WO2023001986A1

WO2023001986A1 - Systems and methods for pain modelling

Info

Publication number: WO2023001986A1
Application number: PCT/EP2022/070547
Authority: WO
Inventors: Cornelis Petrus Hendriks; Lieke Gertruda Elisabeth Cox; Valentina LAVEZZO; Murtaza Bulut; Nicholas Walker; Jan Jasper VAN DEN BERG
Original assignee: Koninklijke Philips N.V.
Priority date: 2021-07-23
Filing date: 2022-07-21
Publication date: 2023-01-26

Abstract

:Systems and methods are proposed for modelling pain experienced by a subject as a result of movement of the subject. Such proposals are based on supplementing the use of a JSM with a tribology simulation model. Results/outputs obtained from the JSM and tribology simulation model may be provided as input to a biophysical model for the subject to obtain a stress or strain value for the subject.

Description

Systems and methods for pain modelling

FIELD OF THE INVENTION

The invention relates to modelling human subjects, and more particularly to the field of models of biological function (commonly referred to as digital twins).

BACKGROUND OF THE INVENTION

A recent development in healthcare is the so-called ‘digital twin’ concept. In this concept, a digital representation or computational simulation (i.e. the Digital Twin (DT)) of a physical system is provided and connected to its physical counterpart, for example through the Internet of things as explained in US 2017/286572 Al. Through this connection, the DT typically receives data pertaining to the state of the physical system, such as sensor readings or the like, based on which the digital twin can predict the actual or future status of the physical system, e.g. through simulation.

A DT has been defined as “a set of virtual information constructs that mimics the structure, context and behavior of an individual or unique physical asset that is dynamically updated with data from its physical twin throughout its life-cycle. Although this definition targets engineering systems, a broad interpretation covers the use of DT in a diverse array of other application areas such as healthcare and information systems. Thus, DTs may be constructed for processes and living entities, and the lifecycle may be the period over which the digital twin is needed to support decision-making. DT may also be used over fixed time periods, such as for surgery or in critical decision points for physical systems. In essence, a digital twin is an in silico model that brings together the technology to map, monitor and control real-world entities by continually receiving and integrating data from a physical twin to provide an up-to-date digital representation of the physical entity.

Such DT technology is also becoming of interest in the medical field, as it provides an approach to more efficient medical care provision. For example, a DT may be built using imaging data of a subject (i.e. patient), e.g. a person suffering from a diagnosed medical condition as captured in the imaging data.

The DT(s) of a subject (i.e. subject-specific computational simulations) may serve a number of purposes. Firstly, the DT(s) (rather than the patient) may be subjected to a number of virtual tests, e.g. treatment plans, to determine which treatment plan is most likely to be successful to the patient. This reduces the number of tests that physically need to be performed on the actual patient. Secondly, the DT(s) of a subject may be used to predict the onset, treatment (outcome) or development of medical conditions of the subject. That is, the DT(s) of a subject may offer a healthcare professionals advanced visualization and/or physical insights into health information of the subject, thus supporting improved Clinical Decision Support (CDS)

By way of example, a personalized Joint and Musculo-Skeletal model (JSM) is known which simulates forces predictive of joint movement pain (i.e. predicts “expected pain”). The inputs of such models are imposed movements and the outputs of are the predicted force(s) acting on joints during the movement(s). However, the link(s) between force(s) and pain are complex, and obtaining an understanding pain experienced as a result of joint movement is therefore challenging.

SUMMARY OF THE INVENTION

The invention is defined by the claims.

According to examples in accordance with an aspect of the invention, there is provided a method for pain modelling for a subject, the method comprising: obtaining a directional force value for the subject as an output from a joint and musculo-skeletal model, JSM, for an anatomical structure of the subject, the directional force value being determined by the JSM based on an input movement of the subject provided to the JSM; inputting the directional force value to a tribology simulation model to obtain, as an output from the tribology simulation model based on the directional force value, a fictional force value for the subject; and inputting the directional force value and the frictional force values to a biophysical model for the subject to obtain, as an output from the biophysical model, a stress or strain value for the subject.

Embodiments propose concepts for modelling pain experienced by a subject as a result of movement of the subject. Such proposals are based on supplementing the use of a JSM with a tribology simulation model. Results/outputs obtained from the JSM and tribology simulation model may be provided as input to a biophysical model for the subject to obtain a stress or strain value for the subject. That is, proposed are concepts that extend a JMS kinematic and force model with a biophysical model (e.g. a tissue deformation model) to simulate the local stresses and strains in the structures of a joint. The obtained stress or strain value(s) for the subject may be used as an indication (e.g. surrogate/substitute value or suggestion) of pain. Indeed, it is anticipated that obtained stress or strain value(s) may be better (e.g. more accurate and/or more reliable) indicators of pathological pain experienced by a subject than a directional force value provided by the JSM on its own.

