System for estimating a stress condition of an individual

Abstract

The present invention relates to a system for estimating a stress condition of an individual, the system comprising a mobile device and a network unit, the mobile device being connected to the network unit and to one or more sensors, the mobile device comprising circuitry configured to: for each occasion of a plurality of occasions: measure a set of physiological parameters using the one or more sensors, and transmit first data relating to the set of physiological parameters to the network unit; and prompt the individual to input a perceived stress-level for the occasion via a user interface of the mobile device, and transmit second data relating to the perceived stress-level to the network unit.

Claims

1. A system for estimating a stress condition of an individual, the system comprising a mobile device and a network unit, the mobile device being connected to the network unit and to one or more sensors, the mobile device comprising circuitry configured to: for each occasion of a plurality of occasions: measure a set of physiological parameters using the one or more sensors, and transmit first data relating to the set of physiological parameters to the network unit; and prompt the individual to input a perceived stress-level for the occasion via a user interface of the mobile device, and transmit second data relating to the perceived stress-level to the network unit; wherein the network unit comprises circuitry configured to, for each occasion of the plurality of occasions: receive the first data from the mobile device, extracting a set of physiological parameters from the first data, and determine for the occasion a measured stress metric by applying a first predetermined stress metric function to at least the extracted set of physiological parameters; receive the second data the mobile device, extracting a perceived stress level from the second data, and determine for the occasion a perceived stress-metric by applying a second predetermined stress-metric function to at least the extracted perceived stress-level; and calculate a stress-level discrepancy based on a difference between the measured stress-metric and the perceived stress-metric; generate, based on the calculated stress-level discrepancies calculated at the plurality of occasions and a stress-level discrepancy threshold, a feedback signal indicative of a stress condition of the individual.

2. The system according to claim 1, wherein the network unit is configured to: upon generation of the feedback signal, transmit the feedback signal to the mobile device; and wherein the mobile device is further configured to provide feedback to the individual based on the received feedback signal.

3. The system according to claim 1, wherein the network unit is configured to: upon generation of the feedback signal, transmit the feedback signal to a device separate from the mobile device.

4. The system according to claim 1, wherein the circuitry of the network unit is configured to generate the feedback signal indicating a stress condition in response to a threshold number of the calculated stress-level discrepancies exceeding the stress-level discrepancy threshold.

5. The system according to claim 1, wherein the circuitry of the network unit is configured to generate the feedback signal indicating a stress condition in response to an average of the calculated stress-level discrepancies exceeding the stress-level discrepancy threshold.

6. The system according to claim 1, wherein the mobile device is configured to, for an occasion of a plurality of occasions, transmit the first data and the second data in separate transmissions, wherein each the first and second data further indicates a point in time of the occasion.

7. The system according to claim 1, wherein the mobile device is configured to, for an occasion of a plurality of occasions, transmit the first data and the second data in a same transmission.

8. The system according to claim 1, wherein the mobile device is configured to encrypt the perceived stress-level and the set of physiological parameters, wherein the first data comprises the encrypted set of physiological parameters, and wherein the second data comprises the encrypted perceived stress-level.

9. The system according to claim 1, wherein one or more sensors are included in the mobile device, and wherein the mobile device is configured to be worn in contact with the skin of the individual.

10. The system according to claim 1, wherein one or more sensors are included in a second mobile device configured to be worn in contact the skin of the individual, and wherein the mobile device is configured to be wirelessly connected to the second mobile device and to receive physiological parameters measured by the at least one sensor included in the second mobile device and include the received physiological parameters in the set of physiological parameters.

11. The system according to claim 1, wherein one or more sensors are non-contact sensors wirelessly connected to the mobile device or included in the mobile device, and wherein the mobile device is configured to include the physiological parameters measured by the one or more non-contact sensors in the set of physiological parameters.

