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EP4637643A1 - Procédé de commande d'un comportement de mouvement d'une articulation artificielle - Google Patents

Procédé de commande d'un comportement de mouvement d'une articulation artificielle

Info

Publication number
EP4637643A1
EP4637643A1 EP23840650.8A EP23840650A EP4637643A1 EP 4637643 A1 EP4637643 A1 EP 4637643A1 EP 23840650 A EP23840650 A EP 23840650A EP 4637643 A1 EP4637643 A1 EP 4637643A1
Authority
EP
European Patent Office
Prior art keywords
mlsv
rule set
machine learning
sensor
joint
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP23840650.8A
Other languages
German (de)
English (en)
Inventor
Roland Auberger
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Otto Bock Healthcare Products GmbH
Original Assignee
Otto Bock Healthcare Products GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Otto Bock Healthcare Products GmbH filed Critical Otto Bock Healthcare Products GmbH
Publication of EP4637643A1 publication Critical patent/EP4637643A1/fr
Pending legal-status Critical Current

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A61F2/50—Prostheses not implantable in the body
    • A61F2/60—Artificial legs or feet or parts thereof
    • A61F2/64—Knee joints
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A61F2/50—Prostheses not implantable in the body
    • A61F2/68—Operating or control means
    • A61F2/70—Operating or control means electrical
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A61F2/50—Prostheses not implantable in the body
    • A61F2/68—Operating or control means
    • A61F2/70—Operating or control means electrical
    • A61F2002/701—Operating or control means electrical operated by electrically controlled means, e.g. solenoids or torque motors
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A61F2/50—Prostheses not implantable in the body
    • A61F2/68—Operating or control means
    • A61F2/70—Operating or control means electrical
    • A61F2002/704—Operating or control means electrical computer-controlled, e.g. robotic control
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A61F2/50—Prostheses not implantable in the body
    • A61F2/76—Means for assembling, fitting or testing prostheses, e.g. for measuring or balancing, e.g. alignment means
    • A61F2002/7615—Measuring means

