Surgical postoperative rehabilitation nursing auxiliary device and method based on Internet of Things

Abstract

The invention relates to the technical field of medical rehabilitation equipment, in particular to a surgical postoperative rehabilitation nursing auxiliary device and method based on the Internet of Things, and the device comprises a bearing part, a driving part, a base, a monitoring part and a remote interaction part, the driving part comprises a first driving assembly for providing relative resistance for a using object and a second driving assembly for driving the thigh and the shank to move relatively. The method comprises the steps that in a single training period, the passive muscle activation degree and the active muscle activation degree are determined to determine the rehabilitation degree of the muscle function of a using object; according to the limb movement track and the muscle strength balance condition of the feature movement part, the movement control ability of the using object is determined, and a rehabilitation training scheme of the using object is adjusted according to the rehabilitation degree or the movement control ability. According to the invention, comprehensive monitoring and remote transmission of the rehabilitation process of the use object are realized, and the rehabilitation process can be accurately controlled by medical personnel.

Description

Technical Field

[0001] The present invention relates to the technical field of medical rehabilitation equipment, and in particular to an Internet of Things-based surgical postoperative rehabilitation nursing auxiliary device and method. Background Art

[0002] Surgical joint surgeries such as knee replacement, shoulder repair, and elbow fracture internal fixation can affect the normal function of joints to varying degrees, such as limited joint movement, muscle atrophy at the surgical site, and uncoordinated joint movement. In order to help users recover their joint mobility, joint rehabilitation trainers are often used for postoperative rehabilitation of joint surgery to help users increase the range of joint movement, improve movement coordination, prevent joint adhesion, promote joint function recovery, and enhance muscle strength around joints.

[0003] Chinese Patent Publication No.: CN211750847U discloses a lower limb joint rehabilitation device, including a driving mechanism, a calf support rod, a thigh support rod, a first base and a second base, a slider is slidably connected to the driving mechanism, the calf support rod is hingedly connected to the thigh support rod, and the end of the calf support rod away from the thigh support rod is hingedly connected to a foot bracket; the driving mechanism is arranged between the first base and the second base, a first push rod and a second push rod are hingedly connected to the slider, the end of the first push rod away from the slider is hingedly connected to the calf support rod, a first connecting rod is hingedly connected between the first base and the calf support rod, the end of the second push rod away from the slider is hingedly connected to the first connecting rod, and a second connecting rod is hingedly connected between the first connecting rod and the thigh support rod.

[0004] It can be seen that the above-mentioned lower limb joint rehabilitation device mainly improves support stability through the double-link design, and can produce a large change in joint movement angle in a short movement distance, which is consistent with the physiological curve. However, there are still the following problems: lack of resistance device, inability to carry out progressive resistance training, and limited muscle strength improvement. Lack of intelligent monitoring and remote management cannot meet the medical staff's precise control of the rehabilitation process. Summary of the invention

[0005] To this end, the present invention provides a surgical postoperative rehabilitation nursing auxiliary device and method based on the Internet of Things, so as to overcome the problem that the postoperative rehabilitation nursing auxiliary device in the prior art lacks a resistance device, intelligent monitoring and remote management, and thus cannot satisfy the medical staff's precise control of the rehabilitation process.

[0006] To achieve the above objectives, on the one hand, the present invention provides a surgical postoperative rehabilitation nursing auxiliary device based on the Internet of Things, comprising:

[0007] The supporting part is used to support the lower limbs of the user, including a thigh support frame, a calf support frame and a foot support frame;

[0008] The driving part includes a first driving assembly and a second driving assembly, wherein the first driving assembly is fixedly connected to the sole support frame to provide resistance to the user relative to the movement direction of the user during active training, and the second driving assembly is respectively hinged to the thigh support frame and the calf support frame to drive the thigh support frame and the calf support frame to perform reverse relative flexion and extension movements during passive training;

[0009] A base, the top of which is provided with a slide rail slidably connected to the second drive assembly, for providing guidance and support for the movement of the second drive assembly, and the slide rail extends along the main axis of the device;

[0010] The monitoring unit is arranged at the characteristic motion part of the user, and includes a first monitoring component for real-time monitoring of passive electromyographic signals and active electromyographic signals of the characteristic motion part of the user, and a second monitoring component for real-time monitoring of the joint activity angle of the user during passive training and the position information of the characteristic motion part during active training;

[0011] A remote interaction unit is connected to the monitoring unit and is used to upload the monitoring information of the monitoring unit to the medical control terminal, determine the degree of recovery of the muscle function and the motor control ability of the user according to the detection data of the monitoring unit, and adjust the training time and initial relative resistance of the user according to the degree of recovery or the motor control ability, so as to realize remote rehabilitation monitoring of the user.

[0012] On the other hand, the present invention also provides a surgical postoperative rehabilitation nursing assistance method applied to a surgical postoperative rehabilitation nursing assistance device based on the Internet of Things, comprising:

[0013] In a single training cycle, the passive muscle activation is determined according to the passive electromyographic signal and the joint activity angle, the active muscle activation is determined according to the active electromyographic signal, and the degree of rehabilitation of the muscle function of the user is determined according to the passive muscle activation and the active muscle activation;

[0014] Determine the initial relative resistance of the user during active training according to the user's weight;

[0015] Determine the limb movement trajectory according to the position information of the characteristic movement part, determine the muscle strength balance of the characteristic movement part according to the active electromyographic signal, and determine the movement control ability of the user according to the limb movement trajectory, the preset movement trajectory and the muscle strength balance;

[0016] The training time and initial relative resistance of the user are adjusted according to the rehabilitation degree or the motor control ability.

[0017] Furthermore, it also includes: determining active signal strength and active waveform characteristics according to the active electromyographic signal, determining the degree of muscle fatigue according to the active signal strength or active waveform characteristics, and adjusting the training time and initial relative resistance according to the degree of muscle fatigue.

