Intelligent interconnected electric wheelchair for old people

Abstract

The invention discloses an intelligent interconnection electric wheelchair for old people, which comprises a wheelchair body and an intelligent interconnection system, and is characterized in that the wheelchair body comprises a wheelchair frame, a cushion part fixed at the top of the wheelchair frame, and a backrest part fixed at the top of the cushion part; a headrest part is mounted at the top of the backrest part, and a plurality of armrests are mounted on the side, facing the headrest part, of the backrest part; through introduction of an intelligent interconnection system and cooperation of an intelligent panel, a distance detection assembly and a subsystem, the intelligent spine protection system not only pays attention to the comfort level of the elderly user in the use process, but also improves the comfort level of the elderly user through accurate and personalized spine protection guide regulation and control, emergency situation processing and comprehensive consideration of user experience and safety. The sitting posture health of the user is protected, comprehensive health guarantee and comfortable experience are provided for senile users with osteoporosis, arthritis and the like, and the health risk caused by aggravation of original disease symptoms due to long-time poor sitting posture is greatly avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of wheelchairs, and in particular to motor-driven ones, specifically to an intelligent interconnected electric wheelchair for the elderly. Background Art

[0002] As the global population ages, the quality of life and health issues of the elderly population are receiving increasing attention from society. Among them, osteoporosis and arthritis, as common chronic diseases among the elderly, seriously affect patients' daily activities and quality of life. For such elderly users, the rationality of the design and use of electric wheelchairs as auxiliary mobility tools is particularly important.

[0003] However, the electric wheelchairs currently on the market often ignore the sitting posture needs of user groups with specific diseases, which leads to long-term poor sitting posture during use, aggravating the symptoms of the original disease and even causing new health problems.

[0004] Therefore, it is necessary to improve the deficiencies in the prior art to solve the above problems. Summary of the invention

[0005] The present invention overcomes the deficiencies of the prior art and provides an intelligent interconnected electric wheelchair for the elderly.

[0006] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: an intelligent interconnected electric wheelchair for the elderly, comprising: a wheelchair body and an intelligent interconnection system,

[0007] The wheelchair body comprises: a wheelchair frame, a cushion portion fixed on the top of the wheelchair frame, and a backrest portion fixed on the top of the cushion portion; a headrest portion is installed on the top of the backrest portion, and a plurality of armrests are installed on the side of the backrest portion facing the headrest portion, and an electric translation mechanism for electrically driving the wheelchair frame to move is provided on the wheelchair frame;

[0008] The intelligent interconnection system includes: an intelligent panel rotatably connected to one end of the armrest, a distance detection component arranged on the backrest and the headrest, and a subsystem arranged inside the intelligent panel;

[0009] The smart panel is used to display information to the user and perform touch-screen operations, and send user data to the subsystem; the distance detection component is used to respectively detect the distance data of the user's hips, waist and neck relative to the backrest; the subsystem is used to match the waist force according to the user data, and calculate and analyze the user's waist force value based on the distance data of the distance detection component using a set threshold, and perform morphological reminders or emergency operations.

[0010] In a preferred embodiment of the present invention, the subsystem includes: a control module, a data collection and processing module, a machine learning model, a threshold module, a voice reminder module, an information recording module and an emergency module;

[0011] The control module is used to issue control instructions;

[0012] The data collection and processing module is used to collect the user data, including basic information, body data and spine diagram of the user, and pre-process the user data to ensure data quality;

[0013] The machine learning model is used to learn and simulate according to the body data and the spine diagram, match and output a waist force table of a user with similar body data and spine morphology, and calculate the user's sitting posture according to the distance data to determine the user's current waist force value;

[0014] The threshold module is used to set the user's waist force threshold, and the distance threshold of the user's hips, waist and neck relative to the backrest, and determine whether the waist force value and the distance data exceed the threshold;

[0015] The voice reminder module is used to give a voice reminder of the corresponding posture to the user when the waist force value exceeds a set threshold;

[0016] The information recording module is used to store and record the waist force value change data;

[0017] The emergency module is used to trigger an emergency operation when the distance data exceeds a threshold.

[0018] In a preferred embodiment of the present invention, the smart panel is provided with a plurality of speakers for outputting voice reminders to the user, and the speakers are electrically connected to the voice reminder module.

[0019] In a preferred embodiment of the present invention, the distance detection component includes: a first distance sensor arranged on the side of the backrest near the bottom for detecting the distance of the user's buttocks relative to the backrest, a second distance sensor arranged on the side of the backrest near the middle for detecting the distance of the user's waist relative to the backrest, and a third distance sensor arranged on the side of the headrest for detecting the distance of the user's neck relative to the backrest; the first distance sensor, the second distance sensor and the third distance sensor are electrically connected to the control module respectively.

[0020] In a preferred embodiment of the present invention, the electric translation mechanism includes: two rear drive wheels and two universal front wheels rotatably connected to the bottom of the wheelchair frame, and a drive motor installed on the rear drive wheels; a motor driver for controlling the operation of the drive motor is installed at one end of the armrest, and a joystick for manually controlling the running direction of the rear drive wheels is installed on the top of the motor driver, the joystick is electrically connected to the motor driver, and the motor driver is electrically connected to the drive motor.

