WO2020039428A1 - Classification par une machine d'une réponse psychophysiologique significative - Google Patents

Classification par une machine d'une réponse psychophysiologique significative Download PDF

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Publication number
WO2020039428A1
WO2020039428A1 PCT/IL2019/050924 IL2019050924W WO2020039428A1 WO 2020039428 A1 WO2020039428 A1 WO 2020039428A1 IL 2019050924 W IL2019050924 W IL 2019050924W WO 2020039428 A1 WO2020039428 A1 WO 2020039428A1
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Prior art keywords
stress
stimulations
sensor
series
computer program
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PCT/IL2019/050924
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English (en)
Inventor
Katerina KON
Dmitry GOLDENBERG
Elliot SPRECHER
Yuval Oded
Zohar HANAN
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Sensority Ltd.
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Application filed by Sensority Ltd. filed Critical Sensority Ltd.
Publication of WO2020039428A1 publication Critical patent/WO2020039428A1/fr
Priority to US17/178,914 priority Critical patent/US20210169415A1/en

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/20—Movements or behaviour, e.g. gesture recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/15—Biometric patterns based on physiological signals, e.g. heartbeat, blood flow

Definitions

  • the invention relates to the field of machine learning.
  • Human psychophysiological behavior can be described as a combination of different physiological stress types. Stress, in turn, may be described as a physiological response to internal or external stimulation, and can be observed in physiological indicators. When external or internal stimulations are created, they may cause the activation of the hypothalamus brain system to activate different processes, which influence the autonomic nervous system and sympathetic and parasympathetic systems, which ultimately control the physiological systems of the human body.
  • Psychological testing like all diagnostic activities, involves using specific observations to ascertain underlying, less readily observable, characteristics.
  • polygraph testing is used as a direct measure of physiological responses and as an indirect indicator of whether an examinee is telling the truth, based on the belief that deceptive answers will produce physiological responses that can be differentiated from those associated with non-deceptive answers.
  • a method comprising operating at least one hardware processor for receiving, as input, physiological parameters data measured in a human subject in response to a series of stimulations; determining a global stress signal associated with said series of stimulations, based, at least in part, on one or more states of stress detected in said physiological parameters data; and analyzing said global stress signal to detect one or more significant responses (SR), wherein each of said SRs is associated with one of said series of stimulations.
  • SR significant responses
  • a system comprising at least one hardware processor; and a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor to: receive, as input, physiological parameters data measured in a human subject in response to a series of stimulations, determine a global stress signal associated with said series of stimulations, based, at least in part, on one or more states of stress detected in said physiological parameters data, and analyze said global stress signal to detect one or more significant responses (SR), wherein each of said SRs is associated with one of said series of stimulations.
  • SR significant responses
  • a computer program product comprising a non-transitory computer-readable storage medium having program instructions embodied therewith, the program instructions executable by at least one hardware processor to: receive, as input, physiological parameters data measured in a human subject in response to a series of stimulations; determine a global stress signal associated with said series of stimulations, based, at least in part, on one or more states of stress detected in said physiological parameters data; and analyze said global stress signal to detect one or more significant responses (SR), wherein each said SRs is associated with one of said series of stimulations.
  • SR significant responses
  • said analyzing comprises temporally segmenting said global stress signal into a plurality of analysis windows, wherein each of said analysis windows corresponds, at least partially, to one of said stimulations. [0010] In some embodiments, each of said analysis windows corresponds, at least partially, to more than one of said stimulations.
  • At least some of said analysis windows overlap.
  • At least some of said analysis windows begin within a specified time period of a start point of one of said stimulations, and end within a specified time period of an end point of one of said stimulations.
  • said specified time period is between 1 and 15 seconds.
  • said analyzing further comprises calculating an SR score for each of said analysis windows, wherein said calculating is based on at least one of: an integral of the global stress signal taken over the analysis window; mean values of one or more temporal segments within the analysis window; standard deviation among one or more temporal segments within the analysis window; a maximum value within an analysis window; and a minimum value within an analysis window.
  • said SR score is calculated relative to a baseline which corresponds to a start point of one of said analysis windows.
