CN117204993A - Intelligent artificial limb movement pattern recognition method and device, intelligent artificial limb and storage medium - Google Patents
Intelligent artificial limb movement pattern recognition method and device, intelligent artificial limb and storage medium Download PDFInfo
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Abstract
The invention discloses an intelligent artificial limb movement pattern recognition method, an intelligent artificial limb movement pattern recognition device, an intelligent artificial limb and a storage medium, wherein the method comprises the following steps: acquiring motion data, supporting pressure data and corner data of an intelligent artificial limb in a preset time period, wherein the supporting pressure data reflects the supporting pressure of the intelligent artificial limb, and the corner data reflects the rotation angle of a rotating shaft of a knee joint in the intelligent artificial limb; determining movement frequency information of the intelligent artificial limb based on the movement data and the rotation angle data, wherein the movement frequency information reflects the swing frequency of the knee joint; and determining a movement mode of the intelligent artificial limb based on the movement frequency information and the supporting pressure data. The invention can realize dynamic real-time analysis of the motion mode of the intelligent artificial limb, is convenient for identifying the motion mode of the intelligent artificial limb in a determined way, and further provides safe and personalized service for users.
Description
Technical Field
The invention relates to the technical field of artificial limbs, in particular to an intelligent artificial limb movement pattern recognition method and device, an intelligent artificial limb and a storage medium.
Background
Along with the development of society, the convenience of traffic and the continuous improvement of industrialization level lead to more and more patients who have amputation caused by machine trauma, car accidents and the like, the amputation brings a lot of inconvenience to the patients, and the basic life ability is lost. It is therefore becoming increasingly urgent to develop a smart prosthesis that helps amputees achieve basic life capabilities. The intelligent artificial limb needs to have the functions of assisting a patient in walking, running and the like, and the intelligent artificial limb needs to have the capability of identifying different movement modes of walking, running and the like to realize the functions of walking, running and the like. In the prior art, the motion mode of the intelligent artificial limb cannot be accurately identified, and the use of a user is affected.
Accordingly, there is a need for improvement and advancement in the art.
Disclosure of Invention
The invention aims to solve the technical problems that the prior art cannot accurately identify the motion mode of the intelligent artificial limb and influences the use of a user.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
In a first aspect, the present invention provides a method for identifying a motion pattern of an intelligent artificial limb, wherein the method comprises:
acquiring motion data, supporting pressure data and rotation angle data of an intelligent artificial limb in a preset time period, wherein the supporting pressure data reflects supporting pressure in the intelligent artificial limb, and the rotation angle data reflects the rotation angle of a rotating shaft of a knee joint in the intelligent artificial limb;
determining movement frequency information of the intelligent artificial limb based on the movement data and the rotation angle data, wherein the movement frequency information reflects the swing frequency of the knee joint;
and determining a movement mode of the intelligent artificial limb based on the movement frequency information and the supporting pressure data.
In one implementation, the acquiring motion data, support pressure data, and rotation angle data of the intelligent prosthesis within a preset time period includes:
acquiring the movement speed and the acceleration of the knee joint based on a preset inertial sensor, and taking the movement speed and the acceleration as the movement data;
acquiring rotation angle data of a rotating shaft in the knee joint based on a preset angle sensor;
and collecting the supporting pressure data based on a pressure sensor preset on the intelligent artificial limb.
In one implementation, the determining the motion frequency information of the intelligent prosthesis based on the motion data and the rotation angle data includes:
determining a periodic motion rule of the knee joint in a preset time period based on the motion data, and determining speed change data of the knee joint in a fixed period based on the periodic motion rule of the knee joint in the preset time period, wherein the speed change data is used for reflecting the speed of speed change in the fixed period;
determining a motion rule of the rotating shaft in a periodic motion in a preset time period based on the rotation angle data, and determining rotation angle change data of the rotating shaft in the same fixed period based on the motion rule of the rotating shaft in the periodic motion in the preset time period, wherein the rotation angle change data are used for reflecting the speed of rotation angle change in the fixed period;
and determining the movement frequency information based on the speed change data and the rotation angle change data.
In one implementation, the determining the movement frequency information based on the speed change data and the rotation angle change data includes:
Comparing the speed change data with a preset speed change threshold range;
comparing the rotation angle change data with the rotation angle change threshold range;
if the speed change data is larger than the speed change threshold range and the rotation angle change data is larger than the rotation angle change threshold range, determining the movement frequency information as a first frequency;
if the speed change data is smaller than the speed change threshold range and the rotation angle change data is smaller than the rotation angle change threshold range, determining the movement frequency information as a second frequency;
wherein the first frequency is greater than the second frequency.
In one implementation, the determining the movement pattern of the intelligent prosthesis based on the movement frequency information and the support pressure data includes:
determining a motion state of the intelligent prosthesis based on the motion frequency information;
and determining a movement mode of the intelligent artificial limb based on the movement state and the supporting pressure data.
In one implementation, the determining the motion state of the intelligent prosthesis based on the motion frequency information includes:
if the motion frequency information is the first frequency, determining that the motion state of the intelligent artificial limb is a fast-walking state;
And if the motion frequency information is the second frequency, determining that the motion state of the intelligent artificial limb is a slow-step state.
