Nursing auxiliary device for chronic skeletal pain exercise

Abstract

The invention provides a nursing auxiliary device for chronic skeletal pain exercise, and relates to the technical field of medical instruments. The nursing auxiliary device for chronic skeletal pain exercise comprises a control platform, a lower supporting piece, an upper supporting piece and a foot supporting piece, the upper supporting piece is arranged at the upper end of the lower supporting piece, the foot supporting piece is arranged at the lower end of the lower supporting piece, the control platform is arranged on one side of the lower supporting piece, and the control platform is arranged on the other side of the lower supporting piece. First connecting pieces are arranged at the positions, close to the two sides, of the upper end face of the lower supporting piece, second connecting pieces are arranged at the positions, close to the two sides, of the lower end face of the upper supporting piece, and the two first connecting pieces are rotationally connected with the two second connecting pieces correspondingly. Legs are completely supported through the upper supporting piece, the lower supporting piece and the foot supporting piece, pressure points and unnecessary stress are reduced, and a more comprehensive treatment effect can be provided through combined use of thermal therapy and electrotherapy.

Description

Technical Field

[0001] The invention relates to the technical field of medical devices, in particular to a nursing auxiliary device for chronic bone pain exercise. Background Art

[0002] Chronic bone pain is persistent or recurring pain that usually lasts three months or longer. It may stem from a variety of causes, including but not limited to bone disease, joint problems, muscle or soft tissue injuries, and nerve problems. The management of chronic bone pain often requires a multidisciplinary approach, including medication, physical therapy, lifestyle adjustments, and psychological support. People with chronic bone pain may need special assistive devices to ensure safety and effectiveness when exercising.

[0003] There are still some problems in the use of existing nursing auxiliary devices for chronic bone pain exercises. When exercising to treat chronic bone pain in the legs, the leg support is insufficient and the auxiliary effect is single. Different patients may respond differently to the treatment. A unified treatment plan may not provide the best effect, and a single treatment method may not be able to fully solve the problem of chronic pain. Therefore, technical personnel in this field provide a nursing auxiliary device for chronic bone pain exercises to solve the problems raised in the above background technology. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In response to the shortcomings of the prior art, the present invention provides a nursing auxiliary device for chronic bone pain exercise, which solves the problems of insufficient leg support and single auxiliary effect during exercise treatment for chronic bone pain in the legs, different patients may respond differently to the treatment, a unified treatment plan may not provide the best effect, and a single treatment method may not be able to fully solve the problem of chronic pain.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a nursing auxiliary device for chronic bone pain exercise, comprising a control platform, a lower support, an upper support and a foot support, wherein the upper support is arranged at the upper end of the lower support, the foot support is arranged at the lower end of the lower support, the control platform is arranged at one side of the lower support, the upper end surface of the lower support is provided with a first connecting member at both sides, the lower end surface of the upper support is provided with a second connecting member at both sides, the two first connecting members are rotatably connected with the two second connecting members respectively, the upper and lower rear end surfaces of the upper and lower support are provided with a power supply structure at the upper center, the upper and lower support are provided with a heating plate at the center of the rear inner wall, and the centers of the two inner side walls of the upper support and the two inner side walls of the lower support are provided with a plurality of electrodes arranged up and down;

[0008] The power supply structure includes a shell, a cover plate, a battery and a signal processing control module. The battery is arranged at the lower center of the shell, the signal processing control module is arranged inside the shell at the upper end of the battery, and the cover plate is arranged on the rear end surface of the shell.

[0009] Preferably, skin galvanic response sensors and electromyography sensors are arranged vertically on the rear inner wall of the upper support member and the rear inner wall of the lower support member.

[0010] Preferably, the skin conductivity change rate of the galvanic skin response sensor is:

[0011]

[0012] Threshold setting: setting the threshold of GSR change to judge the patient's pain and emotional state;

[0013] The EMG sensor signal strength:

[0014]

[0015] Threshold setting, set the threshold of EMG signal intensity, which is used to judge muscle tension and pain response.

