Patent Description:
<CIT> discloses an interface module for receiving input from a user of an exoskeleton indicative of a particular sensitivity level for a particular action.

The present invention seeks to more accurately detect a state transition following a state transition intention input.

Accordingly, the present invention provides a computer implemented method as defined in appended claim <NUM> and an exoskeleton system as defined in appended claim <NUM>.

It should be noted that the figures are not drawn to scale and that elements of similar structures or functions are generally represented by like reference numerals for illustrative purposes throughout the figures. It also should be noted that the figures are only intended to facilitate the description of the preferred embodiments. The figures do not illustrate every aspect of the described embodiments and do not limit the scope of the present disclosure.

This application discloses example embodiments pertaining to the design and implementation of novel systems and methods for the recognition of the intent of users of a powered exoskeleton. In various embodiments, methods for intent recognition in exoskeleton devices, in their simplest form, can allow the user to provide direct indication of their intent through manual entry (by using buttons, for example), while other methods can be designed to eliminate dependence on direct interaction for normal operation. This disclosure describes systems and methods that in various examples allow a user to provide input that may improve the accuracy of a device's intent recognition without slaving the device to user commands.

The embodiments described herein offer a substantial improvement over alternative methods of intent recognition in exoskeletons. For example, one alternative method for intent recognition in exoskeleton devices is a fully supervised approach that provides the user or a device proctor (e.g., a physical therapist) the ability to directly indicate a desired change in the user's intended behavior. These methods can tie triggers in state behavior of the device directly to manual inputs, but this requires the user to indicate a wide variety of behaviors such as sitting, standing, going up or down stairs, walking, running and the like.

In an effort to reduce the user's direct involvement in the device behavior, in another alternative unsupervised methods have been developed that use device sensors to automatically determine the user's intended maneuver without direct interaction from the operator. This reduces the burden on the user and presents the potential to increase the number of operational modes the device can recognize, but it introduces the risk of false intent recognition. Some methods have been developed which use a combination of automated and direct identification, but they still present the same challenges. Various systems and methods described herein allow the operator to directly provide information to the device, without the input of the operator directly manipulating the operating state of the exoskeleton.

This disclosure teaches methods for developing various embodiments of a semi-supervised intent recognition method. This approach can allow an operator to provide the device with additional information that the device can use to manipulate the behavior of the device. In these embodiments, the user can provide direct input to the machine to enhance its decision making ability without directly dictating the decisions to be made.

In some embodiments, the direct input provided by the user is not correlated directly with a specific maneuver (e.g., taking a step). Another embodiment provides the user with only a single input of direct intent indication through the source of a button. In this embodiment, this button does not correlate with a specific maneuver such as walking, running, or standing. Instead, the button only indicates the user's desire or intent to change behavior. In one example, if the operator is walking and plans to transition to standing or running, the user would only need to push the state transition intention button to alert the device to anticipate a potential decision. In such an embodiment, the device behavior is not being fully supervised or directly manipulated by the user's indicated intent. Instead, when the user specifies that a change in intent is possibly coming, the device can implement a method to be more sensitive to responding to user behaviors and then respond accordingly, such as assisting the user in physically transitioning from walking to running or standing, or even doing nothing.

In further embodiments, the direct input provided by the user can be correlated with a specific maneuver but the device is still not directly manipulated by the indicated intent. An embodiment can include a user in the sitting position wearing the device, where the device has a single button. In this position, even if the button is typically used to describe a change in intended behavior, from a sitting position the only valid change in behavior for this device in this embodiment is to stand up. As a result, a single button press can be directly correlated with a single maneuver, such as a sit-to-stand transition. However, in this embodiment, the device may not be directly supervised by the user's indicated intent. As a result, the device does not change behavior immediately or as a direct reaction to the press of the button but instead becomes more sensitive to detecting a sit-to-stand transition initiated by the user.

An exoskeleton system can respond in a variety of ways to the semi-supervised intent of the operator. In one embodiment, the exoskeleton device can use the indication of intent from the operator to begin monitoring the device sensors to look for change in behavior. In another embodiment, the indicated intent can be used to increase the sensitivity of a set of unsupervised intent recognition methods that are already running. This can be done by allowing the exoskeleton device to lower the required confidence to initiate a change in intended maneuver. In yet another embodiment, the indicated intent can be treated as just another sensor input. The device can then provide the user's indicated intent along with the device sensors into a traditionally unsupervised intent recognition method. This can be desirable in the case of using data driven intent recognition algorithms that leverage machine learning algorithms to infer the appropriate points of change in intent. It is important to note that the previously described embodiments are descriptive but not inclusive of all the potential additional embodiments that can leverage the semi-supervised indication of user intent and should therefore not be construed to be limiting.

In various embodiments, a user can provide the exoskeleton device with a semi-supervised manual indication of intent through a variety of input methods. In no way does the source of the input method limit or restrict the application of the disclosed systems and methods when it comes to incorporating the semi-supervised input to form a better estimate of the user's intended behavior. Some of the potential input methods include, but are not limited to, the following: physical button attached to the device; unique button press (e.g., double click or long press); discrete gesture (e.g., wave arms, tap foot); spoken commands; mobile device interface; interpreted manual input through another sensor input (e.g., inferring a knock on the device through watching an IMU signal); and the like.

For the purpose of clarity, example embodiments are discussed in the context of design and implementation of exoskeleton systems (e.g., as shown in <FIG>); however, systems and methods described and shown herein can have application to a wide range of worn devices where the device is using onboard sensors for the purpose of recognizing the intended behavior of the user. A specific example of this is footwear, specifically active footwear, where the device uses included sensors to determine the intended behavior of the operator such that it can report statistics or adapt the performance characteristics for the user. In these applications, the designers will be met with the same issues surrounding balancing the safety of a fully supervised intent recognizer with the usability of an unsupervised option. The application of a semi-supervised method as disclosed in this document can be a solution to balancing these needs in other powered worn devices as well.

