FES riding control method, system, electronic device and computer program product

Abstract

The present invention discloses an FES cycling control method, system, electronic device, and computer program product, relating to the technical field of rehabilitation training methods. The FES cycling control method combines a flywheel, an electromagnetic clutch, and a bicycle crank to provide an FES cycling control method for the quadriceps femoris of patients with spinal cord injury. This method addresses technical difficulties in FES-based fixed cycling rehabilitation exercises, such as the limited torque of electrically stimulated muscles, the difficulty of precise control of muscles as nonlinear elements, and the tendency for long-term continuous electrical stimulation to induce muscle fatigue. The practical effects of FES-based fixed cycling rehabilitation exercises still need to be improved. Compared with traditional electrical stimulation treatment methods, the present invention effectively reduces the intensity of electrical stimulation, improves energy utilization, and achieves energy-saving effects while ensuring rehabilitation needs.

Description

Technical Field

[0001] The present invention relates to the technical field of rehabilitation training methods, and in particular to an FES riding control method, system, electronic equipment and computer program product. Background Art

[0002] Spinal cord injury is a direct or indirect external injury to the structure and function of the corresponding part of the spine, which can cause varying degrees of lower limb movement disorders in patients, and can even lead to complete paralysis of the patient's lower limbs. If left untreated, the muscles of the lower limbs will gradually atrophy after paralysis. This may have adverse effects on the patient's urinary system, respiratory system, and heart, and may even cause problems such as cramps and pain. The "2023 Survey Report on the Quality of Life and Disease Burden of People with Spinal Cord Injury in China" points out that the number of spinal cord injury patients in China has increased dramatically. Therefore, the rehabilitation of patients with spinal cord injury is an urgent issue that needs to be addressed.

[0003] Rehabilitation medicine, an exercise-based treatment, is widely used in the recovery and treatment of hemiplegic stroke patients, and has a significant effect on improving patients' limb function. However, hemiplegic patients lose their limb movement ability and experience muscle atrophy, which increases the difficulty of rehabilitation. The introduction of the functional electrical stimulation (FES) bicycle system has helped to carry out rehabilitation work, positively impacting patients' limb movement function recovery and improving their ability to care for themselves.

[0004] The hardware components of the FES bicycle rehabilitation training system primarily include core components such as a main control computer, bicycle, functional electrical stimulator, sensors, and auxiliary motors. The key to achieving the rehabilitation goals of the FES bicycle rehabilitation training system lies in the implementation of rehabilitation training methods, which in turn relies heavily on the system's control strategy. The design of the control strategy fully considers the patient's condition and closely integrates with medical rehabilitation theory to ensure that the patient does not suffer additional harm during training. Due to the complexity of the human muscle system, the FES bicycle rehabilitation training system is often considered a nonlinear and time-varying system. Therefore, how to closely integrate with medical rehabilitation theory, design a correct control strategy, and improve the system's stability and anti-interference capabilities has attracted the attention of researchers both domestically and internationally. A series of related control strategies have been proposed, such as neural networks, fuzzy control, and deep learning.

[0005] However, current control strategies face the following two problems: First, during cycling rehabilitation training, the electrodes need to be tested multiple times before they can be placed in the optimal position, which may cause certain mechanical difficulties; second, many current FES bicycle rehabilitation training systems use flywheel devices to improve riding stability, but considering that the leg muscles of people with spinal cord injuries are in a weak state, external braking devices are usually used to control the required speed, resulting in energy loss and consumption. Summary of the Invention

[0006] The purpose of the present invention is to provide an FES riding control method, system, electronic device and computer program product, which combines a flywheel, an electromagnetic clutch and a bicycle crank to provide an FES riding control method for the quadriceps of patients with spinal cord injury. This method solves the technical difficulties in fixed cycling rehabilitation exercises based on FES technology, such as the limited torque of the muscles driven by electrical stimulation, the difficulty in accurately controlling the muscles as nonlinear elements, and the easy induction of muscle fatigue by long-term continuous electrical stimulation. The actual effect of fixed cycling rehabilitation exercises based on FES technology still needs to be improved. Compared with traditional electrical stimulation treatment methods, the present invention effectively reduces the electrical stimulation intensity and improves energy utilization while ensuring rehabilitation needs, achieving energy-saving effects.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] A FES riding control method, comprising the following steps:

