Sarcopenia rehabilitation strategy generation method and related devices

Abstract

The application is suitable for the technical field of intelligent algorithm, and provides a sarcopenia rehabilitation strategy generation method and related equipment, including: acquiring four-limb skeletal muscle data, whole-body muscle density data and bone density value data of a target user, and acquiring muscle function data of the target user according to a user type of the target user and according to a preset rule; performing profiling on the target user by a preset diagnosis algorithm based on the four-limb skeletal muscle data, the whole-body muscle density data and the muscle function data, to obtain a profiling result; and selecting a rehabilitation strategy for the target user in a preset database according to the user type, the profiling result and the bone density value data.

Description

Technical Field

[0001] This application belongs to the field of intelligent algorithm technology, and in particular relates to a method and related equipment for generating rehabilitation strategies for sarcopenia. Background Technology

[0002] Existing sarcopenia diagnostic equipment suffers from five major deficiencies: First, insufficient accuracy in muscle mass measurement. Traditional bioelectrical impedance analysis (BIA) devices only estimate muscle mass through limb impedance, failing to accurately scan the distribution and density of muscles throughout the body, resulting in low recognition rates for early localized muscle loss. Second, limited functionality. They can only perform diagnosis and cannot generate personalized exercise plans for different populations (such as the elderly, bedridden patients, and patients with osteoporosis), requiring manual planning by medical staff, which is inefficient and poorly adaptable. Third, inadequate radiation safety design. Standardized designs for DXA scanning module scattering shielding and dose control for special populations are lacking, making it difficult to meet hospital radiation safety compliance requirements. Fourth, a lack of exercise effect tracking and plan iteration mechanisms. Training effectiveness cannot be quantitatively evaluated, and plans cannot be dynamically adjusted based on changes in patient muscle mass, hindering long-term intervention. Fifth, a lack of integration between the device and mobile devices. Patient training completion and physical feedback data cannot be synchronized to the device in real time, preventing medical staff from remotely monitoring intervention effects. Summary of the Invention

[0003] This application provides a method and related equipment for generating sarcopenia rehabilitation strategies, which can address the limitations of existing sarcopenia diagnosis and rehabilitation strategy formulation technologies.

[0004] In a first aspect, embodiments of this application provide a method for generating a sarcopenia rehabilitation strategy, including: Acquire skeletal muscle data of the target user's limbs, whole-body muscle density data, and bone density data, and acquire muscle function data of the target user according to the user type and preset rules. Based on the skeletal muscle data of the limbs, the whole body muscle density data, and the muscle function data, a profile of the target user is created using a preset diagnostic algorithm to obtain the profile result. Based on the user type, the profile results, and the bone density data, a rehabilitation strategy for the target user is selected from a preset database.

[0005] Optionally, the step of obtaining the target user's limb skeletal muscle data, whole-body muscle density data, and bone density value data includes: A heat map of the distribution of muscles throughout the body is obtained, and the skeletal muscle data of the limbs, the total muscle density data, and the bone density value data of the target user are calculated based on the heat map of the distribution of muscles throughout the body.

[0006] Optionally, the step of obtaining the muscle function data of the target user according to the user type and a preset rule includes: Based on the user type of the target user, determine the avoidance evaluation items that the target user cannot perform according to preset rules; Based on the avoidance evaluation items and the preset evaluation item set, the target evaluation items for the target user are determined, and the muscle function data of the target user is obtained according to the target evaluation items.

[0007] Optionally, the preset database includes a first set of motion strategies for different user types, a second set of motion strategies for different profile results, and a third set of motion strategies for different bone density values.