[0018] Further, the passive high-frequency region and the passive low-frequency region of the passive signal strength are determined based on the passive electromyography threshold, a single passive activation degree is determined according to the passive high-frequency region and the passive low-frequency region, and the passive muscle activation degree is determined according to the single passive activation degree;

[0019] The passive electromyography threshold is determined according to the joint activity angle.

[0020] Furthermore, the active high-frequency region and the active low-frequency region of the active signal strength are determined based on the active electromyography threshold, a single active activation degree is determined according to the active high-frequency region and the active low-frequency region, and the active muscle activation degree is determined according to the single active activation degree.

[0021] Furthermore, the first weight and the second weight corresponding to the passive muscle activation degree and the active muscle activation degree are respectively determined according to the rehabilitation training stage of the user to determine the rehabilitation degree, and the training time and the initial relative resistance are adjusted according to the rehabilitation degree.

[0022] Furthermore, the position change and posture change of the lower limbs of the subject are determined based on the position information to determine the limb movement trajectory, and the activation degree difference of the characteristic movement part is determined based on the single active activation degree and the preset key activation degree to determine the muscle strength balance state.

[0023] Furthermore, the trajectory similarity is determined according to the limb movement trajectory and the preset movement trajectory, and the motion control ability of the user is determined according to the trajectory similarity and the muscle strength balance status, and the training time and initial relative resistance are adjusted according to the motion control ability.

[0024] Furthermore, the integrated electromyographic value is determined according to the active signal strength, and the degree of muscle fatigue is determined according to the comparison result between the integrated electromyographic value and the initial integrated electromyographic value, or the waveform complexity is determined according to the active waveform characteristics to determine the complexity change, and the degree of muscle fatigue is determined according to the complexity change.

[0025] Further, adjusting the training time and the initial relative resistance according to the comparison result of the muscle fatigue degree and the preset fatigue degree includes:

[0026] If the muscle fatigue degree is greater than a preset fatigue degree, shortening the training time and reducing the initial relative resistance;

[0027] If the muscle fatigue level is less than or equal to the preset fatigue level, the training time is increased and the initial relative resistance is increased.

[0028] Compared with the prior art, the beneficial effect of the present invention is that the rehabilitation assistive device of the present invention integrates the functions of support, drive, monitoring and remote interaction, and realizes active training and passive training by setting the first drive unit and the second drive unit, which helps the user to obtain comprehensive rehabilitation training at different stages. And combined with high-precision electromyographic signal and motion parameter monitoring, it realizes comprehensive monitoring and remote transmission of the user's rehabilitation process, satisfies the medical staff's precise control of the rehabilitation process, improves the timeliness and effectiveness of rehabilitation training, and improves the personalization and accuracy of rehabilitation training.

[0029] Furthermore, the present invention can accurately evaluate the passive muscle activation and active muscle activation of the user by real-time monitoring of the user's passive electromyographic signals, joint activity angles, and active electromyographic signals, and thus determine the degree of rehabilitation of muscle function. At the same time, the position information of the characteristic motion parts and the active electromyographic signals are combined to analyze the limb movement trajectory and muscle strength balance of the user, so as to accurately evaluate the user's motor control ability, and further realize dynamic adjustment of the user's rehabilitation training program based on the degree of rehabilitation or motor control ability, so as to realize real-time monitoring of the user's rehabilitation progress and facilitate medical staff to adjust the rehabilitation plan, realize personalized and precise rehabilitation treatment, and effectively improve rehabilitation efficiency.

[0030] Furthermore, the present invention realizes accurate assessment of the degree of muscle fatigue of the subject by real-time monitoring and analyzing active electromyographic signals to determine active signal strength and active waveform characteristics when the subject is actively training, and dynamically adjusts the rehabilitation training program accordingly, thereby further improving the pertinence and effectiveness of rehabilitation training, helping to accelerate the rehabilitation process of the subject, and reducing potential risks caused by overtraining or undertraining.

[0031] Furthermore, the present invention determines the weights corresponding to the passive muscle activation and the active muscle activation respectively according to the rehabilitation training stage of the user to comprehensively evaluate the user's rehabilitation degree, taking into account both the user's recovery state with the assistance of external force and the user's active recovery state under autonomous activities, which helps to achieve real-time monitoring of the user's rehabilitation progress, thereby facilitating medical staff to adjust the rehabilitation plan and improve rehabilitation efficiency.

[0032] Furthermore, the present invention determines the position change and posture change of the lower limbs of the user according to the position information of the characteristic motion parts to determine the limb movement trajectory, and at the same time determines the activation degree difference of the characteristic motion parts according to the single active activation degree and the preset key activation degree to determine the muscle strength balance state, and further judges the motion control ability of the user, thereby determining the rehabilitation status of the user, which helps to achieve real-time monitoring of the rehabilitation progress of the user, thereby facilitating medical staff to adjust the rehabilitation plan and improve rehabilitation efficiency.

[0033] Furthermore, the present invention combines trajectory similarity and muscle strength balance to comprehensively evaluate the user's motor control ability, providing doctors with a more accurate and objective means of evaluating rehabilitation effects, which helps to promptly identify problems and adjust rehabilitation plans, thereby improving the pertinence and effectiveness of rehabilitation training and accelerating the user's rehabilitation process.

[0034] Furthermore, the present invention determines the initial relative resistance of the user during active training according to the user's weight, and adjusts the initial relative resistance according to the user's recovery degree, thereby realizing the combination with the user's actual recovery process, avoiding the harm of excessive relative resistance to the user, and helping to further protect the user's health and ensure safety during training. At the same time, it further realizes personalized training, improves training efficiency, and accelerates the user's recovery process.

