CN-121987187-A - Quantifiable fall risk determination method, electronic device, storage medium and product

CN121987187ACN 121987187 ACN121987187 ACN 121987187ACN-121987187-A

Abstract

The embodiment of the application provides a quantifiable falling risk determination method, electronic equipment, a storage medium and a product, and relates to the technical field of data processing; the method comprises the steps of calculating a body function scoring result of a target object based on a preset quantization scoring rule, obtaining maximum angle range data, limb function quantization data and gravity center displacement quantization data of a specific body part of the target object in a preset movement test action, obtaining a quantifiable falling risk value of the target object based on a preset weight coefficient and a quantification fusion model, and determining the falling risk level of the target object according to the preset risk level grading rule and the quantifiable falling risk value of the target object. The embodiment of the application is used for providing a technical scheme capable of quantifying the falling risk of the old with considerable and accurate.

Inventors

GUO YUANCHAO
YANG FANG
GUO FENGXIAN
YU HUAJUN
HU TIANHUA

Assignees

上海际知医疗科技有限公司

Dates

Publication Date: 20260508
Application Date: 20260127

Claims (10)

1. A quantifiable fall risk determination method, the method comprising: Acquiring a primary screening result of the falling risk of a target object, and acquiring body function evaluation parameters of the target object acquired by timing equipment when the primary screening result of the falling risk reaches a preset falling risk judgment threshold; calculating a body function scoring result of the target object according to the body function evaluation parameter of the target object based on a preset quantization scoring rule; Determining the falling risk level of the target object according to a preset risk level classification rule and a body function scoring result of the target object; Acquiring maximum angle range data of a specific body part of the target object in a preset motion test action through a three-dimensional motion capture analysis system, acquiring limb function quantification data of the target object through a healthy body adaptation test system, and acquiring center of gravity displacement quantification data of the target object in the preset balance test action through a balance training and test system; Based on a quantitative fusion model, carrying out fusion calculation on the maximum angle range data of the specific body part, the lower limb strength quantitative data and the gravity center displacement quantitative data to obtain a quantifiable falling risk value of the target object; and determining a falling risk result of the target object according to the falling risk grade of the target object and the quantifiable falling risk value of the target object.
2. The method of claim 1, wherein the body function assessment parameters of the target object are obtained by performing body function tests on the target object by the timing device, wherein the body function tests include a 4 meter pace test, a four-stage balance test, a time-keeping standing walking test, and a chair sitting standing test; the body function evaluation parameters of the target object comprise a first completion time for completing the 4-meter pace test, a second completion time for completing the chair sitting and standing test and a third completion time for completing the timing and standing and walking test, wherein the duration time of each action in the four-stage balance test is equal to the second completion time for completing the chair sitting and standing test.
3. The method according to claim 2, wherein calculating the body function score of the target object based on the body function evaluation parameter of the target object based on the preset quantitative scoring rule comprises: determining a first scoring result based on the preset quantization scoring rule, the first completion time, the duration time, and the second completion time; Determining a second scoring result based on the preset quantization scoring rule and the third completion time; And determining the somatic function scoring result of the target object according to the preset risk quantification rule, the first scoring result and the second scoring result.
4. The method of claim 3, wherein determining a first scoring result based on the preset quantization scoring rule, and the first completion time, the duration, and the second completion time comprises: Determining a pace score of a 4-meter pace test based on the preset quantization score rule and the first completion time; Determining a balance score of the four-stage balance test based on the preset quantization score rule and the duration of each action in the four-stage balance test; Determining a sitting score of the chair sitting up test based on the preset quantitative score rule and the second completion time of the chair sitting up test; and determining the first scoring result according to the pace score of the 4-meter pace test, the balance score of the four-stage balance test and the sitting position score of the chair sitting up test.
5. The method of claim 3, wherein determining the somatic function score for the target subject based on the first score, the second score, and the preset risk quantification rule comprises: Determining that the somatic function scoring result of the target object is one of high risk, medium risk, low risk and abnormality-free according to the preset risk quantification rule, the first scoring result and the second scoring result; When the body function scoring result of the target object is that the abnormality is not generated, acquiring the standing time of a single foot in the four-stage balance test; and if the standing time of the single foot is smaller than the preset duration, determining that the body function scoring result of the target object is low risk, otherwise, determining that the body function scoring result of the target object is abnormal-free.
6. The method of any of claims 1-5, wherein the three-dimensional motion capture analysis system comprises an RGB camera, an infrared camera, and a depth camera, a first processing module, and human joint model standard data; the RGB camera is used for collecting two-dimensional images of the target object in the preset motion test action; the depth camera is used for collecting depth information of the target object in the preset motion test action; the infrared camera is used for collecting two-dimensional contour data of the target object in the preset motion test action; The first processing module is used for reconstructing the three-dimensional joint posture of the target object according to the two-dimensional image, the depth information, the two-dimensional contour data and the human body joint point model standard data so as to determine the maximum angle range data of the specific part of the target object; the health body energy adaptation testing system comprises body energy adaptation rod equipment, terminal equipment, a built-in force sensor, a laser ranging module and a communication module, wherein the terminal equipment is communicated with the body energy adaptation rod equipment; The force sensor is used for acquiring force signals generated when the lower limb of the target object pushes the body energy adapting rod device so as to acquire knee stretching muscle force data of the target object; The laser ranging module is used for acquiring a distance signal when the target object holds the body-fit energy bar equipment to finish the forward extending action so as to acquire functional forward extending distance data of the target object; The communication module is used for transmitting the knee extension muscle strength data and the functional forward extension distance data to the terminal equipment; The terminal equipment is used for receiving the knee extension muscle strength data and the functional extension distance data, converting the knee extension muscle strength data into a standardized muscle strength value, and converting the functional extension distance data into a standardized extension value so as to obtain limb function quantized data of the target object; the balance training and testing system comprises a plurality of gravity sensors and a second processor, wherein the gravity sensors are uniformly distributed at the bottom of the balance training and testing system; each gravity sensor is used for collecting a gravity distribution value of the target object in a standing state; the processor is configured to: Respectively acquiring a plurality of gravity distribution values corresponding to each moment in a preset test duration according to a preset sampling frequency under the state that the target object is in an eye opening state and an eye closing state, and calculating a plurality of gravity center position coordinates corresponding to the target object under the eye opening state and the eye closing state according to the difference values of the gravity distribution values; generating a first barycentric position enveloping area map when the target object executes a preset balance test action in an eye opening state according to a plurality of barycentric position coordinates corresponding to the target object in the eye opening state, and generating a second barycentric position enveloping area map when the target object executes the preset balance test action in an eye closing state according to a plurality of barycentric position coordinates corresponding to the target object in the eye closing state; And respectively calculating a first area value of the first center-of-gravity position envelope area diagram and a second area value of the second center-of-gravity position envelope area diagram, and taking the first area value and the second area value as center-of-gravity displacement quantized data of the target object in a preset balance test action.
7. The method according to any one of claims 1-5, further comprising: and determining a corresponding fall prevention scheme according to a fall risk result of the target object, wherein the fall prevention scheme comprises at least one of dynamic balance training, static balance training, muscle strength training, walking training and living instruction scheme.
8. An electronic device comprising a memory and a processor, the memory configured to store computer program instructions, the processor configured to execute the computer program instructions to cause the electronic device to implement the method of any one of claims 1 to 10.
9. A computer-readable storage medium comprising computer program instructions; An electronic device executing the computer program instructions such that the electronic device implements the method of any one of claims 1 to 10.
10. A computer program product comprising a computer program which, when executed by a processor, implements the method of any of claims 1-10.