[0008] Optionally, the method further includes: Acquire new limb skeletal muscle data, new whole-body muscle density data, and new bone density value data of the target user after implementing the rehabilitation strategy at a preset cycle; Based on the new limb skeletal muscle data, the new whole-body muscle density data, and the whole-body muscle density data, the evaluation data of the rehabilitation strategy is determined according to a preset judgment rule; If the evaluation data indicates that the rehabilitation strategy is effective, then a new rehabilitation strategy for the target user is selected from the preset database based on the new limb skeletal muscle data, the new whole-body muscle density data, and the new bone density value data. If the assessment data does not significantly indicate the effectiveness of the rehabilitation strategy, then the change rate of limb skeletal muscle is calculated based on the new limb skeletal muscle data and the limb skeletal muscle data, and the change rate of whole-body muscle density is calculated based on the new whole-body muscle density data and the whole-body muscle density data. The rate of change of skeletal muscle in the limbs is compared with the rate of change of muscle density in the whole body. If the rate of change of skeletal muscle in the limbs is less than the rate of change of muscle density in the whole body, the new skeletal muscle data of the limbs is displayed to obtain an exercise strategy for the new skeletal muscle data of the limbs, and the rehabilitation strategy is updated based on the exercise strategy to obtain the first updated strategy. If the rate of change of total muscle density is less than the rate of change of skeletal muscle in the limbs, the rate of change of total muscle density is displayed to obtain an exercise strategy for the rate of change of total muscle density, and the rehabilitation strategy is updated based on the exercise strategy to obtain a second updated strategy.

[0009] Optionally, the method further includes: Obtain the identity information of the target user, and bind the identity information with the limb skeletal muscle data, the whole body muscle density data, the bone density value data, the user type, the muscle function data, the rehabilitation strategy, new limb skeletal muscle data, new whole body muscle density data, new bone density value data, new rehabilitation strategy, first update strategy and second update strategy to obtain a dataset for the target user; Based on the dataset, statistical data with a preset period as the horizontal axis is generated and displayed.

[0010] Optionally, the step of displaying the statistical data includes: The statistical data is displayed using encrypted transmission.

[0011] Secondly, embodiments of this application provide a sarcopenia rehabilitation strategy generation device, the device comprising: The data acquisition module is used to acquire the skeletal muscle data of the target user's limbs, the whole body muscle density data, and the bone density value data, and to acquire the target user's muscle function data according to the target user's user type and preset rules. The profiling module is used to create a profile of the target user based on the limb skeletal muscle data, the whole body muscle density data, and the muscle function data, using a preset diagnostic algorithm, and obtain the profile result. The strategy generation module is used to select a rehabilitation strategy for the target user from a preset database based on the user type, the profile results, and the bone density value data.

[0012] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the sarcopenia rehabilitation strategy generation method as described in any of the first aspects above.

[0013] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the sarcopenia rehabilitation strategy generation method as described in any one of the first aspects above.

[0014] Fifthly, embodiments of this application provide a computer program product that, when run on a terminal device, causes the terminal device to execute the sarcopenia rehabilitation strategy generation method described in any one of the first aspects.

[0015] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0016] The beneficial effects of this application embodiment compared with the prior art are as follows: By acquiring the limb skeletal muscle data, whole-body muscle density data, and bone density value data of the target user, and according to the user type of the target user, muscle function data of the target user is acquired according to preset rules; based on the limb skeletal muscle data, the whole-body muscle density data, and the muscle function data, a profile of the target user is created through a preset diagnostic algorithm to obtain the profile result; according to the user type, the profile result, and the bone density value data, a rehabilitation strategy for the target user is selected from a preset database, that is, by accurately acquiring the whole-body muscle mass, muscle density, and distribution, and combining multi-dimensional muscle function assessment, sarcopenia diagnosis is completed; a multi-scenario / population exercise program database is built-in to automatically generate personalized exercise muscle-building programs suitable for the general population and special populations; and a muscle mass change comparison and dynamic iteration of the program are realized through an exercise effect tracking module. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a method for generating a sarcopenia rehabilitation strategy according to an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a sarcopenia rehabilitation strategy generation device provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a computer-readable storage medium provided in an embodiment of this application. Detailed Implementation

[0019] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0020] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0021] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0022] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [the described condition or event] is detected," or "in response to detection of [the described condition or event]."

