Patent Description:
<CIT> discloses a method and system for modelling behaviour and pain-related state of an individual, the method comprising: receiving a log of use dataset associated with communication behaviour of the individual during a time period; receiving a supplementary dataset characterizing activity of the individual during the time period; receiving a survey dataset including responses, to at least one of a set of symptom-assessment surveys, associated with a set of time points of the time period; generating a predictive analysis of a pain-related state of the individual associated with at least a portion of the time period, from at least one of the log of use dataset, the supplementary dataset, and the survey dataset; and generating an alert upon detection that a set of parameters from the predictive analysis of the pain-related state satisfy a threshold condition.

<CIT> discloses systems and methods for managing pain of a subject. A system includes a first sensor circuit to sense a first signal indicative of a functional state of the subject, a second sensor circuit to sense a second signal different from the first signal, and a controller circuit.

According to a first aspect, there is provided a pain-management system configured to:.

Advantageously, such a system can guide the user to change their behaviours in a way that improves their health and wellbeing with a reduced likelihood of exceeding a pain limit of their choosing. This can greatly increase a user's / patient's quality of life (QoL).

The system may be configured to modify the matched-pain-parameters-log-entry to generate the modified-pain-parameters-log-entry by increasing or decreasing one or more user-input-parameters of the matched-pain-parameters-log-entry.

The system may be configured to repeat the compare step a plurality of times up to a predetermined maximum number. The predetermined maximum number may vary over time.

The system may be further configured to:.

The system may be configured to store the plurality of weighting-values associated with a user-identifier, wherein the user-identifier is uniquely associated with an individual user's profile.

The system may be further configured to:
modify the functionality of the user interface over time such that the user is presented with additional mechanisms for providing the user-input-parameters.

The user-input-parameters and / or the target-input-parameters may include one or more of the following characteristics:.

The AI processor may be configured to store pain-parameters-log-entries in memory, wherein each pain-parameters-log-entry comprises:.

Each pain-parameters-log-entry may further comprise:
a user-identifier that is uniquely associated with an individual user for which the plurality of user-input-parameters and the user-input-pain-score relates.

The system may be configured to determine a pain trigger by:.

The system may be configured to determine a pain protector by:.

The plurality of user-input-parameters may comprise one or more sensed-input-parameters. The sensed-input-parameters may be provided directly or indirectly from a sensor.

The user-input-parameters may comprise one or more of:.

There is also disclosed a computer-implemented method comprising:.

There is also provided a pain-management system comprising:.

Such a system can include any of the features and functionality that are described herein.

There is also disclosed a personal-management system configured to:.

There is also disclosed a personal-management system comprising:.

Such systems and methods can provide a platform that can guide the user to change their behaviours in a way that improves their health and wellbeing with a reduced likelihood of exceeding a limit of a personal-characteristic (which may be pain, stress, etc. (further examples are described below)) of their choosing. This can greatly increase a user's / patient's QoL. In another example, systems and methods disclosed herein can be used for elite training, for instance to improve the maximum capacity / performance for each sportsperson in any sport. Therefore, examples disclosed herein can also be used to improve physical performance.

It will be appreciated that any of the features and functionality that is described herein with respect to pain-management systems can also be implemented in a similar way for non-pain related systems.

There may be provided a computer program, which when run on a computer, causes the computer to configure any apparatus, including a circuit, controller, converter, or device disclosed herein or perform any method disclosed herein. The computer program may be a software implementation, and the computer may be considered as any appropriate hardware, including a digital signal processor, a microcontroller, and an implementation in read only memory (ROM), erasable programmable read only memory (EPROM) or electronically erasable programmable read only memory (EEPROM), as non-limiting examples. The software may be an assembly program.

The computer program may be provided on a computer readable medium, which may be a physical computer readable medium such as a disc or a memory device, or may be embodied as a transient signal. Such a transient signal may be a network download, including an internet download. There may be provided one or more non-transitory computer-readable storage media storing computer-executable instructions that, when executed by a computing system, causes the computing system to perform any method disclosed herein.

Chronic pain represents one of the most serious challenges to the citizens and the economy in Europe. According to the research, one in five Europeans suffers from chronic pain. Chronic pain is defined as pain that lasts for longer than <NUM>-<NUM> months beyond the expected period of healing. On average, people live with their chronic pain for up to <NUM> years. The pain affects negatively the patients' everyday life as it has a substantial impact on the daily activities. Chronic pain is also associated with psychological disorders, such as anxiety and depression. People with chronic pain are prone to drug dependency and the risk of suicide in chronic pain sufferers is at least doubled.

Many studies have found that pain is commonly overlooked and thus undertreated and sometimes not treated at all. The majority of EU countries lack specific clinical guidelines for managing chronic pain. Many patients never meet a clinician with enough knowledge about the best ways to cope with pain. The primary health care service is often the first and the only contact that the patients have in such cases - meeting a pain specialist is only available for a very fortunate few. Only <NUM> in <NUM> chronic pain patients in Europe has ever met a pain specialist and only <NUM>% has been going through a pain management program. Other studies indicate that as low as <NUM>% of the chronic pain patients ever meet a pain specialist for their disease. Here it is important to highlight that primary care physicians (PCPs) often don't feel competent in treatment of chronic pain. Only <NUM>% of PCPs feel confident managing chronic pain and over half (<NUM>%) of them are not confident about what to do when a person still complains about pain. As a result of a highly limited access to pain specialists, chronic pain is improperly treated in the vast majority of cases. One of the consequences of mismanagement of pain is opiate abuse.

One or more of the embodiments disclosed herein can empower patients to manage the multidimensional aspects of chronic pain, thereby achieving much better outcomes for the patients without requiring face-to-face patient interactions.

As will be discussed in detail below, examples disclosed herein relate to an advanced self-management tool for chronic pain based on Artificial Intelligence (AI). Systems described herein can analyse data provided by each individual and, leveraging machine learning functionalities, identify pain triggers and pain protectors for the specific patient. The patient can receive concrete and actionable pieces of advice on how to mitigate the negative effects of chronic pain and improve his or her quality of life (QoL).

