CN114012742A - Control system of hip joint power assisting device - Google Patents
Control system of hip joint power assisting device Download PDFInfo
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- CN114012742A CN114012742A CN202210002289.5A CN202210002289A CN114012742A CN 114012742 A CN114012742 A CN 114012742A CN 202210002289 A CN202210002289 A CN 202210002289A CN 114012742 A CN114012742 A CN 114012742A
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- 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
- B25J9/1679—Programme controls characterised by the tasks executed
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- 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
Abstract
The invention relates to a control system of a hip joint power assisting device, which comprises: the system comprises a motion information acquisition module, a decision control module and an execution module; the motion information acquisition module is used for acquiring motion information of joints required by human motion; and the decision control module is used for acquiring the motion state of the wearer by utilizing the motion information of the joints required by the motion of the human body, determining the power-assisted strategy according to the motion state of the wearer and controlling the corresponding execution module to execute the power-assisted strategy according to the power-assisted strategy. The technical scheme provided by the application not only improves the accuracy of the motion mode identification of the lower limbs of the human body, but also improves the smoothness and the stability when the motion modes of the lower limbs of the human body are switched, so that the safety index and the user experience of a wearer are improved.
Description
Technical Field
The invention belongs to the technical field of hip joint assistant devices, and particularly relates to a control system of a hip joint power assisting device.
Background
With the gradual increase of the aging population and the increase of the population of the elderly with cerebral apoplexy and sequelae of different degrees, the medical field generally considers that the hip joint walking aid can effectively help the later-period rehabilitation of the population. The lower limbs of the human body have various states in the process of movement, and the requirements of different states on the power assisting mode of the hip joint power assisting device are different.
The existing wearable hip joint walking aid is simple in control strategy, single in type of the adopted sensor and low in accuracy of identification of motion modes of lower limbs of a human body, so that a simple assistance function under a limited mode can be realized, and the problems of smoothness and stability of the lower limb motion mode switching are generally solved.
Disclosure of Invention
In view of the above, the present invention provides a control system for a hip joint assist device to solve the problem of low accuracy in identifying a motion pattern of a lower limb of a human body in the prior art.
According to a first aspect of embodiments of the present application, there is provided a control system for a hip joint assist device, the system being adapted for use with a hip joint assist device, the system comprising: the system comprises a motion information acquisition module, a decision control module and an execution module;
the motion information acquisition module is used for acquiring motion information of joints required by human motion;
the decision control module is used for acquiring the motion state of the wearer by utilizing the motion information of the joints required by the human motion, and determining the power-assisted strategy according to the motion state of the wearer so as to control the corresponding execution module to execute the power-assisted strategy according to the power-assisted strategy.
Further, the motion information collecting module includes: an inertial measurement unit, an encoder and a pressure sensor;
the inertial measurement unit is used for measuring the angle of the limb part relative to a world coordinate system and sending the angle relative to the world coordinate system to the decision control module;
the encoder is used for measuring the relative angle between two body parts connected with the joint position where the encoder is located and sending the relative angle to the decision control module;
the pressure sensor is used for measuring the pressure value of the installation position where the pressure sensor is located and sending the pressure value to the decision control module.
Further, the decision control module is specifically configured to:
and obtaining the motion state and the confidence coefficient of the wearer by taking the motion information of the joints required by the human motion as the input of a preset mode recognition algorithm.
Further, the decision control module is further configured to: and acquiring the preset pattern recognition algorithm.
Further, the obtaining the preset pattern recognition algorithm includes:
and training by taking the motion information of the joints required by the historical human motion as an input layer training sample of the deep neural network classifier and taking the motion state and the confidence coefficient of the historical wearer as an output layer training sample of the deep neural network classifier to obtain the preset pattern recognition algorithm.
Further, the decision control module is specifically configured to:
and obtaining a power-assisted strategy by taking the motion state and the confidence coefficient of the wearer as the input of a preset machine learning algorithm.
Further, the decision control module is further configured to: and acquiring the preset machine learning algorithm.
Further, the obtaining the preset machine learning algorithm includes:
and training by taking the historical motion state and confidence coefficient of the wearer as input layer training samples of the machine learning algorithm and taking the historical power-assisted strategy as output layer training samples of the machine learning algorithm to obtain the preset machine learning algorithm.