[0021] In a preferred embodiment of the present invention, a storage battery for providing power supply is installed at the bottom of the wheelchair frame, and the storage battery is electrically connected to the motor driver, the drive motor and the intelligent interconnection system respectively.

[0022] In a preferred embodiment of the present invention, a plurality of F-shaped support rods are installed on the side of the seat cushion portion facing the universal front wheel, and a foot pedal for supporting the foot is fixed to the bottom of the F-shaped support rod.

[0023] The present invention provides a control method for an intelligent interconnected electric wheelchair for the elderly, comprising the following steps:

[0024] S1, obtaining the user's basic information and physical data, as well as the user's spine map;

[0025] S2. According to the user's body data and spine diagram, matching and outputting waist force tables with body data and spine morphology similar to the user in different postures;

[0026] S3, setting the lumbar force threshold, as well as the thresholds of the distances of the hips, waist and neck relative to the backrest;

[0027] S4, real-time detection of the distance data of the hips, waist and neck relative to the backrest, when within the threshold range, real-time calculation of the user's spinal morphology, judgment of whether the waist force value under the current morphology is within the threshold range, when the waist force value exceeds the threshold range, output of the corresponding part of the voice reminder, and record and store the change data;

[0028] S5. Detect the distance data of the hips, waist and neck relative to the backrest. When the distance data exceeds the threshold range, an extreme situation is triggered and an emergency operation is performed.

[0029] In a preferred embodiment of the present invention, the step S1 includes the following sub-steps:

[0030] S11, obtaining basic information and body data of the user, as well as a spine diagram of the user; including basic information related to age, gender, height and weight, and body data of shoulder width and pelvic width;

[0031] S12. Remove duplicate, incomplete or abnormal data items from basic information and physical data, and normalize them to ensure that the data are on the same scale;

[0032] S13, denoising and contrast enhancement of the spine image, and performing spine segmentation and spine curve fitting processing to ensure that the user's spine morphology can be recognized.

[0033] In a preferred embodiment of the present invention, the step S4 includes the following sub-steps:

[0034] S41, real-time detection to obtain distance data of the hip, waist and neck relative to the backrest respectively, and determine whether the distance data is within a threshold range;

[0035] S42, if the distance data is within the threshold range, calculate the waist force value under the current spine morphology of the user in real time according to the distance data detected in real time, and determine whether the waist force value is within the threshold range;

[0036] Calculate the waist force value F of the user under the current spine shape wa :

[0037] F wa =k1·D wa +k2·(D hi -D wa )+k3·(D ne -D wa )+k4·A pe +k5·A sh , where D wa is the distance between the waist and the backrest, D hi is the distance between the buttocks and the backrest, D ne is the distance between the neck and the backrest, A pe is the pelvic width, A sh is shoulder width, C sp is the coefficient of vertebral morphological characteristics, k1, k2, k3, k4, k5 are proportional coefficients;

[0038] S43, if the waist force value is within the threshold range, then end;

[0039] S44: If the waist force value exceeds the threshold range, a voice reminder for adjusting the corresponding part is output to the user, and the change data of the waist force value is recorded and stored.

[0040] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0041] (1) The present invention provides an intelligent interconnected electric wheelchair for the elderly. By introducing an intelligent interconnected system and cooperating with an intelligent panel, a distance detection component and a subsystem, the present invention not only focuses on the comfort of elderly users during use, but also protects the user's sitting posture health through precise and personalized spinal protection guidance and regulation, emergency situation handling and comprehensive consideration of user experience and safety. It provides a full range of health protection and comfort experience for elderly users with osteoporosis, arthritis and other diseases, and greatly avoids the health risks caused by long-term poor sitting posture and aggravation of the original disease symptoms.

[0042] (2) In the present invention, through the setting of subsystems, with the cooperation of the control module, the data collection and processing module, the machine learning model, the threshold module, the voice reminder module, the information recording module and the emergency module, the user's sitting posture management is made more comprehensive and systematic. By combining the individualized analysis of the user with the reminder of the patient to restrain himself from sitting posture, the posture optimization can be personalized for the user himself to ensure that the user maintains the best sitting posture with waist stress, which can reduce the burden of self-management for the patient. In particular, for elderly patients with weak self-discipline, instant feedback is provided to maintain the correct wheelchair sitting posture, avoid users from maintaining bad sitting posture for a long time, and thus improve the effect of elderly users using wheelchairs.

