Systems and methods for neural pathways creation/reinforcement by neural detection with virtual feedback

The present invention relates generally to physical therapy technologies, and more particularly to systems for creating/reinforcing neural pathways by neural detection with virtual feedback.

There are, unfortunately, many people in the world who suffer from debilitating conditions that cause partial or total loss of physical motor abilities. Some of these conditions include neurological disorders, brain injuries, muscular dystrophy, amyotrophic lateral sclerosis (ALS), and/or incomplete spinal cord injuries. In these situations, a patient may lose motor function of a hand, an arm, a leg, a finger, etc. due to the brain injury, and thus, the patient may not be able to move the affected limb or may have limited flexion. Oftentimes, the problem is in the injured part of the brain, and although the affected limb may not be damaged, the part of the brain controlling the motor function of the affected limb may not function. In these cases, it is possible for the motor function of the affected limb to be regained if new neural pathways associated with the particular motor functions are created in the patient's brain bypassing the injured portions of the brain. This is possible because of the principle of neuroplasticity, which refers to the ability of the brain to transfer brain activity associated with a given function to a different location, i.e., to create new neural pathways.

Several techniques have been developed that leverage the principle of neuroplasticity. One of those techniques is the mirror therapy (MT) technique. Mirror therapy utilizes a mirror-based illusion in which a healthy limb is perceived by the patient as the affected limb. Thus, a patient experiencing loss of motor function on the right hand may use the left hand in mirror therapy to provide the illusion that the right hand is moving, thereby tricking the patient's brain into thinking that the right hand is moving when it is actually not. This illusion stimulates creation of new pathways in the injured brain that are associated with motor functions of the affected limb. However, mirror therapy is wholly ineffectual where there is no healthy limb that can be used. Additionally, the effect of the mirror technique may be limited, as the patient may be fully aware that a mirror is being used to provide the illusion that the affected limb is moving. Additionally, this technique provides no means of assisted physical feedback that assists the affected limb in actually moving, and thus, the physical therapy duration may be long.

Virtual reality has also been used to immerse patients in a virtual environment, which may be navigated by the patient. The exposure to the created virtual environment may create stimuli in the patient's brain that may be associated with the virtual function. However, these techniques do not provide a robust system for physical therapy that seeks to create/reinforce new neural pathways associated with motor functions of an affected limb.

Thus, the current state of the art is technologically deficient and there are currently no mechanisms that are directed to physical therapy in which a neural pattern of a patient's brain is associated to a particular target action of an affected limb, and then providing a virtual representation of the target action when the neural pattern is detected, while the affected limb may not perform the target function.

The present invention is directed to devices, systems, and methods that implement a technological solution for implementing therapeutic devices that create/reinforce neural pathways associated with a target motor function using neural pattern detection with virtual feedback. The systems, devices, and techniques disclosed herein may be used in physical therapy, where an affected limb may have limited motor function, or flexion, due to an injured part of the brain. Using the techniques of the present disclosure, a system may be provided that may allow a patient to create/reinforce new pathways associated with the lost motor function. Thus, a patient may be able to regain at least part of the lost motor function.

In one embodiment, a method of providing virtual feedback of a neural pattern to create a neural pathway corresponding to an action of an affected limb of a physical therapy patient with brain injury is provided. The method includes detecting the neural pattern and determining a target action associated with the neural pattern. The method also includes generating a virtual feedback including a virtual action corresponding to the target action. In some aspects, the virtual action may be performed in a virtual representation of the affected limb, where a brain injury of a user may partially or wholly prevent the affected limb from performing the target action. The method may also include causing the virtual representation of the affected limb to be positioned within the virtual environment such that the virtual representation of the affected limb may be presented to the physical therapy patient in lieu of the real-world presence of the affected limb. For example, in some embodiments, the position of the virtual representation within the virtual environment may correspond to the position of the affected limb in the real world. The method may further include performing the virtual action corresponding to the target action by the virtual representation of affected limb, such that the virtual action is presented to the physical therapy patient to simulate the target action in the real-world.

