A lower limb diagnosis and training system

Abstract

The present invention relates to a lower limb diagnosis and training system, belonging to the field of medical diagnosis and rehabilitation training. The system comprises: a detection unit, which is used to collect data information related to the patient's lower limbs; and a control unit, which is used to analyze and process the data information to calculate the diagnosis result. The control unit can calculate the nerve function index based on the nerve electrical signal parameters, the joint mobility index based on the joint mobility, and the muscle strength index based on the muscle strength evaluation value, and then calculate the initial lower limb function comprehensive score, and use the initial lower limb function comprehensive score as the initial diagnosis result to output a control signal of the numerical range of the combination of pulling forces in multiple directions, so that the electromyographic signal parameters and blood flow parameters obtained by the detection unit in real time during the training process of the patient can be used by the control unit to correct the initial lower limb function comprehensive score, so as to obtain a dynamic lower limb function comprehensive score for adjusting the specific numerical values ​​of each pulling force distribution.

Description

Technical Field

[0001] The present invention relates to the field of medical diagnosis and rehabilitation training, and in particular to a lower limb diagnosis and training system. Background Art

[0002] Lower limb disorders are complications that may occur in the lower limbs of patients with long-term paralysis or patients who are suddenly bedridden for a long time due to an emergency. They include foot drop, venous thrombosis, etc. Lower limb venous thrombosis, commonly known as deep vein thrombosis, is a disease in which blood clots form in the deep veins, usually in the legs. This condition may lead to serious health problems, including pulmonary embolism (when blood clots move to the lungs). Foot drop refers to obvious dorsiflexion weakness of the ankle and foot. This disease is mostly caused by factors such as nerve and muscle lesions, resulting in paralysis of the dorsiflexor muscles of the foot and toes innervated by the deep peroneal nerve. The dorsiflexor muscles include the tibialis anterior, extensor hallucis longus, and extensor digitorum longus. If the common peroneal nerve is completely damaged, the peroneus longus and brevis muscles will appear, and local tissue paralysis will occur, leading to foot drop, foot inversion and other deformities.

[0003] Currently, for long-term bedridden patients due to lower limb paralysis or other reasons, if no intervention is taken, insufficient blood perfusion in their limbs will lead to neuromuscular atrophy (an important cause of foot drop) and lower limb venous thrombosis. Therefore, for bedridden patients with abnormalities of the tibial muscle or common peroneal nerve or patients with lower limb disorders, lower limb muscle group training and nerve innervation rehabilitation training are particularly important for preventing or alleviating lower limb complications.

[0004] In the prior art, there are many prior arts for adjusting the movement trajectory or rehabilitation training of patients with foot drop based on electromyographic signal feedback. CN213554904U discloses a foot drop walking aid with electromyographic feedback function and a mobile terminal, which includes a host and a mobile terminal, wherein the host includes a sensor module, an electromyographic detection module, an electrical stimulation module, a central processing unit, an electrode assembly, a wireless transmission module and a power management module, and wireless communication is performed between the host and the mobile terminal. The power management module includes a sensor module, an electromyographic detection module, an electrical stimulation module, and a central processing unit. The electromyographic detection module arranged on the leg is used to provide active electrical stimulation training for the patient when the patient is walking. CN110151177A discloses a foot drop detection device based on surface electromyographic signals. The device can detect whether a patient suffers from foot drop according to surface electromyographic signals. Specifically, the device brings the characteristic parameters of the collected patient's foot movement muscle signals into the identified foot drop model to identify whether the patient has a foot drop problem.

[0005] However, in the prior art, traditional rehabilitation systems usually only have a few preset training programs, and rely heavily on medical staff (especially physical therapists or rehabilitation therapists) to make judgments and select corresponding training programs based on their experience. When medical staff do not have enough experience, it is difficult for patients to receive professional guidance and help in a timely manner. They cannot accurately understand the actual condition of their lower limbs, nor can they complete targeted rehabilitation training, which may lead to more serious complications. In addition, incorrect or mismatched training programs will not only affect the training effect of patients, but may also cause serious consequences such as limb injuries to patients. Therefore, how to accurately diagnose the status of patients' lower limbs and match reasonable training programs based on the diagnosis results is a technical difficulty that urgently needs to be solved in this field.

[0006] In addition, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making the present invention, but due to space limitations, not all details and contents are listed in detail. However, this does not mean that the present invention does not have the characteristics of these prior arts. On the contrary, the present invention already has all the characteristics of the prior art, and the applicant reserves the right to add relevant prior art to the background technology. Summary of the invention

[0007] In view of the deficiencies of the prior art, the present invention provides a lower limb diagnosis and training system to solve at least part of the above-mentioned technical problems.

[0008] The invention discloses a lower limb diagnosis and training system, which comprises: a detection unit, which is used to collect data information related to the patient's lower limbs; and a control unit, which is used to analyze and process the data information acquired by the detection unit to calculate a diagnosis result. The control unit can calculate a neural function index for comprehensively reflecting neural function based on the neural electrical signal parameters of some nerve groups related to the patient's lower limbs obtained by the detection unit, calculate a joint mobility index for comprehensively reflecting joint mobility based on the joint range of motion of some joints, and calculate a muscle strength index for comprehensively reflecting muscle strength based on the muscle strength evaluation values ​​of some muscles, and then calculate an initial lower limb function comprehensive score for characterizing the initial state of the patient's lower limbs according to the generated multiple indices, and use the initial lower limb function comprehensive score as the initial diagnosis result to output a control signal of a numerical range of a combination of pulling forces in multiple directions applied to the patient's lower limbs to drive it to complete the corresponding mechanical passive training training movements, so that the electromyographic signal parameters of some muscles and the blood flow parameters of some deep veins obtained in real time by the detection unit during the patient's training based on these pulling forces can be used by the control unit to correct the initial lower limb function comprehensive score, so as to obtain a dynamic lower limb function comprehensive score for adjusting the specific numerical values ​​of the distribution of each pulling force.

[0009] The present invention discloses a lower limb diagnosis and training system, which comprehensively evaluates the functional status of the patient's lower limbs through multi-dimensional data collection and analysis. The system includes a detection unit and a control unit. The detection unit collects data such as neural electrical signals, joint range of motion and muscle strength. The control unit calculates the neural function index (NFI), joint range of motion index (ROMI) and muscle strength index (MSI) to generate an initial lower limb function comprehensive score. During the training process, the detection unit collects electromyographic signals and blood flow parameters in real time. The control unit corrects the initial lower limb function comprehensive score based on these data, generates a dynamic lower limb function comprehensive score, and dynamically adjusts training parameters, such as the distribution of pulling force, to ensure the safety and effectiveness of the training. Through data-driven rehabilitation management, the system provides personalized training plans, improves the scientific nature of diagnosis and treatment and the rehabilitation effect, and significantly improves the rehabilitation quality and satisfaction of patients.