Turning to <FIG>, an example of an embodiment of an exoskeleton system <NUM> being worn by a human user <NUM> is illustrated. As shown in this example, the exoskeleton system <NUM> comprises a left and right leg actuator unit <NUM>, 110R that are respectively coupled to a left and right leg <NUM>, 102R of the user. In this example illustration, portions of the right leg actuator unit 110R are obscured by the right leg 102R; however, it should be clear that in various embodiments the left and right leg actuator units <NUM>, 110R can be substantially mirror images of each other.

The leg actuator units <NUM> can include an upper arm <NUM> and a lower arm <NUM> that are rotatably coupled via a joint <NUM>. A bellows actuator <NUM> extends between plates <NUM> that are coupled at respective ends of the upper arm <NUM> and lower arm <NUM>, with the plates <NUM> coupled to separate rotatable portions of the joint <NUM>. A plurality of constraint ribs <NUM> extend from the joint <NUM> and encircle a portion of the bellows actuator <NUM> as described in more detail herein. One or more sets of pneumatic lines <NUM> can be coupled to the bellows actuator <NUM> to introduce and/or remove fluid from the bellows actuator <NUM> to cause the bellows actuator <NUM> to expand and contract as discussed herein.

The leg actuator units <NUM>, 110R can be respectively coupled about the legs <NUM>, 102R of the user <NUM> with the joints <NUM> positioned at the knees <NUM>, 103R of the user <NUM> with the upper arms <NUM> of the leg actuator units <NUM>, 110R being coupled about the upper legs portions <NUM>, 104R of the user <NUM> via one or more couplers <NUM> (e.g., straps that surround the legs <NUM>). The lower arms <NUM> of the leg actuator units <NUM>, 110R can be coupled about the lower leg portions <NUM>, 105R of the user <NUM> via one or more couplers <NUM>. As shown in the example of <FIG>, an upper arm <NUM> can be coupled to the upper leg portion <NUM> of a leg <NUM> above the knee <NUM> via two couplers <NUM> and the lower arm <NUM> can be coupled to the lower leg portion <NUM> of a leg <NUM> below the knee <NUM> via two couplers <NUM>. It is important to note that some of these components can be omitted in certain embodiments, some of which are discussed within. Additionally, in further embodiments, one or more of the components discussed herein can be operably replaced by an alternative structure to produce the same functionality.

As discussed herein, an exoskeleton system <NUM> can be configured for various suitable uses. For example, <FIG> and <FIG> illustrate an exoskeleton system <NUM> being used by a user during skiing. As shown in <FIG> and <FIG> the user can wear the exoskeleton system <NUM> and a skiing assembly <NUM> that includes a pair of ski boots <NUM> and pair of skis <NUM>. In various embodiments, the lower arms <NUM> of the leg actuator units <NUM> can be removably coupled to the ski boots <NUM> via a coupler <NUM>. Such embodiments can be desirable for directing force from the leg actuator units <NUM> to the skiing assembly. For example, as shown in <FIG> and <FIG>, a coupler <NUM> at the distal end of the lower arm <NUM> can couple the leg actuator unit <NUM> to the ski boot <NUM> and a coupler <NUM> at the distal end of the upper arm <NUM> can couple the leg actuator unit <NUM> to the upper leg <NUM> of the user <NUM>.

The upper and lower arms <NUM>, <NUM> of a leg actuator unit <NUM> can be coupled to the leg <NUM> of a user <NUM> in various suitable ways. For example, <FIG> illustrates an example where the upper and lower arms <NUM>, <NUM> and joint <NUM> of the leg actuator unit <NUM> are coupled along lateral faces of the top and bottom portions <NUM>, <NUM> of the leg <NUM>. <FIG> illustrate another example of an exoskeleton system <NUM> where the joint <NUM> is disposed laterally and adjacent to the knee <NUM> with a rotational axis K of the joint <NUM> being disposed coincident with a rotational axis of the knee <NUM>. The upper arm <NUM> can extend from the joint <NUM> along a lateral face of the upper leg <NUM> to an anterior face of the upper leg <NUM>. The portion of the upper arm <NUM> on the anterior face of the upper leg <NUM> can extend along an axis U. The lower arm <NUM> can extend from the joint <NUM> along a lateral face of the lower leg <NUM> from a medial location at the joint <NUM> to a posterior location at a bottom end of the lower leg <NUM> with a portion extending along axis L that is perpendicular to axis K.

In various embodiments, the joint structure <NUM> can constrain the bellows actuator <NUM> such that force created by actuator fluid pressure within the bellows actuator <NUM> can be directed about an instantaneous center (which may or may not be fixed in space). In some cases of a revolute or rotary joint, or a body sliding on a curved surface, this instantaneous center can coincide with the instantaneous center of rotation of the joint <NUM> or a curved surface. Forces created by a leg actuator unit <NUM> about a rotary joint <NUM> can be used to apply a moment about an instantaneous center as well as still be used to apply a directed force. In some cases of a prismatic or linear joint (e.g., a slide on a rail, or the like), the instantaneous center can be kinematically considered to be located at infinity, in which case the force directed about this infinite instantaneous center can be considered as a force directed along the axis of motion of the prismatic joint. In various embodiments, it can be sufficient for a rotary joint <NUM> to be constructed from a mechanical pivot mechanism. In such an embodiment, the joint <NUM> can have a fixed center of rotation that can be easy to define, and the bellows actuator <NUM> can move relative to the joint <NUM>. In a further embodiment, it can be beneficial for the joint <NUM> to comprise a complex linkage that does not have a single fixed center of rotation. In yet another embodiment, the joint <NUM> can comprise a flexure design that does not have a fixed joint pivot. In still further embodiments, the joint <NUM> can comprise a structure, such as a human joint, robotic joint, or the like.