In other embodiments, a system for providing virtual feedback of a neural pattern to create a neural pathway corresponding to an action of an affected limb of a physical therapy patient with brain injury may be provided. The system may include a neural pattern detector configured to detect the neural pattern, and a controller communicatively coupled to the neural pattern detector. In some aspects, the controller and the neural pattern detector may configured for wired and/or wireless communications. The controller may be configured to determine a target action associated with the neural pattern and to generate a virtual feedback including a virtual action corresponding to the target action. In some aspects, the virtual action may be performed in a virtual representation of the affected limb, where a brain injury of a user may partially or wholly prevent the affected limb from performing the target action. The system may also include a virtual representation unit configured to generate a virtual representation of the affected limb and to present the virtual representation of the affected limb in a position within the virtual environment such that the virtual representation of the affected limb is presented to the physical therapy patient in lieu of the real-world presence of the affected limb. For example, in some embodiments, the position of the virtual representation within the virtual environment may correspond to the position of the affected limb in the real world. The virtual representation unit may also be configured to display the virtual action, corresponding to the target action, as being performed by the virtual representation of affected limb, such that the virtual action is presented to the physical therapy patient to simulate the target action in the real-world.

In yet another embodiment, an apparatus for providing virtual feedback of a neural pattern to create a neural pathway corresponding to an action of an affected limb of a physical therapy patient with brain injury may be provided. The apparatus may include a memory and at least one processor coupled to the memory. The at least one processor may be configured to detect the neural pattern and to determine a target action associated with the neural pattern. The at least one processor may also be configured to generate a virtual feedback including a virtual action corresponding to the target action. In some aspects, the virtual action may be performed in a virtual representation of the affected limb, where a brain injury of a user may partially or wholly prevent the affected limb from performing the target action. The at least one processor may also be configured to cause the virtual representation of the affected limb to be positioned within the virtual environment such that the virtual representation of the affected limb is presented to the physical therapy patient in lieu of the real-world presence of the affected limb. For example, in some embodiments, the position of the virtual representation within the virtual environment may correspond to the position of the affected limb in the real world. The at least one processor may be further configured to perform the virtual action corresponding to the target action by the virtual representation of affected limb, such that the virtual action is presented to the physical therapy patient to simulate the target action in the real-world.

In some embodiments, further functionality for training and learning may be provided. For example, systems in accordance with the present disclosure may provide functionality to determine a baseline measurement of a neural pattern of a patient that may be used to train the system to facilitate detection of neural patterns for a particular patient during operations. Additionally, a system in accordance with the present disclosure may include generation of physical feedback that includes an assisted physical action corresponding to the target action associated with the neural pattern, wherein the assisted physical action may be performed in the real-world environment. In aspects, the assisted physical action may be performed by an assisted physical feedback unit that may be configured to assist or to force movement of a target site by means of mechanical force, or to provide resistance to movement of the target site. In embodiments, the assisted physical action performed in the real-world may correspond to the virtual action performed in the virtual environment.

The foregoing has outlined rather broadly the features and technical advantages of the present invention in order that the detailed description of the invention that follows may be better understood. Additional features and advantages of the invention will be described hereinafter which form the subject of the claims of the invention. It should be appreciated by those skilled in the art that the conception and specific embodiment disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present invention. It should also be realized by those skilled in the art that such equivalent constructions do not depart from the spirit and scope of the invention as set forth in the appended claims. The novel features which are believed to be characteristic of the invention, both as to its organization and method of operation, together with further objects and advantages will be better understood from the following description when considered in connection with the accompanying figures. It is to be expressly understood, however, that each of the figures is provided for the purpose of illustration and description only and is not intended as a definition of the limits of the present invention.

Various features and advantageous details are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known starting materials, processing techniques, components, and equipment are omitted so as not to unnecessarily obscure the invention in detail. It should be understood, however, that the detailed description and the specific examples, while indicating embodiments of the invention, are given by way of illustration only, and not by way of limitation. Various substitutions, modifications, additions, and/or rearrangements within the spirit and/or scope of the underlying inventive concept will become apparent to those skilled in the art from this disclosure.

illustrates a systemconfigured in accordance with embodiments of the present disclosure. The configuration of systemmay facilitate operations for creating and/or reinforcing neural pathways associated with target actions by using neural pattern detection with various feedback techniques as described in embodiments of the present disclosure.