[0009] Determine the cycling cadence required for the complex cycling exercise and obtain actual cycling cadence feedback signals through sensors on the bicycle;

[0010] comparing the actual cycling cadence feedback signal with the desired cycling cadence and providing the resulting error signal to the controller to change the stimulation pulse width accordingly;

[0011] The crank torque is determined based on the stimulation pulse width and the crank angle;

[0012] Based on the real-time acquired angular velocity of the crank and the angular velocity of the flywheel, the electric clutch is controlled using the Sugeno-type fuzzy reasoning method so that the angular velocity of the crank reaches the angular velocity required by the crank torque, and rehabilitation cycling exercise is performed.

[0013] According to the above technical solution, the controller uses the Mamdani-type fuzzy inference method to change the pulse width; the specific execution steps of the controller include:

[0014] The angle error e between the actual rhythm curve of the knee joint and the predefined rhythm curve of the knee joint and the change △e of the angle error between the actual rhythm curve of the knee joint and the predefined rhythm curve of the knee joint are used as input variables; the pulse width △L of the electrical stimulation signal is used as the output variable;

[0015] Fuzzify the input variables e and △e to obtain the fuzzy set of error e and the fuzzy set of error variation △e;

[0016] Combining the system characteristics and control requirements, set the fuzzy control rules of Mamdani type fuzzy reasoning method;

[0017] The fuzzy set of error e and the fuzzy set of error variation △e are calculated together with the inference results of fuzzy control rules, and the fuzzy relationship between input variables and output variables is determined to obtain the fuzzy output variable pulse width △L.

[0018] In general, the membership of the input or output variable to the fuzzy set is calculated through the membership relationship. This process is called fuzzification, and a Gaussian membership function can be used.

[0019] According to the above technical solution, the fuzzy set of the error e is A={NB1,NS1,Z1,PS1,PB1}, where NB1 represents negative large, NS1 represents negative small, Z1 represents zero, PS1 represents positive small, and PB1 represents positive large; the fuzzy subset domain corresponding to the fuzzy set of the error e is {-1, -0.8, -0.6, -0.4,

[0020] -0.2,0,0.2,0.4,0.6,0.8,1};

[0021] The fuzzy set of the error change △e is B = {NB2, NS2, Z2, PS2, PB2}, where NB2 represents large negative, NS2 represents small negative, Z2 represents zero, PS2 represents small positive, and PB2 represents large positive. The fuzzy subset domain corresponding to the fuzzy set of the error change △e is {-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1}.

[0022] According to the above technical solution, the fuzzy control rule of the Mamdani type fuzzy inference method is:

[0023]

[0024] In the table, NBL, NSL, ZL, PSL and PBL together constitute the fuzzy set of the output variable pulse width △L, NBL represents negative large, NSL represents negative small, ZL represents zero, PSL represents positive small, and PBL represents positive large. The fuzzy subset domain corresponding to the fuzzy set of the pulse width △L is {-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1}.

[0025] The electric clutch engages and disengages the flywheel and crank depending on the actual situation. The specific implementation logic is as follows: when the angular velocity of the crank exceeds the required speed, that is, there is too much energy in the system, and the angular velocity of the flywheel is lower than that of the crank, the clutch engages the flywheel and crank to slow down the movement.

[0026] On the other hand, when the crank's angular velocity is lower than the required speed, assistance is required. If the flywheel's angular velocity is higher than the crank's, it engages to assist and accelerate motion. The flywheel engages the crank, absorbing excess energy from the system, storing it as kinetic energy and slowing motion. Furthermore, after being loaded with kinetic energy, the flywheel engages the crank, releasing its energy into the system and accelerating motion.

[0027] Among them, the fuzzy set of the bicycle crank angle α is {PR, PL}, where PR represents the crank angle indicating that the rider is currently actively pedaling the right crank, and PL represents the crank angle indicating that the rider is currently actively pedaling the left crank. The crank torque can be determined by determining the bicycle crank angle α and the stimulation pulse width.