[0010] According to a preferred embodiment, the detection part includes a first detection unit for obtaining electromyographic signal parameters, and the first detection unit can be arranged on the muscles innervated by the deep peroneal nerve, including the tibialis anterior muscle, the extensor hallucis longus muscle and the extensor digitorum longus muscle, wherein the control unit can perform preliminary processing on the electromyographic signal parameters of multiple muscles obtained by the first detection unit, so as to obtain the integral value, the root mean square value and the frequency center of the electromyographic signal of each muscle respectively, and then calculate the muscle activity index.

[0011] The first detection unit configured by the detection unit of the present invention can be arranged on the muscles innervated by the deep peroneal nerve, such as the tibialis anterior muscle, the extensor hallucis longus muscle and the extensor digitorum longus muscle, and the electromyographic signals of these muscles are collected in real time through the surface electromyography (sEMG) sensor. The control unit performs preliminary processing on these electromyographic signals, calculates the integral value (IEMG), the root mean square value (RMS) and the frequency center (MF) of the electromyographic signals, and thus obtains the muscle activity index. This precise muscle activity assessment not only helps to detect muscle dysfunction at an early stage, but also provides personalized guidance for rehabilitation training. For example, for patients with weak muscle strength, the system can recommend more training movements to enhance muscle strength; for patients with severe muscle fatigue, the system can recommend appropriate rest and recovery time. This refined assessment and guidance not only improves the effect of rehabilitation training, but also reduces the risk of secondary injury caused by improper training of patients, and improves the overall quality and safety of rehabilitation. In addition, by real-time monitoring of muscle activity, the system can also adjust the training intensity in time to ensure the safety and effectiveness of training.

[0012] According to a preferred embodiment, the detection part includes a second detection unit for obtaining neural electrical signal parameters, and the second detection unit can be arranged at the common peroneal nerve, wherein the control unit can perform preliminary processing on the neural electrical signal parameters of the common peroneal nerve obtained by the second detection unit, so as to respectively obtain the nerve conduction velocity and electrical signal amplitude of the common peroneal nerve, and then calculate the neural function index.

[0013] The second detection unit configured in the detection part of the present invention can be arranged at the common peroneal nerve, and the nerve electrical signals of the common peroneal nerve are collected in real time through a nerve conduction velocity meter (NCS). The control unit performs preliminary processing on these nerve electrical signals, calculates the nerve conduction velocity (NCV) and the electrical signal amplitude (A), and thus obtains the nerve function index. This precise nerve function assessment not only helps to detect nervous system dysfunction at an early stage, but also provides personalized guidance for rehabilitation training. For example, for patients with slower nerve conduction velocity, the system can recommend more nerve regeneration training movements; for patients with lower electrical signal amplitude, the system can recommend increasing the training intensity of nerve stimulation. This refined assessment and guidance not only improves the effect of rehabilitation training, but also reduces the risk of secondary injury to patients due to improper training, and improves the overall quality and safety of rehabilitation. In addition, by real-time monitoring of nerve function, the system can also adjust the training plan in a timely manner to ensure the safety and effectiveness of the training, thereby achieving a more efficient rehabilitation effect.

[0014] According to a preferred embodiment, the detection part includes a third detection unit for obtaining blood flow parameters of the femoral vein and the popliteal vein, wherein the control unit is capable of performing preliminary processing on the blood flow parameters of multiple deep veins obtained by the third detection unit to obtain the blood flow velocity of each deep vein, and then calculate the blood flow index.

[0015] The third detection unit configured in the detection part of the present invention can collect the blood flow parameters of the femoral vein and popliteal vein in real time through a Doppler blood flow monitor. The control unit performs preliminary processing on these blood flow parameters, calculates the blood flow velocity of each deep vein, and thus obtains the blood flow index. This precise blood circulation assessment not only helps to detect blood circulation disorders at an early stage, but also provides personalized guidance for rehabilitation training. For example, for patients with slower blood flow velocity, the system can recommend more training movements to promote blood circulation; for patients at risk of thrombosis, the system can recommend regular blood flow monitoring and appropriate preventive measures. This refined assessment and guidance not only improves the effect of rehabilitation training, but also reduces the risk of complications caused by blood circulation disorders in patients, and improves the overall quality and safety of rehabilitation. In addition, by real-time monitoring of blood circulation conditions, the system can also adjust the training plan in a timely manner to ensure the safety and effectiveness of the training, thereby achieving a more efficient rehabilitation effect.

[0016] According to a preferred embodiment, the detection part includes a fourth detection unit for obtaining the angles of the hip joint, the knee joint and the ankle joint, wherein the control unit is capable of performing preliminary processing on the angles of the multiple joints obtained by the fourth detection unit to obtain the joint mobility of each joint, and then calculate the joint mobility index.

[0017] The fourth detection unit configured in the detection part of the present invention includes a joint measurement tool (such as an angle meter) so that medical staff can manually use the joint measurement tool to collect the angle parameters of the hip joint, knee joint and ankle joint. The control unit performs preliminary processing on these angle parameters and calculates the joint mobility of each joint to obtain the joint mobility index. This precise joint activity assessment not only helps to detect joint dysfunction at an early stage, but also provides personalized guidance for rehabilitation training. For example, for patients with less joint mobility, the system can recommend more joint activity training movements; for patients with stiff joints, the system can recommend increasing the training intensity of joint flexibility. This refined evaluation and guidance not only improves the effect of rehabilitation training, but also reduces the risk of limited activity caused by joint dysfunction in patients, and improves the overall quality and safety of rehabilitation. In addition, by real-time monitoring of joint mobility, the system can also adjust the training plan in time to ensure the safety and effectiveness of the training, thereby achieving a more efficient rehabilitation effect.

[0018] According to a preferred embodiment, the detection part includes a fifth detection unit for obtaining muscle strength evaluation values ​​of the quadriceps femoris and the hamstring muscles, wherein the control unit is capable of performing preliminary processing and normalization on the muscle strength evaluation values ​​of multiple muscles obtained by the fifth detection unit to calculate a muscle strength index.