As shown in, systemmay include controller, neural pattern detector, virtual feedback generator, physical feedback generator, database, virtual representation unit, assisted feedback unit, flexion detector, processor, memory, and input/output (I/O) unit. These components may cooperatively operate to provide functionality in accordance with the discussion herein. In embodiments, as will be discussed in more detail below, the operations of systemmay include a training and learning process, and an operational process. The training and learning process may facilitate configuring and setting up systemfor operational use, and may include operations for determining baseline neural patterns, for identifying and/or determining neural patterns associated with particular target actions, and/or for storing those neural patterns associated with the target actions. The operational process may include operations for detecting a neural pattern, for determining a target action associated with the neural pattern, for providing feedback, and/or for controlling the feedback based on flexion measurements.

It is noted that as used herein, the term flexion may relate to motion related to a user's motor function. In some cases, flexion information may be provided by a motion associated with motor function (e.g., opening/closing of a fist, extending/contracting an arm, opening/closing jaw, etc.) which may be detected via motion detectors. In these cases, the movement caused by the motor function provides information about the flexion related to the motor function.

It is further noted that as used herein, a neural pattern may refer to a pattern corresponding to a pattern of activity in the brain. In some aspects, the pattern of activity may be produced by electrical activity, magnetic activity, blood flow activity, and/or other biological or metabolic activity within the brain, or a combination thereof. A neural pattern may also be referred to as a brainwave pattern, neural oscillations pattern, etc. The neural pattern may be associated with a particular thought, which may itself be associated with a desired target action. This neural pattern may be detected in accordance with aspects of the present disclosure.

As used herein, a target action may refer to an intended or desired action related to a patient's motor function. For example, for a therapy patient with limited hand flexion, a target action may refer to a desired action of closing the hand, or to a desired action of opening the hand. In some aspects, the target action may be an action related to a motor function that a patient may not be able to perform, or may be limited, due to the brain injury, although the patient may desire and/or intend to perform the action. In embodiments discussed herein, the target action may be associated with a particular neural pattern.

It is also noted that the discussion herein focuses on operations with respect to an affected limb. However, it will be appreciated that the present disclosure is also applicable to situations in which the motor function affected is related to a site that may not necessarily be referred to as a limb, e.g., a facial muscle. Thus, it will be appreciated that the devices and methods discussed in the present disclosure may also be applicable when the affected motor function is associated with any muscle or muscle group of a patient.

In accordance with embodiments of the present disclosure, neural pattern detectormay include a detector configured to detect a patient's neural pattern and to provide the detected neural pattern to controller. In some aspects, neural pattern detectormay be configured to detect a pattern of activity corresponding to one or more of electrical activity, magnetic activity, blood flow activity, and/or other biological and metabolic activity within the brain. In embodiments, neural pattern detectormay include at least one of an electroencephalography (EEG) detector, a magnetoencephalography (MEG) detector, a positron-emission tomography (PET) detector, a magnetic resonance imaging (MRI) detector, a functional MRI (fMRI) detector, a computed tomography (CT) detector, a single-photon emission CT (SPECT) detector, a functional Near-Infrared Spectroscopy (fNIR) detector, and/or any detector configured to detect a corresponding neural pattern. It is noted that the foregoing exemplary types of neural pattern detectors have been provided for purposes of illustration, rather than by way of limitation, and that neural pattern detectormay include other types of neural pattern detectors in accordance with aspects of the present disclosure.