[0028] According to the above technical solution, the Sugeno-type fuzzy reasoning method execution steps include:

[0029] The input signal is the angular velocity ω of the crank and the angular velocity Ω of the flywheel, and the output signal is the electric clutch control signal; the electric clutch control signal includes an electric clutch start signal and an electric clutch stop signal;

[0030] Fuzzify the input signals of the crank angular velocity ω and the flywheel angular velocity Ω to determine the fuzzy set of the crank angular velocity ω and the fuzzy set of the flywheel angular velocity Ω;

[0031] Construct fuzzy rules for clutch control;

[0032] Calculate the inference results of the clutch control fuzzy rules triggered by the fuzzy sets of crank angular velocity ω and flywheel angular velocity Ω;

[0033] By using the weighted average defuzzification method, the inference results generated by the fuzzy rules of trigger clutch control are changed into clear values ​​between 0 and 1;

[0034] Compare the clarity value with the threshold value, if the clarity value is greater than or equal to the threshold value, the clarity value is converted to 1, and the output signal is the electric clutch start signal to activate the electric clutch;

[0035] If the clarity value is less than the threshold, the clarity value is converted to 0, and the output signal is an electric clutch stop signal to deactivate the electric clutch.

[0036] According to the above scheme, the fuzzy set C of the angular velocity ω of the crank is {VerySlow, Slow, Fast, VeryFast}; the fuzzy subset corresponding to the fuzzy set of the angular velocity ω is {0, 100, 200, 300, 400, 500, 600};

[0037] The fuzzy set D of the angular velocity Ω of the flywheel is {VerySlow, Slow, Fast, VeryFast}, and the fuzzy subset corresponding to the fuzzy set of the angular velocity Ω of the flywheel is {0, 100, 200, 300, 400, 500, 600}.

[0038] According to the above technical solution, the clutch control fuzzy rule is:

[0039]

[0040] In the table, CrankVel represents the fuzzy set of the crank angular velocity ω, FlyVel represents the fuzzy set of the flywheel angular velocity Ω, Off represents the electric clutch stop signal, and On represents the electric clutch start signal.

[0041] In another embodiment, a FES riding control system includes:

[0042] A signal acquisition module is used to determine the cycling rhythm required for the cycling exercise and obtain the actual cycling rhythm feedback signal through the sensor on the bicycle;

[0043] a Mamdani inference module for comparing the actual cycling cadence feedback signal with the desired cycling cadence and providing the resulting error signal to the controller to change the stimulation pulse width accordingly;

[0044] a crank torque determination module, which determines the crank torque according to the stimulation pulse width and the crank angle;

[0045] The clutch control module controls the electric clutch using a Sugeno-type fuzzy reasoning method based on the real-time acquired crank angular velocity and flywheel angular velocity, so that the crank angular velocity reaches the speed required by the crank torque, thereby performing rehabilitation cycling exercise.

[0046] In another embodiment, an electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the above method.

[0047] In another embodiment, a computer program product includes a computer program / instruction, which implements the above method when executed by a processor.

[0048] The present invention discloses an FES riding control method, system, electronic device, and computer program product, which have the following beneficial effects:

[0049] 1. This FES cycling control method combines a flywheel, an electromagnetic clutch, and a bicycle crank to create a new closed-loop control method for the quadriceps muscles of spinal cord injury patients. Compared to traditional electrical stimulation treatments, this method effectively reduces electrical stimulation intensity, improves energy utilization, and achieves energy savings while ensuring rehabilitation needs. At the same time, it also achieves the desired rehabilitation effect.

[0050] 2. The FES riding control method uses fuzzy control to handle uncertainty and noise in input data, enabling the system to maintain stable performance despite various complex situations. By combining a flywheel and electromagnetic clutch, the system can automatically absorb and release energy to cope with energy fluctuations during riding, improving the system's robustness and reliability.

