WO2003099525A2 - Principe de rehabilitation robotique de la demarche par un deplacement optimal de la hanche - Google Patents

Principe de rehabilitation robotique de la demarche par un deplacement optimal de la hanche Download PDF

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Publication number
WO2003099525A2
WO2003099525A2 PCT/US2003/015754 US0315754W WO03099525A2 WO 2003099525 A2 WO2003099525 A2 WO 2003099525A2 US 0315754 W US0315754 W US 0315754W WO 03099525 A2 WO03099525 A2 WO 03099525A2
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subject
pelvis
robot
torso
pneumatic
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PCT/US2003/015754
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English (en)
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WO2003099525A3 (fr
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David J. Reinkensmeyer
Susan Harkema
V. Reggie Edgerton
Chia-Yu E. Wang
James E. Bobrow
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The Regents Of The University Of California
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Priority to AU2003247386A priority Critical patent/AU2003247386A1/en
Publication of WO2003099525A2 publication Critical patent/WO2003099525A2/fr
Publication of WO2003099525A3 publication Critical patent/WO2003099525A3/fr

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J17/00—Joints
    • B25J17/02—Wrist joints
    • B25J17/0258—Two-dimensional joints
    • B25J17/0266—Two-dimensional joints comprising more than two actuating or connecting rods
    • 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
    • A61H3/008—Appliances for aiding patients or disabled persons to walk about using suspension devices for supporting the body in an upright walking or standing position, e.g. harnesses

Definitions

  • This invention relates generally to a method and device for controlling the stepping motion of a subject undergoing locomotion rehabilitation.
  • BWS body weight supported
  • One robotic device for locomotion training is the Lokomat, which consists of four rotary joints, driven by precision ball screws connected to DC motors, which are mounted onto a motorized exoskeleton to manipulate a patient's legs in gait-like trajectories (5).
  • Another device is the Mechanized Gait Trainer (MGT), which comprises two foot plates connected to a double crank and rocker system that is singly actuated by an induction motor via a planetary gear system and drives a patient's legs in a walking pattern (8).
  • MGT Mechanized Gait Trainer
  • the ARTHuR robot makes use of a linear motor and a two degree-of-freedom mechanism to measure and manipulate leg movement during stepping with good backdriveability and force control (13).
  • Other devices under development include HealthSouth's Autoambulator, and a more sophisticated version of the MGT that can move the footplates along arbitrary three degree- of-freedom trajectories.
  • Robotic devices for gait training preferably exhibit good backdriveability, defined as low intrinsic endpoint mechanical impedance (10), or accurate reproduction at the input end of a mechanical transmission of a force or motion that is applied at the output end (15).
  • Good backdriveability offers several important benefits for robotic therapy devices (13), including the ability for the device to act as a passive motion capture device. In such a passive motion capture mode, the patient's movement ability can be quantified, and the therapist can manually specify desired, patient-specific training motions for the device.
  • Dynamic motion optimization provides a formalized method for determining motions for underconstrained tasks, and may reveal novel strategies for achieving the tasks. It has been used with success to simulate human control over such activities as diving, jumping, and walking (1 , 9, 11 ).
  • the present invention provides a method of locomotion training which involves shifting a subject's pelvis without directly touching the subject's legs.
  • the method comprises: (a) providing a surface; (b) supporting the subject over the surface so that at least one of the subject's legs is positioned on the surface; and (c) shifting the supported subject's pelvis, which causes the subject's legs to move along the surface.
  • the surface can be fixed or moveable.
  • the pelvis can be shifted manually or robotically.
  • the subject is suspended on a treadmill and the pelvis is shifted by attaching a robot to the subject's torso.
  • a leg swing motion is created by moving the pelvis without contact with the legs.
  • the present invention also provides a method of determining a locomotion training strategy using dynamic motion optimization.
  • a locomotion training strategy is a sequence of body segment trajectories that can be imposed on a subject to obtain a desired gait.
  • the method comprises (a) formulating an optimal control problem for a locomotory model, (b) inputting joint parameters, (c) solving the optimal control problem, and (d) deriving a sequence of body segment trajectories in accordance with the optimization.
  • the model can be of any animal but is preferably a human model. In certain embodiments, an under-actuated human model can be employed and the trajectories can be leg or pelvic trajectories.
  • the present invention further provides a robotic device for manipulating and/or measuring the pelvic motion of a subject undergoing locomotion training.
  • the device comprises at least one backdriveable robot for attaching to the torso of the subject and for applying force to the subject's pelvis.