Definitions

  • the invention relates to a method for controlling a movement behavior of an artificial joint, in particular an artificial knee joint, which has an upper part and a lower part mounted thereon so as to be pivotable about a pivot axis, between which a device for influencing the pivotability or the pivoting of the upper part relative to the lower part is arranged, which is coupled to a control device in which a rule set is stored and which activates, deactivates or modulates the device on the basis of input values for the rule set in order to influence the pivoting or pivotability.
  • Methods and control parameters for controlling the artificial joint are stored in the rule set.
  • Artificial joints in particular artificial knee joints, are arranged in prostheses and orthoses, whereby prostheses replace missing limbs in terms of their function and, if necessary, also in terms of their external appearance.
  • Orthoses are applied to limbs and are used to guide, limit and, if necessary, influence the movement of a natural limb.
  • Orthoses have orthosis joints that are arranged or formed between an upper part and a lower part. The upper part and the lower part each have fastening devices for fastening the orthosis to the limb. Fastening devices are arranged on prostheses with which the prosthesis can be fixed to the limb stump or the patient.
  • dampers are arranged to influence the pivoting movement or the pivotability, which are coupled to a control device via which the dampers or drives are activated, deactivated or their behavior is changed.
  • Dampers can for example, they can be designed as purely passive devices such as linear hydraulics, rotary hydraulics or magnetorheological dampers.
  • Mechanical brakes can also influence the pivoting ability or the pivoting movement of the upper part relative to the lower part.
  • Drives are in particular electric motors and other energy storage devices that can initiate or support a movement or counteract a movement in order to slow down a pivoting movement. With an appropriate circuit, it is also possible to use drives to lock the joint and thus cancel the pivoting ability.
  • the control device activates, deactivates or modulates the device for influencing the pivoting or pivotability, for example on the basis of sensor data that is transmitted to the control device.
  • Sensors are arranged on the artificial joint or on the attachments such as the prosthetic shaft, distal prosthetic component or orthotic splint.
  • the sensors can also be arranged on the limb on the treated side or on the contralateral side.
  • state machines that are stored in the control device are used to control the change in resistance.
  • a rule set can contain several state machines that are dynamically activated depending on the situation.
  • the sensor data is used to determine the state of the prosthesis or orthosis and how an adjustment device, for example for a valve, must be activated or deactivated in order to generate a certain movement behavior.
  • valves are completely or partially closed in order to change the cross-section of a fluid connection in order to influence the corresponding movement of a joint.
  • a control system for a prosthetic knee joint with a state machine is described in EP 549 855 B1.
  • a method for controlling at least one actuator from an orthopedic device with an electronic control device is known.
  • the control device is coupled to the actuator and at least one sensor and has at least one electronic Processor for processing sensor data.
  • At least one state machine is stored in the control device, in which states of the orthopedic device and state transitions of the actuator are determined.
  • a classification is stored in the control device, in which sensor data and/or states are automatically classified in a classification process. The classification process and the state machine can be used in combination. Based on the classification and the states, a decision is made about the manner of activating or deactivating the actuator as a control signal.
  • a control system for a lower extremity prosthesis and a method for controlling the prosthesis in which the prosthesis has a control motor for a prosthetic knee joint, a control motor for an ankle joint, a connecting rod and a sleeve.
  • An IMU is used to record gait information of a healthy person under different conditions and to create a training data set from this.
  • a neural network model is created and trained with the collected data in a simulation environment.
  • the conventional neural network model is implemented in a lower extremity prosthesis in a control device.
  • the control device receives an input signal from the IMU, an instruction is issued to a joint of the lower extremity taking into account the trained network model.
  • the object of the present invention is to provide a method with which, even without angle sensors, it can be detected with sufficient accuracy when and how the pivoting or pivotability must be influenced, whereby the operational effort is as low as possible.
  • the method for controlling a movement behavior of an artificial joint which has an upper part and a lower part pivotably mounted thereon about a pivot axis, between which a device for influencing the pivotability or pivoting of the upper part relative to the lower part is arranged, which is coupled to a control device in which a rule set is stored and which activates, deactivates or modulates the device on the basis of input values for the rule set in order to influence the pivoting or pivotability, is characterized in that sensor values, e.g. from an IMU arranged on the upper part or the lower part or from IMUs arranged on the upper part and the lower part, which are recorded during use of the artificial joint, are fed to at least one machine learning-based estimation method (MLSV), e.g.
  • MLSV machine learning-based estimation method
