A METHOD, A SYSTEM AND COMPUTER PROGRAM PRODUCTS FOR ASSESSING THE BEHAVIORAL PERFORMANCE OF A USER

Abstract

A computer implemented method, system and computer programs for assessing the behavioral performance of a user. The method includes a) receiving data regarding behavioral information of a user and relating to a first or a second period of time; b) obtaining a user typical behavioral model and a user temporary behavioral model and c) comparing them providing a user behavior deviation; d) selecting a group of individuals for a comparison with the user; e) performing for the individuals steps a) to c) in the same first and second periods of time, and processing the data of the individuals providing a group typical behavioral model (RGR1), a group temporary behavioral model (RGT1) and a group behavioral deviation; f) performing a comparison between the user deviation and the user temporary model with the group deviation an the group temporary model, and using the result to assess the behavioral performance of the user.

Claims

1. A computer implemented method for assessing the behavioral performance of a user, the method comprising using one or more processors of a computing system performing following steps: a) receiving data regarding behavioral information of a user, said data relating to a first and/or a second period of time; b) obtaining: b1) a user typical behavioral model by processing extracted features characterizing a repetitive conduct of the user from the received data related to first period of time, and b2) a user temporary behavioral model by processing extracted features characterizing a temporary conduct of the user from the received data related to a second period of time; c) comparing said obtained user typical behavior model with said user temporary behavior model providing a user behavior deviation; d) selecting a group of individuals whose behaviors are to be used as reference points for a comparison with the user and dividing the selected group of individuals into at least one group category; e) performing for the individuals included in said at least one group category steps a) to c) in the same first and second periods of time, and processing the data of the individuals included in said at least one group providing a group typical behavioral model (RGR1), a group temporary behavioral model (RGT1) and a group behavioral deviation established through a comparison between them; f) performing a comparison between the user behavioral deviation and the user temporary behavior model with the group behavioral deviation an the group temporary behavior model (RGT1), and using the result of said comparison to assess the behavioral performance of the user.

2. The method of claim 1, wherein said first period of time is of a long duration of said second period of time, the first period of time comprising a duration of at least one to several months or several years and the second period of time comprising a duration from one day to a month.

3. The method of claim 1, further comprising sending an alarm to a healthcare staff or to a care network of the user if a significant behavioral irregularity of the user relevant for a mental condition of the user is detected in said assessment.

4. The method of claim 3, wherein said alarm comprises an audible and/or a visual sound.

5. The method of claim 3, wherein the alarm is outputted in a mobile computing device.

6. The method of claim 1, wherein the at least one group category being selected at least according to: a contact relationship in a communication network; a same or similar geographical location; a same or similar demographics; a same or similar daily activity; same or similar mobility patterns; or a same disease, of the individuals with the user.

7. The method of claim 1, wherein said received data in step a) is pre-processed by means of applying at least one of a noise reduction technique, an anonymization technique, a data cleaning technique, or a resampling technique, and aligned in time.

8. The method of claim 7, further comprising storing in a database the pre-processed and aligned data.

9. The method of claim 1, wherein said data being automatically captured by a data acquisition device comprising a sensor device including at least one of a wearable sensor device, a mobile computing device or an ambient sensor device.

10. The method of claim 1, wherein said data being automatically captured by a data acquisition device comprising a processor acquiring digital footprints generated by the user through the usage of electronic devices, said footprints including data captured from an Internet provider network or a mobile operator network and comprising communication dynamics, Internet browsing activities and/or mobility patterns.

11. A system for assessing behavioral performance of a user, the system comprising: at least one processor; and a memory including instructions that, when executed by the at least one processor cause the processor to implement a method according to claim 1.

12. The system of claim 11, wherein a data acquisition device, comprising a sensor device including at least one of a wearable sensor device, a mobile computing device or an ambient sensor device, is configured for capturing said data.

13. The system of claim 11, wherein a data acquisition device, comprising a processor acquiring digital footprints generated by the user through the usage of electronic devices, said footprints including data captured from an Internet provider network or a mobile operator network and f comprising communication dynamics, Internet browsing activities and/or mobility patterns, is configured for capturing said data.

14. A computer program product comprising software program code instructions which when loaded into a computer system controls the computer system to perform each of the methods according to claim 1.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The previous and other advantages and features will be more fully understood from the following detailed description of embodiments, with reference to the attached drawings, which must be considered in an illustrative and non-limiting manner, in which:

[0027] FIG. 1 illustrates some examples of sources of data that can be used as a proxy to the user's behavior.

[0028] FIG. 2 illustrates the different steps performed by present invention for building temporal and typical behavioral models, according to different embodiments.

[0029] FIG. 3 illustrates how the information is extracted for the behavioral assessment.

[0030] FIG. 4 illustrates an example of an automatic comparative and contextual behavioral assessment.

DETAILED DESCRIPTION OF THE INVENTION

[0031] The present invention allows for an automatic monitoring and assessment of users' behavior and detection of significant behavioral changes. The automatically collected data is processed and utilized, by one or more processor of a computing system (not illustrated), to build behavioral models of monitored users as well as of sets of other individuals (referred to as reference groups—RGs). The behavioral assessment is based on the analysis of the users (or patients) temporary behavior, his/her base-line behavior (or typical behavior) and the behavioral trends of his/her RGs within the same time frame.

[0032] The reference group—RG of individuals whose behavior is aggregated and used for a relative comparison to a monitored user's behavior can include individuals that: are in the network of close contacts, from the same/similar geographical area (district, city, country, etc.), from the same/similar demographics, have the same/similar daily activities (e.g. same company), have the same/similar mobility patterns (e.g. similar commuting routes), share the same symptoms/disease, among others.