  • said SR score reflects an absolute value difference relative to said baseline.
  • said series of stimulations are selected from the group consisting of test questions, visual stimulations, auditory stimulations, and verbal stimulations.
  • said series of stimulations is a questionnaire comprising one or more sets of test questions, wherein each of said sets comprises an identical number of test questions arranged in a different order.
  • each of said sets comprises relevant questions and irrelevant questions.
  • said one or more states of stress are each detected by applying a trained machine learning classifier, and wherein said trained machine learning classifier is trained based, at least in part, on a training set comprising: (i) physiological parameters data measured in a plurality of human subjects in response to a series of stimulus segments, wherein each stimulus segment is configured for inducing a specified state of stress; and (ii) labels associated with each of said stimulus segments, wherein said labels correspond to said states of stress.
  • said states of stress are selected from the group consisting of: neutral stress, cognitive stress, positive emotional stress, and negative emotional stress.
  • said global stress signal is calculated, at least in part, as an aggregate value of at least some of said states of stress.
  • the method further comprises detecting, and said program instructions are further executable to detect, a state of continuous expectation stress, wherein said detecting of one or more SRs is further based, at least in part, on said detected state of continuous expectation stress.
  • said physiological parameters data are acquired using one or more of: an infrared (IR) sensor; a skin surface temperature sensor; a skin conductance sensor; a respiration sensor; a peripheral capillary oxygen saturation (Sp02) sensor; an electrocardiograph (ECG) sensor; a blood volume pulse (BVP) sensor; a heart rate sensor; a surface electromyography (EMG) sensor; an electroencephalograph (EEG) acquisition sensor; a joint bend sensor; and a muscle activity sensor
  • IR infrared
  • a skin surface temperature sensor a skin conductance sensor
  • a respiration sensor a peripheral capillary oxygen saturation (Sp02) sensor
  • ECG electrocardiograph
  • BVP blood volume pulse
  • EMG surface electromyography
  • EEG electroencephalograph
  • FIG. 1 is a block diagram of a system for training a machine learning classifier to detect a state of stress in a human subject, according to an embodiment
  • FIG. 2 is a flowchart of a data analysis process of an exemplary system for detecting a state of 'significant response' in a subject, according to an embodiment
  • Fig. 3 is a block diagram schematically illustrating an exemplary psycho-physiological stress test protocol, according to an embodiment
  • Fig. 4 is a block diagram schematically illustrating an exemplary psycho-physiological significant response questionnaire, according to an embodiment.
  • SR state of 'significant response'
  • SR may be defined as consistent, significant, and timely physiological responses in a subject, in connection with responding to a relevant test question.
  • a significant response detected in a subject may indicate an intention on part of the subject to provide a false or deceptive answer to the test question.
  • a dedicated global stress global analysis (GSGA) algorithm of the present invention may be configured for detecting SR based, at least in part, on an input comprising various stress signals detected in a subject.
  • the various stress signals may be detected using a machine learning classifier configured for detecting one or more individual categories of stress in a subject, including, but not limited to:
  • Neutral stress A neutral state in which stimulations do not induce cognitive or emotional responses.
  • Cognitive stress Stress associated with cognitive processes, e.g., when a subject is asked to perform a cognitive task, such as to solve a mathematical problem.
  • Positive emotional stress Stress associated with positive emotional responses, e.g., when a subject is exposed to images inducing positive feelings, such as happiness, exhilaration, delight, etc.
  • Negative emotional stress Stress associated with negative emotional responses, e.g., when a subject is exposed to images inducing fear, anxiety, distress, anger, etc.
  • a 'global stress' signal may be further determined, wherein global stress may be defined as an aggregate value of the one or more individual stress states in the subject.
  • a global stress value in a subject may be determined by summing the values of detected cognitive and/or emotional stress in the subject. In some variations, the aggregating may be based on a specified ratio between the individual stress categories.
  • the present invention may further provide for detection of an additional state of stress known as 'continuous expectation stress' in a subject, wherein continuous expectation stress may be defined as a state of suspenseful anticipation, e.g., when a subject is expecting an imminent significant or consequential event.
