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Neuromuscular model-based sensing and control paradigm for a robotic leg — Massachusetts Institute Of Technology (US9975249B2)

Massachusetts Institute Of Technology · Google Patents

Google Patents · Patents · License: Open Access

hughm.herr massachusettsinstituteoftechnology

patent, google patents, intellectual property, US9975249B2, Massachusetts Institute Of Technology, Hugh M. Herr, en, 2018

ABSTRACT

Abstract

A neuromuscular model-based controller for a robotic limb having at least one joint includes a neuromuscular model having a muscle model, muscle geometry and reflex feedback loop to determine at least one torque or impedance command to be sent to the robotic limb. One or more parameters that determine relation between feedback data and activation of the muscle model are adjusted consequent to sensory data from at least one of an intrinsic sensor and an extrinsic sensor. A controller in communication with the neuromuscular model is configured to receive the at least one torque or impedance command and controls at least one of position, torque and impedance of the robotic limb joint.

Description

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No. 13/865,840, filed Apr. 18, 2013, which claims the benefit of U.S. Provisional Application No. 61/635,223, filed on Apr. 18, 2012. The entire teachings of the above applications are incorporated herein by reference.

BACKGROUND

Generally, existing commercially available prostheses, such as active ankle prostheses, are only able to reconfigure joint angle in response to very limited external factors. For example, available microprocessor-controlled ankle prostheses typically are only able to reconfigure ankle joint angle during a swing phase, requiring several strides to converge to a terrain-appropriate ankle position at first ground contact. Further, such ankle prostheses generally do not provide sufficient stance phase power for normal gait, and therefore cannot adapt biomimetically to changes in terrain slope and walking speed. Known control schemes for microprocessor-controlled ankle-foot prostheses rely upon fixed ankle state relationships deemed appropriate for walking at target speeds and across known terrains. Although somewhat effective at their intended steady-state gait speed and terrain, such controllers generally do not allow for adaptation to environmental disturbances such as speed transients and rapid intra-step terrain variations.

Therefore, a need exists for a controller of a robotic limb, such as a robotic leg or ankle, and a method for controlling a robotic limb, that overcomes or minimizes the above-referenced problems.

SUMMARY OF THE INVENTION

The invention generally is directed to a neuromuscular model-based controller for controlling at least one robotic limb joint of a robotic limb, and a method for controlling a robotic limb that includes at least one robotic limb joint. The neuromuscular model-based controller of the invention and the method of its use both embody a neuromuscular model-based sensing and control paradigm for a robotic limb.

In one embodiment, the neuromuscular model-based controller for controlling at least one robotic limb joint of a robotic limb of the invention includes a neuromuscular model including a muscle model, muscle geometry and a reflex feedback loop, wherein the reflex feedback loop conveys feedback data of at least one of muscle force, muscle length and muscle velocity of the muscle model, to thereby adjust activation of the muscle model, and wherein one or more parameters that determine the relation between the feedback data and activation of the muscle model are adjusted consequent to sensing data from at least one of an intrinsic and extrinsic sensor, such as at least one of a velocity of the robotic limb and a terrain underlying the robotic limb, the neuromuscular model employing the muscle model and the muscle geometry, comprising muscle joint moment arms, to determine at least one of a torque command and an impedance command. A control system of the model-based neuromechanical controller is in communication with the neuromuscular model, whereby the control system receives at least one of the torque command and the impedance command from the neuromuscular model and controls at least one of position, torque and impedance of the robotic limb joint.

In another embodiment, the invention is a method for controlling a robotic limb that includes at least one robotic limb joint. The method includes transmitting a measured joint state of the robotic limb to a neuromuscular model, the neuromuscular model including a muscle model, muscle geometry and a reflex feedback loop, whereby the measured joint state of the robotic limb is employed by the muscle geometry and the muscle model to determine at least one of a muscle force, a muscle length and a muscle velocity, and whereby at least one of the muscle force, muscle length and muscle velocity is conveyed by the reflex feedback loop as feedback data to thereby adjust activation of the muscle model, and wherein the one or more parameters that determine the relation between the feedback data and activation of the muscle model are further adjusted consequent to at least one of velocity of the robotic limb and a terrain underlying the limb, the neuromuscular model employing the muscle model to determine at least one of a torque command and an impedance command. The at least one of the torque command and the impedance command are transmitted to a control system. Optionally, at least one of a joint state, a joint torque and a joint impedance of the robotic limb are measured. At least one of the joint state, joint torque and joint impedance of the robotic limb are transmitted to the control system, whereby the control system adapts at least one of the torque command and the impedance command in response to the respective measured joint state, joint torque and joint impedance of the robotic limb to thereby obtain the current command for controlling at least one of the robotic limb joint position, torque and impedance. Alternatively, the control system can perform an open loop control framework where torque and impedance are not measured and fed back to the control system; rather, joint torque and impedance are controlled through modulation of motor current, either directly in the case of open loop torque control, or through a proportional-derivative control on measured joint state in the case of open loop impedance control. In the case of position control, the control system can perform an integration of the torque command to generate a position command. The system can then compare this position command to the measured robotic joint state and apply a feedback control.

