CN108926813B

CN108926813B - Training method based on human body balance data

Info

Publication number: CN108926813B
Application number: CN201710362929.2A
Authority: CN
Inventors: 安健; 黄一宁; 张珏; 方竞
Original assignee: Peking University
Current assignee: Peking University
Priority date: 2017-05-22
Filing date: 2017-05-22
Publication date: 2020-01-21
Anticipated expiration: 2037-05-22
Also published as: CN108926813A

Abstract

The invention discloses a training method based on human body balance data, and belongs to the field of rehabilitation training methods. The training method comprises the steps of firstly carrying out real-time data acquisition on an individual under eight preset interactive posture balance tasks, then analyzing the acquired data by using a nonlinear dynamics method to obtain the quantitative values of the overall balance function of the individual and the limb coordination capacity of the individual, and finally comparing the quantitative values with indexes in a database to obtain the maximum angles and the corresponding maximum time for keeping balance of the eight postures of the human body, thereby realizing the balance training of the eight postures of the human body. The invention can complete the evaluation of the balance function and the corresponding training only by the intelligent mobile phone carried by the human body, is not limited by time and space, and can test and train the balance function of the human body for a long time.

Description

Training method based on human body balance data

Technical Field

The invention relates to a training method based on human body balance data, and belongs to the field of rehabilitation training methods.

Background

Balance is an important function of the human body, and the ability to balance in daily life is particularly important for maintaining various postures, performing various activities, and generating appropriate responses to external disturbances.

At present, a plurality of methods for evaluating and training the balance function are provided, wherein the traditional subjective observation method is simple, easy, convenient, visual and quick to operate, but is too rough and subjective, lacks objective quantification standards and can only be used for preliminary screening of patients suspected of having balance function disorder. The scale method is easy to quantify and convenient to compare, but is complex and time-consuming to operate, is influenced by human factors, and has high error. The pressure plate test is simple and quick to operate, but has strong specialization and higher cost, and is only suitable for research and application. These evaluations and tests result in poor balance function, which is not sufficiently noticed by the general public, and even more not scientifically and effectively improved. Therefore, a method which is convenient for the public to quickly and effectively carry out objective evaluation and scientific improvement on the self balance function under the conventional environment at any time is urgently needed to be provided.

Disclosure of Invention

In order to solve the problem that the existing training method for the human body balance function is lacked, the invention provides a training method based on human body balance data, which can evaluate the balance function for the public at any time and any place and provide a training implementation scheme.

The technical scheme of the invention is as follows:

a training method based on human body balance data comprises the following steps:

1) under the condition that the lower part of the human body is immobile, the upper part of the human body inclines forwards, backwards, leftwards, rightwards, leftwards forwards, leftwards, backwards, rightwards forwards and rightwards backwards, and acceleration sensors and angular velocity sensors are utilized to acquire real-time data of acceleration and angular velocity signals of 8 groups of postures of the human body;

specifically, a sensor arranged in a mobile phone is used for collecting real-time data of acceleration and angular velocity signals of a human body under an interactive posture balancing task. The interactive posture balancing task has 8 types, including: when the upper body is tilted forward, backward, leftward, rightward, leftward forward, leftward backward, rightward forward, rightward backward, under the condition that the lower body of the individual is immobile; the interaction mode is that the next preset posture balancing task to be completed is prompted to the individual through mobile phone voice and flicker, and the mobile phone sends out a prompt tone when the current posture balancing task is completed; the collection time is 1 minute to 15 minutes and the sampling frequency is 100Hz to 300 Hz.

2) Carrying out phase space reconstruction on the real-time data of 8 groups of postures of the human body through delay time tau and embedded dimension E to obtain 8 groups of E-dimensional time sequences Z_i(i ═ 1, 2., E), further performing phase-space mutual prediction on the 8 groups of time sequences pairwise by using a local prediction method, and taking the predicted similarity degree as a quantitative value of the coordination capacity of the limbs;

3) 8 groups of E-dimensional time sequences Z obtained by phase space reconstruction_i(

i

1, 2.. times.e.) performing multi-scale entropy analysis to obtain 8 sample entropies

Wherein

The maximum difference value of two adjacent data in the original acceleration data is obtained; weighting the normalized sample entropy to be used as a quantitative value of the integral balance function of the human body;

4) comparing the quantitative value of the limb coordination ability and the quantitative value of the integral human body balance function with indexes of a database respectively to obtain maximum angles and corresponding maximum time for keeping balance of 8 postures of the human body;

5) the angle and the corresponding time for keeping balance of each posture of the human body are longer than the maximum angle and the corresponding maximum time obtained above, and 8 posture balance training of the human body is realized.

