WO2023204981A1 - Procédé de personnalisation de performances d'exosquelettes - Google Patents

Procédé de personnalisation de performances d'exosquelettes Download PDF

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Publication number
WO2023204981A1
WO2023204981A1 PCT/US2023/017836 US2023017836W WO2023204981A1 WO 2023204981 A1 WO2023204981 A1 WO 2023204981A1 US 2023017836 W US2023017836 W US 2023017836W WO 2023204981 A1 WO2023204981 A1 WO 2023204981A1
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assistance
exoskeleton
walking
data
joint
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PCT/US2023/017836
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English (en)
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Patrick SLADE
Steven H. COLLINS
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The Board Of Trustees Of The Leland Stanford Junior University
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Publication of WO2023204981A1 publication Critical patent/WO2023204981A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H1/00—Apparatus for passive exercising; Vibrating apparatus ; Chiropractic devices, e.g. body impacting devices, external devices for briefly extending or aligning unbroken bones
    • A61H1/02—Stretching or bending or torsioning apparatus for exercising
    • A61H1/0237—Stretching or bending or torsioning apparatus for exercising for the lower limbs
    • A61H1/0266—Foot
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H3/00—Appliances for aiding patients or disabled persons to walk about
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/0006—Exoskeletons, i.e. resembling a human figure
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1615—Programme controls characterised by special kind of manipulator, e.g. planar, scara, gantry, cantilever, space, closed chain, passive/active joints and tendon driven manipulators
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
    • B62D—MOTOR VEHICLES; TRAILERS
    • B62D57/00—Vehicles characterised by having other propulsion or other ground- engaging means than wheels or endless track, alone or in addition to wheels or endless track
    • B62D57/02—Vehicles characterised by having other propulsion or other ground- engaging means than wheels or endless track, alone or in addition to wheels or endless track with ground-engaging propulsion means, e.g. walking members
    • B62D57/032—Vehicles characterised by having other propulsion or other ground- engaging means than wheels or endless track, alone or in addition to wheels or endless track with ground-engaging propulsion means, e.g. walking members with alternately or sequentially lifted supporting base and legs; with alternately or sequentially lifted feet or skid
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H3/00—Appliances for aiding patients or disabled persons to walk about
    • A61H2003/007—Appliances for aiding patients or disabled persons to walk about secured to the patient, e.g. with belts
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
    • A61H2201/16—Physical interface with patient
    • A61H2201/1602—Physical interface with patient kind of interface, e.g. head rest, knee support or lumbar support
    • A61H2201/164—Feet or leg, e.g. pedal
    • A61H2201/1642—Holding means therefor
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
    • A61H2201/16—Physical interface with patient
    • A61H2201/1602—Physical interface with patient kind of interface, e.g. head rest, knee support or lumbar support
    • A61H2201/165—Wearable interfaces
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
    • A61H2201/50—Control means thereof
    • A61H2201/5007—Control means thereof computer controlled
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
    • A61H2201/50—Control means thereof
    • A61H2201/5023—Interfaces to the user
    • A61H2201/5038—Interfaces to the user freely programmable by the user
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
    • A61H2201/50—Control means thereof
    • A61H2201/5058—Sensors or detectors
    • A61H2201/5069—Angle sensors
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H2230/00—Measuring physical parameters of the user
    • A61H2230/62—Posture
    • A61H2230/625—Posture used as a control parameter for the apparatus
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/40—Robotics, robotics mapping to robotics vision
    • G05B2219/40305—Exoskeleton, human robot interaction, extenders

Definitions

  • This invention relates to methods of personalizing the performance of exoskeletons.
  • Exoskeletons that assist leg movement show promise for enhancing personal mobility, but have yet to provide real-world benefits. Millions of people have mobility impairments that make walking slower and more fatiguing, while millions more have occupations that require strenuous locomotion. In research laboratories, exoskeletons can increase walking speed and reduce the energy required to walk, but these benefits have not translated to real-world conditions.
  • a wearable method for personalizing wearable exoskeletons that may substantially improve mobility in patients.
  • patients have selected wearable exoskeletons via trial-and- error, and current methods for customizing exoskeletons are costly and time-consuming relative to the presented invention.
  • the approach uses a machine learning model and wearable sensors to evaluate the energy costs of exoskeleton assistance conditions involving whole-body coordination.