Further, the system further comprises:
the energy supply module is used for supplying power to a control system of the hip joint power assisting device;
and the communication module is used for performing information interaction among the motion information acquisition module, the decision control module and the execution module.
By adopting the technical scheme, the invention can achieve the following beneficial effects: the motion information of the joints required by the human motion is acquired through the motion information acquisition module, the decision control module acquires the motion state of a wearer by utilizing the motion information of the joints required by the human motion, and determines the power-assisted strategy according to the motion state of the wearer so as to control the corresponding execution module to execute the power-assisted strategy according to the power-assisted strategy, so that the accuracy of the motion mode identification of the lower limbs of the human body is improved, the smoothness and the stability of the motion mode of the lower limbs of the human body during switching are also improved, and the safety index and the user experience of the wearer are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of a control system for a hip assist device according to an exemplary embodiment;
FIG. 2 is a schematic diagram illustrating an application scenario of a control system of a hip assist device according to an exemplary embodiment;
in the figure, 11 is a motion information acquisition module, 12 is a decision control module, 13 is an execution module, and 14 is an energy supply module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
Fig. 1 is a schematic diagram illustrating a control system of a hip joint assist device according to an exemplary embodiment, as shown in fig. 1, the system including: the motion information acquisition module 11, the decision control module 12 and the execution module 13;
the motion information acquisition module 11 is used for acquiring motion information of joints required by human motion;
the decision control module 12 is configured to obtain a motion state of the wearer by using motion information of joints required by human motion, and determine a power-assisted strategy according to the motion state of the wearer, so as to control the corresponding execution module 13 to execute the power-assisted strategy according to the power-assisted strategy.
According to the control system of the hip joint power assisting device provided by the embodiment of the invention, the motion information of the joints required by the motion of the human body is acquired through the motion information acquisition module 11, the decision control module 12 acquires the motion state of the wearer by using the motion information of the joints required by the motion of the human body, and determines the power assisting strategy according to the motion state of the wearer so as to control the corresponding execution module 13 to execute the power assisting strategy according to the power assisting strategy, so that the accuracy of the motion mode identification of the lower limbs of the human body is improved, the smoothness and the stability of the motion mode switching of the lower limbs of the human body are also improved, and the safety index and the user experience of the wearer are improved.
Further optionally, the motion information acquiring module 11 includes: an inertial measurement unit, an encoder and a pressure sensor;
the inertial measurement unit is used for measuring the angle of the limb part relative to the world coordinate system and sending the angle relative to the world coordinate system to the decision control module 12;
the encoder is used for measuring the relative angle between two body parts connected with the joint position where the encoder is located and sending the relative angle to the decision control module 12;
and the pressure sensor is used for measuring the pressure value of the installation position of the pressure sensor and sending the pressure value to the decision control module 12.
It should be noted that the inertial measurement unit can measure the acceleration and angular velocity of the movement of the limb part to which it is attached, and with these measured values as inputs, the posture fusion algorithm (Mahony or Madgwick) can be used to calculate the movement information of the angle of the limb part to which it is attached relative to the world coordinate system. In this case, the installation position of the inertial measurement unit may be: left and right foot surfaces, left and right shanks, left and right thighs, waist of trunk, left and right big arms, left and right small arms, and the like. The inertial measurement unit may be, but is not limited to, fixed by a fixing member, and send the motion information data to the decision control module 12 by a wireless communication technology or a wired connection, etc.
In some alternative embodiments, the type of encoder may be, but is not limited to, an incremental encoder or an absolute encoder, etc. The encoder can be installed at a human joint and can measure the relative motion (angle and angular speed) between two parts of a human body connected with the joint. In this case, the installation position of the encoder may be, but is not limited to: left and right ankle joints, left and right knee joints, left and right hip joints, left and right shoulder joints, left and right elbow joints, etc. The encoder may be fixed by a fixing member, and send the motion information data to the decision control module 12 by wireless communication technology or wired connection.
It should also be noted that pressure sensors are typically mounted on the sole of a foot to measure the pressure between the foot mounting location and the ground. In this case, the installation position of the pressure sensor may be, but is not limited to: toe tips, forefoot, midfoot or heel, etc. The pressure sensor can be fixed by a fixing part, and the motion information data is sent to the decision control module 12 by a wireless communication technology or a wired connection and the like.