In one implementation, the determining the movement pattern of the intelligent prosthesis based on the movement state and the support pressure data includes:
comparing the support pressure data with a preset pressure threshold range;
if the motion state of the intelligent artificial limb is a fast-walking state and the supporting pressure data is larger than the pressure threshold range, determining that the motion mode of the intelligent artificial limb is a running mode;
if the motion state of the intelligent artificial limb is a fast walking state and the supporting pressure data is smaller than the pressure threshold range, determining that the motion mode of the intelligent artificial limb is a fast walking mode;
and if the motion state of the intelligent artificial limb is a slow walking state and the supporting pressure data is smaller than the pressure threshold range, determining that the motion mode of the intelligent artificial limb is a slow walking mode.
In a second aspect, an embodiment of the present invention further provides an intelligent artificial limb movement pattern recognition device, where the device includes:
the data acquisition module is used for acquiring motion data, supporting pressure data and rotation angle data of the intelligent artificial limb in a preset time period, wherein the supporting pressure data reflects the supporting pressure of the intelligent artificial limb, and the rotation angle data reflects the rotation angle of a rotating shaft of a knee joint in the intelligent artificial limb;
The frequency analysis module is used for determining the movement frequency information of the intelligent artificial limb based on the movement data and the rotation angle data, wherein the movement frequency information reflects the swing frequency of the knee joint;
and the pattern analysis module is used for determining the movement pattern of the intelligent artificial limb based on the movement frequency information and the supporting pressure data.
In one implementation, the frequency analysis module includes:
the speed analysis unit is used for determining a periodic motion rule of the knee joint in a preset time period based on the motion data and determining speed change data of the knee joint in a fixed period based on the periodic motion rule of the knee joint in the preset time period, wherein the speed change data are used for reflecting the speed of speed change in the fixed period;
the rotation angle analysis unit is used for determining a motion rule of the rotating shaft in a periodic motion in a preset time period based on the rotation angle data and determining rotation angle change data of the rotating shaft in the same fixed period based on the motion rule of the rotating shaft in the periodic motion in the preset time period, wherein the rotation angle change data are used for reflecting the speed of rotation angle change in the fixed period;
And the frequency determining unit is used for determining the movement frequency information based on the speed change and the rotation angle change.
In one implementation, the frequency determining unit includes:
the first data comparison subunit is used for comparing the speed change data with a preset speed change threshold range;
a second data comparison subunit configured to compare the rotation angle change data with the rotation angle change threshold range;
the first frequency determining subunit is configured to determine the motion frequency information as a first frequency if the speed change data is greater than the speed change threshold range and the rotation angle change data is greater than the rotation angle change threshold range;
the second frequency determining subunit is configured to determine the motion frequency information as a second frequency if the speed change data is smaller than the speed change threshold range and the rotation angle change data is smaller than the rotation angle change threshold range; wherein the first frequency is greater than the second frequency.
In one implementation, the pattern analysis module includes:
the state analysis unit is used for determining the motion state of the intelligent artificial limb based on the motion frequency information;
And the mode determining unit is used for determining the movement mode of the intelligent artificial limb based on the movement state and the supporting pressure data.
In a third aspect, an embodiment of the present invention further provides an intelligent artificial limb, where the intelligent artificial limb includes a socket, a knee joint, and an intelligent artificial limb movement pattern recognition device described in the foregoing solution.
In a fourth aspect, an embodiment of the present invention further provides an intelligent prosthesis, where the intelligent prosthesis includes a memory, a processor, and an intelligent prosthesis motion pattern recognition program stored in the memory and capable of running on the processor, and when the processor executes the intelligent prosthesis motion pattern recognition program, the processor implements the steps of the intelligent prosthesis motion pattern recognition method according to any one of the above schemes.
In a fifth aspect, an embodiment of the present invention further provides a computer readable storage medium, where the computer readable storage medium stores an intelligent prosthesis motion pattern recognition program, where the intelligent prosthesis motion pattern recognition program, when executed by a processor, implements the steps of the intelligent prosthesis motion pattern recognition method according to any one of the foregoing aspects.
The beneficial effects are that: compared with the prior art, the invention provides an intelligent artificial limb movement pattern recognition method, which comprises the steps of firstly obtaining movement data, supporting pressure data and rotation angle data of an intelligent artificial limb in a preset time period, wherein the supporting pressure data reflects the supporting pressure of the intelligent artificial limb, and the rotation angle data reflects the rotation angle of a rotating shaft of a knee joint in the intelligent artificial limb. And then, determining the movement frequency information of the intelligent artificial limb based on the movement data and the rotation angle data, wherein the movement frequency information reflects the swing frequency of the knee joint. And finally, determining the movement mode of the intelligent artificial limb based on the movement frequency information and the supporting pressure data. The invention can realize dynamic real-time analysis of the motion mode of the intelligent artificial limb, is convenient for identifying the motion mode of the intelligent artificial limb in a determined way, and further provides safe and personalized service for users.
Drawings
Fig. 1 is a flowchart of a specific implementation of an intelligent artificial limb movement pattern recognition method according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of an intelligent artificial limb according to an embodiment of the present invention.
Fig. 3 is a functional schematic diagram of an intelligent artificial limb movement pattern recognition device according to an embodiment of the present invention.