  • the detection may be based on at least one of (i) a specified combination of one or more of the other stress categories detected in the subject, (ii) a machine learning classifier trained to detect continuous expectation stress in other stress signals, and (iii) a machine learning classifier trained to detect continuous expectation stress in physiological parameters acquired from a test subject.
  • the machine learning classifier may be trained on a training set comprising physiological parameters data acquired from a plurality of test participants, wherein the physiological parameters data is associated with various categories of stress induced in each test participant.
  • the physiological parameters data set on which the training set is based may be acquired by a suitable acquisition system, during the course of administering one or more psycho-physiological test protocols to the test participants, wherein the test protocols may be configured for inducing one or more of the individual categories of stress.
  • the detection of the categories of stress may be based, at least in part, on determining a typical range of physiological parameters associated with each such category of stress.
  • Fig. 1 is a block diagram of an exemplary system 100 according to an embodiment of the present invention.
  • System 100 as described herein is only an exemplary embodiment of the present invention, and in practice may have more or fewer components than shown, may combine two or more of the components, or a may have a different configuration or arrangement of the components.
  • the various components of system 100 may be implemented in hardware, software or a combination of both hardware and software.
  • system 100 may comprise a dedicated hardware device, or may form an addition to or extension of an existing device.
  • system 100 may comprise a hardware processor 110 having a stress classifier 110a, an expectation stress detector 110b, and a GSGA algorithm module 110c; a control module 112, and a non-volatile memory storage device 114; a physiological parameters module 116 having, e.g., a sensors module 116a and an imaging device 116b; environment control module 118; communications module 120; and user interface 122.
  • System 100 may store in storage device 114 software instructions or components configured to operate a processing unit (also “hardware processor,” “CPU,” or simply “processor), such as hardware processor 110.
  • the software components may include an operating system, including various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitating communication between various hardware and software components.
  • physiological parameters module 116 may be configured for acquiring a plurality of physiological parameters data from human subjects.
  • sensors module 116a may comprise at least some of:
  • skin conductance sensor e.g., a galvanic skin response (GSR) sensor
  • ECG electrocardiograph
  • BVP blood volume pulse
  • PPG photoplethysmography
  • EEG electroencephalograph
  • imaging device 116b may comprise any device that captures images and represents them as data. Imaging devices 116b may be optic -based, but may also include depth sensors, radio frequency imaging, ultrasound imaging, infrared imaging, and the like. In some embodiments, imaging device 116b may be a Kinect or a similar motion sensing device, capable of, e.g., IR imaging. In some embodiments, imaging device 116b may be configured to detect RGB (red-green-blue) spectral data. In other embodiments, imaging device 116b may be configured to detect at least one of monochrome, ultraviolet (UV), near infrared (NIR), and short-wave infrared (SWIR) spectral data.
  • UV ultraviolet
  • NIR near infrared
  • SWIR short-wave infrared
  • environment control module 118 comprises a plurality of sensors configured for monitoring environmental conditions at a testing site. Such sensors may include, e.g., lighting and temperature conditions, to ensure consistency in environmental conditions among multiple test subjects. For example, environment control module 118 may be configured to monitor an optimal ambient lighting in the test environment between 1500-3000 lux units, e.g., 2500. In some embodiments, environment control module 118 may be configured to monitor an optimal ambient temperature in the test environment, e.g., between 22-24° C.
  • communications module 120 may be configured for connecting system 100 to a network, such as the Internet, a local area network, a wide area network and/or a wireless network. Communications module 120 facilitates communications with other devices over one or more external ports, and also includes various software components for handling data received by system 100.
  • a user interface 122 comprises one or more of a control panel for controlling system 100, display monitor, and a speaker for providing audio feedback.
  • system 100 includes one or more user input control devices, such as a physical or virtual joystick, mouse, and/or click wheel.
  • system 100 comprises one or more of a peripherals interface, RF circuitry, audio circuitry, a microphone, an input/output (I/O) subsystem, other input or control devices, optical or other sensors, and an external port.
  • modules and applications correspond to a set of instructions for performing one or more functions described above.
  • These modules i.e., sets of instructions
  • control module 112 is configured for integrating, centralize and synchronize control of the various modules of system 100.