In one embodiment of the neuromuscular model-based controller, the control system includes a feedforward gain, a lead compensator and a friction compensator to adapt at least one of the torque command and the impedance command, and thereby obtain the current command. In another embodiment, the control system further includes a motor controller for driving an actuator of the robotic limb joint with the current command. In still another embodiment, the neuromuscular model-based controller includes at least one sensor at the robotic limb, wherein the sensor includes at least one member of the group consisting of an angular joint displacement sensor, a velocity sensor, a torque sensor, an impedance sensor, and an inertial measurement unit, to thereby generate joint state data.

In yet another embodiment of the neuromuscular model-based controller, the joint state data includes a measured robotic limb joint angle and joint angular velocity measured by the at least one sensor. The control system can further include a parallel spring model that employs the measured robotic limb joint angle to thereby further modulate at least one of the torque command and the impedance command. The muscle geometry can be configured to determine a muscle moment arm and a muscle tendon length using the measured joint angle. In one embodiment, the muscle model includes a muscle tendon unit having a contractile element in a series elastic element, wherein the muscle model determines muscle force using the muscle tendon length and a stimulation input from the reflex feedback loop. In one particular embodiment, the muscle model includes at least one of the bilinear muscle model, a Hill-type muscle model and a clutch-spring model.

The reflex feedback loop can be configured as a local feedback loop, whereby the reflex feedback loop is configured to receive muscle feedback information, e.g., muscle force feedback, from the muscle model and to provide stimulation input to the muscle model. In one embodiment, the muscle force feedback is a positive force feedback. In another embodiment, reflex feedback loop is configured to mimic the stretch reflex of an intact human muscle.

In still another embodiment, the neuromuscular model and the control system are configured to control the robotic limb, wherein the robotic limb is a leg, and where the neuromechanical controller further includes a finite state machine synchronized to the walking gait cycle, the finite state machine being configured to receive intrinsic data from at least one of the sensors and to determine a gait phase of the robotic leg using the intrinsic data received.

In various embodiments, the neuromuscular model and the control system are configured to control a robotic leg comprising a knee joint, an ankle joint, a knee joint and hip joint, or any combination thereof.

In an embodiment of the method of controlling a robotic limb that includes at least one robotic hip joint, the method further includes the step of transmitting the measured joint angle state of the robotic limb joint to the control system, whereby at least one of the torque command and the impedance command is further adapted by the measured joint angle state to thereby obtain the current command for controlling the robotic limb joint. In another embodiment, the reflex feedback loop applies a delay and a gain to at least one of the muscle force, muscle length and muscle velocity, which is added to an offset stimulation to thereby obtain a neurostimulation signal that is employed to adjust activation of the muscle model. In yet another embodiment, at least one of the delay and gain is modulated by the velocity of the robotic limb and the underlying terrain.

As for another embodiment, a method further includes the steps of switching the reflex feedback loop between an on position and an off position, so that the reflex feedback loop is operating when an individual wearing the robotic limb is in a stance phase, and low pass filtering the neurostimulation signal with a time constant.

This invention has many advantages. For example, the neuromuscular model-based controller and method for controlling a robotic limb that includes at least one robotic limb joint enables adaptation to at least one of velocity of the robotic limb and terrain underlying the robotic limb. Further, the controller and method of use of the controller of the invention adjusts parameters that determine the relation between muscle force, muscle length and muscle velocity of a muscle model employed to control the robotic limb joint to thereby adjust activation of the muscle model and, consequently, to modulate a torque command and an impedance command employed to actuate a control system in response to feedback from the robotic limb, or in response to stimulus from the individual wearing the robotic limb joint, thereby resulting in not only an objective response to changes in the environment, but also a response to the intent of the wearer, as conveyed by extrinsic signals, such as electromyographic signals.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

Other aspects, advantages and novel features of the invention will become more apparent from the following detailed description of the invention when considered in conjunction with the accompanying drawings wherein:

FIG. 1 is a block diagram of an exemplary embodiment of a general neuromuscular model architecture, according to one aspect of the present invention;

FIG. 2 depicts a full leg neuromuscular model including Hill-type muscle-tendon units;

FIG. 3 illustrates an example musculoskeletal walking model. Only three contractile muscles act about the model's ankle and hip joints capable of performing non-conservative work, namely the Ankle Plantar Flexor (APF), Hip Extensor (HE) and Hip Flexor (HF). All remaining muscle-tendon units of the leg are modeled as an isometric muscle in series with a compliant tendon, namely Ankle Dorsiflexor (AD), Ankle-Knee Posterior (AKP), Knee Flexor (KF), Knee Extensor (KE), Knee-Hip Posterior (KHP) and Knee-Hip Anterior (KHA). The hip joint also includes a unidirectional, linear torsional spring representing the dominant ligaments that tend to flex that joint, namely Hip Ligament.