Fig. 4 is a schematic block diagram of an intelligent artificial limb according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and effects of the present invention clearer and more specific, the present invention will be described in further detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment provides an intelligent artificial limb movement pattern recognition method, and when the intelligent artificial limb movement pattern recognition method is specifically applied, movement data, supporting pressure data and corner data of an intelligent artificial limb in a preset time period can be firstly obtained, wherein the supporting pressure data reflect supporting pressure of the intelligent artificial limb, and the corner data reflect rotation angle of a rotating shaft of a knee joint in the intelligent artificial limb. And then, determining the movement frequency information of the intelligent artificial limb based on the movement data and the rotation angle data, wherein the movement frequency information reflects the swing frequency of the knee joint. And finally, determining the movement mode of the intelligent artificial limb based on the movement frequency information and the supporting pressure data. The embodiment can realize dynamic real-time analysis of the motion mode of the intelligent artificial limb, is convenient for identifying the motion mode of the intelligent artificial limb in a determined manner, and further provides safe and personalized service for users.
The intelligent artificial limb movement pattern recognition method of the present embodiment is applicable to an intelligent artificial limb including a controller that can be used to implement the intelligent artificial limb movement pattern recognition method of the present embodiment. In another implementation manner, the intelligent artificial limb movement pattern recognition method of the embodiment can also be applied to a terminal device, wherein the terminal device can be connected with an intelligent artificial limb, and the terminal device can be an intelligent product terminal such as a computer, a mobile phone and the like and is used for executing the intelligent artificial limb movement pattern recognition method. Specifically, as shown in fig. 1, the intelligent artificial limb movement pattern recognition method comprises the following steps:
step S100, obtaining motion data, supporting pressure data and rotation angle data of the intelligent artificial limb in a preset time period, wherein the supporting pressure data reflect supporting pressure of the intelligent artificial limb, and the rotation angle data reflect rotation angle of a rotating shaft of a knee joint in the intelligent artificial limb.
The movement of the intelligent artificial limb is realized based on the movement of the knee joint, and the movement and deformation of each part in the knee joint can be directly related to the movement data of the whole knee joint, for example, the movement data of the knee joint are different when a user walks and runs with the intelligent artificial limb, and the supporting pressure data of the intelligent artificial limb are also different when the user walks and runs. In order to accurately analyze the movement mode of the knee joint, the intelligent artificial limb of the embodiment can acquire movement data, supporting pressure data and corner data of the intelligent artificial limb in a preset time period in the process of being used by a user. The motion data reflect the motion speed and the acceleration of the knee joint, the support pressure data reflect the support pressure of the sole plate in the intelligent artificial limb, and the rotation angle data reflect the rotation angle of the rotating shaft of the knee joint in the intelligent artificial limb.
As shown in fig. 2, the intelligent artificial limb of the embodiment comprises a leg main body 11 and a knee joint 12 rotatably connected with the leg main body 11 through a rotating shaft, the rotating shaft is used for driving the leg main body to rotate, flexible movement of the knee joint 12 is achieved, the knee joint 12 is located at the top of the leg main body 11, the intelligent artificial limb further comprises a receiving cavity 3, the receiving cavity 3 is fixedly connected with the knee joint 12, the receiving cavity 3 is used for being installed on a thigh of a user, a plurality of sensors (including an inertial sensor, an angular inertial sensor and the like) are arranged in the receiving cavity 3, a resistance device 2 is arranged in a cavity in the leg main body 11, and the bottom end of the resistance device 2 is hinged with the leg main body 11. The resistance device 2 of the present embodiment is a hydraulic cylinder, and the resistance device 2 is used for coordinating the movement between the connection portion and the leg main body, and for regulating the relative position between the connection portion and the leg main body.
In one implementation, step S100 in this embodiment specifically includes the following steps:
step S101, acquiring the movement speed and the acceleration of the knee joint based on a preset inertial sensor, and taking the movement speed and the acceleration as movement data;
step S102, acquiring rotation angle data of a rotating shaft in the knee joint based on a preset angle sensor;
And step S103, acquiring the supporting pressure data based on a pressure sensor preset on the intelligent artificial limb.
Specifically, the intelligent artificial limb of the embodiment is provided with an inertial sensor and an angular inertial sensor. The inertial sensor may collect the motion speed and acceleration of the knee joint 12, which are the motion data of the intelligent prosthesis. The angle sensor may be used to collect rotational angle data of the shaft in the knee joint 12. In addition, the intelligent prosthesis of the present embodiment is provided with a pressure sensor that may be provided on the knee joint 12, foot pipe, ankle joint or sole plate, and may collect support pressure data of the intelligent prosthesis.
In another implementation, the motion data of the present embodiment may also be implemented based on image analysis techniques. In particular applications, the present embodiment may acquire moving image information of the knee joint 12, analyze the moving image information, identify the knee joint 12 in the moving image information, and further identify position information of the knee joint 12 at different moments based on the moving image information, determine a movement path and a movement displacement of the knee joint 12, and further determine a movement speed, an acceleration and a rotation angle (a rotation angle of a rotation shaft in the knee joint 12) of the knee joint 12. The present embodiment may help accurately analyze the movement pattern of the knee joint 12 based on analysis of movement speed, acceleration, and rotation angle.
And step 200, determining the movement frequency information of the intelligent artificial limb based on the movement data and the rotation angle data, wherein the movement frequency information reflects the swing frequency of the knee joint.