  • system 100 may be configured for acquiring a data set comprising physiological parameters from a plurality of human test participants, wherein the physiological parameters are being acquired in the course of administering one or more psycho-physiological test protocols to each of the participants (as will be further described below with reference to Fig. 3).
  • System 100 may then use the data set to generate a training set for training stress classifier 110a to classify one or more categories of stress.
  • a trained stress classifier 110a may be configured for determining a global stress signal based, at least in part, on the classification of stress categories in physiological parameters data.
  • expectation stress detector 110b may be configured for receiving the output from stress classifier 110a to further detect continuous expectation stress in the signals. In other embodiments, expectation stress detector 110b may be configured for detecting continuous expectation stress based on, e.g., one of a machine learning classifier trained to detect continuous expectation stress in other stress signals, and a machine learning classifier trained to detect continuous expectation stress in physiological parameters acquired from a test subject.
  • a trained stress classifier 110a may then receive as input physiological parameters data acquired from a test subject in answering one or more relevant questions, and process the data to output stress signals, e.g., a global stress signal.
  • GSGA algorithm 110c may be configured for detecting SR based, at least in part, on processing (i) a global stress signal received from stress classifier 110a, and/or (ii) a continuous expectation stress signal received from expectation stress detector 110b.
  • GSGA 110c may be configured for detecting SR based on additional and/or alternative sources of input including, but not limited to, additional and/or other stress signals, and/or raw physiological parameters data acquired from a subject.
  • a data set generated by system 100 for the purpose of generating the training set for stress classifier 110a may be based on physiological parameters data acquired from between 50 and 450 test participants, e.g., 150 test participants. In other embodiments, the number of participants may be smaller or greater. In some embodiments, all participants may undergo identical test protocols. In other embodiments, sub-groups of test participants selected at random from a pool of potential participants may be administered different versions of the test protocol.
  • a test protocol may be administered by a specialist, be a computer- based test, or combine both approaches.
  • test participants may be seated near the specialist so as to induce a degree of phycological pressure in the participant, however, in such a way that test participant and specialist do not directly face each other, to avoid any undue influence of the specialist on the participant.
  • participants may be instructed to sit upright, with both legs touching the ground, and to avoid, to the extent possible, body, head, and/or hand movements.
  • test participants may be selected from a pool of potential participants comprising substantially similar numbers of adult men and women.
  • potential test participants may undergo a health and psychological screening, e.g., using a suitable questionnaire, to ensure that no test participant has a medical and/or mental condition which may prevent the participant from participating in the test, adversely affect test results, and/or manifest in adverse side effects for the participant.
  • test participants may be screened to ensure to no test participant takes medications which may affect test results, and/or currently or generally suffers adverse health conditions, such as cardiac disease, high blood pressure, epilepsy, mental health issues, consumption of alcohol and/or drugs within the most recent 24 hours, and the like.
  • physiological parameters module 116 may be configured for continuously acquiring and monitoring, during the course of administering the test protocols to participants, a plurality of physiological parameters from the participant. Such physiological parameters may include, but are not limited to, a video stream of the whole body, the face alone, and/or other body parts, of the participant, taken by imaging device 116b. In other embodiments, physiological parameters module 116 may be configured for taking measurements relating to bodily temperature; heart rate; heart rate variation (HRV); blood pressure; blood oxygen saturation; skin conductance; respiratory rate; eye blinks; ECG; EMG; EEG; PPG; finger/wrist bending; and/or muscle activity. Similarly, environment control module 118 may be configured for continuously monitoring ambient conditions during the course of administering the test protocol, including, but not limited to, ambient temperature and lighting.
  • HRV heart rate variation
  • environment control module 118 may be configured for continuously monitoring ambient conditions during the course of administering the test protocol, including, but not limited to, ambient temperature and lighting.
  • each psycho-physiological test protocol comprises a series of between 2 and 6 stages. During each of the stages, participants may be exposed to between 1 and 4 stimulation segments, each configured to induce one of the different categories of stress described above, including neutral emotional or cognitive stress, cognitive stress, positive emotional stress, negative emotional stress, and/or continuous expectation stress.
