Rehabilitation device and rehabilitation method for Alzheimer's disease patient based on big data

Abstract

The invention relates to the technical field of rehabilitation devices, and provides a rehabilitation device and method for a patient with Alzheimer's disease based on big data, the rehabilitation device for the patient with Alzheimer's disease based on big data comprises a machine body, a seat, a lower limb training mechanism, an upper limb training mechanism, a processor and a display screen, the seat is installed on the machine body, and the upper limb training mechanism is installed on the machine body; the lower limb training mechanism is installed on the seat and used for training the lower limbs of the patient, the lower limb training mechanism comprises a supporting frame, the supporting frame is movably arranged on the seat, a crank is rotationally connected to the supporting frame, a pedal is rotationally connected to the crank, and the upper limb training mechanism is installed on the machine body and used for training the upper limbs of the patient. The processor is installed on the machine body and used for storing and analyzing monitoring data of a patient, and the display screen is installed on the machine body and used for playing training audio and video data. By means of the technical scheme, the problems that in the prior art, the training content and mode are fixed and single, and the rehabilitation effect is limited are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of rehabilitation devices, and in particular to a rehabilitation device and method for Alzheimer's patients based on big data. Background Art

[0002] Alzheimer's disease, also known as Alzheimer's disease, is a progressive neurodegenerative disease with an insidious onset. Clinically, it is characterized by global dementia manifestations such as memory impairment, aphasia, apraxia, agnosia, visual-spatial skill impairment, executive dysfunction, and personality and behavioral changes. Modern medical research has confirmed that early intervention and early benefits: timely intervention can delay the progression of dementia, that is, rehabilitation training for Alzheimer's patients.

[0003] Rehabilitation training includes cognitive training, occupational training, music therapy and exercise training. Traditional rehabilitation devices for Alzheimer's patients rely on internally stored audio and video materials for training during use. The content of the audio and video materials is fixed, and it is difficult to carry out targeted rehabilitation treatment based on the condition and living conditions of different Alzheimer's patients. The rehabilitation effect is relatively limited, and most of the current rehabilitation devices are separate devices, that is, each training is a separate device, cognitive training and exercise training are separated, and it is difficult to achieve synchronous training and provide greater sensory stimulation and training to patients. Summary of the Invention

[0004] The present invention proposes a rehabilitation device and rehabilitation method for Alzheimer's patients based on big data, which solves the problems in the existing technology of fixed and single training content and methods and relatively limited rehabilitation effects.

[0005] The technical solutions of the present invention are as follows: A rehabilitation device for Alzheimer's patients based on big data, comprising a body and: a seat, the seat being mounted on the body; A lower limb training mechanism is installed on the seat and is used to train the patient's lower limbs. The lower limb training mechanism includes: A support frame is movably arranged on the seat, the support frame is rotatably connected to a crank, and the crank is rotatably connected to a pedal.

[0006] An upper limb training mechanism, which is installed on the body and is used to train the patient's upper limbs. The upper limb training mechanism includes: A pull rod, the pull rod is connected to a load-bearing plate via a pull rope, a weight block is installed on the load-bearing plate, and a guide shaft for the pull rope to pass through is installed on the top of the body; a processor, mounted on the body and configured to store and analyze patient monitoring data; A display screen is mounted on the machine body and is electrically connected to the processor for playing training audio and video materials.

[0007] In order to adapt to patients of different body shapes, the seat can be raised and lowered on the machine body.

[0008] In order to improve the comfort of patients, it also includes a backrest. A lifting frame is provided on the body in a liftable manner. The backrest is installed on the lifting frame. An armrest is provided below the backrest in a liftable manner. The seat is fixedly connected below the armrest.

[0009] In order to improve the flexibility of use, the lifting frame is rotatably connected to a rotating frame via a rotating shaft, and the backrest is fixedly installed on the rotating frame.

[0010] To accommodate the rotation of the backrest and the seat, the guide shaft includes a fixed shaft and a rotating shaft. The fixed shaft is fixedly mounted on the lifting frame, and the rotating shaft is fixedly mounted on the rotating frame. The fixed shaft and the rotating shaft are rotatably connected.

[0011] In order to adapt to the training of different strengths, an insertion rod is fixedly connected to the load-bearing plate, and a socket matching the insertion rod is opened on the weight block.

