Sasm detection system and method based on rehabilitation robot

Abstract

The invention discloses a spasm detection system and method based on a rehabilitation robot. The system comprises a motion execution module; the information acquisition module is used for acquiring the actual position and the actual output force of the preset part of the motion execution module in the preset time; the motion planning module is used for sending a control signal to the motion execution module or the spasm detection module; the spasm detection module is used for calculating the comprehensive spasm degree of the patient according to the offset between the actual position and the preset position and the difference value between the actual output force and the preset maximum output force, and when the comprehensive spasm degree is larger than the preset comprehensive spasm degree, the motion execution module is controlled to take corresponding spasm treatment measures. The spasm detection module can judge whether the patient has spasm or not based on the offset between the actual position of the preset part and the preset position, so that the rehabilitation robot can be controlled to take corresponding measures, and secondary injury to the patient is avoided.

Description

Technical Field

[0001] This invention relates to the field of rehabilitation training, and more particularly to a spasticity detection system and method based on a rehabilitation robot. Background Technology

[0002] Rehabilitation robots are considered wearable devices for special environments, with functions such as assisting the disabled to walk, providing rehabilitation therapy, and reducing labor intensity. They are a product of the combination of robotics and medical technology, helping disabled patients regain motor function and reintegrate into society.

[0003] Rehabilitation robots are currently mainly used for upper or lower limb motor dysfunction caused by stroke, brain injury, spinal cord injury, nerve injury, muscle injury and orthopedic diseases. They help patients remodel the brain's motor nerves, restore the brain's control over upper limb movement, and thus improve patients' daily living abilities.

[0004] The main characteristics of spasticity in stroke patients are abnormally high muscle tone and paroxysmal muscle contractions. Increased muscle tone results in persistent stiffness of the limbs at rest, and resistance to passive traction increases significantly with speed (speed-dependent), typically leading to fixed postural abnormalities in the upper limb flexors and lower limb extensors (such as elbow flexion and foot drop). Paroxysmal spasticity manifests as sudden, brief, and intense muscle contractions, often accompanied by pain, and may be triggered by fatigue, electrolyte imbalance, or abnormal nerve excitability. Furthermore, spasticity can also cause joint deformities, motor dysfunction, and gait abnormalities (such as circumduction).

[0005] When patients use rehabilitation robots for rehabilitation training, the robots guide them to move along a preset trajectory. In existing technologies, when a patient experiences spasms while using the rehabilitation robot, their movement is restricted due to abnormally increased muscle tension and paroxysmal muscle contractions. If the rehabilitation robot continues to move according to the original program, it will strain the patient, causing secondary injury. Summary of the Invention

[0006] To address the aforementioned technical problems, the present invention aims to provide a spasticity detection system and method based on a rehabilitation robot. This system can detect the offset between the actual and preset positions of a preset part of the rehabilitation robot, the difference between the preset maximum output force and the actual output force, and determine whether the patient is experiencing spasticity during rehabilitation training with the robot. This allows the system to control the rehabilitation robot to take appropriate countermeasures to avoid causing secondary injury to the patient.

[0007] To achieve the above objectives, the present invention aims to provide a spasticity detection system based on a rehabilitation robot, comprising:

[0008] The module includes a motion execution module, an information acquisition module, a motion planning module, and a spasticity detection module.

[0009] The motion execution module is used to obtain control signals from the spasticity detection module or the motion planning module and execute the control signals;

[0010] The information acquisition module is used to obtain the actual position and actual output force of the preset part of the motion execution module;

[0011] The motion planning module is used to send control signals to the motion execution module or the spasticity detection module to control the motion execution module to drive the patient to move along a preset trajectory with a preset maximum output force;

[0012] The spasticity detection module is used to obtain the preset position and preset maximum output force of the preset part at a preset time from the motion planning module, and to obtain the actual position and actual output force of the preset part from the information acquisition module. Based on the offset between the actual position and the preset position and the difference between the actual output force and the preset maximum output force, the module calculates the patient's comprehensive spasticity degree. When the comprehensive spasticity degree is greater than the preset comprehensive spasticity degree, the module controls the motion execution module to take corresponding spasticity treatment measures.