[0051] 3. The FES riding control method and fuzzy control method enable the system to dynamically adjust the electrical stimulation parameters and the state of the flywheel / clutch according to actual conditions, thereby providing a smoother and more comfortable riding experience, and improving the patient's participation and rehabilitation enthusiasm by reducing unnecessary muscle fatigue and discomfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 This is a schematic diagram of a FES riding control method;

[0054] Figure 2 is the membership graph of error e;

[0055] Figure 3 It is the membership diagram of the error variation △e;

[0056] Figure 4 is the membership diagram of the pulse width △L of the electrical stimulation signal;

[0057] Figure 5 This is a schematic diagram of the stimulation intensity of rehabilitation cycling exercise using traditional methods;

[0058] Figure 6 This is a schematic diagram of the stimulation intensity of rehabilitation cycling exercise performed using an FES cycling control method of the present invention. DETAILED DESCRIPTION

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0060] Considering the person and bicycle as a whole, the pedal mechanism is positioned at 0 and 180 degrees, with the hip joint and crank at the same level. At these points, it's difficult for the leg to generate significant torque to rotate the crank, representing the transition point between extension and flexion torque. However, healthy individuals can overcome these points by utilizing a complex interplay of muscle actions, which are difficult to generate with FES because they involve deep muscles that are difficult to stimulate with surface electrodes.

[0061] To solve this problem, each pedaling cycle is divided into three phases based on crank angle:

[0062] Pushing phase: During this phase, the quadriceps muscles are stimulated to provide knee extension and increase cycling speed. During this phase, the parallel legs are in a resting stage.

[0063] Resistance phase: During this phase, the quadriceps muscles are stimulated to extend the leg and resist the movement if needed. This phase occurs when the legs are in the resting phase with the legs parallel.

[0064] Rest phase: During this phase, the quadriceps muscles are at rest and are not stimulated.

[0065] Therefore, cadence control is important for monitoring the training effect at a specific speed during FES cycling.

[0066] Example 1, the present invention discloses a FES riding control method, that is, a rhythm control method in FES riding. Figure 1 As shown, the following steps are included:

[0067] S1, determining the required riding rhythm for the cycling exercise and obtaining an actual riding rhythm feedback signal from a sensor on the bicycle;

[0068] S2. Comparing the actual cycling rhythm feedback signal with the desired cycling rhythm, and providing the generated error signal to the controller to adopt a Mamdani-type fuzzy inference method to change the stimulation pulse width accordingly, the specific execution steps comprising: using the angle error e between the actual knee joint rhythm curve and the predefined knee joint rhythm curve and the change in the angle error △e between the actual knee joint rhythm curve and the predefined knee joint rhythm curve as input variables; and using the pulse width △L of the electrical stimulation signal as the output variable;

[0069] Fuzzify the input variables e and △e to obtain the fuzzy set of error e and the fuzzy set of error variation △e;

[0070] Combining the system characteristics and control requirements, set the fuzzy control rules of Mamdani type fuzzy reasoning method;

[0071] The fuzzy set of error e and the fuzzy set of error variation △e are calculated together with the inference results of fuzzy control rules, and the fuzzy relationship between input variables and output variables is determined to obtain the fuzzy output variable pulse width △L.

[0072] Among them, the fuzzy set of error e is A = {NB1, NS1, Z1, PS1, PB1}, where NB1 represents negative large, NS1 represents negative small, Z1 represents zero, PS1 represents positive small, and PB1 represents positive large; the fuzzy subset domain corresponding to the fuzzy set of error e is {-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1};

[0073] The fuzzy set of error variation △e is B = {NB2, NS2, Z2, PS2, PB2}, where NB2 represents negative large, NS2 represents negative small, Z2 represents zero, PS2 represents positive small, and PB2 represents positive large. The fuzzy subset domain corresponding to the fuzzy set of error variation △e is {-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1}. The membership diagram of error variation △e is shown in the figure. Figure 3 .

[0074] The fuzzy control rules of the Mamdani type fuzzy inference method are:

[0075]

[0076] In the table, NB L NS L 、Z L 、PS L and PB L Together they constitute the fuzzy set of the output variable pulse width △L, NB L Indicates negative large, NS LIndicates negative small, Z L Indicates zero, PS L Indicates positive small, PB L The fuzzy subset domain corresponding to the fuzzy union of positive pulse width △L is {-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1}.