[0043] (3) In the present invention, by obtaining the user's basic information, body data and spinal map, and performing denoising, contrast enhancement and spinal segmentation, the user's spinal morphology can be accurately identified. Combined with the machine learning algorithm, the lumbar force table that best suits the user's personal characteristics in different postures can be matched for the user. The personalized matching method not only improves the accuracy, but also ensures that the user can get the best spinal support in different sitting postures, effectively reducing the pressure on the spine of patients with osteoporosis and arthritis. At the same time, the real-time detected distance data is used to determine the threshold range of the waist force value obtained therefrom, and the output correction of the voice reminder is realized, guiding the user to adjust the sitting posture to reduce the burden on the waist. The instant feedback mechanism helps to prevent further spinal damage caused by long-term poor sitting posture.

[0044] (4) In the present invention, by combining the threshold with the emergency mode, the unexpected situations that elderly users may face are taken into consideration, such as sudden loss of consciousness resulting in excessive forward leaning of the body, etc., and potential risks can be discovered and responded to in the first place, which greatly improves the safety of elderly users in emergency situations and reduces the occurrence of accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 is a front view of a three-dimensional structure diagram of a wheelchair body according to a preferred embodiment of the present invention;

[0047] Figure 2 is a back-view stereoscopic structural diagram of a wheelchair body according to a preferred embodiment of the present invention;

[0048] Figure 3 is a structural schematic diagram of an intelligent interconnection system according to a preferred embodiment of the present invention;

[0049] Figure 4 is a schematic diagram of the structure of a subsystem of a preferred embodiment of the present invention;

[0050] Figure 5 is a flow chart of a control method of a smart interconnected electric wheelchair for the elderly according to a preferred embodiment of the present invention;

[0051] In the figure: 1. wheelchair body; 11. wheelchair frame; 12. seat cushion; 13. backrest; 14. headrest; 15. armrest; 2. intelligent interconnection system; 3. intelligent panel; 4. distance detection component; 41. first distance sensor; 42. second distance sensor; 43. third distance sensor; 5. subsystem; 51. control module; 52. data collection and processing module; 53. machine learning model; 54. threshold module; 55. voice reminder module; 56. information recording module; 57. emergency module; 6. speaker; 7. rear drive wheel; 71. universal front wheel; 72. drive motor; 73. motor driver; 74. joystick; 8. energy storage battery; 9. F-type support rod; 91. pedal. DETAILED DESCRIPTION

[0052] 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.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0054] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the scope of protection of the present application. In addition, the terms "first", "second", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, features defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of the invention, unless otherwise specified, "multiple" means two or more.

[0055] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0056] Application Overview:

[0057] Impact of bad sitting posture:

[0058] For example, the buttocks are away from the chair and the back is against the backrest: in this sitting position, the body weight is mainly concentrated on a small area of ​​the buttocks, especially the back of the ischium and the coccyx area. For elderly users with osteoporosis, this concentrated pressure can easily lead to an increased risk of fractures. At the same time, since the whole body slides downward and lacks effective support, it not only aggravates the body's fatigue, but may also affect the blood circulation of the lower limbs, further affecting health.

[0059] If the buttocks are against the backrest and the back is leaning forward: In this sitting position, the front of the ischium bears the weight, and the lumbar spine is forced to bend forward excessively, which increases the pressure on the lumbar intervertebral disc, accelerates the loss of lumbar bone, and increases the risk of lumbar fracture. In addition, the long-term tension of the back muscles can easily cause muscle spasms and pain, affect the comfort of sitting, and may increase the risk of falling due to muscle weakness. For arthritis patients, this posture will also change the physiological curvature of the spine, aggravate the wear and degeneration of the lumbar spine and surrounding joints, and induce or aggravate arthritis symptoms.

[0060] In view of the above technical problems, the concept of the present invention is to propose an intelligent interconnected electric wheelchair for the elderly.

[0061] like Figure 1 and Figure 3 As shown, a smart interconnected electric wheelchair for the elderly comprises: a wheelchair body 1 and a smart interconnected system 2, wherein the wheelchair body 1 comprises: a wheelchair frame 11, a cushion portion 12 fixed to the top of the wheelchair frame 11, and a backrest portion 13 fixed to the top of the cushion portion 12; a headrest portion 14 is installed on the top of the backrest portion 13, and a plurality of armrests 15 are installed on the side of the backrest portion 13 facing the headrest portion 14, and an electric translation mechanism for electrically driving the wheelchair frame 11 to move is arranged on the wheelchair frame 11; the smart interconnected system 2 comprises: a smart panel 3 rotatably connected to one end of the armrest 15, A distance detection component 4 is arranged on the backrest portion 13 and the headrest portion 14, and a subsystem 5 is arranged inside the smart panel 3; the smart panel 3 is used to display information to the user and perform touch-screen operations by itself, and send the user data to the subsystem 5; the distance detection component 4 is used to respectively detect the distance data of the user's buttocks, waist and neck relative to the backrest portion 13; the subsystem 5 is used to match the waist force according to the user data, and calculate and analyze the user's waist force value according to the distance data of the distance detection component 4 using a set threshold value, and perform morphological reminders or emergency operations.