[0012] In order to ensure the stability of the lifting of the load-bearing plate, a limit rod is installed on the lifting frame, and the load-bearing plate and the limit rod are in sliding fit.

[0013] A rehabilitation method for Alzheimer's patients based on big data, using the above-mentioned rehabilitation device for Alzheimer's patients based on big data, includes the following steps: S1. Adjust the seat height and support frame position according to the patient's height and body shape. The lifting frame adjusts the backrest height to suit the patient's body shape. S2. The patient sits on the chair with his feet on the ground. When upper limb training is required, the patient pulls the pull rod to move the pressure plate up and down to train the patient's upper limbs. S3: When lower limb training is required, the patient places his feet on the pedals and trains his legs by pedaling. S4. When cognitive training is required, the processor connects to the monitoring system to analyze the patient's behavior and actions, and combines the input patient information to determine the patient's condition and formulate a corresponding training method. The monitoring system also captures the portraits of the patient's relatives and friends, or uploads images of the patient's relatives and friends, and projects the portraits of the patient's relatives and friends onto the display screen for the patient to identify, thereby providing cognitive training for the patient. S5, the processor can also play music or images through the display screen to stimulate and train the patient's senses.

[0014] The working principle and beneficial effects of the present invention are: 1. In the present invention, the rotation of the crank and the pedals simulates the pedaling action of a bicycle to train the patient's lower limbs, and the pull rod drives the pull rope to pull the load-bearing plate and the weight block to train the patient's upper limbs, thereby achieving more comprehensive exercise training for the patient.

[0015] 2. In the present invention, the processor is connected to big data to analyze and process the patient's medical condition information and behavioral videos, and targeted cognitive training projects are formulated. At the same time, the portraits of the patient's relatives and friends are recorded and processed and transmitted to the display screen for the patient to identify. The familiar faces stimulate the patient's senses and memory, thereby improving the effect of rehabilitation training.

[0016] 3. In the present invention, the lifting and rotating of the seat and the movement of the support frame can adapt to the training of patients of different heights and body shapes, and has good adaptability.

[0017] 4. Therefore, compared with the separation training and fixed training content in the existing technology, the present invention uses a processor based on big data technology to judge the conditions of different patients and design targeted training programs. At the same time, it integrates cognitive training, occupational training, music therapy and exercise training into one device to provide patients with diversified training and improve rehabilitation effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] Figure 1 This is a schematic structural diagram of the present invention from a first viewing angle; Figure 2 It is a structural schematic diagram of the second viewing angle of the present invention; Figure 3 It is a structural schematic diagram of the machine body, lifting frame, screw feeding mechanism and rotating shaft of the present invention; Figure 4 It is a structural schematic diagram of the machine body and roller of the present invention; Figure 5 This is a schematic structural diagram of the lifting frame and the rotating shaft of the present invention; Figure 6 This is a schematic structural diagram of the seat, backrest, display screen and lower limb training mechanism of the present invention; Figure 7 This is a schematic structural diagram of the chair, armrests and lower limb training mechanism of the present invention; Figure 8 This is a schematic structural diagram of the backrest and rotating frame of the present invention; Figure 9 Schematic diagram of the structure of the upper limb training mechanism of the present invention; Figure 10 For the present invention Figure 2 Schematic diagram of the local enlarged structure at A in the middle; Figure 11 Schematic diagram of the framework of the processor system of the present invention.

[0020] In the picture: 1. Machine body; 2. Rollers; 3. Seat; 4. Backrest; 5. Lifting frame; 6. Screw feed mechanism; 7. Rotating shaft; 8. Rotating frame; 9. Armrest; 10. Electric cylinder 1; 11. Processor; 12. Display screen; 13. Mounting frame; 101. Support frame; 102. Crank; 103. Pedal; 104. Sliding seat 1; 105. Moving plate; 106. Sliding seat 2; 107. Electric cylinder 2; 108. Electric cylinder 3; 201. Pull rod; 202. Pull rope; 203. Load-bearing plate; 204. Weight block; 205. Insert rod; 206. Limit rod; 207. Fixed axis; 208. Rotating axis. DETAILED DESCRIPTION

[0021] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described 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 making any creative efforts are within the scope of protection of the present invention.