[0035] Furthermore, the present invention determines the degree of muscle fatigue of the user according to the active signal strength integrated electromyographic value or the waveform complexity of the active waveform feature, and dynamically adjusts the training plan of the user according to the degree of muscle fatigue, which helps to personalize and adapt to the actual situation of the user under different rehabilitation progress, avoid insufficient or excessive training problems, help improve training efficiency, improve the pertinence and effectiveness of rehabilitation training, and accelerate the rehabilitation process of the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a schematic diagram of the structure of a surgical postoperative rehabilitation nursing auxiliary device based on the Internet of Things according to an embodiment of the present invention;

[0037] Figure 2 This is a step diagram of a surgical postoperative rehabilitation nursing auxiliary method according to an embodiment of the present invention;

[0038] Figure 3 A diagram showing the steps of determining the muscle strength balance state according to an embodiment of the present invention;

[0039] Figure 4 A diagram showing the steps of determining the muscle fatigue degree of a user according to an embodiment of the present invention;

[0040] In the figure, 101, thigh support frame; 102, calf support frame; 103, sole support frame; 201, first drive assembly; 202, second drive assembly; 3, base. DETAILED DESCRIPTION

[0041] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0042] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0043] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0044] See also Figure 1 As shown, it is a structural schematic diagram of a surgical postoperative rehabilitation nursing auxiliary device based on the Internet of Things according to an embodiment of the present invention. Specifically, the present invention provides a surgical postoperative rehabilitation nursing auxiliary device based on the Internet of Things, including:

[0045] The supporting part is used to support the lower limbs of the user, including a thigh supporting frame 101, a calf supporting frame 102 and a foot supporting frame 103;

[0046] The driving part includes a first driving component 201 and a second driving component 202. The first driving component 201 is fixedly connected to the sole support frame 103 to provide resistance to the user relative to the movement direction of the user during active training. The second driving component 202 is hinged to the thigh support frame 101 and the calf support frame 102 respectively to drive the thigh support frame 101 and the calf support frame 102 to perform reverse relative flexion and extension movements during passive training.

[0047] The base 3 has a slide rail on the top thereof which is slidably connected to the second drive assembly 202 to provide guidance and support for the movement of the second drive assembly 202. The slide rail extends along the main axis of the device.

[0048] The monitoring unit is arranged at the characteristic motion part of the user, and includes a first monitoring component for real-time monitoring of passive electromyographic signals and active electromyographic signals of the characteristic motion part of the user, and a second monitoring component for real-time monitoring of the joint activity angle of the user during passive training and the position information of the characteristic motion part during active training;

[0049] A remote interaction unit is connected to the monitoring unit and is used to upload the monitoring information of the monitoring unit to the medical control terminal, determine the degree of recovery of the muscle function and the motor control ability of the user according to the detection data of the monitoring unit, and adjust the training time and initial relative resistance of the user according to the degree of recovery or the motor control ability, so as to realize remote rehabilitation monitoring of the user.

[0050] It is understandable that the user who needs lower limb rehabilitation training after surgery, such as the user who has undergone fracture, joint replacement and other operations, can perform rehabilitation training by using a rehabilitation assistive device to restore the function of the lower limbs. The supporting part in the rehabilitation assistive device is used to support the lower limbs of the user to ensure that the limbs of the user are stably supported during the training process. The first drive component 201 is used to provide relative resistance during active training (such as the user bending and straightening the lower limbs by himself), simulating the resistance in real walking or exercise, and enhancing the muscle strength of the user. The second drive component 202 is used to drive the thigh support frame 101 and the calf support frame 102 to move relative to each other during passive training (such as the user is temporarily unable to move the lower limbs by himself), simulating walking movements, and helping the user to restore the function of the lower limbs.

[0051] It is understandable that electromyographic signals are electrical signals generated when muscles are active, which can reflect the activity state and strength of muscles. By monitoring the passive electromyographic signals and active electromyographic signals of the characteristic motion parts of the user in real time through the first monitoring component, the muscle recovery and training effect of the user can be evaluated. At the same time, the second monitoring component collects the joint activity angle of the user during passive training and the position information of the characteristic motion parts during active training, which can help medical staff understand the training status of the user and adjust the training plan.

[0052] In a specific embodiment, the characteristic motion part is the key muscle group of the thigh and calf of the user when performing lower limb flexion and extension exercises. Preferably, the thigh support bracket 101 and the calf support bracket 102 are made of aluminum alloy or carbon fiber material brackets and are provided with pads. At the same time, the sole support bracket 103 is an ergonomic sole shape, which can fix the user's foot to prevent sliding. The first drive component 201 is a hydraulic system, which provides power through a hydraulic cylinder and provides a certain resistance. The second drive component 202 is driven by a motor, and the motor drives the support bracket to move through a transmission device (such as a chain or belt). There is an anti-skid pad at the bottom of the base 3, and the slide rail is made of high-strength steel to ensure stability. A linear guide rail and a slider are provided on the slide rail to guide the smooth movement of the second drive component 202. The first monitoring component is an electromyographic sensor, which is attached to the characteristic motion part and is used to monitor the electromyographic signals during passive and active training in real time to reflect the muscle activity state. The second monitoring component includes an angle sensor and a position sensor. The angle sensor is arranged at the hinge of the thigh support frame 101 and the calf support frame 102, and is used to monitor the joint activity angle in real time; the position sensor is installed near the characteristic movement part, and is used to track the movement trajectory and position information of the object during active training. The remote interaction part is provided with a data transmission module and a control module. The data transmission module adopts wireless communication technology such as Wi-Fi or Bluetooth to transmit the monitoring data to the medical control terminal in real time. The control module has a built-in microprocessor, which is responsible for monitoring data processing, analysis and communication with the remote medical control terminal. In implementation, the supporting part, monitoring part, driving part and related structural components in the remote interaction part can be determined according to actual conditions, which are not specifically limited here and will not be repeated.

[0053] The rehabilitation assistive device of the present invention integrates support, driving, monitoring and remote interaction functions, and realizes active training and passive training by setting a first driving unit and a second driving unit, which helps the user to obtain comprehensive rehabilitation training at different stages. In addition, combined with high-precision electromyographic signal and motion parameter monitoring, it realizes comprehensive monitoring and remote transmission of the user's rehabilitation process, satisfies the medical staff's precise control of the rehabilitation process, improves the timeliness and effectiveness of rehabilitation training, and improves the personalization and accuracy of rehabilitation training.