[0019] The fifth detection unit configured in the detection part of the present invention can collect the muscle strength evaluation values ​​of the quadriceps and hamstrings in real time through a handheld muscle tester. The control unit performs preliminary processing and normalization on these muscle strength evaluation values ​​to obtain a muscle strength index. This accurate muscle strength assessment not only helps to detect insufficient muscle strength at an early stage, but also provides personalized guidance for rehabilitation training. For example, for patients with weaker muscle strength, the system can recommend more training movements to enhance muscle strength; for patients with uneven muscle strength, the system can suggest increasing the intensity of muscle balance training. This refined evaluation and guidance not only improves the effect of rehabilitation training, but also reduces the risk of limited activity due to insufficient muscle strength in patients, and improves the overall quality and safety of rehabilitation. In addition, by real-time monitoring of muscle strength, the system can also adjust the training plan in time to ensure the safety and effectiveness of the training, thereby achieving a more efficient rehabilitation effect.

[0020] According to a preferred embodiment, the control unit can generate a control signal for adjusting the intelligent training device based on the calculated comprehensive score of lower limb function, and the intelligent training device is configured to drive the patient's lower limbs to complete corresponding mechanical passive training training movements based on the control signal output by the control unit, wherein the control unit can output the working parameters of the intelligent training device based on the range of the patient's comprehensive score of lower limb function, so as to drive the patient to perform training movements with corresponding motion range, motion speed and motion frequency.

[0021] The control unit of the present invention can generate a control signal for adjusting the intelligent training device based on the calculated diagnostic results, thereby driving the patient's lower limbs to complete the corresponding mechanical passive training program. This control signal generation mechanism based on the diagnostic results enables the system to dynamically adjust the training program according to the patient's lower limb function comprehensive score (LLFCS), thereby realizing personalized rehabilitation training. This dynamically adjusted training program not only improves the scientificity and pertinence of the training, but also reduces the risk of secondary injury to the patient due to improper training. By generating a mechanical passive training program corresponding to the patient's lower limb status, the system can achieve precise rehabilitation, which not only improves the effect of rehabilitation training, but also shortens the rehabilitation cycle and reduces medical costs. The dynamic adjustment mechanism based on LLFCS enables the system to continuously optimize the training program according to the patient's rehabilitation progress, thereby achieving continuous improvement in rehabilitation effects.

[0022] According to a preferred embodiment, the intelligent training device includes a foot cover that is sleeved on the forefoot area of ​​the patient's foot and a traction part that adjusts the patient's foot movement posture by controlling the movement direction of the foot cover, wherein the traction part includes a plurality of traction belts that are respectively used to be connected to fixings at different positions on the outer surface of the foot cover, and the lengths of the corresponding traction belts are respectively adjusted by mutual cooperation of corresponding control elements, thereby driving the patient's foot to complete training movements of stretching the front end of the foot to the right, hooking the front end of the foot, and stretching the front end of the foot to the left that are consistent with the range of the patient's lower limb function comprehensive score.

[0023] The intelligent training device of the present invention includes a foot cover that is sleeved on the forefoot area of ​​the patient's foot and a pulling part that adjusts the patient's foot movement posture by controlling the movement direction of the foot cover. The pulling part includes a plurality of traction belts that are respectively used to connect to fixings at different positions on the outer surface of the foot cover. The lengths of the corresponding traction belts are respectively adjusted by the mutual cooperation of corresponding control elements, thereby driving the patient's foot to complete training movements that are consistent with the range of its lower limb function comprehensive score (LLFCS). The design of multiple traction belts enables the system to achieve multi-directional foot movement control, thereby covering more training movements and improving the diversity and comprehensiveness of training. Through this layout design, the system can achieve precise foot movement control, ensure the accuracy and stability of training movements, and thus improve the rehabilitation training effect.

[0024] According to a preferred embodiment, the traction belt includes a second traction belt that can be connected to a second fixing piece arranged on the outer surface of the foot cover, the position of the second fixing piece corresponds to the front end of the patient's forefoot area, the first fixing piece and the third fixing piece are located on both sides of the second fixing piece and are respectively connected to the first traction belt and the third traction belt, so that when the intelligent training device drives the patient's foot to perform training movements, it can use one of the traction belts as the main one, and be assisted by the other two traction belts, and dynamically adjust the traction force corresponding to the range of the patient's lower limb function comprehensive score based on the training movements performed.

[0025] The layout design of the traction belt configured in the intelligent training device of the present invention enables the system to achieve multi-directional foot movement control, thereby covering more training movements and improving the diversity and comprehensiveness of training. Through this main and auxiliary combined control method, the system can achieve precise foot movement control and ensure the accuracy and stability of training movements. Based on the training movements performed, the traction force corresponding to the range of the patient's lower limb function comprehensive score (LLFCS) is dynamically allocated to these traction belts, and the system can implement a highly personalized training plan. This dynamic allocation mechanism not only improves the scientificity and pertinence of the training, but also reduces the risk of secondary injuries to patients due to improper training, and improves the overall quality and safety of rehabilitation.

[0026] According to a preferred embodiment, the other end of each traction belt connected to the fixing piece can be connected to the corresponding control element, wherein the second control element connected to the second traction belt can be arranged in the hollow cavity of the base surrounding the patient's lower limbs, and the first control element and the third control element respectively connected to the first traction belt and the third traction belt can be arranged in the hollow cavity of the swinging piece located on both sides of the heel cover, and the heel cover can be put on the patient's heel and connected to the foot cover through an elastic elastic member.

[0027] The intelligent training device of the present invention not only improves the portability and wearability of the system through such a compact and flexible design, but also ensures the stability of the foot during training. The use of elastic parts enables the foot cover to be quickly reset after the training action is completed, thereby achieving continuous training action. In addition, the use of heel covers also provides better support for patients and reduces the discomfort that may occur during training. Through this compact and flexible design, the system can achieve efficient and comfortable rehabilitation training, which not only improves the effect and safety of training, but also provides more innovation space for future rehabilitation training, such as combining Internet of Things technology and virtual reality technology to achieve remote monitoring and immersive training experience, further enhancing the patient's rehabilitation experience and training effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1A hardware connection diagram of the lower limb diagnosis and training system provided by the present invention;

[0029] Figure 2 A schematic diagram of the layout of the first detection unit and the second detection unit provided by the present invention;

[0030] Figure 3 A schematic diagram of the sampling position of the third detection unit provided by the present invention;

[0031] Figure 4 A schematic diagram of the sampling position of the fifth detection unit provided by the present invention;

[0032] Figure 5 A schematic diagram of the structure of the intelligent training device provided by the present invention at one viewing angle;

[0033] Figure 6 A schematic diagram of the structure of the intelligent training device provided by the present invention from another perspective;

[0034] Figure 7 A schematic diagram of wearing the intelligent training device provided by the present invention;

[0035] Figure 8 A diagram showing the use status of the intelligent training device provided by the present invention when performing the front end hook back of the foot;

[0036] Fig. 9 A diagram showing the use state of the intelligent training device provided by the present invention when the front end of the foot is stretched to the left;

[0037] Fig.10 A diagram showing the use state of the intelligent training device provided by the present invention when the front end of the foot is stretched to the right;

[0038] Fig.11 This is a schematic diagram of the specific structure of the control element provided by the present invention.