  • the inventors demonstrate in an exemplary embodiment that for an ankle exoskeleton assistance, a data-driven optimized approach reduced energy consumption in walking across different speeds and inclines in a statistical comparison with existing methods.
  • the presented method used to uncover the relationship between kinematics and energy costs may additionally extend to other assistive devices.
  • Assistive devices like exoskeletons have shown remarkable promise for maintaining physical health and improving mobility.
  • Optimizing exoskeleton assistance offers remarkable improvements to mobility compared to hand tuned assistance profiles.
  • Existing methods for optimizing assistance require hours of walking and equipment that is infeasible for everyday use.
  • the inventors present a wearable method for optimizing ankle exoskeleton assistance to minimize human energy cost during walking.
  • Wearable exoskeletons capable of personalizing assistance may substantially improve mobility for populations like older adults.
  • An exoskeleton has an actuator capable of applying torque around a joint.
  • a movement is performed that includes at least two movement cycles. The movement includes motion of the joint.
  • Joint angle, joint velocity and control parameters for a plurality of data points or bins are collected during two of the at least two movement cycles.
  • a trained model has been developed which is a mapping joint angle, joint velocity and control parameters for data points for the movement cycles as input and a single performance value as output. Collected the joint angles, the joint velocities and the control parameters are inputted into the trained model, and obtaining a single performance value for the performed movement. The single performance value is then used to adjust torque actuator control parameters to control the torque actuator of the exoskeleton.
  • the trained model is trained at at-least one speed or speed range for the movement cycle.
  • the movement cycle is a gait cycle.
  • the joint is an ankle joint.
  • the method is personalized in a real-world environment.
  • the method is personalized during walking in a real-world environment.
  • the movement cycles are performed at different speeds.
  • FIGs. 1-H show according to an exemplary embodiment of the invention data-driven exoskeleton optimization.
  • FIG. 1A during optimization, the participant walks with the exoskeleton and experiences a sequence of assistance conditions, or control laws, each defining a pattern of exoskeleton torque. The optimizer's goal is to identify the torque pattern that maximizes performance.
  • FIG. IB ankle motions for each stride are recorded from sensors on the exoskeleton.
  • FIG. 1C all possible pairs of conditions are then compared. For each pair, differences in segmented motion data (A) are calculated by subtraction.
  • FIG. 1A segmented motion data
  • FIG. IE a logistic function uses the pair coefficient to compute the probability (pij) that the first assistance condition is more beneficial than the second condition.
  • FIG. IF the score for each condition (5) is computed by summing the probabilities of all pairs that include that condition.
  • FIG. 1G conditions are then ranked by score and used to update an optimizer.
  • FIG. 1H the optimizer selects a set of k new control laws to evaluate. This optimization process is repeated until convergence criteria are satisfied, in this case a set number of evaluations having been completed. During real-world experiments, optimization was performed on the exoskeleton's microcontroller.
  • FIGs. 2A-E show according to an exemplary embodiment of the invention data-driven optimization results.
  • FIG. 2A exoskeleton assistance was applied using a tethered ankle exoskeleton emulator.
  • FIG. 2B Assistance parameters optimized using the data-driven method converged to within 5% of the parameters identified using metabolic optimization, but in one quarter the time.
  • FIG. 2C individual subjects had unique data-driven optimized parameters, centered around the generic assistance parameters.
  • FIG. 2E optimized torque patterns varied by walking condition, with similar changes in data-driven and metabolic optimized parameters. Data-driven and metabolic optimized assistance led to similar reductions in metabolic rate when walking at 0.75 m/s (slow), 1.25 m/s (normal), 1.75 m/s (fast), and on a 10 degrees incline.
  • FIGs. 3A-C show according to an exemplary embodiment of the invention speed-adaptive control.
  • FIG. 3A the speed-adaptive controller interpolated between previously optimized assistance parameters to estimate the optimal parameters for each step based on walking speed.
  • FIG. 3B ground truth and estimated walking speed for a representative participant. Speed was estimated on each step using a model that took stride period as an input (FIG. 1A). The shaded region represents the mean ⁇ one standard deviation.
  • FIGs. 4A-D show according to an exemplary embodiment of the invention untethered ankle exoskeleton.
  • FIG. 4A a participant walking in a community setting wearing the exoskeleton.