[0062] It should be noted that the cushion part 12, the backrest part 13 and the headrest part 14 facing the user can be detachably provided with a flexible skin-friendly pad to improve the user's comfort, and can be detached and washed after long-term use; in the wheelchair body 1, the wheelchair frame 11 is used to achieve the overall support effect, and the cushion part 12, the backrest part 13 and the headrest part 14 complete the flexible support of the three sides of the user, and with the cooperation of the electric translation mechanism, the wheelchair frame 11 is electrically driven to move, and at the same time, the introduction of the intelligent interconnection system 2, with the cooperation of the intelligent panel 3, the distance detection component 4 and the subsystem 5, makes the present invention not only focus on the comfort of elderly users during use, but also protect the user's sitting posture health through precise and personalized spinal protection guidance and regulation, emergency situation handling and comprehensive consideration of user experience and safety, etc., providing all-round health protection and comfort experience for elderly users with osteoporosis and arthritis, etc., and greatly avoiding the health risks caused by long-term poor sitting posture and aggravation of the original disease symptoms.

[0063] like Figure 4As shown, in some embodiments, the subsystem 5 includes: a control module 51, a data collection and processing module 52, a machine learning model 53, a threshold module 54, a voice reminder module 55, an information recording module 56 and an emergency module 57; the control module 51 is used to issue control instructions; the data collection and processing module 52 is used to collect user data, including basic information, body data and spinal diagrams related to the user, and pre-process the user data to ensure data quality; the machine learning model 53 is used to learn and simulate according to the body data and spinal diagram, match and output similar body data and The lumbar force table of the spine morphology is used to calculate the user's sitting posture according to the distance data to determine the user's current lumbar force value; the threshold module 54 is used to set the user's lumbar force threshold, as well as the distance threshold of the user's hips, waist and neck relative to the backrest 13, and determine whether the lumbar force value and distance data exceed the threshold; the voice reminder module 55 is used to give the user a voice reminder of the corresponding posture when the lumbar force value exceeds the set threshold; the information recording module 56 is used to store and record the lumbar force value change data; the emergency module 57 is used to trigger an emergency operation when the distance data exceeds the threshold.

[0064] It should be noted that the user uses the smart panel 3 to provide basic information, body data and spinal diagram to the data collection and processing module 52. The data collection and processing module 52 completes the collection of user data and preprocesses the user data. The control module 51 issues instructions to transmit the preprocessed body data and spinal diagram features to the machine learning model 53. The machine learning model 53 performs learning and simulation, and matches and outputs the waist force table of the user's similar body data and spinal morphology to the smart panel 3 for display to the user. The user uses the smart panel 3 to send the threshold module 54 through the control module 51. Send instructions to set the waist force threshold and the distance threshold of the user's hips, waist and neck relative to the backrest 13; the distance detection component 4 detects the distance data of the user's hips, waist and neck relative to the backrest in real time, and transmits it to the threshold module 54 to determine whether the distance data is within the threshold range; if the distance data is within the threshold range, the threshold module 54 transmits the distance data to the machine learning model 53, and the machine learning model 53 calculates the waist force value of the user in the current sitting posture according to the distance data, and transmits it back to the threshold module 54 to determine whether the waist force value is within the threshold range; If the waist force value exceeds the threshold range, the threshold module 54 transmits a signal to the control module 51, and the control module 51 issues an instruction to control the voice reminder module 55 to output a voice signal of the corresponding part voice reminder content, and controls the information recording module 56 to store and record the waist force value change data; if the distance data exceeds the threshold range, the threshold module 54 transmits a signal to the control module 51, and the control module 51 issues an instruction to control the emergency module 57 to trigger the emergency operation; through the setting of the subsystem 5, in the control module 51, the data collection and processing module 52, the machine learning model 53, the threshold module 54. With the cooperation of the voice reminder module 55, the information recording module 56 and the emergency module 57, the user's sitting posture management becomes more comprehensive and systematic. By combining the individualized analysis of the user with the reminder of the patient's self-sitting posture restraint, the user's posture can be optimized in a personalized manner to ensure that the user maintains the best sitting posture with the waist stressed. This can reduce the burden of self-management for the patient, and provide instant feedback, especially for elderly patients with weak self-discipline, to maintain the correct wheelchair sitting posture, and avoid the user maintaining a bad sitting posture for a long time, thereby improving the effect of elderly users using wheelchairs.

[0065] like Figure 1 and Figure 2 As shown, in some embodiments, the smart panel 3 is provided with a plurality of speakers 6 for outputting voice reminders to the user, and the speakers 6 are electrically connected to the voice reminder module 55 .

[0066] It should be noted that, through the setting of the speaker 6, the voice reminder module 55 can convert the designed voice reminder content into a voice signal, and play it through the speaker 6, so as to achieve the voice adjustment reminder of the user's posture.

[0067] In some embodiments, the distance detection component 4 includes: a first distance sensor 41 disposed on the side near the bottom of the backrest 13 for detecting the distance of the user's buttocks relative to the backrest 13, a second distance sensor 42 disposed on the side near the middle of the backrest 13 for detecting the distance of the user's waist relative to the backrest 13, and a third distance sensor 43 disposed on the side of the headrest 14 for detecting the distance of the user's neck relative to the backrest 13; the first distance sensor 41, the second distance sensor 42 and the third distance sensor 43 are electrically connected to the control module 51, respectively.