[0039] Reference numerals list

[0040] 100: intelligent training device; 110: first traction belt; 120: second traction belt; 130: third traction belt; 140: adjusting member; 150: swinging member; 160: base; 1601: pulley; 170: heel cover; 180: foot cover; 190: elastic member; 200: control unit; 300: detection unit; 310: first detection unit; 320: second detection unit; 330: third detection unit; 340: fourth detection unit; 350: fifth detection unit. DETAILED DESCRIPTION

[0041] The following is a detailed description with reference to the accompanying drawings.

[0042] Preferably, if Figure 1As shown, the lower limb diagnosis and training system of the present invention may include: a detection unit 300 for collecting data information related to the patient's lower limbs, and a control unit 200 for analyzing and processing the data information obtained by the detection unit 300.

[0043] Preferably, the control unit 200 can be configured as a remote terminal, such as an intelligent device having data processing and data storage capabilities, and can be communicatively connected to a hospital management system.

[0044] Preferably, the detection unit 300 may include a first detection unit 310 for detecting myoelectric signal parameters of some muscles of the patient's lower limbs and a second detection unit 320 for detecting neural electrical signal parameters of some nerve groups of the patient's lower limbs.

[0045] Preferably, if Figure 2 As shown, the first detection unit 310 can detect the electromyographic signal parameters of some muscles of the patient's lower limbs through a surface electromyography (sEMG) sensor to evaluate the electrical activity of the lower limb muscles, especially the muscles innervated by the deep peroneal nerve (such as the tibialis anterior muscle, the extensor hallucis longus muscle, and the extensor digitorum longus muscle), wherein the surface electromyography (sEMG) sensor can be arranged in the tibialis anterior muscle (front side of the calf), the extensor hallucis longus muscle (front and outside of the calf) and / or the extensor digitorum longus muscle (front part of the calf). The method of collecting electromyographic signal parameters using a surface electromyography (sEMG) sensor can refer to the following paper: Lienhard, K., Cabasson, A., Meste, O., & Colson, S. (2015). sEMG during Whole-Body Vibration Contains Motion Artifacts and Reflex Activity.. Journal of sports science & medicine, 14 1, 54-61.

[0046] Preferably, if Figure 2 As shown, the second detection unit 320 can detect the nerve electrical signal parameters of some nerve groups of the patient's lower limbs through a nerve conduction velocity meter (NCS) to evaluate the function of the common peroneal nerve, including nerve conduction velocity and nerve signal integrity, wherein the nerve conduction velocity meter (NCS) can be arranged on the common peroneal nerve (outside of the calf, at the fibular head). The method of collecting nerve electrical signal parameters using a nerve conduction velocity meter (NCS) can refer to the following paper: Kane, N., & Oware, A. (2012). Nerve conduction and electromyography studies. Journal of Neurology, 259, 1502-1508.

[0047] Preferably, if Figure 2 As shown, the first detection unit 310 and the second detection unit 320 can be arranged at corresponding positions of the patient's lower limbs in the form of patches. Before attaching the patches, the skin needs to be prepared (such as cleaning and exfoliation) to reduce resistance.

[0048] Preferably, if Figure 3 As shown, the detection unit 300 may also include a third detection unit 330 for obtaining blood flow parameters of the patient's lower limbs, so as to evaluate the blood perfusion of the lower limbs and help determine the risk of venous thrombosis, wherein the third detection unit 330 can collect blood parameters of the patient's main deep veins (such as the femoral vein and popliteal vein) in the thigh and calf by using a Doppler blood flow monitor.

[0049] Preferably, the detection unit 300 may also include a fourth detection unit 340 for obtaining the joint range of motion of the patient's lower limbs, so as to evaluate the range of motion of each joint of the lower limbs to determine the degree of functional limitation, wherein the fourth detection unit 340 may be configured with a joint measurement tool (such as an goniometer) so that medical staff can use the joint measurement tool to measure the patient's hip joint, knee joint and ankle joint, and send the measured data to the control unit 200 via the terminal for recording.

[0050] Preferably, if Figure 4 As shown, the detection unit 300 may also include a fifth detection unit 350 for obtaining a muscle strength evaluation value of the patient's lower limbs, so as to evaluate the strength of the main muscle groups of the patient's lower limbs, especially dorsiflexion and plantar flexion, wherein the fifth detection unit 350 can measure the main muscle groups of the patient's hip joint, knee joint and ankle joint (such as quadriceps, hamstrings, etc.) through a handheld muscle tester.

[0051] Preferably, the detection unit 300 can be communicatively connected with the control unit 200, so that the corresponding data information acquired by each detection unit can be sent to the control unit 200, and the control unit 200 performs data processing.

[0052] Preferably, the control unit 200 can perform preliminary processing on the data information obtained by each detection unit.

[0053] Preferably, the control unit 200 performs the following preliminary processing on the electromyographic signal parameters of the multiple muscles (i.e., tibialis anterior, extensor hallucis longus, and extensor digitorum longus) acquired by the first detection unit 310:

[0054] Calculate the integral value (IEMG):

[0055]

[0056]

[0057] ,

[0058] Among them, IEMG 胫骨前肌 is the integral value of the electromyographic signal of the tibialis anterior muscle, IEMG 踇长伸肌 IEMG is the integral value of the myoelectric signal of the extensor hallucis longus muscle. 趾长伸肌 is the integral value of the electromyographic signal of the extensor digitorum longus, EMG 胫骨前肌 (t) is the electromyographic signal of the tibialis anterior muscle at time t, EMG 踇长伸肌 (t) is the electromyographic signal of the extensor hallucis longus at time t, EMG 趾长伸肌 (t) is the electromyographic signal of the extensor digitorum longus at time t. The above electromyographic signal can be obtained by a surface electromyography sensor arranged at the corresponding muscle position.