Proposed embodiments may thus provide the advantage of providing more accurate predictions/estimations of a subject’s (predicted or experienced) pain.

Embodiments therefore propose one or more concepts for a personalized (i.e. subject-specific) pain modelling method which makes use of a tribology simulation model and biophysical model for a subject the DT generated signals. Embodiments may thus facilitate improved (i.e. more accurate and/or reliable) pain prediction, taking into account a physiological process of pain and the conditions under which pain measurements are carried out.

As a result, systems and methods are proposed for pain diagnosis and treatment. Such proposals may be based on obtaining a directional force value for a subject as an output from a JSM and then inputting the directional force value to a tribology simulation model to obtain a fictional force value for subject. The directional force value and the frictional force values may then be input to a biophysical model for the subject to obtain a stress or strain value for the subject, wherein the stress or strain value provides an indication of pain experienced by the subject.

Embodiments may be based on using a biophysical model of the subject (e.g. human patient) to account for a complex relation between the normal force output of a JSM, a local stress and strain distribution, the macro- and micro-geometry of the subject, joint movement (e.g. rotation), mechanical properties, and the deformation of the anatomical structures in the joint. In this way. embodiments may cater for the fact that human structures and local stress and strain values are subject-specific, thus facilitating adaptation of pain modelling.

Proposed embodiments may provide personalized and/or dynamic pain modelling concepts that cater for varying parameters and/or conditions.

In some embodiments, the JSM for the subject may be configured to model a macro-geometry of the anatomical structure of the subject. Further, the tribology simulation model may be configured to model a micro-geometry of the anatomical structure of the subject.

In an embodiment, the tribology simulation model may be configured to model a wear volume of the anatomical structure of the subject as a function of directional force. By way of example, the tribology simulation model may be configured to model a wear volume of the anatomical structure of the subject according to the following equation: V = k*Fn*s, wherein: V is wear volume; k is a wear factor; Fn is directional force; and s is joint slide distance.

In some proposed embodiments, the biophysical model may be configured to model subchondral bone properties of the subject.

The input movement may, for example, comprise a joint rotation.

Some proposed embodiments may further comprise: obtaining, from an empirical pain prediction model, a prediction of perceived pain for the subject based on the input movement; associating the obtained prediction of perceived paint for the subject with a description of at least one environmental or physiological parameter for which the prediction was obtained; and analyzing the obtained stress or strain value for the subject based on the prediction of perceived pain for the subject and the description associated with the prediction of perceived pain for the subject.

By way of example, the at least one environmental or physiological parameter may comprise at least one of: ambient temperature; time of input movement; emotional state of the subject; consumed medication; weight of the subject; presence of caregiver; and submerged state of the subject.

In an embodiment, analyzing may comprise: comparing the obtained stress or strain value for the subject against the prediction of perceived pain; and determining a relative contribution of pathological pain based on the comparison result.

Some embodiments may also comprise determining a movement recommendation for the subject based on the analysis result, the movement recommendation comprising a value of a movement parameter for the subject.

Accordingly, the following benefits/advantages may be provided by proposed embodiments:

- Improved accuracy of pain prediction, for example by taking into account a physiological process of pain and the conditions under which pain measurements are carried out;

- Enablement of comparison between expected and perceived pain, including its progression over time;

- Facilitation of disease progression as a function of lifestyle. Embodiments may therefore be of particular use in relation to clinical decision making. Exemplary usage applications may for example include predicting the onset, treatment (outcome) or development of medical conditions and/or medical procedures. Embodiments may thus be of particular use in relation to medical care management and/or prediction.

According to examples in accordance with yet another aspect of the invention, there is provided a computer program product comprising computer program code means which, when executed on a computing device having a processing system, cause the processing system to perform all of the steps of the method described above.

According to another aspect of the invention, there is provided a system for pain modelling for a subject, the system comprising: an input interface configured to obtain a directional force value (Fn) for the subject as an output from a JSM for an anatomical structure of the subject, the directional force value (Fn) being based on an input movement (Q) of the subject provided to the JSM; a tribology simulation model component configured to receive the directional force value (Fn) and to determine a fictional force value (Ff) for subject based on the directional force value (Fn); and biophysical model for the subject configured to receive the directional force value (Fn) and the frictional force value (Ff) and to determine a stress or strain value (e) for the subject based on the directional force value (Fn) and the frictional force values (Ff).