After the motion data and the corner data of the intelligent artificial limb are determined, the intelligent artificial limb can analyze the motion frequency information according to the motion data and the corner data, and the motion frequency information reflects the swing frequency of the knee joint. The intelligent artificial limb can analyze the step frequency, namely the swing frequency of the intelligent artificial limb according to the movement speed, the acceleration and the rotation angle data of the rotating shaft in the preset time period.
In one implementation manner, the step S200 specifically includes the following steps:
step S201, determining a periodic motion rule of the knee joint in a preset time period based on the motion data, and determining speed change data of the knee joint in a fixed period based on the periodic motion rule of the knee joint in the preset time period, wherein the speed change data is used for reflecting the speed change speed in the fixed period;
step S202, determining a motion rule of the rotating shaft in a periodic motion in a preset time period based on the rotation angle data, and determining rotation angle change data of the rotating shaft in the same fixed period based on the motion rule of the rotating shaft in the periodic motion in the preset time period, wherein the rotation angle change data are used for reflecting the speed of rotation angle change in the fixed period;
Step S203, determining the motion frequency information based on the speed change data and the rotation angle change data.
Specifically, in this embodiment, after the movement speed, acceleration and rotation angle data of the knee joint 12 are obtained, the movement speed, acceleration and rotation angle data can be analyzed respectively, so as to determine the movement rule of the knee joint 12. The intelligent artificial limb of the embodiment can record the movement speed and the acceleration of the knee joint 12 and the rotation angle data of the rotating shaft in real time when a user uses the intelligent artificial limb, and then comprehensively analyze the movement speed, the acceleration and the rotation angle data of the rotating shaft in a preset time period to analyze the movement speed, the acceleration and the rotation angle rule of the rotating shaft. Since the movement of the knee joint 12 is periodic when the intelligent prosthesis is normally used by a user, whether walking or running, the movement speed and acceleration of the knee joint 12 and the rotation angle of the rotation shaft are also periodically changed. For this purpose, the intelligent prosthesis may determine a periodic motion law of the knee joint 12 in a preset time period based on the acquired motion speed and acceleration of the knee joint 12, and then determine speed change data of the knee joint 12 in a fixed period based on the periodic motion law of the knee joint 12 in the preset time period, where the speed change data is used to reflect the speed change speed in the fixed period. That is, the present embodiment can analyze how the movement speed of the knee joint changes and whether the movement speed changes fast or slow, i.e., the speed change data, in a fixed period of time. In one implementation, the velocity change data may also be an amount of change in the velocity of movement of the knee joint over a fixed period of time. For example, when the intelligent artificial limb is used by a user in a walking scene, the intelligent artificial limb is called a complete cycle from leg lifting to floor to leg lifting, and the movement speed and acceleration of the knee joint 12 between different cycles are basically not changed, so that the embodiment can analyze a fixed cycle, and in the time of the fixed cycle, based on the movement speed and acceleration of the knee joint 12, how the speed of the knee joint 12 changes and how much the speed change amount is are analyzed, so as to obtain speed change data. Similarly, the intelligent artificial limb can also determine the motion rule of the rotating shaft in periodic motion within a preset time period based on the collected rotating angle of the rotating shaft. And determining rotation angle change data of the rotating shaft in the same fixed period based on a motion rule that the rotating shaft periodically moves in a preset period, wherein the rotation angle change data are used for reflecting the speed of rotation angle change in the fixed period. That is, the present embodiment can analyze how the rotation angle data of the rotation axis of the knee joint changes and whether the rotation angle changes fast or slow, and thus obtain the rotation angle change data within a fixed period of time. In one implementation, the rotation angle change data may also be the amount of change in the rotation angle of the knee joint over a fixed period of time. For example, when the intelligent artificial limb is used by a user in a walking scene, the intelligent artificial limb is called a complete period from leg lifting to floor to leg lifting, and the corner data of the knee joint 12 basically changes little between different periods, so that the embodiment can analyze a fixed period, and in the time of the fixed period, based on the corner data of the knee joint 12, how the corner of the knee joint 12 changes and what the corner change amount is, further corner change data is obtained, and the speed of corner change is determined.
Further, the present embodiment compares the speed change data with a preset speed change threshold range, and compares the rotation angle change data with the rotation angle change threshold range. The speed change threshold range and the rotation angle change threshold range are preset and are used for measuring the speed change speed and the rotation angle change speed. If the speed change data is greater than the speed change threshold range and the rotation angle change data is greater than the rotation angle change threshold range, it is indicated that the movement speed and rotation angle change of the knee joint 12 are both rapid, and the movement frequency information at this time can be determined to be the first frequency. If the speed change data is smaller than the speed change threshold range and the rotation angle change data is smaller than the rotation angle change threshold range, it is indicated that the movement speed and rotation angle change of the knee joint 12 are slow, and the movement frequency information at this time can be determined to be the second frequency. Wherein the first frequency is greater than the second frequency. Therefore, the present embodiment can accurately analyze the speed of the knee joint 12 and the rotation angle of the rotating shaft based on the comprehensive analysis of the movement speed and the rotation angle.
And step 300, determining the movement mode of the intelligent artificial limb based on the movement frequency information and the supporting pressure data.
After the motion frequency information is obtained, the embodiment can analyze the motion mode of the intelligent artificial limb based on the motion frequency information and the supporting pressure data, and the motion mode of the embodiment comprises the following steps: slow walking mode, fast walking mode, and running mode. Since the support pressure data of the sole plate are different when the intelligent artificial limb is in different movement modes, the movement modes can be accurately and quickly analyzed by combining the movement frequency information and the support pressure data.