FIGS. 4A-C illustrate example walking finite state machines. The state machines for ( 4 A) ankle, ( 4 B) knee and ( 4 C) hip are shown. The state machines turn on and off the muscle reflex controllers and initiate the isometric muscle force generation. Each isometric muscle is disengaged automatically when its series spring returns to its equilibrium position after an energy storage and release sequence. The state transitions are facilitated by gait events. For example, from state 3 to state 1 , the ankle state controller engages the Ankle Dorsiflexor at heel strike, and from state 1 to state 2 , the knee state controller engages the Ankle-Knee Posterior at maximum knee flexion.

FIGS. 5A-B illustrate the relationship between metabolic cost of transport (COT) and the maximum cross-correlation coefficient (R). FIG. 5A shows data for one representative participant ( participant # 1, Table I). Each closed circle is one forward dynamic solution that can walk for at least 20 seconds without falling down and where model walking speed falls within the range from 1.2 m/sec to 1.35 m/sec. The dashed line and shaded area represent meanÂ±one standard deviation for human metabolic COT data from the literature (Herr and Grabowski, 2011). The open diamond is the model's optimal solution because it falls within the shaded region and has the highest R value. The open square is the model solution of Anderson & Pandy (2001). FIG. 5B illustrates the same relationships as in FIG. 5A for the remaining eight subjects (P2-P9).

FIGS. 6A-F depict joint kinetic and kinematic predictions. Shown are model predictions for ( 6 A) ankle angle, ( 6 B) knee angle, ( 6 C) hip angle, ( 6 D) ankle torque, ( 6 E) knee torque and ( 6 F) hip torque. The black curves are the average of 10 walking model cycles from the optimal solution plotted in FIG. 5A . The grey curves are human biological data from Herr and Popovic (2008) of the modeled participant ( Participant # 1 in Table I). The dotted curves are one standard deviation from the biological data mean (N=7 walking trials). Maximum cross-correlation coefficient (R) values are shown for each model prediction.

FIG. 7 depicts ground reaction force (GRF) prediction. Vertical GRF (upper curves) and horizontal GRF (lower curves) for biological walking (grey lines) and model walking (black lines) are shown for one representative participant ( Participant # 1, Table I). Only data from the stance phase (0Ë62% of gait cycle) are shown. The dotted curves are one standard deviation from the biological data mean (N=7 walking trials). Model GRFs are the mean of 10 walking stance phases from the optimal solution shown in FIG. 5A . All data are normalized by body weight. Maximum cross-correlation coefficient (R) values are shown for each model prediction.

RELATED APPLICATIONS

BACKGROUND

Therefore, a need exists for a controller of a robotic limb, such as a robotic leg or ankle, and a method for controlling a robotic limb, that overcomes or minimizes the above-referenced problems.

SUMMARY OF THE INVENTION

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary embodiment of a general neuromuscular model architecture, according to one aspect of the present invention;

FIG. 2 depicts a full leg neuromuscular model including Hill-type muscle-tendon units;

FIG. 8 depicts potential, kinetic and elastic mechanical energy predictions. The thin grey curve is the sum of gravitational potential and kinetic mechanical energies during steady-state model walking of one representative participant ( Participant # 1, Table I). The black curve is the estimated elastic potential energy from all the springs within the walking model. The thick grey curve is the model's total mechanical energy. Each dotted line is one standard deviation about the mean (N=10 walking model cycles). Energy curves are normalized by body weight and the center of mass height during quiet standing (See Table I for values).

FIG. 9A illustrates an example musculoskeletal model applied in a prosthesis controller.

FIG. 9B is a block diagram illustrating an example reflex-based controller.

FIG. 10 depicts gastrocnemius activation, force, contractile element length and contractile element velocity estimated by the data-driven muscle-tendon model.

FIG. 11 shows an example schematic and control architecture of a prosthetic apparatus.