[0013] In some embodiments, the spasm detection module includes a dystonia detection unit, which is used to calculate the duration for which the offset between the actual position and the preset position is greater than a preset offset and the actual output force is greater than a preset maximum output force of a preset proportion, and uses the ratio of the duration to the preset spasm detection time as the degree of dystonia.

[0014] In some embodiments, the spasm detection module includes a paroxysmal spasm detection unit. The paroxysmal spasm detection unit is used to select the actual position of the preset part at the current time as a reference position when the offset between the actual position and the preset position is greater than a preset offset. After a preset detection period, the actual position of the preset part is selected as the paroxysmal spasm detection position. The paroxysmal offset of the paroxysmal spasm detection position relative to the reference position is calculated, and the ratio of the paroxysmal offset to a preset paroxysmal detection amount is used as the paroxysmal spasm degree.

[0015] In some embodiments, the spasm treatment measures include controlling the motion execution module to stop moving or controlling the motion execution module to move in the opposite direction.

[0016] According to another aspect of this application, a spasticity detection method based on a rehabilitation robot is further provided, comprising:

[0017] The motion execution module of the control rehabilitation robot drives the patient to move along a preset trajectory with a preset maximum output force;

[0018] Obtain the preset position and preset maximum output force of the preset part of the motion execution module at a preset time;

[0019] The actual position and actual output force of the preset part of the rehabilitation robot at the preset time are obtained;

[0020] The patient's overall spasticity is calculated based on the offset between the actual position and the preset position, the difference between the actual output force and the preset maximum output force; in response to the overall spasticity being greater than the preset overall spasticity, the motion execution module is controlled to perform spasticity treatment measures, the spasticity treatment measures including controlling the motion execution module to stop moving or controlling the motion execution module to move in the opposite direction.

[0021] In some implementations, the step of calculating the patient's overall spasticity includes:

[0022] The duration for which the offset is greater than a preset offset and the difference between the actual output force and the preset maximum output force is greater than a preset proportion of the preset maximum output force is calculated.

[0023] The degree of muscle tone spasm is determined based on the ratio of the duration to the preset spasm detection time.

[0024] In some implementations, the step of calculating the patient's overall spasticity includes:

[0025] In response to the offset between the actual position and the preset position being greater than the preset offset, the actual position of the preset part of the rehabilitation robot at the current moment is selected as the reference position. After a preset detection cycle, the actual position of the preset part is selected as the paroxysmal spasm detection position. The paroxysmal offset of the paroxysmal spasm detection position relative to the reference position is calculated. The ratio of the paroxysmal offset to the preset paroxysmal detection amount is taken as the paroxysmal spasm degree.

[0026] In some implementations, the step of calculating the patient's overall spasticity further includes:

[0027] The overall spasticity is obtained by fusing the muscle tone spasticity and the paroxysmal spasticity according to a preset ratio.

[0028] In some implementations, the overall degree of spasticity ,in The coefficient for abnormal muscle tone and spasticity. Paroxysmal spasm coefficient, muscle tone spasm degree Paroxysmal spasm degree . Attached Figure Description

[0029] The preferred embodiments will now be described in a clear and easy-to-understand manner, in conjunction with the accompanying drawings, to further explain the above-mentioned characteristics, technical features, advantages, and implementation methods of the present invention.

[0030] Figure 1 This is a flowchart of a preferred embodiment of the spasticity detection method based on a rehabilitation robot of the present invention;

[0031] Figure 2 This is a sub-flowchart of a spasticity detection method based on a rehabilitation robot according to a preferred embodiment of the present invention;

[0032] Figure 3 This is another sub-flowchart of the spasticity detection method based on a rehabilitation robot, which is a preferred embodiment of the present invention;

[0033] Figure 4 This is a block diagram of a spasticity detection system based on a rehabilitation robot, according to a preferred embodiment of the present invention. Detailed Implementation

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings and other implementation methods can be obtained based on these drawings without any creative effort.

[0035] To keep the drawings concise, each figure only schematically shows the parts relevant to the invention, and these do not represent the actual structure of the product. Furthermore, to facilitate understanding, in some figures, only one of components with the same structure or function is schematically depicted, or only one is labeled. In this document, "one" not only means "only one," but can also mean "more than one."