In one implementation, step S300 in this embodiment specifically includes the following steps:
step S301, determining the motion state of the intelligent artificial limb based on the motion frequency information;
step S302, determining the movement mode of the intelligent artificial limb based on the movement state and the supporting pressure data.
In the present embodiment, since the dynamic frequency information reflects the swing frequency of the knee joint 12, that is, the step frequency, the first frequency is greater than the second frequency. Therefore, if the motion frequency information is the first frequency, the motion state of the intelligent artificial limb is determined to be a fast-walking state. And if the motion frequency information is the second frequency, determining that the motion state of the intelligent artificial limb is a slow-step state. The intelligent prosthesis then compares the support pressure data to a predetermined pressure threshold range, which is predetermined, for measuring whether the support pressure of the plantar plate exceeds the threshold range. If the motion state of the intelligent artificial limb is a fast-walking state and the support pressure data is greater than the pressure threshold range, it is indicated that the intelligent artificial limb is moving fast at this time and is stepping on the ground with force (the intelligent artificial limb is stepping on the ground with force while running, and thus the support pressure data of the sole plate is relatively large), so that it is possible to determine that the motion mode of the intelligent artificial limb is the running mode. If the motion state of the intelligent artificial limb is a fast-walking state and the supporting pressure data is smaller than the pressure threshold range, the intelligent artificial limb is indicated to move fast at the moment, but the sole plate does not pedal the ground with force and is supported in a relatively gentle manner (the intelligent artificial limb does not pedal the ground with force when walking, so the supporting pressure data of the sole plate is relatively small), and the motion mode of the intelligent artificial limb can be determined to be a fast-walking mode. If the motion state of the intelligent artificial limb is a slow-walking state and the supporting pressure data is smaller than the pressure threshold range, the intelligent artificial limb moves slowly at the moment, and the sole plate of the intelligent artificial limb does not pedal the ground forcefully, so that the motion mode of the intelligent artificial limb can be determined to be a slow-walking mode. Of course, in other embodiments, the present embodiment may further analyze the overall posture change of the knee joint by combining the movement speed, acceleration, rotation angle, etc. of the knee joint 12, so as to further analyze whether the movement mode of the knee joint is in the riding mode.
In summary, the present embodiment may first obtain motion data, support pressure data, and rotation angle data of the intelligent artificial limb in a preset time period, where the support pressure data reflects support pressure of the intelligent artificial limb, and the rotation angle data reflects a rotation angle of a rotation shaft of a knee joint in the intelligent artificial limb. And then, determining the movement frequency information of the intelligent artificial limb based on the movement data and the rotation angle data, wherein the movement frequency information reflects the swing frequency of the knee joint. And finally, determining the movement mode of the intelligent artificial limb based on the movement frequency information and the supporting pressure data. The embodiment can realize dynamic real-time analysis of the motion mode of the intelligent artificial limb, is convenient for identifying the motion mode of the intelligent artificial limb in a determined manner, and further provides safe and personalized service for users.
Based on the above embodiment, the present invention further provides an intelligent artificial limb movement pattern recognition device, which can be applied to an intelligent artificial limb, as shown in fig. 3, and the device includes: a data acquisition module 10, a frequency analysis module 20 and a pattern analysis module 30. Specifically, the data acquisition module 10 is configured to acquire motion data, support pressure data, and rotation angle data of the intelligent prosthesis within a preset time period, where the support pressure data reflects support pressure of the intelligent prosthesis, and the rotation angle data reflects a rotation angle of a rotation shaft of a knee joint in the intelligent prosthesis. The frequency analysis module 20 is configured to determine, based on the motion data and the rotation angle data, motion frequency information of the intelligent prosthesis, where the motion frequency information reflects a swing frequency of the knee joint. The pattern analysis module 30 is configured to determine a movement pattern of the intelligent prosthesis based on the movement frequency information and the support pressure data.
In one implementation, the data acquisition module 10 includes:
the motion data acquisition unit is used for acquiring the motion speed and the acceleration of the knee joint based on a preset inertial sensor and taking the motion speed and the acceleration as the motion data;
the rotation angle acquisition unit is used for acquiring rotation angle data of the rotating shaft in the knee joint based on a preset angle sensor;
and the pressure data acquisition unit is used for acquiring the supporting pressure data based on a pressure sensor preset on the intelligent artificial limb.
In one implementation, the frequency analysis module includes:
the speed analysis unit is used for determining a periodic motion rule of the knee joint in a preset time period based on the motion data and determining speed change data of the knee joint in a fixed period based on the periodic motion rule of the knee joint in the preset time period, wherein the speed change data are used for reflecting the speed of speed change in the fixed period;
the rotation angle analysis unit is used for determining a motion rule of the rotating shaft in a periodic motion in a preset time period based on the rotation angle data and determining rotation angle change data of the rotating shaft in the same fixed period based on the motion rule of the rotating shaft in the periodic motion in the preset time period, wherein the rotation angle change data are used for reflecting the speed of rotation angle change in the fixed period;
And the frequency determining unit is used for determining the movement frequency information based on the speed change and the rotation angle change.