[0022] Example 1, as Figures 1 to 10 As shown, this embodiment proposes a rehabilitation device for Alzheimer's patients based on big data, including a body 1, a roller 2 is provided at the bottom of the body 1, and the device can be moved. The roller 2 is provided with a locking device to fix the position of the body 1. It also includes a seat 3, a lower limb training mechanism, an upper limb training mechanism, a processor 11 and a display screen 12. Compared with the separation training and fixed training content in the prior art, the present invention uses the processor 11 based on big data technology to judge the condition of different patients and design training programs in a targeted manner. At the same time, cognitive training, occupational training, music therapy and exercise training are integrated into one device, so as to provide patients with diversified training and improve the rehabilitation effect.

[0023] The seat 3 is liftable and arranged on the body 1. In order to improve the comfort of the patient, it also includes a backrest 4. A lifting frame 5 is liftable on the body 1. A screw feed mechanism 6 is installed on the body 1. The lifting frame 5 and the body 1 are in sliding cooperation, and the lifting frame 5 and the screw nut on the screw feed mechanism 6 are fixedly connected. The backrest 4 is installed on the lifting frame 5. The lifting frame 5 is rotatably connected to the rotating frame 8 through the rotating shaft 7. The backrest 4 is fixedly mounted on the rotating frame 8. An armrest 9 is liftable below the backrest 4. The seat 3 is fixedly connected to the bottom of the armrest 9. The rear side of the backrest 4 is fixed. An electric cylinder 10 is installed, and the seat 3 is fixedly connected to the output end of the electric cylinder 10. The armrest 9 and the rotating frame 8 are in sliding cooperation. Through the rotation cooperation between the rotating frame 8 and the lifting frame 5, the angles of the backrest 4 and the seat 3 can be adjusted to change the patient's orientation. The use is more flexible and can adapt to the use in different environments. The lifting frame 5 can be driven to rise and fall by the screw feed mechanism 6 to adjust the height of the backrest 4. The seat 3 can be driven to rise and fall by the electric cylinder 10 to adjust the height of the seat 3 to adapt to the use of patients of different heights and body shapes.

[0024] like Figures 6 and 7 As shown, the lower limb training mechanism is installed on the seat 3 and is used to train the patient's lower limbs. The lower limb training mechanism includes a support frame 101, which is movably set on the seat 3. The support frame 101 is rotatably connected to the crank 102, and the crank 102 is rotatably connected to the pedal 103. The bottom of the seat 3 is fixedly connected to a sliding seat 104, and the sliding seat 104 is slidably connected to a moving plate 105. The end of the moving plate 105 is fixedly connected to a sliding seat 2 106. The support frame 101 and the sliding seat 2 106 are slidably connected. An electric cylinder 2 107 is installed on the sliding seat 104, and the sliding seat 2 106 is fixedly connected to the electric cylinder 2 1 07, an electric cylinder three 108 is installed on the sliding seat, and the support frame 101 is fixedly connected to the output end of the electric cylinder three 108. The electric cylinder two 107 can drive the support frame 101 to move closer to or away from the seat 3, and the electric cylinder three 108 can drive the support frame 101 to move up and down, and adjust the height of the crank 102 and the pedal 103. The position and height of the support frame 101 can be adjusted according to the height and leg length of the patient. At the same time, the height and position of the support frame 101 can be adjusted to the patient's pedaling angle to meet the different training needs of different patients. The patient's lower limbs can be trained through movements similar to cycling.

[0025] like Figure 1 and Figure 9As shown, the upper limb training mechanism is installed on the body 1 and is used to train the patient's upper limbs. The upper limb training mechanism includes a pull rod 201, and the pull rod 201 is connected to a load-bearing plate 203 through a pull rope 202. A weight block 204 is installed on the load-bearing plate 203. A guide shaft for the pull rope 202 to pass through is installed on the top of the body 1. In order to adapt to training with different strengths, an insertion rod 205 is fixedly connected to the load-bearing plate 203, and a socket matching the insertion rod 205 is opened on the weight block 204. In order to ensure the stability of the lifting of the load-bearing plate 203, a limit rod 206 is installed on the lifting frame 5. The load-bearing plate 203 and the limit rod 206 are in sliding cooperation. In order to adapt to the rotation of the backrest 4 and the seat 3, the guide shaft includes a fixed shaft 207 and a rotating shaft 208. The fixed shaft 207 is fixedly installed on the lifting frame 5, and the rotating shaft 208 is fixedly installed on the rotating frame 8, and the fixed shaft 207 and the rotating shaft 208 are rotatably connected. The rotating shaft 7 is a hollow shaft. The fixed shaft 207 and the rotating shaft 208 pass through the rotating shaft 7. The fixed shaft 207 is n-shaped. The patient holds the pull rod 201 and pulls the pull rope 202 downward, driving the pull rope 202 to lift the load-bearing plate 203. According to different strength training needs, a suitable number of weight blocks 204 are selected and installed on the insertion rod 205, so as to adjust the force required for pulling down to meet different training needs. The load-bearing plate 203 is limited during the lifting process by the limit rod 206 to ensure stability and reduce the occurrence of safety accidents. The upper limb training mechanism and the lower limb training mechanism can be used to train the patient's upper and lower limbs, meeting the exercise training needs of Alzheimer's patients.