[0068] It should be noted that the first distance sensor 41 is facing the user's sciatic region, and is used to provide buttocks detection to help maintain the sitting posture of the buttocks. The second distance sensor 42 is facing the user's thoracic spine region, and is used to provide waist detection to help maintain the upright posture of the upper body. The third distance sensor 43 is facing the user's cervical spine region, and is used to provide neck detection to prevent the head from leaning forward excessively. Through the cooperation of the first distance sensor 41, the second distance sensor 42 and the third distance sensor 43, the subsystem 5 can provide real-time detection of the distance between the user's buttocks, waist and neck relative to the backrest 13, thereby providing effective distance data.

[0069] In some embodiments, the electric translation mechanism includes: two rear drive wheels 7 and two universal front wheels 71 rotatably connected to the bottom of the wheelchair frame 11, and a drive motor 72 installed on the rear drive wheels 7; a motor driver 73 for controlling the operation of the drive motor 72 is installed at one end of the armrest 15, and a joystick 74 for manually controlling the running direction of the rear drive wheels 7 is installed on the top of the motor driver 73, the joystick 74 is electrically connected to the motor driver 73, and the motor driver 73 is electrically connected to the drive motor 72.

[0070] It should be noted that the two rear drive wheels 7 adopt a dual-wheel differential drive mode to meet the needs of the electric wheelchair when traveling in different directions. The user pushes and pulls the joystick 74 at one end of the armrest 15 according to the required direction of travel. The motor driver 73 can be used to control the drive motor 72 on the rear drive wheel 7 to rotate forward or reverse, so that it drives the two rear drive wheels 7 to rotate accordingly, thereby driving the electric wheelchair to move. The operation is controllable and all are located at the hand, which improves the overall convenience of use and helps to improve the user's controllability of the electric wheelchair.

[0071] It can be understood that the motor driver 73 can control the drive motor 72 to realize various electric control functions of the electric wheelchair. The control circuit of the motor driver 73 can be realized by simple programming by technicians in this field, which is common knowledge in this field. Therefore, this application document no longer explains the control method and circuit connection in detail, and therefore will not be described in detail.

[0072] In some embodiments, a storage battery 8 for providing power supply is installed at the bottom of the wheelchair frame 11, and the storage battery 8 is electrically connected to the motor driver 73, the drive motor 72 and the intelligent interconnection system 2 respectively.

[0073] It should be noted that, by providing the energy storage battery 8 , it is possible to supply the electric energy required by the motor driver 73 , the drive motor 72 and the intelligent interconnection system 2 .

[0074] In some embodiments, a plurality of F-shaped support rods 9 are installed on the side of the seat cushion portion 12 facing the universal front wheel 71 , and a foot pedal 91 for supporting the foot is fixed to the bottom of the F-shaped support rod 9 .

[0075] It should be noted that the number of F-shaped support rods 9 is preferably two. When the user sits on the seat cushion portion 12, the foot pedals 91 at the bottom of the two F-shaped support rods 9 can serve as a support surface for the feet, and the top of the foot pedals 91 is provided with anti-slip treatment, such as linear or dotted anti-slip patterns, to improve the friction effect with the shoe surface of the foot.

[0076] like Figure 5 As shown, the present invention provides a control method for an intelligent interconnected electric wheelchair for the elderly, comprising the following steps:

[0077] S1, obtaining the user's basic information and physical data, as well as the user's spine map;

[0078] S2. According to the user's body data and spine diagram, matching and outputting waist force tables with body data and spine morphology similar to the user in different postures;

[0079] S3, setting the lumbar force threshold, as well as the thresholds of the distances of the hips, waist and neck relative to the backrest;

[0080] S4, real-time detection of the distance data of the hips, waist and neck relative to the backrest, when within the threshold range, real-time calculation of the user's spinal morphology, judgment of whether the waist force value under the current morphology is within the threshold range, when the waist force value exceeds the threshold range, output of the corresponding part of the voice reminder, and record and store the change data;

[0081] S5. Detect the distance data of the hips, waist and neck relative to the backrest. When the distance data exceeds the threshold range, an extreme situation is triggered and an emergency operation is performed.

[0082] In some specific embodiments, the step of S1 includes the following sub-steps:

[0083] S11, obtaining basic information and physical data of the user, and a spine map of the user;

[0084] S12. Remove duplicate, incomplete or abnormal data items from basic information and physical data, and normalize them to ensure that the data are on the same scale;

[0085] S13, denoising and contrast enhancement of the spine image, and performing spine segmentation and spine curve fitting processing to ensure that the user's spine morphology can be recognized.

[0086] In this embodiment, in step S11, basic information includes but is not limited to age, gender, height and weight, physical data includes but is not limited to body shape, shoulder width, pelvic width, and spine image users can obtain medical images of the spine through X-ray or CT technology.