In one implementation, the frequency determining unit includes:
the first data comparison subunit is used for comparing the speed change data with a preset speed change threshold range;
a second data comparison subunit configured to compare the rotation angle change data with the rotation angle change threshold range;
the first frequency determining subunit is configured to determine the motion frequency information as a first frequency if the speed change data is greater than the speed change threshold range and the rotation angle change data is greater than the rotation angle change threshold range;
the second frequency determining subunit is configured to determine the motion frequency information as a second frequency if the speed change data is smaller than the speed change threshold range and the rotation angle change data is smaller than the rotation angle change threshold range; wherein the first frequency is greater than the second frequency.
In one implementation, the pattern analysis module includes:
the state analysis unit is used for determining the motion state of the intelligent artificial limb based on the motion frequency information;
And the mode determining unit is used for determining the movement mode of the intelligent artificial limb based on the movement state and the supporting pressure data.
In one implementation, the state analysis unit includes:
the first state analysis subunit is used for determining that the motion state of the intelligent artificial limb is a fast-step state if the motion frequency information is the first frequency;
and the second state analysis subunit is used for determining that the motion state of the intelligent artificial limb is a slow-step state if the motion frequency information is the second frequency.
In one implementation, the mode determining unit includes:
a pressure comparison subunit, configured to compare the support pressure data with a preset pressure threshold range;
the first mode determining subunit is used for determining that the motion mode of the intelligent artificial limb is a running mode if the motion state of the intelligent artificial limb is a fast-walking state and the supporting pressure data is larger than the pressure threshold range;
the second mode determining subunit is used for determining that the motion mode of the intelligent artificial limb is a fast walking mode if the motion state of the intelligent artificial limb is a fast walking state and the supporting pressure data is smaller than the pressure threshold range;
And the third mode determining subunit is used for determining that the motion mode of the intelligent artificial limb is a slow walking mode if the motion state of the intelligent artificial limb is a slow walking state and the supporting pressure data is smaller than the pressure threshold range.
The working principle of each module in the intelligent artificial limb movement pattern recognition device of the embodiment is the same as that of each step in the method embodiment, and is not repeated here.
Based on the above embodiment, the invention further provides an intelligent artificial limb, wherein the intelligent artificial limb comprises a receiving cavity, a knee joint and the intelligent artificial limb movement pattern recognition device in the above embodiment.
Based on the above embodiments, the present invention also provides a smart prosthesis, and a schematic block diagram of the smart prosthesis may be shown in fig. 4. The smart prosthesis may include one or more processors 100 (only one shown in fig. 4), a memory 101, and a computer program 102, such as a smart prosthesis movement pattern recognition program, stored in the memory 101 and executable on the one or more processors 100. The execution of the computer program 102 by the one or more processors 100 may implement the various steps of an embodiment of the intelligent prosthesis movement pattern recognition method. Alternatively, the one or more processors 100, when executing the computer program 102, may perform the functions of the various modules/units of the intelligent prosthetic motion pattern recognition device embodiment, without limitation.
In one embodiment, the processor 100 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In one embodiment, the memory 101 may be an internal storage unit of the electronic device, such as a hard disk or a memory of the electronic device. The memory 101 may also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash card (flash card) or the like, which are provided on the electronic device. Further, the memory 101 may also include both an internal storage unit and an external storage device of the electronic device. The memory 101 is used to store computer programs and other programs and data required by the intelligent prosthesis. The memory 101 may also be used to temporarily store data that has been output or is to be output.
It will be appreciated by those skilled in the art that the functional block diagram shown in FIG. 4 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not intended to limit the smart prosthesis to which the present inventive arrangements may be applied, and that a particular smart prosthesis may include more or less components than those shown, or may incorporate some of the components, or may have a different arrangement of components.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of a computer program, which may be stored on a non-transitory computer readable storage medium, that when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, operational database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual operation data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (14)
1. An intelligent artificial limb movement pattern recognition method, which is characterized by comprising the following steps:
acquiring motion data, supporting pressure data and rotation angle data of an intelligent artificial limb in a preset time period, wherein the supporting pressure data reflects supporting pressure of the intelligent artificial limb, and the rotation angle data reflects the rotation angle of a rotating shaft of a knee joint in the intelligent artificial limb;
determining movement frequency information of the intelligent artificial limb based on the movement data and the rotation angle data, wherein the movement frequency information reflects the swing frequency of the knee joint;
and determining a movement mode of the intelligent artificial limb based on the movement frequency information and the supporting pressure data.
2. The method for identifying a movement pattern of an intelligent prosthesis according to claim 1, wherein the acquiring movement data, support pressure data and rotation angle data of the intelligent prosthesis in a preset time period comprises:
acquiring the movement speed and the acceleration of the knee joint based on a preset inertial sensor, and taking the movement speed and the acceleration as the movement data;
acquiring rotation angle data of a rotating shaft in the knee joint based on a preset angle sensor;
and acquiring supporting pressure data based on a pressure sensor preset on the intelligent artificial limb.
3. The intelligent prosthesis movement pattern recognition method according to claim 2, wherein the determining movement frequency information of the intelligent prosthesis based on the movement data and the rotation angle data includes:
determining a periodic motion rule of the knee joint in a preset time period based on the motion data, and determining speed change data of the knee joint in a fixed period based on the periodic motion rule of the knee joint in the preset time period, wherein the speed change data is used for reflecting the speed of speed change in the fixed period;
Determining a motion rule of the rotating shaft in a periodic motion in a preset time period based on the rotation angle data, and determining rotation angle change data of the rotating shaft in the same fixed period based on the motion rule of the rotating shaft in the periodic motion in the preset time period, wherein the rotation angle change data are used for reflecting the speed of rotation angle change in the fixed period;
and determining the movement frequency information based on the speed change data and the rotation angle change data.