The input interface may be further adapted to obtain, from an empirical pain prediction model, a prediction of perceived pain for the subject based on the input movement (Q). Further, the system may further comprise: a description component configured to associate the obtained prediction of perceived paint for the subject with a description of at least one environmental or physiological parameter for which the prediction was obtained; and an analysis unit configured to analyze the obtained stress or strain value (e) for the subject, the prediction of perceived pain for the subject based, and the description associated with the prediction of perceived pain for the subject.

In an embodiment, the analysis unit may be further configured to determine a movement recommendation for the subject based on the analysis result, the movement recommendation comprising a value of a movement parameter for the subject.

The system may be further configured to generate a control instruction for a sensor or medical equipment based on the determined stress or strain value (e). In this way, a sensor and/or medical equipment may be controlled according to modelling/prediction results generated by embodiments. Dynamic and/or automated control concepts may therefore be realized by proposed embodiments.

Further, proposed concepts may provide a clinical decision support comprising a system according to a proposed embodiment.

These and other aspects of the invention will be apparent from and elucidated with reference to the embodiment(s) described hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the invention, and to show more clearly how it may be carried into effect, reference will now be made, by way of example only, to the accompanying drawings, in which:

Figure 2 is a flow diagram of a method for verifying a digital twin of a biological system of a subject according to a proposed embodiment;

Figure 3 depicts a modification to the method of Figure 2;

Figure 4 is a simplified block diagram of a system according to an embodiment;

Figure 5 is a graph depicting exemplary outputs of the biophysical model and the empirical pain model of Figure 4; and

Figure 6 is a simplified block diagram of a computer within which one or more parts of an embodiment may be employed

DETAILED DESCRIPTION OF THE EMBODIMENTS

The invention will be described with reference to the Figures.

It should be understood that the detailed description and specific examples, while indicating exemplary embodiments of the apparatus, systems and methods, are intended for purposes of illustration only and are not intended to limit the scope of the invention. These and other features, aspects, and advantages of the apparatus, systems and methods of the present invention will become better understood from the following description, appended claims, and accompanying drawings. It should be understood that the Figures are merely schematic and are not drawn to scale. It should also be understood that the same reference numerals are used throughout the Figures to indicate the same or similar parts.

The invention provides concepts for modelling pain a subject. Such concepts may make use of a JSM and a tribology simulation model to generate inputs to a biophysical model for the subject. Based on such inputs, the biophysical model may determine a stress or strain value (e) for the subject, wherein the stress or strain value is proposed to provide an improved (e.g. more accurate and/or more reliable) indication of pathological pain experienced by a subject.

In particular, embodiments propose to extend a known JMS kinematic and force model with a biophysical model (e.g. a tissue deformation model) to simulate local stresses and strains in innervated structures of the joints. Such local stresses and strains determine the response (e.g. firing rate) of local nerve endings and mechanoreceptors.

Use of a biophysical model is proposed to account for one or more relationships between the normal force output of the JSM model and the local stress and strain distribution. Such relationships may be complex and depend on various factors such as the macro- and micro-geometry of a joint of the subject, joint movement, mechanical properties of the joint, and deformation of the anatomical structures in the joint. The JSM model is based on the macro- geometry (such as bone length, muscle size etc.), not on the micro-geometry (such as the detailed shapes of the bones, meniscus, pathologies etc.). It is proposed that, as a surrogate for pain, the average or maximum stress or strain may be used. This pain surrogate may be much closer to the pathological pain than the joint normal force. Accordingly, using proposed embodiments, an “expected pain” may be estimated more accurately than with a JSM model alone.

Referring to Figure 1, there is shown a schematic illustration of a cross-sectional view of a knee joint of subject. The illustration depicts the application of a biophysical model to translate a joint normal force Fn (output of a JSM model) to local stresses and strains which cause pain signals. It is proposed that the average or maximum stress or strain e may provide an indication of pain, wherein e = f (F_n, F_f, G, Gm, Q), wherein: the joint normal force F_n is an output of a JSM model; the friction force F_f is an output of a tribology simulation model; the macro-geometry G used for the JSM model; micro-geometry Gm is modelled by tribology simulation; and joint movement Q is observed or otherwise detected/determined using one or more sensors.

