# US 2021/0343389 A1 · Systems and methods of pain treatment

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- Number: US 2021/0343389 A1
- Document ID: US20210343389A1
- Status: Published application
- Kind: A1
- Application number: 17/273,675
- Filed: 2019-09-09
- Published: 2021-11-04
- Earliest claimed: 2018-09-07
- Applicants: Lucine
- Inventors: Marine COTTY-ESLOUS
- Web page: https://patentgrep.com/patent/US20210343389A1

## CPC

- G16H 20/30: relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
- A61B 5/0205: Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
- A61B 5/021: Measuring pressure in heart or blood vessels
- A61B 5/0531: Measuring skin impedance
- A61B 5/0816: {Measuring devices for examining respiratory frequency (measuring frequency of electric signals G01R23/00)}
- A61B 5/1107: {Measuring contraction of parts of the body, e.g. organ or muscle}
- A61B 5/14542: {for measuring blood gases (A61B5/14551 takes precedence)}
- A61B 5/165: {Evaluating the state of mind, e.g. depression, anxiety}
- A61B 5/442: {Evaluating skin mechanical properties, e.g. elasticity, hardness, texture, wrinkle assessment}
- A61B 5/4803: {Speech analysis specially adapted for diagnostic purposes}
- A61B 5/4824: {Touch or pain perception evaluation (A61B5/4821 takes precedence)}
- A61B 5/7267: {involving training the classification device}
- G06N 20/00: Machine learning
- G06V 40/174: {Facial expression recognition}
- G16H 20/70: relating to mental therapies, e.g. psychological therapy or autogenous training
- G16H 30/40: for processing medical images, e.g. editing
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- A61B 2503/40: Animals
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

A computer-implemented method for determining a pain treatment for a person or an animal with a pain condition, including identifying, by a processor, a level of pain being experienced by the person or animal. A method in which a level of pain experienced by the person is determined by obtaining a multimodal image or video the person, and determining the level of pain, on the basis of this multimodal image or video, via a trained Machine Learning Algorithm. The Machine Learning Algorithm is previously trained based on a training multimodal image or video of different subjects, each annotated by a benchmark pain level, determined by a biometrist and/or a health care professional based on extensive biometric data concerning the subject considered.

## Claims

### Claim at position 1

1-17. (canceled)

### Claim 18

A computer-implemented method for determining a pain treatment for a person or an animal with a pain condition, by a processor of a computer system, the method comprising:
identifying, by the processor, a level of pain being experienced by the person or animal, and
determining, by the processor, a pain treatment for the person or the animal based on the identified level of pain.

### Claim 19 (depends on 18)

The method according to claim 18, wherein the pain treatment comprises causing one or more devices to provide one or more sensory signals to the person or the animal, the one or more sensory signals having a wavelength, frequency and pattern suitable for treating, reducing, or alleviating the pain condition in the person or the animal with the pain condition.

### Claim 20 (depends on 18)

The method according to claim 18, wherein the treatment, reduction or alleviation of the pain condition of the user is measured by at least one of an endomorphic response of the user or an oxytocin response of a user.

### Claim 21 (depends on 18)

The method according to claim 18, wherein determining the pain treatment further comprises determining a cognitive therapy for the person or animal with the pain condition based on at least a pain level of the person or animal.

### Claim 22 (depends on 18)

The method according to claim 18, wherein determining the pain treatment comprises obtaining one or more markers of pain, the one or more markers of pain including at least one of objective or subjective markers of pain, selected from: facial expressions, facial markers, direct input from the person with the pain condition, sensed data on the person' s physiology and mental state.

### Claim 23 (depends on 22)

The method according to claim 22, wherein the determining the pain treatment using the one or more markers of pain comprises implementing a trained Machine Learning Algorithm, or comprises looking up associations between the one or more markers of pain and pain treatments.

### Claim 24 (depends on 18)

The method according to claim 18, for determining the pain treatment for said person, wherein the computer system is programmed to execute the following steps, in order to determine the level of pain experienced by the person:
obtaining a multimodal image or video, representing at least a face and an upper part of a body of the person, and comprising a recording of a voice of the person; and
determining said level of pain by means of a trained Machine Learning Algorithm parametrized by a set of trained coefficients, the Machine Learning Algorithm receiving input data that comprises at least said multimodal image or video, the Machine Learning Algorithm outputting output data that comprises at least said level of pain, the Machine Learning Algorithm determining said output data from said input data, on the basis of said trained coefficients;
the trained coefficients of the Machine Learning Algorithm having been previously set by training the Machine Learning Algorithm using several sets of annotated training data, each set being associated to a different subject and comprising:
training data, comprising at least a training multimodal image or video, representing at least the face and an upper part of the body of the subject considered and comprising a recording of the voice of the subject; and
annotations associated with the training data, that comprise a benchmark pain level representative of a pain level experienced by the subject represented in the multimodal training image or video, the benchmark pain level having been determined, by at least one of a biometrist or a health care professional, based on extensive biometric data concerning that subject, these biometric data comprising at least positions, within the training image, of at least one of some remarkable points of the face of the subject or distances between these remarkable points.