[0023] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0024] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0025] like Figure 1 As shown in the embodiment of this application, a method for generating a sarcopenia rehabilitation strategy is provided, including: S101. Obtain the skeletal muscle data of the target user's limbs, the whole body muscle density data, and the bone density value data, and obtain the muscle function data of the target user according to the user type of the target user and a preset rule. For example, muscle function data include at least one of grip strength, corrected grip strength, leg strength, walking speed, 5 sit-ups time, and balance sway value; S102. Based on the skeletal muscle data of the limbs, the whole body muscle density data, and the muscle function data, a profile of the target user is created using a preset diagnostic algorithm to obtain the profile result. For example, the diagnostic rule of the preset diagnostic algorithm is: if ASM / H² < male 7.0kg / m² / female 5.4kg / m², or muscle density < male 70mg / cm³ / female 65mg / cm³, it is determined as "insufficient muscle mass"; Assess muscle function (meeting one of the following criteria is sufficient): If grip strength < 28kg for men / 18kg for women (for obese patients with BMI ≥ 28kg / m², corrected grip strength < 30kg for men / 20kg for women), leg strength < 30% of body weight, walking speed < 1.0m / s, time to 5 sit-ups > 12 seconds, and balance sway value > 5cm, it is judged as "decreased muscle function". Result interpretation: Insufficient muscle mass + decreased muscle function → positive sarcopenia; insufficient muscle mass only → pre-sarcopenia; both meet the criteria → negative sarcopenia.

[0026] S103. Based on the user type, the profile results, and the bone density data, select a rehabilitation strategy for the target user from a preset database.

[0027] By acquiring limb skeletal muscle data, whole-body muscle density data, and bone density data of the target user, and obtaining muscle function data of the target user according to the user type and preset rules; based on the limb skeletal muscle data, whole-body muscle density data, and muscle function data, a profile of the target user is created using a preset diagnostic algorithm to obtain the profile result; based on the user type, the profile result, and the bone density data, a rehabilitation strategy for the target user is selected from a preset database, that is, sarcopenia diagnosis is completed by accurately acquiring whole-body muscle mass, muscle density, and distribution, combined with multi-dimensional muscle function assessment; a built-in multi-scenario / population exercise program database is built-in to automatically generate personalized muscle-building exercise programs suitable for the general population and special populations; and a muscle mass change comparison and dynamic iteration of the program are realized through an exercise effect tracking module.

[0028] In one possible implementation, the step of acquiring the target user's limb skeletal muscle data, whole-body muscle density data, and bone density value data includes: A heat map of the distribution of muscles throughout the body is obtained, and the skeletal muscle data of the limbs, the total muscle density data, and the bone density value data of the target user are calculated based on the heat map of the distribution of muscles throughout the body.

[0029] In one possible implementation, the step of obtaining the muscle function data of the target user according to the user type and a preset rule includes: Based on the user type of the target user, determine the avoidance evaluation items that the target user cannot perform according to preset rules; Based on the avoidance evaluation items and the preset evaluation item set, the target evaluation items for the target user are determined, and the muscle function data of the target user is obtained according to the target evaluation items.

[0030] For example, if the patient is bedridden in a nursing home, the system automatically blocks the infrared walkway (walking test) and balance board (balance test) assessment items, and only retains the grip strength sensor and the leg strength sensor under the mattress; if the patient has sarcopenia combined with osteoporosis, the system automatically blocks the single-leg standing assessment item of the balance board, and only retains the balance test assisted by the chair, to avoid high-risk movements.

[0031] In one possible implementation, the preset database includes a first set of motion strategies for different user types, a second set of motion strategies for different profile results, and a third set of motion strategies for different bone density values.

[0032] For example, the preset database includes: ordinary elderly sarcopenia patients (over 65 years old, core muscle loss + poor balance, hospital / community clinic setting, no complications): single training session duration 25 minutes, training 3 times a week. The training is divided into four phases: Warm-up (5 minutes), including seated ankle circles (1 minute per side, 10 circles clockwise and 10 circles counterclockwise), shoulder circles (10 circles forward and backward, 2 minutes in total), and abdominal breathing (slowly inhale for 4 seconds, hold your breath for 2 seconds, exhale for 6 seconds, 5 times in total, 2 minutes); Strength training (12 minutes), including seated leg raises (15 repetitions per side, 3 sets in total, with 0.5kg weight on the ankles, keeping the knees straight while raising the legs), and resistance band abdominal bridges (hold for 15 seconds per set, 3 sets in total, with the resistance band around the waist and the hips raised 5cm off the bed); Balance training (6 minutes), including single-leg standing (8 seconds per side, 3 sets in total, with arms hanging naturally, and light support from a wall is acceptable); and Relaxation (2 minutes), including leg stretches (1 minute per side, seated forward bend to touch the toes, with the intensity limited to the level of pain relief). Safety tips: To avoid bending over at an angle exceeding 45°, medical personnel or family members should be present to protect you during balance training to prevent falls.