The database comprises ten grades of data sets of the overall balance ability and the limb coordination ability of the crowd, and each grade is divided into 1-4 layers from small to large; the maximum angle theta of the individual keeping balance under each posture task of each layer of each grade is recorded in the database_ijk(i-1, 2.. 8; j-1, 2.. 10; k-1, 2, 3, 4) and a corresponding maximum time T_ijk(i-1, 2.. 8; j-1, 2.. 10; k-1, 2, 3, 4), where i represents 8 pose tasks, j represents ten levels, and k represents four levels.

The training amount is determined by the maximum angle and the corresponding maximum time for the human body to keep balance under 8 posture tasks, so that the balance function of the human body is improved. Wherein the training amount is the maximum angle for keeping balance at theta_ijkTo theta_ij(k+1)Corresponding to the maximum time at T_ijkTo T_ij(k+1)In the meantime.

The invention has the following advantages:

the invention is not limited by time and space, can evaluate and train the human body balance function at any time and any place only by using the intelligent mobile phone carried by the individual without purchasing any other equipment, and provides long-term detection; the invention provides an individualized training scheme according to the self condition of an individual, and can scientifically and effectively improve the self balance function.

Drawings

FIG. 1 is a schematic flow diagram of the process of the present invention;

FIG. 2 is a schematic diagram of 8 pose tasks for an individual in the method of the present invention;

FIG. 3 is a flow chart of balance training in the method of the present invention.

Detailed Description

The invention will be further illustrated by the following specific examples in order to better understand the invention, without however being limited thereto.

FIG. 1 is a schematic flow chart of the method of the present invention, which is mainly divided into data acquisition, calculation of quantitative values of the overall balance function of the human body and quantitative values of the coordination ability of the limbs, database comparison and training scheme formulation. The whole system can form a closed loop by using Bayesian estimation, and the individual balance capability is continuously improved.

First, a basic file of an individual is established according to the input name, sex, age, height, weight, medical history and the like.

When the individual balance function evaluation is carried out, the acceleration and angular velocity signals of a human body are collected in real time under eight preset interactive posture balance tasks by utilizing acceleration and angular velocity sensors arranged in the smart phone, the collection time is 1-15 minutes, the sampling frequency is 100-300 Hz, and the method is divided into the following steps:

(1) after a test is started by clicking on a mobile phone application program interface, the body of an individual is naturally upright, two feet are closed, two arms are crossed at the chest, and the mobile phone is held by two hands to stick a screen in front of the chest;

(2) as shown in fig. 2, the individual keeps the lower body below the waist still, the upper body randomly tilts in eight directions, namely, forward 1, backward 2, leftward 3, rightward 4, leftward front 5, leftward back 6, rightward front 7 and rightward back 8 according to the prompt tone of the mobile phone, and after each tilt, the mobile phone is heard to give out the prompt tone "beep" to restore the upright state to prepare the next action;

(3) after hearing the prompt sent by the mobile phone, the mobile phone is taken down from the chest and the data is clicked and saved.

Carrying out phase space reconstruction on real-time data acquired under 8 posture balance tasks preset when a human body stands through delay time tau and embedded dimension E to obtain 8 groups of E-dimensional time sequences Z_iAnd (i ═ 1, 2., E), further performing phase-space mutual prediction on the 8 time series in pairs by using a local prediction method, wherein the predicted similarity degree is used as a quantitative value of the coordination capacity of the limbs. 8 groups of E-dimensional time sequences Z obtained by phase space reconstruction_i(i ═ 1, 2.., E) andperforming line multi-scale entropy analysis to obtain 8 sample entropies

Wherein

The maximum difference value of two adjacent data in the original acceleration data is obtained; weighting the normalized sample entropy as the quantitative value of the integral balance function of the human body, wherein the weight is 8 maximum Lyapunov indexes of the collected data under each posture balance task

Respectively comparing the calculated quantitative value of the overall balance function and the quantitative value of the limb coordination ability of the individual with indexes in a database to obtain the maximum angle and the corresponding maximum time for keeping balance of the human body under the preset eight posture balance tasks; the database of the human body balance function and the limb coordination ability comprises ten grades of data sets of the overall balance ability and the limb coordination ability, and each grade is divided into 1-4 layers from small to large; the maximum angle theta for keeping the individuals of each grade balanced under each posture task is recorded in the database of the human body balance function and the limb coordination ability_ijk(i-1, 2.. 8; j-1, 2.. 10; k-1, 2, 3, 4) and a corresponding maximum time T_ijk(i 1, 2.. 8;

j

1, 2.. 10;

k

1, 2, 3, 4), where i, j, k represent eight pose tasks, ten levels, four levels, respectively.