### Claim 25 (depends on 24)

The method according to claim 24, wherein, for each set of annotated training data, the extensive biometric data that is taken into account to determine the benchmark pain level considered further comprises at least some of the following data:
skin aspect data comprising at least one of a shine, a hue or a texture feature of the skin of the face of the subject;
bone data, representative of a left versus right imbalance of dimensions of at least one type of bone growth segment of said subject;
muscle data, representative of at least one of a left versus right imbalance of dimensions of at least one type of muscle of the subject or representative of a contraction level of a muscle of the subject;
physiological data comprising at least one of electrodermal data, breathing rate data, blood pressure data, oxygenation rate data or an electrocardiogram of the subject;
corpulence data, derived from scanner data, representative of a volume or mass of all or part of the body of the subject;
genetic data, comprising data representative, for several generations within a family of the subject, of epigenetic modifications resulting from an impact of pain.

### Claim 26 (depends on 25)

The method according claim 25, wherein the annotations of each set of annotated training data further comprise at least some of the extensive biometric data, from which the benchmark pain level has been determined.

### Claim 27 (depends on 26)

The method according to claim 26, wherein the output data determined by the Machine Learning Algorithm further comprises inferred biometric data concerning the person whose level of pain is determined, said biometric data comprising at least one of:
skin aspect data comprising at least one of a shine, a hue or a texture feature of the skin of the face of the person;
bone data, representative of a left versus right imbalance of the dimensions of at least one type of bone growth segment of said person;
muscle data, representative of at least one of a left versus right imbalance of the dimensions of at least one type of muscle of the person or representative of a contraction level of a muscle of the person;
physiological data comprising at least one of electrodermal data, breathing rate data, blood pressure data, oxygenation rate data or cardiac activity data relative to the person;
corpulence data, representative of the volume or mass of all or part of the body of the person; or
genetic data, comprising data representative, for several generations within the family of the person, of epigenetic modifications resulting from the impact of pain.

### Claim 28 (depends on 24)

The method according to claim 24, wherein:
the output data determined by the Machine Learning Algorithm further comprises temporal features concerning the pain experienced by the person, that specify at least one of whether the pain experienced by the person is chronic or acute, or whether the person had already experienced pain in the past; and
the annotations of each set of annotated training data further comprise temporal training features relative to the pain experienced by the subject represented in the training image of the set considered, the temporal training features specifying at least one of whether the pain experienced by the subject is chronic or acute, or whether the subject had already experienced pain in the past, these temporal features having been determined on the basis of the extensive biometric data concerning the subject.

### Claim 29 (depends on 24)

The method according to claim 24, wherein the determination of said output data is achieved by the Machine Learning Algorithm without resorting to an identification, within the multimodal image or video of the face of the person, of predefined, conventional types of facial movements.

### Claim 30 (depends on 24)

The method according to claim 24, comprising the setting of the coefficients of the Machine Learning Algorithm, said setting comprising the following steps:
gathering the sets of annotated training data, associated respectively to the different subjects, each set being obtained by executing the following sub-steps:
acquiring the training data associated to the subject considered, that comprise the training multimodal image or video that represents at least the face and an upper part of the body of the subject, and that comprises a recording of the voice of the subject;
determining the annotations associated to the training data acquired, these annotations comprising at least the benchmark pain level representative of a pain level experienced by the subject represented in said image or video, the benchmark pain level being determined by at least one of the biometrist or the health care professional on the basis of said extensive biometric data concerning the subject; and
setting the coefficients of the Machine Learning Algorithm by training the Machine Learning Algorithm on the basis of the sets of annotated training data previously gathered.

### Claim 31 (depends on 18)

A computer implemented method for treating pain of a person or an animal with a pain condition, the method being implemented by a processor of a computer system, the method comprising:
determining a pain treatment for a person, according to the method of claim 18; and
providing to the person the pain treatment previously determined by the computer system, by sending instructions to one or more devices associated with the person with the pain condition, the devices being arranged to provide one or more sensory signals to the person, at a wavelength, frequency and pattern suitable for treating, reducing, or alleviating the pain condition in the person or the animal.

### Claim 32 (depends on 18)

A system for determining a pain treatment, the system comprising a computer system having a processor, the processor being arranged to perform the method of claim 18.

### Claim 33 (depends on 18)

A system for determining a pain treatment, the system comprising a computer system having a processor, the processor being arranged to perform the method of claim 18, further comprising an imaging device and a microphone for acquiring the multimodal image or video of the person, the system being realized in the form of a hand-held portable electronic device.

### Claim 34 (depends on 31)

A system for determining a pain treatment, the system comprising a computer system having a processor, the processor being arranged to perform the method of claim 31, comprising said one or more devices associated with the person, said one or more devices comprising one or more of a device for providing visual output or a virtual reality headset, the processor being arranged to send said instructions to said device or virtual reality headset, for providing said pain treatment to the person.