<FIG> shows an example embodiment of how a pain management system <NUM> can be trained to develop an individualised pain-management model for each user, i.e. a truly patient-centric model. In this example, the pain-management model is represented by a plurality of weighting-values W <NUM> that are stored in a computer memory <NUM>.

The system <NUM> includes a user interface <NUM> that receives a plurality of user-input-parameters <NUM>. The plurality of user-input-parameters <NUM> represent activities and / or properties of a user during a defined period of time; more particularly, activities and / or properties that can influence the degree of chronic pain that is experienced by the user. The activities can relate to work, physical activity and leisure time, for example. The properties can relate to one or more measured or determined characteristics of the user's body such as heart rate, blood pressure, temperature, etc..

A user-input-parameter <NUM> can include one or more of the following characteristics, depending upon the type of parameter: (i) a duration-characteristic, which represents the duration that the user performed the activity / or exhibited the property; (ii) an intensity-characteristic, which represents the intensity with which the user performed the activity; (iii) a satisfaction-characteristic, which represents the degree of satisfaction that the user experienced when performing the activity; and (iv) a type-characteristic. Specific examples of user-input-parameters <NUM> that relate to activities will be described below with reference to <FIG>. Specific examples of user-input-parameters <NUM> that relate to properties of the user will be described below with reference to <FIG>.

As shown in <FIG> the user-interface <NUM> also receives a user-input pain-score <NUM>, which represents a degree of pain experienced by the user during the same defined period of time. This can represent one or more pain values. The user-input pain-score <NUM> can represent the degree of pain experienced by the user that is associated with the corresponding user-input-parameters <NUM>.

In this example, the user-input pain-score <NUM> represents the average pain value that the user experienced over a defined period of time (such as a day). As shown in the screenshot of <FIG>, a user-input-parameter <NUM> can also relate to the user's pain, but be different to the user-input pain-score <NUM>. In this example, again as shown in <FIG>, the user-input-parameters <NUM> include (i) the maximum and minimum pain values experienced over a predetermined period of time; and (ii) the duration for which the user experienced pain (optionally the duration that the user experienced pain at a certain level).

In this example the user-input-parameters <NUM> represent properties / activities performed by a user over a <NUM>-hour period, and the user-input pain-score <NUM> represents one or more pain values over the same <NUM>-hour period. Optionally, each of the user-input-parameters <NUM> and the user-input pain-score <NUM> can be stored in memory with a date-identifier. The date-identifier can be associated with the corresponding user-input-parameters <NUM> / user-input pain-score <NUM> when the user enters the information. For instance, the date-identifier may be provided by the user via the user interface <NUM> by the user selecting the date that the user-input-parameters <NUM> and the user-input pain-score <NUM> relate to. In other examples, the date-identifier may be implemented as a timestamp. Optionally the date-identifier can be attributed to the user-input-parameters <NUM> / user-input pain-score <NUM> when the information is received at an AI processor <NUM>. Use of such a date-identifier can beneficially enable the user-input-parameters <NUM> / user-input pain-score <NUM> to be inspected later on as being associated with a specific date. For instance, it can be possible for plots of the user-input-parameters <NUM> / user-input pain-score <NUM> to be generated to illustrate how the values have changed over time, or to perform any statistical analysis that is considered useful.

It will be appreciated from the description that follows that the user provides the user-input pain-score <NUM> so that the pain-management model can be trained for the individual user. Depending upon the data that is provided, the pain-management model may be sufficiently trained such that it can be used to provide useful predictions of pain (or any other output described herein) after a defined period of time, such as a few days, <NUM> or <NUM> days, or a couple of weeks. The length of time that it takes to adequately train the pain-management model can be affected by the variability in the user's daily activities - if there is a high degree of variability, then it can take longer to train the pain-management model to an acceptable level.

The user-interface <NUM> sends the user-input-parameters <NUM> to an AI processor <NUM>. It will be appreciated that the AI processor <NUM> may be co-located on a device that provides the user-interface <NUM> to the user, or may be located remotely from a device that provides the user-interface <NUM>. In some embodiments, the device that provides the user-interface <NUM> may be a user's smartphone because it is portable and readily available for the user to input the user-input-parameters <NUM>. The functionality of the AI processor <NUM> may be cloud-based, such that the user-interface <NUM> provides the user-input-parameters <NUM> and the user-input pain-score <NUM> to the AI processor <NUM> over the internet. In this way a server-side application can be provided.

The AI processor <NUM> can implement any artificial intelligence algorithm that is known in the art, including a neural network. For example, the AI processor <NUM> may train an artificial neural network (ANN) using the user-input-parameters <NUM> as inputs, and the user-input-pain-score <NUM> as the ground truth (that, is the result that the ANN is intended to produce for the user-input-parameters <NUM> that have been provided). The AI processor <NUM> can apply a plurality of weighting-values W <NUM> (which may also be referred to as weight factors) to the plurality of user-input-parameters <NUM> in order to determine a calculated-pain-score, and adjust the plurality of weighting-values W <NUM> based on the difference between the calculated-pain-score and the user-input pain-score <NUM>. As is known in the art, this can involve using mathematics to train an AI engine. For example, input weighting-values and output weighting-values can be iteratively calculated in epochs according to a learning rate, and then error correcting mathematics can be applied to minimize the difference between the average pain level entered by the user (the user-input-pain-score <NUM>) and the calculated average pain level suggested by the AI engine (the calculated-pain-score). In this way, the AI processor <NUM> can adjust the plurality of weighting-values W <NUM> based on an optimization routine so that the calculated-pain-score tends towards the user-input pain-score <NUM> and the model is trained. More generally, the AI processor <NUM> can set / train a plurality of weighting-values W <NUM> of a neural network based on the plurality of user-input-parameters <NUM> and the user-input pain-score <NUM> for the user.

In this way, the AI processor <NUM> determines a plurality of weighting-values W <NUM> for the ANN that represent the pain-management model for the individual user. The plurality of weighting-values W <NUM> can be stored in computer memory <NUM>, as shown schematically in <FIG>, such that they can be retrieved later on for further training (during which they can be modified and saved back to memory <NUM>), or for application to received information to generate an output for the user.

