SEQ Pj) among said second detection results (R1, R2, Rk); and, if said personalized sequence (SEQ__Pj) is not found during a predetermined time interval (T_p), * a step of notification (E400) of anomaly to a user; characterized in that when the central unit (UC) detects the presence of the second person (P2) within said delimited environment (ENV) during the execution of the learning step (E000) or of the sub-step acquisition (E310), the execution of the current step (E000, E310) is suspended for the duration of this presence. 2. Quantification method according to the preceding claim, further comprising: ® a verification step (E600) of the personalized sequence (SECLPj) and, in the event of variation greater than a predefined threshold, "an update step (E700) of the personalized sequence (SECLPj), characterized in that 2 7 when the central unit (UC) detects the presence of the second person (P2) within said delimited environment (ENV) during the execution of the update step (E700), this execution is suspended for the duration of this presence. 3. Quantification method according to one of the preceding claims, characterized in that each sensor (Cj) is associated with an exclusion list (LEj), composed of identifiers of sensors (Ci, Ck) arranged in said delimited environment (ENV) of so that simultaneous or quasi-simultaneous detection results from a sensor (Cj) and from a sensor belonging to its associated exclusion list (LEj) indicate the presence of the second person (P2). 4. Quantification method according to one of the preceding claims, characterized in that: ® an indicator (INDj) is associated with said personalized sequence (SECLPj), said indicator (INDj) being able to take a first predetermined value (V1) if said personalized sequence (SEQ__Pj) is recognized in the second detection results (R1, R2, Rk) received from said sensors (Cj); in that : • the monitoring step (E300) further comprises a sub-step for assigning (E330) to said indicator (INDj) an effective value (Vej), according to the result of the search sub-step (E320) ; and in that : “The quantification method further comprises a step of transmitting (E500) to a user the effective value (Vej) of said indicator. 5. Quantification method according to one of the preceding claims, characterized in that the learning step (E000) comprises: * a step for obtaining (E100) a generic sequence (SEQj) of results capable of being detected by said sensors (Cj), said generic sequence (SEQj) being defined by at least one parameter (mj, nj); “A step of determining (E200), as a function of first detection results (r1, r2, rk) received from said sensors (Cj) of a personalized value (vmj__p, vnj__p), of said at least one parameter (mj, nj ) defining said generic sequence (SEQj) for the person (P) operating in the delimited environment (ENV), said at least one parameter (mj, nj) being designated as personalized parameter (mj__p, nj___p) when it takes the value personalized (vmj__p, vnj__p), said personalized sequence (SEQ_Pj) being derived from said generic sequence (SEQj), when it is defined by said at least one personalized parameter (mj ... p, nj ___p). 6. Quantification method according to the preceding claim, characterized in that: * the determination step (E200) further comprises a sub-step of identification (E250) of a time period during which the personalized sequence (SEQ Pj) is found among the first detection results (r1, r2, rk ), with a number of occurrences greater than a first predefined threshold (P1), called personalized time slot (T_Pj), and in that: * the predetermined time interval (T_p) is equal to the personalized time slot (T_Pj). 7. Method according to one of the preceding claims, in which the first learning step (E000) is executed as long as a predetermined condition relating to the personalized sequence (SEQ_Pj) is not fulfilled. 8. Method according to one of the preceding claims, in which the plurality of sensors (Cj) comprises: * presence sensors; * motion sensors; * accelerometers arranged on the doors. * devices for measuring electrical consumption arranged on electrical outlets supplying a first group of predetermined household appliances; accelerometers located on the doors of a second group of predetermined household appliances. 9. Method according to one of the preceding claims, in which the anomaly notification step (E400) comprises: • the sending of an alert message (MA) if at least one sequence of detection results (SEQ__A) received from said sensors (Cj) indicates a variation of the personalized sequence (SEQ_Pj) of a first predefined type; * the sending of a prevention message (MP) if a plurality of sequences of detection results (SEQ__D1, SEQ__D2, ... SEQ__D__m) received from said sensors (Cj) indicate a variation of the personalized sequence (SECLPj) d 'a second predefined type. 10. Computer program comprising instructions for the execution of the steps of the quantification method according to any one of claims 1 to 9 when said program is executed by a computer. 11. Central unit (UC) for a system for monitoring (SYS) the activity of a person (P) operating in a delimited environment (ENV), in which are arranged several sensors (Cj) connected to said central unit (UC) , including: * learning means (CPU, MEM, TEMPO) of a personalized sequence (SEQ__Pj) of detection results capable of being received from said sensors (Cj), said personalized sequence (SEQ „Pj) characterizing the activity of the person (P) operating in the demarcated environment (ENV); * means for acquiring (RX) second detection results (R1, R2, ..Rk) received from said sensors (Cj), * means for searching (CPU, MEM, TEMPO) for said personalized sequence (SEQ__Pj) among said second detection results (R1, R2, Rk); * means of notification (TX) of anomalies to a user; characterized in that said central processing unit (UC): * is able to detect the presence of a second person (P2) within said delimited environment (ENV), and in that: "When it detects the presence of the second person (P2) within said delimited environment (ENV) during the execution of a learning step (E000) of the personalized sequence (SEQ__Pj) or of a sub-step acquisition (E310) of second detection results (R1, R2, Rk) received from said sensors (Cj), said central unit (UC) is configured to suspend the execution of the current step (E000, E310) for the duration of this presence.