[0036] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0037] In this document, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0038] Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0039] refer to Figures 1 to 3 This application provides a spasticity detection method 100 based on a rehabilitation robot. The spasticity detection method 100 based on a rehabilitation robot can detect the offset between the actual position and the preset position of a preset part of the rehabilitation robot and the difference between the actual output force and the preset maximum output force during the rehabilitation training process of the rehabilitation robot. It can then determine whether the patient has spasticity, so as to control the rehabilitation robot to take corresponding measures to avoid causing secondary injury to the patient.

[0040] For example, when the rehabilitation robot determines that the patient is experiencing spasm based on the offset, the measures taken by the rehabilitation robot include, but are not limited to: immediately stopping the movement of the patient or moving the patient in the opposite direction, massaging the spasmodic area of ​​the patient, and applying cold or hot compresses to the spasmodic area of ​​the patient.

[0041] refer to Figure 1 Specifically, the spasticity detection method 100 based on a rehabilitation robot includes:

[0042] S101: The motion execution module 10 of the control rehabilitation robot drives the patient to move along a preset trajectory with a preset maximum output force;

[0043] S102: Obtain the preset position and preset maximum output force of the preset part of the motion execution module 10 at a preset time;

[0044] S103: Obtain the actual position and actual output force of the preset part of the rehabilitation robot at the preset time;

[0045] S104: Calculate the patient's overall spasticity based on the offset between the actual position and the preset position, the difference between the actual output force and the preset maximum output force; in response to the overall spasticity being greater than the preset overall spasticity, control the motion execution module to perform spasticity treatment measures, the spasticity treatment measures including controlling the motion execution module to stop moving or controlling the motion execution module to move in the opposite direction.

[0046] In step S101 above, the motion execution module 10 of the rehabilitation robot can drive the patient to move along a preset trajectory to complete the rehabilitation training for the patient. For example, the rehabilitation robot can drive the patient's arm movement, leg movement, or walking movement, etc. By pre-setting a corresponding control program within the rehabilitation robot, it can control the rehabilitation robot to drive the patient to move along a preset trajectory. The specific type of movement trajectory completed by the rehabilitation robot should not constitute a limitation of this application.

[0047] Specifically, the motion execution module 10 of the rehabilitation robot includes a driver 11, a drive motor 12, and a motion component 13. The driver 11 is connected to the drive motor 12 and is adapted to receive external control signals and convert these signals into appropriate electrical output to the drive motor 12 to control its speed, torque, and direction. Precise control of the drive motor 12 is achieved by adjusting parameters such as current, voltage, and frequency. The motion component 13 is adapted to contact a preset body part of the patient and is connected to the power output shaft of the drive motor 12. When the drive motor 12 is operating, it drives the motion component 13 to move the preset body part of the patient along a preset trajectory.

[0048] In step S102 above, before the motion execution module 10 moves the patient along a preset trajectory, the motion execution module 10 obtains a control signal from the motion planning module 20 or the spasticity detection module 30. Based on the control signal, the motion execution module 10 moves the patient along the preset trajectory. Based on the control signal, the motion trajectory of the motion component 13 of the motion execution module 10 can be obtained over a future period of time; that is, based on the control signal, the preset position and preset maximum output force of the motion component 13 at a preset time in the future can be obtained.

[0049] Specifically, step S102 is executed by a spasticity detection module 30 of the rehabilitation robot. That is, the spasticity detection module 30 is communicatively connected to the motion planning module 20 and can obtain the control signal from the motion planning module 20 to control the movement of the motion execution module 10, thereby obtaining the preset position of the preset part (moving component 13) of the motion execution module 10 at a preset time. In some modified embodiments, the motion execution module 10 can also obtain the control signal from the motion planning module 20 to control its movement.

[0050] In step S103 above, an information acquisition module 40 of the rehabilitation robot acquires the actual position and actual output force of the moving component 13 of the motion execution module 10 at a preset time. The information acquisition module 40 includes a position sensor. In some embodiments, the position sensor is a position feedback sensor disposed inside the rehabilitation robot, such as, but not limited to, an encoder, potentiometer, or rotary transformer. In some embodiments, the position sensor is a position measurement sensor disposed outside the rehabilitation robot, such as, but not limited to, a vision sensor (2D / 3D camera), laser displacement sensor, or wire-type displacement sensor. In some embodiments, the position sensor may also include a combination of the aforementioned position feedback sensor disposed inside the rehabilitation robot and the position measurement sensor disposed outside the rehabilitation robot.