  • the robot can be powered by pneumatic, hydraulic or electric actuators.
  • the robot comprises a plurality of pneumatic actuators, which are preferably pneumatic cylinders.
  • the robotic device can be used to manipulate a subject's pelvis in order to move the subject's legs.
  • the pelvis can be manipulated for its own sake without regard for leg movement.
  • the device can be used to manipulate the pelvis while the legs are also manipulated, either robotically or manually by a therapist.
  • the present invention is further directed to a system for locomotion therapy.
  • the system comprises (a) a surface, (b) a support system for supporting a subject over the surface so that at least one of the subject's legs is positioned on the surface, and (c) a robotic device comprising at least one backdriveable robot for attaching to the torso of the supported subject and for applying force to the pelvis of the supported subject.
  • Figure 1 is a perspective view of a suspended person undergoing locomotion training in accordance with the present invention.
  • Figure 2 is a perspective view showing a preferred embodiment of the robotic device
  • Figure 3 is a close-up view of the rod ends of three pneumatic cylinders which compose a robot of the present invention
  • Figure 4 is a flow chart illustrating a hierarchical control system for a pneumatically actuated robot
  • Figure 5 is a schematic representation of the joints used to model a human for dynamic motion optimization
  • Figures 6A and 6B are graphs showing the workspace of a robotic device of the present invention.
  • Figures 7A - D show the inferred positions of an actual human subject's hips throughout stepping as captured by a robotic device of the present invention
  • Figures 8A - D show the calculated average trajectory per step of the passive motion capture data of Figure 6;
  • Figure 9A-C are graphic representations of one-half of the gait cycle found by motion capture of an actual human subject;
  • Figures 10A - C are graphic representations of the optimized motion computed for a fully actuated human model;
  • Figures 11 A-G are graphs showing the joint motions for a fully actuated human model
  • Figures 12A - G are graphs showing the joint torques for a fully actuated human model
  • Figures 13A - C are graphic representations of the optimized motion computed for an under-actuated human model
  • Figures 14A - G are graphs showing the joint motions for an under-actuated human model.
  • Figures 15A and 15B are graphs showing the stance hip torques for an under-actuated human model.
  • a subject 2 is suspended over a moveable surface 4 and a robotic device is attached to the subject's torso.
  • the moveable surface can be a surface provided by devices well known in the art such as a motorized treadmill, a conveyor belt, or a moving walkway.
  • a suitable suspension system 6 such as a counterweight, spring, or pneumatic system is also well known in the art.
  • the suspension system can partially unload the subject's weight to a desired level of support.
  • the subject can be held and supported over the surface by the robotic device itself without the need for a separate support system.
  • a specific embodiment of the robotic device comprises a pair of backdriveable pneumatic robots 10 that attach to the back of a belt 12 worn by a subject.
  • Each robot comprises three pneumatic cylinders 14 that are rotatably connected to a support pillar 15, in this case via ball-joints.
  • Two cylinders lie coplanar in the horizontal plane and connect to the support pillar through a cross-bar 16; the third cylinder lies in an oblique plane to provide upward forces.
  • Each robot has three degrees of freedom and exhibits good backdriveability.
  • the post 19 is connected to a revolute joint
  • Each three-cylinder robot can be mounted to an adjustable slide that allows the robots to be moved vertically to accommodate subjects of various hip heights.
  • the mounting of the pneumatic cylinders on ball joints minimizes the moments that can be imparted onto the pistons, preventing damage to the cylinders.
  • the resulting system has five degrees of freedom, relative to the axes in Figure 2, providing control of three translations, i.e., side-to-side, forward-and-back, up-and-down, and two rotations, i.e., pelvic swivel about the Z-axis, and pelvic tilt about the Y axis.
  • One rotation cannot be controlled - pelvic rotation about the X-axis.
  • the cylinders attach to the belt behind the subject, allowing the subject to swing the arm naturally during gait, and providing an unobstructed view for the subject.
  • the cylinders can be angled in from the sides with sufficient spacing to allow a subject to enter the device via a wheelchair, and to allow a therapist to access the subject from both behind and on the sides.
  • the device can be used to measure and record the movements and body segment trajectories of a subject.
  • the pneumatic cylinders are vented and the device is used in a passive mode.
  • the cylinders are instrumented with linear potentiometers.
  • the position and orientation of the pelvis can be inferred in real-time from the potentiometer measurements using the forward kinematics of the mechanism.
  • the device can be used to playback desired movements including movement previously recorded or specified by a therapist.