  • an artificial neural network which calculates an estimated value for a kinetic or kinematic parameter, e.g. joint angle, or an expected kinetic or kinematic parameter from the sensor values and this estimated value is fed to the rule set as an input value, e.g. for the joint angle signal, and is used therein as a criterion for the activation, deactivation or modulation of the device for influencing the pivotability or pivoting.
  • a kinetic or kinematic parameter e.g. joint angle
  • an expected kinetic or kinematic parameter e.g. joint angle
  • the rule set e.g. for the joint angle signal
  • the machine learning-based estimation method(s) continuously calculates the joint angle and, if applicable, another value or values that can be derived from it.
  • the proposed method creates a virtual joint angle sensor that provides a calculated value or estimated value for the joint angle, in particular the knee angle.
  • the virtual sensor in the machine learning-based estimation method is based on an evaluation of the data from the sensor or sensors, in particular an IMU or several IMUs, wherein the machine learning-based estimation method (MLSV) has been trained in particular to calculate the joint angle.
  • the method is not limited to artificial joint devices of the lower extremity, in particular artificial knee joints or ankle joints, but can also be used for other artificial joints, for example for a hip joint or joints of the upper extremity, for example an elbow joint, shoulder joint or wrist.
  • a machine learning-based estimation method can, for example, be implemented as an artificial neural network (ANN), such as a multilayer perceptron, or can include an ANN.
  • ANN artificial neural network
  • black box models and regression methods are available, in which internal model and calculation parameters are optimized in a training process using data obtained in advance.
  • One use case for an MLSV is estimating a knee angle from the data of an IMU mounted on the upper or lower part.
  • the training data can be obtained by using a system that has both a knee angle sensor and an IMU. With this data, the MLSV is trained to estimate the knee angle from the IMU data by using the data from the additional knee angle sensor as "ground truth".
  • the "ground truth” thus includes a Reference result for the training process in which the MLSV learns to estimate the reference result only from the available source data, e.g. IMU data.
  • the MLSV can be used as a virtual sensor to estimate the knee angle.
  • a physical angle sensor can be replaced by this.
  • the MLSV can also be trained using "unsupervised learning" methods.
  • Such a calculation or determination of a joint angle or knee angle without a direct joint angle sensor is helpful for trip protection in artificial knee joints.
  • the flexion resistance and the extension resistance can be set separately, trip protection can be implemented in artificial knee joints that are controlled by a control device with a microprocessor by increasing the flexion resistance at the time of the reversal of movement in the swing phase. This time is also reliably determined using the calculated value or estimated value for the knee angle or the knee angular velocity calculated in the MLSV.
  • the reversal of movement in the swing phase is a clear and specific situation that is relatively easy to recognize and therefore inaccuracies in the calculation or estimation can be easily compensated for or are not significant.
  • the joint angle input value is thus provided via an angle sensor without directly detecting the joint angle, which means that significantly less operational effort and less effort is required for assembly and calibration.
  • the estimation method based on machine learning continuously determines several estimated values for one or more kinematic or kinetic parameters, so that, for example, not only the joint angle, but also a load, a load profile, accelerations, the spatial orientation of a component with respect to gravity or the like can be calculated without having to carry out a direct measurement of the parameter sought.
  • the sensor values are determined by at least one IMU, for example to estimate the kinetic or kinematic parameter(s) of the joint angle between the upper part and the lower part using the MLSV(s).
  • Several identical or different MLSVs can also be used to determine several estimated values for different kinematic or kinetic parameters, which then form the basis for the further process. If several estimated values for different kinematic or kinetic parameters are available, these are made available to the rule set as input parameters.
  • additional sensor data and/or state variables are fed to the rule set as input values, so that these additional sensor data and/or state variables are used as criteria for activating, deactivating or modulating the device for influencing the pivotability or pivoting of the upper part relative to the lower part.
  • This is particularly advantageous in safety-critical cases, since any inaccuracies that may occur in the MLSV can be balanced or compensated for by a plausibility check based on the additional sensor data.
  • Safety-critical activations, deactivations or modulations are therefore not carried out exclusively based on the results of the machine learning-based estimation method in one embodiment, but are secured by additional parameters, measurements or calculations.
  • the MLSV is supplied with previously obtained sensor data from a database and trained.
  • the previously determined data which is stored in the database, makes it easier for the MLSV to estimate the probability of a certain situation.
  • the database can be constantly updated and linked to the MLSV in order to achieve an enlarged data base and greater accuracy.
  • the database is updated during operation in order to achieve real-time optimization.
  • phases of inactivity e.g. during loading or when not in use, during which the system can be updated based on additional data.
  • This data can be collected during operation, which means it contains patient-specific information.