12. The system according to claim 1, wherein the circuitry of the mobile device is further configured to, for each occasion of the plurality of occasions, transmit third data to the network unit, the third data comprising at least one from the list of: metadata relating to the individual, and metadata relating to the occasion of the plurality of occasions, wherein the circuitry of the network unit is further configured to, for each occasion of the plurality of occasions, receive the third data from the mobile device, extract the metadata from the third data, and use the metadata as input in at least one of the first and second predetermined stress-metric function to determine at least one of the measured stress metric and the perceived stress-metric.

13. The system according to wherein the one or more sensors comprises at least one from the list of: a galvanic skin response sensor, an electroencephalogram sensor, a photoplethysmogram sensor, a bio-impedance sensor, an electromyogram sensor, an electrooculogram sensor, an electrocardiogram sensor, an accelerometer, a camera, an audio recognition device, and a gyroscope.

14. The system according to claim1, wherein the system comprises a plurality of further mobile devices, wherein each of the further mobile devices is connected to a second network unit and to one or more sensors configured for measuring a set of physiological parameters of a respective further individual, wherein each of the further individuals belongs to a first group of individuals or a second group of individuals, wherein the individuals of the first group are classified as mentally healthy and the individuals of the second group are classified as mentally un-healthy based on a stress-related criteria, wherein the circuitry of the second network unit is further configured to calculate the stress-level discrepancy threshold in a model phase comprising: for each individual of the first and second group of individuals: receive, on a plurality of occasions, first data relating a set of physiological parameters measured by the one or more sensors configured for measuring a set of physiological parameters of the individual, and for each occasion, extracting the set of physiological parameters from the first data, and determine a measured stress metric by applying the first predetermined stress metric function to at least the extracted set of physiological parameters; receive, for each of the plurality of occasions, second data relating to a user perceived stress-level of the individual, extracting a user perceived stress-level from the second data, and determine a perceived stress metric by applying the second predetermined stress metric function to at least the extracted user perceived stress-level; calculate, for each of the plurality of occasions, a stress level discrepancy representing a difference between the measured stress metric and the perceived stress metric, and associating the stress level discrepancy to group of the individual; wherein the circuitry is further configured to: calculate the stress-level discrepancy threshold based on the calculated stress level discrepancies for the first and the second group of individuals, wherein the second network unit is configured to communicate the stress-level discrepancy threshold to the network unit, or wherein the network unit comprises the second network unit.

15. The system of claim 14, wherein the circuitry of the network unit is configured to calculate the stress-level discrepancy threshold using at least one from the list of: a clustering algorithm, a mean square error metric, Euclidean distance, and statistical interquartile difference.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The above, as well as additional objects, features and advantages of the present inventive concept, will be better understood through the following illustrative and non-limiting detailed description, with reference to the appended drawings. In the drawings like reference numerals will be used for like elements unless stated otherwise. In the drawings, dashed objects are used for optional features of the invention.

[0056] FIG. 1 schematically shows a mobile device according to embodiments,

[0057] FIG. 2 schematically shows by way of example the mobile device of FIG. 1 connected to a network unit,

[0058] FIG. 3 schematically shows by way of example a system of a plurality of mobile devices of FIG. 1 connected to the network unit of FIG. 2.

[0059] FIG. 4 shows a method for estimating physiological stress of an individual according to embodiments,

[0060] FIG. 5 shows a method for determining a stress-level discrepancy threshold according to embodiments.

DETAILED DESCRIPTION

[0061] The present disclosure relates to a new methodology towards mental disease prevention and interception, i.e. detecting a disease before there are any symptoms. Although literature agrees that disease interception is key towards early interventions and reduction of healthcare costs, techniques are lacking. Currently, the common practice to detect mental health diseases is by using questionnaires. However, these are subjective, time-consuming and based on spot-checks only. Additionally, questionnaires are often only used when a patient goes to a therapist for treatment. This stage is already past the interception stage as the patient in this case noticed his/her complaints. Physiological signals could provide a continuous, objective monitoring techniques that can be applied for large population screenings. However, as described above, it is not clear how these physiological signals could differentiate between healthy and un-healthy subjects. In the present disclosure, an invention focusing on the discrepancy between self-reported health indicators and predicted health based on physiological sensing is disclosed as a tool for disease interception.

