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

Abstract

The invention discloses an FES riding control method and system, electronic equipment and a computer program product, and relates to the technical field of rehabilitation training methods. According to the FES riding control method, a flywheel, an electromagnetic clutch and a crank of a bicycle are combined, the FES riding control method is provided for quadriceps femoris muscles of a patient with spinal cord injury, and the problems that in fixed riding rehabilitation exercise based on the FES technology, the torque limited by electrical stimulation driving muscles is limited, the muscles serving as nonlinear elements are difficult to control accurately, and the rehabilitation effect is poor are solved. Compared with a traditional electrical stimulation treatment method, the electrical stimulation treatment method has the advantages that the electrical stimulation intensity is effectively reduced, the energy utilization rate is increased, and the energy utilization rate is increased on the premise that the rehabilitation requirement is met. And the energy-saving effect is achieved.

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 damage to the structure and function of the corresponding stage of the spine by the outside world. It will cause varying degrees of lower limb movement disorders in patients, and even cause complete paralysis of the patient's lower limbs. If not treated in time, the muscles of the lower limbs will gradually atrophy after paralysis. This may have adverse effects on the patient's urinary system, breathing, and heart, and may even cause problems such as cramps and pain. The "Research Report on the Quality of Life and Disease Burden of People with Spinal Cord Injury in China 2023 Edition" pointed out that the number of patients with spinal cord injury in China has increased sharply. Therefore, the rehabilitation of patients with spinal cord injury is an urgent problem that needs to be solved at present.

[0003] Rehabilitation medicine is a treatment method based on exercise. It is widely used in the recovery treatment of hemiplegic stroke patients and has a very significant effect on the patient's limb function training. However, hemiplegic patients lose their limb movement ability and have symptoms of muscle atrophy, which increases the difficulty of rehabilitation medicine. The introduction of the functional electrical stimulation (FES) bicycle system has helped the development of rehabilitation medicine and has a positive effect on the recovery of patients' limb movement function and the improvement of their ability to take care of themselves.

[0004] The hardware part of the FES bicycle rehabilitation training system mainly includes core components such as the main control computer, bicycle, functional electrical stimulator, sensors and auxiliary motors. The key to achieving the rehabilitation goal of the FES bicycle rehabilitation training system lies in the implementation of rehabilitation training methods, and the implementation of rehabilitation training methods depends largely on the control strategy of the rehabilitation training system. The design of the control strategy fully considers the patient's condition and closely combines medical rehabilitation theory to ensure that the patient will not suffer additional injuries during the training process. Due to the complexity of the human muscle system, the FES bicycle rehabilitation training system is usually regarded as a nonlinear and time-varying system. Therefore, how to closely combine medical rehabilitation theory, design a correct control strategy, and improve the stability and anti-interference of the system has attracted much attention from researchers at home and abroad, and a series of related control strategies such as neural networks, fuzzy control and deep learning have been proposed.

[0005] However, the current control strategy faces 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 introduce 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 crank of a bicycle, and provides an FES riding control method for the quadriceps of patients with spinal cord injury. The method solves the technical difficulties that in fixed cycling rehabilitation exercises based on FES technology, the torque of muscles driven by electrical stimulation is limited, muscles as nonlinear elements are difficult to accurately control, and long-term continuous electrical stimulation easily causes muscle fatigue. 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 the energy utilization rate to achieve energy saving while ensuring rehabilitation needs.

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

[0008] A FES riding control method, the steps of which include:

[0009] Determine the cycling cadence required for the complex cycling exercise and obtain the actual cycling cadence feedback signal through the sensor 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 torque of the crank is determined based on the stimulation pulse width and the angle of the crank;

[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 torque of the crank, and rehabilitation cycling is performed.