Some embodiments may also add labels to an empirical pain model which reflect the relevant conditions under which the model is established. Conditions which are relevant are those which may influence pain perception. For example, a labeled sensor output valid for high and low temperature, ShighT = f(0) respectively Si_ow t = f(0). Other label examples includes, time of the day, stress level of the subject (e.g. determined using a wearable sensor arrangement or smartwatch, submerged in water status, presence of caregiver, etc.

By way of further explanation, tribology is the science of friction, lubrication and wear of materials. Applications include the simulation or prediction of the friction force in a mechanical system. The application of tribology to biological systems is called “biotribology”. Tribological concepts have been successfully applied to the human body and diseases such as joint replacement and osteoarthritis. Embodiments propose to incorporate tribology simulation within the pain prediction process, so as to monitor and predict the progression of the disease and its pathology as a function of lifestyle and therapy, and to predict a friction force to refine a stress and strain simulation/prediction model.

The first element of the tribology module is a wear equation that predicts the wear volume V in the subject’s joint. The wear volume is a function of the normal force F_n (output of the JSM), the sliding distance s (which depends on daily life aspects such a walking, movement, exercises, therapy etc.), and a wear factor k. The wear factor k is a system parameter that can be measured in an in-vitro lab test or may be estimated from literature. The wear factor may further depend on the lubrication conditions that, in turn, depend on joint movement that helps to mobilize the joint fluid and lubricate the joints. The relation between the wear volume and the other parameters may be represented by the following equation:

V = k*F_n*s

The wear volume V may gradually alter the micro-geometry Gm of the contact in the joint, which is an input of the biophysical model. As such, a relationship is established between daily life aspects (s, F_n) and the evolution of the stress and strain distribution in time, e = e (time). This allow the prediction or virtual monitoring of the progression of the pathology as a function of daily life and therapy. It may also enable the design of therapies to reduce or minimize wear.

The second element of the tribology module is the Stribeck number that relates the friction force F_f in the contact with the normal force F_n, the contact area A, the fluid viscosity h and the sliding speed v according to the following equation:

S = hnA/Fn

The contact area A and the normal force Fn are output from the biophysical model and the JSM model respectively. The fluid viscosity h and the relationship between S and the friction force F_f can be estimated from lab tests or literature. The friction force F_f, or alternatively the friction coefficient m = F_f / F_n, is an input of the biophysical model.

Based on such proposals, embodiments may provide a concept of pain modelling for a subject. In particular, embodiments may employ a biophysical model to simulate local stresses and strains in the innervated structures of a subject’s joint. Furthermore, embodiments may also employ a tribology module to predict disease progression, and outputs of different pain models may be compared. In this way, more accurate predictions of expected pain may be obtained and/or improved diagnosis and therapy applications may be realized.

Referring now to Figure 2, there is depicted a flow diagram of a method for pain modelling for a subject according to an exemplary embodiment. In this exemplary embodiment, the subject is a human patient.

The method comprises the first step 110 of obtaining a directional force value Fn for the subject as an output from ajoint and musculo-skeletal model (JSM) for an anatomical structure of the subject. Specifically, the JSM for the subject is configured to model a macro geometry of the anatomical structure of the subject, and the directional force value Fn is determined by the JSM based on an input movement Q of the subject provided to the JSM. For example, in this exemplary embodiment of Figure 2, the anatomical structure of the subject is a knee joint of the subject and the input movement Q comprises a rotation of the knee joint.

The directional force value Fn obtained from step 110 is then input to a tribology simulation model in step 120 so as to obtain a fictional force value Ff for subject. Here, the tribology simulation model is configured to model a micro-geometry of the anatomical structure of the knee joint of the subject.

In this example of Figure 2, the tribology simulation model is configured to model a wear volume of the subject’s knee joint as a function of directional force. Specifically, the tribology simulation model is configured to model a wear volume of the anatomical structure of the subject according to the following equation:

V = k*Fn*s wherein: V is wear volume; k is a wear factor; Fn is directional force; and s is joint slide distance.

Then, in step 130, the obtained directional force value Fn (from step 110) and the obtained frictional force value Ff (from step 120) are input to a biophysical model for the subject to obtain, as an output from the biophysical model, a stress or strain value e for the subject. Here, the biophysical model is configured to model subchondral bone properties of the subject.

As detailed above, it is proposed that the stress or strain value e obtained from step 130 may be employed as an indicator or substitute for pathological pain experienced by the subject as a result of the input (joint) movement Q. Accordingly, using the exemplary embodiment of Figure 2, an “expected pain” may be estimated more accurately than with a JSM model alone. It is to be understood that the above-described embodiment of Figure 2 is purely exemplary. Such an embodiment may therefore be adapted and/or modified according to requirements and/or desired application. Also, the method of Figure 2 is only illustrative and the ordering of one or more of the steps may be modified in alternative embodiments.

