阅读说明：本技术 一种用于预测老年人跌倒风险的方法 (Method for predicting falling risk of old people ) 是由 马英楠 高星 王立 赵鹏霞 李少祥 于 2020-05-21 设计创作，主要内容包括：本发明公开了一种用于预测老年人跌倒风险的方法,包括以下步骤：将足底压力区域划分为大踇趾区、第二至第五脚趾区、脚前掌区、足中部区和脚后跟区。并将支撑相分为初始接触段、初始跖骨触地段、初始前足扁平段、足跟离地段和最后接触段；然后以足底压力区域和支撑相为依据,利用Footscan足底压力平板测试系统对受试者进行足底压力测试,得到不同足底压力区域在各支撑相时的压力变化曲线；再利用卷积神经网络和循环神经网络构建深度神经网络模型,并对该预测模型进行训练,选取最优预测模型为足压预测模型；最后将压力变化曲线输入足压预测模型,得到预测值。本发明具有数据测量精度高、特征指标多样和预测准确度好的特点。(The invention discloses a method for predicting falling risk of old people, which comprises the following steps: the plantar pressure area is divided into a hallux, second to fifth toe, forefoot, midfoot and heel areas. And dividing the support phase into an initial contact segment, an initial metatarsal contact segment, an initial forefoot flattening segment, a heel lift segment and a final contact segment; then, taking the sole pressure area and the supporting phase as a basis, and carrying out sole pressure test on the subject by using a Footscan sole pressure flat plate test system to obtain pressure change curves of different sole pressure areas in each supporting phase; then, a deep neural network model is constructed by utilizing the convolutional neural network and the cyclic neural network, the prediction model is trained, and the optimal prediction model is selected as a foot pressure prediction model; and finally, inputting the pressure change curve into a foot pressure prediction model to obtain a predicted value. The invention has the characteristics of high data measurement precision, various characteristic indexes and good prediction accuracy.)

1. A method for predicting the fall risk of an elderly person, comprising the steps of:

dividing a pressure area of a sole into a hallux region, second to fifth toe regions, a forefoot region, a midfoot region and a heel region;

dividing the support phase into an initial contact section, an initial metatarsal contact section, an initial forefoot flat section, a heel off-section and a final contact section;

thirdly, carrying out plantar pressure test on a subject by using the plantar pressure regions and the supporting phases as a basis and utilizing a Footscan plantar pressure flat plate test system to obtain pressure change curves of different plantar pressure regions in the supporting phases;

fourthly, a deep neural network model is built by utilizing the convolutional neural network and the cyclic neural network, the prediction model is trained, and the optimal prediction model is selected as a foot pressure prediction model;

fifthly, inputting the pressure change curve obtained in the third step into a foot pressure prediction model to obtain a predicted value.

2. A method for predicting fall risk of elderly according to claim 1, wherein the specific partitioning method of plantar pressure area in step (r) comprises the following steps:

(1.1) dividing the sole into 10 sub-regions according to a Footscan sole pressure plate test system, specifically: a lateral heel region, a medial heel region, a midfoot region, a fifth metatarsal region, a fourth metatarsal region, a third metatarsal region, a second metatarsal region, a first metatarsal region, second to fifth toe regions, and a hallux region;

(1.2) combining a lateral area and a medial area of the heel to form a heel area, and combining a fifth metatarsal area, a fourth metatarsal area, a third metatarsal area, a second metatarsal area and a first metatarsal area to form a forefoot area; the midfoot region, the second through fifth toe regions and the hallux region remain unchanged.

3. The method for predicting the fall risk of the elderly according to claim 1, wherein the deep neural network model in the step (iv) has a calculation formula as follows:

f_t＝σ(W_xf*x_t+W_hf*h_t-1+b_f)

i_t＝σ(W_xi*x_t+W_hi*h_t-1+b_i)

o_t＝σ(W_xo*x_t+W_ho*h_t-1+b_o)

h_t＝o_t°tanh(c_t)

wherein i_t、f_tAnd o_tIndicating input gate, forgetting gate and output gate, x_tFor input at the current time, h_t-1For the output of the hidden layer at the previous moment, c_tIs a cell state, represents the convolution operation, and ° represents the Hadamard product.