  • a hierarchical control system such as one provided in Bobrow, J.E. and B. W.
  • McDonell "Modeling, Identification, and Control of a Pneumatically Actuated, Force Controllable Robot", IEEE Transactions on Robotics and Automation, vol. 14, pp. 732-42, 1998, can be used for which the actuator dynamics are separated from the rigid body dynamics of the robot.
  • the first step is the inputting of a desired output motion or force 21.
  • a well- established robot control algorithm 22 which uses feedback 23 from the robot position and force sensors, is used to create the desired output motion.
  • One such control algorithm is the "computed torque” method which is known to perform well for robots using electric motors as the actuators.
  • the computed torque method requires that the actuators create a desired torque 24.
  • a nonlinear gas flow control law 25 is then used to ensure that the pneumatic actuators produce the desired torques.
  • the nonlinear control law can use feedback 26 from the actual torques and feedback 28 from the robot position and force sensors.
  • the hierarchical control system permits well-established control laws, like those used for motor driven robots, to be used for the pneumatic device.
  • control laws like those used for motor driven robots
  • the nonlinear compressible air flow dynamics for each cylinder and servovalve are modeled and controlled.
  • pressure sensors are used on both sides of the pistons for feedback in order to achieve fast and accurate force control for each cylinder of the system. This transforms the control problem into one that is standard for robotic control designers.
  • the inner-loop force control law is:
  • V-i, k p - governs response time of the force control subsystem
  • the device has mechanical hard stops that limit pelvic rotation to twelve degrees.
  • Pressure-actuated safety valves vent both sides of each cylinder to leave the system in its passive state in case the main supply pressure is cut.
  • Main supply pressure is vented with an electrically controlled valve when an emergency stop button is pressed.
  • Main supply pressure is also vented when software limits on position, velocity, and pressure are exceeded.
  • a robotic device of the present invention can be used to manipulate and measure the limb movement of a subject undergoing physical training of a limb.
  • the limb is preferably the leg of a subject undergoing locomotion therapy.
  • the present invention further provides a method of determining a locomotion training strategy for a subject supported over a moveable surface such as a treadmill.
  • the problem of determining an appropriate sequence of body segment trajectories for a paralyzed subject can be formulated as an optimal control problem for an under-actuated articulated chain.
  • the optimal control problem can be converted into a discrete parameter optimization, and an efficient gradient- based algorithm can be used to solve it.
  • Motion capture data from a human subject can be compared to the results from the dynamic motion optimization.
  • the present invention makes it possible for a robot to create a gait for the paralyzed subject that is close to that of an unimpaired subject.
  • the head, torso, pelvis, and arms can be combined into a single rigid body referred to as the upper trunk 30.
  • the walking gait cycle can be assumed to be bilaterally symmetric. That is, in the gait cycle, the right-side stance and swing phases are assumed to be identical to the left-side stance and swing phases, respectively. Based on this assumption, only one-half of the gait cycle can be simulated.
  • the joints on the side of the stance phase are referred to as the stance joints and the joints on the side of the swing phase as the swing joints.
  • the stance hip 32 can be modeled as a two degree-of-freedom universal joint rotating about axes oriented in the x- and y-directions.
  • the upper trunk can be assumed to remain at a fixed angle about the z axis.
  • the swing hip 34 can be modeled as a three degree-of-freedom ball joint rotating about axes in the in the x- (i.e. leg adduction/abduction), y- (hip internal/external rotation), and z- (i.e. hip flexion/extension) directions.
  • the knee 36 and ankle 38 can be modeled as one degree-of-freedom hinge joints about the z-axis (knee extension/flexion and ankle dorsal/plantar flexion, respectively).
  • Motion capture data of key body segments for an unimpaired subject during treadmill walking can be obtained using a video-based system (Motion Analysis Corp., Santa Rosa, California). External markers can be attached to the subject at the antero-superior iliac spines (ASISs), knees, ankles, tops of the toes, and backs of the heels. Representative steps can be chosen for comparison with the optimization results. A least squares method can be used to convert the positions of the markers to the link lengths and joint angles based on the forward kinematics of the human model.
  • ASISs antero-superior iliac spines
  • Dynamic properties of the body segments can be estimated using regression equations based on segment kinematic measurements such as shown by Zatsiorsky, V., and Seluyanov, V., "Estimation of the Mass and Inertia Characteristics of the Human Body by Means of the Best Predictive Regression Equations, Biomechanics IX-B 233-239, 1985.