[0054] See also Figure 2 , which is a step diagram of a surgical postoperative rehabilitation nursing auxiliary method according to an embodiment of the present invention. Specifically, the present invention provides a method of a surgical postoperative rehabilitation nursing auxiliary device based on the Internet of Things, comprising:

[0055] Step S1, in a single training cycle, determining the passive muscle activation degree according to the passive electromyographic signal and the joint activity angle, determining the active muscle activation degree according to the active electromyographic signal, and determining the degree of rehabilitation of the muscle function of the user according to the passive muscle activation degree and the active muscle activation degree;

[0056] Step S2, determining the initial relative resistance of the user during active training according to the user's weight;

[0057] Step S3, determining a limb motion trajectory according to the position information of the characteristic motion part, determining a muscle strength balance of the characteristic motion part according to the active electromyographic signal, and determining a motion control ability of the user according to the limb motion trajectory, a preset motion trajectory and the muscle strength balance;

[0058] Step S4, adjusting the training time and initial relative resistance of the user according to the rehabilitation degree or the motor control ability.

[0059] It is understandable that the passive EMG signal reflects the electrical signal generated by the muscles of the user in passive movement, the active EMG signal reflects the electrical signal generated by the muscles of the user in the active training process, and the joint movement angle is the angle between the thigh and calf of the user. The degree of rehabilitation of the muscle function of the user can be comprehensively evaluated based on the passive muscle activation and active muscle activation. At the same time, by monitoring the position information of the characteristic movement parts, the limb movement trajectory of the user can be determined, which helps to understand the movement pattern and rehabilitation progress of the user. The muscle strength balance of the characteristic movement parts is evaluated based on the active EMG signal. If the muscle strength of a certain characteristic movement part is obviously low or high, targeted rehabilitation intervention is required.

[0060] It is understandable that if the user has a higher level of recovery or stronger motor control ability, medical staff can increase the training time and training resistance accordingly; otherwise, it is necessary to reduce the training time and training resistance to avoid overtraining or poor rehabilitation effect.

[0061] It can be understood that the initial relative resistance is the initial resistance provided by the first driving component to the user during active training. Different users have different physical abilities and rehabilitation needs. Weight is one of the important factors affecting the resistance setting. Users with heavier weight require greater resistance to produce muscle stimulation. Therefore, the weight of the user is positively correlated with the initial relative resistance.

[0062] In a specific embodiment, the weight of the user is positively correlated with the initial relative resistance. The greater the weight of the user, the greater the initial relative resistance. When the weight of the user is between 50 and 60 kg, the initial relative resistance is 10 to 15 N. Preferably, when the weight of the user is 55 kg, the initial relative resistance is 12 N. For every 10 kg increase in the weight of the user, the initial relative resistance increases by 5 N. In practice, the value range and preferred value of the initial relative resistance can be determined according to actual conditions, as long as the weight of the user is positively correlated with the initial relative resistance.

[0063] The present invention can accurately evaluate the passive muscle activation and active muscle activation of the user by real-time monitoring of the user's passive electromyographic signals, joint activity angles, and active electromyographic signals, and then determine the degree of rehabilitation of muscle function. At the same time, the position information of the characteristic movement parts and the active electromyographic signals are combined to analyze the limb movement trajectory and muscle strength balance of the user, so as to accurately evaluate the user's motor control ability, and further realize dynamic adjustment of the user's rehabilitation training program based on the degree of rehabilitation or motor control ability, so as to realize real-time monitoring of the user's rehabilitation progress and facilitate medical staff to adjust the rehabilitation plan, realize personalized and precise rehabilitation treatment, and effectively improve rehabilitation efficiency.

[0064] Specifically, in step S4, it also includes determining the active signal strength and the active waveform characteristics according to the active electromyographic signal, determining the degree of muscle fatigue according to the active signal strength or the active waveform characteristics, and adjusting the training time and initial relative resistance according to the degree of muscle fatigue.

[0065] It is understandable that the active signal strength is the amplitude of the electromyographic signal corresponding to the active electromyographic signal, and the active waveform characteristics are the muscle activity patterns of the user during active training. The degree of muscle fatigue can be evaluated through the active signal strength and active waveform characteristics. For example, as muscle fatigue deepens, the active signal strength will decrease, or the active waveform characteristics will change, such as the waveform becomes more irregular or the amplitude decreases. If the degree of muscle fatigue is high, it may be necessary to reduce resistance and increase rest time to avoid excessive strain; if the degree of muscle fatigue is low, the resistance can be maintained or appropriately increased and the training time can be extended to promote the recovery process.

[0066] The present invention realizes accurate assessment of the degree of muscle fatigue of the subject by real-time monitoring and analyzing active electromyographic signals to determine active signal strength and active waveform characteristics when the subject is actively training, and dynamically adjusts the rehabilitation training program accordingly, thereby further improving the pertinence and effectiveness of rehabilitation training, helping to accelerate the rehabilitation process of the subject, and reducing potential risks caused by overtraining or undertraining.

[0067] Specifically, in step S1, the passive high-frequency region and the passive low-frequency region of the passive signal strength are determined based on the passive electromyography threshold, a single passive activation degree is determined according to the passive high-frequency region and the passive low-frequency region, and the passive muscle activation degree is determined according to the single passive activation degree;

[0068] The passive electromyography threshold is determined according to the joint activity angle.

[0069] It can be understood that the single passive activation degree is the degree of muscle activity corresponding to each characteristic movement part, and the passive muscle activation degree is the total muscle activity of each characteristic movement part under passive training time in a single training cycle. The passive electromyography threshold refers to the electromyography threshold at which the muscles of the user begin to become active under passive training, which can be determined according to the joint movement angle. Different joint movement angles result in different stretching and squeezing conditions on the muscles, and different levels of electrical activity generated by the muscles. Therefore, the passive electromyography thresholds corresponding to different joint movement angles are also different. The frequency part of the electromyography signal higher than the passive electromyography threshold is divided into a passive high-frequency area, and the frequency part lower than the passive electromyography threshold is divided into a passive low-frequency area, so as to determine the passive high-frequency ratio. The higher the ratio, the higher the degree of muscle activation under passive training.