FIG. 12 depicts a comparison of the soleus muscle dynamics produced by EMG versus those produced by reflex feedback to the muscle-tendon model. The top plot shows the contributions from the force, length and velocity terms to the stimulation. Here, the stimulation is the solid line, the force term is the dashed-dot line (largest contributor), the length is the dashed line and the velocity term (which goes negative) is the dotted line. In the rest of the plots, the dashed curves are the model outputs given EMG-based activation, while the solid curves are the corresponding variables when the model activation is determined by the reflex structure in equation (2.3). The shaded regions indicate the times where the force, length and velocity feedback terms contribute at least 0.01 to the stimulation. All plots used biological angles for walking trials at 1.25 m s â1 .

FIG. 13 depicts plot of soleus muscle dynamics produced by the reflex-based stimulation (equation (2.3)) for input ankle angles from walking trials at 0.75 m s â1 . The top plot shows the contributions to the stimulation (solid line) from the force (dashed-dot line), length (dashed line) and velocity terms (dotted line). The remaining plots (from top to bottom) show the total activation, muscle force, contractile element length and contractile element velocity. The shaded regions indicate the times where the force, length and velocity feedback terms contribute at least 0.01 to the stimulation.

FIGS. 14A-D depict comparison of prosthesis ankle and knee angles and torques during the clinical trials (measured) with those from a height- and weight-matched subject with intact limbs (biological). Torque that plantar flexes the ankle is defined to be positive and moves the angle in the positive direction. Similarly, torque that flexes the knee is positive and increases the knee angle. The biological values are the thick solid lines (with shaded errors) in each plot while the dashed lines are the values measured on the prosthesis. In the ankle torque plot the commanded torque is shown as a thinner solid line, again with shaded error bars. The knee torque plot compares the torque provided by the clutch-spring mechanism to that provided by the natural gastrocnemius in simulation. The vertical line indicates toe off in each plot.

FIGS. 15A-C depict commanded ankle angles, torques and work loops for three speeds in clinical walking trials. Shown are data for three speeds: 0.75 m s â1 (solid line), 1.0 m s â1 (dashed line) and 1.25 m s â1 (dotted line). In the torque versus angle plot, heel strike is indicated using a circle.

FIG. 16 depicts energy output of the ankle across gait speed. Shown are biological data, net work as commanded by the ankle-foot prosthesis during clinical trials and measured net work during the clinical trials. Inverted triangles, biological; crosses, commanded clinical trials; circles, measured clinical trials; continuous line, biological trend; dashed lines, command trend; dotted lines, measured trend.

FIG. 17 depicts an example statemachine that can be used with a myoelectric controller;

FIG. 18 depicts an EMG profile (normalized EMG amplitude as a function of percent gait cycle) measured from an amputee's gastrocnemius muscle across three walking speeds;

FIG. 19 depicts a mechanical model for an exemplary embodiment of an ankle-foot prosthesis, according to one aspect of the present invention;

FIG. 20 depicts ankle angle as a function of percent gait cycle for level ground walking across three walking speeds;

FIG. 21 depicts ankle torque as a function of percent gait cycle for level ground walking across three walking speeds;

FIG. 22 depicts ankle power as a function of percent gait cycle for level ground walking across three walking speeds;

FIG. 23 depicts ensemble average of net work calculated for three walking speeds;

FIG. 24 depicts ensemble average of peak power calculated for three walking speeds;

FIG. 25 depicts measured average toe off angle for three walking speeds from three data sets;

FIG. 26 depicts average timing at which peak power occurs (percent of gait cycle of peak power) for three walking speeds;

FIG. 27 depicts ankle angle, torque and power measured using a hybrid controller and an intrinsic controller during stair descent;

FIG. 28 depicts ankle angle, torque and power measured using the hybrid controller and the intrinsic controller during stair ascent.

DETAILED DESCRIPTION OF THE INVENTION

In one embodiment, the invention is an apparatus and method for producing biomimetic positions, torques and impedances at the hip, knee and ankle joints of a powered leg prosthesis, orthosis, or exoskeleton during walking and running gaits. Sensory data are collected using intrinsic and/or extrinsic sensors. Intrinsic sensing refers to information collected from sensors located on the wearable robotic device, and extrinsic sensing refers to all information collected from sensors located externally to the wearable device. As an example, in the case of a leg prosthesis, a surface electrode for the measurement of the electromyographic signal from residual limb muscles would be an extrinsic sensor, and an inertial measurement unit located on the device itself would be classified as an intrinsic sensor. Intrinsic sensors measure device positions, motions, forces, torques, pressures, and temperatures. Extrinsic sensors might comprise such mechanical and temperature sensors positioned externally to the wearable device, as well as neural sensors for the determination of user motor intent.