In some examples, the AI processor <NUM> can also store the user-input-parameters <NUM>, the user-input pain-score <NUM> (and optionally the date-identifier) in a memory <NUM>. This memory <NUM> may or may not be the same as the memory <NUM> that stores the weighting-values W <NUM>. This combination of the user-input-parameters <NUM>, the user-input pain-score <NUM> and optionally the date-identifier may be referred to together as a pain-parameters-log-entry, and is illustrated in <FIG> as a "Daily log". In this example, the pain-parameters-log-entry can relate to activities that are performed, and pain that is experienced, over the period of a day. Although it will be appreciated that the period does not have to be one day - in other applications it could be <NUM>, <NUM>, <NUM><NUM> ,<NUM>, or <NUM> hours, as non-limiting examples. As will also be appreciated from the following description, especially in relation to <FIG>, the pain-parameters-log-entry can also include user-parameters that represent properties of each user in addition to, or instead of, activities that are performed by each user.

Especially in examples where the AI processor <NUM> is remote from the user-interface <NUM>, the user interface <NUM> may also communicate a user-identifier to the AI processor <NUM>. Such a user-identifier can be uniquely associated with an individual user's profile. In some examples the user-identifier may be stored as part of a pain-parameters-log-entry. The user interface <NUM> can associate the user-identifier with the corresponding user-input-parameters <NUM> and user-input pain-score <NUM> when the user interface sends this information to the AI processor <NUM>. This can enable the AI processor <NUM> to use the received user-identifier to extract the correct weighting-values <NUM> from memory (i.e. those that are associated with the same user-identifier), such that they can be adjusted for the newly received information and the individual model for the specific user can be updated and further trained. In this way, the AI processor <NUM> can ensure that a model is only used for the (single) associated user, i.e. truly patient-centric, which advantageously ensures that the model is bespoke to each individual person. This has been found to be important for generating an accurate model for each user because pain can be experienced differently, and for different reasons, for each individual.

Advantageously, the pain-management model for each individual user (that can be represented by weighting-values <NUM>) can be used to identify different pain triggers and pain protectors for the specific user.

A pain trigger can be considered as an activity (or a plurality of activities in combination) that causes the user to experience significant pain. In some examples the system <NUM> can determine a pain trigger by processing a plurality of pain-parameter-log-entries, where each entry represents one or more of user-input-parameters <NUM> and a user-input pain-score <NUM>, at least. For instance, the system <NUM> can identify pain-parameter-log-entries that have a user-input pain-score <NUM> that is above a high-pain-trigger-threshold (such as <NUM> out of <NUM>) as high-pain-parameter-log-entries. The system can then perform an analysis of the user-input-parameters <NUM> of the high-pain-parameter-log-entries to determine a correlation-score that represents the degree of correlation between values of corresponding user-input-parameters <NUM> in the high-pain-parameter-log-entries. The analysis can be statistical analysis, machine learning analysis, or any other type of analysis that can determine a degree of correlation between values of corresponding user-input-parameters <NUM> in the high-pain-parameter-log-entries. For example, the system <NUM> can identify one or more user-parameters as pain triggers if they have a correlation-score that satisfies a correlation-criterion. Examples of such a correlation-criterion include: a correlation-score that is above a correlation-threshold; the highest correlation-score for all of the user-parameters; and a predetermined number of the highest correlation-scores for all of the user-parameters (e.g. the <NUM> user-parameters that have the highest correlation-scores). The system can then display the pain triggers to the user (for example using the user interface <NUM>). In this way, the system <NUM> can beneficially determine and display certain user-input-parameters, and their associated values, to a user as a "pain trigger" such that each user can modify their behaviour to avoid those pain triggers and thereby reduce their level of pain in the future.

A pain protector can be considered as an activity (or a plurality of activities in combination) that results in the user experiencing a low amount of pain. In some examples the system <NUM> can determine a pain protector by processing a plurality of pain-parameter-log-entries, where each entry represents one or more of user-input-parameters <NUM> and a user-input pain-score <NUM>, at least. For instance, the system <NUM> can identify pain-parameter-log-entries that have a user-input pain-score <NUM> that is below a low-pain-trigger-threshold (such as <NUM> out of <NUM>) as low-pain-parameter-log-entries. The system can then perform an analysis of the user-input-parameters <NUM> of the low-pain-parameter-log-entries to determine correlation-scores and subsequently identify specific user-parameters as pain protectors in a similar way to that described above for pain triggers. Again, such analysis can be statistical analysis, machine learning analysis, or any other type of analysis. As above, in this way, the system <NUM> can beneficially determine and present certain user-input-parameters, and their associated values, to a user as a "pain protector".

In this way, the pain-management model can be used to teach each individual how to become more active by learning their pain triggers and pain protectors. The human brain can handle only up to four variables rationally at any point in time. An AI engine, provided by the AI processor <NUM>, can be harnessed to process many more, multivariable functional aspects of each individual's pain level. This can allow each chronic pain patient a unique ability for self-management and to take an active role of their condition, which directly translates into a better QoL.

<FIG> shows three example screenshots that will be used to describe examples of user-input-parameters <NUM> and a user-input-pain-score <NUM>, and how they can be provided by a user via a graphical user interface (GUI) to train a pain-management-model for an individual user. A GUI is one example of the user-interface of <FIG>.

The first screenshot shows the GUI before a user has entered any activity or pain information. As shown in <FIG>, the user-input-parameters <NUM> in this example are the following:.

It will be appreciated that any other parameters can also or alternatively be used, and in some examples can be configured by the user. The value for one or more of the parameters described herein, including a stress level parameter for instance, can be determined by processing answers that the user has provided in a questionnaire. The value of a stress level parameter can have a significant effect on a user's pain.

It will also be appreciated that since the user-input-parameters <NUM> will be used to generate an individualised pain-management model, it does not matter if different users score the user-input-parameters <NUM> differently; as long as the user scores the user-input-parameters <NUM> in a consistent way, then the pain-management model will be appropriately trained for that user. Therefore, beneficially, the user does not have to worry about scoring their activities against a standard scoring scheme that is determined by someone else. Furthermore, each user / patient will have their own AI engine trained exclusively on their own data.