[0059] Calculate the root mean square value (RMS):

[0060]

[0061]

[0062] ,

[0063] Among them, RMS 胫骨前肌 is the root mean square value of the electromyographic signal of the tibialis anterior muscle, RMS 踇长伸肌 is the root mean square value of the myoelectric signal of the extensor hallucis longus, RMS 趾长伸肌 is the RMS value of the EMG signal of the extensor digitorum longus, and T is the length of the time window.

[0064] Calculate the frequency center (MF):

[0065]

[0066]

[0067] ,

[0068] Among them, MF 胫骨前肌 MF is the frequency center of the electromyographic signal of the tibialis anterior muscle. 踇长伸肌 MF is the frequency center of the EMG signal of the extensor hallucis longus muscle, 趾长伸肌 is the frequency center of the EMG signal of the extensor digitorum longus, f is the frequency, F is the highest frequency, P 胫骨前肌 (f) is the power spectral density of the tibialis anterior muscle at frequency f, P 踇长伸肌 (f) is the power spectral density of the extensor hallucis longus at frequency f, P 趾长伸肌 (f) is the power spectral density of the extensor digitorum longus at frequency f.

[0069] Preferably, the control unit 200 performs the following preliminary processing on the neural electrical signal parameters of the partial nerve group (i.e., the common peroneal nerve) acquired by the second detection unit 320:

[0070] Calculation of nerve conduction velocity (NCV):

[0071] ,

[0072] Among them, NCV 腓总神经 is the nerve conduction velocity of the common peroneal nerve, distance is the distance between the stimulation point and the recording point, and latency is the time delay from the stimulation point to the recording point.

[0073] Calculate the amplitude:

[0074] ,

[0075] Among them, A 腓总神经 NEURON is the electrical signal amplitude of the common peroneal nerve. 腓总神经 (t) is the electrical signal of the common peroneal nerve at time t.

[0076] Preferably, the control unit 200 performs the following preliminary processing on the blood flow parameters of the plurality of main deep veins (i.e., the femoral vein and the popliteal vein) acquired by the third detection unit 330:

[0077] Recording blood flow velocity:

[0078]

[0079] ,

[0080] Among them, BFV 股静脉 is the blood flow velocity in the femoral vein, BFV 腘静脉 is the blood flow velocity in the popliteal vein.

[0081] Preferably, the control unit 200 performs the following preliminary processing on the measurement angles of the multiple joints (i.e., the hip joint, the knee joint, and the ankle joint) acquired by the fourth detection unit 340:

[0082] Calculate joint range of motion:

[0083]

[0084]

[0085] ,

[0086] Among them, ROM 髋关节 The range of motion of the hip joint, ROM 膝关节 The range of motion of the knee joint, ROM 踝关节is the range of motion of the ankle joint, θ 髋关节 is the angle of the hip joint, θ 膝关节 is the angle of the knee joint, θ 踝关节 is the angle of the ankle joint.

[0087] Preferably, the control unit 200 performs the following preliminary processing on the muscle strength evaluation values ​​of the multiple muscles (i.e., the quadriceps femoris and the hamstring muscles) obtained by the fifth detection unit 350:

[0088] Record muscle strength assessment values:

[0089]

[0090] ,

[0091] Among them, Strength 股四头肌 Strength is the muscle strength assessment value of the quadriceps femoris. 腘绳肌 This is an assessment of the muscle strength of the hamstrings.

[0092] Preferably, the control unit 200 may normalize the data obtained by the previous processing. If any data obtained by the previous processing is X, then the data after normalization is X′. In other words, the normalized data is represented in the above manner in the present invention.

[0093] Preferably, the control unit 200 can calculate an initial lower limb function comprehensive score for evaluating the initial state of the patient's lower limbs based on part of the data obtained after normalization processing, wherein the initial lower limb function comprehensive score can be calculated through at least part of the static parameters before starting training to obtain a preliminary diagnosis result of the patient's lower limbs.

[0094] Preferably, the initial lower limb function composite score (LLFCS 初始 ) is calculated by the following formula:

[0095] ,

[0096] Among them, NFI is the neurological function index, which is used to comprehensively reflect neurological function; ROMI is the joint range of motion index, which is used to comprehensively reflect joint range of motion; MSI is the muscle strength index, which is used to comprehensively reflect muscle strength.

[0097] Furthermore, the neurological function index (NFI) can be calculated by the following formula:

[0098] .

[0099] Furthermore, the range of motion index (ROMI) can be calculated using the following formula:

[0100] .

[0101] Furthermore, the muscle strength index (MSI) can be calculated by the following formula:

[0102] .

[0103] Preferably, the static parameters may include a nerve function index, a joint range of motion index, and a muscle strength index, wherein the nerve conduction velocity and amplitude of the common peroneal nerve involved in the nerve function index are generally considered to be relatively stable parameters, especially in a short period of time (such as during a training session). Therefore, before starting training, the detection unit 300 may only be equipped with detection units related to static parameters.

[0104] Preferably, based on the calculated initial lower limb function comprehensive score, a preliminary diagnosis of the patient's lower limbs can be achieved, so that an adapted training program (range) can be determined according to the preliminary diagnosis result, so that the patient can start training. Preferably, when the patient is ready to start training, the detection unit 300 can be equipped with a detection unit related to the dynamic parameters, so that the control unit 200 can calculate a dynamic lower limb function comprehensive score for correcting the initial lower limb function comprehensive score according to the patient's dynamic parameters during training, so that the training program can be accurately adjusted based on the dynamic lower limb function comprehensive score.

[0105] Preferably, the initial lower limb function composite score (LLFCS 动态 ) is calculated by the following formula:

[0106] ,

[0107] Among them, MAI is the muscle activity index, which is used to comprehensively reflect the intensity of muscle activity; BFI is the blood flow index, which is used to comprehensively reflect the blood flow situation.

[0108] Furthermore, the muscle activity index (MAI) can be calculated by the following formula:

[0109] .

[0110] Furthermore, the blood flow index (BFI) can be calculated by the following formula:

[0111] .