[0070] In a specific embodiment, the passive high frequency ratio is used to characterize the single passive activation, the single passive activation is the ratio of the passive high frequency area to the passive low frequency area, and the passive muscle activation is the average of the single passive activation corresponding to each characteristic motion part. In implementation, the single passive activation and passive muscle activation can be determined according to actual conditions, for example, the passive muscle activation can also be determined comprehensively according to the muscle physiological characteristics of each characteristic motion part, the mechanical environment during exercise, etc., which is not specifically limited here and will not be repeated.

[0071] In a specific embodiment, the passive electromyographic threshold is determined according to the joint movement angle. Specifically, if the joint movement angle is less than or equal to the preset angle, the passive electromyographic threshold is positively correlated with the joint movement angle. If the joint movement angle is greater than the preset angle, the passive electromyographic threshold is the electromyographic threshold at the preset angle. The value range of the preset angle is 70° to 80°. In implementation, the passive electromyographic threshold can be determined according to actual conditions. As long as the joint movement angle is less than or equal to the preset angle, the passive electromyographic threshold is positively correlated with the joint movement angle. When the joint movement angle exceeds the preset angle, the passive electromyographic threshold no longer increases. No specific limitation is made here, and no further description is given.

[0072] Specifically, in step S1, the active high-frequency area and the active low-frequency area of ​​the active signal strength are determined based on the active electromyography threshold, a single active activation degree is determined according to the active high-frequency area and the active low-frequency area, and the active muscle activation degree is determined according to the single active activation degree.

[0073] It can be understood that the single active activation degree is the degree of muscle activity corresponding to each characteristic movement part, and the active muscle activation degree is the total muscle activity of each characteristic movement part during the active training time in a single training cycle. The active electromyography threshold refers to the electromyography threshold at which the muscles of the user begin to become active under active training. The frequency part of the electromyography signal that is higher than the active electromyography threshold is divided into the active high-frequency area, and the frequency part that is lower than the active electromyography threshold is divided into the active low-frequency area, so as to determine the proportion of active high frequency. The higher the proportion, the higher the degree of activation of the muscle under active training.

[0074] In a specific embodiment, the single active activation is characterized by the active high frequency ratio, the single active activation is the ratio of the area of ​​the active high frequency area to the area of ​​the active low frequency area, and the active muscle activation is the average of the single active activation corresponding to each characteristic motion part. In implementation, the single active activation and active muscle activation can be determined according to actual conditions, for example, the active muscle activation can also be determined comprehensively according to the muscle physiological characteristics of each characteristic motion part, the mechanical environment during exercise, etc., which is not specifically limited here and will not be repeated.

[0075] In a specific embodiment, the active electromyography threshold value is 150Hz to 200Hz, and preferably, the active electromyography threshold value is 170Hz. The frequency range of 170Hz and above is regarded as the active high-frequency area, and the rest is the active low-frequency area. In implementation, the active electromyography threshold value can be determined according to actual conditions, and is not specifically limited here and will not be repeated.

[0076] Specifically, in step S1, the first weight and the second weight corresponding to the passive muscle activation degree and the active muscle activation degree are respectively determined according to the rehabilitation training stage of the user to determine the rehabilitation degree;

[0077] It is understandable that rehabilitation training for users is usually divided into different stages. As the rehabilitation training time increases, the user's muscle activity ability can be improved. In the early stage of rehabilitation training, the user's muscle strength is weak, the passive training accounts for a large proportion, and the corresponding first weight is large, while the active training accounts for a small proportion, and the corresponding second weight is small. As the rehabilitation process progresses, the user's muscle strength is restored, the proportion of active training will increase, and the corresponding second weight will also increase.

[0078] In a specific embodiment, the first weight and the second weight are determined according to the current rehabilitation training stage of the user, and the sum of the first weight and the second weight is 1. Assuming that the rehabilitation training stage is divided into a primary stage, an intermediate stage and an advanced stage, the primary stage corresponds to a first weight of 0.75 and a second weight of 0.25, the intermediate stage corresponds to a first weight of 0.5 and a second weight of 0.5, and the advanced stage corresponds to a first weight of 0.25 and a second weight of 0.75. The degree of rehabilitation is determined by combining the first weight, the second weight, the passive muscle activation and the active muscle activation. In the corresponding rehabilitation training stage, the degree of rehabilitation = first weight × passive muscle activation + second weight × active muscle activation. In implementation, the value range and preferred value of the rehabilitation training stage and the first weight and the second weight can be determined according to actual conditions, which are not specifically limited here and will not be repeated.

[0079] Specifically, in step S4, the training time and the initial relative resistance are adjusted according to the degree of rehabilitation, wherein if the degree of rehabilitation is greater than the stage rehabilitation degree, the initial relative resistance is increased and the training time is shortened; if the degree of rehabilitation is less than the stage rehabilitation degree, the initial relative resistance is reduced and the training time is extended; if the degree of rehabilitation is equal to the stage rehabilitation degree, the initial relative resistance and training time are not adjusted.

[0080] It can be understood that the stage rehabilitation degree is the preset stage rehabilitation goal corresponding to different rehabilitation training stages during active training, and the rehabilitation degree is the corresponding rehabilitation degree at different rehabilitation training stages during active training. By adjusting the initial relative resistance according to the comparison results of the rehabilitation degree and the stage rehabilitation degree, it can be ensured that the resistance setting better adapts to the actual rehabilitation degree of the user and promotes the rapid recovery of the user.