[0013] According to the above technical solution, the controller adopts Mamdani-type fuzzy reasoning 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 amount △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 an 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 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] Among them, 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 = {NB 1 ,NS 1 ,Z 1 ,PS 1 ,PB 1}, where NB 1 Indicates negative large, NS 1 Indicates negative small, Z 1 Indicates zero, PS 1 Indicates positive small, PB 1 Indicates positive; 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 variation △e is B={NB 2 ,NS 2 ,Z 2 ,PS 2 ,PB 2}, where NB 2 Indicates negative large, NS 2 Indicates negative small, Z 2 Indicates zero, PS 2 Indicates positive small, PB 2 It means positive, and 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 reasoning 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 function of the electric clutch is to complete the meshing and disengagement of the flywheel and the crank according to 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 the angular velocity of the crank, the clutch engages the flywheel with the crank to slow down the movement;

[0026] On the other hand, when the angular velocity of the crank is lower than the required speed, assistance is required, and if the angular velocity of the flywheel is higher than the angular velocity of the crank, it engages to assist and accelerate the motion. The flywheel engages with the crank, absorbs excess energy in the system, stores it as kinetic energy, and slows down the motion; in addition, the flywheel, after being loaded with kinetic energy, engages with the crank, releases its energy into the system, and accelerates the motion.

[0027] Among them, the fuzzy set of the angle α of the bicycle crank is {PR, PL}, where PR means that the crank angle indicates that the rider is currently actively pedaling the right crank, and PL means that the crank angle indicates that the rider is currently actively pedaling the left crank. The crank torque can be determined by determining the angle α of the bicycle crank 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] Fuzzifying the input signals of the crank angular velocity ω and the flywheel angular velocity Ω, and determining 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 reasoning results of the fuzzy set of the crank's angular velocity ω and the fuzzy set of the flywheel's angular velocity Ω to trigger the clutch control fuzzy rule;

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

[0034] The clarity value is compared with a threshold value, and 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 an 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] Another embodiment, a FES riding control system, comprising:

[0042] A signal acquisition module, used to determine the cycling rhythm required for the complex cycling movement 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 the Sugeno-type fuzzy reasoning method based on the real-time acquired angular velocity of the crank and the angular velocity of the flywheel, so that the angular velocity of the crank reaches the speed required by the torque of the crank, and the rehabilitation riding exercise is performed.

[0046] In another embodiment, 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.

[0047] Another embodiment provides a computer program product, comprising a computer program / instruction, wherein the computer program / instruction implements the above method when executed by a processor.

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

[0049] 1. This FES riding control method combines the flywheel, electromagnetic clutch and bicycle crank, and proposes a new closed-loop control method for the quadriceps of patients with spinal cord injury. Compared with the traditional electrical stimulation treatment method, it effectively reduces the intensity of electrical stimulation, improves the utilization rate of energy, and achieves energy saving while ensuring the rehabilitation needs. At the same time, it also achieves the desired rehabilitation effect.

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

[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 the actual situation, 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 drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 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 3It 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 the traditional method;

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

[0059] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Considering the person and the bicycle as a whole, the pedal mechanism is at 0 and 180 degrees, when the hip joint is at the same level as the crank. At this point, it is difficult for the leg to generate a large torque to rotate the crank, which is the transition point between extension torque and flexion torque. However, healthy people can overcome these points by using complex muscle action interactions, which are difficult to generate through FES because they involve deep muscles that are difficult to stimulate through 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 are stimulated to provide knee extension and increase riding speed. During this phase, the parallel legs are in a resting phase.

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

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

[0065] Therefore, cadence control is important in FES cycling to monitor the training effect at a specific speed.

[0066] Embodiment 1, the embodiment of 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 riding rhythm required for the cycling exercise and obtaining an actual riding rhythm feedback signal through a sensor on the bicycle;

[0068] S2, comparing the actual riding rhythm feedback signal with the required riding rhythm, and providing the generated error signal to the controller to adopt Mamdani type fuzzy reasoning method to change the stimulation pulse width accordingly, and the specific execution steps include: taking 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 amount △e of the angle error between the actual rhythm curve of the knee joint and the predefined rhythm curve of the knee joint as input variables; taking 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 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={NB 1 ,NS 1 ,Z 1 ,PS 1 ,PB 1}, where NB 1 Indicates negative large, NS 1 Indicates negative small, Z 1 Indicates zero, PS 1 Indicates positive small, PB 1 indicates positive; 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 = {NB 2 ,NS 2 ,Z 2 ,PS 2 ,PB 2}, where NB 2 Indicates negative large, NS 2 Indicates negative small, Z 2 Indicates zero, PS 2 Indicates positive small, PB 2The fuzzy set corresponding to the error change △e is positive, and the fuzzy subset domain is {-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1}. The membership diagram of the error change △e is as follows 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 L Indicates negative small, Z L Indicates zero, PS L Indicates positive small, PB L The fuzzy union of positive and large pulse width △L corresponds to the fuzzy subset domain of {-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1}.