In embodiments, neural pattern detectormay comprise an EEG having a plurality of electrodes configured to be placed on a patient's scalp and configured to detect the pattern of activity in the patient's brain. The electrodes may be placed in contact with the patient's scalp in pre-specified locations to obtain measurements from targeted areas of the brain. In some embodiments, neural pattern detectormay include a wearable cap or net into which electrodes are embedded, and which may be placed on top a patient's scalp. The number and/or location of electrodes in neural pattern detectormay be predetermined based on a desired resolution, or may be dynamically determined based on operational observations. For example, neural pattern detectormay be configured with any number of electrodes from 6 to 256 electrodes, depending on operational requirements. Alternatively, or additionally, the number of electrodes in neural pattern detectormay be determined based on measurements taken during operations. For example, it may be determined that neural pattern detectormay not be adequately detecting a neural pattern. In this case, the number of active electrodes in neural pattern detectormay be increased. In another example, it may be determined that neural pattern detectoris adequately detecting a neural pattern. In this other example, the number of electrodes in neural pattern detectormay be decreased without affecting the neural pattern detection operations. In embodiments, the number of electrodes in neural pattern detectormay be changed (increased or decreased) by activating or deactivating individual electrodes. It will be appreciated that deactivating electrodes, when appropriate, may decrease power consumption, which may result in power savings, especially in mobile applications.

It is noted that the neural pattern detected by neural pattern detectormay have certain characteristics, such as a frequency and an amplitude. Thus, a particular neural pattern may be characterized, and in some instances may be identified, by a particular frequency and a particular amplitude. In other embodiments, the neural pattern may be characterized by a digital pattern, such as a particular series or sequence of 0 and 1. In this manner, a neural pattern, once detected, may be analyzed to determine whether the neural pattern is associated to a previously determined neural pattern, or may be analyzed as part of a training and learning process to determine if a change in the target action neural pattern has occurred. Additionally, a detected pattern may be associated with a particular patient and may be stored with an indication of the association to the patient.

Systemmay include controllerconfigured to provide functionality in accordance with aspects of the present disclosure. In some embodiments, controllermay be implemented using a desktop computer, a laptop computer, a smartphone, a tablet computing device, a personal digital assistant (PDA), another type of wired and/or wireless computing device, or part thereof. Controllermay include processor, memory, and database. In embodiments, processormay comprise a processor, a microprocessor, a controller, a microcontroller, a plurality of microprocessors, an application-specific integrated circuit (ASIC), an application-specific standard product (ASSP), or any combination thereof, and may be configured to execute instructions to perform operations in accordance with the disclosure herein. In some aspects, implementations of processormay comprise code segments (e.g., software, firmware, and/or hardware logic) executable in hardware, such as a processor, to perform the tasks and functions described herein. In yet other aspects, processormay be implemented as a combination of hardware and software.

In some aspects, processormay be communicatively coupled to memory. Memorymay comprise read only memory (ROM) devices, random access memory (RAM) devices, one or more hard disk drives (HDDs), flash memory devices, solid state drives (SSDs), other devices configured to store data in a persistent or non-persistent state, or a combination of different memory devices. Memorymay store instructions that, when executed by processor, cause processorto perform operations in accordance with the present disclosure.

In some embodiments, memorymay also be configured to facilitate storage operations. For example, memorymay comprise databasefor storing previously determined or detected neural patterns, target actions, threshold values, user profile information, etc. In aspects, databasemay be integrated into memory, or may be provided as a separate module. In yet other aspects, databasemay be a single database, or may be a distributed database implemented over a plurality of database modules. Databasemay be configured to store information for a plurality of patients. In some cases, the information may be used in training and learning operations, as discussed below.

It is noted that in some embodiments, controllerand neural pattern detectormay be implemented in a single device, rather than separate devices. For example, controllerand neural pattern detectormay be implemented as a single device that may be placed on a patient's head. In this example, the detection of the neural pattern and the processing and analysis may be performed by operations in the single device. In alternative embodiments, controllerand neural pattern detectormay be implemented as separate units, communicatively coupled to one another. As noted above, controllerand neural pattern detectormay configured for wired and/or wireless communications.

In embodiments, controllermay include I/O unit. In some embodiments, I/O unit may include a display, and may be configured to display a graphical user interface (GUI) structured to facilitate input and output operations in accordance with aspects of the present disclosure.I/O unitmay be configured to accept input from users, such as a patient or a therapist that may be used to specify the affected motor function, the associated limb, the target action desired, particular motor function limitations of the patient, etc. Thus, for example, a therapist may use I/O unitto specify a target action that the patient is to perform during therapy. In some embodiments, I/O unitmay be configured to provide output which may present, display, or reproduce the virtual environment. In these cases, a therapist may be able to monitor what the patient is perceiving in the virtual environment.