  • each test stage may last between 20 and 600 seconds. In some embodiments, all stages have an identical length, e.g., 360 seconds. In some embodiments, each segment within a stage may have a length of between 30 and 400 seconds. In some embodiments, test segments designed to induce continuous expectation stress may be configured for lasting at least 360 seconds, so permit the buildup of suspenseful anticipation.
  • the various stages and/or individual segments within a stage may be interspersed with periods of break or recovery configured for unwinding a stress state induced by the previous stimulation.
  • each recovery segment may last, e.g., 120 seconds.
  • recovery segments may comprise exposing a participant to, e.g., relaxing or meditative background music, changing and/or floating geometric images, and/or simple non-taxing cognitive tasks. For example, because emotional stress stimulations may have a heightened and/or more lasting effect on participants, recovery segments following negative emotional stimulations may comprise simple cognitive tasks, such as a dots counting task, configured for neutralizing an emotional stress state in a participant.
  • Fig. 3 is a block diagram schematically illustrating an exemplary psycho-physiological test protocol 300 configured for inducing various categories of stress in a participant, according to an embodiment.
  • system 100 may be configured for acquiring baseline physiological parameters of a test participant, in a state of rest where the participant may not be exposed to any stimulations.
  • the participant may be exposed to one or more stimulations configured to induce a neutral emotional or cognitive state.
  • the participant may be exposed to one or more segments of relaxing or meditative background music, to induce a neutral emotional state.
  • the participant may also be exposed to images incorporating, e.g., changing geometric or other shapes, to induce a neutral cognitive state.
  • the participant may be exposed to one or more cognitive stress segments, which may be interspersed with one or more recovery segments.
  • the participant may be exposed to a Stroop test asking the participant to name a font color of a printed word, where the word meaning and font color may or may not be incongruent (e.g., the word 'Green' may be written variously using a green or red font color).
  • a cognitive stimulation may comprise a mathematical problem task, a reading comprehension task, a 'spot the difference' image analysis task, a memory recollection task, and/or an anagram or letter- rearrangement task.
  • each cognitive task may be followed by a suitable recovery segment.
  • the participant may then be exposed to one or more stimulation segments configured to induce a positive emotional response.
  • the participant may be exposed to one or more video segments designed to induce reactions of laughter, joy, happiness, and the like.
  • Each positive emotional segment may be followed by a suitable recovery segment.
  • the participant may be exposed to one or more stimulations configured to induce a negative emotional response.
  • the participant may be exposed to one or more video segments designed to induce reactions of fear, anger, distress, anxiety, and the like.
  • Each negative emotional segment may be followed by a suitable recovery segment.
  • the participant may be exposed to one or more stimulations configured to induce continuous expectation stress.
  • the participant may be exposed to one or more video segments showing a suspenseful scene from a thriller feature film.
  • Each expectation segments may be also followed by a suitable recovery segments.
  • test protocol 300 is only one possible such protocol.
  • Alternative test protocols may include fewer or more stages, may arrange the stages in a different order, and/or may comprise a different number of stimulation and recovery segments in each stage.
  • test protocols of the present invention may be configured to place, e.g., a negative emotional segment after a positive emotional segment, because negative emotions may be lingering emotions which may affect subsequent segments.
  • stress classifier 110a may be configured for receiving the physiological parameters for each test participant from physiological parameters module 116.
  • Stress classifier 110a may then be configured for temporally associating the physiological parameters data for each participant with the corresponding stimulation segments administered to the participant, using, e.g., appropriate time stamps.
  • the temporally-associated data set may comprise a training set for training stress classifier 110a to predict one or more of the constituent stress categories (i.e., neutral stress, cognitive stress, positive emotional stress, negative emotional stress, and/or continuous expectation stress).
  • stress classifier 110a may also be trained to detect a state of global stress in a human subject based, at least in part, on detecting a combination of one or more of the constituent stress categories in the set of measured physiological parameters.
  • expectation stress detector 110b may be configured for detecting continuous expectation stress based, at least on part, on stress signals detected by stress classifier 110a.