[0062] FIG. 1 shows a mobile device 100 according to embodiments. The mobile device 100 is associated with an individual and configured to be used to aid the estimation of physiological stress of the individual.

[0063] The mobile device 100 may be any device having circuitry configured to collect data from various internal 106a-b and/or externa (peripheral) sensors 106c-d configured to measure physiological parameters of the individual, as well as to other sources for information such as an environmental sensor 114 or the internet 115. The mobile device may for example be a smart watch, smart phone, a camera, or any internet of things (loT) enabled device. The mobile device comprises a power source 105, for example a battery ora unit adapted to receive power via induction.

[0064] The circuitry of the mobile device is configured to, for each occasion of a plurality of occasions, measure a set of physiological parameters of the individual using the one or more sensors 106a-d.

[0065] As described above, the sensors 106a-d may be internal or external to the mobile device 100. The sensors may be contact sensors 106a,c adapted to be worn in contact the skin of the individual and measuring physiological parameters such as heart rate, skin conductance, etc. Examples of such sensors include a galvanic skin response sensor, an electroencephalogram sensor, a photoplethysmogram sensor, a bio-impedance sensor, an electromyogram sensor, and an electrooculogram sensor, an electrocardiogram sensor, a temperature sensor.

[0066] The sensors may be non-contact sensors 106b,d such as a camera, an audio recognition device, or a gyroscope, or a touch screen, which are not adapted to be worn in contact the skin of the individual. Such sensors 106b,d may be configured to measure physiological parameters pertaining to for example movement patterns of an individual, posture of the individual, mood of the individual based on language analysis, etc.

[0067] A peripheral contact sensor 106c may be included in a second mobile device (such as a pulse measuring device, a smart watch, etc.,) configured to be worn in contact the skin of the individual. The mobile device 100 may be configured to be wirelessly connected to the second mobile device and to receive physiological parameters measured by the at least one sensor 106c included in the second mobile device using a receiver 102 of the mobile device 100.

[0068] A peripheral contact sensor 106c may also include a sensor implanted in the individual.

[0069] The mobile device may further be configured to be wirelessly connected to one or more non-contact sensors 106c and receive physiological parameters measured by the one or more non-contact sensors 106c using the receiver 102.

[0070] The mobile device may also comprise one or more sensor 106a, b, which may be contact sensor(s) 106a or non-contact sensor(s) 106b. The internal sensors 106a-b are also configured measure to a set of physiological parameters of the individual.

[0071] The device 100 comprises a processing unit, e.g. a processor 108, which determine first data 116 pertaining to the complete set of physiological parameters measured/collected by the one or more sensors 106a-d for a specific occasion. The first data 116 is transmitted by a transmitter 104 of the first device 100.

[0072] The transmitter 104 and the receiver 102 may be separate units or form a single unit, i.e. a transceiver.

[0073] The mobile device 100 is further configured to, for each occasions of the plurality of occasions, prompt the individual to input a perceived stress-level for the occasion via a user interface 112 of the mobile device 100. The mobile device 100 may prompt the individual using for example vibrations of the mobile device, sounds of the mobile device, light emitted by the mobile device or using any other suitable way of prompting.

[0074] The user interface 112 may for example be a graphical user interface where the user can input the perceived stress-level, or a voice recognition user interface such that the individual can input the perceived stress-level by voice. Any other suitable user interface may be employed.

[0075] The processing unit, e.g. the processor 108, of the mobile device determines second data 118 relating to the perceived stress-level for the specific occasion. The second data 118 is transmitted by a transmitter 104 of the first device 100.

[0076] The mobile device 100 may further be configured to, for each occasion of the plurality of occasions, collect or retrieve metadata relating to the individual, and/or metadata relating to the occasion of the plurality of occasions. The processor may form third data 120 from the metadata and the transmitter 104 may then transmit the third data.

