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SYSTEMS, METHODS AND APPARATUSES FOR ESTIMATING THE STRESS VALENCE OF AN INDIVIDUAL FROM SENSOR DATA

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  • Publication Date:
    Dec 15, 2017
  • Language:
    English
  • Additional Information
    • Patent Number:
      20180174690
    • Filing Authority:
      United States of America (USA)
    • Appl. No:
      201715843294
    • Application Filed:
      Dec 15, 2017
    • Kind Code:
      A1
    • Abstract:
      Systems, methods, apparatuses, and computer program products for inferring the stress valence of an individual from sensor data are provided. One method includes collecting a first set of data from sensor(s) of user device(s), collecting a second set of data including a partial or complete ecological momentary assessment (EMA) response from a user, establishing a set of physiological time-series data for the user based on the first set of data and the second set of data, identifying habit information of the user based on the first set of data. Based on the first set of data and the habit information, detecting a potential trigger point for sending a subsequent EMA to the user, and, at the time of the potential trigger point, calculating an estimate of a stress valence value associated with the subsequent EMA for which an EMA response was at least partially not received.
    • Inventors:
      Bailey, Marc James Ashton (GB); RAHMAN, Md Mahbubur (US)
    • Claim:
      1. A method, comprising:collecting a first set of data from at least one sensor of at least one user device, wherein the first set of data comprises at least one of physiological data or contextual data;collecting a second set of data comprising at least one partial or complete ecological momentary assessment (EMA) response from a user of the at least one user device;establishing a set of physiological time-series data for the user based on the first set of data and the second set of data;identifying habit information related to regularly occurring habits of the user based on the first set of data;based on at least the first set of data and the habit information, detecting a potential trigger point for sending a subsequent EMA to the user, the subsequent EMA being used to ascertain a stress valence of the user at a time of the potential trigger point; andat the time of the potential trigger point, calculating an estimate of a stress valence value associated with the subsequent EMA for which an EMA response was at least partially not received, wherein the stress valence value estimate is calculated based on at least one of the physiological time-series data, the at least one previously collected partial or complete EMA response, the first set of data, or the habit information.
    • Claim:
      2. The method according to claim 1, wherein the potential trigger point comprises a point in time where the first set of data shows a significant change in level that is above or below a pre-defined threshold.
    • Claim:
      3. The method according to claim 2, wherein the calculating of the estimate of the stress valence value comprises performing backtracking to identify past trends in the physiological time-series data of the user.
    • Claim:
      4. The method according to claim 3, wherein the performing comprises iteratively applying an increasing retrospective time window beginning from the time of the potential trigger point until a previously captured EMA response is found representing a change in physiological data, contextual data, or any combination thereof, that is similar to the significant change in the first set of data at the potential trigger point, and wherein the calculating further comprises using a stress valence determined from the previously captured EMA response as the estimated stress valence value for the subsequent EMA.
    • Claim:
      5. The method according to claim 1, the method further comprising placing a habit anchor within the physiological time-series data to identify periods of time during which the regularly occurring habits are happening.
    • Claim:
      6. The method according to claim 1, wherein the detecting of the potential trigger point further comprises using the habit information to disregard physiological arousal unrelated to stress.
    • Claim:
      7. The method according to claim 1, wherein the contextual data comprises at least one of geographical location, time of day, altitude, external temperature, or any combination thereof.
    • Claim:
      8. The method according to claim 1, wherein the physiological data comprises the user's physical or biological characteristics including at least one of heart rate, pulse, core body temperature, respiration rate, blood oxygenation level, blood pressure, skin temperature, cardiac rhythm, or any combination thereof.
    • Claim:
      9. The method according to claim 1, wherein the method further comprises, upon detection of the potential trigger point, sending the subsequent EMA to the at least one user device, and wherein the calculating comprises calculating the estimate of the stress valence value associated with the subsequent EMA when a response to the subsequent EMA is not received.