[0051] The spasm detection module 30 is communicatively connected to the information acquisition module 40. The spasm detection module 30 can obtain the actual position and actual output force of the moving part 13 collected by the information acquisition module 40 from the information acquisition module 40.

[0052] In other words, step S103 is jointly executed by the information acquisition module 40 and the spasm detection module 30. The information acquisition module 40 acquires the actual position and the actual output force and then transmits them to the spasm detection module 30.

[0053] In step S104 above, the spasticity detection module 30, based on the acquired preset position and the actual position, can determine the offset of the actual position relative to the preset position. The spasticity detection module 30 can also calculate the difference between the actual output force and the preset maximum output force. The spasticity detection module 30 can calculate the patient's overall spasticity degree based on the offset, the difference between the actual output force and the preset maximum output force. In response to the overall spasticity degree being greater than the preset overall spasticity degree, the module 30 controls the motion execution module 10 to perform spasticity treatment measures, including controlling the motion execution module 10 to stop movement or controlling the motion execution module 10 to reverse movement. When the overall spasticity degree is greater than the preset overall spasticity degree, the spasticity detection module 30 sends a control command to the motion execution module 10 to control the motion execution module 10 to stop driving the patient's movement or to reverse movement. It should be noted that when the offset is greater than the preset offset, it indicates that the patient is no longer able to follow the motion component 13 along the preset trajectory, indicating that the patient has experienced spasm. The motion execution module 10 can stop the motion or reverse the motion in time to avoid causing the patient in a spasm state to continue to move, thereby protecting the patient and avoiding secondary injury to the patient.

[0054] In some modified embodiments, when the spasticity detection module 30 determines that the offset between the preset position and the actual position is greater than a preset offset, the spasticity detection module 30 calculates the patient's overall spasticity degree. Specifically, the spasticity detection module 30 includes an offset detection unit 31 and a spasticity detection unit 32. The offset detection unit 31 is used to determine the offset between the preset position and the actual position, and can compare the offset with the preset offset to determine whether the offset is greater than the preset offset. The spasticity detection unit 32 is used to calculate the patient's overall spasticity degree. The spasticity detection unit 32 can also compare the detected overall spasticity degree with a threshold. When the overall spasticity degree is greater than the threshold, it sends a corresponding control command to the motion execution module 10 to control the motion execution module 10 to stop driving the patient's movement.

[0055] It should be noted that spasticity in stroke patients can be mainly classified into dystonic spasticity and paroxysmal spasticity. Dystonic spasticity is characterized by abnormally high muscle tone, persistent stiffness of the limbs at rest, and a significant increase in resistance with increasing speed during passive traction (speed-dependent), typically leading to fixed postural abnormalities in the upper limb flexors and lower limb extensors (such as elbow flexion and foot drop). Paroxysmal spasticity is characterized by paroxysmal muscle contractions, manifested as sudden, brief, and intense muscle contractions, often accompanied by pain, and may be triggered by fatigue, electrolyte imbalance, or abnormal nerve excitability. Furthermore, spasticity can also lead to joint deformities, motor dysfunction, and gait abnormalities (such as circling gait). In this application, the spasticity detection unit 32 can calculate a comprehensive degree of spasticity based on the characteristics of the dystonic spasticity and / or the paroxysmal spasticity.

[0056] refer to Figure 2 The steps for calculating the patient's overall spasticity include:

[0057] S1041: Calculate the duration for which the offset is greater than a preset offset and the difference between the actual output force and the preset maximum output force is greater than a preset proportion of the preset maximum output force;

[0058] S1042: Determine the degree of muscle tone spasm based on the ratio of the duration to the preset spasm detection time, and use the degree of muscle tone spasm as the comprehensive spasm degree.