[0026] The processor 11 is installed on the body 1 and is used to store and analyze the patient's monitoring data. The display screen 12 is installed on the body 1. The display screen 12 and the processor 11 are electrically connected and are used to play training audio and video materials. Based on big data technology, the processor 11 is connected to the network and can transmit the patient's information, photos of the patient's relatives and friends and other information to the processor 11. At the same time, the monitoring system and the processor 11 can be connected to shoot and analyze the patient's daily behavior and movements to judge the patient's condition and conduct targeted training according to the patient's degree of dementia. At the same time, the patient's recognition ability, that is, cognitive ability, is tested and stimulated through portraits familiar to the patient, so as to better conduct rehabilitation training for the patient. At the same time, the processor 11 stores images, voices, music and videos, which can be used for the rehabilitation of the patient's memory and cognitive ability, and for the patient. Training and testing, while playing music or video to activate and stimulate the patient's senses, further improving the patient's rehabilitation effect, the display screen 12 is rotatably mounted on one of the armrests 9 through the mounting bracket 13, and the display screen 12 rotates with the rotation of the seat 3, so that the patient faces the display screen 12, which is convenient for the patient to watch the picture on the display screen 12, and at the same time, the display screen 12 is driven to rotate by the mounting bracket 13, and the angle of the display screen 12 is adjusted to reduce the obstruction caused to the patient's sitting up and adapt to the viewing needs of different angles. The patient can interact with the display screen 12 through voice, and the display screen 12 is a touch screen, which can realize the patient's training in a variety of ways, and buttons are installed on the armrest 9 for making right or wrong judgments or selections in certain test questions. The left button corresponds to the negative option, and the right button corresponds to the confirmation option. Figure 11 The system in the processor includes an information processing system and a rehabilitation training system, wherein the information processing system includes an information acquisition module for collecting and entering the patient's basic information, underlying diseases, patient monitoring videos, and images of the patient's relatives and friends, and also includes an information analysis module for evaluating the patient's condition and formulating targeted training plans and test questions. The rehabilitation training system includes an input module, an output module, an image testing module, a movement training module, and a music therapy module, wherein the input module includes a camera, a microphone, and an armrest button. The camera collects the patient's training movements, thereby evaluating and counting the patient's training movements. The microphone is used to collect the patient's voice for language training or receiving patient instructions. The image testing module, the movement training module, and the music therapy module correspond to the patient's cognitive training, movement training, and music therapy, respectively. The information of the test questions or training items and the guiding movements can be played through the display screen and the voice playback device to carry out diversified training for the patient. The networking of the processor 11, the use of big data, and the audio and video materials of the training are all existing technologies well known to those skilled in the art and will not be elaborated here.

[0027] Example 2: This example proposes a rehabilitation method for Alzheimer's patients based on big data, using the above-mentioned rehabilitation device for Alzheimer's patients based on big data, including the following steps: S1. Adjust the height of the seat 3 and the position of the support frame 101 according to the patient's height and body shape. The lifting frame 5 adjusts the height of the backrest 4 to suit the patient's body shape. S2. The patient sits on the seat 3 with his feet on the ground. When upper limb training is required, the patient pulls the pull rod 201 to move the pressure plate up and down to train the patient's upper limbs. S3: When lower limb training is required, the patient places his feet on the pedals 103 and performs leg training by pedaling the pedals 103; S4. When cognitive training is required, the processor 11 is connected to the monitoring system to analyze the patient's behavior and actions, and the patient's condition is judged based on the input patient information, and a corresponding training method is formulated. The monitoring system captures portraits of the patient's relatives and friends, or uploads images of the patient's relatives and friends, and projects the portraits of the patient's relatives and friends onto the display screen 12 for the patient to identify, thereby performing cognitive training on the patient; S5. The processor 11 may also play music or images through the display screen 12 to stimulate and train the patient's senses.