In this example, the user-input-pain-score <NUM> is the average pain experienced by the user in the day.

The first screenshot also shows one way in which the user can provide the date <NUM> with which the user-input-parameters <NUM> and the user-input-pain-score <NUM> are associated. As discussed above, the user interface can determine a date-identifier based on the date <NUM> that the user provides.

The second screenshot in <FIG> shows examples of information that the user has provided for the user-input-parameters, and the corresponding characteristics. More particularly, as non-limiting examples:.

Although not shown in this example, one or more of the input-parameters may also have a type-characteristic that identifies a specific type of activity. For instance, the physical-activity-parameter may have a type-characteristic that can represent different types of physical activity, such as cycling, running, swimming, etc..

In this example, each duration-characteristic corresponds to an amount of time spent performing the activity (or experiencing pain for the pain-range-parameter) during a single day.

As will be discussed below, in some examples one or more of the characteristics for a parameter may be automatically provided by associated software. For instance, software is known in the art that can automatically classify parameters such as sleep, rest and physical activity from signals that are available from wearable sensors. Such known software can also determine associated duration-characteristics.

The third screenshot shows in more detail how a user can provide input for the specific characteristics of the work-parameter.

It has been found that after a short training period, the pain-management model can be adequately trained such that it is competent to make suggestions on how a user can plan their day, for example to achieve the maximum level of function at the lowest possible level of pain. The use of the AI processor can enable multiple types of data to be collected and analysed such that multifaceted causal aspects of the individual's chronic pain can be determined.

<FIG> shows an example embodiment of how a trained pain management system <NUM> can be used to help the user achieve a desired level of pain in a day. Features of <FIG> that are also shown in <FIG> will be given corresponding reference numbers in the <NUM> series, and will not necessarily be described in detail again here.

In this example, the user provides a target-pain-score <NUM> to the user-interface <NUM>. The target-pain-score <NUM> represents a level of pain that the user considers acceptable when performing a variety of activities during a defined period of time. In some examples, the user can also provide one or more target-input-parameters <NUM> (which may be referred to as fixed activities), which typically will be a subset of the full list of input-parameters that are available. As will be discussed below, the system <NUM> can then provide one or more calculated-user-parameters <NUM> based on the target-pain-score <NUM> and the target-input-parameters <NUM>. The calculated-user-parameters <NUM> can represent suggestions for values for one or more of the input-parameters (that are not provided as an input by the user) that are expected to achieve the target-pain-score <NUM>.

In this example, the user-interface <NUM> sends the target-pain-score <NUM> and the entered target-input-parameters <NUM> to a processor <NUM> that will perform some processing on the target-pain-score <NUM> and the entered target-input-parameters <NUM> before using the pain-management model. As will be discussed in detail below.

In addition to receiving the target-pain-score <NUM> and the entered target-input-parameters <NUM> from the user interface <NUM>, the processor <NUM> in this implementation also receives one or more settings <NUM>. The settings <NUM> may represent one or more types of input-parameter (activity) that are to be increased or decreased. The settings <NUM> may for example include user-settings, default-settings, or physician-settings as discussed in more detail below.

User-settings can represent one or more types of input-parameter (activity) that the user has indicated are to be increased or decreased. The user-settings <NUM> may be received directly from the user interface <NUM>, or they may be retrieved from a computer memory that stores a user profile. The user-settings may represent one or more types of input-parameter (activity) that the user is looking to increase or decrease. This can be a type of input-parameter that is expected to provide a health benefit to each user, which may be an activity that is important to them. For this specific example, we will assume that the user-setting represents a desire to increase the duration of the physical-activity-parameter. Although it will be appreciated that the user-setting <NUM> can represent a preference for increasing or decreasing any input-parameter.

In some examples the user-settings can represent one or more types of input-parameter (activity) that the system <NUM> has determined should be increased or decreased. For example, the system may determine user-settings based on one or more pieces of information in a user's profile. In one implementation, the system <NUM> can process one or more pain-location-settings that the user has included in their profile to determine appropriate user-settings. This can include, as a non-limiting example, determining a user-setting to increase a duration-characteristic for a rest-parameter if a pain-location-setting indicates lower back pain. Such a determination can be performed by the system <NUM> according to any suitable algorithm or accessing any database / look-up table that is associated with the system <NUM>.

Default-settings can also represent one or more types of input-parameter (activity) that are to be increased or decreased, but are not necessarily user defined. This can include an indication that all types of input-parameter are equally important and therefore should be increased or decreased equally. That is, in some examples the settings <NUM> may indicate that all of the input-parameters are to be increased or decreased at the same rate.

Physician-settings can represent one or more types of input-parameter (activity) that a physician has indicated are to be increased or decreased. Advantageously the physician can therefore provide information such that the user can change their behaviours in a way that the physician has identified as likely to improve their quality of life.

The processor <NUM> initially inspects a set of historic pain-parameter-log-entries (daily logs) that are stored in memory <NUM> associated with the same user, to identify the pain-parameters-log-entry that is a match with the received target-pain-score <NUM> and the entered target-input-parameters <NUM>. For instance, the processor <NUM> may determine a degree of correlation between (i) the received target-pain-score <NUM> and the entered target-input-parameters <NUM>; and (ii) each of the pain-parameter-log-entries that are stored in memory <NUM>. The processor <NUM> may then select the pain-parameters-log-entry that has the highest correlation as a matched-pain-parameters-log-entry, or at least one that has a sufficiently high correlation such that it is expected to provide a reasonable starting point for the subsequent processing.