[0077] The metadata may be measured by an environmental sensor 114 connected to the mobile device, for example a light sensor 114, a weather sensor 114, and/or a microphone 114, which can collect metadata relating to the occasion.

[0078] The metadata may also be retrieved from the internet 115. Such metadata may comprise data relating to the stock market, sports results, weather, the political climate, etc.

[0079] The metadata may also be retrieved from a memory 110 of the mobile device and comprise metadata relating to the individual such as age, social status, weight, demographical data, etc.

[0080] The mobile device 100 may comprise an internal clock (not shown in the figures) to keep track of time and coordinate the collection/retrieval of physiological parameters, the perceived stress-level and optionally the metadata.

[0081] The mobile device may receive data at regular intervals from peripheral sensors 106c-d by own motion of the peripheral sensors 106c-d, or request such data from the sensors 106c-d at regular intervals.

[0082] The first 116, second 118, and optionally the third 120 data relating to a specific occasion may be transmitted in a single transmission, or in different transmissions. The first, second and third data may further indicate a point in time of the occasion and/or identification data pertaining to the individual. The mobile device, e.g. the processor 108, may be configured to encrypt any of the transmitted data 116, 118, 120. In other words, the mobile device 100 may be configured to encrypt the perceived stress-level and the set of physiological parameters (and optionally the metadata), wherein the first data 116 comprises the encrypted set of physiological parameters, and/or the second data 118 comprises the encrypted perceived stress-level, and/or the third data 120 comprises the encrypted metadata. Any encryption method may be used. For example, an asymmetric encryption method may be used, where the mobile device 100 comprises the public key for encryption, which facilitates easy updating of software of a plurality of mobile devices.

[0083] FIG. 2 shows a system including the mobile device 100 of FIG. 1 wirelessly connected to a network unit 200. The network unit 200 is configured to receive the first 116, second 118 and optionally the third 120 data from the mobile device 100 using e.g. a transceiver 202 (or a separate receiver).

[0084] The network unit comprises circuitry (e.g. one or more processors 204) for processing the received data 116, 118, 120. In other words, the network unit 200 comprising circuitry configured to, for each occasion of the plurality of occasions: receive the first data 116 from the mobile device 100 and extracting (optionally decrypting) a set of physiological parameters from the first data. Similarly, the network unit 200 comprising circuitry configured to, for each occasion of the plurality of occasions, receive the second data 118 the mobile device, and extracting (optionally decrypting) a perceived stress level from the second data 118.

[0085] Optionally, the network unit 200 comprising circuitry configured to, for each occasion of the plurality of occasions, receive the third data 120 the mobile device, and extracting (optionally decrypting) metadata from the third data 120.

[0086] For each occasion, a measured stress metric is determined, by applying a first predetermined stress metric function to at least the extracted set of physiological parameters. The first predetermined metric function may be implemented using a logistic regression, a SVM, a decision tree, a random forest, a neural network, hierarchical Bayesian, hidden Markov models, etc.

[0087] The processor 204 is thus configured to determine measured stress-metric for the individual and for the occasion t: S.sub.(m_ind, t)=F1(p1, p2 . . . ), where F1 is the predetermined stress metric function, p1, p2 . . . are the physiological parameters extracted from the first data 116. In some embodiments, also metadata (m1, m2 . . . ) from the third data 120 is included, i.e. S.sub.(m_ind,t)=F1(p1, p2 . . . , m1, m2 . . . ).

[0088] For each occasion, a perceived stress metric is determined, by applying a second predetermined stress metric function to at least the perceived stress level.

[0089] The processor 204 is thus configured to determine perceived stress-metric for the individual and for the occasion t: S.sub.(p_ind, t)=F2(PSL), where F2 is the predetermined stress metric function, and PSL is the perceived stress-level from the second data 118. In some embodiments, also metadata (m1, m2 . . . ) from the third data 120 is included, i.e. S.sub.(p_ind, t)=F2(PSL, m1, m2 . . . ).