[0059] Specifically, in step S1041 above, the information acquisition module 40 can acquire the actual position of the preset part of the motion execution module 10 and the magnitude of the actual output force of the preset part. For example, the information acquisition module 40 can acquire the magnitude of the actual output force of the moving component 13 of the motion execution module 10. In some embodiments, the information acquisition module 40 can calculate the magnitude of the actual output force of the moving component 13 based on the current of the motor driving the moving component 13. In some embodiments, the information acquisition module 40 can also indirectly sense the magnitude of the actual output force of the moving component 13 based on the force of the encoder. For example, when the moving component 13 is subjected to force, it will cause a slight shift (or positional deviation) in the joint movement speed. The encoder detects the speed change, and combined with the inertial parameters of the moving component 13 (such as mass, moment of inertia, inertia tensor, etc.), the magnitude of the force on the moving component 13 is calculated using a dynamic model.

[0060] It should be noted that the motion planning module 20 can obtain the preset maximum output force and preset position of the rehabilitation robot within a preset time, that is, it can obtain the preset maximum output force and preset position of the preset part of the motion execution module 10 within a preset time. The spasticity detection unit 32 can compare the preset maximum output force with the actual output force. When the actual output force is greater than the preset maximum output force, timing begins to record the duration for which the actual output force is greater than a preset proportion of the preset maximum output force. The muscle tone spasticity is determined based on the ratio of the duration to the preset spasticity detection time, and the muscle tone spasticity is used as the comprehensive spasticity. It should be noted that during the process of recording the duration for which the actual output force is greater than the preset maximum output force, a duration threshold is preset. When the duration reaches the threshold, the muscle tone spasticity is determined by the ratio of the duration to the preset spasticity detection time. Similarly, the length of the preset spasticity detection time is also preset, and the length of the preset spasticity detection time is greater than the duration threshold.

[0061] Specifically, the spasm detection unit 32 further includes a muscle tone spasm detection unit 321, which performs the following steps: calculating the duration for which the actual output force of the rehabilitation robot at a preset position is greater than the preset maximum output force; and determining the degree of muscle tone spasm based on the ratio of the duration to the preset spasm detection time.

[0062] For example, the offset of the actual position of the moving component 13 of the motion execution module 10 relative to the preset position is: The preset offset is The actual output force of the moving part 13 at time i is The preset maximum output force of the moving part 13 at time i is The abnormal tension output coefficient is The preset time for spasm detection is... Then when ,and The duration is calculated from the start time. Muscle spasticity .

[0063] It should be noted that when comparing with the actual output force, it is compared with the preset maximum output force of the preset ratio, that is, the preset maximum output force is multiplied by the tension abnormal output coefficient.

[0064] refer to Figure 3 The steps for detecting the patient's overall spasticity include:

[0065] S1043: In response to the fact that the offset between the actual position and the preset position is greater than the preset offset, the actual position of the preset part of the rehabilitation robot at the current moment is selected as the reference position. After a preset detection period, the actual position of the preset part is selected as the paroxysmal spasm detection position. The paroxysmal offset of the paroxysmal spasm detection position relative to the reference position is calculated. The ratio of the paroxysmal offset to the preset paroxysmal detection amount is taken as the paroxysmal spasm degree.

[0066] In some modified embodiments, detecting the patient's overall spasticity further includes:

[0067] S1044: The comprehensive spasm degree is obtained by fusing the muscle tone spasm degree and the paroxysmal spasm degree according to a preset ratio.

[0068] Furthermore, the spasm detection unit 32 also includes a paroxysmal spasm detection unit 322. In step S1043 above, when the offset detection unit 31 detects that the offset between the actual position and the preset position is greater than the preset offset, the paroxysmal spasm detection unit 322 can select the actual position of the moving part 13 at the current moment as the reference position. The actual position of the moving part 13 at the preset moment can be obtained by the information acquisition module 40. After a preset detection period after obtaining the reference position, the actual position of the moving part 13 is obtained again by the information acquisition module 40 as the paroxysmal spasm detection position. The spasm detection unit 32 calculates the paroxysmal offset of the paroxysmal spasm detection position relative to the reference position, and the ratio of the paroxysmal offset to the preset paroxysmal detection value is used as the paroxysmal spasm degree.

[0069] It should be noted that the preset detection period is the number of paroxysmal spasm detection periods, and the duration and number of the paroxysmal detection periods are set based on the characteristics of paroxysmal spasms. The paroxysmal detection quantity is also an offset set based on the characteristics of paroxysmal spasms.