4. A method for predicting fall risk of elderly people as claimed in claim 3, wherein the training of the prediction model in step ④ is performed on a single sample by representing any sample as X ═ { X1, X2, …, xL }, where L is the length of the sequence and xi is a vector of M dimensions, and then dividing X into N subsequences, where X ═ PT1, PT2 …, PTN }, and each subsequence Pti ∈ R^M×1Denoted as Pti ═ { x1Ti, …, x1Ti }, where 1 is the length of each subsequence, x^k _Ti∈R^MRepresenting the value of the ith subsequence at the time point k, and the data input format after each sample sequence is divided into subsequences is (N, l, M).

5. The method for predicting the fall risk of the elderly as recited in claim 4, wherein the training method of the deep neural network model in the step (iv) comprises the following steps:

(4.1) carrying out dynamic balance ability test on different subjects by taking the subjects as training sets, and dividing the subjects into a high-fall risk group, a low-fall risk group and a test result ambiguous group according to the test results;

(4.2) carrying out static balance ability test on the subjects with uncertain test results, and dividing the subjects into a high-fall risk group and a low-fall risk group according to the test results;

(4.3) carrying out sole pressure test on the subject by using a Footscan sole pressure flat plate test system according to the sole pressure areas and the supporting phases to obtain pressure change curves of different sole pressure areas in each supporting phase;

(4.4) inputting the pressure change curve obtained in the step (4.3) into the deep neural network model according to the test results in the steps (4.1) and (4.2), training the deep neural network model by adopting a supervision mode, randomly initializing the weight of the model, and selecting the learning rate by 10^-3The objective is to minimize the cross entropy loss function and use Adam's preferredAnd optimizing the weight value by using a chemometric algorithm to obtain an optimal prediction model.

6. The method for predicting the fall risk of the elderly according to claim 1, wherein the Footscan plantar pressure flat plate test system detects the uni-podal univariate, uni-podal multivariate and multi-podal multivariate of the subject; the single-foot univariate is the pressure change data of a single foot in a certain plantar pressure area, the single-foot multivariate is the pressure change data of the single foot in a plurality of plantar pressure areas, and the multi-foot multivariate is the pressure change data of the double feet in the plurality of plantar pressure areas.

Technical Field

The invention relates to the field of behavior identification and judgment, in particular to a method for predicting falling risk of old people.

Background

The walking is the most basic and natural movement form which is completed under the coordination and coordination of various organs and muscles of the human body, and the walking capability is the basic guarantee for the independent activities of the old and the realization of the self-care of life. The main execution unit of human walking is the lower limbs, and 28 bones involved in the movement of the lower limbs are from the feet. Therefore, the plantar pressure in the walking process contains rich gait information, and the plantar pressure is often used for researching special people, such as abnormal gait conditions of the old, so that the falling risk of the old in the walking process is evaluated in real time, and theoretical support and practical guidance are provided for falling prevention and intervention of the old.

At present, the common methods for evaluating the falling risk of the old are an observation method, a scale method, a test method and an instrument detection method, wherein the observation method requires that the testers have related clinical experience, and the accuracy is low. The scale method and the test method are easily interfered by human factors during evaluation, are only suitable for being used as preliminary diagnosis in clinical practice and are not suitable for daily prediction and evaluation of the fall risk of the elderly. The instrument detection method mainly comprises the steps of firstly manually screening features by means of a plantar pressure testing platform to serve as detection factors, then recording feature values of a human body when the human body walks through the testing platform, and then obtaining a detection result by carrying out technology on the feature values through the traditional machine learning methods such as Logistic regression analysis and a support vector machine.

However, the prediction mode can only select discrete and single characteristic indexes as detection factors when selecting the characteristics, and the whole gait or balance process is not enough to be summarized, so that the prediction accuracy is low; moreover, the feature extraction and the selection of the subsequent classifier are mutually independent processes in many researches, and the features and the classifiers cannot be simultaneously optimized according to the classification result, so that the problems of low detection efficiency and poor generalization capability of the instrument detection method are caused. In addition, the data obtained by detecting the plantar pressure test platform has the characteristics of high latitude, high variation, multivariable, time dependence, nonlinearity and the like, so that the difficulty in analyzing the prediction result is further increased. Therefore, the existing prediction method for the falling risk of the old has the problems of low data measurement precision, single characteristic index and poor prediction accuracy.