    • Claim:
      10. An apparatus, comprising:at least one processor; andat least one memory including computer program code,the at least one memory and the computer program code configured, with the at least one processor, to cause the apparatus at least tocollect a first set of data from at least one sensor of at least one user device, wherein the first set of data comprises at least one of physiological data or contextual data;collect a second set of data comprising at least one partial or complete ecological momentary assessment (EMA) response from a user of the at least one user device;establish a set of physiological time-series data for the user based on the first set of data and the second set of data;identify habit information related to regularly occurring habits of the user based on the first set of data;detect, based on at least the first set of data and the habit information, a potential trigger point for sending a subsequent EMA to the user, the subsequent EMA being used to ascertain a stress valence of the user at a time of the potential trigger point; andat the time of the potential trigger point, calculate an estimate of a stress valence value associated with the subsequent EMA for which an EMA response was at least partially not received, wherein the stress valence value estimate is calculated based on at least one of the physiological time-series data, the at least one previously collected partial or complete EMA response, the first set of data, or the habit information.
    • Claim:
      11. The apparatus according to claim 10, wherein the potential trigger point comprises a point in time where the first set of data shows a significant change in level that is above or below a pre-defined threshold.
    • Claim:
      12. The apparatus according to claim 11, wherein the at least one memory and the computer program code are further configured, with the at least one processor, to cause the apparatus at least to perform backtracking to identify past trends in the physiological time-series data of the user in order to estimate the stress valence value associated with the subsequent EMA.
    • Claim:
      13. The apparatus according to claim 12, wherein the dynamic temporal backtracking comprises iteratively applying an increasing retrospective time window beginning from the time of the potential trigger point until a previously captured EMA response is found representing a change in physiological data, contextual data, or any combination thereof, that is similar to the significant change in the first set of data at the potential trigger point, and wherein the stress valence determined from the previously captured EMA response is used as the estimated stress valence value for the subsequent EMA.
    • Claim:
      14. The apparatus according to claim 10, wherein the at least one memory and the computer program code are further configured, with the at least one processor, to cause the apparatus at least to place a habit anchor within the physiological time-series data to identify periods of time during which the regularly occurring habits are happening.
    • Claim:
      15. The apparatus according to claim 10, wherein the at least one memory and the computer program code are further configured, with the at least one processor, to cause the apparatus at least to detect the potential trigger point at least by using the habit information to disregard physiological arousal unrelated to stress.
    • Claim:
      16. The apparatus according to claim 10, wherein the contextual data comprises at least one of geographical location, time of day, altitude, external temperature, or any combination thereof.
    • Claim:
      17. The apparatus according to claim 10, wherein the physiological data comprises the user's physical or biological characteristics including at least one of heart rate, pulse, core body temperature, respiration rate, blood oxygenation level, blood pressure, skin temperature, cardiac rhythm, or any combination thereof.
    • Claim:
      18. The apparatus according to claim 10, wherein the at least one user device comprises at least one of a smart phone, smart watch, fitness tracker, other wearable device, or any combination thereof.
    • Claim:
      19. The apparatus according to claim 10, wherein, upon detection of the potential trigger point, the at least one memory and the computer program code are further configured, with the at least one processor, to cause the apparatus at least to send the subsequent EMA to the at least one user device, and to calculate the estimate of the stress valence value associated with the subsequent EMA when a response to the subsequent EMA is not received.
    • Claim:
      20. A computer program, embodied on a non-transitory computer readable medium, the computer program configured to control a processor to perform a process, comprising:collecting a first set of data from at least one sensor of at least one user device, wherein the first set of data comprises at least one of physiological data or contextual data;collecting a second set of data comprising at least one partial or complete ecological momentary assessment (EMA) response from a user of the at least one user device;storing the first set of data and the second set of data;establishing a set of physiological time-series data for the user based on the first set of data and the second set of data;identifying habit information related to regularly occurring habits of the user based on the first set of data;based on at least the first set of data and the habit information, detecting a potential trigger point for sending a subsequent EMA to the user, the subsequent EMA being used to ascertain a stress valence of the user at a time of the potential trigger point; andat the time of the potential trigger point, calculating an estimate of a stress valence value associated with the subsequent EMA for which an EMA response was at least partially not received, wherein the stress valence value estimate is calculated based on at least one of the physiological time-series data, the at least one previously collected partial or complete EMA response, the first set of data, or the habit information.