By way of example, it is proposed that a prediction of perceived pain for the subject may be leveraged according to an embodiment. Further, this may be extended to control operations of connected medical treatment(s).

For instance, referring to Figure 3, there is depicted a flow diagram of a method according to another embodiment, wherein the method is a modified version of the method of Figure 2. Specifically, the embodiment of Figure 3 comprises all of the steps of the method of Figure 2, but further comprises the step 140 of analyzing the obtained stress or strain value (e) for the subject

In more detail, a prediction of perceived pain for the subject is obtained from a conventional empirical pain prediction model based on the input movement Q.

The obtained prediction of perceived paint for the subject is then associated with a description of at least one environmental or physiological parameter for which the prediction was obtained. For example, an environmental or physiological parameter may include: ambient temperature; time of input movement; emotional state of the subject; consumed medication; weight of the subject; presence of caregiver; or a submerged state of the subject (e.g. whether the subject’s joint is submerged in water).

Then, obtained stress or strain value e for the subject can be analyzed based on the prediction of perceived pain for the subject and the description associated with the prediction of perceived pain for the subject. For example, such analysis may comprise: comparing the obtained stress or strain value e for the subject against the prediction of perceived pain; and determining a relative contribution of pathological pain based on the comparison result.

In a further embodiment, a movement recommendation for the subject may be determined based on the analysis result. Such a movement recommendation may, for example, comprise a value of a movement parameter for the subject (e.g. a maximum angle of displacement, a maximum speed or force, etc.).

Referring now to Figure 4, there is depicted an illustration of a system according to an embodiment. The system is for modelling pain of a subject according to an exemplary proposal. In this example, the subject is an elderly human requiring regular medical assessment and the modelling process is configured to model pain experienced by the subject as a result of undergoing movement.

The system 400 comprises an input interface 410 that is configured to obtain a directional force value Fn for the subject as an output from a JSM 415 for an anatomical structure of the subject. Here, the directional force value Fn output from the JSM 415 is based on an input movement Q of the subject that is provided as an input to the JSM 415.

Specifically, the input movement Q of the subject is provided by a human kinetic data gathering module 418 that is provided from subject observations 420 (e.g. movement sensor data and/or subject inputs describing movements made the subject). The subject observations 420 are also provided to a pain estimation module 421 which, in turn, provides an estimation of pain to a conventional empirical pain prediction model 422. The empirical pain prediction model 422 also receives the input movement Q from the human kinetic data gathering module 418.

The JSM 415 uses macro-geometry G which is gathered from the subject’s medical records 424 by a medical data gathering module 426. Micro-geometry G_m is also gathered from the subject’s medical records 424 by the medical data gathering module 426, and provided to the input interface 410 (for subsequent use by a tribology simulation model component/module 430 of the system 400).

The tribology simulation model component/module 430 of the system 400 is configured to receive the directional force value Fn and to determine a fictional force value Ff for subject (based on the directional force value Fn. The tribology simulation model component/module 430 is also configured to determine an evolvement of the microgeometry Gm of the anatomical structure of the subject (based on the directional force value Fn).

A bone remodeling simulation 435 of the system 400 is configured to receive the directional force value Fn and to determine a mechanical properties E of the anatomical structure of the subject (based on the directional force value Fn).

The system 400 also comprises a biophysical model 440 for the subject. The biophysical model 440 is configured to receive the directional force value Fn, the frictional force value Ff, the microgeometry G_m,the input movement Q, and the mechanical properties E. Based on these received inputs, the biophysical model 440 determines a stress or strain value (e) for the subject. That is, the biophysical model 440 determines s(t)= f(F_n, F_f, G, G_m, Q, E). The input interface 410 is also adapted to obtain, from the empirical pain prediction model 422, a prediction S of perceived pain for the subject based on the input movement (Q), where S=F(0).

Further, the system 400 also comprises a description component 460 that is configured to associate the obtained prediction of perceived pain for the subject with a description of at least one environmental or physiological parameter for which the prediction was obtained.

An analysis unit 450 of the system 400 is then configured to analyze the obtained stress or strain value e, the prediction S of perceived pain for the subject based, and the description associated with the prediction of perceived pain for the subject. In this example, the analysis unit 450 determines (and outputs) a comparison of the prediction S of perceived pain and the stress or strain value e for the subject (i.e. it outputs D = S - e).