[0070] For example, when At that time, the actual position of the preset part of the current rehabilitation robot will be determined. As the reference position The preset number of detection cycles is c, and the actual position of the preset part after c detection cycles is... This serves as the detection location for the paroxysmal spasm. Paroxysmal offset. , The magnitude of the position vector. Paroxysmal spasm degree. ,in It is the preset intermittent detection quantity.

[0071] The spasticity detection unit 32 further includes a fusion unit 323. In step S1044 above, the muscle tone spasticity and the paroxysmal spasticity are fused according to a preset ratio to obtain the comprehensive spasticity. That is, the comprehensive spasticity. ,in The coefficient for abnormal muscle tone and spasticity. This represents the paroxysmal spasm coefficient. When This allows the patient to be determined to be in a state of spasticity. It should be noted that by combining the degree of muscle tone spasticity and the degree of paroxysmal spasticity according to a preset ratio, and comparing the combined spasticity degree with a threshold, the accuracy of the spasticity assessment can be improved.

[0072] In some modified embodiments, it is also possible to use only the degree of paroxysmal spasm. The patient's condition is determined by comparing the result with a corresponding threshold. In some modified embodiments, the degree of muscle tone spasticity can also be used as the sole criterion. The patient is compared with a corresponding threshold to determine whether the patient is in a state of spasm.

[0073] refer to Figure 4 According to another aspect of this application, a spasticity detection system 200 based on a rehabilitation robot is further provided, including the aforementioned motion execution module 10, motion planning module 20, spasticity detection module 30, and information acquisition module 40. The information acquisition module 40 is used to acquire the actual position of a preset part of the motion execution module 10 at a preset time; the motion planning module 20 is used to send a control signal to the motion execution module 10 to control the motion execution module 10 to move the patient along a preset trajectory; the spasticity detection module 30 is used to acquire the preset position of the preset part at the preset time from the motion planning module 20 and the actual position of the preset part at the preset time from the information acquisition module 40. The spasticity detection module 30 can also calculate the offset between the actual position and the preset position, and control the motion execution module 10 to stop moving the patient when the offset is greater than a preset offset.

[0074] The spasticity detection module 30 includes the offset detection unit 31 and the spasticity detection unit 32. The offset detection unit 31 is used to determine the offset based on the actual position and the preset position, and to determine whether the offset is greater than the preset offset. The spasticity detection unit 32 is used to detect the patient's overall spasticity when the offset is greater than the preset offset.

[0075] The spasm detection unit 32 includes a muscle tone spasm detection unit 321. The information acquisition module 40 can also acquire the actual output force of the preset part of the motion execution module 10 at the preset time; the muscle tone spasm detection unit 321 can obtain the preset maximum output force of the preset part of the motion execution module 10 at the preset time from the motion planning module 20; the muscle tone spasm detection unit 321 can also calculate the duration for which the actual output force is greater than the preset maximum output force, and use the ratio of the duration to the preset spasm detection time as the muscle tone spasm degree, and use the muscle tone spasm degree as the comprehensive spasm degree.

[0076] The spasm detection unit 32 further includes a paroxysmal spasm detection unit 322 and a fusion unit 323. The paroxysmal spasm detection unit 322 is used to select the actual position of the preset location at a preset time as a reference position when the offset between the actual position and the preset position is greater than a preset offset; after a preset detection period, the actual position of the preset location is used as the paroxysmal spasm detection position; the paroxysmal offset of the paroxysmal spasm detection position relative to the reference position is calculated; and the ratio of the paroxysmal offset to a preset paroxysmal detection value is used as the paroxysmal spasm degree. The fusion unit 323 is used to fuse the muscle tone spasm degree and the paroxysmal spasm degree according to a preset ratio to obtain the comprehensive spasm degree.

[0077] The various modules and units of the spasticity detection system 200 based on rehabilitation robots have been described in the above-mentioned spasticity detection method based on rehabilitation robots, and will not be repeated here.

[0078] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the invention. The advantages of the present invention have been fully and effectively realized. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments; any variations or modifications can be made to the implementation of the present invention without departing from these principles.