[0081] In a specific embodiment, if the rehabilitation degree is greater than the stage rehabilitation degree, the initial relative resistance of the corresponding rehabilitation training stage is increased, and the increase in initial relative resistance = (rehabilitation degree / stage rehabilitation degree-1) × initial relative resistance. If the rehabilitation degree is less than the stage rehabilitation degree, the initial relative resistance of the corresponding rehabilitation training stage is reduced, and the decrease in initial relative resistance = (1-rehabilitation degree / stage rehabilitation degree) × initial relative resistance. Assuming that the rehabilitation training stages are the primary stage, the intermediate stage and the advanced stage, the corresponding stage rehabilitation degrees are 0.3, 0.6 and 1 respectively. In implementation, the value range and preferred value of the stage rehabilitation degree can be determined according to actual conditions, which are not specifically limited here and will not be repeated.

[0082] In a specific embodiment, the range of the adjustment amount of the training time is 5 minutes to 10 minutes, and preferably, the adjustment amount of the training time is 8 minutes. In implementation, the range of the adjustment amount of the training time and the preferred value can be determined according to actual conditions, and are not specifically limited here and will not be repeated.

[0083] The present invention determines the weights corresponding to the passive muscle activation and the active muscle activation respectively according to the rehabilitation training stage of the user to comprehensively evaluate the user's rehabilitation degree, taking into account both the user's recovery state with the assistance of external force and the user's active recovery state under autonomous activities, which helps to achieve real-time monitoring of the user's rehabilitation progress, thereby facilitating medical staff to adjust the rehabilitation plan and improve rehabilitation efficiency.

[0084] At the same time, the present invention determines the initial relative resistance of the user during active training according to the user's weight, and adjusts the initial relative resistance according to the user's recovery degree, so as to realize the combination with the user's actual recovery process, avoid the harm of excessive relative resistance to the user, and help to further protect the user's health and ensure safety during training. At the same time, it further realizes personalized training, improves training efficiency, and accelerates the recovery process of the user.

[0085] See also Figure 3 As shown, it is a step diagram for determining the muscle strength balance status in an embodiment of the present invention; specifically, in step S3, the position change and posture change of the lower limbs of the object are determined according to the position information to determine the limb movement trajectory, and the activation degree difference of the characteristic movement part is determined according to the single active activation degree and the preset key activation degree to determine the muscle strength balance status.

[0086] It can be understood that by capturing the position information and posture information of the characteristic movement parts of the lower limbs of the user during active training in real time through the second monitoring component, the movement trajectory of the lower limbs can be determined, which helps to understand the movement pattern and movement accuracy of the user.

[0087] It is understandable that, under normal circumstances, muscles have certain activation patterns and synergistic relationships when completing specific movements. If some muscles are activated too early or too late, or the degree of activation is significantly different from normal, it may lead to an imbalance in muscle strength. When muscles are bending and straightening, their activation order and degree of activation are usually different. For example, in the early stage of leg bending, the biceps femoris and other posterior thigh muscles contract first, driving the calf to move backward, and then the posterior calf muscles move to complete the bending movement together. At this time, the muscles on the posterior thigh and the posterior calf are the main force sites, and the degree of activation is relatively high. During the leg straightening process, the activation order is also different. The activation order is the quadriceps femoris, rectus femoris, lateralis, vastus medialis, and posterior calf muscles on the front of the thigh. During the straightening process, the quadriceps femoris on the front of the thigh is the main force site.

[0088] It is understandable that the greater the activation difference, the more imbalanced the muscle strength is, and the muscle strength of certain characteristic movement parts is weaker or stronger, requiring targeted rehabilitation intervention. Therefore, by monitoring the single active activation corresponding to each characteristic movement part to determine the activation difference of the characteristic movement part, the muscle strength balance of the user can be determined to provide a scientific basis for adjusting the rehabilitation plan.

[0089] In a specific embodiment, the position information is the coordinates of the characteristic motion part in three-dimensional space. By comparing the coordinate positions per second during active training, the position change can be determined. At the same time, the flexion and extension angles of the user's thigh and calf can be determined through the position information, so as to determine the joint posture changes of the user when doing flexion and extension exercises. By combining the position changes and posture changes per second, the limb movement trajectory of the lower limb can be determined. In implementation, the limb movement trajectory of the user can also be determined by an inertial motion capture system or a fusion of multiple sensor data, which is not specifically limited here and will not be repeated.

[0090] In a specific embodiment, the preset key activation ranges from 0.3 to 0.5, and preferably, the preset key activation ranges from 0.4. If the single active activation is greater than the preset key activation, it means that the corresponding characteristic motion part is activated. The activation difference is the absolute difference between the single active activation and the preset key activation. The muscle strength balance is determined according to the activation difference corresponding to each characteristic motion part. If the activation differences are all within the normal range, it indicates that the muscle strength balance is good; if the number of activation differences exceeding the normal range is greater than the preset number threshold, the muscle strength balance is poor. The normal range ranges from 0.08 to 0.15, and preferably, the normal range ranges from 0.1, and the preset number threshold ranges from 1 to 3, and preferably, the preset number threshold is 2. In implementation, the preset key activation and the muscle strength balance can be determined according to the actual situation, which is not specifically limited here and will not be repeated.

[0091] The present invention determines the position change and posture change of the lower limbs of the user according to the position information of the characteristic movement parts to determine the limb movement trajectory, and at the same time determines the activation degree difference of the characteristic movement parts according to the single active activation degree and the preset key activation degree to determine the muscle strength balance state, and further judges the movement control ability of the user. The rehabilitation status of the user is determined, which helps to realize real-time monitoring of the rehabilitation progress of the user, thereby facilitating medical staff to adjust the rehabilitation plan and improve rehabilitation efficiency.

[0092] Specifically, in step S3, the trajectory similarity is determined according to the limb movement trajectory and the preset movement trajectory, and the movement control ability of the user is determined according to the trajectory similarity and the muscle strength balance status;

[0093] Specifically, in step S4, the training time and the initial relative resistance are adjusted according to the motor control ability, including shortening the training time and increasing the initial relative resistance if the motor control ability is good, maintaining the training time and the initial relative resistance if the motor control ability is average, and extending the training time and reducing the initial relative resistance if the motor control ability is poor.