As a next step, the processor <NUM> can modify the matched-pain-parameters-log-entry based on the settings <NUM>. This can involve increasing or decreasing one or more user-input-parameters of the matched-pain-parameters-log-entry. For this example, where the settings <NUM> represent a desire to increase the duration of the physical-activity-parameter, the processor <NUM> will increase the duration-characteristic of the physical-activity-parameter in the matched-pain-parameters-log-entry. The processor <NUM> may temporarily save this modified entry as a modified-pain-parameters-log-entry. In some examples the processor <NUM> can increase the duration-characteristic by a predefined amount that may be coded (e.g. hard-coded) into the software, or may be provided as part of the settings <NUM>. For instance, the predetermined amount may be a predetermined period of time or may be a predetermined percentage increase. Optionally, the processor <NUM> may include the original target-input-parameters <NUM> in the modified-pain-parameters-log-entry without amendment. That is, the processor <NUM> can prevent the original target-input-parameters <NUM> from being modified (at least initially) on the basis that the user has indicated that these input-parameters are fixed.

The processor <NUM> then sends the full set of input-parameters of the modified-pain-parameters-log-entry <NUM> to the AI processor <NUM>. The AI processor <NUM> applies a neural network using each user's individual pain-management-model (as defined by the weighting-values <NUM> stored in memory <NUM>) to the modified-pain-parameters-log-entry <NUM> in order to determine a calculated-pain-score <NUM>. In this way, the system uses a neural network that has been trained for the individual user to determine one or more calculated-user-parameters <NUM> based on the target-pain-score <NUM> and the target-input-parameters <NUM>, wherein at least one of the calculated-user-parameters <NUM> is set based on the user-settings <NUM>. The AI processor <NUM> then sends the calculated-pain-score <NUM> back to the processor <NUM>. In some examples, the user interface <NUM> can then present the one or more calculated-user-parameters <NUM> (and optionally the calculated-pain-score <NUM>) to each user.

In this example, the processor <NUM> compares the calculated-pain-score <NUM> to the target-pain-score <NUM>. If the calculated-pain-score <NUM> is greater than the target-pain-score <NUM>, then the modified-pain-parameters-log-entry <NUM> may be considered unacceptable because it results in too much pain for the user. (It will be appreciated that the calculated-pain-score <NUM> may be greater than the target-pain-score <NUM> because the duration of physical activity has been increased in this example. ) If this is the case, the processor <NUM> can adjust one or more of the input-parameters (for instance not one that relates to the target-input-parameters <NUM> or the physical-activity-parameter that is identified by the settings <NUM>) of the modified-pain-parameters-log-entry <NUM>. For instance, the processor <NUM> may increase the duration-characteristic of the rest-parameter or decrease the intensity-characteristic of the work-parameter, as non-limiting examples. The processor <NUM> can adjust the one or more other input-parameters based on a predetermined set of rules, which may or may not be provided as part of the user-settings <NUM> to indicate which activities are less important to the user.

The processor <NUM> then sends the full set of revised input-parameters of the modified-pain-parameters-log-entry <NUM> to the AI processor <NUM>. The AI processor <NUM> applies a neural network using the user's individual pain-management-model (as defined by the weighting-values <NUM> stored in memory <NUM>) to the adjusted modified-pain-parameters-log-entry <NUM> in order to determine a revised calculated-pain-score <NUM>. The processor <NUM> compares the revised calculated-pain-score <NUM> to the target-pain-score <NUM>, and will continue around the loop of adjusting the one or more other input-parameters and revising the calculated-pain-score <NUM> until the calculated-pain-score <NUM> is less than or equal to the target-pain-score <NUM>, or until a predetermined number of iterations have been performed. Once either of these requirements is satisfied, the processor <NUM> sends the last iteration of the modified-pain-parameters-log-entry <NUM> to the user interface <NUM> such that it can display the calculated-user-parameters <NUM> to the user. If the processor <NUM> performs the predetermined number of iterations without achieving the target-pain-score <NUM>, then the processor <NUM> can instruct the user interface <NUM> to display an appropriate message to the user, such as: "Unable to identify a combination of activities without exceeding the target pain level". In this way, the processor <NUM> can repeat the compare step one or more times for the adjusted calculated-pain-score, in some applications a plurality of times up to a predetermined maximum number.

Assuming that the predetermined number of iterations is not reached, the calculated-user-parameters <NUM> represent a set of activities that the user should be able to perform without exceeding their target-pain-score <NUM>, while increasing or decreasing one of the user-parameters in line with the settings <NUM>. In this way, the system can advantageously guide the user to change their behaviours in a way that improves their health and wellbeing with a reduced likelihood of exceeding a pain limit of their choosing. This can greatly increase a user's / patient's quality of life (QoL). Beneficially, the system <NUM> can try to increase the activities of choice that the user has indicated in the settings as being of high value to the user, until the set pain threshold is reached. Furthermore, it may not be possible for a patient / user to be able to recognise what changes can to be made to their behaviours without a system disclosed herein because there can be too many variables for the human brain to be able to compute to identify correlations between activities undertaken and the associated pain. The systems described herein can be used to improve the QoL of patients / user irrespective of a sub-optimal current activity balance, e.g. whether they are over-active or under-active in their daily activities. This can be achieved through use of appropriate settings <NUM>. It will be appreciated that a user can be over-active or under-active in relation to specific activities. For instance, a person that is afraid to move can be considered as over-active in their amount of rest during a day, and a person that refuses to accept a permanent injury and continues as before can be considered as over-active in physical activity, work, etc. but under-active in rest. In some implementations, the pain-management model can be used to calculate the optimal exercise time and intensity for every single patient. Systems described herein can guide the user to find the right activity balance, e.g. balance between activity time and rest time for that specific user.

In some examples, the processor <NUM> can compare the value of each input-parameter in the modified-pain-parameters-log-entry <NUM> with the pain-parameters-log-entries stored in memory <NUM>. If the processor <NUM> determines that the value of an input-parameter in the modified-pain-parameters-log-entry <NUM> is outside of a range of the corresponding input-parameters in the pain-parameters-log-entries, then the processor can cause an error message to be displayed to the user. Such an error message may be "You are asking for values the AI engine is not trained on. You have not performed such activities before!". Also, the processor <NUM> may not pass the modified-pain-parameters-log-entry <NUM> to the AI processor <NUM> for determining the calculated-pain-score <NUM>. This is on the basis that the resultant calculated-pain-score <NUM> may not be reliable because the AI engine has not been trained on such values.

<FIG> shows example screenshots that will be used to describe how the user-interface of <FIG> can be used to provide the user with calculated-user-parameters <NUM>.