In some embodiments, as shown in fig. 2, the motion information collecting module 11 may be, but is not limited to, disposed at two positions of the left hip joint and the right hip joint, at the ankle, at the instep, at the wrist, in front of the chest, etc. of the hip joint assisting device shown in fig. 2. The execution module 13 may be, but is not limited to, disposed at the left hip joint and the right hip joint as shown in fig. 2, etc. The decision control module 12 may be, but is not limited to being, disposed at the rear center of the belt as shown in fig. 2.
It can be understood that the execution module 13 and the motion information acquisition module 11 can be combined in a modularized manner, so that the user can freely match the actual situation of the user, the application range is wider, and the manufacturing cost is lower.
In some alternative embodiments, the execution module 13 may include, but is not limited to, a motor. After the decision control module 12 obtains the power-assisted strategy, the power-assisted strategy is used for providing power assistance for the wearer by controlling the working state of the motor, so that the wearer can complete different exercises.
Further optionally, the decision control module 12 is specifically configured to:
taking the motion information of joints required by human motion as the input of a preset pattern recognition algorithm to obtain the motion state and the confidence coefficient of the wearer;
and (4) obtaining a power-assisted strategy by taking the motion state and the confidence coefficient of the wearer as the input of a preset machine learning algorithm.
Specifically, optionally, the motion state of the wearer may include, but is not limited to: squatting, standing, stepping, walking, gait amplitude variation, frequency conversion, ascending and descending and the like. The confidence level of the wearer's motion state may be, but is not limited to: 0% to 100%.
For example, it is assumed that the motion information of the joints required for the motion of the human body is input into a preset pattern recognition algorithm, and the motion state of the wearer is squat, walking or the like. Wherein, the confidence coefficient of the squatting is 60%, the confidence coefficient of the walking is 30%, and the confidence coefficients of the other motion states are 10%, the motion state of the wearer and the confidence coefficient thereof are taken as the input of a preset machine learning algorithm, and the obtained power-assisted strategy is the power-assisted strategy of the squatting.
It should be noted that, after the motion state and the confidence level of the wearer are obtained, if the motion information of the joint required by the human motion of the wearer still changes all the time, the decision control module 12 does not immediately control the execution module 13 to execute the power-assisted strategy, but continuously updates the power-assisted strategy along with the change of the motion information of the joint required by the human motion of the wearer and the change of the motion state and the confidence level of the wearer, until the confidence level of the motion state of a certain wearer is greater than the confidence level threshold, and executes the power-assisted strategy. One skilled in the art can define the specific value of the confidence threshold based on experimental data, etc., for example, the confidence threshold is 90%.
Further optionally, the decision control module 12 is further configured to: and acquiring a preset pattern recognition algorithm.
Specifically, optionally, the obtaining of the preset pattern recognition algorithm includes:
and taking the motion information of the joints required by the historical human motion as input layer training samples of the deep neural network classifier, taking the motion state of the historical wearer and the confidence coefficient of the motion state as output layer training samples of the deep neural network classifier, and training to obtain a preset pattern recognition algorithm.
Further optionally, the decision control module 12 is further configured to: and acquiring a preset machine learning algorithm.
Specifically, optionally, the obtaining of the preset machine learning algorithm includes:
and training by taking the historical motion state and confidence coefficient of the wearer as input layer training samples of the machine learning algorithm and taking the historical power-assisted strategy as output layer training samples of the machine learning algorithm to obtain the preset machine learning algorithm.
It should be noted that the nature of the power assist strategy is to control the hip booster by changing the parameter values of the execution module 13. The parameters of the execution module 13 may include, but are not limited to: moment, angle and angular velocity.
It can be understood that the preset machine learning algorithm is obtained through training of the machine learning algorithm, so that information of the motion information acquisition modules 11 in different joint positions and different types is fused, all motion information and mutual correlation information are fully utilized, a more accurate and more stable prediction result is output, the power-assisted output also meets the real requirements of users, the hip joint assistant device is suitable for more use scenes, and the user experience is improved.