Controllermay be configured to receive a detected neural pattern from neural pattern detector, to analyze and process the detected neural pattern, and to drive feedback generation in accordance with the discussion herein. In some aspects, controllermay provide operations to facilitate a training and learning process, and operations to facilitate an operational process, as discussed in more detail below. Controllermay be configured to provide neural pattern information to virtual feedback generatorand/or to physical feedback generator.

In embodiments, controllermay analyze a detected pattern to determine whether the neural pattern is associated to a previously determined neural pattern. For example, controllermay compare a detected neural pattern with neural patterns stored in databaseto determine if the detected neural pattern matches at least one of the stored neural patterns. In some embodiments, the matching may be based on a comparison of the frequency and/or amplitude of the detected neural pattern and the stored neural patterns. For example, controllermay compare the frequency of a neural pattern detected by neural pattern detectorwith the frequency of each of the stored neural patterns in databaseand, upon finding a match, controllermay determine that the detected neural pattern is associated with the matched neural pattern's target action.

Systemmay include virtual feedback generator. Virtual feedback generatormay comprise a processor configured to execute instructions to perform operations in accordance with the present disclosure. In embodiments, implementations of virtual feedback generatormay comprise code segments executable in hardware, such as a processor, to perform the tasks and functions described herein. In yet other aspects, virtual feedback generatormay be implemented as a combination of hardware and software.

As seen in, virtual feedback generatormay be communicatively coupled to controller, and may be configured to receive neural pattern information. Virtual feedback generatormay be configured to analyze the neural pattern information received from controllerand to generate a virtual feedback based on the neural pattern information received from controller. The virtual feedback may comprise a feedback signal that may be sent to virtual representation unit. The feedback signal may include a signal that drives virtual representation unitto represent an action on a virtual representation, or virtual avatar, of the patient.

Virtual representation unitmay include a virtual reality device, an augmented reality device, a mixed reality device, a computer screen, a television screen, a projector, and/or any device configured to display a virtual representation of a patient, or a virtual representation of a portion or part of a patient. For example, virtual representation unitmay display a virtual avatar that may represent the patient undergoing physical therapy. In aspects, the virtual avatar may be configured with the physical attributes of the patient so that the patient may better relate to the virtual avatar. For example, the virtual avatar may include attributes similar to the patient's attributes, such as body shape, skin color, height, weight, hair color, age, gender, etc. In this cases, the virtual avatar may be associated with a particular patient and may be stored in database. In other aspects, the virtual avatar may be generic and may be similar regardless of the particular patient using the system.

In some embodiments, virtual representation unitmay be configured to display the virtual avatar, or a virtual limb, such that the position of the virtual limb within the virtual environment may correspond to the position of the corresponding physical limb of the patient in the real-world environment, and such that the patient may perceive the virtual limb as part of the patient. For example, a virtual avatar limb may be displayed within the virtual environment of virtual representation unitsuch that the virtual avatar limb may be positioned in a location and arrangement corresponding to the position and arrangement of the real-world limb of the patient. In this case, the patient, while looking at the position within the virtual environment corresponding to the position where the real-world limb of the patient may be, may observe the virtual avatar limb instead. Thus, the patient may perceive any action represented in the virtual representation as an action performed by the patient and in this manner, the virtual feedback, represented as a virtual action, may be perceived as a patient's action, thereby contributing to the creation/reinforcement of neural pathways associated with the action.

In embodiments, the action to be represented in the virtual avatar may correspond to the desired target action discussed above. For example, the feedback signal may indicate to virtual representation unitto represent the action of closing a hand into a fist on a virtual representation of a patient, e.g., the virtual avatar. In some aspects, the feedback signal may specify various parameters for the target action to be represented in the virtual avatar. For example, the feedback signal may specify the amount of flexion, the speed of the movement, and/or the force of the movement associated with the action. In the exemplary case of the target action being a closing of a hand, the associated feedback signal may specify how fast the hand is to be closed in the virtual representation, the force with which the hand is to be closed, and/or how much the hand is to be closed.