[0087] In this embodiment, in step S12, it is necessary to remove duplicate data from the acquired basic information and physical data, and identify and process abnormal values: abnormal values ​​may be caused by data entry errors or special circumstances of the patient, and need to be processed according to the actual situation, such as replacing them with mean, median or deleting them; normalization processing is specifically to scale the data so that it falls into a small specific interval, such as [0,1] or [-1,1], which helps to eliminate the influence of the dimension on the result, and can adopt minimum or maximum normalization, Z-score normalization, etc.

[0088] In this embodiment, in step S13, it is necessary to remove noise from the acquired spinal image to improve the image quality, and use a spatial domain denoising method to perform denoising in the image spatial domain, such as median filtering or mean filtering, to remove noise by calculating the statistical characteristics of the neighborhood around the pixel point; enhance the contrast between the spine and other soft tissues in the image to make the spine more prominent, and use adaptive histogram equalization to automatically adjust the distribution of the histogram according to the local characteristics of the image to achieve the effect of enhancing the contrast; use the edge detection operator of the Sobel operator or the Canny operator to detect the contour line of the spine in the spinal image, and then use this edge information to segment, and accurately segment the spine from the image for subsequent analysis or evaluation; use a polynomial function to fit the contour line of the spine to obtain a mathematical expression of the spinal curve.

[0089] Specifically, polynomial curve fitting is used to estimate a polynomial function through a set of data points to describe the trend or shape of these data points, and select an n-order polynomial model:

[0090] p(x)=a0+a1x+a2x+…+a n x n , where a0, a1, ..., a n are the coefficients of the polynomial, and the x term is the horizontal coordinate of the data point;

[0091] Use the least squares method to minimize the squared error between the data points and the polynomial curve and solve for the coefficients: Among them, (x i ,y i ) are the data points on the contour line, and m is the number of data points;

[0092] Convert the above minimization problem into a linear algebra problem and solve the coefficients by solving the linear equations: a0, a1, ..., a n , use statistical indicators (such as mean square error or R square value) to evaluate the fitting effect.

[0093] In some specific implementation schemes, in step S2, the waist force table refers to the force value of the human waist in different postures, such as sitting, sitting with hunched back, etc. For example, under a normal spine, in a standard sitting posture, that is, the buttocks and back are close to the backrest, there is a fist of space between the waist and the backrest, and the neck is naturally upright, the waist force value is 140kg; in a sitting with hunched back, that is, the buttocks are close to the backrest, the back and neck are tilted forward at 45°, and the waist force value is 185kg; wherein, different postures refer to the user's bending or other postures based on the distance of the buttocks, waist and neck relative to the backrest when maintaining a certain posture based on the user's body data and spinal characteristics; the matching output adopts a machine learning algorithm to enable it to automatically learn and discover rules and patterns from the data, and then use these rules and patterns to perform prediction, classification, clustering, and dimensionality reduction tasks. Preferably, a deep learning model is used, combined with artificial intelligence algorithms for modeling and simulation, and neural network tools are used for regression learning.

[0094] Specifically, the machine learning algorithm calculates the ideal distance D between the waist and the backrest for matching different postures. id :D id =k1·A pe +k2·A sh +k3·C sp ;

[0095] Calculate the waist force F wa :

[0096] F wa = k1·(D wa -D id )+k2·(D hi -D id )+k3·(D ne -D id )+k4·C sp +k5·A pe ·A sh , where D wa is the distance between the waist and the backrest, D hi is the distance between the buttocks and the backrest, Dne is the distance between the neck and the backrest, A pe is the pelvic width, A sh is shoulder width, C sp is the coefficient of spinal morphological characteristics, k1, k2, k3, k4, k5 are proportional coefficients, specifically, when establishing a preliminary mathematical model, the model links the physiological measurement data with the lumbar force value, and the model contains unknown proportional coefficients. The collected data are subjected to curve fitting or regression analysis using statistical software or data analysis libraries (such as MATLAB, Python's NumPy or SciPy, etc.). During the fitting process, the value of the proportional coefficient is adjusted until the difference between the lumbar force value predicted by the model and the actual measured value is minimized; for example, it is found through experimental data that for every 1 cm increase in the distance between the waist and the backrest, the lumbar force value increases by an average of 0.5 kg, then the value of k1 can be set to 0.5, so that the model can match the lumbar force value according to the specific situation of the user;

[0097] User's spine morphological characteristic coefficient C sp If the user has scoliosis or hunchback, the F wa Calculation of: F wa =F wa ·(1+α·|C sp |), where α It is a coefficient adjusted according to the severity of the user's spinal morphology;

[0098] For example, the user's spine morphology coefficient C sp is 0.7, indicating that the user has a certain degree of scoliosis. Medical experts determine the coefficient α of the severity of the spinal morphology to be 0.5 based on the user's specific situation. If the original calculated waist force value F wa is 140kg, then the adjusted waist force value will be F wa =140·(1+0.5·|0.7|)=189kg. The adjusted waist force value reflects the additional waist force caused by the user's spinal morphology deviation.