[0094] It is understandable that the trajectory similarity reflects the degree of proximity between the subject's movement pattern and the expected rehabilitation goal. If the actual limb movement trajectory is closer to the preset movement trajectory, the muscle strength of the characteristic movement part can maintain a relative balance at different movement stages and under different loads, and there is no obvious force imbalance that causes movement deformation or loss of control, indicating that the movement control ability is strong and the subject has a strong recovery ability. The movement control ability is helpful in evaluating the rehabilitation effect of the subject.

[0095] In a specific embodiment, the actual limb movement trajectory of the user is compared with the preset movement trajectory, and the mean square error (MSE) is used to measure the trajectory similarity between the trajectories. The smaller the MSE value, the closer the actual trajectory is to the preset trajectory, the higher the trajectory similarity, and the stronger the motor control ability. When the trajectory similarity is higher than 80% and the muscle strength balance is good, the user is judged to have good motor control ability; when the trajectory similarity is between 60% and 80% and the muscle strength balance is good, the motor control ability is judged to be average and further rehabilitation training is required; when the trajectory similarity is lower than 60% and the muscle strength balance is poor, the motor control ability is judged to be poor and focus intervention is required.

[0096] In a specific embodiment, the value range of the adjustment amount of the initial relative resistance is 10% to 15% of the initial relative resistance. Preferably, the value of the adjustment amount of the initial relative resistance is 12% of the initial relative resistance. In implementation, the value range and preferred value of the adjustment amount of the initial relative resistance can be determined according to actual conditions, and are not specifically limited here and will not be repeated.

[0097] The present invention combines trajectory similarity and muscle strength balance to comprehensively evaluate the user's motor control ability, providing doctors with a more accurate and objective means of evaluating rehabilitation effects, which helps to promptly identify problems and adjust rehabilitation plans, thereby improving the pertinence and effectiveness of rehabilitation training and accelerating the user's rehabilitation process.

[0098] See also Figure 4 As shown, it is a step diagram for determining the degree of muscle fatigue of the user according to an embodiment of the present invention. Specifically, in step S4, the integrated electromyographic value is determined according to the active signal strength, and the degree of muscle fatigue is determined according to the comparison result between the integrated electromyographic value and the initial integrated electromyographic value, or the waveform complexity is determined according to the active waveform characteristics to determine the complexity change, and the degree of muscle fatigue is determined according to the complexity change.

[0099] It is understandable that muscles generate electromyographic signals during contraction, and the integrated electromyographic value reflects the total electrical activity of the muscles during the active training period in a single training cycle. During exercise, in order to maintain a certain force output, the amplitude of the electromyographic signal will increase, and the integrated electromyographic value will also increase accordingly. When the muscle fatigue reaches a certain level, the muscle contraction ability decreases, and the integrated electromyographic value will decrease. Therefore, the muscle fatigue state can be determined by the integrated electromyographic value and the initial integrated electromyographic value.

[0100] It is understandable that under normal circumstances, the active EMG signals of muscles have a certain regularity and complexity, which is caused by the orderly discharge of different motor units. When the muscle is fatigued, the discharge pattern of the motor unit will become disordered, and the waveform of the EMG signal will change, showing that the waveform complexity decreases and becomes more irregular. Therefore, by analyzing the active waveform characteristics to determine the change in waveform complexity, the degree of muscle fatigue can be inferred.

[0101] In a specific embodiment, the integrated EMG value is the average of the integral active signal strengths corresponding to each characteristic movement part in the active training time period in a single training cycle, and the initial integrated EMG value is the average of the integral active signal strengths corresponding to each characteristic movement part in the first 20% of the active training time period in several single training cycles. If the integrated EMG value and the initial integrated EMG value are greater than a preset difference, it indicates muscle fatigue, and the preset difference is 15% to 20% of the initial integrated EMG value. The degree of muscle fatigue is the ratio of the preset difference to the initial integrated EMG value. In implementation, the value range and preferred value of the initial integrated EMG value and the preset difference can be determined according to actual conditions, and are not specifically limited here and will not be repeated.

[0102] In a specific embodiment, the active waveform characteristics can be divided into several parts based on the active training time in a single training cycle, and the corresponding waveform complexity can be determined according to the active waveform characteristics of each part, such as using methods such as approximate entropy (ApEn) and sample entropy (SampEn) to describe the waveform complexity. At the same time, based on the waveform complexity of each part, the complexity change trend is observed to determine the degree of muscle fatigue. If the complexity gradually decreases, the muscle is in a fatigue state. The degree of muscle fatigue is the ratio of the waveform complexity difference between the first part and the last part corresponding to the active training time period in a single training cycle and the waveform complexity of the first part. In implementation, the degree of muscle fatigue can be determined according to actual conditions, which is not specifically limited here and will not be repeated.

[0103] Specifically, in step S4, adjusting the training time and the initial relative resistance according to the comparison result between the muscle fatigue degree and the preset fatigue degree includes:

[0104] If the muscle fatigue degree is greater than a preset fatigue degree, shortening the training time and reducing the initial relative resistance;

[0105] If the muscle fatigue level is less than or equal to the preset fatigue level, the training time is increased and the initial relative resistance is increased.

[0106] It is understandable that when the degree of muscle fatigue is greater than the preset degree of fatigue, it indicates that the muscles of the user are already quite fatigued and exceed their current recovery capacity. In order to avoid overtraining and possible injuries, it is necessary to shorten the training time and reduce the initial relative resistance (i.e., reduce the intensity of training). When the degree of muscle fatigue is less than or equal to the preset degree of fatigue, it indicates that the muscles of the user are in good condition and are able to withstand more training loads. Therefore, the training time can be increased and the initial relative resistance can be increased to further improve the physical fitness of the user.