Systemmay include physical feedback generator. Physical feedback generatormay comprise a processor configured to execute instructions to perform operations in accordance with the present disclosure. In embodiments, implementations of physical feedback generatormay comprise code segments executable in hardware, such as a processor, to perform the tasks and functions described herein. In yet other aspects, physical feedback generatormay be implemented as a combination of hardware and software.

It should be noted that, although controller, virtual feedback generator, and physical feedback generatorare discussed herein a separate modules, in some embodiments, controller, virtual feedback generator, and physical feedback generatormay be part of a single module. For example, in some implementations, virtual feedback generatorand physical feedback generatormay be implemented as code segments executable in processor. Thus, it should be appreciated that the discussion of controller, virtual feedback generator, and physical feedback generatoras separate modules is for purposes of illustrating and emphasizing the functionality of the difference modules, but it should not be construed as limiting, with respect to their implementation, in any way.

As seen in, physical feedback generatormay be communicatively coupled to controller, and may be configured to receive neural pattern information from controller. Physical feedback generatormay be configured to analyze the neural pattern information received from controllerand to generate a physical feedback based on the neural pattern information received from controller. The physical feedback may comprise a physical feedback signal that may be sent to assisted feedback unit. The physical feedback signal may include a signal that drives assisted feedback unitto perform an assisted action corresponding to the physical feedback.

Assisted feedback unitmay include a wearable device that may be placed on a target site associated with an affected motor function, such as a limb with limited flexion, and may be configured to assist or to force movement of the target site by means of mechanical force, or to provide resistance to movement of the target site. For example, assisted feedback unitmay include a glove, an arm or leg brace, or any other wearable device that may be placed upon a target site and may be configured to assist or force bending, twisting, flexing, extending, etc., of the target site. In some aspects, the mechanical means for assisting or forcing the movement may include motors, actuators, hydraulic actuators, pneumatic actuators, pressure-based actuators, etc. In some embodiments, assisted feedback unitmay include mechanical means for preventing the target site from being moved, thereby providing resistance training to be used during therapy to strengthen the target site.

In embodiments, the physical feedback signal may specify various parameters for the target action to be performed by assisted feedback unit. For example, the feedback signal may specify the amount of flexion, the speed of the movement, and/or the direction of the movement associated with the target action. In an exemplary case of a target action being a closing of a hand, the associated physical feedback signal may specify how fast the hand is to be closed, the direction of movement of the hand, and/or how much the hand is to be closed. In some embodiments, assisted feedback unitmay be configured to respond to the physical feedback signal from physical feedback generatorand to position, by use of the mechanical means, the target site to a position that may be specified by the physical feedback signal.

It is noted that, in some embodiments, the target action for assisted feedback unitmay correspond to a target action associated with virtual representation unit, and may be a similar action. For example, a physical feedback signal from physical feedback generatorderived from neural pattern information may specify a target action for closing a hand to be performed by assisted feedback unit. The same neural pattern information may be received by virtual feedback generatorand may result in a virtual feedback signal specifying a target action for closing a hand in the virtual avatar. Thus, a target action of closing a hand may be coordinately performed virtually and physically, and the patient may be able to perceive the action virtually or physically, due to the cooperative operation of system. Additionally, in some embodiments, the same target action may be performed differently in the virtual environment than in the physical environment. For example, where a target action of a target site, such as an affected limb, beyond a particular limit may be counter-indicated for a patient, the target action may be performed virtually with a set of parameter values, and may also be performed physically with a different set of parameter values. In an exemplary case of a target action being opening of a hand, the patient may experience pain when the hand is extended beyond, e.g., 10% of full flexion. In this example, after detecting and analyzing a neural pattern, neural pattern information may be sent from controllerto virtual feedback generatorand physical feedback generator. Virtual feedback generatormay generate a virtual feedback signal specifying a 100% extension of the virtual avatar hand, and virtual representation unitmay display the virtual avatar hand opening with a 100% extension. In this same example, physical feedback generatormay generate a physical feedback signal specifying a 10% extension, and assisted feedback unitmay operate to physically extend to 10% extension of the patient's hand. Thus, in this example, although the physical extension of the patient's limb may be 10%, the virtual representation of the patient's hand may be extended to 100%. Therefore, the patient may perceive a 100% extension, while physically experiencing a 10% extension.