This sensory information is passed to a neuromuscular model of human locomotion, which computes appropriate joint dynamics for the device to provide to the user. The following sections detail a sensing and control mechanism, the neuromuscular models applied in control, a successful application of this mechanism for the control of a powered ankle-foot prosthesis, and one method for inferring neural intent from surface electromyographic (EMG) measurements.

Overview of Sensing and Control Scheme

In one embodiment, a control architecture commands biomimetic torques, impedances and positions to the hip, knee and/or ankle joints of a wearable robotic device during legged locomotion. The model-based control scheme, depicted in FIG. 1 , relies on data collected from at least one of intrinsic and extrinsic mechanical sensors, and extrinsic neural sensors used to infer the motor intent of the user, volitionally and/or non-volitionally. A set of potential intrinsic/extrinsic mechanical sensors may include, but are not limited to: digital encoders or hall-effect sensors to measure joint angular displacement and velocity, torque sensors at the hip, knee and ankle joints, and inertial measurement units (IMU's) located on limb segments to measure limb orientations and translations. Further, extrinsic neural sensors for direct sensing of user intent may include, but are not limited to: electrodes to measure the EMG signals of biological limb musculature, peripheral neural implants for efferent sensing of motor nerve axons, and/or central brain implants for sensing of brain motor commands. A control system of the invention may also include or employ an afferent stimulation using a nerve implant, allowing the user of the wearable robot to better modulate efferent motor commands for volitional or non-volitional control of the wearable device.

Among other sensory modalities, collected intrinsic sensory data provide information about joint state (angular position and velocity) of the hip, knee, and ankle joints. In the case of a transfemoral amputee, for example, joint state at the ankle and knee can be measured through angular sensors. In one embodiment, the hip joint state can be estimated under the assumption that the upper body (torso) maintains a vertical orientation during gait. The hip's angular position and velocity can then be determined using the angular position sensor at the knee and an IMU located between the robotic knee and ankle for the determination of lab frame orientation of the lower leg.

The neuromuscular model used to compute desired joint dynamics may include muscles modeled in a variety of ways. The muscle model may include, but is not limited to, a bilinear muscle model or a Hill-type muscle model. The measured states of the robotic joints are employed to determine the internal state (force length, velocity) of each of the virtual muscle-tendon units of the neuromuscular model employing morphological information of the muscle moment arms about each modeled joint.

The impedance and force of each virtual muscle are additionally governed by the muscle activation, which may be determined from a local reflex loop, an external source, or a combination thereof. In the reflex case, a feedback loop is implemented where virtual muscle force and state are used to produce muscle stimulation, which is then filtered to produce muscle activation as shown in FIG. 1 . This feedback-based control scheme is designed to emulate the force feedback and stretch reflex of an intact human muscle. This reflexive feedback loop can be a linear or non-linear function of virtual muscle force, length and velocity. In one embodiment, the reflexive feedback loop is nonlinear, and comprises a threshold prestim parameter, as well as force and state gains and exponents, or

u ( t )= x+y F [F ( tâÎt F )] Z

+y l [l ( tâÎt l )] Z

+y v [v ( tâÎt v ] Z

v ââ(1)

where x is the prestim parameter, y i are force and state gains, and z i are force and state exponents.

In the purely external source case, a neural sensor is employed to provide some estimate of motor intent, which is then input to an activation dynamics block where a muscle activation is estimated as an input to the muscle model. Typically, such measurements of motor intent would comprise one or more peripheral neural sensors from implants interfacing with nerves and/or muscles, but in the most general case, such motor intent commands could additionally be measured from central brain implants.

In the combination case, intrinsic and/or extrinsic sensory data are employed to modulate the reflex parameters of the neuromuscular model. In the combination case, the framework of FIG. 1 describes a procedure where the reflexive parameters are modulated by the controller either within a single gait cycle, and/or from gait cycle to gait cycle in an updating manner, based upon detected variations in gait speed and/or terrain. In one embodiment, gait speed and terrain condition are detected by intrinsic sensing, and the virtual force output of at least one modeled muscle and its associated state gains/exponents (y i and z i within equation 1) are adjusted either within a gait cycle and/or from gait cycle to gait cycle in an updating manner. In another embodiment, extrinsic efferent neural signals from muscles and/or periperial nerves are employed to modulate reflexive parameters, such as force and state gains and exponents. For example, as is described in Example III, infra, measured calf muscle EMG in a transtibial amputee can be used to modulate the gain of a positive torque feedback during the stance period of a walking gait cycle, providing the amputee direct volitional control over powered plantar flexion during terminal stance in walking. It will be understood by those of skill in the art that stimulation through a nerve implant to reflect intrinsic/extrinsic mechanical sensory data as an afferent feedback signal would allow the user of the wearable robot to better modulate efferent neural motor commands for a desired prosthetic/orthotic dynamical response.