As shown in FIG. 3, the training amount is selected according to the global balance function, and the training amount is referenced to the maximum angle θ of the last training_ijkMaximum time T corresponding to maintaining balance_ijkThe training amount is increased one level at a time, 1/4 which is the difference between the two levels. The training amount as above is (theta)_ijk，T_ijk) Then the current training amount is [ theta ]_ijk+0.25(θ_i(j+1)k-θ_ijk)，T_ijk+0.25(T_i(j+1)k-T_ijk)]The next training amount is [ theta ]_ijk+0.50(θ_i(j+1)k-θ_ijk)，T_ijk+0.50(T_i(j+1)k-T_ijk)]Until the training amount reaches (theta)_i(j+1)k，T_i(j+1)k) And then, evaluating the overall balance function and the limb coordination ability of the individual again, and updating the training scheme.

The number of eight training tasks for posture balance is proportionally selected according to the degree of insufficiency of the individual limb coordination function, and the typical tangential angle theta under the preset eight interactive posture balance tasks is preset_iThe sum of which is theta_sum，

Then each interactive pose balancing task appears in the next training in a proportion of (theta)_sum-θ_i)/θ_sum。

Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make numerous possible variations and modifications to the present invention, or modify equivalent embodiments, using the methods and techniques disclosed above, without departing from the scope of the present invention. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention are still within the scope of the protection of the technical solution of the present invention, unless the contents of the technical solution of the present invention are departed.

Claims

1. A training method based on human body balance data comprises the following steps:

2) passing real-time data of 8 groups of postures of human body through delay time tau and embedding dimensionE, carrying out phase space reconstruction to obtain 8 groups of E-dimensional time sequences Z_i(i ═ 1, 2., E), further performing phase-space mutual prediction on the 8 groups of time sequences pairwise by using a local prediction method, and taking the predicted similarity degree as a quantitative value of the coordination capacity of the limbs;

3) 8 groups of E-dimensional time sequences Z obtained by phase space reconstruction_i(i 1, 2.. times.e.) performing multi-scale entropy analysis to obtain 8 sample entropies

Wherein

2. The training method of claim 1, wherein step 1) comprises: and acquiring real-time data by using a mobile phone internally provided with an acceleration sensor and an angular velocity sensor.

3. The training method of claim 2, wherein the step of collecting comprises:

1) after a test is started by clicking on a mobile phone application program interface, the body of an individual is naturally upright, two feet are closed, two arms are crossed at the chest, and the mobile phone is held by two hands to stick a screen in front of the chest;

2) the individual keeps the lower body below the waist still, the upper body randomly inclines forwards 1, backwards 2, leftwards 3, rightwards 4, leftwards forwards 5, leftwards backwards 6, rightwards forwards 7 and rightwards backwards 8 in eight directions according to the prompt tone of the mobile phone, and after each inclination, the mobile phone is heard to give out the prompt tone which is bleeped, and then the upright state is recovered to prepare the next action;

3) after hearing the prompt sent by the mobile phone, the mobile phone is taken down from the chest and the data is clicked and saved.

4. The training method of claim 3, wherein each ramp time is 1 minute to 15 minutes and the sampling frequency is 100Hz to 300 Hz.

5. The training method of claim 1, wherein the weight in step 3) is 8 maximum lyapunov exponents of the data collected under each posture balancing task

6. The training method of claim 1, wherein the database index in step 4) comprises ten levels, each level having 1-4 layers from small to large.

7. The training method of claim 6, wherein each posture of each level of human body maintains a balanced maximum angle θ_ijk(i-1, 2.. 8; j-1, 2.. 10; k-1, 2, 3, 4) and a corresponding maximum time T_ijk(i-1, 2.. 8; j-1, 2.. 10; k-1, 2, 3, 4), where i represents eight pose tasks, j represents ten levels, and k represents four levels.

8. The training method of claim 7, wherein the angle at which the human body maintains balance under 8 posture tasks satisfies θ_ijkTo theta_ij(k+1)In the middle, the time for keeping balance is satisfied at T_ijkTo T_ij(k+1)In the meantime.

9. Training method according to claim 7Characterized in that the human body maintains a balanced angle theta under the posture balancing task_iThe sum of which is theta_sum，

Each pose balancing task occurs in the training at a rate of (θ)_sum-θ_i)/θ_sum。