Systemmay also include flexion detector. Although in the embodiment shown inflexion detectoris shown communicatively coupled to physical feedback generator, in some embodiments, flexion detectormay be communicatively coupled to controller, and may be configured to provide measurements with respect to a physical position and conformation of a target site. It is also noted that, although flexion detectorand assisted feedback unitare shown as separate units, in some embodiments, flexion detector, may be physically or functionally integrated within assisted feedback unit. In embodiments, flexion detectormay be configured to be placed on a target site and may include sensors configured to measure conformation, location, movement, speed, velocity, tilt, position, force, etc., of the target site. For example, flexion detectormay be configured to take measurements with respect to a flexion status of a limb, including whether a limb has moved or may be moving, the speed of the movement, the force of the movement, the extent of the movement, etc. In this manner, the measurements taken by flexion detectormay indicate whether a limb is extended or contracted, how much the limb has extended or contracted, with what force the limb was extended or contracted, etc.

In some embodiments, the flexion measurements measured by flexion detectormay be provided to controllerand/or virtual feedback generator. In these embodiments, the flexion measurements may be used to modify the virtual representation of the limb. For example, the flexion measurements may indicate that a limb is extended to 10%, and the profile information may indicate that the patient has a maximum flexion of 20%. In this case, the virtual representation of the limb may be displayed as 50% extended, because 50% of 20% is 10%. These capabilities of flexion detectormay also allow modifying the virtual representation of the limb to reflect progress during therapy. For example, a patient, at the initial stages of therapy may only be capable of 10% flexion of an affected limb. In this case, during operation, a 10% flexion detected by flexion detectormay be determined to correspond to 100% flexion in the virtual avatar. Based on this, virtual feedback generatormay cause virtual representation unitto display a virtual action of the virtual avatar limb of 100% flexion. At a subsequent time during therapy the flexion limit of the affected limb may be determined to have increased to 20% flexion. In this case, during operation, a 20% flexion detected by flexion detectormay be determined to correspond to 100% flexion in the virtual avatar, whereas a 10% flexion detected by flexion detectormay be determined to correspond to 50% flexion in the virtual avatar. Based on this, virtual feedback generatormay cause virtual representation unitto display a virtual action of the virtual avatar limb of 100% flexion when the flexion detected by flexion detectoris 20%, and may cause virtual representation unitto display a virtual action of the virtual avatar limb of 50% flexion when the flexion detected by flexion detectoris 10%.

As will be appreciated, these features allow a system implemented in accordance with the present disclosure to be configured to adapt the virtual feedback to the therapy progress. In some embodiments, the therapy progress may be determined via visual inspection by the therapist, or may be determined based on automatic measurements made by, e.g., flexion detector. In yet further embodiments, the modifications of the virtual feedback may be performed automatically, based on the determined therapy progress. As such, it should be appreciated that the feedback modifications may be adaptive in nature. For example, a therapist may determine that a patient has 10% motion at the beginning of therapy. In this case, the therapist may manually configure systemto specify the 10% motion of the patient. The system may then use the manual configuration to configure the virtual feedback such that 10% motion, as measured by the flexion detector, corresponds to 100% motion in the virtual representation. Additionally,

Operations of systemto provide functionality in accordance with the present disclosure will now be discussed with respect to the flowcharts shown in.shows a diagram illustrating example blocks executed to implement one aspect of the present disclosure. The procedure according to this example aspect details steps for implementing a learning and training process in accordance with the present disclosure. At block, a predetermined action is identified. In some embodiments, the predetermined action may be a known action, and/or may be an action that is achievable. For example, the predetermined action may comprise moving a virtual ball from one position to another, or may comprise extending or contracting an unaffected limb of a patient.