[0099] The calculated F wa It is presented to the user in the form of a graph, which can show the changes in lumbar force under different sitting postures, so that the matching output can be completed.

[0100] In some specific implementation schemes, in step S3, the setting of the lumbar force threshold should be performed by floatingly setting the threshold in the lumbar force table output by the user in different sitting postures with the posture with the smallest lumbar force value, so that the threshold setting can be personalized as much as possible; and the threshold setting of the distance between the hips, waist and neck and the backrest should be as large as possible compared to the distance between the ordinary sitting posture and the backrest, so as to avoid the error of triggering extreme situations.

[0101] In some specific embodiments, the step of S4 includes the following sub-steps:

[0102] S41, real-time detection to obtain distance data of the hip, waist and neck relative to the backrest respectively, and determine whether the distance data is within a threshold range;

[0103] S42, if the distance data is within the threshold range, calculate the waist force value under the current spine morphology of the user in real time according to the distance data detected in real time, and determine whether the waist force value is within the threshold range;

[0104] S43, if the waist force value is within the threshold range, then end;

[0105] S44: If the waist force value exceeds the threshold range, a voice reminder for adjusting the corresponding part is output to the user, and the change data of the waist force value is recorded and stored.

[0106] In this embodiment, in step S42, for the distance data detected in real time, the waist force value F of the user under the current spine shape is calculated. wa :

[0107] F wa =k1·D wa +k2·(D hi -D wa )+k3·(D ne -D wa )+k4·A pe +k5·A sh , where D wa is the distance between the waist and the backrest, D hi is the distance between the buttocks and the backrest, D ne is the distance between the neck and the backrest, A pe is the pelvic width, A sh is shoulder width, C sp is the coefficient of vertebral morphological characteristics, k1, k2, k3, k4, k5 are proportional coefficients;

[0108] Determine whether the calculated waist force value exceeds the set threshold:

[0109] isWithinRange=F wa ≤T wa , where T wa It is the waist force threshold set in step S3.

[0110] In this embodiment, in step S44, the voice reminder for adjusting the corresponding part is specifically to determine the characteristics of the spinal morphology according to the waist force value in step S42, and guide the cervical spine, thoracic spine or ischial spine to adjust to the correct sitting posture with the waist force value within the threshold range, such as, please adjust the sitting posture, let the buttocks close to the back of the chair, please keep the back upright, and do not lean the head forward, etc.; during the real-time detection process, key information such as the waist force value, the distance data of the buttocks, waist and neck relative to the backrest can be continuously collected, and the user's subsequent data analysis and query can be carried out, and the mobile phone APP, computer and other smart devices can be connected via Bluetooth or Wi-Fi to timely understand the user's posture changes. At the same time, the user can set adjustment, recording and other functions.

[0111] Increase the interconnection effect and provide more convenient and efficient management methods.

[0112] In some specific implementation schemes, in step S5, if the real-time detected distance data of the hips, waist and neck relative to the backrest exceeds the threshold range, an extreme situation is triggered, such as the user suddenly losing consciousness, causing excessive forward leaning, etc., so that the user can perform emergency operations, including but not limited to emergency braking, issuing emergency alarms or automatically dialing emergency contacts, etc., to ensure the safety of the user in an emergency and reduce the occurrence of accidents.

[0113] The above is based on the ideal embodiment of the present invention. Through the above description, it is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is limited by the attached claims rather than the above description, and it is intended to include all changes within the meaning and scope of the equivalent elements of the claims. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0114] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A smart interconnected electric wheelchair for the elderly, comprising: The wheelchair body and intelligent interconnection system are characterized by: The wheelchair body comprises: a wheelchair frame, a cushion portion fixed on the top of the wheelchair frame, and a backrest portion fixed on the top of the cushion portion; a headrest portion is installed on the top of the backrest portion, and a plurality of armrests are installed on the side of the backrest portion facing the headrest portion, and an electric translation mechanism for electrically driving the wheelchair frame to move is provided on the wheelchair frame; The intelligent interconnection system includes: an intelligent panel rotatably connected to one end of the armrest, a distance detection component arranged on the backrest and the headrest, and a subsystem arranged inside the intelligent panel; The smart panel is used to display information to the user and perform touch-screen operations, and send user data to the subsystem; the distance detection component is used to respectively detect the distance data of the user's hips, waist and neck relative to the backrest; the subsystem is used to match the waist force according to the user data, and calculate and analyze the user's waist force value based on the distance data of the distance detection component using a set threshold, and perform morphological reminders or emergency operations.