[0028] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A rehabilitation device for Alzheimer's patients based on big data, comprising a body (1), characterized in that: Also includes: A seat (3), the seat (3) being mounted on the machine body (1); A lower limb training mechanism, which is mounted on the seat (3) and is used to train the patient's lower limbs; An upper limb training mechanism, the upper limb training mechanism being mounted on the machine body (1) and being used to train the patient's upper limbs; A processor (11), the processor (11) being mounted on the body (1) and configured to store and analyze patient monitoring data; A display screen (12), the display screen (12) is mounted on the body (1), the display screen (12) is electrically connected to the processor (11), and is used to play training audio and video materials.

2. The rehabilitation device for Alzheimer's patients based on big data according to claim 1, characterized in that: The lower limb training mechanism comprises: A support frame (101) is movably arranged on the seat (3), a crank (102) is rotatably connected to the support frame (101), and a pedal (103) is rotatably connected to the crank (102).

3. The rehabilitation device for Alzheimer's patients based on big data according to claim 2, characterized in that: The upper limb training mechanism comprises: A pull rod (201) is connected to a load-bearing plate (203) via a pull rope (202), a weight block (204) is installed on the load-bearing plate (203), and a guide shaft for the pull rope (202) to pass through is installed on the top of the body (1).

4. The rehabilitation device for Alzheimer's patients based on big data according to claim 3, characterized in that: The seat (3) can be raised and lowered on the machine body (1).

5. The rehabilitation device for Alzheimer's patients based on big data according to claim 4, characterized in that: It also includes a backrest (4), a lifting frame (5) is provided on the body (1) in a liftable manner, the backrest (4) is mounted on the lifting frame (5), an armrest (9) is provided below the backrest (4) in a liftable manner, and the seat (3) is fixedly connected below the armrest (9).

6. The rehabilitation device for Alzheimer's patients based on big data according to claim 5, characterized in that: The lifting frame (5) is rotatably connected to a rotating frame (8) via a rotating shaft (7), and the backrest (4) is fixedly mounted on the rotating frame (8).

7. The rehabilitation device for Alzheimer's patients based on big data according to claim 6, characterized in that: The guide shaft comprises a fixed shaft (207) and a rotating shaft (208), wherein the fixed shaft (207) is fixedly mounted on the lifting frame (5), and the rotating shaft (208) is fixedly mounted on the rotating frame (8), and the fixed shaft (207) and the rotating shaft (208) are rotatably connected.

8. The rehabilitation device for Alzheimer's patients based on big data according to claim 7, characterized in that: The load-bearing plate (203) is fixedly connected to an insertion rod (205), and the weight block (204) is provided with a socket matching the insertion rod (205).

9. The big data-based rehabilitation device for Alzheimer's patients according to claim 8, characterized in that: A limiting rod (206) is installed on the lifting frame (5), and the load-bearing plate (203) and the limiting rod (206) are in sliding fit.

10. A rehabilitation method for Alzheimer's patients based on big data, using the rehabilitation device for Alzheimer's patients based on big data according to claim 9, characterized in that: The following steps are involved: S1. Adjust the height of the seat (3) and the position of the support frame (101) according to the patient's height and body shape, and adjust the height of the backrest (4) by the lifting frame (5) to suit the patient's body shape; S2, the patient sits on the chair (3) with his feet on the ground. When upper limb training is required, the patient pulls the pull rod (201) to drive the pressure plate to rise and fall, thereby training the patient's upper limbs; S3, when lower limb training is required, the patient places his feet on the pedal (103) and performs leg training by pedaling the pedal (103); S4. When cognitive training is required, the processor (11) is connected to the monitoring system to analyze the patient's behavior and actions, and the patient's condition is judged in combination with the input patient information, and a corresponding training method is formulated. The monitoring system is used to capture the portraits of the patient's relatives and friends, or upload the images of the patient's relatives and friends, and project the portraits of the patient's relatives and friends onto the display screen (12) for the patient to identify, thereby performing cognitive training on the patient; S5. The processor (11) can also play music or images through the display screen (12) to stimulate and train the patient's senses.

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