  • the data can be made available centrally by the manufacturer, e.g. via the Internet as part of a "field update”.
  • the expected joint angle is fed to the rule set with a lead time of between 0.001 seconds and 1 second.
  • the MLSV can make predictions about the expected behavior of the upper part relative to the lower part and about the development of the current situation, so that it is possible to react more quickly to possible actual changes.
  • the prediction is made possible and more accurate in particular because the MLSV can predict the probability of a future movement or future movement behavior with a high degree of accuracy based on the data in the database. Based on these probabilities or estimated values, a corresponding command is then sent or prepared via the control device in order to set the corresponding behavior, i.e.
  • the probabilities of a movement situation or a state of the joint are calculated from the sensor values of the IMU in the control device or the MLSV and then fed to the rule set as an input value.
  • the MLSV thus forms a virtual sensor that determines exactly one variable, for example the knee angle, and then transmits this variable as an input variable to the control device.
  • other variables can also be calculated or predicted, such as forces, moments, the nature of the ground or gradients of the subsoil, so that this variable is also calculated by the MLSV with the corresponding probability of occurrence and fed to the control device as an input variable.
  • sensor values from previous journals or time periods can also be fed into the machine learning-based estimation method in order to increase the accuracy for determining the estimated value and, in particular, to also provide the MLSV with information on the movement history.
  • the estimation method or methods based on machine learning are based in one embodiment on Gaussian processes, whereby the MLSV(s) provides, in addition to the estimated values for the kinematic or kinetic parameter(s), information on the confidence interval for the respective estimated value, which is then used in the further calculation.
  • the confidence interval for the respective estimated value is used, for example, as a criterion for activation, deactivation or modulation and, if necessary, transmitted together with the estimated value determined in each case and, if necessary, the rule set.
  • This probabilistic approach uses kernel functions and, in addition to the estimated value, also provides the associated confidence interval, i.e. a statement on the quality of the estimate.
  • the additional information is of particular interest for the control of orthopedic facilities, as it can be used to determine the weighting of the influence of the estimated value when controlling the artificial joint in the rule set.
  • a small confidence interval corresponds to a good estimation accuracy and thus a high level of trustworthiness of the estimated value.
  • D i.e. the estimated value can be included in the control with a higher weighting than a less reliable estimated value with a large confidence interval.
  • Estimates of the information on the ground conditions and the gradient of the ground are particularly useful for controlling artificial ankle joints or prosthetic feet.
  • the foot position at the end of the swing phase can be modulated depending on the estimated gradient of the ground.
  • An estimate of the height difference overcome is helpful for optimizing behavior on stairs.
  • Artificial knee joints can modulate the resistance or support behavior depending on these parameters, e.g. when walking downhill, they can offer increased flexural resistance as the gradient increases. When walking uphill, a support moment can be adapted to the height difference to be overcome. All of these optimization Additional information that supports the support of artificial joints can be determined as estimated values by appropriately trained MLSVs.
  • MLSVs some of which are based on different methods
  • P parameters
  • a parameter for the soil condition can be estimated using an ANN, while at the same time a joint angle is estimated using a method based on Gaussian processes.
  • Figure 1 - a schematic view of a leg prosthesis
  • Figure 2 - a schematic view of a leg prosthesis in a bent position
  • Figure 4 - a diagram of a knee angle estimation
  • Figure 5 - a schematic view of a leg orthosis
  • Figure 6 a diagram of a parameter regression
  • Figure 7 - a schematic representation of a prosthetic ankle joint.
  • FIG. 1 shows a schematic representation of a prosthetic knee joint as part of a leg prosthesis.
  • the prosthetic knee joint has an upper part 10 and a lower part 20, which are pivotably mounted on one another about a pivot axis 15.
  • a prosthetic foot 60 is arranged on the distal end of the lower part 20.
  • a prosthetic shaft or another device for receiving a thigh stump or for securing to a person is arranged or formed on the upper part 10.
  • the hydraulic damper is designed with a hydraulic chamber or a cylinder which is arranged or designed in a housing or base body 31.
  • a piston 32 is movably mounted in the cylinder.
  • the piston 32 can be displaced along the longitudinal extent of the cylinder and is fastened to a piston rod 33 which protrudes from the housing or base body 31.
  • the piston 32 divides the cylinder into chambers which are fluidically connected to one another via a hydraulic line.
  • the base body 31 or the housing can be pivotally mounted on the lower part 20 at a fastening point 23 in order to prevent the piston 32 from tilting during a pivoting movement of the upper part 10 relative to the lower part 20.
  • the end of the piston rod 33 facing away from the piston 32 is attached to the upper part 10, in the embodiment shown on an arm to increase the distance to the pivot axis 15, at an upper fastening point 21.
  • the piston 32 is pressed downwards so that the volume of a flexion chamber decreases, and correspondingly the volume of an extension chamber increases, reduced by the volume of the retracting piston rod 33.