    • CPC Classification Code:
      A61B 5/01 20130101 LA20180621BHUS; A61B 5/7282 20130101 LI20180621BHUS; A61B 5/165 20130101 LI20180621BHUS; A61B 5/02055 20130101 LI20180621BHUS; A61B 5/486 20130101 LI20180621BHUS; A61B 5/4857 20130101 LI20180621BHUS; G16H 80/00 20180101 FI20180621BHUS
    • IPCR Classification Code:
      A61B 5/16 20060101ALI20180621BHUS; G16H 80/00 20060101AFI20180621BHUS; A61B 5/00 20060101ALI20180621BHUS; A61B 5/0205 20060101ALI20180621BHUS
    • Rights:
      User is aware and acknowledges that Lighthouse IP shall retain all right, title and interest in and to this record and its structure under relevant and applicable copyright laws. User receives no ownership or any other rights to this record and its structure. User is aware and confirms accepting the terms and conditions of use as defined in the relevant user agreement either with Lighthouse IP or with its partner(s).
    • Date Entry:
      20180621
    • Accession Number:
      US20180174690A1
  • Citations
    • ABNT:
      INVENTOR, B. M. J. A. (GB); INVENTOR, R. M. M. (US). Systems, Methods and Apparatuses for Estimating the Stress Valence of an Individual from Sensor Data. [s. l.], 2017. Disponível em: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=gpd&AN=US20180174690A1. Acesso em: 1 out. 2020.
    • AMA:
      Inventor BMJA (GB), Inventor RMM (US). Systems, Methods and Apparatuses for Estimating the Stress Valence of an Individual from Sensor Data. 20171215 2017. Accessed October 1, 2020. http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=gpd&AN=US20180174690A1
    • APA:
      Inventor, B. M. J. A. (GB), & Inventor, R. M. M. (US). (2017). Systems, Methods and Apparatuses for Estimating the Stress Valence of an Individual from Sensor Data.
    • Chicago/Turabian: Author-Date:
      Inventor, Bailey, Marc James Ashton (GB), and RAHMAN, Md Mahbubur (US) Inventor. 2017. “Systems, Methods and Apparatuses for Estimating the Stress Valence of an Individual from Sensor Data.” http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=gpd&AN=US20180174690A1.
    • Harvard:
      Inventor, B. M. J. A. (GB) and Inventor, R. M. M. (US) (2017) ‘Systems, Methods and Apparatuses for Estimating the Stress Valence of an Individual from Sensor Data’. Available at: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=gpd&AN=US20180174690A1 (Accessed: 1 October 2020).
    • Harvard: Australian:
      Inventor, BMJA (GB) & Inventor, RMM (US) 2017, ‘Systems, Methods and Apparatuses for Estimating the Stress Valence of an Individual from Sensor Data’, viewed 1 October 2020, .
    • MLA:
      Inventor, Bailey, Marc James Ashton (GB), and RAHMAN, Md Mahbubur (US) Inventor. Systems, Methods and Apparatuses for Estimating the Stress Valence of an Individual from Sensor Data. 20171215 2017. EBSCOhost, search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=gpd&AN=US20180174690A1.
    • Chicago/Turabian: Humanities:
      Inventor, Bailey, Marc James Ashton (GB), and RAHMAN, Md Mahbubur (US) Inventor. “Systems, Methods and Apparatuses for Estimating the Stress Valence of an Individual from Sensor Data,” 20171215 2017. http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=gpd&AN=US20180174690A1.
    • Vancouver/ICMJE:
      Inventor BMJA (GB), Inventor RMM (US). Systems, Methods and Apparatuses for Estimating the Stress Valence of an Individual from Sensor Data. 2017 20171215 [cited 2020 Oct 1]; Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=gpd&AN=US20180174690A1