[0033] For bedridden sarcopenic patients in nursing homes (long-term bed rest, muscle loss in limbs + limited joint mobility, nursing home setting, no complications): Each training session lasts 20 minutes, 5 times a week. The training is divided into three phases: Warm-up phase (5 minutes), including passive ankle rotation (assisted by caregiver, 10 circles per side, 5 circles clockwise and 5 circles counterclockwise), active finger grasping (clenching the fist and then fully extending it counts as one repetition, 15 repetitions as one set, 2 sets in total), and abdominal breathing (same as for ordinary elderly patients, 5 repetitions); Strength training phase (12 minutes), including straight leg raises in bed (10 repetitions per side as one set, 2 sets in total, raise the leg to a 30° angle with the bed surface, hold for 3 seconds and then slowly lower it), and side-lying leg raises (lying on the healthy side, straighten the lower leg and raise it 10cm, hold for 3 seconds, 8 repetitions per side as one set). The exercise consists of two sets of exercises: assisted bridge exercise (the caregiver supports the patient's lower back with both hands, lifts the patient's buttocks 5cm off the bed, holds for 5 seconds, 8 repetitions per set, 2 sets total); and arm flexion and extension with an elastic band (the elastic band is looped around the wrist, the arm is straightened and then slowly bent, 12 repetitions per set, 2 sets total, elastic band resistance 5 lbs). The relaxation phase (3 minutes) includes passive knee stretches (the caregiver assists in slowly bending the knee to 90°, holds for 5 seconds, then slowly lowers it, 5 repetitions per side); and passive shoulder stretches (the caregiver supports the patient's shoulder with one hand and pulls the elbow with the other, slowly stretching it to the opposite side, holding for 10 seconds on each side). Safety tips: The intensity of passive movements should be limited to a level that does not cause pain to the patient. During the bridge exercise, the caregiver must support the lower back throughout to avoid excessive stress on the lower back.

[0034] For patients with sarcopenia and osteoporosis (total muscle loss + bone mineral density T-score ≤ -2.5, hospital / community outpatient setting, with osteoporosis as a complication): Each training session lasts 25 minutes, 3 times a week. The training consists of four phases: Warm-up (5 minutes), including slow in-place marching (0.6 m / s, feet on the ground, 2 minutes), shoulder circles (10 circles forward and backward, 2 minutes), and standing hip circles (hands on hips, 8 circles clockwise and 8 circles counterclockwise, 1 minute per side); Strength training (15 minutes), including standing wall squats (back against the wall, knees bent at a 45° angle, avoiding extending beyond the toes, hold for 20 seconds, 3 sets), seated resistance band chest expansions (resistance band around the chest, arms extended horizontally outwards to maximum range, 15 repetitions, 3 sets, resistance band 10 lbs), and standing heel raises (slow...). The exercises include: 1) Slowly rise onto your toes to the highest point, hold for 2 seconds, then slowly lower. Repeat 20 times for one set, for a total of 2 sets. 2) Seated leg raises (seated position, alternately raise each leg 10cm, hold for 3 seconds, 12 times per side for one set, for a total of 2 sets, without weights). 3) Balance training phase (3 minutes): This includes standing on one leg while holding onto a chair (lightly holding onto the chair surface, stand on one leg for 8 seconds, 3 sets per side), and walking in a straight line from heel to toe while holding onto a chair (walk 5 meters in a straight line while holding onto the chair surface, once). 4) Relaxation phase (2 minutes): This includes standing leg stretches (holding onto a chair surface, extend one leg backward, gently pull on the ankle with your hand, hold for 10 seconds per side), and shoulder stretches (with assistance, one hand on the shoulder, the other pulling the elbow to the opposite side, hold for 10 seconds per side). Safety tips: Avoid bending over, carrying heavy loads, and violent impact movements throughout. When squatting against a wall, ensure your knees do not extend beyond your toes. Balance training must be performed on a flat surface, and a caregiver must be present to supervise.