Disclosure of Invention

It is an object of the present invention to provide a method for predicting the fall risk of an elderly person. The method has the characteristics of high data measurement precision, various characteristic indexes and good prediction accuracy.

The technical scheme of the invention is as follows: a method for predicting the fall risk of an elderly person, comprising the steps of:

dividing a pressure area of a sole into a hallux region, second to fifth toe regions, a forefoot region, a midfoot region and a heel region;

dividing the support phase into an initial contact section, an initial metatarsal contact section, an initial forefoot flat section, a heel off-section and a final contact section;

thirdly, carrying out plantar pressure test on a subject by using the plantar pressure regions and the supporting phases as a basis and utilizing a Footscan plantar pressure flat plate test system to obtain pressure change curves of different plantar pressure regions in the supporting phases;

fourthly, a deep neural network model is built by utilizing the convolutional neural network and the cyclic neural network, the prediction model is trained, and the optimal prediction model is selected as a foot pressure prediction model;

fifthly, inputting the pressure change curve obtained in the third step into a foot pressure prediction model to obtain a predicted value.

In the foregoing method for predicting a fall risk of an elderly person, the specific partitioning method of the plantar pressure region in the step (i) includes the following steps:

(1.1) dividing the sole into 10 sub-regions according to a Footscan sole pressure plate test system, specifically: a lateral heel region, a medial heel region, a midfoot region, a fifth metatarsal region, a fourth metatarsal region, a third metatarsal region, a second metatarsal region, a first metatarsal region, second to fifth toe regions, and a hallux region;

(1.2) combining a lateral area and a medial area of the heel to form a heel area, and combining a fifth metatarsal area, a fourth metatarsal area, a third metatarsal area, a second metatarsal area and a first metatarsal area to form a forefoot area; the midfoot region, the second through fifth toe regions and the hallux region remain unchanged.

In the foregoing method for predicting the fall risk of the elderly, the calculation formula of the ConvLSTM prediction model in the step (iv) is as follows:

f_t＝σ(W_xf*x_t+W_hf*h_t-1+b_f)

i_t＝σ(W_xi*x_t+W_hi*h_t-1+b_i)

o_t＝σ(W_xo*x_t+W_ho*h_t-1+b_o)

h_t＝o_t ^οtanh(c_t)

wherein i_t、f_tAnd o_tIndicating input gate, forgetting gate and output gate, x_tFor input at the current time, h_t-1For the output of the hidden layer at the previous moment, c_tIn the unit state, represents convolution operation, and o represents the Hadamard product.

In the method for predicting the fall risk of the elderly, the prediction model in step ④ is trained on a single sample by representing any one sample as X ═ X1, X2, …, xL, where L is the sequenceThe column length, xi, is an M-dimensional vector, and X is then divided into N subsequences, where X ═ PT1, PT 2., PTN }, each subsequence Pti ∈ R^M×1Denoted as Pti ═ { x1Ti, …, x1Ti }, where 1 is the length of each subsequence, x^k _Ti∈R^MRepresenting the value of the ith subsequence at the time point k, and the data input format after each sample sequence is divided into subsequences is (N, 1, M).

In the foregoing method for predicting a fall risk of an elderly person, the training method of the deep neural network model in step (iv) includes the following steps:

(4.1) carrying out dynamic balance ability test on different subjects by taking the subjects as training sets, and dividing the subjects into a high-fall risk group, a low-fall risk group and a test result ambiguous group according to the test results;

(4.2) carrying out static balance ability test on the subjects with uncertain test results, and dividing the subjects into a high-fall risk group and a low-fall risk group according to the test results;

(4.3) carrying out sole pressure test on the subject by using a Footscan sole pressure flat plate test system according to the sole pressure areas and the supporting phases to obtain pressure change curves of different sole pressure areas in each supporting phase;

(4.4) inputting the pressure change curve obtained in the step (4.3) into the deep neural network model according to the test results in the steps (4.1) and (4.2), training the deep neural network model by adopting a supervision mode, randomly initializing the weight of the model, and selecting the learning rate by 10^-3And taking the minimized cross entropy loss function as a target, and optimizing the weight by selecting an Adam optimization algorithm to obtain an optimal prediction model.