In various embodiments, leg actuator unit <NUM> (e.g., comprising bellows actuator <NUM>, joint structure <NUM>, constraint ribs <NUM> and the like) can be integrated into a system to use the generated directed force of the leg actuator unit <NUM> to accomplish various tasks. In some examples, a leg actuator unit <NUM> can have one or more unique benefits when the leg actuator unit <NUM> is configured to assist the human body or is included into a powered exoskeleton system <NUM>. In an example embodiment, the leg actuator unit <NUM> can be configured to assist the motion of a human user about the user's knee joint <NUM>. To do so, in some examples, the instantaneous center of the leg actuator unit <NUM> can be designed to coincide or nearly coincide with the instantaneous center of rotation of the knee (e.g., aligned along common axis K as shown in <FIG>). In one example configuration, the leg actuator unit <NUM> can be positioned lateral to the knee joint <NUM> as shown in <FIG>, <FIG>, <FIG>, and <FIG> (as opposed to in front or behind). In another example configuration, the leg actuator unit <NUM> can be positioned behind the knee <NUM>, in front of the knee <NUM>, on the inside of the knee <NUM>, or the like. In various examples, the human knee joint <NUM> can function as (e.g., in addition to or in place of) the joint <NUM> of the leg actuator unit <NUM>.

For clarity, example embodiments discussed herein should not be viewed as a limitation of the potential applications of the leg actuator unit <NUM> described within this disclosure. The leg actuator unit <NUM> can be used on other joints of the body including but not limited to the elbow, hip, finger, spine, or neck, and in some embodiments, the leg actuator unit <NUM> can be used in applications that are not on the human body such as in robotics, for general purpose actuation, or the like.

Some embodiments can apply a configuration of a leg actuator unit <NUM> as described herein for linear actuation applications. In an example embodiment, the bellows <NUM> can comprise a two-layer impermeable/inextensible construction, and one end of the constraining ribs <NUM> can be fixed to the bellows <NUM> at predetermined positions. The joint structure <NUM> in various embodiments can be configured as a series of slides on a pair of linear guide rails, where the remaining end of each constraining rib <NUM> is connected to a slide. The motion and force of the fluidic actuator can therefore be constrained and directed along the linear rail.

<FIG> is a block diagram of an example embodiment of an exoskeleton system <NUM> that includes an exoskeleton device <NUM> that is operably connected to a pneumatic system <NUM>. The exoskeleton device <NUM> comprises a processor <NUM>, a memory <NUM>, one or more sensors <NUM>, a communication unit <NUM> and a user interface <NUM>. A plurality of actuators <NUM> are operably coupled to the pneumatic system <NUM> via respective pneumatic lines <NUM>. The plurality of actuators <NUM> includes a pair of knee-actuators <NUM>, 130R that are positioned on the right and left side of a body <NUM>. For example, as discussed above, the example exoskeleton system <NUM> shown in <FIG> can comprise a left and right leg actuator unit <NUM>, 110R on respective sides of the body <NUM> as shown in <FIG>.

In various embodiments, the example system <NUM> can be configured to move and/or enhance movement of the user wearing the exoskeleton system <NUM>. For example, the exoskeleton device <NUM> can provide instructions to the pneumatic system <NUM>, which can selectively inflate and/or deflate the bellows actuators <NUM> via pneumatic lines <NUM>. Such selective inflation and/or deflation of the bellows actuators <NUM> can move one or both legs <NUM> to generate and/or augment body motions such as walking, running, jumping, climbing, lifting, throwing, squatting, skiing or the like. In further embodiments, the pneumatic system <NUM> can be manually controlled, configured to apply a constant pressure, or operated in any other suitable manner.

In some embodiments, such movements can be controlled and/or programmed by the user <NUM> that is wearing the exoskeleton system <NUM> or by another person. In some embodiments, the exoskeleton system <NUM> can be controlled by movement of the user. For example, the exoskeleton device <NUM> can sense that the user is walking and carrying a load and can provide a powered assist to the user via the actuators <NUM> to reduce the exertion associated with the load and walking. Similarly, where a user <NUM> wears the exoskeleton system <NUM> while skiing, the exoskeleton system <NUM> can sense movements of the user <NUM> (e.g., made by the user <NUM>, in response to terrain, or the like) and can provide a powered assist to the user via the actuators <NUM> to enhance or provide an assist to the user while skiing.

Accordingly, in various embodiments, the exoskeleton system <NUM> can react automatically without direct user interaction. In further embodiments, movements can be controlled in real-time by a controller, joystick or thought control. Additionally, some movements can be pre-preprogrammed and selectively triggered (e.g., walk forward, sit, crouch) instead of being completely controlled. In some embodiments, movements can be controlled by generalized instructions (e.g. walk from point A to point B, pick up box from shelf A and move to shelf B).

In various embodiments, the exoskeleton device <NUM> can be operable to perform methods or portions of methods described in more detail below. For example, the memory <NUM> can include non-transient computer readable instructions, which if executed by the processor <NUM>, can cause the exoskeleton system <NUM> to perform methods or portions of methods described herein. The communication unit <NUM> can include hardware and/or software that allows the exoskeleton system <NUM> to communicate with other devices, including a user device, a classification server, other exoskeleton systems, or the like, directly or via a network.

In some embodiments, the sensors <NUM> can include any suitable type of sensor, and the sensors <NUM> can be located at a central location or can be distributed about the exoskeleton system <NUM>. For example, in some embodiments, the exoskeleton system <NUM> can comprise a plurality of accelerometers, force sensors, position sensors, pressure sensors and the like, at various suitable positions, including at the arms <NUM>, <NUM>, joint <NUM>, actuators <NUM> or any other location. Accordingly, in some examples, sensor data can correspond to a physical state of one or more actuators <NUM>, a physical state of a portion of the exoskeleton system <NUM>, a physical state of the exoskeleton system <NUM> generally, and the like. In some embodiments, the exoskeleton system <NUM> can include a global positioning system (GPS), camera, range sensing system, environmental sensors, or the like.

The user interface <NUM> can include various suitable types of user interfaces, including one or more of a physical button, a touch screen, a smart phone, a tablet computer, a wearable device and the like. For example, in some embodiments the exoskeleton system <NUM> can comprise an embedded system that includes a user interface <NUM> or the exoskeleton device <NUM> can be operably connected to a separate device (e.g., a smart phone) via a wired or wireless communication network (e.g., Bluetooth, Wi-Fi, the Internet, or the like).

The pneumatic system <NUM> can comprise any suitable device or system that is operable to inflate and/or deflate the actuators <NUM> individually or as a group. For example, in one embodiment, the pneumatic system can comprise a diaphragm compressor as disclosed in related patent application <CIT> and/or a poppet valve system as described in <CIT>, which issued as <CIT>.