[0090] As described above, F2 may according to some embodiments only be used to make sure that the the two stress metrics S.sub.(m_ind, t), S.sub.(p_ind, t) are defined in a same scale, e.g. the Likert scale.

[0091] For each occasion t, a stress-level discrepancy is calculated .sub.(ind, t)=S.sub.(m_ind, t)S.sub.(p_ind, t). Based on the calculated discrepancies .sub.ind at the plurality of occasions (t1 . . . tn) and a stress-level discrepancy threshold DT, the processor generates a feedback signal FS indicative of a stress condition of the individual.

[0092] FS=F3(.sub.ind, t1) . . . .sub.(ind, tn), DT), where F3 is a function for determining if the individual may be in risk of developing a stress condition or not.

[0093] For example, according to some embodiments, the circuitry of the network unit is configured to generate the feedback signal indicating a stress condition in response to a threshold number of the calculated stress-level discrepancies exceeding the stress-level discrepancy threshold. The threshold number may in some embodiments depend on the variance of the plurality of discrepancies, or be a static threshold such as 50%, 66%, etc., of the number of discrepancies.

[0094] In other embodiments, the circuitry of the network unit is configured to generate the feedback signal indicating a stress condition in response to an average of the calculated stress-level discrepancies exceeding the stress-level discrepancy.

[0095] The network unit 200 may, upon generation of the feedback signal, transmit (sing the transceiver 202 or a separate transmitter) the feedback signal 202 to the mobile device 202. The mobile device may in this case be configured to provide feedback to the individual based on the received feedback signal. Alternatively, or additionally, the network unit is configured to, upon generation of the feedback signal, transmit the feedback signal to a device 204 separate from the mobile device. In the example of FIG. 2, the device 204 is associated with a caretaker of the individual, but other stakeholders such as a spouse, a parent or an employer may also receive the feedback signal.

[0096] FIG. 3 shows an embodiment where the network unit 200 further is configured to determine the stress-level discrepancy threshold DT. Such functionality will be described in conjunction with FIG. 5. In this embodiment, the network unit is connected to a plurality of further mobile devices 100a . . . n. Each of the further mobile devices 100a . . . n is connected to one or more sensors configured for measuring a set of physiological parameters of a respective further individual. Each of the further individuals belong to a first group of individuals or a second group of individuals. The individuals of the first group are classified S502 as mentally healthy and the individuals of the second group are classified as mentally un-healthy based on a stress-related criterion. For example, a self-test questionnaire (such as a perceived stress scale (PSS) test, or a depression, anxiety, stress scale, DASS, test) may be used to classify the individuals as healthy or at risk.

[0097] The network unit 200 is in this embodiment configured to, for each individual of the first and second group of individuals S504: [0098] receive on a plurality of occasions first data 302 relating a set of physiological parameters measured by the one or more sensors configured for measuring a set of physiological parameters of the individual, and for each occasion, extracting the set of physiological parameters from the first data, and determine S506 a measured stress metric by applying the first predetermined stress metric function to at least the extracted set of physiological parameters; [0099] receive for each of the plurality of occasions second data 302 relating to a user perceived stress-level of the individual, extracting a user perceived stress-level from the second data, and determine S508 a perceived stress metric by applying the second predetermined stress metric function to at least the extracted user perceived stress-level; [0100] calculate S508 for each of the plurality of occasions a stress level discrepancy representing a difference between the measured stress metric and the perceived stress metric and associating the stress level discrepancy to group of the individual.

[0101] In FIG. 3, for ease of explanation, the data 302 received from each of the mobile devices 100a . . . n is intended to comprise the first, second and optionally the third data as described in conjunction with FIGS. 1-2. To keep track of, at the network unit, which individual that is associated with which received data 302, the data 302 advantageously comprises identification data as well. The information of which group a specific individual belongs to may be included in the data 302 or may be determined by the network unit using e.g. identification data in the received data 302 and a table of mappings between individuals and groups stored in the network unit 200.

[0102] Using the stress-level discrepancies calculated for the first and second group of individuals, for the plurality of occasions, the stress-level discrepancy threshold is calculated S512.