  • An electric motor can be arranged in the housing 31 to generate pressure within one of the chambers, which drives a pump (not shown) in order to apply pressure to the hydraulic fluid within one of the two chambers and thereby move the piston 32 within the cylinder in one direction or the other. This causes a flexion movement or an extension movement of the orthopedic device in the form of the prosthetic leg.
  • the electric motor for driving the pump is an option that can be used in one embodiment in combination with the linear damper 30. In principle, a drive or motor is not necessary for a passive prosthetic knee joint.
  • An alternative embodiment of the device 30 provides a rotary damper, in particular a rotary hydraulic system, a magnetorheological resistance device or an electric motor in generator mode instead of a linear damper, in particular a linear hydraulic system. A combination of several of the resistance devices or drives mentioned is also implemented in one embodiment.
  • the device 30 can also have a linear actuator, a rotary drive or a combination of the described technologies.
  • An actuator 34 is arranged inside the housing 31 or on the housing 31 and is coupled to at least one control valve 35, via which the hydraulic resistance in the device 30 can be changed.
  • the actuator 34 is coupled to a control device 40, which activates, deactivates or modulates the actuator 34 on the basis of sensor values in order to be able to provide an adapted resistance and, if necessary, a hydraulic lock. If the device 30 is designed as a magnetorheological resistance device, the resistances are changed by activating, deactivating or modulating a magnetic field, the actuator 34 is then the electromagnet or the magnetic coil. If the device 30 also contains active drives, the control device 40 also provides commands for the active energy output by the drive.
  • a sensor 50 for detecting the spatial orientation of the lower part 20 or the upper part 10 is arranged on both the upper part 10 and the lower part 20.
  • the sensor 50 for detecting the spatial orientation is only arranged on the upper part 10.
  • This sensor 50 which is designed as an IMU (inertial measurement unit), is used to determine the solid angle or the absolute angle to a fixed spatial orientation, for example the direction of gravity, during use of the prosthetic knee joint.
  • the IMU as a sensor 50 can also detect other status data, in particular status data relating to the artificial knee joint. In particular, positions, angular positions, speeds, accelerations, forces and their progressions or changes are detected as status data.
  • the determined solid angle of the upper part 10 and/or the lower part 20 or another status variable is transmitted to the control device 40 as an input variable.
  • the control device 40 modulates, activates or deactivates the actuator 34 in order to otherwise change the flow resistance in the device 30 in the form of a hydraulic damper, the viscosity, the braking force or the force counteracting the flexion movement.
  • an energy storage device in particular in the form of an accumulator, is assigned to it.
  • the energy storage device can be directly be arranged next to the actuator 34 or at another location in the orthopaedic device where more space is available or where this appears advantageous due to the weight distribution.
  • mechanical energy stores such as springs or flywheels are also provided in embodiments.
  • control device 40 and at least one further sensor 50 can be arranged on the prosthesis foot 60. All sensors arranged on the prosthesis or orthosis are coupled to a control device 40 and their sensor values serve as the basis for controlling the actuator 34 of the device 30 if this is designed as a damper, or as input signals for a motor control if the device 30 is designed as a motor. In the case of magnetorheological damping, the sensor values are used to control the magnetic field or its change.
  • the actuator 34 is controlled on the basis of the sensor data, in particular the spatial positions as well as position data and data on the load, orientation, acceleration and/or deformation of other components, for example to reduce or increase a swivel resistance, to limit an end stop and/or to generate or support a relative movement between the upper part 10 and the lower part 20.
  • Figure 2 shows a schematic representation of a prosthetic knee joint with an upper part 10, a lower part 20 and a device 30 for influencing the pivotability or the pivoting of the component 10 relative to the lower part 20 between the upper part 10 and the lower part 20 in a bent position.
  • the upper part 10 can be pivoted relative to the lower part 20 about the pivot axis 15 against the resistance of the device 30 and has an IMU as a sensor 50.
  • a joint angle a in the embodiment shown the knee angle a, is changed between the upper part 10 and the lower part 20.
  • the joint angle a is measured on the front between the upper part 10 and the lower part 20.
  • At least one sensor 50 is arranged on the prosthetic foot 60 on the lower part 20, which in turn is coupled to the control device 40 and which detects a load on the foot part 60 or a pivoting or position of the foot part 60 in space or relative to the lower part 20.
  • Figure 3 shows a flow chart of the control.
  • Sensor values of an IMU as sensor 50 for example acceleration values and/or orientations in space of IMUs that are arranged on the upper part 10 and/or the lower part 20 of an orthopedic joint device, for example a prosthetic knee joint or an orthotic knee joint, are transmitted to a machine learning-based estimation method (MLSV).
  • the MLSV can be trained both for regression tasks for estimating output variables or probabilities and for classification tasks.
  • the sensor values from the IMU 50 are evaluated within the MLSV with regard to a possible knee angle a.