[0112] Preferably, the weight coefficients in the above formulas can be determined based on clinical experience and literature. Specifically, the weight coefficients in each formula can be determined by one or more of the following methods: conducting a literature review and meta-analysis, systematically searching for published research literature, extracting weight coefficients related to muscle activity, nerve function, blood flow, joint mobility and muscle strength, and using statistical software for meta-analysis to obtain a comprehensive weight coefficient; organizing expert consensus, forming a group of experts in the fields of rehabilitation medicine, biomechanics, sports science, etc., and reaching a consensus on the importance of each parameter through multiple rounds of Delphi surveys and discussions; conducting experimental verification, designing a series of experiments, recruiting a certain number of patients, using standard detection methods and instruments to record their various parameter data, determining the weight coefficients of each parameter through statistical analysis, and verifying the prediction effect and accuracy of the model; using machine learning and data-driven methods to collect a large number of patients' various parameter data, using feature selection and model training algorithms, and extracting the weight coefficients of each parameter from the trained model. Exemplarily, based on the above method, these weight coefficients can be set to: α1=0.4; α2=0.3; α3=0.3; β1=0.6; β2=0.4; γ1=0.5; γ2=0.5; δ1=0.4; δ2=0.4; δ3=0.4; ε1=0.5; ε2=0.5; λ1=0.21; λ2=0.39; λ3=0.18; λ4=0.33; λ5=0.28; λ6=0.61.

[0113] The comprehensive lower limb function score proposed in this invention integrates and evaluates multiple key parameters in stages, including static parameters in the pre-training stage (such as neural electrical signals, joint mobility and muscle strength) and dynamic parameters during training (such as electromyographic signals and blood flow parameters), thus achieving a comprehensive and dynamic evaluation of the patient's lower limb function. This two-stage evaluation method not only overcomes the limitations of single parameter evaluation and improves the accuracy and reliability of diagnosis, but also provides a more precise scientific basis for the formulation of personalized evaluation and rehabilitation programs. 初始 Based on the integration of static parameters, it helps medical staff to develop preliminary rehabilitation training plans and set reasonable range of motion, speed and frequency. 动态 By updating and revising LLFCS in real time 初始, reflecting the progress and immediate status of rehabilitation, and guiding the dynamic adjustment of training programs. This dynamic feedback mechanism enables medical staff to discover and solve potential problems more promptly, optimize training parameters according to the patient's real-time status, and thus improve the rehabilitation effect. In addition, LLFCS provides a standardized evaluation framework that is suitable for large-scale clinical research and data analysis, and promotes the development of rehabilitation medicine. Combined with artificial intelligence and big data technology, LLFCS not only serves as the core evaluation indicator of the intelligent rehabilitation system, realizes automated rehabilitation training and management, improves rehabilitation efficiency, and reduces the workload of medical staff, but also enhances the patient's rehabilitation experience. From a social and economic perspective, this efficient rehabilitation assessment and training method can shorten the rehabilitation cycle, reduce the waste of medical resources, reduce medical expenses, and significantly improve the quality of life of patients.

[0114] Preferably, the control unit 200 uses the calculated LLFCS as a diagnosis result, and then outputs a control signal for applying a pulling force or a combination of pulling forces in multiple directions to the patient's lower limbs to drive the patient to complete the corresponding mechanical passive training training action, wherein the control unit 200 can firstly calculate the pulling force according to the LLFCS. 初始 Initially determine the training goals, range of motion, range of motion speed, frequency of motion, and range of tension, and then 动态 Real-time update and revision of LLFCS 初始 , so as to determine a specific value or adjustment range within the above range. Preferably, the control signal output by the control unit 200 can be sent to any training device that can apply a pulling force or a combination of pulling forces in multiple directions to the patient's lower limbs, thereby driving the patient's lower limbs to complete the corresponding mechanical passive training training movements. Preferably, the lower limb diagnosis and training system of the present invention includes an intelligent training device 100 that is communicatively connected to the control unit 200, wherein the intelligent training device 100 can drive the patient's lower limbs to complete the corresponding mechanical passive training training movements in response to the control signal output by the control unit 200.

[0115] like Figure 5~Figure 7 As shown, the lower limb diagnosis and training system may include an intelligent training device 100 fixed to the patient's leg, and the intelligent training device 100 includes a base 160 that surrounds the patient's lower limb so that the intelligent training device 100 can be fixed relative to the patient, a foot cover 180 that is sleeved on the forefoot area of ​​the foot, and a pulling part that adjusts the foot movement posture by controlling the movement direction of the foot cover 180.

[0116] Preferably, the pulling part comprises a first pulling belt 110, a second pulling belt 120 and a third pulling belt 130, one end of the first pulling belt 110, the second pulling belt 120 and the third pulling belt 130 are respectively connected to the corresponding control elements, and the other ends thereof are respectively connected to the fixing members arranged on the outer surface of the foot cover 180. Preferably, the fixing members on the outer surface of the foot cover 180 correspond to the outer side of the patient's forefoot plantar region, the inner side of the patient's forefoot plantar region, and the front end of the patient's forefoot plantar region, respectively. Preferably, the second pulling belt 120 can be connected to the second fixing member arranged on the outer surface of the foot cover 180, and the second fixing member corresponds to the front end of the patient's forefoot plantar region, wherein the first fixing member and the third fixing member are located on both sides of the second fixing member, and the pulling belt located on the right side of the second pulling belt 120 is the first pulling belt 110 when viewed from a top view, and the pulling belt located on the left side of the second pulling belt 120 is the third pulling belt 130.

[0117] Preferably, the first traction belt 110, the second traction belt 120 and the third traction belt 130 can be respectively controlled by the first control element, the second control element and the third control element to complete the extension and retraction, wherein, based on the instruction sent by the control terminal, the first control element, the second control element and the third control element can cooperate with each other to adjust the length of the corresponding first traction belt 110, the second traction belt 120 and the third traction belt 130 respectively so that the foot can complete the training actions including but not limited to stretching the front end of the foot to the right, hooking the front end of the foot and stretching the front end of the foot to the left, such as Figure 8~Figure 10As shown. Preferably, the main function of hooking the front end of the foot back is to activate the tibialis anterior muscle and enhance the dorsiflexion function of the foot. This training action is mainly completed by the second traction belt 120 and assisted by the first traction belt 110 and the third traction belt 130, wherein the pulling force of the second traction belt 120 is mainly responsible for hooking the front end of the foot upward, and the pulling forces of the first traction belt 110 and the third traction belt 130 assist the second traction belt 120 to enhance the hooking effect. Preferably, the main function of stretching the front end of the foot to the left is to activate the extensor hallucis longus and enhance the leftward stretching function of the foot. This training action is mainly completed by the third traction belt 130 and assisted by the first traction belt 110 and the second traction belt 120, wherein the pulling force of the third traction belt 130 is mainly responsible for stretching the front end of the foot to the left, and the pulling force of the first traction belt 110 assists the third traction belt 130 to enhance the stretching effect, and the pulling force of the second traction belt 120 assists in stabilizing the foot to prevent excessive inversion or eversion. Preferably, the main function of stretching the front end of the foot to the right is to activate the extensor digitorum longus and enhance the rightward stretching function of the foot. This training action is mainly completed by the first traction belt 110 and assisted by the second traction belt 120 and the third traction belt 130. Among them, the pulling force of the first traction belt 110 is mainly responsible for stretching the front end of the foot to the right, the pulling force of the second traction belt 120 assists in stabilizing the foot to prevent excessive inversion or eversion, and the pulling force of the third traction belt 130 assists the third traction belt 130 to enhance the stretching effect. Preferably, in the case of configuring the above three traction ropes, the traction part can also be provided with other traction ropes to expand the types of rehabilitation training.