[0077] S3, determining the crank torque based on the stimulation pulse width and the crank angle;

[0078] S4. Based on the real-time acquired crank angular velocity and flywheel angular velocity, the electric clutch is controlled using a Sugeno-type fuzzy inference method so that the crank angular velocity reaches the speed required for crank torque, and rehabilitation cycling exercise is performed. The specific execution steps include:

[0079] The input signal is the angular velocity of the crank ω and the angular velocity of the flywheel Ω, and the output signal is the electric clutch control signal; the electric clutch control signal includes the electric clutch start signal and the electric clutch stop signal;

[0080] Fuzzify the input signals of the crank angular velocity ω and the flywheel angular velocity Ω to determine the fuzzy set of the crank angular velocity ω and the fuzzy set of the flywheel angular velocity Ω;

[0081] Construct fuzzy rules for clutch control;

[0082] Calculate the inference results of the clutch control fuzzy rules triggered by the fuzzy sets of crank angular velocity ω and flywheel angular velocity Ω;

[0083] By using the weighted average defuzzification method, the inference results generated by the fuzzy rules of trigger clutch control are changed into clear values ​​between 0 and 1;

[0084] Compare the clarity value with the threshold value, if the clarity value is greater than or equal to the threshold value, the clarity value is converted to 1, and the output signal is the electric clutch start signal to activate the electric clutch;

[0085] If the clarity value is less than the threshold, the clarity value is converted to 0, and the output signal is an electric clutch stop signal to deactivate the electric clutch.

[0086] Among them, the fuzzy set C of the crank angular velocity ω is {VerySlow, Slow, Fast, VeryFast}; the fuzzy subset corresponding to the fuzzy set of angular velocity ω is {0, 100, 200, 300, 400, 500, 600};

[0087] The fuzzy set D of the angular velocity Ω of the flywheel is {VerySlow, Slow, Fast, VeryFast}, and the fuzzy subset corresponding to the fuzzy set of the angular velocity Ω of the flywheel is {0, 100, 200, 300, 400, 500, 600}.

[0088] Fuzzy rules for clutch control:

[0089]

[0090] In the table, CrankVel represents the fuzzy set of the crank angular velocity ω, FlyVel represents the fuzzy set of the flywheel angular velocity Ω, Off represents the electric clutch stop signal, and On represents the electric clutch start signal.

[0091] The specific text language description is as follows:

[0092] 1. If the crank angular velocity is too slow and the flywheel angular velocity is too slow, the clutch closes;

[0093] 2. If the crank angular velocity is too slow and the flywheel angular velocity is slow, the clutch opens;

[0094] 3. If the crank angular velocity is too slow and the flywheel angular velocity is fast, the clutch opens;

[0095] 4. If the crank angular velocity is too slow and the flywheel angular velocity is too fast, the clutch opens;

[0096] 5. If the crank angular velocity is slow and the flywheel angular velocity is slow, the clutch is closed;

[0097] 6. If the crank angular velocity is slow and the flywheel angular velocity is fast, the clutch is closed;

[0098] 7. If the crank angular velocity is slow and the flywheel angular velocity is too fast, the clutch opens;

[0099] 8. If the crank angular velocity is slow and the flywheel angular velocity is too slow, the clutch opens;

[0100] 9. If the crank angular velocity is fast and the flywheel angular velocity is too slow, the clutch opens;

[0101] 10. If the crank angular velocity is fast and the flywheel angular velocity is slow, the clutch is open;

[0102] 11. If the crank angular velocity is high and the flywheel angular velocity is high, the clutch is closed;

[0103] 12. If the crank angular velocity is high and the flywheel angular velocity is too high, the clutch closes;

[0104] 13. If the crank angular velocity is too fast and the flywheel angular velocity is too slow, the clutch opens;

[0105] 14. If the crank angular velocity is too fast and the flywheel angular velocity is slow, the clutch opens;

[0106] 15. If the crank angular velocity is too fast and the flywheel angular velocity is fast, the clutch opens;

[0107] 16. If the crank angular velocity is too fast and the flywheel angular velocity is too high, the clutch closes;

[0108] Example 2

[0109] Rehabilitation cycling exercises were performed using the traditional method and the method proposed by the present invention. Figure 5 It is the traditional method. Figure 6 This is the method proposed by the present invention. It can be seen that the proposed control method can achieve energy savings of 20%-25%. The fuzzy control method can handle uncertainty and noise in the input data, allowing the system to maintain stable performance in various complex situations. By combining a flywheel and an electromagnetic clutch, the system can automatically absorb and release energy to cope with energy fluctuations during riding, improving the system's robustness and reliability.