[0107] In a specific embodiment, the muscle fatigue degree is standardized to be between 0 and 1, and the preset fatigue degree has a value range of 0.5 to 0.7, and preferably, the preset fatigue degree has a value of 0.6. In implementation, the preset fatigue degree has a value range and a preferred value that needs to be determined according to actual conditions, and is not specifically limited here and will not be described in detail.

[0108] The present invention determines the degree of muscle fatigue of the user according to the active signal strength integrated electromyographic value or the waveform complexity of the active waveform feature, and dynamically adjusts the training plan of the user according to the muscle fatigue degree, which helps to personalize and adapt to the actual situation of the user under different rehabilitation progress, avoids the problems of insufficient training or excessive training, helps to improve training efficiency, improves the pertinence and effectiveness of rehabilitation training, and accelerates the rehabilitation process of the user.

[0109] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A surgical postoperative rehabilitation nursing auxiliary device based on the Internet of Things, characterized in that: include: The supporting part is used to support the lower limbs of the user, including a thigh support frame, a calf support frame and a foot support frame; The driving part includes a first driving assembly and a second driving assembly, wherein the first driving assembly is fixedly connected to the sole support frame to provide resistance to the user relative to the movement direction of the user during active training, and the second driving assembly is respectively hinged to the thigh support frame and the calf support frame to drive the thigh support frame and the calf support frame to perform reverse relative flexion and extension movements during passive training; A base, the top of which is provided with a slide rail slidably connected to the second drive assembly, for providing guidance and support for the movement of the second drive assembly, and the slide rail extends along the main axis of the device; A monitoring unit is arranged at a characteristic motion part of the user, and includes a first monitoring component for real-time monitoring of passive electromyographic signals and active electromyographic signals at the characteristic motion part, and a second monitoring component for real-time monitoring of the joint activity angle of the user during passive training and the position information of the characteristic motion part during active training; A remote interaction unit is connected to the monitoring unit and is used to upload the monitoring information of the monitoring unit to the medical control terminal, determine the degree of recovery of the muscle function and the motor control ability of the user according to the detection data of the monitoring unit, and adjust the training time and initial relative resistance of the user according to the degree of recovery or the motor control ability, so as to realize remote rehabilitation monitoring of the user.

2. A surgical postoperative rehabilitation nursing assistance method applied to the surgical postoperative rehabilitation nursing assistance device based on the Internet of Things as claimed in claim 1, characterized in that: include: In a single training cycle, the passive muscle activation is determined according to the passive electromyographic signal and the joint activity angle, the active muscle activation is determined according to the active electromyographic signal, and the degree of rehabilitation of the muscle function of the user is determined according to the passive muscle activation and the active muscle activation; Determine the initial relative resistance of the user during active training according to the user's weight; Determine the limb movement trajectory according to the position information of the characteristic movement part, determine the muscle strength balance of the characteristic movement part according to the active electromyographic signal, and determine the movement control ability of the user according to the limb movement trajectory, the preset movement trajectory and the muscle strength balance; The training time and initial relative resistance of the user are adjusted according to the rehabilitation degree or the motor control ability.

3. The surgical postoperative rehabilitation nursing auxiliary method according to claim 2, characterized in that: Also includes: The active signal strength and the active waveform characteristics are determined according to the active electromyographic signal, and the degree of muscle fatigue is determined according to the active signal strength or the active waveform characteristics, and the training time and the initial relative resistance are adjusted according to the degree of muscle fatigue.

4. The surgical postoperative rehabilitation nursing auxiliary method according to claim 2, characterized in that: Determine the passive high-frequency area and the passive low-frequency area of ​​the passive signal strength based on the passive electromyography threshold, determine a single passive activation degree according to the passive high-frequency area and the passive low-frequency area, and determine the passive muscle activation degree according to the single passive activation degree; The passive myoelectric threshold is determined according to the joint activity angle.

5. The surgical postoperative rehabilitation nursing auxiliary method according to claim 2, characterized in that: The active high frequency region and the active low frequency region of the active signal strength are determined based on the active electromyography threshold, a single active activation degree is determined according to the active high frequency region and the active low frequency region, and the active muscle activation degree is determined according to the single active activation degree.

6. The surgical postoperative rehabilitation nursing auxiliary method according to claim 5, characterized in that: According to the rehabilitation training stage of the user, the first weight and the second weight corresponding to the passive muscle activation degree and the active muscle activation degree are respectively determined to determine the rehabilitation degree, and the training time and the initial relative resistance are adjusted according to the rehabilitation degree.

7. The surgical postoperative rehabilitation nursing auxiliary method according to claim 2, characterized in that: The position change and posture change of the lower limbs of the subject are determined according to the position information to determine the limb movement trajectory, and the activation degree difference of the characteristic movement part is determined according to the single active activation degree and the preset key activation degree to determine the muscle strength balance state.

8. The surgical postoperative rehabilitation nursing auxiliary method according to claim 7, characterized in that: The trajectory similarity is determined according to the limb movement trajectory and the preset movement trajectory, and the motion control ability of the user is determined according to the trajectory similarity and the muscle strength balance status, and the training time and initial relative resistance are adjusted according to the motion control ability.

9. The surgical postoperative rehabilitation nursing auxiliary method according to claim 3, characterized in that: Determine the integrated EMG value according to the active signal strength, determine the degree of muscle fatigue according to the comparison result between the integrated EMG value and the initial integrated EMG value, or determine the waveform complexity according to the active waveform characteristics to determine the complexity change, and determine the degree of muscle fatigue according to the complexity change.

10. The surgical postoperative rehabilitation nursing auxiliary method according to claim 9, characterized in that: Adjusting the training time and initial relative resistance based on the comparison of the muscle fatigue level with the preset fatigue level includes: If the muscle fatigue degree is greater than a preset fatigue degree, shortening the training time and reducing the initial relative resistance; If the muscle fatigue level is less than or equal to the preset fatigue level, the training time is increased and the initial relative resistance is increased.

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