The control system of the hip joint power assisting device further supports that the output of a sensor based on the principles of inertial measurement or stress deformation measurement, muscle electrical signals, a brain-computer interface and the like is used as motion information, and the preset pattern recognition algorithm and the preset machine learning algorithm are combined, so that the accuracy of power assisting and pattern recognition is improved.
To further understand the boosting strategy, the embodiments of the present invention also provide some specific examples to explain the boosting strategy:
for example, when the user goes upstairs, the leg lifting target position of the swing legs of the user is higher than the leg lifting target position during normal walking, the leg lifting assisting force is larger than the leg lifting assisting force during normal walking, and meanwhile, the supporting legs are provided with larger downward supporting assisting force. When the user goes down stairs, the leg lifting target position of the swing legs of the wearer is lower than that of the swing legs during normal walking. And a power assisting strategy similar to the power assisting mode is adopted when the vehicle ascends and descends.
For another example, if the wearer enters a standing exercise state during walking, the assisting torque generated by the executing module 13 on the left and right sides can be smoothly switched to a non-assisting state. If the left and right joints of the wearer move forward at the same time, the hip joint assist device will switch to the squat assist mode. Two upward supporting assisting forces are generated at the left side and the right side, and the assisting forces are generated smoothly. In addition, in the walking process, if the walking speed of a wearer is reduced or improved and the stride is changed, the hip joint power assisting device can continuously update and learn through a machine learning algorithm to adjust a power assisting strategy, so that the wearer can still obtain correct and compliant power assisting experience.
For another example, when the wearer takes a forward step in the standing mode, a small assisting force is generated to compensate the gravity moment when the thighs lift the legs. If the hip joint assistant device returns to the walking assistance mode again after the walking assistance, the hip joint assistant device smoothly superimposes an additional assistance obtained according to the result in the assistance strategy predicted by the machine learning algorithm on the basis of the gravity compensation assistance, so that a wearer can obtain a better and more obvious assistance experience.
It can be understood that the magnitude of the additional assistance obtained according to the result of the machine learning algorithm in different motion phases of the joint can be dynamically adjusted in real time according to a preset or dynamically generated assistance curve, so that normal assistance to a normal wearer is realized, and different targeted assistance is performed on the affected side and the normal side of the wearer with symptoms such as hemiplegia. The main force curve is a key point sequence which generally represents the magnitude of the assisting force at different key stages.
Further optionally, the decision control module 12 is further configured to:
when the motion information of the joints required by the motion of the human body exceeds the corresponding motion data threshold, the control execution module 13 limits the motion state of the wearer, and ensures that the wearer is in a safe motion state.
It should be noted that, in the embodiment of the present invention, the "motion data threshold" is not limited, and a person skilled in the art may set the threshold according to experimental data and the like. In some embodiments, the motion data threshold may be, but is not limited to, a threshold of angular acceleration, angular velocity, angle, pressure value, and the like, collected by the motion information collection module 11. The general motion data threshold refers to a motion data threshold corresponding to a motion state exceeding the body limit of a normal person.
Further optionally, the system further comprises:
the energy supply module 14 is used for supplying power to a control system of the hip joint power assisting device;
and the communication module is used for performing information interaction among the motion information acquisition module 11, the decision control module 12 and the execution module 13.
It should be noted that, as shown in fig. 2, the energy supply module 14 may be, but is not limited to being, disposed at the rear center of the belt as shown in fig. 2, and integrated with the decision control module 12.
In some embodiments, but not limited to, the assistance may be implemented by providing a communication module on the decision control module 12, and sending an assistance signal generated by the assistance strategy to the corresponding execution module 13.
It should be noted that the energy supply module 14 may include, but is not limited to, various wearable or external chemical batteries, external power transmission interfaces, and other devices that can provide the required electric energy, mechanical energy, or other forms of energy for the system to work. But is also not limited to, a rechargeable battery. The communication module may be implemented by, but not limited to, wireless communication technology or wired connection.
In some embodiments, the decision control module 12 may implement control (enable, shut down, charge, discharge) of the energy supply module 14 through, but not limited to, human-machine interaction (power on, power off, or emergency stop, etc.), an automatic protection program, and the like.