[0035] In one possible implementation, the method further includes: Acquire new limb skeletal muscle data, new whole-body muscle density data, and new bone density value data of the target user after implementing the rehabilitation strategy at a preset cycle; Based on the new limb skeletal muscle data, the new whole-body muscle density data, and the whole-body muscle density data, the evaluation data of the rehabilitation strategy is determined according to a preset judgment rule; If the evaluation data indicates that the rehabilitation strategy is effective, then a new rehabilitation strategy for the target user is selected from the preset database based on the new limb skeletal muscle data, the new whole-body muscle density data, and the new bone density value data. If the assessment data does not significantly indicate the effectiveness of the rehabilitation strategy, then the change rate of limb skeletal muscle is calculated based on the new limb skeletal muscle data and the limb skeletal muscle data, and the change rate of whole-body muscle density is calculated based on the new whole-body muscle density data and the whole-body muscle density data. The rate of change of skeletal muscle in the limbs is compared with the rate of change of muscle density in the whole body. If the rate of change of skeletal muscle in the limbs is less than the rate of change of muscle density in the whole body, the new skeletal muscle data of the limbs is displayed to obtain an exercise strategy for the new skeletal muscle data of the limbs, and the rehabilitation strategy is updated based on the exercise strategy to obtain the first updated strategy. If the rate of change of total muscle density is less than the rate of change of skeletal muscle in the limbs, the rate of change of total muscle density is displayed to obtain an exercise strategy for the rate of change of total muscle density, and the rehabilitation strategy is updated based on the exercise strategy to obtain a second updated strategy.

[0036] For example, muscle mass change indicators: an increase of ≥3% in ASM / H² is considered "significant effect," an increase of 1%-3% is considered "moderate effect," and an increase of <1% is considered "poor effect." A decrease in muscle mass is directly considered "regression." Functional indicator changes: A grip strength increase of ≥5%, leg strength increase of ≥3%, walking speed increase of ≥0.1m / s, a reduction of ≥1 second in 5 sit-ups, and a decrease in balance sway value of ≥0.5cm are considered "functional improvement." Meeting 2 or more of these indicators is considered "no functional change," meeting 0-1 indicators is considered "no functional change," and a decrease in any indicator is considered "functional regression." Exercise compliance indicators: A weekly actual training frequency / planned frequency of ≥80% is considered "good compliance," 60%-80% is considered "moderate compliance," and <60% is considered "poor compliance."

[0037] An exemplary, highly effective iterative strategy: If a patient's muscle mass changes (ASM / H² increase ≥3%), functional index changes (grip strength increase ≥5%, walking speed increase ≥0.1m / s, or sit-up time shortened by ≥1 second, meeting two or more criteria), and exercise adherence (weekly training frequency completion ≥80%) all meet the criteria, the strategy is considered "highly effective." During iteration, target muscle group training is strengthened, strength training weight is increased by 0.5-1kg (0.2-0.3kg for bedridden patients), the number of repetitions per set is increased by 3-5, and the weekly training frequency is increased by 1 time. Simultaneously, one new targeted exercise is added (e.g., if core muscle group results are good, a Russian twist with resistance band is added).

[0038] The "moderate effect" iterative strategy is as follows: If the patient's muscle mass increases by 1%-3%, functional indicators show no significant change, or exercise compliance is 60%-80%, the effect is considered "moderate." During iteration, optimize the movement combinations, replacing standing movements with sitting movements (to improve completion rate), adding 1-2 fun movements (such as single-leg standing on a balance mat instead of single-leg standing on flat ground); shorten the duration of each training session (from 25 minutes to 20 minutes, and for bedridden patients from 20 minutes to 15 minutes), while extending the rest time between each set of movements (from 30 seconds to 1 minute).

[0039] Poor Performance Iteration Strategy: If a patient's muscle mass increase is <1%, functional indicators show no improvement, or exercise adherence is <60%, the outcome is considered "poor." During iteration, the cause is first analyzed through exercise logs (e.g., joint pain preventing leg training, excessively long training time leading to poor adherence), and then targeted adjustments are made: If the problem is discomfort with the movement, replace it with a low-load movement (e.g., side-lying leg raises instead of seated leg kicks); if the problem is poor adherence, break each training session into "10 minutes x 2 times / day," and add "completion reward reminders" to the mobile app (e.g., push notifications like "Completed 3 training sessions this week, muscle mass has increased by 1.2%, keep it up!").