By way of example, Figure 5 is a graph depicting exemplary outputs of the biophysical model 440 (i.e. simulated mechanical stimulus e) and the empirical pain model 422 (i.e. output S). That is, Figure 5 depicts exemplary outputs of the biophysical model 440 and the empirical pain model 422.

The outputs can be compared and analysed in different ways. Firstly, the difference D = S - e or the ratio S/e can be monitored over time. Optionally scaled values can be used, e.g. S* = S/Smax or e* = e/amax in order to compare relative values instead of absolute values because the units of S and e are different. Secondly, when the mechanical stimulus e shows an asymptote or a maximum, as in Fig. 5, the tipping point amax can be used to identify the pain level caused by pathological pain, Ppath. Changes over time in D = S - e or S/e indicate changes in the relative contribution of pain due to pathological changes and pain due to increased sensitivity. Thus, the value Ppath provides an indication of the relative contribution of pathological pain.

Based on the analysis result, a diagnosis, prediction and management suggestion module 460 determines a movement recommendation 460 comprising a value of a movement parameter for the subject. This may be provided to a personalized therapy module 470 for determining personalized (i.e. subject-specific) therapy recommendations.

From the above description, it will be appreciated that proposed embodiments facilitate prediction of pain for a subject. However, concepts are also proposed for additional applications/usages. For instance, modelled pain results may be used to provide personalized recommendations and/or medical interventions. By way of further explanation of the proposed concept(s), example implementations of the exemplary system will now be considered Bone re-modelling simulation

Subchondral bone microarchitecture changes in osteoarthritis patients. To calculate stresses and strains e in the bone and overlying cartilage tissues correctly, the system takes into account the subchondral bone properties. Moreover, progression of bone adaptation can be simulated over time, and effects of treatments on the subchondral bone could be predicted, such as bone realignment, but also effect of weight loss or different loads as result of lifestyle adaptations. These results can then be used as input for pain progression predictions.

- Personalized therapy development

The output of the biophysical model may be used to design and teach adjusted movements for a subject. The output may also be used to adjust medical equipment (e.g. braces) for a specific subject and/or to design exercise plans for minimum stress and strain in the critical locations. For example:

The biophysical model may be used to limit movements below Q < qi in order to teach how to pathological damage and pain can be limited. For example, in case it is required to prevent further damage, or in the design of an effective brace;

In case it is required that the patient exceeds movements Q > qi for rehabilitation purposes or to reduce over-sensitivity, the expected pain level (represented by e) may be visualized with augmented or virtual reality. The subject in this case may see that the mechanical load in the joint is actually not increasing while the joint rotation is increasing;

Personalized exercises and movements may be designed that fulfil a motoric function with a lower maximum stress in the joint tissues than the patient is currently doing. This helps to reduce disease progression and to reduce pathological pain;

The biophysical model output, e.g. the local stress or strain level, may be shown to a user (e.g. therapist) while manipulating a subject. Real-time feedback during joint manipulations may therefore be provided;

From the above-described embodiments and examples, it will be understood that the proposed concepts may be particularly relevant to medical facilities (e.g. hospitals) where many users may be involved in a subject care process. In particular, embodiments may be of yet further relevance to physiotherapy applications.

It is to be understood that the above examples and embodiments are only illustrative and the ordering of one or more of the steps may be modified in alternative embodiments. By way of further example, Figure 6 illustrates an example of a computer 600 within which one or more parts of an embodiment may be employed. Various operations discussed above may utilize the capabilities of the computer 600. For example, one or more parts of a system for pain modelling for a subject may be incorporated in any element, module, application, and/or component discussed herein. In this regard, it is to be understood that system functional blocks can run on a single computer or may be distributed over several computers and locations connected across a cloud-based system (e.g. connected via internet). That is, at least part of the system and data may be stored and executed in one or more cloud- based systems, of which the computer 600 may be part.

The computer 600 includes, but is not limited to, PCs, workstations, laptops, PDAs, palm devices, servers, storages, and the like. Generally, in terms of hardware architecture, the computer 600 may include one or more processors 610, memory 620, and one or more I/O devices 630 that are communicatively coupled via a local interface (not shown). The local interface can be, for example but not limited to, one or more buses or other wired or wireless connections, as is known in the art. The local interface may have additional elements, such as controllers, buffers (caches), drivers, repeaters, and receivers, to enable communications. Further, the local interface may include address, control, and/or data connections to enable appropriate communications among the aforementioned components.