[0016] Preferably, the signal processing control module further includes a signal receiving module, a signal sending module, a signal processing module, a temperature control module and an electrode output power adjustment module, and the control platform further includes a signal receiving module, a signal sending module, a signal processing module and a neural stimulation feedback system. The neural feedback system control module controls the output power of the heating plate and the electrode in the following detailed steps:

[0017] S1. Data collection, simultaneously collects changes in leg skin conductivity, data reflecting emotions and pain levels, and muscle electrical activity, reflecting muscle tension and pain response, and continuously monitors the patient's skin electrical response and muscle activity;

[0018] S2. Signal processing: filtering and denoising the collected data to improve signal quality and extract key features from the pre-processed data, such as the amplitude of skin conductivity changes and the intensity of EMG signals;

[0019] S3. Therapeutic equipment adjustment, according to the extracted features, applies the preset proportional-integral-differential control algorithm to adjust the output power of the heating plate and the electrode. The signal processing control module adjusts the output power of the heating plate and the electrode according to the algorithm results. Its proportional-integral-differential control algorithm uses three terms, proportional, integral and differential, to adjust the output. The proportional term P is proportional to the current error and directly affects the output. The integral term I is proportional to the cumulative sum of past error values, which helps to eliminate steady-state errors. The differential term D is proportional to the error change rate, which helps to predict the trend of trace errors. The formula is:

[0020]

[0021] Where u(t) is the control output, e(t) is the error, and K p , K i and K d They are proportional, integral and derivative gains respectively;

[0022] Output power adjustment:

[0023] P new =P current ±ΔP

[0024] Among them, P new is the new output power, P current is the current output power, ΔP is the power change adjusted according to the data. By real-time monitoring of the patient's skin conductivity and muscle activity, the PID control algorithm can adjust the treatment parameters according to the patient's immediate response and provide personalized treatment plans. Different patients respond differently to heat and electrotherapy. PID control allows the intensity of treatment to be adjusted according to the specific needs of the individual. The PID control algorithm can accurately control the output power of the heating plate and electrodes to ensure the safety and effectiveness of the treatment process;

[0025] S4. Data recording and integration: recording sensor data and treatment parameters during each treatment, integrating data from multiple treatments, and performing trend analysis;

[0026] S5. Optimize the treatment process. Adjust the treatment parameters based on the results of data integration, optimize the treatment process, and make personalized treatment adjustments based on the patient's specific response.

[0027] Preferably, the optimization process described in step S5 above relies on a machine learning algorithm to optimize the treatment process. The machine learning algorithm can be used to learn the treatment effect from historical data and predict the best treatment parameters based on new data. The labeled historical data is used to train the model, and the clustering algorithm is used to identify patterns and groups in the data. The strategy is learned through interaction with the environment to maximize the cumulative reward. The loss function is:

[0028]

[0029] Among them, L(θ) is the loss function, y i It is the actual effect of treatment. is the treatment result predicted by the model, θ is the model parameter. Through treatment process optimization, the treatment parameters are adjusted according to the results of data integration, the treatment process is optimized, and personalized treatment adjustments are made according to the patient's specific response. By optimizing the treatment parameters, the time required for each treatment can be shortened and the treatment efficiency can be improved. By providing the best personalized treatment, the rehabilitation process can be accelerated and the recovery time can be reduced.

[0030] Preferably, a plurality of heating wires are arranged vertically inside the two heating plates, and the plurality of heating wires are individually controlled. The plurality of electrodes are grouped in pairs horizontally, and the plurality of groups of electrodes are individually controlled, so that they can be individually controlled according to the specific position of the legs, and hot compress and electrotherapy can be performed on the specific position.

[0031] (III) Beneficial effects

[0032] The present invention provides a nursing auxiliary device for chronic bone pain exercise, which has the following beneficial effects:

[0033] 1. In the present invention, 3D technology is used to scan the patient's legs, and 3D printing technology is used to print out the upper support member, the lower support member and the foot support member according to the size of the patient's legs, so as to better fit the patient's legs. The legs are fully supported by the upper support member, the lower support member and the foot support member, reducing pressure points and unnecessary stress.

[0034] 2. In the present invention, heat therapy through the heating plate can dilate blood vessels, promote local blood circulation, help deliver more oxygen and nutrients to the injured area, accelerate the healing process, help relax tense muscles, and relieve muscle spasms and stiffness. Electrotherapy, especially transcutaneous electrical nerve stimulation, can relieve pain by interfering with the transmission of pain signals and help maintain muscle mass and strength, especially when the patient is unable to perform active movements.

[0035] 3. In the present invention, by real-time monitoring of the patient's skin conductivity and muscle activity, the PID control algorithm can adjust the treatment parameters according to the patient's immediate response and provide a personalized treatment plan. Different patients have different responses to thermal therapy and electrotherapy. PID control allows the treatment intensity to be adjusted according to the specific needs of the individual. The PID control algorithm can accurately control the output power of the heating plate and the electrode to ensure the safety and effectiveness of the treatment process.