It should be noted that, the parameter setting of the power-assisted curve can be realized by, but not limited to, using a human-computer interaction interface of a mobile phone, a tablet computer, a PC or other customized handheld terminal, and the like, and by connecting manners such as wired, wireless, local or remote. The human-computer interaction interface obtains the numerical value of the current assistance curve and displays the numerical value of the current assistance curve to a user through the connection mode, and the user can perform operations such as point selection, dragging and input through the human-computer interaction interface to modify the numerical value of the key point in the assistance curve. And then, the modified result is transmitted back to a control system of the hip joint assistant device through the connection mode, so that the adjustment of the assistance curve is realized, the adjustment can be automatically performed through a machine learning algorithm according to the use data of the wearer and the gait motion data of the cloud, and the adjustment can be performed through the combination of the two methods.
It should be noted that the exercise information collection module 11 is not limited to two positions of the left and right hip joints in the joints required for the movement of the human body, and can be located at any position on the wearer, such as the lower limbs, the upper limbs, the waist, and the outside of the body of the patient (e.g., the exercise information collection module 11 disposed outside), and so on.
According to the control system of the hip joint power assisting device provided by the embodiment of the invention, the motion information of the joints required by the motion of the human body is acquired through the motion information acquisition module 11, the decision control module 12 acquires the motion state of the wearer by using the motion information of the joints required by the motion of the human body, and determines the power assisting strategy according to the motion state of the wearer so as to control the corresponding execution module 13 to execute the power assisting strategy according to the power assisting strategy, so that the accuracy of the motion mode identification of the lower limbs of the human body is improved, the smoothness and the stability of the motion mode switching of the lower limbs of the human body are also improved, and the safety index and the user experience of the wearer are improved.
It is understood that the corresponding specific contents in the system embodiments provided above may be mutually referred to, and are not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (9)
1. A control system for a hip joint assist device, the control system being adapted for use with a hip joint assist device, the system comprising: the system comprises a motion information acquisition module, a decision control module and an execution module;
the motion information acquisition module is used for acquiring motion information of joints required by human motion;
the decision control module is used for acquiring the motion state of the wearer by utilizing the motion information of the joints required by the human motion, and determining the power-assisted strategy according to the motion state of the wearer so as to control the corresponding execution module to execute the power-assisted strategy according to the power-assisted strategy.
2. The system of claim 1, wherein the motion information collection module comprises: an inertial measurement unit, an encoder and a pressure sensor;
the inertial measurement unit is used for measuring the angle of the limb part relative to a world coordinate system and sending the angle relative to the world coordinate system to the decision control module;
the encoder is used for measuring the relative angle between two body parts connected with the joint position where the encoder is located and sending the relative angle to the decision control module;
the pressure sensor is used for measuring the pressure value of the installation position where the pressure sensor is located and sending the pressure value to the decision control module.
3. The system of claim 1, wherein the decision control module is specifically configured to:
and obtaining the motion state and the confidence coefficient of the wearer by taking the motion information of the joints required by the human motion as the input of a preset mode recognition algorithm.
4. The system of claim 3, wherein the decision control module is further configured to: and acquiring the preset pattern recognition algorithm.
5. The system of claim 4, wherein the obtaining the preset pattern recognition algorithm comprises:
and training by taking the motion information of the joints required by the historical human motion as an input layer training sample of the deep neural network classifier and taking the motion state and the confidence coefficient of the historical wearer as an output layer training sample of the deep neural network classifier to obtain the preset pattern recognition algorithm.
6. The system of claim 1, wherein the decision control module is specifically configured to:
and obtaining a power-assisted strategy by taking the motion state and the confidence coefficient of the wearer as the input of a preset machine learning algorithm.
7. The system of claim 6, wherein the decision control module is further configured to: and acquiring the preset machine learning algorithm.
8. The system of claim 7, wherein the obtaining the preset machine learning algorithm comprises:
and training by taking the historical motion state and confidence coefficient of the wearer as input layer training samples of the machine learning algorithm and taking the historical power-assisted strategy as output layer training samples of the machine learning algorithm to obtain the preset machine learning algorithm.
9. The system of claim 1, further comprising:
the energy supply module is used for supplying power to a control system of the hip joint power assisting device;
and the communication module is used for performing information interaction among the motion information acquisition module, the decision control module and the execution module.
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