[0110] Example 3

[0111] An FES riding control system, comprising:

[0112] A signal acquisition module is used to determine the cycling rhythm required for the cycling exercise and obtain the actual cycling rhythm feedback signal through the sensor on the bicycle;

[0113] a Mamdani inference module for comparing the actual cycling cadence feedback signal with the desired cycling cadence and providing the resulting error signal to the controller to change the stimulation pulse width accordingly;

[0114] a crank torque determination module, which determines the crank torque according to the stimulation pulse width and the crank angle;

[0115] The clutch control module controls the electric clutch using a Sugeno-type fuzzy reasoning method based on the real-time acquired crank angular velocity and flywheel angular velocity, so that the crank angular velocity reaches the speed required by the crank torque, thereby performing rehabilitation cycling exercise.

[0116] This enables the system to dynamically adjust the electrical stimulation parameters and the status of the flywheel / clutch according to actual conditions, thereby providing a smoother and more comfortable riding experience. By reducing unnecessary muscle fatigue and discomfort, it improves patient participation and rehabilitation enthusiasm.

[0117] Example 4

[0118] An electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the above method.

[0119] Example 5

[0120] A computer program product comprises a computer program / instruction, wherein the computer program / instruction implements the above method when executed by a processor.

[0121] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0122] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A FES riding control method, characterized in that: The steps include: Determine the cycling cadence required for the complex cycling exercise and obtain actual cycling cadence feedback signals through sensors on the bicycle; The actual riding cadence feedback signal is compared with the desired riding cadence, and the resulting error signal is provided to a controller to change the stimulation pulse width accordingly; the controller uses a Mamdani-type fuzzy inference method to change the pulse width; the controller specifically performs the following steps: The angle error e between the actual rhythm curve of the knee joint and the predefined rhythm curve of the knee joint and the change △e of the angle error between the actual rhythm curve of the knee joint and the predefined rhythm curve of the knee joint are used as input variables; the pulse width △L of the electrical stimulation signal is used as the output variable; Fuzzify the input variables e and △e to obtain the fuzzy set of error e and the fuzzy set of error variation △e; Combining the system characteristics and control requirements, set the fuzzy control rules of Mamdani type fuzzy reasoning method; Based on the fuzzy set of the error e and the fuzzy set of the error variation Δe, according to the fuzzy control rule, a fuzzy relationship between input variables and output variables is determined to obtain a fuzzy output variable pulse width ΔL; The crank torque is determined based on the stimulation pulse width and the crank angle; The electric clutch is controlled using a Sugeno-type fuzzy inference method based on the real-time acquired crank angular velocity and flywheel angular velocity, so that the crank angular velocity reaches the speed required by the crank torque.

2. A FES riding control method according to claim 1, characterized in that: The fuzzy set of the error e is A={NB1,NS1,Z1,PS1,PB1}, where NB1 represents negative large, NS1 represents negative small, Z1 represents zero, PS1 represents positive small, and PB1 represents positive large; the fuzzy subset domain corresponding to the fuzzy set of the error e is {-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1}; The fuzzy set of the error change △e is B = {NB2, NS2, Z2, PS2, PB2}, where NB2 represents large negative, NS2 represents small negative, Z2 represents zero, PS2 represents small positive, and PB2 represents large positive. The fuzzy subset domain corresponding to the fuzzy set of the error change △e is {-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1}.