In the method for predicting the falling risk of the old, the Footscan plantar pressure flat plate test system detects the single-foot univariate, single-foot multivariate and multi-foot multivariate of a subject during detection; the single-foot univariate is the pressure change data of a single foot in a certain plantar pressure area, the single-foot multivariate is the pressure change data of the single foot in a plurality of plantar pressure areas, and the multi-foot multivariate is the pressure change data of the double feet in the plurality of plantar pressure areas.

Compared with the prior art, the invention has the following advantages:

(1) according to the invention, through a deep neural network model formed by combining a convolutional neural network and a cyclic neural network, local spatial features of different layers of data can be respectively captured through a plurality of convolutional kernels, the number of model training parameters is reduced through a weight sharing mechanism, and the operation efficiency is improved, while the cyclic neural network is good at processing long-term dependence and nonlinear dynamic change in a time sequence; therefore, the deep neural network model can actively learn the characteristics with identification force from the original data by considering the spatial characteristics of the pressure distribution in the pressure area of the sole and the dynamic characteristics of the pressure distribution changing along with time, and a more accurate prediction effect is achieved;

(2) on the basis of a deep neural network model, the pressure data of different plantar pressure areas and the contact time with the ground are detected, so that the invention can obtain rich and perfect data variables after plantar pressure test, and the data are input into the deep neural network model, so that the deep neural network model can extract and screen features from more complete original data, thereby obtaining the most representative feature set for prediction, effectively improving the feature extraction precision and reducing the prediction result deviation of a single feature caused by high variation and nonlinear characteristics compared with a manual feature screening mode, and further achieving the feature index diversity and the data measurement precision;

(3) when the plantar pressure is detected, the plantar pressure area is divided again, and the pressure contact time area is divided into a plurality of support phases, so that detection data of plantar pressure testing can be classified, the plantar pressure and complicated data variables detected by an inertial sensor are simplified, the calculation amount required by a deep neural network model is reduced while the prediction accuracy is ensured, the deep neural network is convenient to classify samples, and the detection efficiency of the invention is improved;

(4) according to the invention, the plantar pressure region and the support phase are used as detection factors, and the detection data of the single-foot univariate, the single-foot multivariate and the multi-foot multivariate of the testee can be respectively obtained, so that the selection of characteristic indexes is further perfected, and the detection data can be correlated and supplemented with each other in the training and prediction of the deep neural network model, so that the deviation of high-variation and nonlinear single characteristics on the prediction effect is reduced, obvious regularity difference is presented in complex time and space characteristics, and the data measurement precision and the prediction accuracy are further improved;

(5) on the basis of the detection data, the calculation formula of the deep neural network model and the training process of the sample are further optimized, so that the calculation mode of the deep neural network model can be adapted to the detection data obtained by testing, and the detection efficiency and the prediction accuracy of the deep neural network model are further improved;

(6) under the mutual cooperation of the effects, the method can eliminate the process of manually screening the characteristics, screen out proper characteristics for prediction on the basis of the original data by utilizing the deep neural network model, and perform end-to-end learning from the input of the original data and the output of a final result, thereby avoiding the subjectivity and experience requirements caused by manually selecting the characteristics and improving the prediction accuracy of the method; the detection data are obtained by taking the plantar pressure area and the supporting phase as the basis, and the deep neural network model is used for calculation, so that the method can screen the time and space characteristics, perfects the selection range of characteristic variables, enables the characteristic variables to be correlated with each other, and further improves the prediction accuracy of the deep neural network model;

therefore, the method has the characteristics of high data measurement precision, various characteristic indexes and good prediction accuracy.

Drawings

FIG. 1 is a schematic view of the distribution of pressure areas on the sole of a foot;

FIG. 2 is a distribution diagram of sub-regions of the sole of a foot of the flat panel testing system;

FIG. 3 is a time phase division schematic of the support phase;

FIG. 4 is a schematic diagram of the internal structure of a deep neural network model;

FIG. 5 is a schematic diagram of a training process for a single sample in a deep neural network model;

fig. 6 is a graph of the pressure change in the midfoot region of a subject.

Detailed Description

The invention is further illustrated by the following figures and examples, which are not to be construed as limiting the invention.