Claims

1. A spasticity detection system based on a rehabilitation robot, characterized in that, include: The module includes a motion execution module, an information acquisition module, a motion planning module, and a spasticity detection module. The motion execution module is used to obtain control signals from the spasticity detection module or the motion planning module and execute the control signals; The information acquisition module is used to obtain the actual position and actual output force of the preset part of the motion execution module; The motion planning module is used to send control signals to the motion execution module or the spasticity detection module to control the motion execution module to drive the patient to move along a preset trajectory with a preset maximum output force; The spasticity detection module is used to obtain the preset position and preset maximum output force of the preset part at a preset time from the motion planning module, and to obtain the actual position and actual output force of the preset part from the information acquisition module. Based on the offset between the actual position and the preset position and the difference between the actual output force and the preset maximum output force, the module calculates the patient's comprehensive spasticity degree. When the comprehensive spasticity degree is greater than the preset comprehensive spasticity degree, the module controls the motion execution module to take corresponding spasticity treatment measures.

2. The spasticity detection system based on a rehabilitation robot according to claim 1, characterized in that, The spasm detection module includes a muscle tone spasm detection unit, which is used to calculate the duration during which the offset between the actual position and the preset position is greater than a preset offset and the actual output force is greater than a preset maximum output force of a preset proportion, and uses the ratio of the duration to the preset spasm detection time as the muscle tone spasm degree.

3. The spasticity detection system based on a rehabilitation robot according to claim 1, characterized in that, The spasm detection module includes a paroxysmal spasm detection unit. The paroxysmal spasm detection unit is used to select the actual position of the preset part at the current time as a reference position when the offset between the actual position and the preset position is greater than the preset offset. After a preset detection period, the actual position of the preset part is selected as the paroxysmal spasm detection position. The paroxysmal offset of the paroxysmal spasm detection position relative to the reference position is calculated, and the ratio of the paroxysmal offset to the preset paroxysmal detection amount is used as the paroxysmal spasm degree.

4. The spasticity detection system based on a rehabilitation robot according to any one of claims 1 to 3, characterized in that, The spasm treatment measures include controlling the motion execution module to stop moving or controlling the motion execution module to move in the opposite direction.

5. A spasticity detection method based on a rehabilitation robot, characterized in that, include: The motion execution module of the control rehabilitation robot drives the patient to move along a preset trajectory with a preset maximum output force; Obtain the preset position and preset maximum output force of the preset part of the motion execution module at a preset time; The actual position and actual output force of the preset part of the rehabilitation robot at the preset time are obtained; The patient's overall spasticity is calculated based on the offset between the actual position and the preset position, the difference between the actual output force and the preset maximum output force; In response to the overall spasticity level being greater than a preset overall spasticity level, the motion execution module is controlled to perform spasticity treatment measures, which include controlling the motion execution module to stop moving or controlling the motion execution module to move in the opposite direction.

6. The method for detecting spasticity in a rehabilitation robot according to claim 5, characterized in that, The steps for calculating the patient's overall spasticity include: The duration for which the offset is greater than a preset offset and the difference between the actual output force and the preset maximum output force is greater than a preset proportion of the preset maximum output force is calculated. The degree of muscle tone spasm is determined based on the ratio of the duration to the preset spasm detection time.

7. The method for detecting spasticity in a rehabilitation robot according to claim 6, characterized in that, The steps for calculating the patient's overall spasticity include: In response to the offset between the actual position and the preset position being greater than the preset offset, the actual position of the preset part of the rehabilitation robot at the current moment is selected as the reference position. After a preset detection cycle, the actual position of the preset part is selected as the paroxysmal spasm detection position. The paroxysmal offset of the paroxysmal spasm detection position relative to the reference position is calculated. The ratio of the paroxysmal offset to the preset paroxysmal detection amount is taken as the paroxysmal spasm degree.

8. The method for detecting spasticity in a rehabilitation robot according to claim 7, characterized in that, The steps for calculating the patient's overall spasticity also include: The overall spasticity is obtained by fusing the muscle tone spasticity and the paroxysmal spasticity according to a preset ratio.

9. The method for detecting spasticity in a rehabilitation robot according to claim 8, characterized in that, Comprehensive spasticity ,in The coefficient for abnormal muscle tone and spasticity. Paroxysmal spasm coefficient, muscle tone spasm degree Paroxysmal spasm degree .

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