As discussed herein, various suitable exoskeleton systems <NUM> can be used in various suitable ways and for various suitable applications. However, such examples should not be construed to be limiting on the wide variety of exoskeleton systems <NUM> or portions thereof that are within the scope the present disclosure. Accordingly, exoskeleton systems <NUM> that are more or less complex than the examples of <FIG>, <FIG>, <FIG>, <FIG>, <FIG> and <FIG> are within the scope of the present disclosure.

Additionally, while various examples relate to an exoskeleton system <NUM> associated with the legs or lower body of a user, further examples can be related to any suitable portion of a user body including the torso, arms, head, legs, or the like. Also, while various examples relate to exoskeletons, it should be clear that the present disclosure can be applied to other similar types of technology, including prosthetics, body implants, robots, or the like. Further, while some examples can relate to human users, other examples can relate to animal users, robot users, various forms of machinery, or the like.

As discussed herein, various embodiments relate to a method of semi-supervised intent recognition for wearable devices such as an exoskeleton system <NUM>. Semi-supervised intent recognition methods of various embodiments can be distinguished from fully-supervised intent recognition methods and unsupervised intent recognition methods as described in more detail below.

Turning to <FIG>, an example state machine <NUM> for an exoskeleton system <NUM> is illustrated, which includes a plurality of system states and transitions between the system states. More specifically, the state machine <NUM> is shown comprising a sitting state <NUM>, from which the exoskeleton system <NUM> can transition to a stand state <NUM> via a sitting-stand transition <NUM>. The exoskeleton system <NUM> can transition from the stand state <NUM> to a standing state <NUM> via a stand-standing transition <NUM>. The exoskeleton system <NUM> can transition from the standing state <NUM> to a sit state <NUM> via a standing-sit transition <NUM>. The exoskeleton system <NUM> can transition from the sit state <NUM> to a sitting state <NUM> via a sit-sitting transition <NUM>.

For example, where a user <NUM> is sitting in a chair, the exoskeleton system <NUM> can be in a sitting state <NUM> and when the user <NUM> wants to stand up, the exoskeleton system <NUM> can move from sitting <NUM> to standing <NUM> via the stand state <NUM>, which moves the user <NUM> from a sitting position to a standing position. Where the user <NUM> is standing by a chair, the exoskeleton system <NUM> can be in a standing state <NUM> and when the user <NUM> wants to sit in the chair, the exoskeleton system <NUM> can move from standing <NUM> to sitting <NUM> via the sit state <NUM>, which moves the user <NUM> from a standing position to a sitting position.

Also, as shown in the state machine <NUM>, the exoskeleton system <NUM> can move from the standing-state <NUM> to a walk state <NUM> via a standing-walk transition <NUM>. The exoskeleton system <NUM> can move from the walk state <NUM> to the standing state <NUM> via a walk-standing transition <NUM>. For example, where a user <NUM> is standing <NUM>, the user <NUM> can choose to walk <NUM> and can choose to stop walking <NUM> and return to standing <NUM>.

The example state machine <NUM> is used herein for purposes of illustration only and should not be construed to be limiting on the wide variety of state machines for an exoskeleton system <NUM> that are within the scope and sprit of the present disclosure. For example, some embodiments can include a simpler state machine having only standing and walking states <NUM>, <NUM>. Further embodiments can include additional states such as a running state from the walking state <NUM>, or the like.

Turning to <FIG>, an example of a fully-supervised intent recognition method <NUM> is illustrated in the context of the state machine <NUM> of <FIG> and a user interface <NUM> (see <FIG>) having an A-button <NUM>. In a fully-supervised state machine of various examples, the user <NUM> provides a direct manual input to an interface <NUM> to dictate the initiation of a single unique transition from one state to another, upon which the exoskeleton system <NUM> is slaved to initiate that transition. In this example, that manual input is represented by a button press of the A-button <NUM>. The A-Button <NUM> is shown mapped to a single transition (i.e., standing-walk transition <NUM>) from a standing state <NUM> to a walk state <NUM>. If button A is pressed and the exoskeleton system <NUM> detects that the user <NUM> is in a safe configuration to initiate a transition to walking <NUM>, the exoskeleton system <NUM> will initiate a transition <NUM> to the walk state <NUM> from the standing state <NUM>. In other words, in this example, Button A can only trigger the standing-walk transition <NUM> from the standing state <NUM> to the walking state <NUM>, with all other transitions (i.e., <NUM>, <NUM>, <NUM>, <NUM>, <NUM>) being unavailable via a button press of the A-Button <NUM>. This transition, if successfully completed, will result in the device wearer to physically transition from standing to walking in this example.

Turning to <FIG>, an example of a fully supervised intent recognition method <NUM> is illustrated in the context of the state machine <NUM> of <FIG> and a user interface <NUM> having a first and second button <NUM>, <NUM>. More specifically, expanding on the example of <FIG>, to deal with multiple transitions in a fully supervised intent recognition system, Button A is mapped to a single transition <NUM> from standing state <NUM> to walk state <NUM> as discussed above. Additionally, the B-button <NUM> is shown mapped to a single transition (i.e., sitting-stand transition <NUM>) from sitting state <NUM> to stand state <NUM>.

As discussed herein, if the A-Button <NUM> is pressed and the user <NUM> is safe, the exoskeleton system <NUM> initiates a transition from standing <NUM> to walk <NUM>. If the B-button <NUM> is pressed, the exoskeleton system <NUM> initiates a sitting-stand transition <NUM> from sitting <NUM> to stand <NUM>, causing the user <NUM> to stand up from sitting. From there, the exoskeleton system can then interpret whether the user <NUM> has made it fully into the standing state <NUM> from the stand state <NUM> through the device interpreted stand-standing transition <NUM>, and, if not, can abort the sitting-stand transition <NUM> as a safety measure and return the user to sitting. In other words, pressing the B-button <NUM> on the interface <NUM> can trigger the sitting-stand transition <NUM> from sitting <NUM> to a stand state <NUM>, and the exoskeleton device <NUM> will then transition <NUM> to the standing state <NUM> unless an error occurs, in which case the device would return to the sitting state <NUM>.