3. A FES riding control method according to claim 1, characterized in that: The fuzzy control rules of the Mamdani type fuzzy inference method are: In the table, NB L NS L , Z L 、PS L and PB L Together they constitute the fuzzy set of the output variable pulse width △L, NB L Indicates negative large, NS L Indicates negative small, Z L Indicates zero, PS L Indicates positive small, PB L It means positive, and the fuzzy subset domain corresponding to the fuzzy union of the pulse width △L is {-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1}.

4. The FES riding control method according to claim 1, characterized in that: The Sugeno-type fuzzy reasoning method execution steps include: The input signal is the angular velocity ω of the crank and the angular velocity Ω of the flywheel, and the output signal is the electric clutch control signal; the electric clutch control signal includes an electric clutch start signal and an electric clutch stop signal; Fuzzify the input signals of the crank angular velocity ω and the flywheel angular velocity Ω to determine the fuzzy set of the crank angular velocity ω and the fuzzy set of the flywheel angular velocity Ω; Construct fuzzy rules for clutch control; Calculate the inference results of the clutch control fuzzy rules triggered by the fuzzy sets of crank angular velocity ω and flywheel angular velocity Ω; By using the weighted average defuzzification method, the inference results generated by the fuzzy rules of trigger clutch control are changed into clear values ​​between 0 and 1; Compare the clarity value with the threshold value, if the clarity value is greater than or equal to the threshold value, the clarity value is converted to 1, and the output signal is the electric clutch start signal to activate the electric clutch; If the clarity value is less than the threshold, the clarity value is converted to 0, and the output signal is an electric clutch stop signal to deactivate the electric clutch.

5. The FES riding control method according to claim 4, characterized in that: The fuzzy set C of the crank angular velocity ω is {VerySlow, Slow, Fast, VeryFast}; the fuzzy subset corresponding to the fuzzy set of the angular velocity ω is {0, 100, 200, 300, 400, 500, 600}; The fuzzy set D of the angular velocity Ω of the flywheel is {VerySlow, Slow, Fast, VeryFast}, and the fuzzy subset corresponding to the fuzzy set of the angular velocity Ω of the flywheel is {0, 100, 200, 300, 400, 500, 600}.

6. The FES riding control method according to claim 4, characterized in that: The clutch control fuzzy rule: In the table, CrankVel represents the fuzzy set of the crank angular velocity ω, FlyVel represents the fuzzy set of the flywheel angular velocity Ω, Off represents the electric clutch stop signal, and On represents the electric clutch start signal.

7. A FES riding control system, characterized in that: include: A signal acquisition module is used to determine the cycling rhythm required for the cycling exercise and obtain the actual cycling rhythm feedback signal through the sensor on the bicycle; a Mamdani inference module for comparing the actual cycling cadence feedback signal with the desired cycling cadence and providing the resulting error signal to a controller to change the stimulation pulse width accordingly; the controller using a Mamdani-type fuzzy inference method to change the pulse width; The controller specifically performs the following steps: The angle error e between the actual rhythm curve of the knee joint and the predefined rhythm curve of the knee joint and the change △e of the angle error between the actual rhythm curve of the knee joint and the predefined rhythm curve of the knee joint are used as input variables; the pulse width △L of the electrical stimulation signal is used as the output variable; Fuzzify the input variables e and △e to obtain the fuzzy set of error e and the fuzzy set of error variation △e; Combining the system characteristics and control requirements, set the fuzzy control rules of Mamdani type fuzzy reasoning method; Based on the fuzzy set of the error e and the fuzzy set of the error variation Δe, according to the fuzzy control rule, a fuzzy relationship between input variables and output variables is determined to obtain a fuzzy output variable pulse width ΔL; a crank torque determination module, which determines the crank torque according to the stimulation pulse width and the crank angle; The clutch control module controls the electric clutch using a Sugeno-type fuzzy reasoning method based on the real-time acquired crank angular velocity and flywheel angular velocity, so that the crank angular velocity reaches the speed required by the crank torque, thereby performing rehabilitation cycling exercise.

8. An electronic device, characterized in that: include: at least one processor; And, a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of claims 1 to 6.

9. A computer program product, characterized in that The method comprises a computer program / instruction, which implements the method according to any one of claims 1 to 6 when executed by a processor.

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