  • FIG. 4B the exoskeleton consists of (1) a 0.3 kg battery pack worn on the waist, (2) a motor and drum transmission, (3) electronics to receive sensor data, command the motor, and perform optimization, (4) a carbon fiber frame to transmit forces to the body, and (5) a lightweight shoe.
  • the motor can apply a peak torque of 54 Nm when walking at 1.5 m/s, sufficient to match optimized assistance parameters identified in emulator experiments.
  • FIG. 4D the motor temperature during 30 minutes of walking with maximum assistance reached approximately 35 degrees C, well below the 75 degrees C thermal limit of the motor. An exponential fit indicated the steady-state temperature was 35.4 degrees C.
  • FIGs. 5A-H show according to an exemplary embodiment of the invention real-world optimization of exoskeleton assistance.
  • FIG. 5A participant walking on the public validation course.
  • FIG. 5A map of the 566-meter course used for optimization and validation.
  • FIG. 5C distribution of self-selected walking speeds and
  • FIG. 5D walking bout durations during optimization and validation compared to previously recorded distributions of real-world walking data.
  • FIG. 5E as assistance was optimized over one hour of naturalistic bouts of walking, the convergence parameter (cr) continually improved.
  • FIG. 5F optimized parameters for each participant were unique. In the original drawing in the priority document red squares depict values for generic assistance.
  • FIG. 5A participant walking on the public validation course.
  • FIG. 5A map of the 566-meter course used for optimization and validation.
  • FIG. 5C distribution of self-selected walking speeds
  • FIG. 5D walking bout durations during optimization and validation compared to previously recorded distributions of real-world walking data.
  • FIG. 5E as assistance was optimized over
  • the boxes extend from the lower to upper quartile values of the data, with a line at the median and a dot at the mean.
  • the whiskers extend between the minimum and maximum of the data values.
  • FIG. 6 shows according to an exemplary embodiment of the invention model weights for the data-driven classification model.
  • the differences in ankle angle and velocity averaged across all pairs of assistance conditions from the training data are shown in black as shown in the original drawing in the priority document.
  • the model associates these differences in motion at each point in the gait cycle with a contribution to a lower or higher metabolic cost, shown as a background color of blue or red as shown in the original drawing in the priority document, respectively. Darker colors indicate greater importance.
  • FIGs. 7A-C show according to an exemplary embodiment of the invention speed-adaptive control.
  • FIG. 7A the participant calibrates the walking speed estimation by walking at several known speeds (using a treadmill or GPS). These measured stride durations with ground truth speed measurements are used to fit an affine equation with linear regression.
  • FIG. 7B These lines of best fit estimate walking speeds for new stride durations.
  • FIG. 7C the speed-adaptive controller relates these walking speed estimates to exoskeleton assistance parameters by interpolating between assistance parameters previously optimized at fixed speeds.
  • FIG. 8 shows according to an exemplary embodiment of the invention optimizing assistance during real-world bouts of walking.
  • the exoskeleton assistance parameters were adjusted each step by using stride duration (t stride) to estimate walking speed and perform speed-adaptive control. Assistance parameters were optimized during real-world walking when the participant reached a sufficient number of strides (z) during a bout of walking. If data for the current set of exoskeleton control laws were collected then an optimizer updated its internal parameters and selected a promising set of new control laws, otherwise the condition number was incremented, and the next control law was applied to the user. We simultaneously optimized parameters for different bins of walking speeds (b), due to the variation in speed during natural walking.
  • the mean parameter values (p) of the other bins were also updated based on how much each bin had converged, represented by a change in the sigma which is the step size of the optimizer. This approach rapidly adapted to the participant early in the optimization and focused on updating parameters for common walking speeds as the optimization progresses.
  • FIG. 9 shows according to an exemplary embodiment of the invention additional untethered exoskeleton treadmill condition evaluations.
  • Walking with data- driven optimized assistance reduced the metabolic cost of walking compared to normal shoes during several additional treadmill conditions, indicating it may perform well during a wide range of common walking activities. These conditions included walking at 1.25 m/s with a 5 degrees incline, walking at 1.5 m/s with a weight vest adding approximately 20% of the participant’s bodyweight, and climbing stairs at a rate of 50 steps per minute.
  • FIG. 10 shows according to an exemplary embodiment of the invention the method of controlling torque parameters of an exoskeleton using a trained model outputting a single performance value.