2. The intelligent interconnected electric wheelchair for the elderly according to claim 1, characterized in that: The subsystem includes: a control module, a data collection and processing module, a machine learning model, a threshold module, a voice reminder module, an information recording module and an emergency module; The control module is used to issue control instructions; The data collection and processing module is used to collect the user data, including basic information, body data and spine diagram of the user, and pre-process the user data to ensure data quality; The machine learning model is used to learn and simulate according to the body data and the spine diagram, match and output a waist force table of a user with similar body data and spine morphology, and calculate the user's sitting posture according to the distance data to determine the user's current waist force value; The threshold module is used to set the user's waist force threshold, and the distance threshold of the user's hips, waist and neck relative to the backrest, and determine whether the waist force value and the distance data exceed the threshold; The voice reminder module is used to give a voice reminder of the corresponding posture to the user when the waist force value exceeds a set threshold; The information recording module is used to store and record the waist force value change data; The emergency module is used to trigger an emergency operation when the distance data exceeds a threshold.

3. The intelligent interconnected electric wheelchair for the elderly according to claim 2, characterized in that: The smart panel is provided with a plurality of speakers for outputting voice reminders to the user, and the speakers are electrically connected to the voice reminder module.

4. The intelligent interconnected electric wheelchair for the elderly according to claim 2, characterized in that: The distance detection component includes: a first distance sensor arranged on the side of the backrest near the bottom for detecting the distance between the user's buttocks and the backrest, a second distance sensor arranged on the side of the backrest near the middle for detecting the distance between the user's waist and the backrest, and a third distance sensor arranged on the side of the headrest for detecting the distance between the user's neck and the backrest; the first distance sensor, the second distance sensor and the third distance sensor are electrically connected to the control module respectively.

5. The intelligent interconnected electric wheelchair for the elderly according to claim 1, characterized in that: The electric translation mechanism includes: two rear drive wheels and two universal front wheels rotatably connected to the bottom of the wheelchair frame, and a drive motor installed on the rear drive wheels; a motor driver for controlling the operation of the drive motor is installed at one end of the armrest, and a joystick for manually controlling the running direction of the rear drive wheels is installed on the top of the motor driver, the joystick is electrically connected to the motor driver, and the motor driver is electrically connected to the drive motor.

6. The intelligent interconnected electric wheelchair for the elderly according to claim 5, characterized in that: An energy storage battery for providing power supply is installed at the bottom of the wheelchair frame, and the energy storage battery is electrically connected to the motor driver, the drive motor and the intelligent interconnection system respectively.

7. The intelligent interconnected electric wheelchair for the elderly according to claim 5, characterized in that: A plurality of F-shaped support rods are installed on the side of the cushion portion facing the universal front wheel, and a foot pedal for supporting the feet is fixed at the bottom of the F-shaped support rod.

8. A control method for a smart interconnected electric wheelchair for the elderly based on any one of claims 1-7, characterized in that: The following steps are involved: S1, obtaining the user's basic information and physical data, as well as the user's spine map; S2. According to the user's body data and spine diagram, matching and outputting waist force tables with body data and spine morphology similar to the user in different postures; S3, setting the lumbar force threshold, as well as the thresholds of the distances of the hips, waist and neck relative to the backrest; S4, real-time detection of the distance data of the hips, waist and neck relative to the backrest, when within the threshold range, real-time calculation of the user's spinal morphology, judgment of whether the waist force value under the current morphology is within the threshold range, when the waist force value exceeds the threshold range, output of the corresponding part of the voice reminder, and record and store the change data; S5. Detect the distance data of the hips, waist and neck relative to the backrest. When the distance data exceeds the threshold range, an extreme situation is triggered and an emergency operation is performed.

9. The control method of a smart interconnected electric wheelchair for the elderly according to claim 8, characterized in that: The step S1 includes the following sub-steps: S11, obtaining basic information and body data of the user, as well as a spine diagram of the user; including basic information related to age, gender, height and weight, and body data of shoulder width and pelvic width; S12. Remove duplicate, incomplete or abnormal data items from basic information and physical data, and normalize them to ensure that the data are on the same scale; S13, denoising and contrast enhancement of the spine image, and performing spine segmentation and spine curve fitting processing to ensure that the user's spine morphology can be recognized.

10. The control method of a smart interconnected electric wheelchair for the elderly according to claim 8, characterized in that: The step S4 includes the following sub-steps: S41, real-time detection to obtain distance data of the hip, waist and neck relative to the backrest respectively, and determine whether the distance data is within a threshold range; S42, if the distance data is within the threshold range, calculate the waist force value under the current spine morphology of the user in real time according to the distance data detected in real time, and determine whether the waist force value is within the threshold range; Calculate the waist force value F of the user under the current spine shape wa : F wa =k1·D wa +k2·(D hi -D wa )+k3·(D ne -D wa )+k4·A pe +k5·A sh , where D wa is the distance between the waist and the backrest, D hi is the distance between the buttocks and the backrest, D ne is the distance between the neck and the backrest, A pe is the pelvic width, A sh is shoulder width, C sp is the coefficient of vertebral morphological characteristics, k1, k2, k3, k4, k5 are proportional coefficients; S43, if the waist force value is within the threshold range, then end; S44: If the waist force value exceeds the threshold range, a voice reminder for adjusting the corresponding part is output to the user, and the change data of the waist force value is recorded and stored.

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