[0036] 4. In the present invention, by optimizing the treatment process, the treatment parameters are adjusted according to the results of data integration, the treatment process is optimized, and personalized treatment adjustments are made according to the patient's specific response. By optimizing the treatment parameters, the time required for each treatment can be shortened and the treatment efficiency can be improved. By providing the best personalized treatment, the rehabilitation process can be accelerated and the recovery time can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A perspective view of the present invention;

[0038] Figure 2 A three-dimensional diagram of another viewing angle of the present invention;

[0039] Figure 3 is a rear cross-sectional view of the present invention;

[0040] Figure 4 It is a front view of the present invention;

[0041] Figure 5 is a side view of the present invention;

[0042] Figure 6 It is a flow chart of the control module of the neural feedback system of the present invention.

[0043] Among them, 1. lower support member; 2. upper support member; 3. foot support member; 4. bandage hole; 5. first connecting member; 6. second connecting member; 7. electrode; 8. galvanic skin response sensor; 9. power supply structure; 901. shell; 902. cover plate; 903. battery; 904. signal processing control module; 10. heating plate; 11. electromyography sensor; 12. control platform. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] Embodiment 1:

[0046] like Figure 1-6 As shown, an embodiment of the present invention provides a nursing auxiliary device for chronic bone pain exercise, including a control platform 12, a lower support member 1, an upper support member 2 and a foot support member 3, wherein the upper support member 2 is arranged at the upper end of the lower support member 1, the foot support member 3 is arranged at the lower end of the lower support member 1, the control platform 12 is arranged at one side of the lower support member 1, a first connecting member 5 is arranged at both sides of the upper end surface of the lower support member 1, a second connecting member 6 is arranged at both sides of the lower end surface of the upper support member 2, the two first connecting members 5 are rotatably connected to the two second connecting members 6 respectively, and the patient's legs are scanned using 3D technology, and 3D printing technology is used. The upper support member 2, the lower support member 1 and the foot support member 3 are printed out according to the size of the patient's legs to better fit the patient's legs. The legs are fully supported by the upper support member 2, the lower support member 1 and the foot support member 3 to reduce pressure points and unnecessary stress. A power supply structure 9 is provided at the upper center of the rear end surface of the upper support member 2 and the lower support member 1, and a heating plate 10 is provided at the center of the rear inner wall of the upper support member 2 and the lower support member 1. Heat therapy is performed through the heating plate 10. Multiple electrodes 7 are arranged vertically at the center of the two inner walls of the upper support member 2 and the center of the two inner walls of the lower support member 1, and electrotherapy is performed through the multiple electrodes 7.

[0047] The power supply structure 9 includes a shell 901, a cover plate 902, a battery 903 and a signal processing control module 904. The battery 903 is arranged at the lower center of the shell 901, the signal processing control module 904 is arranged inside the shell 901 at the upper end of the battery 903, and the cover plate 902 is arranged on the rear end surface of the shell 901. Electric energy is supplied by the battery 903, and the control signal of the control platform 12 is received by the signal processing control module 904, and then the heating plate 10 and the electrode 7 are controlled to perform thermal therapy and electrotherapy.

[0048] The upper support member 2 and the lower support member 1 are provided with a skin galvanic response sensor 8 and an electromyography sensor 11 arranged in an upper and lower arrangement on the rear inner wall. The skin galvanic response sensor 8 and the electromyography sensor 11 are used to monitor leg reactions in real time.

[0049] Skin conductivity change rate of the galvanic skin response sensor 8:

[0050]

[0051] Threshold setting: setting the threshold of GSR change to judge the patient's pain and emotional state;

[0052] EMG sensor 11 signal strength:

[0053]

[0054] Threshold setting, set the threshold of EMG signal intensity, which is used to judge muscle tension and pain response.

[0055] The signal processing control module 904 also includes a signal receiving module, a signal sending module, a signal processing module, a temperature control module and an electrode output power adjustment module. The control platform 12 also includes a signal receiving module, a signal sending module, a signal processing module and a neural stimulation feedback system. The neural feedback system control module controls the output power of the heating plate 10 and the electrode 7 in detail as follows:

[0056] S1. Data collection, simultaneously collects changes in leg skin conductivity, data reflecting emotions and pain levels, and muscle electrical activity, reflecting muscle tension and pain response, and continuously monitors the patient's skin electrical response and muscle activity;

[0057] S2. Signal processing: filtering and denoising the collected data to improve signal quality and extract key features from the pre-processed data, such as the amplitude of skin conductivity changes and the intensity of EMG signals;