Accordingly, the A-Button <NUM> can only trigger the standing-walk transition <NUM> from the standing state <NUM> to the walking state <NUM> and the B-button <NUM> can only trigger the sitting-stand transition <NUM> from the sitting state <NUM> to the standing state <NUM>, with all other transitions (i.e., <NUM>, <NUM>, <NUM>, <NUM>) being unavailable via a button press of the A-button <NUM> or B-button <NUM>.

Turning to <FIG>, another example of a fully supervised intent recognition method <NUM> is illustrated in the context of the state machine <NUM> of <FIG> and a user interface <NUM> (see <FIG>) having an A-button <NUM>. Specifically, <FIG> illustrates another variation of a fully-supervised state machine <NUM> where the A-Button <NUM> is mapped such that if the exoskeleton system <NUM> is in a standing state <NUM> and the user <NUM> is safe, pressing the A-Button <NUM> will cause the exoskeleton system <NUM> to initiate the standing-walk transition <NUM> to the walk state <NUM>, and if the exoskeleton system <NUM> is in a sitting state <NUM> and the user <NUM> is safe, the exoskeleton system <NUM> will initiate the sitting-stand transition <NUM> to the stand state <NUM>, after which the exoskeleton system <NUM> will then interpret whether there has been a successful transition <NUM> to the standing state <NUM> and behave accordingly. This example button configuration is similar to the previous example of <FIG> having dual buttons A and B <NUM>, <NUM> except that the same button <NUM> is mapped to two specific transitions <NUM>, <NUM> instead of one transition respectively. As such, in this example of a fully supervised intent recognition method, a single button press is mapped to one, and only one, transition, regardless of whether one, two, or a plurality of buttons is used to indicate that button press.

Fully-supervised intent recognition methods as discussed above can be distinguished from unsupervised intent recognition methods. For example, <FIG> illustrates an example of an un-supervised intent recognition method. More specifically, <FIG> illustrates an unsupervised state machine <NUM> where the user <NUM> provides no direct manual input to the intent recognition of the exoskeleton system <NUM>. Instead, the exoskeleton system <NUM> is continuously monitoring sensor inputs and interpreting what state the exoskeleton system <NUM> is currently in and what transition the user <NUM> is attempting to initiate. Once the threshold for a possible transition from the currently detected state is reached based on sensor data (e.g., from sensors <NUM>) and the user <NUM> is interpreted as being in a safe configuration, the exoskeleton system <NUM> can then initiate the interpreted transition.

In contrast to the fully supervised intent recognition methods discussed in <FIG>, each of the transitions <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM> shown in <FIG> are device-interpreted transitions where the exoskeleton system <NUM> determines the current state (i.e., sitting <NUM>, stand <NUM>, standing <NUM>, sit <NUM> and walk <NUM>) and determines what transition, if any, the user is attempting to initiate. Accordingly, the example user interface <NUM> of <FIG> is without a button or other element or mechanism that allows the user <NUM> to initiate one or more specific transitions (although the user interface <NUM> can have other suitable functionalities). In other words, the unsupervised method of <FIG> does not allow the user <NUM> to provide input to indicate a desire to make a transition or to initiate a transition, whereas the supervised intent recognition methods discussed in <FIG> do allow the user <NUM> to initiate a transition to some or all states through the user interface <NUM>.

As discussed herein, fully supervised intent recognition methods and unsupervised intent recognition methods can be distinguished from semi-supervised intent recognition methods as described in more detail below. For example, <FIG> illustrates an example embodiment of a semi-supervised intent recognition method. Specifically, <FIG> illustrates a semi-supervised state machine <NUM> where user <NUM> provides direct manual input to the intent recognition of the exoskeleton system <NUM> indicating that the exoskeleton system <NUM> should look for a state transition from the current state, where the current state is known to or determined by the exoskeleton system <NUM> at the time of the manual input by the user <NUM>.

Such an increased observance of a state transition can be accomplished in various suitable ways such as by lowering one or more thresholds for interpreting whether a transition is occurring, which can increase the chance that a transition is observed from the sensor inputs (e.g., from sensor data received from sensors <NUM>).

After the manual input (e.g., the button X <NUM> being pressed in this example), if a state transition is detected, the exoskeleton system <NUM> then proceeds to initiate the detected state transition. However, if no state transition is detected, the exoskeleton system <NUM> takes no action, and after a predefined timeout, the exoskeleton system <NUM> stops looking for transitions, returning the exoskeleton system <NUM> into a normal state of readiness for the next manual input.

In other words, in various embodiments, the exoskeleton system <NUM> can monitor and respond to the movements of a user <NUM> in a normal operation state including identifying and initiating various state transitions (e.g., any possible state transition as shown in the example of <FIG>) with the identifying of the state transitions being associated with a first set of one or more thresholds, criteria, or the like. In response to an input from a user (e.g., pressing single button X <NUM>), the exoskeleton system <NUM> can still monitor and respond to the movements of the user <NUM>, but according to a second set of one or more thresholds, criteria, or the like, such that identifying state transitions is made easier compared to normal operation under the first set.

More specifically, for some sets of sensor data, a given state transition would not be identified as being present when the exoskeleton system <NUM> is operating under the first set but would be identified as being present under the second set of one or more thresholds, criteria, or the like. Accordingly, in various embodiments, by the user <NUM> providing a given input (e.g., pressing single button X <NUM> ), the exoskeleton system <NUM> can become more sensitive to identifying state transitions.

In various embodiments, sensitivity to state transitions initiated by the user <NUM> can be based on possible state transitions given the state that the user <NUM> and exoskeleton system <NUM> are currently in. Accordingly, in various embodiments, after an indication of an intention to make a state change is received (e.g., via the user <NUM> pushing the X-button <NUM>) a determination can be made as to what state the user <NUM> and exoskeleton system <NUM> are currently in and sensitivity to potential state changes by the user <NUM> can be tuned based on the determined current state.