Once the force of each virtual muscle spanning a joint is determined (by the implemented muscle model), each muscle force is multiplied by its biologically-realistic muscle moment arm and then all muscle torque contributions are summed around the joint to produce a net torque and impedance estimate. The model estimates are then sent to the controller ( FIG. 1 ) as the desired net torque and impedance for each robotic joint. The controller tracks these desired values at each joint to produce human-like joint forces and impedances. In the case where the human user's motor intent is to control device joint position, the controller integrates the desired joint torque to achieve a joint position estimate, and then modulates device joint position to achieve that desired position.

One embodiment of a neuromuscular model-based controller for controlling at least one robotic limb joint of a robotic limb, and a method of employing the neuromuscular model-based controller of the invention are represented schematically in FIG. 1 . As shown therein, neuromuscular model-based controller 100 includes neuromuscular model 102 which, in turn, includes muscle model 104 , muscle geometry 106 , reflex feedback loop 108 and reflex architecture 110 . Neuromuscular model 102 , as well as each of muscle model 104 , muscle geometry 106 and reflex feedback loop 108 , are processors, or components of processors, such as designated computer processors that are configured to perform functions described and associated with each of neuromuscular model 102 , muscle model 104 , muscle geometry 106 and reflex feedback loop 108 . Alternatively, neuromuscular model 102 , as well as each of muscle model 104 , muscle geometry 106 and reflex feedback loop 108 , are algorithmic procedures of a single global algorithm or computer code run by a single processor or a plurality of processors. Detailed descriptions of each of neuromuscular model 102 , muscle model 104 , muscle geometry 106 and reflex feedback loop 108 are described, for example, in U.S. Publication No. 2010/0324699, U.S. application Ser. No. 12/698,128, entitled: Model-Based Neuromechanical Controller for a Robotic Leg, by Hugh M. Herr et al., filed Feb. 1, 2010, the teachings of which are incorporated herein by reference in their entirety.

Reflex feedback loop 108 conveys feedback data of at least one of muscle force, muscle length and muscle contractile velocity of muscle model 104 to thereby adjust activation of muscle model 104 . Parameters that determine the relation between feedback data and activation of muscle model 104 through activation dynamics 112 are adjusted consequent to at least one of a velocity of a robotic limb, such as biomimetic robotic leg 120 , and a terrain underlying the robotic limb. Parameters that determine the relation between the feedback data and activation of muscle model are dictated by a reflex architecture 110 that can be a linear or non-linear function of virtual muscle force, muscle length and muscle velocity. For example, in one embodiment, the reflex feedback loop is nonlinear, and includes a threshold prestim parameter, as well as force and state gains and exponents, and is represented by the Formula (1), shown, supra. A formula representing a linear function of virtual muscle force, muscle length and muscle contractile velocity can be represented, for example, by Equation (2.3), in Example II, infra.

Neuromuscular model 102 further employs muscle model 104 and muscle geometry 106 to determine at least one of a torque command and an impedance command.

In the embodiment shown in FIG. 1 , activation of muscle model 104 occurs by virtue of reflex architecture 110 , which are equations employed to convert muscle force, muscle length and velocity into stimulation signals (i.e., reflex feedback equations). Examples of reflex feedback equations are described below, at Section 2.3 of Example I, infra, and, specifically with respect to Equations (5)-(11). Feedback data conveyed by reflex feedback loop 108 is adjusted by at least one of intrinsic and extrinsic sensing 114 which, optionally, are determined, at least in part, by switch 116 of an individual wearing robotic limb while walking.

âIntrinsic sensingâ means sensing by a robotic limb of its own state, such as that of a robotic ankle sensing its own angle, orientation, acceleration and torque of a component motor. The examples of intrinsic sensing are described infra, in Example II, with respect to ankle angle measurements.

âExtrinsic sensingâ means external measurements made by sensors employed by the device, such as electromyographic measurements of a residual limb of an individual wearing a robotic limb controlled by the invention. An example of extrinsic sensing according to the one embodiment of the invention is described infra, in Example III, at Section 3.3.2, with respect to an EMG measurement model described therein.

âActivation dynamicsâ as that term is employed herein, refers to differential equations that employ input muscle stimulation, for example, and shapes it to approximate muscle activation. Examples of suitable equations are described infra, at Example I, at Equation (4) and Example II, at Equation 2.1.

âReflex architecture,â as that term is employed herein, refers to equations that are employed to turn muscle force, length and velocity into stimulation signals (i.e. reflexive feedback equations). Examples of such equations are shown as Formula (1), supra, as Equations (5) through (11), in Example I, infra, and as Equation (2.3) in Example II, infra.