[0040] Discomfort Iteration Strategy: If a patient's exercise log reports discomfort symptoms such as joint pain or dizziness, it is considered "discomfort has occurred." During iteration, the movements that cause discomfort are immediately marked and removed, the overall training intensity is reduced by 10%-15% (e.g., the resistance of the elastic band is reduced from 10 pounds to 8 pounds), and the warm-up and cool-down time is extended (each by 2 minutes). The next evaluation cycle is shortened to 2 weeks, focusing on monitoring whether the discomfort symptoms have been relieved.

[0041] When a patient meets all of the following criteria for two consecutive assessment cycles (a total of 2 months), the system will automatically switch from the "intervention period" to the "maintenance period": muscle mass reaches the target (ASM / H² ≥ 7.0 kg / m² for men / 5.4 kg / m² for women, and muscle density ≥ 70 mg / cm³ for men / 65 mg / cm³ for women), functional indicators reach the target (grip strength ≥ 28 kg for men / 18 kg for women, walking speed ≥ 1.0 m / s, time to sit up ≤ 12 seconds for 5 times, balance sway value ≤ 5 cm), exercise compliance ≥ 80% and no exercise-related discomfort feedback.

[0042] In one possible implementation, the method further includes: Obtain the identity information of the target user, and bind the identity information with the limb skeletal muscle data, the whole body muscle density data, the bone density value data, the user type, the muscle function data, the rehabilitation strategy, new limb skeletal muscle data, new whole body muscle density data, new bone density value data, new rehabilitation strategy, first update strategy and second update strategy to obtain a dataset for the target user; Based on the dataset, statistical data with a preset period as the horizontal axis is generated and displayed.

[0043] For example, a unique electronic file is created for each patient, which is linked to the patient's basic information (name, ID number, gender, age, height, weight, BMI, underlying diseases, type of sarcopenia), data from previous whole-body scans (stored on a timeline the total muscle mass, ASM / H², muscle density, and absolute and change values ​​of muscle mass in each part (limbs, trunk, core muscles) for each DXA scan, such as "Left lower limb muscle mass: 5.2kg initially → 5.5kg after 1 month, an increase of 5.8%"), data from previous functional assessments (trends in grip strength, leg strength, walking speed, sit-up time, and balance sway value), and exercise log data (daily training completion rate, training duration, and physical feedback entered by the patient through a mobile APP, such as "80% of training completed today, slight knee pain during leg strength training").

[0044] The system automatically triggers effect assessments every one month (with a maintenance period of three months). When triggered, an "assessment reminder" is pushed to medical staff and patients via the touchscreen of the human-computer interaction unit and a mobile APP. During the assessment, the system automatically retrieves the patient's most recent and first (or previous cycle) scan data, functional data, and exercise logs, calculates the changes, compares them with the assessment indicators, and generates a "Muscle Mass Change Comparison Report" (including text descriptions and bar / line graphs to visually display changes in muscle mass at each site), a "Functional Indicator Trend Report" (presenting the historical values ​​and rates of change of each functional indicator in tabular form), and a "Comprehensive Exercise Effect Assessment Report" (clearly indicating "Significant / Average / Poor / Regression" and analyzing the reasons, such as "Average effect, the reason is that exercise compliance is only 65%, and it is recommended to optimize training time"). All reports can be exported in PDF format and can be printed or synchronized to the hospital's HIS system via Wi-Fi.

[0045] In one possible implementation, the step of displaying the statistical data includes: The statistical data is displayed using encrypted transmission.

[0046] For example, when displaying the statistical data via encrypted transmission, different data transmission protocols are selected based on the transmission distance. Bluetooth 5.0 BLE (Low Power) protocol is used, with a communication distance ≤10 meters, primarily for short-range, rapid data interaction. It supports bidirectional communication between the device and the app: the device pushes scan progress (e.g., "DXA scan completed 30%, please remain still"), assessment step reminders (e.g., "Scan completed, please proceed to the assessment table for grip strength measurement"), and text previews of diagnostic results (excluding high-resolution scan images, only including "Sarcopenia positive / early / negative" and core indicator values) to the app; the app transmits manually entered height and weight (if the ID card cannot be successfully read), and real-time exercise log data (e.g., "Today's training completion rate 100%, no discomfort") to the device. Bluetooth transmission uses AES-128-bit encryption to ensure data transmission security, with a single data transmission latency ≤1 second and a transmission success rate ≥99%.