  • continuous expectation stress may be detected based on training a machine learning classifier using a training set comprising raw physiological parameters data acquired from a plurality of human test participants, wherein such physiological parameters are acquired in the course of administering one or more psycho-physiological test protocols configured for inducing continuous expectation stress.
  • machine learning classifier may be trained using data analysis windows of between 60-120 seconds each.
  • GSGA algorithm 110c may be configured for detecting SR in a global stress signal received from stress classifier 110a, wherein the global stress signal is detected in physiological parameters data acquired from a subject in the course of answering an SR protocol.
  • the SR protocol may be configured for administering under similar environmental conditions to those described above with reference to test protocol 300 and Fig. 3.
  • the SR protocol comprises one or more stages, e.g., 3 stages, wherein each stage comprises a set of between 5 and 10 SR 'triggers.
  • the triggers may comprise simple questions.
  • the triggers may be additional and/or other verbal, audio and/or visual stimulations.
  • each set may include, e.g., 7 identical questions comprising, e.g., 5 'relevant' questions (i.e., questions related to an event that is germane to the participant) and 2 'irrelevant' questions.
  • Each stage may repeat the same set of question in a different order.
  • the subject is afforded exactly 20 seconds to answer each question. Following each set of question, the participant may get 30 seconds of rest.
  • the exemplary SR protocol is only one possible such protocol.
  • Alternative test protocols may include fewer or more trigger sets, may arrange the sets in a different order, and/or may comprise a different number of triggers.
  • a system such as system 100 in Fig. 1, may be configured for continuously acquiring and monitoring, during the course of administering the SR protocol to a subject, a plurality of physiological parameters from the participants similar to those described with reference to test protocol 300 above.
  • physiological parameters may include, but are not limited to, a video stream of the whole body, the face, and/or other body parts, of the participants; bodily temperature; heart rate; blood pressure; skin conductance; respiratory rate; blood oxygen saturation; ECG; EMG; EEG; finger/wrist bending; eye blinking; and/or muscle flexion.
  • environment control module 118 may be configured for continuously morning ambient conditions, including, but not limited to, ambient temperature and lighting.
  • GSGA algorithm 110c may be configured for analyzing a global stress signal received from stress classifier 110a based on segmenting the signal into a plurality of analysis windows.
  • GSGA algorithm 110c may be configured for defining analysis windows based on time stamps, such that each window corresponds to a single trigger in the SR protocol.
  • each such analysis window may start at the start-time of the corresponding trigger, and end, e.g., between 1-5 seconds before the end of the trigger.
  • analysis windows may be defined in a variety of ways, including, but not limited to, with respect to analysis window length, number of triggers covered within a window, start/end times of each window, and/or sections of overlap between consecutive windows.
  • GSGA algorithm 110c may be configured for calculating, with respect to each analysis window, an SR score based, at least in part, on the measured global stress signal in each analysis window.
  • the SR score may be based, at least in part, on an integral of the global stress signal taken over the analysis window, relative to a baseline value.
  • GSGA algorithm 110c may be configured for calculating a combined SR score for each identical question in the 3 stages of the SR protocol, e.g., based on a mean score of the 3 appearances of the question.
  • GSGA algorithm 110c may then be configured for comparing the SR score of each question in the SR protocol to known detected responses acquired in a preliminary stage.
  • GSGA algorithm 110c may then assign a nominal value of, e.g., between 1-6 to each SR score, wherein 6 represents a response most likely to constitute SR.
  • the baseline value may be measured as within 0-2 seconds from the start of the trigger.
  • GSGA algorithm 110c may be configured for calculating an absolute value of the change in global stress signal from the baseline, based on the observation that, in different subjects, SR may be expressed variously as increasing or decreasing (relief) trends of the global stress signal.
  • SR detection may be further based on additional and/or other statistical calculations with respect to each analysis window, or segments of an analysis window (dubbed epochs). Such statistical calculations may include, but are not limited to, mean values of the various epochs within an analysis window, standard deviation among epochs, and/or maximum value and minimum value within an analysis window.