  • Passive torque-angle properties of the hip, knee, and ankle joints can be measured for the subject with a motorized dynamometer (Biodex Inc., Shirley, New York).
  • the dynamometer can impose slow isovelocity movements at the joints and can measure applied torques and resulting joint angles.
  • Joints can be measured in a gravity-eliminated configuration, or, if not possible, torques due to gravity can be estimated and subtracted.
  • the joints can be modeled as nonlinear springs in which the joint torque is a polynomial function of the joint angle. A least squares method can be used to obtain the best-fit polynomial of order 3 for the torque-angle properties of each of the joints.
  • a robot is assumed to be capable of moving the pelvis such that the stance hip moves along a normal, unimpaired trajectory, while simultaneously lifting the swing hip to control movement of the swing leg.
  • the robot-assisted motion is assumed to be initiated when the treadmill has pulled the stance leg backward to the position from which swing would normally be initiated, with the foot's horizontal and vertical velocity equal to zero.
  • the robot-generated motion can then initiate the transition from stance to swing, driving the leg toward the desired foot-fall location.
  • the swing leg can be modeled as a paralyzed (i.e. unactuated) linkage with specified passive torque-angle properties.
  • This problem can be addressed mathematically as an optimal control problem for an under-actuated system.
  • the goal is to obtain a normal swing phase of the paralyzed leg, starting with the leg in an extended position with zero initial joint velocities by shifting the pelvis.
  • the motion of the stance hip found from video capture data of an unimpaired subject can be used as an input to an under-actuated human model.
  • the stance hip joint center locations can be approximated using B-spline curves based on the motion capture data.
  • the swing motion can be considered to be an optimal control problem as follows:
  • Equation (2) represents the dynamics for the human model with the 10 joint coordinates q, the joint forces or torques ⁇ , and the measured passive torques due to soft tissue stiffness ⁇ st .
  • H(q) is the
  • ⁇ x , ⁇ 2 , and r 3 are the generalized forces associated with the translation of the stance hip (and are not included in the cost function since the position of the stance hip was specified by the motion capture data); r 4 and ⁇ 5 are the moments corresponding to the two rotations of the stance hip (controlled by the robot); ⁇ 6 , r 7 , and r 8 are the swing hip moments
  • Equation (1) is a penalty function used to avoid collision of the swing leg with the stance leg and the ground and to achieve the final desired position. This was achieved by introducing two functions which penalized the penetration of the swing leg with the stance leg and the ground.
  • the joint trajectories can be interpolated by uniform, C 4 continuous quintic B-spline polynomials over the knot space of an ordered time sequence.
  • the system can be modeled as an under-actuated system with two actuated joints (q 4 and q $ ) and five passive, or unactuated, joints (c 6 , q ⁇ , q & , q_, and g-io).
  • the dynamics of such a hybrid dynamic system can be solved efficiently by a Lie group formulation such as one provided by Sohl, G. A., and Bobrow, J.
  • an initial trajectory is required for the actuated joints.
  • the trajectory identified from motion capture can be used as an initial trajectory.
  • the dynamics of the partially actuated system can be integrated numerically from the given initial conditions using a numerical solution function such as Matlab's function " ode45", and a dynamics software such as the Cstorm dynamics software provided by Sohl, G. A., and Bobrow, J.
  • Motions can be generated by this dynamic motion optimization using different weighting coefficients for different cases. Weighting coefficients can be chosen based on experience with many simulations by guaging how accurately the coefficients produce the desired motions of the pelvis and leg. In each case, 8 variable parameters can be used for each of the actuated joints. Joint torques can be computed for the human model based on the estimated dynamic properties and the B-spline joint trajectories.
  • Dynamic motion optimization provides a useful tool for investigating novel strategies for assisting in locomotion rehabilitation (16). Finding strategies by observation of therapists is also desirable, but may miss some valuable strategies because therapists are limited in control relative to robots. Dynamic motion optimization also provides a formal means to automatically generate strategies on a patient-by-patient basis by including patient-specific passive joint and reflex properties in the simulation. In addition, as a patient begins to recover control over some muscles, this activation can be modeled and included in the simulation. As the patient recovers walking ability, the simulations can progress from unactuated, to partially actuated, to fully actuated simulations, with the optimization algorithm automatically determining the appropriate assistance strategy for each recovery state.
  • Example 1 shows the robotic device in motion capture mode.
  • Each robot of the device uses three 1.5" diameter pneumatic cylinders, each cylinder with a 12" stroke.