âMuscle modelâ as that term is employed herein means a processor or component of a processor, employing differential equations that make up inputs, such as muscle length, muscle velocity and activation, such as is determined by activation dynamics, to muscle force, such as a bilinear or Hill-type muscle model. The muscle model typically includes a tendon component so that inputs to the differential equations employed include both muscle and tendon length, in addition to muscle activation determined by differential equations employed by the activation dynamics. Examples of suitable expressions of these relations are described infra at Example I, at Equations (1) through (3).

âMuscle geometry,â as that term is employed herein, is a module, or component of a module of a computer processor that maps muscle-tendon length and muscle-tendon moment arm and lever arms from measured joint angles. For example, in the case of prosthesis control, âjoint angle inputâ is the angle of the robotic joint, as measured by intrinsic sensing, which is converted by, for example, a polynomial fit or a lookup table to muscle-tendon length and muscle-tendon moment arm or lever arm, as described below in Example II, at Section E.

Torque, Impedance, Position control system 118 receives at least one of a desired joint torque command and desired joint impedance command from neuromuscular model 102 . Control system 118 adapts at least one of the torque command and impedance command from neuromuscular model 102 in response to at least one of a respective measured torque and a measured impedance of a robotic limb of biomimetic robotic leg 120 to thereby obtain a current command that is directed from control system 118 to biomimetic robotic leg 120 to thereby control at least one of position, torque and impedance of biomimetic robotic leg 120 , which includes a robotic limb joint. A joint state is conveyed from biomimetic leg 120 to muscle geometry 106 .

âTorque, impedance, position controlâ as that phrase is employed herein, means a hardware controller of a robotic prosthetic device. An example of such a hardware controller is described infra, at Example II, FIG. 11 and accompanying text.

âBiomimetic robotic leg,â as that term is employed herein refers to a robotic ankle, robotic knee or combination thereof, and can include a control system component such as torque, impedance, position controller 118 of the invention. Examples of suitable biomimetic robotic legs, are described infra at Examples II and III.

The following are non-limiting examples that illustrate embodiments of the invention.

Example I

Human Walking Models

Two human walking models are described that may be applied in the control of prosthetic, orthotic and exoskeletal components. It is important to note that these are only two of many models that may be used for this purpose. These models do, however, lend themselves well to the task; since they include reflex feedback-based neural control schemes that fit neatly into the framework illustrated in FIG. 1 .

a. Neuromuscular Model with Hill-Type Muscle Elements

In this section, a lumped-parameter neuromuscular model for walking is described that includes Hill-type representations of all the major muscle groups of the leg ( FIG. 2 ). FIG. 2 illustrates neuromuscular model 200 comprising ankle 202 , knee 204 and hip 206 joints connected by rigid bodies representing the human trunk 214 and two, three-segment legs, each leg including a foot 208 , a shank 210 , and a thigh 212 . As shown in FIG. 2 , nine muscle-tendon units were modeled: soleus (SOL) 216 , gastrocnemius (GAS) 218 , tibialis anterior (TA) 220 , vastii group (VAS) 222 , biceps femoris short head (BFSH) 224 , hamstring group (HAM) 226 , rectus femoris (RF) 228 , gluteus maximus (GLU) 230 , and hip flexors, e.g, iliopsoas (ILL) 232 .

Human walking data are critical for the advancement of the model. The following are the data types used in building the model and the methods for collecting them:

Motion capture is used to track the motion of the participant's body segments. This provides kinematic data that are used to determine joint angles. An instrumented treadmill allows measurement of the three-dimensional forces applied by the participant to the walking surface. These kinetic data are collected synchronously with the kinematic data for use in an inverse dynamics analysis. Measurements of the electrical activity of the dominant muscles of the leg are made by placing EMG electrodes on the skin overlying the bellies of the muscles of interest during walking trials. From this we identify the times and magnitudes of activation of each muscle by the nervous system. Throughout walking trials the participant's inspired and expired gas volume and composition are recorded and analyzed to calculate steady-state metabolic energy consumption. From this, the metabolic cost of transport can be determined for each walking condition. B-mode ultrasound imaging is used to record images of muscle fascicles in vivo during walking trials. Images are collected at 50 Hz and then digitized to provide measurements of muscle fascicle lengths throughout the gait cycle.