With reference to the first screenshot, the user can provide the target-pain-score <NUM> as an average pain level in this example, using a slider. The user has also provided information for two of the input-parameters in this implementation, which results in them being considered as target-input-parameters <NUM>. The target-input-parameters <NUM> in the first screenshot of <FIG> are: a sleep-parameter and a work-parameter. These target-input-parameters <NUM> can be referred to as fixed activities. The user could also, or instead, provide information for any of the other input-parameters <NUM>, in which case the system would move them to the list of fixed activities and they would be processed as target-input-parameters. In this way, any activities (input-parameters) for which the user has provided input will be considered as fixed activities.

Once the user is satisfied with the information that they have entered, they can cause the system to calculate and display the calculated-user-parameters <NUM>. In this example by selecting a "Suggest activities" button <NUM>. The system will perform the processing that is described in detail with reference to <FIG> such that it displays calculated-user-parameters <NUM> to the user, as shown in the second screenshot.

As also shown in the second screenshot, in this example the user is provided with an "Explore your pain" button <NUM>, which they can press to explore how modifying one or more of the input-parameters would affect their expected level of pain. Optionally, the user can modify one or more of the calculated-user-parameters <NUM> (and in some examples also modify one or more of the target-input-parameters <NUM>) before they press the "Explore your pain" button <NUM>. For instance, the user can interact with the duration values or the sliders that are shown in the second screenshot to adjust a characteristic associated with one or more of the input-parameters.

After the user selects the "Explore your pain" button <NUM>, they will be presented with the third screenshot. Using this screenshot, they can adjust the levels of any of the input-parameters <NUM> (here both the calculated-user-parameters and the target-input-parameters). Once the user has adjusted the input-parameters <NUM>, they can select the "Suggest pain" button <NUM> to determine a new calculated-pain-score. The fourth screenshot of <FIG> shows the calculated-pain-score <NUM> along with the associated input-parameters <NUM>. In this example, the user has increased the duration of rest from <NUM> hours to <NUM> hours, which has caused their expected level of pain to reduce to a level of <NUM> (shown as the calculated-pain-score <NUM>) from a level of <NUM> (shown as the target-pain-score <NUM>).

In this way, the system can present to the user, via the user interface, an option (the "Explore activities" button <NUM>) for modifying one or more of the calculated-user-parameters <NUM>. Once the system receives one or more modified calculated-user-parameters from the user interface (that are representative of user input), it can apply a neural network that has been trained for the individual user to the modified calculated-user-parameters to determine a modified calculated-pain-score and present the modified calculated-pain-score using the user interface. Advantageously this can provide the user with the functionality to fine tune the proposed set of daily activities to better suit their needs, while still not expecting to exceed their target-pain-score.

In some examples, the system can automatically determine and present calculated-user-parameters <NUM> to the user in response to one or more predefined triggers. For instance, as soon as the user enters information for a sleep-parameter (which can be expected to be shortly after the user wakes up in the morning), the system may determine and present calculated-user-parameters <NUM> in accordance with one or more settings (as discussed above). In this way, the system can proactively propose a daily log for the user that is intended to change their behaviours in line with the one or more settings.

<FIG> shows an example embodiment of how a trained pain management system <NUM> can be used to help the user predict an expected level of pain based on their planned activities. Features of <FIG> that are also shown in either <FIG> or <FIG> will be given corresponding reference numbers in the <NUM> series, and will not necessarily be described in detail again here.

In this example, the user provides target-user-parameters <NUM> to the user-interface <NUM>. The target-user-parameters <NUM> represents a set of activities that the user would like to perform in a day, and for which they would like a prediction of their expected level of pain.

The system <NUM> can then provide a calculated-pain-score <NUM> based on the target-user-parameters <NUM>. This can be achieved by the user-interface <NUM> sending the target-user-parameters <NUM> to the AI processor <NUM>. The AI processor <NUM> then applies the weighting-values W <NUM> to the target-user-parameters <NUM> to determine the calculated-pain-score <NUM>.

It will be appreciated that the functionality described with reference to <FIG> is similar to part of the functionality that is described with reference to <FIG>, in that it provides the user with the opportunity to modify the target-user-parameters <NUM> to see how the modifications affect the calculated-pain-score <NUM>. The user can therefore plan a day that achieves an appropriate balance between being able to perform the activities that they wish to, while reducing the likelihood that they will exceed what they have set as a maximum level of pain.

<FIG> provides an overview of the functionality that can be implemented by any of the AI processors described herein. <FIG> schematically illustrates a neural network that has an input layer, one hidden layer in this example (although it will be appreciated that there could be any number of hidden layers) and an output-layer. The output layer can provide a prognosis (in this example a calculated-pain-score).

<FIG> shows an example screenshot that can be displayed to a user via any of the user interfaces disclosed herein. <FIG> shows how the work-parameter <NUM> can be expanded by a user to show historic information for that parameter along with the associated pain-score. This historic information can be provided to the user interface from a memory that stores pain-parameter-log-entries, such as the memory that is shown in <FIG>. In this way, the user can browse the stored historic information to look for correlations between specific activities and increases or decreases in pain.

<FIG> shows another example embodiment of how a pain management system <NUM> can be trained to develop an individualised pain-management model for a user. Features of <FIG> that are also shown in <FIG> will be given corresponding reference numbers in the <NUM> series, and will not necessarily be described in detail again here.

In addition to the features of <FIG>, the system <NUM> of <FIG> includes a UI controller <NUM>. As will be discussed in detail below, the UI controller <NUM> is used to modify the functionality of the user interface <NUM> such that over time the user is presented with additional mechanisms for providing the user-input-parameters <NUM>.

Initially, the user interface <NUM> may provide the user with a display screen that enables them to enter the user-input-parameters <NUM> using sliders in the same way that is illustrated in <FIG>. This can be referred to as a first-user-input-mechanism. The UI controller <NUM> provides a UI-control-signal to the user interface <NUM> that instructs the user interface <NUM> to activate the first-user-input-mechanism such that the user can enter the user-input-parameters <NUM> using the first-user-input-mechanism.