  • a data-driven model was developed that relates human motion during exoskeleton-assisted walking to metabolic energy consumption that can be used outside the laboratory. Human movement arises from the interaction between the inertia of our body segments and forces from the environment and our muscles. The inventors hypothesized that careful analysis could extract meaningful information about muscular energy expenditure from subtle changes in motion data.
  • participants walked with exoskeleton assistance in about 3,600 different conditions while data were recorded from both laboratory equipment that measure biomechanical outcomes and low-cost, portable sensors on the exoskeleton.
  • the inventors trained a logistic regression model using this dataset (Extended Data FIGs. 1A-H).
  • the data-driven classification model compared sensor data from two assistance conditions and classified which assistance condition provided a larger benefit.
  • the model inputs were ankle angle and ankle velocity, segmented by gait cycle, and the torque parameters for each assistance condition.
  • the model then estimated the likelihood that the first assistance condition resulted in lower metabolic energy expenditure.
  • the user experienced a set of assistance conditions, the data-driven model compared all possible pairs of conditions, the conditions were ranked, and an optimization algorithm (CMA-ES) updated the estimate of the optimal parameters and generated a new set of assistance conditions to evaluate (FIGs. 1A-H). This process was repeated until convergence criteria were met.
  • CMA-ES optimization algorithm
  • Data-driven optimization can use the information embedded in our movements to identify exoskeleton assistance patterns that are as effective as those found with laboratory-based methods, but in one quarter of the time.
  • the data-driven optimization evaluated eight sets of assistance conditions in 32 min, four times faster than the state-of-the-art approach using indirect respirometry to measure metabolic rate (FIG. 2B).
  • Data-driven and metabolic optimization approaches identified the same subject-specific adjustments to assistance (FIG. 2C). Assistance optimized using data-driven and metabolic approaches resulted in similar metabolic cost, significantly lower than the metabolic cost of walking with the exoskeleton in a zero-torque mode or with a generic assistance profile (FIG. 2D).
  • the exoskeleton was designed to apply the range of optimal torque profiles identified in the tethered optimization study (FIGs. 2A-E) while having minimum mass (1.2 kg per ankle).
  • a brushless motor and custom drum transmission applied torque about the ankle joint, while portable electronics sensed the user's motion and performed real-time control and optimization (FIG. 4B).
  • the exoskeleton provided a peak torque of 54 Nm (FIG. 4C), which was about 50% to 75% of the biological ankle torque of participants in this invention.
  • Torque was controlled using a mixture of classical feedback control and iterative learning, with a tracking error of less than 1% of the peak torque. Maximum assistance could be applied continually without overheating the motor (FIG. 4D).
  • the battery weighed 0.3 kg and powered the exoskeleton for at least 30 minutes on a single charge. While the energy cost of carrying mass near a distal joint is high, locating motors and electronics near the assisted joint results in efficient power transmission, simpler design, and lower total weight, which can yield large net benefits.
  • Real-world optimized assistance increased self-selected walking speed and reduced the metabolic energy expended per distance traveled during naturalistic walking.
  • participants performed a fixed set of outdoor walking bouts with varying durations and speeds, while ground-truth metabolic rate and speed were measured.
  • Real-world optimization could improve the effectiveness of robotic devices that assist people in diverse contexts, from workers with physically demanding jobs to people with mobility impairments. Assistance could aid a variety of tasks, such as stair climbing or lifting, and improve other aspects of performance, such as balance or joint pain.
  • additional training data could be collected in the laboratory and used to train new data-driven optimizers, illuminating the information contained within the body’s movements for each task.
  • the learned models could be made more general, progressively connecting to more fundamental relationships between movement and performance outcomes. Data from laboratory-based emulation and optimization experiments could simultaneously provide design guidelines for new products.
  • the objective was to personalize exoskeleton assistance during real-world walking.
  • a data-driven optimization was used, which uses portable sensors in the exoskeleton to personalize assistance for each participant.
  • data-driven optimized assistance would provide similar reductions in the metabolic cost of walking as metabolic optimized assistance, and significantly larger reductions than generic assistance or normal shoes.
  • a power analysis that eight participants were the necessary sample size to validate the data- driven optimization. This analysis used a power of 0.8, alpha value of 0.05, and previous experimental results where metabolic optimized assistance (1.44 ⁇ 0.15 W/kg) provided significantly larger metabolic reductions than generic assistance 9 (1.64 W/kg).