The collected kinematic and kinetic data are used to obtain information about the joints and muscles of the leg. The motion capture data are used to scale the full body model of the SIMM software (Musculographics, Inc). This model represents the average anatomy of a male human as determined by extensive cadaver studies (Delp, S., Loan, J., Hoy, M., Zajac, F., Topp, E., and Rosen, J. (1990), An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures, IEEE Transactions on Biomedical Engineering, 37(8): 757-767), and is widely accepted as the professional standard in the field. The resulting representation of each subject is used to compute joint angles, muscle-tendon unit lengths, and muscle-tendon unit moment arms from the motion capture data. Adding the collected force plate data and utilizing the SIMM/SDFAST dynamics pipeline, joint torques of the hip, knee, and ankle are computed. For each walking protocol, all reasonable trajectories are averaged and used as model inputs.

The morphological parameters governing force production in the tendons of the model are then estimated using an optimization procedure similar to the one conducted in [5]. This procedure uses the kinematic, kinetic, electromyographic, and ultrasound data collected in walking trials at self-selected speed on level ground. As discussed earlier, the kinematic and kinetic data are used to determine joint moments, muscle-tendon lengths, and muscle-tendon moment arms about each joint. The electromyographic data are used to estimate the muscle activations of all major muscle groups spanning the hip, knee, and ankle joints. The ultrasound data are used to determine muscle fascicle lengths. We model each muscle as Hill-type and, as in (Krishnaswamy, P., Brown, E. N., Herr, H. M. (2011) Human Leg Model Predicts Ankle Muscle-Tendon Morphology, State, Roles and Energetics in Walking. PLoS Comput Biol 7(3): e1001107. doi:10.1371/journal.pcbi.1001107), hypothesize that the muscle-tendon morphology of the leg has evolved to maximize the economy of walking at self-selected speed over level ground.

The estimated activations, muscle-tendon lengths, and muscle-tendon moment arms are then input to the model, with the muscle-tendon morphological parameters being chosen as those that minimize the metabolic cost of walking. The optimization is guided by the constraints that the output joint moments match those computed from the data (within some tolerance). If reliable muscle fascicle length measurements are available, the optimization may be further constrained to produce output muscle fascicle lengths that match these measurements (again within some tolerance). We thoroughly examine the solution space for this optimization problem, focusing on the region that is energetically efficient and closely matches the biological data. The final results of this identification step are sets of muscle-tendon parameters that, for each subject, accurately describe the muscle groups of the leg that dominate sagittal plane motion in walking.

Once the muscle-tendon morphologies for each subject are determined, we explore control of the system. The collected EMG data are used to infer reflexive feedback schemes for each muscle actuator through a procedure similar to (Markowitz, J. Krishnaswamy, P., Eilenberg, M. F., Endo, K., Barnhart, C., and Herr, H. (2011) Speed adaptation in a powered transtibial prosthesis controlled with a neuromuscular model. Philosophical Transactions of the Royal Society B, 366: 1621-31). The muscle reflexes comprise linear and/or nonline

CLAIMS

Claims ( 9 )

What is claimed is:

1. A neuromuscular model-based controller for controlling at least one robotic limb joint of a robotic limb, the controller comprising:

a) at least one extrinsic sensor that detects at least one extrinsic signal of a subject wearing the robotic limb;

b) a neuromuscular model in communication with the at least one extrinsic sensor, the neuromuscular model comprising parameters that are adjusted by activation dynamics in response to the at least one extrinsic signal from the extrinsic sensor, the neuromuscular model thereby determining at least one of a position command, a torque command and an impedance command; and

c) a control system in communication with the neuromuscular model, whereby the control system receives at least one of the position command, the torque command and the impedance command from the neuromuscular model and controls at least one of position, torque and impedance of the robotic limb joint.

2. The neuromuscular model-based controller of claim 1 , wherein the neuromuscular model includes a muscle model, whereby parameters that determine the relation between muscle feedback data and activation of the muscle model are adjusted consequent to the at least one extrinsic signal of the extrinsic sensor, wherein the muscle feedback data include at least one of muscle length, muscle contractile velocity, and muscle force.

3. The neuromuscular model-based controller of claim 1 , wherein the at least one extrinsic signal is an electromyographic signal.

4. The neuromuscular model-based controller of claim 1 , wherein the position command is generated by integration of the torque command.

5. The neuromuscular model-based controller of claim 3 wherein the control system compares the position command to a robotic joint state and applies a feedback control to the robotic limb.

6. The neuromuscular model-based controller of claim 1 , wherein the extrinsic sensor is a brain sensor.

7. The neuromuscular model-based controller of claim 1 , wherein the extrinsic sensor is an extrinsic neural sensor.

8. The neuromuscular model-based controller of claim 7 , wherein the extrinsic neural sensor includes at least one member selected from the group consisting of: an electrode to measure an electromyographic signal of biological limb musculature; peripheral neural implants for efficient sensing of motor neuron axons; and central brain implants for sensing of brain motor commands.

9. The neuromuscular model-based controller of claim 1 , further including a reflex architecture.

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