After the user has entered user-input-parameters <NUM> for a predetermined number of periods of time (such as a predetermined number of days), or after a predetermined period of time, the UI controller <NUM> can provide a UI-control-signal to the user interface <NUM> that instructs the user interface <NUM> to additionally or alternatively activate a further-user-input-mechanism.

<FIG> shows three example screenshots that will be used to describe examples of different user-input-mechanisms.

The first screenshot of <FIG> is the same as the second screenshot of <FIG>, and shows how a user can use sliders to enter the information for the user-input-parameters. As indicated above, this can be referred to as a first-user-input-mechanism. Also shown in the first screenshot is a "Timer" button <NUM> associated with each of the user-input-parameters. Selection of the "Timer" button <NUM> can enable the user to access a further-input-mechanism. In this example, the "Timer" button <NUM> may be deactivated (such that it cannot be selected by the user) until the user has provided user-input-parameters for at least a predetermined number of days (for example at least <NUM>, <NUM> or <NUM> days). Such deactivation may be implemented in accordance with a UI-control-signal received from the UI controller <NUM>. In this way, the system <NUM> can ensure that the user is comfortable with providing information using the first-user-input-mechanism before additional mechanisms are made available to the user. This can enable an improved continued use of the user interface because it can result in the user being able to reliably and accurately provide the required information to the system so that the pain-management-model can be trained effectively.

The second screenshot in <FIG> illustrates a display that can be presented to the user after the "Timer" button <NUM> has been selected. The display of the second screenshot is one example of a further-user-input-mechanism. In this example, the timer can count down from a target-time, which is set by the user as a goal in this implementation. The user interface <NUM> can then convert the target-time to a duration-characteristic of the associated user-input-parameter once the timer has counted down to zero.

The third screenshot in <FIG> illustrates an alternative display that can be presented to the user after the "Timer" button <NUM> has been selected. The display of the third screenshot is another example of a further-user-input-mechanism. The display of the third screenshot can also be accessed by pressing a "Stopwatch" button <NUM> that is shown in the second screenshot. In this example, the stopwatch can count up from zero to measure the duration of the associated activity. Once the user stops and saves the stopwatch, the user interface can convert the duration of the stopwatch to a duration-characteristic of the associated user-input-parameter.

A yet further example of a further-user-input-mechanism can be accessed by a user selecting a "Guide" button <NUM>. As shown in the first screenshot, there may be a "Guide" button <NUM> associated with each of the user-input-parameters. Alternatively, there may be a single "Guide" button that applies to all, or a subset, of the user-input-parameters. If the user selects a "Guide" button <NUM>, then the user interface can pre-populate the information for the associated user-input-parameter (or parameters) based on historic information. For instance, the system may extract historic information for the user-input-parameter (or parameters) from memory (which may be stored as parts of pain-parameters-log-entries as discussed above), and then perform a statistical operation on these historic user-input-parameters to calculate guide-input-parameter values. One example of a simple statistical operation is an averaging operation, such as a mean. Another example of a statistical operation is a mode, such that the value of the user-input-parameter that has been provided most often is offered to the user as a guide. The user can then log the guide-input-parameter values as they are if they do not need changing, or can modify them before submitting them to the AI processor <NUM> for further processing.

<FIG> shows another example embodiment of a pain management system <NUM> according to the present disclosure. The pain management system <NUM> can be used to train an individualised pain-management model for a user in a similar way to the system of <FIG> and / or to help a user predict an expected level of pain based on their planned activities in a similar way to the system of <FIG>. Features of <FIG> that are also shown in an earlier figure will be given corresponding reference numbers in the <NUM> series.

The system <NUM> of <FIG> includes one or more sensors <NUM> that can provide (directly or indirectly after pre-processing <NUM>) one or more sensed-input-parameters <NUM> to the user interface <NUM>. The user interface <NUM> can then process the sensed-input-parameters <NUM> in the same way as the user-input-parameters <NUM> in any of the examples described herein. In some examples the pre-processing <NUM>, as it is described here, can include a known algorithm that classifies activities based on the sensor signals. For instance, algorithms are known that can identify sleep, rest and physical activities from sensor signals, and can therefore also determine at least a duration associated with those activities for providing to the user interface <NUM> as sensed-input-parameters <NUM>.

The sensed-input-parameters <NUM> can be used as additional or alternative inputs by the AI processor <NUM> to train a pain-management-model along with a user-input-pain-score <NUM> and / or to determine a calculated-pain-score (not shown in <FIG>) or calculated-user-parameters (also not shown) using a trained model.

Advantageously, the sensors <NUM> can be used to automatically gather the information for the input-parameters such that a user does not have to manually enter the information themselves. In this way, the sensed-input-parameters <NUM> can be considered as a subset of the user-input-parameters <NUM>.

The sensors <NUM> can be provided as part of a wearable device (such as a smart watch or a fitness / activity tracker) or a user's smartphone. Such devices are known to be able to provide information in relation to various activities, such as a user's sleep, physical activity, rest etc. This activity information (at least some of which can be considered as pre-classified) can be provided to the user interface as sensed-input-parameters <NUM> (in some cases following a pre-processing operation <NUM>). In some examples, when the user interface <NUM> receives sensed-input-parameters <NUM> it can automatically push a message to the user (for example using the user interface <NUM>) that requests the user to provide information to populate any characteristics of the activity that have not been provided as part of the sensed-input-parameters <NUM>. As one example, if a sensed-input-parameter <NUM> relates to physical-activity-parameter, then the sensor <NUM> may be able to provide a duration-characteristic for the activity but not a satisfaction-characteristic. In which case, the user interface <NUM> may provide the user with an opportunity to manually input the satisfaction-characteristic as part of a user-input-parameter <NUM>.

Also, the sensors <NUM> can be used to provide sensed-input-parameters <NUM> that relate to properties of a user, such as one or more measured or determined characteristics of the user's body. As non-limiting examples these can include a heart-rate-parameter, a blood-pressure-parameter, a temperature-parameter, etc..