[0103] For example, a joint discrepancy may be calculated for the healthy and the un-healthy group of individuals, whereby the stress-level discrepancy threshold can be calculated based on the two joint discrepancies. The circuitry of the network unit 200 may configured to calculate the stress-level discrepancy threshold using one from the list of: a clustering algorithm, a mean square error metric, a F.sub.1 score metric, an accuracy metric, and Cohen's kappa matric. In other words, the assumption is that the discrepancy (i.e. false positives and/or false negatives) will be smaller for healthy subjects and larger the more severe the condition of the patient (i.e. the physiological response does not represent the perceived health correctly). E.g. the clustering can be used to identify a threshold stress-level discrepancy threshold to alarm patients at risk and/or caregivers. In some embodiments, a gradual indicator may be employed and included in the feedback signal (202 in FIG. 2) which may represent the distance to the centroid of the clusters, or the distance to the threshold stress-level discrepancy.

[0104] It should be noted that the embodiment of FIG. 3 is just by way of example. In FIG. 3 it is exemplified that it is the same network device 200 that generates a feedback signal indicative of a stress condition (e.g. according to FIG. 4) of an individual and calculates the stress-level discrepancy threshold (e.g. according to FIG. 5). However, in other embodiments, the network unit of FIG. 2 is instead configured to receive the stress-level discrepancy threshold from a remote (second) network unit configure to calculate the stress-level discrepancy threshold. In other words, the network unit of FIG. 2 and the network unit of FIG. 3 may be located in the same network (or may be located in another network). In the later case, the network unit of FIG. 2 and the (second) network unit of FIG. 3 exchange the stress-level discrepancy threshold when needed.

[0105] FIG. 4 shows according to embodiments a method for estimating physiological stress of an individual in a system comprising a mobile device and a network unit, the mobile device being connected to the network unit and to one or more sensors. The method comprises the steps of:

[0106] for each occasion of a plurality of occasions: [0107] measuring S402, by the mobile device, a set of physiological parameters using the one or more sensors, and transmitting S408 first data relating to the set of physiological parameters to the network unit; [0108] prompting S404 the individual to input a perceived stress-level for the occasion via a user interface of the mobile device, and transmitting S408 second data relating to the perceived stress-level to the network unit,

[0109] The method may optionally comprise for each occasion of a plurality of occasions: determining S406, by the mobile device, metadata relating to the individual, and/or to the occasion of the plurality of occasions and transmitting S408 third data comprising the metadata to the network unit.

[0110] The steps S402, S404, optionally S406 and S408 are repeated for each occasion of the plurality of occasions.

[0111] The method further comprises, for each occasion of the plurality of occasions, receiving S410 the first data from the mobile device, extracting a set of physiological parameters from the first data, and determining S412 for the occasion a measured stress metric by applying a first predetermined stress metric function to at least the extracted set of physiological parameters. Optionally, the metadata is also employed as described above for determining S412 the measured stress metric.

[0112] The method further comprises, for each occasion of the plurality of occasions, receiving S410 the second data from the mobile device, extracting a perceived stress level from the second data, and determining S414 for the occasion a perceived stress-metric by applying a second predetermined stress-metric function to at least the extracted perceived stress-level. Optionally, the metadata is also employed as described above for determining S414 the perceived stress metric.

[0113] The method further comprises, for each occasion of the plurality of occasions, calculating S416 a stress-level discrepancy based on a difference between the measured stress-metric and the perceived stress-metric.

[0114] The steps S410, S412, S414, S416 are repeated for each occasion of the plurality of occasions.

[0115] The method further comprises the step of generating S418, based on the calculated stress-level discrepancies calculated at the plurality of occasions and a stress-level discrepancy threshold, a feedback signal indicative of a stress condition of the individual.

[0116] In the above the inventive concept has mainly been described with reference to a limited number of examples. However, as is readily appreciated by a person skilled in the art, other examples than the ones disclosed above are equally possible within the scope of the inventive concept, as defined by the appended claims.