The processor 610 is a hardware device for executing software that can be stored in the memory 620. The processor 610 can be virtually any custom made or commercially available processor, a central processing unit (CPU), a digital signal processor (DSP), or an auxiliary processor among several processors associated with the computer 600, and the processor 610 may be a semiconductor based microprocessor (in the form of a microchip) or a microprocessor.

The memory 620 can include any one or combination of volatile memory elements (e.g., random access memory (RAM), such as dynamic random access memory (DRAM), static random access memory (SRAM), etc.) and non-volatile memory elements (e.g., ROM, erasable programmable read only memory (EPROM), electronically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), tape, compact disc read only memory (CD-ROM), disk, diskette, cartridge, cassette or the like, etc.). Moreover, the memory 620 may incorporate electronic, magnetic, optical, and/or other types of storage media. Note that the memory 620 can have a distributed architecture, where various components are situated remote from one another, but can be accessed by the processor 610. The software in the memory 620 may include one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing logical functions. The software in the memory 620 includes a suitable operating system (O/S) 650, compiler 660, source code 670, and one or more applications 680 in accordance with exemplary embodiments. As illustrated, the application 680 comprises numerous functional components for implementing the features and operations of the exemplary embodiments. The application 680 of the computer 600 may represent various applications, computational units, logic, functional units, processes, operations, virtual entities, and/or modules in accordance with exemplary embodiments, but the application 680 is not meant to be a limitation.

The operating system 650 controls the execution of other computer programs, and provides scheduling, input-output control, file and data management, memory management, and communication control and related services. It is contemplated by the inventors that the application 680 for implementing exemplary embodiments may be applicable on all commercially available operating systems.

Application 680 may be a source program, executable program (object code), script, or any other entity comprising a set of instructions to be performed. When a source program, then the program is usually translated via a compiler (such as the compiler 660), assembler, interpreter, or the like, which may or may not be included within the memory 620, so as to operate properly in connection with the O/S 650. Furthermore, the application 680 can be written as an object oriented programming language, which has classes of data and methods, or a procedure programming language, which has routines, subroutines, and/or functions, for example but not limited to, C, C++, C#, Pascal, BASIC, API calls, HTML, XHTML, XML, ASP scripts, JavaScript, FORTRAN, COBOL, Perl, Java, ADA, .NET, and the like.

The EO devices 630 may include input devices such as, for example but not limited to, a mouse, keyboard, scanner, microphone, camera, etc. Furthermore, the I/O devices 430 may also include output devices, for example but not limited to a printer, display, etc. Finally, the EO devices 630 may further include devices that communicate both inputs and outputs, for instance but not limited to, a NIC or modulator/demodulator (for accessing remote devices, other files, devices, systems, or a network), a radio frequency (RF) or other transceiver, a telephonic interface, a bridge, a router, etc. The EO devices 630 also include components for communicating over various networks, such as the Internet or intranet.

If the computer 600 is a PC, workstation, intelligent device or the like, the software in the memory 620 may further include a basic input output system (BIOS) (omitted for simplicity). The BIOS is a set of essential software routines that initialize and test hardware at startup, start the 0/S 650, and support the transfer of data among the hardware devices. The BIOS is stored in some type of read-only -memory, such as ROM, PROM, EPROM, EEPROM or the like, so that the BIOS can be executed when the computer 600 is activated.

When the computer 600 is in operation, the processor 610 is configured to execute software stored within the memory 620, to communicate data to and from the memory 620, and to generally control operations of the computer 670 pursuant to the software. The application 680 and the O/S 650 are read, in whole or in part, by the processor 610, perhaps buffered within the processor 610, and then executed.

When the application 680 is implemented in software it should be noted that the application 680 can be stored on virtually any computer readable medium for use by or in connection with any computer related system or method. In the context of this document, a computer readable medium may be an electronic, magnetic, optical, or other physical device or means that can contain or store a computer program for use by or in connection with a computer related system or method.

The application 680 can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this document, a "computer-readable medium" can be any means that can store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer readable medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium.

The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

A single processor or other unit may fulfill the functions of several items recited in the claims.

It will be understood that the disclosed methods are computer-implemented methods. As such, there is also proposed a concept of a computer program comprising code means for implementing any described method when said program is run on a processing system.

The skilled person would be readily capable of developing a processor for carrying out any herein described method. Thus, each step of a flow chart may represent a different action performed by a processor, and may be performed by a respective module of the processing processor.

As discussed above, the system makes use of a processor to perform the data processing. The processor can be implemented in numerous ways, with software and/or hardware, to perform the various functions required. The processor typically employs one or more microprocessors that may be programmed using software (e.g. microcode) to perform the required functions. The processor may be implemented as a combination of dedicated hardware to perform some functions and one or more programmed microprocessors and associated circuitry to perform other functions.