[0118] Preferably, the traction part further comprises a heel sleeve 170 and an adjusting member 140 for stabilizing the heel of the patient. The heel sleeve 170 is connected to the adjusting member 140, and the other end of the adjusting member 140 is connected to the base 160 through a telescopic fourth control element, wherein the distance between the heel sleeve 170 and the base 160 can be adjusted by changing the length of the adjusting member 140, so as to adapt to the foot morphology of different patients. Preferably, the heel sleeve 170 can be connected to the foot sleeve 180 through an elastic elastic member 190, wherein when the foot sleeve 180 is displaced based on the pulling action of the traction belt, the elastic member 190 can be elastically deformed accordingly, and can be restored to the initial state based on the elastic action when the pulling action of the traction belt is removed, so that the foot sleeve 180 is reset. Preferably, the distance between the heel sleeve 170 and the foot sleeve 180 can be adjusted by adjusting the length and / or setting position of the elastic member 190, so as to adapt to the feet of different patients.

[0119] Preferably, air bags may be provided on the inner side walls of the foot cover 180 and the heel cover 170, and the patient's foot may be better wrapped by adjusting the amount of air in the air bags to adapt to the foot morphology of different patients. Furthermore, the inner side of the foot cover 180 and the heel cover 170 may also be manually filled with flexible material to wrap the patient's foot to save costs.

[0120] Preferably, based on the coordination of the length changes of the first traction belt 110, the second traction belt 120 and the third traction belt 130, the medical staff can remotely adjust the patient's exercise mode through the medical terminal. For example, for patients with lower limb paralysis who have poor ligament and muscle ability, the medical staff can reduce the amplitude of the patient's foot hooking back and ankle rotation and increase the exercise frequency by adjusting the amplitude and frequency of the length changes of the first traction belt 110, the second traction belt 120 and the third traction belt 130, so that the patient can get effective rehabilitation training when wearing the rehabilitation training device, and can avoid the damage to the calf and foot caused by improper rehabilitation training.

[0121] Preferably, the first control element, the second control element and the third control element are all provided with a pulley 1601 and a motor for controlling the rotation of the pulley 1601. Fig.11 As shown, the motor can control the length of the corresponding traction belt by controlling the direction and number of rotations of the pulley 1601.

[0122] Preferably, if Figure 7 As shown, the base 160 is configured as a leg ring with a hollow chamber to accommodate the second control element. The leg ring is connected by Velcro or other connection methods to make the two ends of the leg ring close and fixed, so that the base 160 can be stabilized at the corresponding position of the leg.

[0123] Preferably, swinging members 150 having hollow chambers for accommodating the first control element and the third control element respectively may be provided on both sides of the heel cover 170, so that the first traction belt 110 and the third traction belt 130 can extend toward the swinging members 150 located on both sides of the heel cover 170 respectively, and their lengths can be adjusted under the driving action of the motor built into the swinging members 150.

[0124] Preferably, the control unit 200 can be configured according to the patient's initial lower limb function comprehensive score (LLFCS) 初始) is in a numerical range, and its training target and corresponding motion range, motion speed range, motion frequency and other motion parameters are determined, wherein the motion parameters can be used to reflect the expected value of the training action of the patient's lower limbs (mainly the foot) to complete the mechanical passive training under the drive of the intelligent training device 100. Further, the control unit 200 can generate the working parameters of the intelligent training device 100 according to the determined motion parameters, wherein the working parameters generated by the control unit 200 may include the range of pulling forces applied to the patient's lower limbs (mainly the foot) by the three traction belts (through the foot cover 180). Further, the pulling force applied by the first traction belt 110 is F1, the pulling force applied by the second traction belt 120 is F2, and the pulling force applied by the third traction belt 130 is F3, wherein F1, F2, and F3 are all less than or equal to F, and F is the maximum pulling force, which is related to the motor set by the corresponding control element. Preferably, the control unit 200 can first select a set of standard values ​​in the above training program range to start the training, and can calculate the dynamic lower limb function comprehensive score (LLFCS) according to the calculated dynamic lower limb function comprehensive score (LLFCS) during the training process. 动态 ) Determine the specific value of the training program and adjust it dynamically. Preferably, when determining the pulling force applied by the three pulling belts of the intelligent training device 100, the control unit 200 can determine the distribution of the pulling force based on the value range of the lower limb function comprehensive score, the training action to be completed, and the role of the corresponding pulling belt in the training action to be completed, and adjust it in real time.

[0125] Preferably, the control unit 200 can obtain the working parameters of the intelligent training device 100 for different types of patients by looking up a table. Furthermore, for a patient who is bedridden for a long time, the control unit 200 can generate or adjust the working parameters of the intelligent training device 100 according to the preliminary diagnosis results according to the rules shown in the following table:

[0126]

[0127] For example, for a patient who has been bedridden for a long time, based on LLFCS 初始 Determine a set of standard values ​​or based on LLFCS 动态 When adjusting the working parameters of the intelligent training device 100, the control unit 200 may obtain the following table, wherein the LLFCS in the table may refer to LLFCS 初始 or LLFCS 动态 :

[0128]

[0129] It should be noted that the above-mentioned specific embodiments are exemplary, and those skilled in the art can come up with various solutions inspired by the disclosure of the present invention, and these solutions also belong to the disclosure scope of the present invention and fall within the protection scope of the present invention. Those skilled in the art should understand that the present invention specification and its drawings are illustrative and do not constitute a limitation of the claims. The scope of protection of the present invention is defined by the claims and their equivalents. The present invention specification contains multiple inventive concepts, such as "preferably", "according to a preferred embodiment" or "specifically", all of which indicate that the corresponding paragraph discloses an independent concept, and the applicant reserves the right to file a divisional application based on each inventive concept. Throughout the text, the features guided by "preferably" are only an optional method and should not be understood as being required. Therefore, the applicant reserves the right to abandon or delete the relevant preferred features at any time.