In some examples, a sensed-input-parameter <NUM> can include a timestamp. The timestamp can be associated with a start time of an activity / measure property and / or an end time of an activity / measure property. Optionally, the timestamp can be stored in memory <NUM> as part of a pain-parameter-log-entry. In this way, the stored timestamps can be used as part of the processing to identify pain triggers and protectors that is described above. Furthermore, in some examples the timestamp can be part of an input-parameter that is provided as an input to the ANN. In this way the timestamp can influence the training of the pain-management-model, such that it can also be including in subsequent processing to determine a calculated-pain-score or calculated-user-parameters using the trained model.

In addition, in some examples, the AI processor <NUM> may receive one or more environmental-input-parameters (not shown) that can potentially influence how the user experiences pain. For example, the environmental-input-parameters may represent one or more of: ambient air temperature, weather conditions, altitude, and location of the user. Such environmental-input-parameters may be provided to the AI processor <NUM> by an appropriate sensor. In some examples, one or more of the environmental-input-parameters can be retrieved from an online web service - for instance, a GPS sensor of with a device associated with a user (such as their smartphone) can provide the web service with the location of the user, and the web service can provide one or more environmental-input-parameters (such as weather, temperature, etc.) to the user interface <NUM> (or directly to the AI processor <NUM>) based on the location. Again, the user interface <NUM> / AI processor <NUM> can process such environmental-input-parameters in the same way as the user-input-parameters <NUM> in any of the examples described herein.

<FIG> illustrates schematically a computer-implemented method according to the present disclosure. The method of <FIG> generally corresponds to at least some of the functionality that is described above with reference to <FIG> and <FIG>.

At step <NUM>, the method involves receiving a target-pain-score that represents a level of pain that the user considers acceptable during a defined period of time. At step <NUM>, the method receives one or more target-input-parameters which represent properties and / or activities of a user during the same defined period of time. In the same way as described above, the one or more target-input-parameters are a subset of a full list of input-parameters that are available. At step <NUM>, the method receives one or more user-settings that represent one or more input-parameters that the user is looking to increase or decrease. It will be appreciated that it does not matter which order steps <NUM>, <NUM> and <NUM> are performed in. Indeed, one or of the steps they be performed at the same time.

At step <NUM>, the method continues by using a neural network that has been trained for the individual user to determine one or more calculated-user-parameters based on the target-pain-score and the target-input-parameters. As discussed in detail above, at least one of the calculated-user-parameters is set based on the user-settings. Then at step <NUM>, the method includes presenting the one or more calculated-user-parameters using a user interface. Presenting the calculated-user-parameters in this way can enable a user to change their behaviours / activities in order to improve their health and wellbeing while being able to control the amount of pain that they experience.

In other examples, the general principles that are discussed in detail herein can be applied to applications that are not necessarily associated with pain. Such a system can be referred to as a personal-management system, in that it can be a system that is trained such that it is bespoke to an individual. Such examples can receive a target-score (which is a more generic version of the target-pain-score that is described above) that represents a score / level of a personal-characteristic that the user considers acceptable during a defined period of time. Non-limiting examples of personal-characteristics can include: stress, anxiety, depression, burn-out, fatigue, quality of life, long-term coronavirus / COVID (or the long term effects of any other disease or condition), mental health wellbeing, and personal performance (such as in one or more specific activities, including, but not limited to, elite sports).

Chronic pain is a major health problem world-wide. Over <NUM> million individuals in USA (<NPL>) are suffering and a majority patients find even the most up-to-date treatments falling short due to lack of efficacy or significant side effects. PainDrainer™ is a digital coach for self-management of pain, powered by artificial intelligence, aiming to improve the quality of life (QoL). (The PainDrainer™ tool corresponds to the systems and methods that are described above. ) It utilizes the concept of the Acceptance and Commitment Therapy (<NPL>), believed to constitute a core component in evidence-based treatment of chronic pain. This trial is study that tests key elements, such as patient acceptance and improvement in QoL.

A one-arm, open label study was performed at the Koman Family Outpatient Pavilion at UC San Diego Health. Fifteen (<NUM>) eligible patients (<NUM>% women), suffering from neck, shoulder, and/or lower back pain were included after signing an informed consent. The pilot study was performed in two phases, representing different user experience, with <NUM> patients in the first phase and <NUM> patients in the second phase, using PainDrainer™ a tool for self-management of pain. PROMIS Pain Interference 6a validated questionnaire was used to measure changes in Pain Interference/Quality of Life and Pain Intensity. From the PROMIS questionnaire, the T-score was calculated. The statistical significance (p-value) between the T-scores was then estimated, using a one-tail, paired T-test. The difference in T-score was also compared to the Minimally Important Difference (MID) seen in pain management (<NPL>).

Claim 1:
A pain-management system for changing a user's behaviours in a way that improves their health and wellbeing, the system configured to:
receive a target-pain-score (<NUM>) that represents a level of pain that the user considers acceptable during a defined period of time;
receive one or more target-input-parameters (<NUM>) which represent properties and / or activities of the user during the same defined period of time, wherein the one or more target-input-parameters (<NUM>) are a subset of a full list of input-parameters that are available;
receive one or more settings (<NUM>) that represent one or more input-parameters that are to be increased or decreased, and wherein the one or more input-parameters represent one or more physical activity performed by the user;
use a neural network that has been trained for the individual user to determine one or more calculated-user-parameters (<NUM>) based on the target-pain-score (<NUM>) and the target-input-parameters (<NUM>), wherein at least one of the calculated-user-parameters (<NUM>) is set based on the settings (<NUM>) and wherein the calculated-user-parameters (<NUM>) include suggestions for values for one or more of the settings (<NUM>) that are expected to achieve the target-pain-score (<NUM>), and wherein the one or more calculated-user-parameters (<NUM>) comprise:
(i) a duration-characteristic, which represents a duration for the user to perform the physical activity that is associated with the setting (<NUM>); and / or
(ii) an intensity-characteristic, which represents an intensity for the user to perform the physical activity that is associated with the setting (<NUM>); and
present the one or more calculated-user-parameters using a user interface (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>) to enable the user to change their behaviours / activities in order to improve their health and wellbeing while being able to control the amount of pain that they experience.