Rehabilitation physiotherapy system for preventing hand and foot necrosis of scleroderma patient based on big data
A scleroderma and big data technology, applied in the direction of image data processing, patient-specific data, physical therapy or behavior, etc., can solve the problems of insufficient rehabilitation and inconvenient detection of scleroderma patients, and achieve the goal of improving accuracy Effect
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Embodiment 1
[0034] Example 1, please refer to figure 1 , a rehabilitation physiotherapy system based on big data to prevent necrosis of hands and feet of patients with scleroderma, the rehabilitation physiotherapy system includes a database module 1, a diagnosis acquisition module 2, a processing module 3 and a physiotherapy module 4;
[0035] The symptom data of scleroderma patients is stored in the database module 1;
[0036] The diagnosis acquisition module 2 is used to acquire the patient's condition data;
[0037] The processing module 3 is used for processing according to the acquired condition data to obtain a corresponding physiotherapy plan;
[0038] The physical therapy module 4 is used for performing physical therapy on the patient according to the obtained physical therapy plan.
[0039]Stored in the database module 1 are early symptom pictures, developmental period symptom pictures and sclerotic period symptom pictures of patients with scleroderma;
[0040] The diagnosis a...
Embodiment 2
[0049] Example 2, please refer to image 3 , Embodiment 2 adds multiple measurement points on the basis of Embodiment 1, and performs change and total value processing and analysis on the data of multiple measurement points to obtain more accurate disease results. Patients can choose the method of Embodiment 1 according to their needs. A simple and direct detection method, or choose the relatively more accurate detection method in Example 2. The processing module 3 further includes a control unit 33, the control unit 33 is configured with a control strategy, and the control strategy includes: starting from the end of the patient's finger, controlling the pressure detection every first distance to the side of the finger close to the torso The sensor 22 and the pressure range detection mechanism 23 measure the corresponding position once, and the elastic force values detected by the pressure detector 22 are marked as Tls1 to Tlsm in turn, and the concave distances detected by ...
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