Claims

1. A lower limb diagnosis and training system, comprising: A detection unit (300), used to collect data information related to the patient's lower limbs; The control unit (200) is used to analyze and process the data information acquired by the detection unit (300) to calculate and obtain a diagnosis result, and is characterized in that: The control unit (200) can calculate a nerve function index for comprehensively reflecting nerve function based on the nerve electrical signal parameters of some nerve groups related to the patient's lower limbs obtained by the detection unit (300), calculate a joint mobility index for comprehensively reflecting joint mobility based on the joint mobility of some joints, and calculate a muscle strength index for comprehensively reflecting muscle strength based on the muscle strength evaluation values ​​of some muscles, and then calculate an initial lower limb function comprehensive score for characterizing the initial state of the patient's lower limbs based on the generated multiple indexes, and use the initial lower limb function comprehensive score as the initial diagnosis result to output a control signal of a numerical range of a combination of pulling forces in multiple directions applied to the patient's lower limbs to drive the patient to complete the corresponding mechanical passive training training movements, so that the electromyographic signal parameters of some muscles and the blood flow parameters of some deep veins obtained in real time by the detection unit (300) during the patient's training based on these pulling forces can be used by the control unit (200) to correct the initial lower limb function comprehensive score to obtain a dynamic lower limb function comprehensive score for adjusting the specific numerical values ​​of the distribution of each pulling force.

2. The lower limb diagnosis and training system according to claim 1, characterized in that: The detection unit (300) includes a first detection unit (310) for obtaining electromyographic signal parameters, and the first detection unit (310) can be arranged on muscles innervated by the deep peroneal nerve, including the tibialis anterior muscle, the extensor hallucis longus muscle and the extensor digitorum longus muscle, wherein the control unit (200) can perform preliminary processing on the electromyographic signal parameters of multiple muscles obtained by the first detection unit (310) to obtain the integral value, root mean square value and frequency center of the electromyographic signal of each muscle, respectively, and then calculate the muscle activity index.

3. The lower limb diagnosis and training system according to claim 1, characterized in that: The detection section (300) includes a second detection unit (320) for obtaining neural electrical signal parameters, and the second detection unit (320) can be arranged at the common peroneal nerve, wherein the control unit (200) can perform preliminary processing on the neural electrical signal parameters of the common peroneal nerve obtained by the second detection unit (320) to respectively obtain the nerve conduction velocity and electrical signal amplitude of the common peroneal nerve, and then calculate the neural function index.

4. The lower limb diagnosis and training system according to claim 1, characterized in that: The detection section (300) includes a third detection unit (330) for obtaining blood flow parameters of the femoral vein and the popliteal vein, wherein the control unit (200) is capable of pre-processing the blood flow parameters of multiple deep veins obtained by the third detection unit (330) to obtain the blood flow velocity of each deep vein, and then calculate the blood flow index.

5. The lower limb diagnosis and training system according to claim 1, characterized in that: The detection section (300) comprises a fourth detection unit (340) for acquiring angles of a hip joint, a knee joint and an ankle joint, wherein the control unit (200) is capable of performing preliminary processing on the angles of a plurality of joints acquired by the fourth detection unit (340) to obtain the joint range of motion of each joint, and further calculate a joint range of motion index.

6. The lower limb diagnosis and training system according to claim 1, characterized in that: The detection unit (300) comprises a fifth detection unit (350) for obtaining muscle strength evaluation values ​​of the quadriceps femoris and the hamstring muscles, wherein the control unit (200) is capable of performing preliminary processing and normalization processing on the muscle strength evaluation values ​​of the multiple muscles obtained by the fifth detection unit (350) to calculate a muscle strength index.

7. The lower limb diagnosis and training system according to claim 1, characterized in that: The control unit (200) is capable of generating a control signal for adjusting the intelligent training device (100) based on the calculated lower limb function comprehensive score, and the intelligent training device (100) is configured to drive the patient's lower limbs to complete corresponding mechanical passive training training movements based on the control signal output by the control unit (200), wherein the control unit (200) is capable of outputting the working parameters of the intelligent training device (100) based on the range of the patient's lower limb function comprehensive score, so as to drive the patient to perform training movements with corresponding movement range, movement speed and movement frequency.

8. The lower limb diagnosis and training system according to claim 7, characterized in that: The intelligent training device (100) comprises a foot cover (180) sleeved on the forefoot area of ​​the patient's foot and a pulling part for adjusting the patient's foot movement posture by controlling the movement direction of the foot cover (180), wherein the pulling part comprises a plurality of pulling belts respectively used to be connected to fixing members at different positions on the outer surface of the foot cover (180), and the lengths of the corresponding pulling belts are respectively adjusted by the mutual cooperation of corresponding control elements, thereby driving the patient's foot to complete the training movements of stretching the front end of the foot to the right, hooking the front end of the foot, and stretching the front end of the foot to the left, which are in accordance with the range of the comprehensive score of the lower limb function.

9. The lower limb diagnosis and training system according to claim 8, characterized in that: The traction belt comprises a second traction belt (120) which can be connected to a second fixing member arranged on the outer surface of the foot cover (180); the second fixing member is located at a position corresponding to the front end of the patient's forefoot area; the first fixing member and the third fixing member are located on both sides of the second fixing member and are respectively connected to the first traction belt (110) and the third traction belt (130), so that when the intelligent training device (100) drives the patient's foot to perform training movements, one of the traction belts can be used as the main force, and the other two traction belts can be used to assist in the performance, and based on the training movements performed, the traction forces corresponding to the range of the patient's lower limb function comprehensive score are dynamically allocated to the traction belts.

10. The lower limb diagnosis and training system according to claim 9, characterized in that: The other end of each traction belt connected to the fixing member can be connected to a corresponding control element, wherein the second control element connected to the second traction belt (120) can be arranged in a hollow cavity of a base (160) surrounding the patient's lower limbs, and the first control element and the third control element respectively connected to the first traction belt (110) and the third traction belt (130) can be arranged in the hollow cavity of a swinging member (150) located on both sides of a heel cover (170). The heel cover (170) can be mounted on the heel of the patient and connected to the foot cover (180) via an elastic elastic member (190).

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