  • the evaluation within the machine learning-based estimation method (MLSV) results in a calculated value or estimated value K, which is processed in the control device 40 instead of a direct knee angle sensor signal.
  • the estimated value K serves as an input value or an input variable for the rule set that is stored in the control device 40.
  • further sensor values from sensors 55 that have not been evaluated by the MLSV can be fed to the control device 40 with the rule set.
  • the control device 40 itself determines a control command for the actuator on the basis of the input values in order to increase or decrease a resistance or to switch a drive on or off.
  • a probability can also be calculated as to which situation the user of the prosthesis or orthosis is currently in or a classification value that corresponds to a specific situation.
  • the estimated value K for the knee angle a is therefore not measured directly via a knee angle sensor, but is preferably determined by the artificial intelligence of the MLSV on the basis of one or more sensor values of the IMU, which is arranged on the upper part 10 or the lower part 20.
  • the artificial intelligence in an artificial neural network KNN or another MLSV, data from a database for training the artificial intelligence or of the MLSV.
  • other variables or input values for the control device 40 can be calculated or estimated on the basis of the sensor values from the IMU and, if applicable, other sensors that are transmitted to the MLSV, for example forces or moments that occur or can be expected due to movement sequences.
  • the limit is between 0.001 seconds and 1 second in order, on the one hand, to still have a noticeable effect of the pre-setting and, on the other hand, to not make too drastic a change in the setting of the device 30 that is not justified by the actual situation in the joint or on the joint.
  • the MLSV can also be supplied with additional sensor signals other than IMU signals in order to improve the prediction or the calculation accuracy for determining the variable to be determined.
  • control functions without a direct joint angle sensor and the rule set within the control device 40 is supplied exclusively with data from one IMU or several IMUs.
  • the accuracy of the determination via the MLSV is sufficient, especially when determining significant events such as the reversal of movement of a lower leg at the end of a swing phase in an orthotic or prosthetic knee joint.
  • Indirect determination without direct knee angle sensors is advantageous in this case, as an additional sensor is not required.
  • Figure 5 shows a schematic representation of an orthosis with a basic structure that corresponds to the prosthesis according to Figure 1.
  • the connection between the artificial joint and the leg is made via connecting devices 101, 201.
  • a drive 70 is also provided, in which an electric motor 70 is coupled to a pulley, possibly via a gear.
  • a V-belt or toothed belt can then be used to bring about or support flexion or extension of the knee joint depending on the direction of rotation of the motor 70.
  • the design with the drive with an electric motor 70 via a mechanical power transmission device and parallel damping via a hydraulic damper 30 can also be used with a prosthetic knee joint.
  • the resistance device in an orthosis can also be formed by a motor, e.g. in generator mode.
  • the direct mechanical coupling of the electric motor as a resistance device 30 with the upper part 10 and the lower part 20 can be carried out via a power transmission device, for example via a spindle drive, so that instead of a piston rod 33 a spindle is driven by turning a Spindle nut, which is driven by the electric motor, is retracted or extended from the housing 31.
  • the motor as a resistance device is coupled to the upper part 10 and the lower part 20 via a gear device, for example via a planetary gear, in order to cause or slow down and influence a displacement of the upper part 10 relative to the lower part 20.
  • control device 40 and at least one IMU as a sensor 50 are arranged on the prosthesis or orthosis.
  • the angle between the upper part 10 and the lower part 20 is determined by evaluating the sensor data from two spatial position sensors or IMU 50. All sensors arranged on the prosthesis or orthosis are coupled to a control device 40, and their sensor values serve as the basis for controlling the actuator 34 of the resistance devices 30 if this is designed as a damper, or as input signals for a motor control for the electric motor 70 if the resistance device 30 is designed as a motor.
  • the sensor values are used to control the magnetic field or its change.
  • the actuator 34 is controlled or the electric motor 70 is activated, deactivated or modulated, for example in order to reduce or increase a swivel resistance, to limit an end stop and/or to generate or support a relative movement between the upper part 10 and the lower part 20.
  • the solid line shows the result of a regression of a parameter plotted on the Y axis from an input value plotted on the X axis.
  • the areas ⁇ o and ⁇ 2o plotted around the solid line indicate the respective confidence intervals.
  • the input data which are supplied in the form of training data, are plotted as points on the solid line. In the area of the training data, the quality of the estimate is better, the confidence intervals are smaller, which means that the quality of the calculated data is improved with an increased amount of training data.
  • Figure 7 shows a schematic representation of a prosthetic ankle joint in which the upper part 10 is a lower leg shaft and the lower part 20 is the prosthetic foot.
  • Sensors 50 are arranged on both the lower part 20 and the upper part 10, which are coupled to the control device 40, via which the actuator 30 or the resistance device is influenced accordingly.
  • the resistance to dorsal flexion and/or plantar flexion is set depending on an estimated value for a kinetic or kinematic parameter.
  • the estimated value is fed to the rule set and can also relate to the inclination of the ground, for example.