Examples of circuitry that may be employed in various embodiments of the present disclosure include, but are not limited to, conventional microprocessors, application specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs).

In various implementations, the processor may be associated with one or more storage media such as volatile and non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM. The storage media may be encoded with one or more programs that, when executed on one or more processors and/or controllers, perform the required functions. Various storage media may be fixed within a processor or controller or may be transportable, such that the one or more programs stored thereon can be loaded into a processor.

Variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure and the appended claims. In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. A computer program may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. If the term “adapted to” is used in the claims or description, it is noted that the term “adapted to” is intended to be equivalent to the term “configured to”. Any reference signs in the claims should not be construed as limiting the scope.

Claims

CLAIMS:

1. A method for pain modelling for a subject, the method comprising: obtaining a directional force value (Fn) for the subject as an output from a joint and musculo-skeletal model, JSM, for an anatomical structure of the subject, the directional force value (Fn) being determined by the JSM based on an input movement (Q) of the subject provided to the JSM; inputting the directional force value (Fn) to a tribology simulation model to obtain, as an output from the tribology simulation model based on the directional force value (Fn), a fictional force value (Ff) for the subject; and inputting the directional force value (Fn) and the frictional force values (Ff) to a biophysical model for the subject to obtain, as an output from the biophysical model, a stress or strain value (e) for the subject.

2. The method of claim 1 , wherein the J SM for the subj ect is configured to model a macro geometry of the anatomical structure of the subject, and wherein the tribology simulation model is configured to model a micro-geometry of the anatomical structure of the subject.

3. The method of claim 1, wherein the tribology simulation model is configured to model a wear volume of the anatomical structure of the subject as a function of directional force.

4. The method of claim 3, wherein the tribology simulation model is configured to model a wear volume of the anatomical structure of the subject according to the following equation:

5. The method of claim 1, wherein the biophysical model is configured to model subchondral bone properties of the subject.

6. The method of claim 1, wherein the input movement (Q) comprises a joint rotation.

7. The method of claim 1, further comprising: obtaining, from an empirical pain prediction model, a prediction of perceived pain for the subject based on the input movement (Q); associating the obtained prediction of perceived paint for the subject with a description of at least one environmental or physiological parameter for which the prediction was obtained; and analyzing the obtained stress or strain value (e) for the subject based on the prediction of perceived pain for the subject and the description associated with the prediction of perceived pain for the subject.

8. The method of claim 7, wherein the at least one environmental or physiological parameter comprises at least one of: ambient temperature; time of input movement; emotional state of the subject; consumed medication; weight of the subject; presence of caregiver; and submerged state of the subject.

9. The method of claim 7, wherein the analyzing comprises: comparing the obtained stress or strain value (e) for the subject against the prediction of perceived pain; and determining a relative contribution of pathological pain based on the comparison result.

10. The method of claim 7, further comprising: determining a movement recommendation for the subject based on the analysis result, the movement recommendation comprising a value of a movement parameter for the subject.

11. A computer program product comprising computer program code means which, when executed on a computing device having a processing system, cause the processing system to perform all of the steps of the method according to claims 1.

12. A system for pain modelling for a subject, the system comprising: an input interface configured to obtain a directional force value (Fn) for the subject as an output from a JSM for an anatomical structure of the subject, the directional force value (Fn) being based on an input movement (Q) of the subject provided to the JSM; a tribology simulation model component configured to receive the directional force value (Fn) and to determine a fictional force value (Ff) for subject based on the directional force value (Fn); and biophysical model for the subject configured to receive the directional force value (Fn) and the frictional force value (Ff) and to determine a stress or strain value (e) for the subject based on the directional force value (Fn) and the frictional force values (Ff)

13. The system of claim 12, wherein the input interface is further adapted to obtain, from an empirical pain prediction model, a prediction of perceived pain for the subject based on the input movement (Q), and wherein the system further comprises: a description component configured to associate the obtained prediction of perceived paint for the subject with a description of at least one environmental or physiological parameter for which the prediction was obtained; and an analysis unit configured to analyze the obtained stress or strain value (e) for the subject, the prediction of perceived pain for the subject based, and the description associated with the prediction of perceived pain for the subject.

14. The system of claim 13, wherein the analysis unit is further configured to determine a movement recommendation for the subject based on the analysis result, the movement recommendation comprising a value of a movement parameter for the subject.

15. A clinical decision support comprising a system for pain modelling for a subject according to claim 13.