For example, referring to <FIG>, where a determination is made that the user is in the sitting state <NUM>, sensitivity to identifying a transition to a stand state <NUM> can be tuned to be more sensitive, whereas other states that are not directly reachable from the sitting state (e.g., walk state <NUM> or sit state <NUM>) can be excluded as potential states that may be detected or identified. Additionally, where multiple state transitions are possible from a given state, sensitivity can be tuned for these multiple potential state transitions. For example, referring to <FIG>, where a determination is made that the user is in the standing state <NUM> sensitivity to identifying a transition to a sit or walk state <NUM>, <NUM> can be tuned to be more sensitive, whereas other states that are not directly reachable from the sitting state (e.g., stand <NUM>) can be excluded as potential states that may be detected or identified.

Having the exoskeleton system <NUM> become more sensitive to state transitions in response to an input from the user <NUM> can be desirable for improving the experience of the user wearing the exoskeleton system <NUM>. For example, during normal operation, the threshold for identifying and responding to state transitions can be high to prevent false-positives of state transitions while also allowing the exoskeleton system <NUM> to respond if necessary where a state transition occurs.

However, where the user intends to initiate a state transition (e.g., moving from sitting to a standing position; moving from a standing position to a sitting position; moving from a standing position to walking; or the like), the user <NUM> can provide an input to indicate the intention to initiate a state transition and the exoskeleton system <NUM> can become more sensitive to state transitions in anticipation of the user <NUM> making the intended state transition. Such increased sensitivity can be desirable for preventing false negatives or failures to identify a state transition being initiated by the user <NUM>.

Also, providing the user <NUM> with a single input to indicate an intention to make a state transition can be desirable because it makes operation of such an exoskeleton system <NUM> much simpler and user-friendly compared to fully supervised systems having multiple buttons mapped to different specific state transitions or systems where a single button is mapped to fewer than all state transitions (e.g., as shown in <FIG>). Providing the user <NUM> with a single input to indicate an intention to make a state transition can be desirable over unsupervised methods because providing the user <NUM> with the ability to indicate an intention to make state transitions helps to prevent false positives and false negatives for state transitions by providing variable sensitivity to state transitions based on user intent or desire to make state transitions, which can be associated with an increased likelihood of a state transition occurring.

To further illustrate the difference between the fully supervised intent recognition methods of <FIG> and semi-supervised method of <FIG>, it can be useful to focus on examples where a user has multiple options for making a state transition from a given state. For example, as shown in the state diagram <NUM> of <FIG>, a user in a standing state <NUM> has the option of transitioning to a sitting state <NUM> via a sit maneuver state <NUM> or the option of transitioning to a walk state <NUM>. As shown in the example of <FIG>, where a user <NUM> presses the button <NUM>, the user <NUM> has the option initiating a standing-sit transition <NUM> or a standing walk-transition <NUM>, and the exoskeleton system <NUM> can be become more sensitive to both potential transitions <NUM>, <NUM> and can respond to the user <NUM> initiating either potential transition <NUM>, <NUM>.

In contrast, as shown in the examples of <FIG>, where the A-button <NUM> is pressed, the user <NUM> will be forced into the standing-walk transition <NUM> or at the very least will not have the option of a standing-sit transition <NUM>, with the standing-sit transition <NUM> being an unavailable action. Accordingly, while fully-supervised methods can limit the options of the movements of the user <NUM>, semi-supervised methods (e.g., as shown in <FIG>) can allow for a user to indicate an intent to make a state transition without explicitly or implicitly specifying one or more specific state transitions. Stated another way, fully-supervised methods can limit the options of the movements of the user <NUM>, whereas semi-supervised methods of various embodiments do not limit the options of the movements of the user <NUM> and allows the exoskeleton system <NUM> to adapt to the movements of the user <NUM> without limitation.

The difference between fully supervised intent recognition and semi-supervised intent recognition can also be illustrated when examining a state machine where one state has a larger number of possible state transitions. For example, <FIG> illustrates an example state machine <NUM> in a fully supervised intent recognition method <NUM> where a standing state <NUM> has eight possible transitions <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM> to eight different system states <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>.

More specifically, a user <NUM> of an exoskeleton system <NUM> has the option of transitioning from a standing state <NUM> to a sit state <NUM> via a standing-sit transition <NUM>; to a walk state <NUM> via a standing-walk transition <NUM>; to a jump state <NUM> via a standing-jump transition <NUM>; to a lunge state <NUM> via a standing-lunge transition <NUM>; to a crouch state <NUM> via a standing-crouch transition <NUM>; to a dive state <NUM> via a standing-dive transition <NUM>; to a sprint state <NUM> via a standing-sprint transition <NUM>; and to a jog state <NUM> via a standing-jog transition <NUM>.

As shown in the example of <FIG>, a user interface <NUM> can have an A-button <NUM> that is mapped to the standing-sit transition <NUM>. When the A-button <NUM> is pressed in this example, the exoskeleton system <NUM> can initiate transitioning to the sit state <NUM> via the standing-sit transition <NUM>, with the other states and transitions being unavailable when the A-button is pushed.

In a similar example, <FIG> illustrates a state machine <NUM> in a supervised intent recognition method <NUM> where a standing state <NUM> has eight possible transitions to eight respective states and four buttons <NUM>, <NUM>, <NUM>, <NUM> are respectively mapped to single transition and state pairs. More specifically, the A-button <NUM> is mapped to the standing-sit transition <NUM>; the B-button <NUM> is mapped the standing-jump transition <NUM>; the C-button <NUM> is mapped to the standing-lunge transition <NUM>; and the D-button <NUM> is mapped to the standing-crouch transition <NUM>.

Similar to the example of <FIG>, the method <NUM> of <FIG> illustrates that each of the buttons <NUM>, <NUM>, <NUM>, <NUM>, when pressed, triggers a transition to the state that the given button is mapped while making the other transitions and states unavailable. In this example, other state transitions are only available when pressing their respective associated buttons from the original state. Although the example of <FIG> illustrates only four buttons mapped to four respective state and transition pairs, in further embodiments, each of the states can be mapped to a respective button. In other words, for the example state machine <NUM> of <FIG> and <FIG> in further embodiments each of the eight state-transition pairs can be mapped to a respective button. Accordingly, where a user <NUM> wants to transition from the standing state <NUM> to another state, the user <NUM> must press a specific button associated with the given state or state transition to initiate the transition to the desired state.