  • GSGA algorithm 110c may be further configured for analyzing the SR scores corresponding to the multiple triggers in an administered the SR protocol, wherein a trigger having the highest SR score (e.g., 6) may be designated as a significant trigger which may have occasioned a significant response in the subject.
  • a trigger having the highest SR score e.g., 6
  • a highest scoring response may indicate an intention on part of the subject to provide a false or deceitful response.
  • GSGA algorithm 110c may be further configured for receiving additional data from expectation stress detector 110b, to determine a state of continuous expectation stress with respect to one of the triggers in the SR protocol.
  • a detected state of continuous expectation stress which may be used in combination with the global stress analysis detailed above, may then provide a further indication that a particular trigger has occasioned a significant response in the subject, with an intention on part of the subject to provide a false or deceitful response.
  • a decline in a continuous expectation stress value may indicate an end of a suspenseful expectation period, in which the subject may have been anticipating an imminent significant trigger.
  • a trigger accompanied by a decline in continuous expectation stress may indicate an SR event.
  • SR protocol 400 may be administered to a plurality of test participants, e.g. 30, to acquire a data set comprising physiological parameters.
  • SR protocol 400 may comprise, e.g., 3 sets of 7 questions each.
  • a participant may be asked, e.g., to select a card from a deck of cards.
  • 5 of the questions in each set may be 'relevant' question about the selected card, wherein the 5 relevant question are bookended by a first and last irrelevant question about other subject.
  • one question will be designated as a 'key' question for which the participant is directed to provide a false answer, and which may not be the first or last question in the set.
  • the participant may know the order of the questions.
  • the 'key' question will appear unexpectedly, so as to build an expectation stress response in the participant (after the first two sets, the participant will be able to deduce the order of questions in the third set).
  • a participant is directed to select a card from a deck comprising, e.g., 5 cards.
  • the participant may be directed to answer 7 questions whose order is known to the participant, wherein a 'key' relevant question in the set must be answered falsely.
  • the participant is afforded exactly 20 seconds to answer each question. Following the first set of question, the participant may get 30 seconds of rest.
  • the participant may be directed to answer the same 7 questions as in set one, however, in a different order, wherein the place of the 'key' question in the order is not known.
  • the participant may be directed to answer the same 7 questions again, in yet another order. Following the second set of question, the participant may get another 30 seconds of rest.
  • the results of the verification stage may be fed to stress classifier 110a and expectation stress detector 110b for analysis.
  • the data from stress classifier 110 and expectation stress detector 110b may then be received by GSGA algorithm 110c, wherein the response for each question may be given an SR score, as detailed above. Because the significant event (e.g., key question) in each set is known, the SR scores can be compared to the expected results, so as to verify the accuracy of the SR scores determined by GSGA algorithm 110c.
  • aspects of the present invention may be embodied as a system, method or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a "circuit,” “module” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.
  • the computer readable medium may be a computer readable signal medium or a computer readable storage medium.
  • a computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.
  • a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
  • a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro magnetic, optical, or any suitable combination thereof.
  • a computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
  • Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
  • Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object- oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
  • LAN local area network
  • WAN wide area network
  • Internet Service Provider for example, AT&T, MCI, Sprint, EarthLink, MSN, GTE, etc.
  • These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
  • the computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
  • the flowcharts and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention.
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).

Abstract

Un procédé comprend les étapes suivantes consistant à: faire fonctionner au moins un processeur matériel pour recevoir, en tant qu'entrée, des données de paramètres physiologiques mesurés chez un sujet humain en réponse à une série de stimulations; déterminer un signal de stress global associé à ladite série de stimulations, sur la base, au moins en partie, d'un ou plusieurs états de stress détectés dans lesdites données de paramètres physiologiques; et analyser ledit signal de stress global pour détecter une ou plusieurs réponses significatives (SR), chacune desdites SR étant associée à l'une desdites séries de stimulations.
PCT/IL2019/050924 2018-08-19 2019-08-19 Classification par une machine d'une réponse psychophysiologique significative WO2020039428A1 (fr)

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WO2018069789A1 (fr) * 2016-10-14 2018-04-19 Facense Ltd. Systèmes et procédés de détection du stress, de l'allergie et de l'asymétrie thermique
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