[0033] The data that is used as a proxy to the user's behavioral patterns can be captured from sensors (including wearable sensors, mobile phones, ambient sensors, etc.), or from a network (the digital footprints generated through the usage of electronic devices, such as the data captured from Internet provider network or mobile operator network and consisted of communication dynamics, Internet browsing activities and mobility patterns). FIG. 1 illustrates some examples of data sources that can be used as a proxy to the user's behavior.

[0034] With reference to FIG. 2, are illustrated the different steps that can be performed by the proposed method for building temporal 203a and base-line behavioral 103a models of a user. As the data 100 from one or multiple sources 50 arrives, preferably (although it is not always necessary) it is being pre-processed 101. Depending on the kind of data different pre-processing may be required such as noise reduction, anonymization, data cleaning, resampling, etc.). Then, the pre-processed data is aligned in time and can be stored in a database 102, or in a temporary buffer.

[0035] The behavior of a monitored user can be modeled for at least two, first and second, different periods of time. In principle, the invention compares two behavioral models—one that is related to a user's base-line behavior 103a (preferably computed over a longer period of time, or first period of time, such as several months, several years or a time period defined by a specific embodiment—e.g. behavior during summer), and the other one that is related to a user's temporary behavior 203a (preferably computed over a shorter time period, or second period of time, such as one day, a weekend, one or more weeks or even a variable period defined by a specific moment—Tij from the moment i until the moment j).

[0036] FIG. 3 illustrates the extracting information steps used for the behavioral assessment 300. For a user, the temporary behavior 203a in a specific time frame Tij is compared to his/her established base-line behavior 103a for determining significant changes. In comparison to the prior-art, present invention involves the comparison of the current behavior 203a and its deviation from the base-line of a monitored subject (Δ.sub.p, Tij) for the specific period of time Tij with the current behavior RGTx, x=1, 2, . . . , n, and its deviation of one or more reference groups (Δ.sub.RG, Tij) in the same time interval.

[0037] The user's behavior and behavioral changes can be characterized with one variable or a set of variables i.e. features extracted from the collected data using the state of the art methods. Quantifying the behavioral change of the reference groups (Δ.sub.RG, Tij) can include different methods and it depends on the types of the considered variables. For instance, one possibility may be by considering the distribution of a certain variable across individuals that belong to one reference group and comparing it to the value of the same variable related to the user's temporary behavior.

[0038] The behavioral assessment 300 of a user during a certain time period Tij is based on the analysis of his/her current behavior 203a, deviation of the behavior in Tij with respect to the user's typical (base-line) behavior, as well as on the current behavior of one or more reference groups, RGTx, x=1, 2, . . . , n, in the same time period Tij and the relative behavioral change of one or more reference groups with respect to the base-line behavior of the individuals in these groups. This information can be used to provide an automatic behavioral assessment and/or mental condition inference and/or physical condition inference. In addition, said information may be delivered to a user or to a human supervisor (such as healthcare staff) for better understanding of the behavior of a monitored user.

Human Supervised Behavioral Assessment

[0039] Using the extracted information of FIG. 3, the behavioral assessment 300 in a certain short time period Tij can be provided by a human such as a personal coach, therapist, a doctor, or even by a user him/herself. In addition to a temporary behavior assessment, the inferred base-line behavior of the related reference groups can provide better understanding of the typical behavior in the user's environment (cultural, geographical, demographical, a group of similar patients, etc.) as opposed to or complementary to the general guidelines related to the recommended behavior.

Automatic Behavioral Assessment

[0040] By using temporary features related to the behavior of a user and the reference group in a time frame Tij, the deviation of behavior within Tij with respect to the base-line behavior (for both a user and the reference groups) combined with the ground-truth information about a user's mental condition within Tij, the proposed method assesses the user's behavior and learns a mental condition model that can be used to automatically infer mental health status using the extracted information (FIG. 4).

[0041] In case of detected irregularities in the user's temporary behavior and the patterns that correspond to the precursors to the crisis or the crisis onset (specifically in case of mental health conditions), the present invention provides a signal that can trigger an alarm to formal or informal healthcare network around the user or to the user him/herself.

ADDITIONAL APPLICATIONS

[0042] Different representations of the inferred behavior of a user and the comparison to the reference groups can be used to provide persuasive applications for stimulating positive behavioral changes.

[0043] The behavior of the reference groups within the time frame Tij provides a proxy of the contextual information around a user that can affect his/her behavior without a need to know the exact reason of a specific behavior. For instance, if a user decreased a number of outgoing calls/SMS but the same trend was perceived among his colleagues the reason can lie in the context at the company such as a close deadline; if a user decreased his/her mobility but the same patterns was inferred for an increased number of individuals in the same city, the reason could lie in the weather or a season change; on the other hand, if no important behavioral changes are detected in the reference groups but only in the user, there is a higher probability that the reason lies at the user's individual level (e.g. related to his/her mental state).

[0044] While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. For example, other aspects may be implemented in hardware or software or in a combination of hardware and software.

[0045] Additionally, the software programs included as part of the invention may be embodied in a computer program product that includes a computer useable medium. For example, such a computer usable medium can include a readable memory device, such as a hard drive device, a flash memory device, a CD-ROM, a DVD/ROM, or a computer diskette, having computer readable program code segments stored thereon. The computer readable medium can also include a communications link, either optical, wired, or wireless, having program code segments carried thereon as digital or analog signals.

[0046] The scope of the present invention is determined by the claims that follow.