4. The intelligent prosthesis movement pattern recognition method of claim 3, wherein the determining the movement frequency information based on the speed change data and the rotation angle change data comprises:
comparing the speed change data with a preset speed change threshold range;
comparing the rotation angle change data with the rotation angle change threshold range;
if the speed change data is larger than the speed change threshold range and the rotation angle change data is larger than the rotation angle change threshold range, determining the movement frequency information as a first frequency;
if the speed change data is smaller than the speed change threshold range and the rotation angle change data is smaller than the rotation angle change threshold range, determining the movement frequency information as a second frequency;
Wherein the first frequency is greater than the second frequency.
5. The intelligent prosthesis movement pattern recognition method of claim 4, wherein the determining the movement pattern of the intelligent prosthesis based on the movement frequency information and the support pressure data comprises:
determining a motion state of the intelligent prosthesis based on the motion frequency information;
and determining a movement mode of the intelligent artificial limb based on the movement state and the supporting pressure data.
6. The method of claim 5, wherein said determining a motion state of the intelligent prosthesis based on the motion frequency information comprises:
if the motion frequency information is the first frequency, determining that the motion state of the intelligent artificial limb is a fast-walking state;
and if the motion frequency information is the second frequency, determining that the motion state of the intelligent artificial limb is a slow-step state.
7. The intelligent prosthesis movement pattern recognition method of claim 6, wherein the determining the movement pattern of the intelligent prosthesis based on the movement state and the support pressure data comprises:
comparing the support pressure data with a preset pressure threshold range;
If the motion state of the intelligent artificial limb is a fast-walking state and the supporting pressure data is larger than the pressure threshold range, determining that the motion mode of the intelligent artificial limb is a running mode;
if the motion state of the intelligent artificial limb is a fast walking state and the supporting pressure data is smaller than the pressure threshold range, determining that the motion mode of the intelligent artificial limb is a fast walking mode;
and if the motion state of the intelligent artificial limb is a slow walking state and the supporting pressure data is smaller than the pressure threshold range, determining that the motion mode of the intelligent artificial limb is a slow walking mode.
8. An intelligent prosthetic motion pattern recognition device, the device comprising:
the data acquisition module is used for acquiring motion data, supporting pressure data and rotation angle data of the intelligent artificial limb in a preset time period, wherein the supporting pressure data reflects the supporting pressure of the intelligent artificial limb, and the rotation angle data reflects the rotation angle of a rotating shaft of a knee joint in the intelligent artificial limb;
the frequency analysis module is used for determining the movement frequency information of the intelligent artificial limb based on the movement data and the rotation angle data, wherein the movement frequency information reflects the swing frequency of the knee joint;
And the pattern analysis module is used for determining the movement pattern of the intelligent artificial limb based on the movement frequency information and the supporting pressure data.
9. The intelligent prosthetic motion pattern recognition device of claim 8, wherein the frequency analysis module comprises:
the speed analysis unit is used for determining a periodic motion rule of the knee joint in a preset time period based on the motion data and determining speed change data of the knee joint in a fixed period based on the periodic motion rule of the knee joint in the preset time period, wherein the speed change data are used for reflecting the speed of speed change in the fixed period;
the rotation angle analysis unit is used for determining a motion rule of the rotating shaft in a periodic motion in a preset time period based on the rotation angle data and determining rotation angle change data of the rotating shaft in the same fixed period based on the motion rule of the rotating shaft in the periodic motion in the preset time period, wherein the rotation angle change data are used for reflecting the speed of rotation angle change in the fixed period;
and the frequency determining unit is used for determining the movement frequency information based on the speed change and the rotation angle change.
10. The intelligent prosthetic motion pattern recognition device of claim 9, wherein the frequency determination unit comprises:
the first data comparison subunit is used for comparing the speed change data with a preset speed change threshold range;
a second data comparison subunit configured to compare the rotation angle change data with the rotation angle change threshold range;
the first frequency determining subunit is configured to determine the motion frequency information as a first frequency if the speed change data is greater than the speed change threshold range and the rotation angle change data is greater than the rotation angle change threshold range;
the second frequency determining subunit is configured to determine the motion frequency information as a second frequency if the speed change data is smaller than the speed change threshold range and the rotation angle change data is smaller than the rotation angle change threshold range; wherein the first frequency is greater than the second frequency.
11. The intelligent prosthetic motion pattern recognition device of claim 10, wherein the pattern analysis module comprises:
the state analysis unit is used for determining the motion state of the intelligent artificial limb based on the motion frequency information;
And the mode determining unit is used for determining the movement mode of the intelligent artificial limb based on the movement state and the supporting pressure data.
12. A smart prosthesis comprising a socket, a knee joint and a smart prosthesis movement pattern recognition device according to any one of the preceding claims 8-11.
13. A smart prosthesis comprising a memory, a processor and a smart prosthesis movement pattern recognition program stored in the memory and executable on the processor, the processor implementing the steps of the smart prosthesis movement pattern recognition method according to any one of claims 1-7 when executing the smart prosthesis movement pattern recognition program.
14. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon an intelligent prosthesis movement pattern recognition program, which when executed by a processor, implements the steps of the intelligent prosthesis movement pattern recognition method according to any one of claims 1-7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117442398A (en) * | 2023-12-22 | 2024-01-26 | 浙江强脑科技有限公司 | Intelligent artificial limb adjusting method, device, terminal and medium based on gait difference |
| CN117442397A (en) * | 2023-12-22 | 2024-01-26 | 浙江强脑科技有限公司 | Intelligent artificial limb movement effect evaluation method, device, terminal and storage medium |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020052663A1 (en) * | 2000-03-29 | 2002-05-02 | Herr Hugh M. | Speed-adaptive and patient-adaptive prosthetic knee |
| US20160310342A1 (en) * | 2013-12-27 | 2016-10-27 | Korea Polytechnic University Industry Academic Cooperation Foundation | System and method for controlling joint angle of knee-joint type walking training robot |
| CN109938892A (en) * | 2019-04-03 | 2019-06-28 | 河北工业大学 | A kind of intelligent lap artificial limb wearer riding condition identifying system |
| CN110755184A (en) * | 2019-11-08 | 2020-02-07 | 哈工大机器人湖州国际创新研究院 | Prosthetic knee joint control method |
| KR20210062796A (en) * | 2019-11-21 | 2021-06-01 | 한국산업기술대학교산학협력단 | A method and apparatus for estimating gait phase based on inertial sensor |
| CN113288121A (en) * | 2021-05-07 | 2021-08-24 | 深圳先进技术研究院 | Gait analysis system |
| CN114167996A (en) * | 2022-02-14 | 2022-03-11 | 浙江强脑科技有限公司 | Sensor-based action pre-judging method and device and storage medium |
| US20220202594A1 (en) * | 2020-12-25 | 2022-06-30 | Nabtesco Corporation | Prosthetic knee joint |
| CN115969590A (en) * | 2023-03-16 | 2023-04-18 | 深圳市心流科技有限公司 | Knee prosthesis, control method and system, intelligent terminal and storage medium |
| CN115998494A (en) * | 2023-02-14 | 2023-04-25 | 浙江强脑科技有限公司 | Anti-falling control method and device for intelligent knee joint |
| CN116265200A (en) * | 2021-12-17 | 2023-06-20 | 沈阳新松机器人自动化股份有限公司 | Control method for automatically adjusting gait of tripping condition of exoskeleton |
| CN116512220A (en) * | 2022-06-21 | 2023-08-01 | 重庆理工大学 | Lower limb assistance exoskeleton and assistance control method |
-
2023
- 2023-11-09 CN CN202311487085.6A patent/CN117204993B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020052663A1 (en) * | 2000-03-29 | 2002-05-02 | Herr Hugh M. | Speed-adaptive and patient-adaptive prosthetic knee |
| US20160310342A1 (en) * | 2013-12-27 | 2016-10-27 | Korea Polytechnic University Industry Academic Cooperation Foundation | System and method for controlling joint angle of knee-joint type walking training robot |
| CN109938892A (en) * | 2019-04-03 | 2019-06-28 | 河北工业大学 | A kind of intelligent lap artificial limb wearer riding condition identifying system |
| CN110755184A (en) * | 2019-11-08 | 2020-02-07 | 哈工大机器人湖州国际创新研究院 | Prosthetic knee joint control method |
| KR20210062796A (en) * | 2019-11-21 | 2021-06-01 | 한국산업기술대학교산학협력단 | A method and apparatus for estimating gait phase based on inertial sensor |
| US20220202594A1 (en) * | 2020-12-25 | 2022-06-30 | Nabtesco Corporation | Prosthetic knee joint |
| CN113288121A (en) * | 2021-05-07 | 2021-08-24 | 深圳先进技术研究院 | Gait analysis system |
| CN116265200A (en) * | 2021-12-17 | 2023-06-20 | 沈阳新松机器人自动化股份有限公司 | Control method for automatically adjusting gait of tripping condition of exoskeleton |
| CN114167996A (en) * | 2022-02-14 | 2022-03-11 | 浙江强脑科技有限公司 | Sensor-based action pre-judging method and device and storage medium |
| CN116512220A (en) * | 2022-06-21 | 2023-08-01 | 重庆理工大学 | Lower limb assistance exoskeleton and assistance control method |
| CN115998494A (en) * | 2023-02-14 | 2023-04-25 | 浙江强脑科技有限公司 | Anti-falling control method and device for intelligent knee joint |
| CN115969590A (en) * | 2023-03-16 | 2023-04-18 | 深圳市心流科技有限公司 | Knee prosthesis, control method and system, intelligent terminal and storage medium |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117442398A (en) * | 2023-12-22 | 2024-01-26 | 浙江强脑科技有限公司 | Intelligent artificial limb adjusting method, device, terminal and medium based on gait difference |
| CN117442397A (en) * | 2023-12-22 | 2024-01-26 | 浙江强脑科技有限公司 | Intelligent artificial limb movement effect evaluation method, device, terminal and storage medium |
| CN117442398B (en) * | 2023-12-22 | 2024-04-09 | 浙江强脑科技有限公司 | Intelligent artificial limb adjusting method, device, terminal and medium based on gait difference |
| CN117442397B (en) * | 2023-12-22 | 2024-04-09 | 浙江强脑科技有限公司 | Intelligent artificial limb movement effect evaluation method, device, terminal and storage medium |
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