[0077] S3, determining the torque of the crank according to the stimulation pulse width and the angle of the crank;

[0078] S4, 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 speed required by the torque of the crank, and the rehabilitation cycling exercise is performed, and the specific execution steps include:

[0079] 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;

[0080] Fuzzifying the input signals of the crank angular velocity ω and the flywheel angular velocity Ω, and determining 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 reasoning results of the fuzzy set of the crank's angular velocity ω and the fuzzy set of the flywheel's angular velocity Ω to trigger the clutch control fuzzy rule;

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

[0084] The clarity value is compared with a threshold value, and 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 an 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 of the angular velocity ω of the crank is C = {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};

[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 angle speed is too slow and the flywheel angle speed is too slow, the clutch is closed;

[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 angle speed is slow and the flywheel angle speed is slow, the clutch is closed;

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

[0103] 12. If the crank angular velocity is fast and the flywheel angular velocity is too fast, 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 is open;

[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 angle speed is too fast and the flywheel angle speed is too fast, the clutch closes;

[0108] Example 2

[0109] Rehabilitation cycling exercise is performed using the traditional method and the method proposed by the present invention respectively. Figure 5 It is the traditional method. Figure 6 It is the method proposed by the present invention. It can be seen that the control method proposed by the present invention can save energy by 20%-25%. The fuzzy control method can handle the uncertainty and noise in the input data, so that the system can still maintain stable performance in the face of various complex situations. By combining the flywheel and the electromagnetic clutch, the system can automatically absorb and release energy to cope with energy fluctuations during riding, thereby improving the robustness and reliability of the system.

[0110] Example 3

[0111] A FES riding control system, comprising:

[0112] A signal acquisition module, used to determine the cycling rhythm required for the complex cycling movement 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 the Sugeno-type fuzzy reasoning method based on the real-time acquired angular velocity of the crank and the angular velocity of the flywheel, so that the angular velocity of the crank reaches the speed required by the torque of the crank, and the rehabilitation riding exercise is performed.

[0116] This enables 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 patient engagement and rehabilitation enthusiasm by reducing unnecessary muscle fatigue and discomfort.

[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 so that the at least one processor can execute 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 article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0122] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached 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 the actual cycling cadence feedback signal through the sensor on the bicycle; 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; The torque of the crank is determined based on the stimulation pulse width and the angle of the crank; 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 speed required by the torque of the crank, and rehabilitation cycling is performed.

2. A FES riding control method according to claim 1, characterized in that: 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 amount △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 an 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 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, the fuzzy relationship between the input variable and the output variable is determined to obtain the fuzzy output variable pulse width ΔL.

3. A FES riding control method according to claim 2, 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}.

4. A FES riding control method according to claim 2, characterized in that: The fuzzy control rules of the Mamdani type fuzzy reasoning 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}.

5. A 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; Fuzzifying the input signals of the crank angular velocity ω and the flywheel angular velocity Ω, and determining 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 reasoning results of the fuzzy set of the crank's angular velocity ω and the fuzzy set of the flywheel's angular velocity Ω to trigger the clutch control fuzzy rule; By using the weighted average defuzzification method, the inference results generated by the fuzzy rules of the trigger clutch control are changed into clear values ​​between 0 and 1; The clarity value is compared with a threshold value, and 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 an 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.

6. A FES riding control method according to claim 5, characterized in that: 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}; 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}.

7. A FES riding control method according to claim 5, 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.

8. A FES riding control system, characterized in that: include: A signal acquisition module, used to determine the cycling rhythm required for the complex cycling movement 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 the controller to change the stimulation pulse width accordingly; 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 the Sugeno-type fuzzy reasoning method based on the real-time acquired angular velocity of the crank and the angular velocity of the flywheel, so that the angular velocity of the crank reaches the speed required by the torque of the crank, and the rehabilitation riding exercise is performed.

9. 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 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 execute the method described in any one of claims 1-7.

10. 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 7 when executed by a processor.

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