Landscapes

  • Health & Medical Sciences (AREA)
  • Transplantation (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Cardiology (AREA)
  • Vascular Medicine (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Orthopedic Medicine & Surgery (AREA)
  • Prostheses (AREA)

Abstract

L'invention concerne un procédé de commande d'un comportement de mouvement d'une articulation artificielle présentant une partie supérieure et une partie inférieure montée de manière à pivoter autour d'un axe de pivotement, parties entre lesquelles est monté un dispositif destiné à influer sur l'aptitude à pivoter ou sur le pivotement de la partie supérieure par rapport à la partie inférieure, ledit dispositif étant couplé à un dispositif de commande, dans lequel est mis en mémoire un ensemble de règles et qui active, désactive ou module le dispositif en fonction des valeurs d'entrée relatives à l'ensemble de règles, de manière à influer sur le pivotement ou l'aptitude à pivoter. Des valeurs de détection d'au moins un capteur disposé au niveau de la partie supérieure ou de la partie inférieure, qui sont enregistrées pendant l'utilisation de l'articulation artificielle, alimentent au moins un procédé d'estimation fondé sur l'apprentissage automatique, lequel calcule en continu une valeur estimée pour un paramètre cinétique ou cinématique ou pour un paramètre cinétique ou cinématique théorique, à partir des valeurs de détection, et cette valeur estimée alimente, en tant que valeur d'entrée, l'ensemble de règles, puis ladite valeur estimée y utilisée en tant que critère pour l'activation, la désactivation ou la modulation.
EP23840650.8A 2022-12-21 2023-12-21 Procédé de commande d'un comportement de mouvement d'une articulation artificielle Pending EP4637643A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102022134381.2A DE102022134381A1 (de) 2022-12-21 2022-12-21 Verfahren zur Steuerung eines Bewegungsverhaltens eines künstlichen Gelenkes
PCT/EP2023/087221 WO2024133649A1 (fr) 2022-12-21 2023-12-21 Procédé de commande d'un comportement de mouvement d'une articulation artificielle

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EP4637643A1 true EP4637643A1 (fr) 2025-10-29

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EP (1) EP4637643A1 (fr)
CN (1) CN120379620A (fr)
DE (1) DE102022134381A1 (fr)
WO (1) WO2024133649A1 (fr)

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5383939A (en) 1991-12-05 1995-01-24 James; Kelvin B. System for controlling artificial knee joint action in an above knee prosthesis
WO2012047721A1 (fr) 2010-09-29 2012-04-12 össur hf Dispositifs prosthétiques et orthétiques et procédés et systèmes pour commander ceux-ci
DE102017131319B4 (de) * 2017-12-27 2019-07-04 Otto Bock Healthcare Products Gmbh Orthopädietechnische Einrichtung und Verfahren zu deren Steuerung
WO2020086721A2 (fr) * 2018-10-23 2020-04-30 Massachusetts Institute Of Technology Commande neurale efférente et afférente d'équilibre de ressort, d'amortissement et de puissance dans des actionneurs élastiques en série pouvant et ne pouvant pas être entraînés en arrière comprenant des mécanismes à rigidité variable en série
DE102020111535A1 (de) 2020-04-28 2021-10-28 Otto Bock Healthcare Products Gmbh Verfahren zur Steuerung zumindest eines Aktuators einer orthopädietechnischen Einrichtung und orthopädietechnische Einrichtung
US20220096249A1 (en) 2020-09-25 2022-03-31 X Development Llc Control using an uncertainty metric
CN113520683B (zh) 2021-07-08 2023-06-16 中国科学技术大学 基于模仿学习的下肢假肢控制系统及方法

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WO2024133649A1 (fr) 2024-06-27
DE102022134381A1 (de) 2024-06-27
CN120379620A (zh) 2025-07-25

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