[0047] Wi-Fi Communication: Utilizes the IEEE 802.11n protocol, operating in the 2.4GHz band, with a communication distance ≤50 meters (unobstructed). Primarily used for long-distance, high-volume data transmission, it interfaces with hospital / community LANs. The device pushes high-definition DXA muscle heatmaps (JPEG format, 300dpi resolution, approximately 2MB file size), complete diagnostic reports (PDF format, including scan data, functional indicators, diagnostic conclusions, and initial exercise plans, approximately 5MB file size), and exercise plan animation videos (MP4 format, 1920×1080 resolution, 30fps frame rate, each movement includes a standard demonstration, error messages, and force point annotations, supports 360° rotation viewing, single video segment size approximately 10MB). The app batch synchronizes historical exercise logs to the device (if the patient enters data offline, it will automatically resume transmission after Wi-Fi is restored). Wi-Fi transmission uses HTTPS protocol encryption and supports breakpoint resumption (if transmission is interrupted, it will resume from the breakpoint upon reconnection, without restarting). High-definition heatmap transmission time ≤10 seconds, complete report transmission time ≤20 seconds.

[0048] For example, to ensure the security and reliability of data transmission, different devices transmitting data need to establish a binding relationship through dual authentication of "device dynamic verification code + last 6 digits of patient's ID card". The device MAC address and Wi-Fi configuration information are automatically saved. No repeated authentication is required when connecting later. The device and the APP only need to be in the same Wi-Fi network or Bluetooth signal range to be automatically recognized.

[0049] For example, statistical data are accompanied by a unique patient identifier (SHA-256 hash of ID number, not plaintext) and a timestamp to avoid mismatch between data and patient information; sensitive data transmitted via Bluetooth (such as diagnostic results) is encrypted with AES-128, and all data transmitted via Wi-Fi is encrypted with HTTPS protocol to prevent data from being stolen or tampered with; patient data in the device database is stored in encrypted form and can only be accessed by authorized medical personnel with a password.

[0050] Secondly, such as Figure 2 As shown in the figure, this application provides a sarcopenia rehabilitation strategy generation device, the device comprising: The data acquisition module 201 is used to acquire the skeletal muscle data of the target user's limbs, the whole body muscle density data and the bone density value data, and to acquire the muscle function data of the target user according to the user type of the target user and a preset rule. The portrait module 202 is used to create a portrait of the target user based on the limb skeletal muscle data, the whole body muscle density data, and the muscle function data, using a preset diagnostic algorithm, and obtain the portrait result. The strategy generation module 203 is used to select a rehabilitation strategy for the target user from a preset database based on the user type, the profile result, and the bone density value data.

[0051] In one possible implementation, such as Figure 3 As shown, this application embodiment provides a terminal device 300, including: a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, it performs the following: acquiring limb skeletal muscle data, whole-body muscle density data, and bone density data of a target user; acquiring muscle function data of the target user according to a preset rule based on the user type of the target user; creating a profile of the target user based on the limb skeletal muscle data, the whole-body muscle density data, and the muscle function data using a preset diagnostic algorithm; and selecting a rehabilitation strategy for the target user from a preset database based on the user type, the profile result, and the bone density data.

[0052] In one possible implementation, such as Figure 4As shown, this application embodiment provides a computer-readable storage medium 400 storing a computer program 411. When executed by a processor, the computer program 411 performs the following: acquiring limb skeletal muscle data, whole-body muscle density data, and bone density data of a target user; acquiring muscle function data of the target user according to a preset rule based on the user type of the target user; creating a profile of the target user based on the limb skeletal muscle data, the whole-body muscle density data, and the muscle function data using a preset diagnostic algorithm to obtain a profile result; and selecting a rehabilitation strategy for the target user from a preset database based on the user type, the profile result, and the bone density data.

[0053] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0054] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0055] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0056] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0057] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0058] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0059] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0060] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for generating a sarcopenia rehabilitation strategy, characterized in that, include: Acquire skeletal muscle data of the target user's limbs, whole-body muscle density data, and bone density data, and acquire muscle function data of the target user according to the user type of the target user and a preset rule; Based on the skeletal muscle data of the limbs, the whole body muscle density data, and the muscle function data, a profile of the target user is created using a preset diagnostic algorithm to obtain the profile result. Based on the user type, the profile results, and the bone density data, a rehabilitation strategy for the target user is selected from a preset database.