[0058] S3. Treatment equipment adjustment: According to the extracted features, the preset proportional-integral-differential control algorithm is applied to adjust the output power of the heating plate 10 and the electrode 7. The signal processing control module 904 adjusts the output power of the heating plate 10 and the electrode 7 according to the algorithm results. The proportional-integral-differential control algorithm uses three terms, namely proportional, integral and differential, to adjust the output. The proportional term P is proportional to the current error and directly affects the output. The integral term I is proportional to the cumulative sum of past error values ​​and helps to eliminate steady-state errors. The differential term D is proportional to the error change rate and helps to predict the trend of trace errors. The formula is:

[0059]

[0060] Where u(t) is the control output, e(t) is the error, and K p , K i and K d They are proportional, integral and derivative gains respectively;

[0061] Output power adjustment:

[0062] P new =P current ±ΔP

[0063] Among them, P new is the new output power, P currentis the current output power, ΔP is the power change adjusted according to the data. By real-time monitoring of the patient's skin conductivity and muscle activity, the PID control algorithm can adjust the treatment parameters according to the patient's immediate response and provide personalized treatment plans. Different patients respond differently to heat and electrotherapy. PID control allows the intensity of treatment to be adjusted according to the specific needs of the individual. The PID control algorithm can accurately control the output power of the heating plate and electrodes to ensure the safety and effectiveness of the treatment process;

[0064] S4. Data recording and integration: recording sensor data and treatment parameters during each treatment, integrating data from multiple treatments, and performing trend analysis;

[0065] S5. Optimize the treatment process. Adjust the treatment parameters based on the results of data integration, optimize the treatment process, and make personalized treatment adjustments based on the patient's specific response.

[0066] According to the optimization process in step S5 above, the treatment process is optimized by using a machine learning algorithm. The machine learning algorithm can be used to learn the treatment effect from historical data and predict the best treatment parameters based on new data. The model is trained using labeled historical data and a clustering algorithm is used to identify patterns and groups in the data. The strategy is learned through interaction with the environment to maximize the cumulative reward. The loss function is:

[0067]

[0068] Among them, L(θ) is the loss function, y i It is the actual effect of treatment. is the treatment result predicted by the model, θ is the model parameter. Through treatment process optimization, the treatment parameters are adjusted according to the results of data integration, the treatment process is optimized, and personalized treatment adjustments are made according to the patient's specific response. By optimizing the treatment parameters, the time required for each treatment can be shortened and the treatment efficiency can be improved. By providing the best personalized treatment, the rehabilitation process can be accelerated and the recovery time can be reduced.

[0069] Multiple heating wires are arranged vertically inside the two heating plates 10, and the multiple heating wires are individually controlled. Multiple electrodes 7 are grouped in two horizontal directions, and the multiple groups of electrodes 7 are individually controlled, so that they can be individually controlled according to the specific position of the legs, and hot compress and electrotherapy can be performed on the specific position.

[0070] Working principle: Use 3D technology to scan the patient's legs, and use 3D printing technology to print out the upper support 2, the lower support 1 and the foot support 3 according to the patient's leg size, so as to better fit the patient's legs. The legs are fully supported by the upper support 2, the lower support 1 and the foot support 3, reducing pressure points and unnecessary stress. Heat therapy through the heating plate 10 can dilate blood vessels, promote local blood circulation, help deliver more oxygen and nutrients to the injured area, accelerate the healing process, help relax tense muscles, and relieve muscle spasms and stiffness. Electrotherapy, especially transcutaneous electrical nerve stimulation, can relieve pain by interfering with the transmission of pain signals and help maintain muscle mass and strength, especially when the patient is unable to perform active movements.

[0071] By real-time monitoring of the patient's skin conductivity and muscle activity, the PID control algorithm can adjust the treatment parameters according to the patient's immediate response and provide a personalized treatment plan. Different patients respond differently to heat therapy and electrotherapy. PID control allows the treatment intensity to be adjusted according to the specific needs of the individual. The PID control algorithm can accurately control the output power of the heating plate 10 and the electrode 7 to ensure the safety and effectiveness of the treatment process. Through treatment process optimization, the treatment parameters are adjusted according to the results of data integration to optimize the treatment process. According to the patient's specific response, personalized treatment adjustments are made. By optimizing treatment parameters, the time required for each treatment can be shortened and the treatment efficiency can be improved. By providing the best personalized treatment, the rehabilitation process can be accelerated and the recovery time can be reduced.