Description

Quantifiable fall risk determination method, electronic device, storage medium and product Technical Field The present application relates to the field of data processing technologies, and in particular, to a quantifiable fall risk determination method, an electronic device, a storage medium, and a product. Background At present, the old people fall risk assessment is divided into four core links of comprehensive assessment, somatic function assessment, environmental assessment and psychological assessment, wherein the comprehensive assessment is carried out by adopting a Morse old people fall risk assessment scale and an old people fall risk assessment tool equivalent scale, and the somatic function assessment covers the modes of a daily life activity Ability (ADL) assessment scale, a timing standing walking test, a Berg balance scale, a Tinetti gait and balance test scale, a functional protrusion test and the like. Current mainstream assessment schemes are by scale assessment. However, the existing evaluation means of the falling risk of the old based on the scale are greatly influenced by subjective factors of evaluation staff, such as the understanding difference of scale scoring standards, subjectivity of observation and judgment and the like, so that the evaluation result is difficult to quantitatively analyze, and objective and accurate quantitative index support is lacked. Disclosure of Invention The application provides a quantifiable falling risk determination method, electronic equipment, storage media and products, and provides a technical scheme capable of quantifying the falling risk of the old with considerable and high accuracy. In a first aspect, the application provides a quantifiable fall risk determination method, the method comprising: Acquiring a primary screening result of the falling risk of a target object, and acquiring body function evaluation parameters of the target object acquired by timing equipment when the primary screening result of the falling risk reaches a preset falling risk judgment threshold; calculating a body function scoring result of the target object according to the body function evaluation parameter of the target object based on a preset quantization scoring rule; Determining the falling risk level of the target object according to a preset risk level classification rule and a body function scoring result of the target object; Acquiring maximum angle range data of a specific body part of the target object in a preset motion test action through a three-dimensional motion capture analysis system, acquiring limb function quantification data of the target object through a healthy body adaptation test system, and acquiring center of gravity displacement quantification data of the target object in the preset balance test action through a balance training and test system; Based on a preset weight coefficient and a quantization fusion model, carrying out fusion calculation on the body function quantization scoring result, the maximum angle range data of the specific body part, the lower limb strength quantization data and the gravity center displacement quantization data to obtain a quantifiable falling risk value of the target object; and determining a falling risk result of the target object according to the falling risk grade of the target object and the quantifiable falling risk value of the target object. In an alternative embodiment, the body function evaluation parameter of the target object is obtained by performing a body function test on the target object through the timing equipment, wherein the body function test comprises a 4-meter pace test, a four-stage balance test, a timing standing walking test and a chair sitting standing test; the body function evaluation parameters of the target object include a first completion time for completing the 4-meter pace test, a duration for completing each action in the four-stage balance test, a second completion time for completing the chair sitting up test, and a third completion time for completing the timer standing up walk test. In an optional implementation manner, the calculating the body function scoring result of the target object according to the body function evaluation parameter of the target object based on the preset quantization scoring rule includes: determining a first scoring result based on the preset quantization scoring rule, the first completion time, the duration time, and the second completion time; Determining a second scoring result based on the preset quantization scoring rule and the third completion time; And determining the somatic function scoring result of the target object according to the preset risk quantification rule, the first scoring result and the second scoring result. In an alternative embodiment, determining the first scoring result based on the preset quantization scoring rule, and the first completion time, the duration, and the second completion time includes: Determining a