  • the metabolic cost of walking was computed with measurements from respirometry equipment. Respirometry equipment was used to measure the volume of carbon dioxide and oxygen exchanged per breath. The Brockway equation was used to compute metabolic energy expenditure in Watts from each breath of carbon dioxide and oxygen measurements taken by the respirometry equipment. Metabolics measurements during indoor exoskeleton experiments were measured with tethered respirometry equipment (Quark CPET, COSMED). Metabolics measurements during outdoor exoskeleton experiments were measured with portable respirometry equipment worn on the participant’s back (K5, COSMED). Participants refrained from all food and drink except for water for at least three hours before experiments that included respirometry measurements. The steady-state metabolic cost was computed by averaging the respirometry measurements during the last three minutes of the six-minute condition.
  • the cumulative metabolic cost was the total energy expended during the condition, including the metabolic cost beyond quiet standing for three minutes following the condition.
  • the energy spent during the return to quiet standing accounted for any deficits in oxygen during walking and delays in respirometry measurements.
  • the cost of transport was calculated as the cumulative metabolic cost divided by the total distance walked.
  • exoskeleton assistance conditions were evaluated to determine the benefits that they provided to the user. These assistance conditions included walking in normal shoes and walking with the exoskeletons in a zero-torque mode, with a fixed generic assistance profile, with metabolic optimized parameters, and with data-driven optimized parameters.
  • Zero-torque mode was a tethered exoskeleton condition where the exoskeleton provided no assistive torques.
  • the zero-torque condition evaluated the effort required to walk with the added weight of the exoskeleton and without the benefits of assistance. Comparing the walking effort during zero torque to a condition with assistance provided an estimate of the metabolic savings that an idealized assistive device could provide if it did not add any additional weight.
  • a generic assistance profile was a fixed set of assistance parameters. In experiments, generic assistance reduced the metabolic cost of walking, but was less beneficial than assistance personalized for each individual.
  • the tethered exoskeleton experiments in this invention used a generic assistance condition computed by averaging the optimized parameters from a previous experiment.
  • the untethered exoskeleton experiments used generic assistance parameters computed by averaging the optimized profiles for three speeds of walking from the tethered experiments in this invention (FIGs. 2A-E).
  • Metabolic optimization relied on metabolic measurements to fine-tune exoskeleton assistance parameters for each participant. Metabolic optimization uses a sample-efficient optimization approach to identifying the exoskeleton control parameters that minimize the metabolic cost of walking for a specific person. To perform metabolic optimization, a participant walked with exoskeleton assistance on a treadmill for two minutes while respirometry measurements were recorded. A minimum of two minutes of respirometry data were required to provide an estimate of the steady-state metabolic cost of walking. Once a fixed number of assistance conditions, referred to as one generation of optimization, were completed, an optimizer ranked the conditions in order of metabolic cost, updated the optimization parameters, and selected a new set of promising assistance conditions to evaluate. We used the same metabolic optimization approach from exoskeleton experiments which used Covariance Matrix Adaptation Evolutionary Strategy as the optimization framework.
  • Data-driven optimization used the exact same optimization framework as metabolic optimization, but relied on a data-driven model to individualize assistance parameters for each participant.
  • the data-driven classification model used portable sensor data to estimate which assistance conditions were the most beneficial to the participant, allowing the assistance conditions to be ranked and used to update the optimization instead of metabolic estimates (FIGs. 1-H).
  • Data from untethered sensors in the exoskeleton were passed into a data-driven classification model.
  • the data-driven classification model estimated the likelihood that one assistance condition provided a larger metabolic reduction than another assistance condition (FIGs. 1-H).
  • the data-driven classification model was a logistic regression model.
  • the model inputs includes carefully processed portable sensor data including four torque parameters, as well as ankle angle and ankle velocity measurements.
  • the torque parameters prescribe the ankle assistance profile and consist of four values: peak torque, peak timing, rise timing, and fall timing.
  • the angle and velocity measurements were sampled from a rotary encoder in the ankle joint of the exoskeleton worn on the left leg.
  • the portable sensor data was processed by segmenting the ankle angle and velocity measurements by gait cycle, whenever a heel strike was detected by the pressure sensor insoles in the exoskeletons. The first six gait cycles of data were discarded. The remaining gait cycles of data were discretized by averaging the measurements into 30 discrete bins and then averaged across all gait cycles for that condition.