In contrast, <FIG> illustrates an example of a semi-supervised intent recognition method <NUM> having the state machine <NUM> as shown in <FIG> and <FIG> and a user interface <NUM> having a single button <NUM> for indicating an intention to make a state transition. As shown in <FIG>, the user <NUM> can be in a standing state <NUM> and can press the X-button <NUM> to indicate the intention or desire to make a state transition, and the exoskeleton system <NUM> can become more sensitive to identifying state transitions, allowing the exoskeleton system <NUM> to initiate any of the eight possible state transitions shown in the example of <FIG> based on whether a state transition is detected from the user's behavior, or, alternatively, choose to not initiate a state transition if none is detected.

In other words, in the semi-supervised intent recognition method <NUM> of <FIG>, because the manual input (X-button <NUM>) only indicates for the exoskeleton system <NUM> to become more sensitive to detecting any possible transition (e.g., by lowering the transition thresholds to possible behaviors) from the current state, all possible state transitions remain possible.

Also, no transition is also possible and the user <NUM> is not forced or required to make a state transition. However, in the fully supervised example of <FIG>, if the B-button <NUM> is pressed and the current standing configuration state <NUM> is deemed safe to the user <NUM> to transition, the exoskeleton system <NUM> will initiate a standing to jump transition <NUM>. Whereas in the example of <FIG>, if X-button <NUM> is pressed and the user <NUM> is doing nothing that indicates a transition should occur, is about to occur, or is occurring, no transition will occur.

Additionally, while various embodiments of semi-supervised intent recognition methods are discussed having a single button (e.g., the X-button <NUM>), it should be clear that various embodiments can comprise a single input type, with one or more input methods for the single input type. For example, in some embodiments, an exoskeleton system <NUM> can comprise a first and second X-button <NUM> disposed respectively on the left and right actuator units 110A, 110B of the exoskeleton system <NUM>, and the user <NUM> can press either of the buttons <NUM> to make the exoskeleton system <NUM> more sensitive or responsive to identifying state transitions. Also, the single input type can be associated with multiple input methods in some embodiments. For example, a user <NUM> can press an X-button <NUM>, can knock on the body of the exoskeleton system <NUM> or provide a voice command to make the exoskeleton system <NUM> more sensitive or responsive to identifying state transitions.

One way to mathematically describe the difference between a fully supervised method and a semi-supervised method is to examine the probability of possible state transitions from a given starting state. In fully supervised methods for various state machines (e.g., state machine <NUM> of <FIG>), the probability of transitioning from standing <NUM> to walk <NUM> can be equal to N (i.e., P(Walk/Standing) = N). The probability of transitioning from standing <NUM> to standing <NUM> is then <NUM>-N (i.e., P(Standing/Standing) = <NUM>-N), in which case the exoskeleton system <NUM>, (e.g., due to a safety feature), did not allow the standing-walk transition to occur. The probability of transitioning from standing <NUM> to sit <NUM> equals <NUM> (i.e., P(Sit/Standing) = <NUM>), because in various fully supervised methods, a manual input can only map a single desired transition from a single starting state.

In a semi-supervised method for such same state machines (e.g., state machine <NUM> of <FIG>), the probability of transitioning from standing <NUM> to walk <NUM> can be equal to A (i.e., P(Walk/Standing) = A). The probability of transitioning from standing <NUM> to standing <NUM> is B (i.e., P(Standing/Standing) = B). The probability of transitioning from standing <NUM> to sit <NUM> is <NUM>-A-B (i.e., P(Sit/Standing) = <NUM>-A-B). This can be because in some embodiments of a semi-supervised intent recognition method, the exoskeleton system <NUM> is left to interpret the desired state transition from the given starting state, allowing the exoskeleton system <NUM> to decide between sit <NUM>, walk <NUM>, or remaining standing <NUM>.

Turning to <FIG>, a semi-supervised intent recognition method <NUM> in accordance with one embodiment is illustrated, which in various examples can be implemented by an exoskeleton device <NUM> of an exoskeleton system <NUM> (see <FIG>). The method <NUM> begins at <NUM> where the exoskeleton system <NUM> operates in a first mode with sensitivity to detecting state transitions at a first sensitivity level. At <NUM>, a determination is made whether a state transition is identified, and if so, the exoskeleton device facilitates the identified state transition at <NUM> and the method <NUM> cycles back to <NUM> where the exoskeleton system <NUM> operates in the first mode with sensitivity to detecting state transitions at the first sensitivity level. However, if at <NUM> a state transition is not identified, then at <NUM> a determination is made whether a state transition intention input is received, and if not, the method <NUM> cycles back to <NUM> where the exoskeleton system <NUM> operates in the first mode with sensitivity to detecting state transitions at the first sensitivity level.

For example, the exoskeleton <NUM> can operate in a normal sensitivity mode (e.g., the first mode) and can identify one or more state transitions being initiated or made by the user <NUM> and can act accordingly to support the user with such identified one or more state transitions as necessary. Also, the exoskeleton system <NUM> can monitor or wait for a state transition intention input to be received, which as discussed herein can be received in various suitable ways such as via pressing a button on a user interface <NUM>, via haptic input, via audio input, or the like.

In various embodiments, the exoskeleton system <NUM> can operate and transition the user <NUM> through some or all available states during a given operating session without a state transition intention input ever being received. For example, exoskeleton system <NUM> can be powered up, operate in various position states and then be powered off without a state transition intention input being received. In other words, in various embodiments, the exoskeleton system <NUM> can be fully functional and have the ability to move through all available position states and transitions without a state transition intention input ever being received.

Returning to the method <NUM>, if a state transition intention input is received at <NUM>, then the method <NUM> continues to <NUM> where the exoskeleton system <NUM> operates in a second mode with sensitivity to detecting state transitions at a second sensitivity level. At <NUM>, a determination is made whether a state transition is identified, and if so, at <NUM> the exoskeleton system <NUM> facilities the identified state transition and the method <NUM> cycles back to <NUM> where the exoskeleton system <NUM> operates in the second mode with sensitivity to detecting state transitions at a second sensitivity level.