At block204, the patient is prompted to think, envision, or take the predetermined action as a desired target action to be performed, thereby stimulating neural activity in the patient's brain corresponding to a neural pattern associated with the predetermined action. At block, a neural pattern of the patient is detected. In some embodiments, the neural pattern may be detected using a neural pattern detector such as neural pattern detector. The detected neural pattern may be determined to be associated with the predetermined target action pattern and it may be stored in a database, such as database. In some aspects, the stored neural pattern may include an indication identifying the neural pattern as a training neural pattern.

At block, the system causes the predetermined action to be performed. In some embodiments, the predetermined action may be performed as a virtual action in virtual representation unit. In other aspects, e.g., when the predetermined action is a physical action by an unaffected limb of the patient, the patient might be prompted to perform the predetermined action.

In embodiments, systemmay be configured with learning capabilities. For example, as noted above, databasemay be used to store neural patterns from different patients. The stored neural patterns may be classified based on types of injuries, baseline neural patterns, type of target action associated with the neural pattern, learning context (e.g., training or operational), etc. Using these classifications, system, under control of controller, may learn particular characteristics of a neural pattern associated with a particular target action and may apply those characteristics to subsequently detected neural patterns to identify the associated target action. Thus, in embodiments using this hive learning, individual training of the system may be bypassed.

The learning capabilities of the system may also include determining progress during therapy. In these embodiments, for a particular patient, a detected neural pattern associated with a target action may be compared with a previously stored neural pattern associated with the target action for the patient. If a difference between the two neural patterns is present, controllermay analyze the difference (e.g., difference in amplitude) and may update the stored neural pattern when it is determined that the new value is more advantageous (e.g., greater amplitude in the detected neural pattern).

shows a diagram illustrating example blocks executed to implement one aspect of the present disclosure. The procedure according to this example aspect details steps for implementing an operational process in accordance with the present disclosure. The operational process may be performed during use of systemto provide neural pathway creation/reinforcement in accordance with aspects of the present disclosure. At block, a neural pattern of a therapy patient is detected. The neural pattern may be detected using a neural pattern detector such as neural pattern detector. In some embodiments, detecting the neural pattern at blockmay be in response to prompting a patient to think or envision a target action, thereby stimulating neural activity in the patient's brain corresponding to a neural pattern associated with the target action. In other embodiments, the patient may not be prompted to think or envision a target action prior to detecting the neural pattern.

At block, a target action associated with the detected neural pattern is determined. In some embodiments, determining a target action associated with the detected neural pattern may include analyzing the detected pattern to determine whether the neural pattern is associated to a previously determined neural pattern. For example, a detected neural pattern may be compared with neural patterns stored in databaseto determine if the detected neural pattern matches at least one of the stored neural patterns. In some embodiments, each of the stored neural patterns may be associated with a target action. The matching of the detected neural pattern with the stored neural patterns may be based on a comparison of the frequency and/or amplitude of the detected neural pattern and the stored neural patterns. For example, controllermay compare the frequency of the neural pattern detected by neural pattern detectorwith the frequency of each of the stored neural patterns in databaseuntil a match is detected. In other embodiments, the matching of the detected neural pattern with the stored neural patterns may be based on a comparison of the digital patterns, such as a particular series or sequence of 0's and 1's, of the stored neural patterns and the detected neural pattern. Upon finding a match for the detected neural pattern from the stored neural patterns, controllermay determine that the detected neural pattern is associated with the target action corresponding to the matched neural pattern. In some embodiments, the matching may not an exact match, and may instead be based on the detected neural pattern being within a threshold value of the matched neural pattern. For example, controllermay compare the frequency or digital pattern of the neural pattern detected by neural pattern detectorwith the frequency or digital pattern of each of the stored neural patterns in databaseand may find a match when the difference between the detected neural pattern and the matched neural pattern is within the threshold value. In embodiments, the matching process described may be used in the learning and training process, as discussed in further detail below.

In some embodiments, the matching of the detected neural pattern with the stored neural patterns may be based alternative or further signal processing to the comparing discussed above. For example, matching the detected neural pattern with the stored neural patterns may be based on pattern timing, pattern intensity, pattern duration, etc.