  • the device can generate about 350 lbs of force in the X-direction, 200 lbs of force in the Y-direction, and 140 lbs of force in the Z-direction, with reference to the X,Y and Z axes of Figure 2, at a 100 PSI supply pressure.
  • the positions of the cylinder rods are measured by an analog voltage signal from potentiometers that are integral within the cylinders. Pressures on each side of each cylinder are measured using low-cost pressure sensors.
  • the system is controlled using Matlab xPC target.
  • FIG. 6A shows the workspace of the device in the horizontal (X-Y) plane, where the X, Y and Z axes are oriented as in Figure 2.
  • a triangle 40 represents a position of the left attachment point to subject
  • a square 42 represents a position of the right attachment point to subject.
  • Figure 6B shows the workspace of the device in the X-Z plane, where a triangle 44 represents a left attachment point position and a square 46 represents a right attachment point position.
  • Position signals were collected from potentiometers on the pneumatic cylinders while an unimpaired subject made 100 steps over a treadmill moving at a constant speed of about 2 m/s. Forward kinematic equations were used to infer the position of the subject's hips throughout the stepping.
  • Figures 7A - D show the inferred positions.
  • Figure 7A shows the position of the subject's left 50 and right 52 hip in the horizontal (X-Y) plane.
  • Figure 7B shows the subject's left 54 and right 56 hip in the X-Y-Z space.
  • Figure 7C shows the subject's left 58 and right 60 hip in the Y-Z plane.
  • Figure 7D shows the subject's left 62 and right 64 hip in the X-Z plane.
  • Figures 8A - D Calculated average hip trajectory per step of the passive motion capture data from Figures 7A - D are shown in Figures 8A - D.
  • Figure 8A shows the calculated trajectory for the left 70 and right 72 hip in the horizontal (X-Y) plane.
  • Figure 8B shows the calculated trajectory for the left hip 74 and right 76 hip in the X-Y-Z space.
  • Figure 8C shows the calculated trajectory for the left 78 and right 80 hip in the Y-Z plane.
  • Figure 8D shows the calculated trajectory for the left 82 and right 84 hip in the X-Z plane.
  • This example shows the use of dynamic motion optimization applied to a fully actuated model. This model simulates normal human control of stepping.
  • Motion capture data was obtained from an unimpaired human subject with a height of 1.95 m and a weight of 75 kg.
  • the sampling rate of motion capture was 60 Hz.
  • the treadmill speed was selected to be 1.25 m/sec to approximate a speed commonly used in step training with BWS training.
  • Figures 9A - C show one representative step with a duration of 0.5 sec that was chosen for comparison with the optimization results.
  • the positions of the external markers were converted to link lengths and joint angles based on forward kinematics.
  • the X, Y and Z axes are oriented as shown in Figure 5.
  • Figure 9A shows the subject's gait along the X-Z plane.
  • Figure 9B shows a side view of the gait along the X-Y plane, where a solid line 90 represents the subject's swing leg during the step cycle and a dashed line 92 represents the configuration of the stance leg.
  • Figure 9C shows a front view of the gait along the Y-Z plane, where the solid line 94 represents the swing leg and the dotted line 96 represents the stance leg.
  • FIG. 10A shows the gait in the X-Z plane.
  • Figure 10B shows the gait in the Y-X plane, with a solid line 100 representing the optimized gait and a dashed line 102 representing the actual human data for comparison.
  • Figure 10C shows the gait in the Y-Z plane.
  • Figure 11A shows the joint angles of the stance hip external/internal rotation for the optimized data 104 and the actual human data 106.
  • Figure 11 B shows the joint angles of the swing hip abduction/reduction for the optimized data 108 and the actual human data 110.
  • Figure 11 C shows the joint angles of the swing hip extention/flexion for the optimized data 112 and the actual human data 114.
  • Figure 11 D shows the joint angles of the ankle plantar/dorsal flexion for the optimized data 116 and the actual human data 118.
  • Figure 11 E shows the joint angles of the stance hip abduction/adduction for the optimized data 120 and the actual human data 122.
  • Figure 11 F shows the joint angles of the swing hip external/internal rotation for the optimized data 124 and the actual human data 126.
  • Figure 11 G shows the joint angles of the knee flexion/extension for the optimized data 128 and the actual human data 130.
  • Figure 12A shows the joint torques of the stance hip external/internal rotation for the optimized data 132 and the actual human data 134.
  • Figure 12B shows the joint torques of the swing hip abduction/reduction for the optimized data 136 and the actual human data 138.