However, if a state transition is not identified at <NUM>, the method <NUM> continues to <NUM> where a determination is made whether a second mode timeout has occurred. If not, the method <NUM> cycles back to <NUM> where the exoskeleton system <NUM> operates in the second mode with sensitivity to detecting state transitions at a second sensitivity level. However, if a second mode timeout is determined, then the method <NUM> cycles back to <NUM> where the exoskeleton system <NUM> operates in the first mode with sensitivity to detecting state transitions at the first sensitivity level.

For example, where a state transition intention input is received by the exoskeleton system <NUM>, the exoskeleton system <NUM> can switch from detecting state transitions at the first sensitivity level in the first mode to detecting state transitions at the second sensitivity level in the second mode, with the first and second sensitivity levels being different. The exoskeleton system <NUM> can monitor for state transitions and can facilitate one or more state transitions that are identified until a timeout for operating in the second mode occurs. However, it is not necessary that state transitions are ever identified and/or facilitated while operating in the second mode before a timeout of the second mode occurs.

As discussed herein, in various examples, the second sensitivity level of the second mode can be more sensitive to detecting or identifying state transitions compared to the first sensitivity level of the first mode. The greater sensitivity of the of the second sensitivity level can be achieved in various suitable ways including lowering one or more thresholds associated with identifying one or more state transitions; removing or modifying criteria for identifying one or more state transitions; or the like. However, in various embodiments, a subset of thresholds and/or criteria of a set of criteria need not be changed, removed or modified. Also, in some embodiments, one or more thresholds can be increased if the overall effect of the difference between the second sensitivity level from the first sensitivity level results in greater overall sensitivity of the second sensitivity level. In further embodiments, the first and second mode can be different in any suitable way such that for some sets of sensor data, a given state transition would not be identified as being present when the exoskeleton system <NUM> is operating in the first mode, but would be identified as being present when the exoskeleton system <NUM> is operating in the second mode.

A second mode timeout can be generated or implemented in various suitable ways. In some embodiments, a second mode timeout can comprise a timer corresponding to the time that a given second mode session has been active (e.g., an amount of time from when a switch from the first mode to the second mode occurs), and the second mode timeout can occur when the timer reaches or exceeds a defined timeout threshold. For example, a timeout threshold can be a number of seconds, minutes, or the like, including <NUM> second, <NUM> seconds, <NUM> seconds, <NUM> seconds, <NUM> seconds, <NUM> seconds, <NUM> seconds, <NUM> seconds, <NUM> minutes, <NUM> minutes, <NUM> minutes, or the like.

Such a timeout threshold can be static or variable. In some embodiments, second mode sessions can last a defined amount of time. In further embodiments, second mode sessions can last a defined amount of time by default but can be extended or shortened based on any suitable criteria, conditions, sensor data, or the like. For example, in some embodiments, a second mode session can end after a state transition is identified and/or the identified state transition is facilitated.

Intent recognition methods can be used in various suitable applications. One example embodiment includes an intent recognition method for a lower extremity exoskeleton system <NUM> for assisting with community mobility of aging adults. The exoskeleton system <NUM> can be designed to assist with transitions between seated and standing positions, ascending and descending stairs, as well as providing assistance during walking maneuvers. In this example, the user is provided with a single input to the exoskeleton system <NUM> in the form of knocking or tapping twice on the exterior of the exoskeleton system <NUM>. This manual interaction by the user <NUM> can be sensed through monitoring integrated accelerometers or other sensors <NUM> of the exoskeleton system <NUM>. The exoskeleton system <NUM> can interpret the input from the user <NUM> as an indication that a change in behavior is coming. The exoskeleton system <NUM> can utilize unsupervised intent recognition methods that monitor the device sensors <NUM> to observe a change in the user's behavior to identify intent; however, the specific methods can be tuned to be very conservative so as to avoid false indications of intent. When the intent is indicated from the user <NUM>, the required confidence threshold for the method can lower, allowing the exoskeleton system <NUM> to be much more sensitive and willing to respond to what it interprets as a triggered motion.

In such an example, the subject may have donned the exoskeleton system <NUM> from a seated position and the only available state transition to the device is to then stand up. When the user <NUM> taps the exoskeleton system <NUM> twice, the exoskeleton system <NUM> can relax the threshold requirements for the stand behavior for a fixed period of time, which for the purpose of this example can be set at <NUM> seconds. If the user <NUM> does not seek to initiate a stand behavior the intent indication will simply time out and return the conservative thresholds. If the user <NUM> does attempt to initiate a stand behavior, the exoskeleton system <NUM> will see the motion and respond with assistance accordingly. Once in a standing position, the user <NUM> can make a variety of actions including walking, transition to sit, ascend stairs or descend stairs. In this case, the user <NUM> can decide to not tap the machine and begin walking. At this point, the device can still respond to the behavior, but it may require a much more confident identification of the targeted behavior.

After stopping walking, the user <NUM> intends to ascend stairs. The user <NUM> taps the device twice to indicate the coming change in intended behavior and then begins to complete the motion. Here, the user's indicated intent does not specify for the exoskeleton system <NUM> what behavior the user <NUM> intends to transition to, only that a transition will likely occur in the near future. The exoskeleton system <NUM> observes the user <NUM> is standing, and using a more sensitive transition threshold the exoskeleton system <NUM> allows for the transition in behavior modes to occur.

Claim 1:
A computer implemented method of semi-supervised intent recognition for an exoskeleton system (<NUM>), the method comprising:
in response to a state transition intention input, changing the exoskeleton system (<NUM>) from operating in a first mode with sensitivity to detecting state transitions at a first sensitivity level to operating in a second mode with sensitivity to detecting state transitions at a second sensitivity level that is more sensitive than the first sensitivity level;
identifying a state transition while operating in the second mode and using the second sensitivity level; and
facilitating the identified state transition by actuating the exoskeleton system (<NUM>).