2. The method for generating a sarcopenia rehabilitation strategy as described in claim 1, characterized in that, The steps of obtaining the target user's limb skeletal muscle data, whole-body muscle density data, and bone density value data include: A heat map of the distribution of muscles throughout the body is obtained, and the skeletal muscle data of the limbs, the total muscle density data, and the bone density value data of the target user are calculated based on the heat map of the distribution of muscles throughout the body.

3. The method for generating a sarcopenia rehabilitation strategy as described in claim 1, characterized in that, The step of obtaining the muscle function data of the target user according to the user type and preset rules includes: Based on the user type of the target user, determine the avoidance evaluation items that the target user cannot perform according to preset rules; Based on the avoidance evaluation items and the preset evaluation item set, the target evaluation items for the target user are determined, and the muscle function data of the target user is obtained according to the target evaluation items.

4. The method for generating a sarcopenia rehabilitation strategy as described in claim 1, characterized in that, The preset database includes a first set of motion strategies for different user types, a second set of motion strategies for different profile results, and a third set of motion strategies for different bone density values.

5. The method for generating a sarcopenia rehabilitation strategy as described in claim 1, characterized in that, The method further includes: Acquire new limb skeletal muscle data, new whole-body muscle density data, and new bone density value data of the target user after implementing the rehabilitation strategy at a preset cycle; Based on the new limb skeletal muscle data, the new whole-body muscle density data, and the whole-body muscle density data, the evaluation data of the rehabilitation strategy is determined according to a preset judgment rule; If the evaluation data indicates that the rehabilitation strategy is effective, then a new rehabilitation strategy for the target user is selected from the preset database based on the new limb skeletal muscle data, the new whole-body muscle density data, and the new bone density value data. If the assessment data does not significantly indicate the effectiveness of the rehabilitation strategy, then the change rate of limb skeletal muscle is calculated based on the new limb skeletal muscle data and the limb skeletal muscle data, and the change rate of whole-body muscle density is calculated based on the new whole-body muscle density data and the whole-body muscle density data. The rate of change of skeletal muscle in the limbs is compared with the rate of change of muscle density in the whole body. If the rate of change of skeletal muscle in the limbs is less than the rate of change of muscle density in the whole body, the new skeletal muscle data of the limbs is displayed to obtain an exercise strategy for the new skeletal muscle data of the limbs, and the rehabilitation strategy is updated based on the exercise strategy to obtain the first updated strategy. If the rate of change of total muscle density is less than the rate of change of skeletal muscle in the limbs, the rate of change of total muscle density is displayed to obtain an exercise strategy for the rate of change of total muscle density, and the rehabilitation strategy is updated based on the exercise strategy to obtain a second updated strategy.

6. The method for generating a sarcopenia rehabilitation strategy as described in claim 5, characterized in that, The method further includes: Obtain the identity information of the target user, and bind the identity information with the limb skeletal muscle data, the whole body muscle density data, the bone density value data, the user type, the muscle function data, the rehabilitation strategy, new limb skeletal muscle data, new whole body muscle density data, new bone density value data, new rehabilitation strategy, first update strategy and second update strategy to obtain a dataset for the target user; Based on the dataset, statistical data with a preset period as the horizontal axis is generated and displayed.

7. The method for generating a sarcopenia rehabilitation strategy as described in claim 6, characterized in that, The step of displaying the statistical data includes: The statistical data is displayed using encrypted transmission.

8. A device for generating sarcopenia rehabilitation strategies, characterized in that, The device includes: The data acquisition module is used to acquire the skeletal muscle data of the target user's limbs, the whole body muscle density data, and the bone density value data, and to acquire the target user's muscle function data according to the target user's user type and preset rules. The profiling module is used to create a profile of the target user based on the limb skeletal muscle data, the whole body muscle density data, and the muscle function data, using a preset diagnostic algorithm, and obtain the profile result. The strategy generation module is used to select a rehabilitation strategy for the target user from a preset database based on the user type, the profile results, and the bone density value data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for generating sarcopenia rehabilitation strategies as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for generating sarcopenia rehabilitation strategies as described in any one of claims 1 to 7.

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