  • the processed data was reshaped into a single vector with 64 values, including the torque parameters, 30 binned values for the ankle angle across the gait cycle, and 30 binned values for the ankle velocity across the gait cycle.
  • the model input also consisted of 64 values, the vector of sensor data from one assistance condition subtracted from the vector of sensor data from a different assistance condition. This difference in the sensor measurements provided the model with information about how the torque and person’s movements varied between the two conditions.
  • Previous data-driven models accurately estimated energy expenditure from wearable sensor data, in part by formatting data by gait cycle.
  • the data-driven classification model was trained to compare two assistance conditions at a time, determining which condition provided a larger reduction in the metabolic cost of walking.
  • Training the data-driven classifier input data from portable sensors and ground truth labels from metabolic measurements during many exoskeleton assistance conditions.
  • the sensor data was taken as input into the model to estimate the likelihood that the first of the compared assistance conditions reduced the metabolic cost of walking more than the second condition. This probability was a continuous value from 0 to 1, with 1 indicating the highest likelihood that the first condition reduced the metabolic cost of walking more than the second condition.
  • the ground truth labels were computed by subtracting the metabolic costs, estimated with two- minutes of respirometry data, for two conditions. A negative valued label indicated the first condition was more beneficial, reducing the metabolic cost of walking more than the second condition.
  • the model was trained with data from previous metabolic optimization experiments, where ten participants walked under approximately 3600 exoskeleton assistance conditions. We also used regularization, a technique that encourages simpler models and avoids overfitting to training data in order to improve model estimates for new data points.
  • the model fitting included a lasso regularization term, penalizing the absolute value of the model weights multiplied by a regularization parameter with a value of 1.
  • the data-driven classification model was trained to capture a relationship between leg movement and the metabolic cost of walking with assistance.
  • the data-driven model classified pairs of assistance conditions using a linear set of weights and the logistic function. These weights can be visualized, but are difficult to interpret (FIGs.
  • the data-driven classification model used in these experiments also took as inputs the subject’s height and weight, but further analysis revealed this information was not relevant to the model classifications.
  • Models trained in exactly the same way, with or without the subject’s height or weight classified 96% of the exoskeleton assistance pairs evaluated in the data-driven optimization experiments the same. Thus, a person’s height and weight were not informative when determining the effectiveness of assistance conditions.
  • a generation of conditions were ranked by using the probability values estimated by the data-driven classifier. Each pair of assistance conditions passed into the data- driven classification model resulted in one probability value of whether the first or second assistance condition provided a larger benefit. All possible pairs of conditions were classified with the data-driven model to receive a set of probability values.
  • Each assistance condition was scored summing the probabilities from all pairs that included that assistance condition. If a condition was the second condition in the pair, the negative probability was added to the total score for that condition.
  • the assistance conditions were ranked by the magnitude of scores, with a larger value indicating the condition was more likely to provide a larger reduction in the metabolic cost of walking (FIGs. 1-H). These ranked conditions replaced the need for metabolic measurements with respirometry, allowing the optimizer to update its internal parameters and generate a promising set of new assistance conditions to evaluate.
  • Tethered exoskeleton experiments were performed in an indoor laboratory setting to determine how effective data-driven optimization was compared to a range of other assistance conditions. Participants wore tethered bilateral ankle exoskeleton emulators. The exoskeleton assistance was governed by a torque pattern parameterized by four parameters: peak magnitude, peak time, rise time, and fall time 7 . The control loop ran at 1000 Hz on a real-time computer (Speedgoat). Exoskeleton sensor measurements were recorded at a rate of 2000 Hz, including pressure values from shoe insoles, commanded torque parameters, applied torque, ankle angle, and ankle velocity.
  • Participants completed eight generations of optimization for each approach. Each generation consisted of eight assistance conditions.
  • the optimizations were initialized with the generic assistance parameters, corresponding to the average optimized parameters of a previous group of expert participants.
  • the optimizations were initialized with a covariance size that included a 20% range of the normalized assistance parameters, corresponding to a sigma value of 0.1.
  • the metabolic optimization conditions lasted two minutes, the minimum time needed to estimate the steadystate metabolic cost with respirometry, requiring a total time of 128 min of walking.