  • Figure 12C shows the joint torques of the swing hip extention/flexion for the optimized data 140 and the actual human data 142.
  • Figure 12D shows the joint torques of the ankle plantar/dorsal flexion for the optimized data 144 and the actual human data 146.
  • Figure 12E shows the joint torques of the stance hip abduction/adduction for the optimized data 148 and the actual human data 150.
  • Figure 12F shows the joint torques of the swing hip external/internal rotation for the optimized data 152 and the actual human data 154.
  • Figure 12G shows the joint torques of the knee flexion/extension for the optimized data 156 and the actual human data 158.
  • This example shows the use of dynamic motion optimization applied to an under-actuated model, which simulates a paralyzed subject.
  • Figure 13A shows the gait in the X-Z plane, with a solid line 160 representing the optimized gait and a dashed line 162 representing the actual human data.
  • Figure 13B shows the gait in the Y-X plane, with a solid line 164 representing the optimized gait and a dashed line 166 representing the actual human data.
  • Figure 13C shows the gait in the Y-Z plane with the solid line 168 representing the optimized gait and the dashed line 170 representing the actual human data.
  • Figure 14A shows the joint angles of the stance hip external/internal rotation for the optimized data 172 and the actual human data 174.
  • Figure 14B shows the joint angles of the swing hip abduction/reduction for the optimized data 176 and the actual human data 178.
  • Figure 14C shows the joint angles of the swing hip extention/flexion for the optimized data 180 and the actual human data 182.
  • Figure 14D shows the joint angles of the ankle plantar/dorsal flexion for the optimized data 184 and the actual human data 186.
  • Figure 14E shows the joint angles of the stance hip abduction/adduction for the optimized data 188 and the actual human data 190.
  • Figure 14F shows the joint angles of the swing hip external/internal rotation for the optimized data 192 and the actual human data 194.
  • Figure 14G shows the joint angles of the knee flexion/extension for the optimized data 196 and the actual human data 198.
  • Figure 15A shows the joint torques of the stance hip external/internal rotation for the optimized data 200 and the actual human data 202.
  • Figure 15B shows the joint torques of the stance hip abduction/adduction for the optimized data 204 and the actual human data 206.
  • the optimizer lifted the swing hip to avoid collision between the swing leg and the ground. At the same time, it twisted the pelvis to pump energy into the paralyzed leg and moved the leg close to the desired final configuration, while avoiding collision between the legs. Thus the optimizer was able to determine a strategy that could achieve repetitive stepping by shifting the pelvis alone. The strategy incorporated a large swivel of the stance hip joint around the y-axis which may be undesirable in step training a real human. Similar optimizations that constrained the stance hip rotation and achieved the desired step pattern were also performed. [0074] The results demonstrate the feasibility of incorporating robotic control of pelvic motion into BWS training.
  • a hip swinging robot can also be useful for loading the stance leg by pressing downward on the stance hip, thus providing load-related sensory input required for stepping at the same time as assisting in swing.

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Abstract

L'invention porte sur un procédé et sur un dispositif robotique d'entraînement à la locomotion. Le procédé consiste à déplacer le bassin d'un sujet sans venir en contact direct avec la jambe du sujet pour permettre le déplacement le long d'une surface mobile. Le dispositif comprend deux robots aptes à reculer, chacun possédant trois cylindres pneumatiques qui se raccordent les uns aux autres au niveau des extrémités de leur tige afin de les fixer au torse du sujet. L'invention porte également sur un procédé de détermination d'une stratégie d'entraînement à la locomotion d'un robot déplaçant le bassin en incorporant une optimisation du mouvement dynamique .
PCT/US2003/015754 2002-05-20 2003-05-20 Principe de rehabilitation robotique de la demarche par un deplacement optimal de la hanche WO2003099525A2 (fr)

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WO2007137150A2 (fr) * 2006-05-18 2007-11-29 Massachusetts Institute Of Technology Interface au niveau du bassin
CN106814610A (zh) * 2017-01-23 2017-06-09 长春工业大学 基于非线性模型预测控制的双足机器人步态优化的信赖域‑sqp方法
TWI684442B (zh) * 2018-07-27 2020-02-11 國立陽明大學 步態學習輔助系統及其應用方法
CN111341412A (zh) * 2020-02-25 2020-06-26 南京理工大学 基于rbf-dmp振荡器的下肢康复型外骨骼步态规划方法
CN114161472A (zh) * 2021-11-17 2022-03-11 深圳市优必选科技股份有限公司 髋腰关节结构及人形机器人

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