[0072] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A nursing auxiliary device for chronic bone pain exercise, comprising a control platform (12), a lower support member (1), an upper support member (2) and a foot support member (3), wherein the upper support member (2) is arranged at the upper end of the lower support member (1), the foot support member (3) is arranged at the lower end of the lower support member (1), and the control platform (12) is arranged at one side of the lower support member (1), characterized in that: The upper end surface of the lower support member (1) is provided with a first connecting member (5) on both sides, the lower end surface of the upper support member (2) is provided with a second connecting member (6) on both sides, the two first connecting members (5) are rotatably connected to the two second connecting members (6) respectively, the upper support member (2) and the lower support member (1) are provided with a power supply structure (9) at the upper center of the rear end surface, the upper support member (2) and the lower support member (1) are provided with a heating plate (10) at the center of the rear inner wall, and a plurality of electrodes (7) are arranged in an upper and lower manner at the center of the two inner walls of the upper support member (2) and the center of the two inner walls of the lower support member (1); The power supply structure (9) comprises a housing (901), a cover plate (902), a battery (903) and a signal processing control module (904); the battery (903) is arranged at the lower center of the housing (901); the signal processing control module (904) is arranged inside the housing (901) at the upper end of the battery (903); and the cover plate (902) is arranged on the rear end surface of the housing (901).

2. The nursing auxiliary device for chronic bone pain exercise according to claim 1, characterized in that: The upper support member (2) and the lower support member (1) are both provided with a skin galvanic response sensor (8) and an electromyography sensor (11) arranged in an upper and lower arrangement on the rear inner wall.

3. The nursing auxiliary device for chronic bone pain exercise according to claim 2, characterized in that: The skin conductivity change rate of the galvanic skin response sensor (8) is: Threshold setting: setting the threshold of GSR change to judge the patient's pain and emotional state; Signal strength of the electromyography sensor (11): Threshold setting, set the threshold of EMG signal intensity, which is used to judge muscle tension and pain response.

4. The nursing auxiliary device for chronic bone pain exercise according to claim 1, characterized in that: The signal processing control module further includes a signal receiving module, a signal sending module, a signal processing module, a temperature control module and an electrode output power adjustment module. The control platform (12) further includes a signal receiving module, a signal sending module, a signal processing module and a neural stimulation feedback system. The neural feedback system control module controls the output power of the heating plate (10) and the electrode (7) in detail as follows: S1. Data collection, simultaneously collects changes in leg skin conductivity, data reflecting emotions and pain levels, and muscle electrical activity, reflecting muscle tension and pain response, and continuously monitors the patient's skin electrical response and muscle activity; S2. Signal processing: filtering and denoising the collected data to improve signal quality and extract key features from the pre-processed data, such as the amplitude of skin conductivity changes and the intensity of EMG signals; S3. The therapeutic device is adjusted. According to the extracted features, a preset proportional-integral-differential control algorithm is applied to adjust the output power of the heating plate (10) and the electrode (7). The signal processing control module (904) adjusts the output power of the heating plate (10) and the electrode (7) according to the algorithm result. The proportional-integral-differential control algorithm uses three terms, namely proportional, integral and differential, to adjust the output. The proportional term P is proportional to the current error and directly affects the output. The integral term I is proportional to the cumulative sum of past error values ​​and helps to eliminate steady-state errors. The differential term D is proportional to the error change rate and helps to predict the trend of trace errors. The formula is: Where u(t) is the control output, e(t) is the error, and K p , K i and K d They are proportional, integral and derivative gains respectively; Output power adjustment: P new =P current ±ΔP Among them, P new is the new output power, P current is the current output power, ΔP is the power change adjusted according to the data; S4. Data recording and integration: recording sensor data and treatment parameters during each treatment, integrating data from multiple treatments, and performing trend analysis; S5. Optimize the treatment process. Adjust the treatment parameters based on the results of data integration, optimize the treatment process, and make personalized treatment adjustments based on the patient's specific response.

5. The nursing auxiliary device for chronic bone pain exercise according to claim 4, characterized in that: According to the optimization process described in step S5 above, the treatment process is optimized by a machine learning algorithm. The machine learning algorithm can be used to learn the treatment effect from historical data and predict the best treatment parameters based on new data. The model is trained using labeled historical data, and a clustering algorithm is used to identify patterns and groups in the data. The strategy is learned through interaction with the environment to maximize the cumulative reward. The loss function is: Among them, L(θ) is the loss function, y i It is the actual effect of treatment. is the treatment outcome predicted by the model, and θ is the model parameter.

6. The nursing auxiliary device for chronic bone pain exercise according to claim 1, characterized in that: A plurality of heating wires are arranged vertically inside the two heating plates (10), and the plurality of heating wires are controlled individually. The plurality of electrodes (7) are grouped in pairs in each horizontal direction, and the plurality of groups of electrodes (7) are controlled individually.

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