  • the data- driven optimization conditions lasted only 30 seconds because people quickly converge to steady state motion, requiring a total time of 32 min of walking. For each participant, the parameters identified using data-driven and metabolic approaches were similar.
  • Participants had previously completed the first two tethered exoskeleton experiments, providing data-driven optimized parameters for walking speeds of 0.75, 1.25 and 1.75 m/s. Participants walked on a treadmill while the speed varied sinusoidally from 0.75 to 1.75 m/s with a period of 30 seconds.
  • Participants completed assistance conditions including walking in normal shoes and walking with the exoskeletons under a zero-torque mode, generic assistance profile, and data-driven optimized parameters using the speed-adaptive controller to adjust assistance with each step.
  • the validation tests were randomized and presented in a double-reversal ABCDDCBA order to mitigate the effects of noise in the metabolics measurements and trial order.
  • the untethered exoskeleton was designed to provide the optimized assistance parameters from the tethered exoskeleton experiments during extended real-world use.
  • the optimized parameters required assistance with a peak torque of 54 Nm while walking 1.5 m/s.
  • the exoskeleton was designed to provide this level of assistance without the motor overheating.
  • a portable battery was selected to allow 30 minutes of continuous walking on a single charge. The device weight was minimized to reduce the metabolic effort required to carry the exoskeleton.
  • the untethered exoskeleton weighed 1.2 kg on each ankle and consisted of the same frame, shoe, and pressure sensor insole as the tethered exoskeleton, with the addition of a portable motor, drum transmission, electronics, and a battery.
  • the brushless motor (AK80-9, CubeMars) contained a single stage 9: 1 gear ratio and internal motor driver electronics.
  • the motor weighed approximately 0.5 kg and was selected to apply the optimized torques and ankle velocities from the tethered exoskeleton experiments by using a 5:1 drum transmission.
  • the custom drum transmission was machined from 7075 aluminum.
  • a cable connected the heelspur to the motor drum. When the motor applied torque to the drum, the cable transmitted this force to create torque about the ankle joint of the exoskeleton.
  • the drum and cable transmission had added benefits of being backdrivable.
  • the cable could also be driven to a slack state to allow the person to move freely when desired.
  • the untethered exoskeleton used a Raspberry Pi 4b microcontroller to read sensor data and perform real-time control and optimization at a rate of 200 Hz.
  • a breakout board enabled sensors to interface with the microcontroller.
  • a step-down voltage converter enabled the electronics to be safely powered by a portable battery.
  • the total weight of electronics was 0.15 kg.
  • a lithium polymer battery had a nominal voltage of 24 V, capacity of 1300 mAh, and weight of 0.3 kg.
  • the design of the untethered exoskeleton required several trade-offs.
  • the highest design priority was providing the 54 Nm peak torque, specified from previous optimization experiments, with the lightest motor and transmission. We considered several factors to ensure that the motor would provide 54 Nm during operation.
  • the motor had to operate at a safe steady - state temperature, to prevent damage to the windings.
  • a brushless motor was selected for its high efficiency and large peak torque capability. While this untethered exoskeleton was designed for the optimized parameters of our experimental participant group, other participants may require a different device with another torque-weight trade-off to provide the same metabolic reductions.
  • Opportunistic optimization overcame the challenges of optimizing assistance during short bouts of real-world walking by accumulating data across many bouts.
  • the opportunistic optimization approach used the same data-driven classification model and optimization method that was validated in the tethered experiments, but only updated the optimization whenever 22 consecutive strides were collected for one control law (FIGs. 3A-C).
  • a fixed number of 22 strides was selected to capture approximately the same number of strides as the 30 second condition duration used in the tethered data-driven optimization experiments. Once this sufficient number of strides were collected, the next assistance condition was applied to the user (FIGs. 3A-C). Similar to the tethered experiments, the first six strides of data were discarded due to the person changing speeds at the beginning of the bout.
  • the data-driven classification model ranked the control laws. This control law ranking updated the optimizer. Separate optimizations were performed for three bins of walking speed of less than 1.22 m/s, between 1.22 and 1.38 m/s, and greater than 1.38 m/s. These speeds were chosen from the 33rd and 66th percentile of real-world walking distributions, providing equal likelihood for the participant to walk in each bin. Speed-adaptive control interpolated assistance based on the speed of each individual step (FIGs. 2A-E). When a condition of data was collected the estimated walking speeds for all steps during that condition were averaged to select which speed bin to store the data for the optimization process.
  • the real-world optimization experiment used the untethered exoskeleton to optimize assistance during naturalistic bouts of walking and then evaluated the optimized assistance profiles.
  • Participants completed a two-day protocol.
  • participants walked outside in a public setting along a path consisting of concrete, asphalt, and brick sidewalks for approximately one hour while the untethered exoskeleton provided assistance during data-driven optimization (FIG. 5B).
  • To emulate naturalistic motion the participants received audio cues to tell them to start and stop walking bouts. The durations of these bouts were randomized from a preselected distribution (FIG.
  • the treadmill conditions consisted of walking at 1.25 m/s, 1.5 m/s, and 1.25 m/s with an incline of 10 degrees.
  • the participant completed each treadmill condition twice, once with real-world optimized assistance and once with normal shoes.
  • the order of the conditions was randomized.
  • One participant completed additional indoor conditions of walking at 1.25 m/s with an incline of 5 degrees, at 1.25 m/s with a backpack load of 20% of their bodyweight, and on a stairmill at 50 steps per minute.
  • Participants completed a series of surveys to evaluate the ease of use, comfort, and functionality of the untethered exoskeleton after the completion of all experiments. Participants completed a System Usability Scale survey to determine how easy the system was to operate. Users reported our exoskeleton was relatively easy to use (Table 1) with a score of 72.5, which was in the 65th percentile among 5000 devices previously surveyed. Participants also completed surveys adapted from the Orthotics and Prosthetics Users' Survey, which acts as a self-report instrument for evaluating the outcomes of prosthetics and orthotics services in a clinically useful manner. In terms of comfort, participants found it easiest to endorse that the weight of the device was manageable, easy to put on, and their clothes were free of wear (Table 2).
  • the untethered exoskeleton was in the 65th percentile of a distribution of 5000 devices evaluated with the System Usability Scale.

Abstract

L'invention concerne un procédé de personnalisation d'une performance d'un exosquelette. Un exosquelette présente un actionneur apte à appliquer un couple autour d'une articulation. Un mouvement est effectué lequel comporte au moins deux cycles de mouvement. Le mouvement comporte un mouvement de l'articulation. L'angle d'articulation, la vitesse d'articulation et des paramètres de commande pour une pluralité de points ou de compartiments de données sont collectés pendant deux desdits deux cycles de mouvement. Un modèle entraîné a été développé qui est un angle d'articulation de mappage, une vitesse d'articulation et des paramètres de commande pour des points de données correspondant aux cycles de mouvement comme entrée et une valeur de performance unique comme sortie. Collectés, les angles d'articulation, les vitesses d'articulation et les paramètres de commande sont saisis dans le modèle entraîné, et une valeur de performance unique correspondant au mouvement effectué est obtenue. La valeur de performance unique est ensuite utilisée pour ajuster des paramètres de commande d'actionneur de couple pour commander l'actionneur de couple de l'exosquelette.
PCT/US2023/017836 2022-04-20 2023-04-07 Procédé de personnalisation de performances d'exosquelettes WO2023204981A1 (fr)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160331557A1 (en) * 2015-05-11 2016-11-17 The Hong Kong Polytechnic University Exoskeleton Ankle Robot
US20180325713A1 (en) * 2017-05-11 2018-11-15 Board Of Regents, The University Of Texas System Lower limb powered orthosis with low ratio actuation
US20190244436A1 (en) * 2018-02-06 2019-08-08 Walmart Apollo, Llc Customized augmented reality item filtering system
US20210378903A1 (en) * 2020-06-05 2021-12-09 Dephy, Inc. Real-time feedback-based optimization of an exoskeleton

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160331557A1 (en) * 2015-05-11 2016-11-17 The Hong Kong Polytechnic University Exoskeleton Ankle Robot
US20180325713A1 (en) * 2017-05-11 2018-11-15 Board Of Regents, The University Of Texas System Lower limb powered orthosis with low ratio actuation
US20190244436A1 (en) * 2018-02-06 2019-08-08 Walmart Apollo, Llc Customized augmented reality item filtering system
US20210378903A1 (en) * 2020-06-05 2021-12-09 Dephy, Inc. Real-time feedback-based optimization of an exoskeleton

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