CN108926814B - Personalized human body balance training system - Google Patents
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Abstract
The invention discloses a personalized human body balance training system, which consists of (1) a motion platform module (2), a signal acquisition module, (3) a signal preprocessing module, (4) an analysis module and (5) a personalized task generation module based on data driving; the method is characterized in that: the individual balance adaptability index is obtained by collecting, preprocessing and analyzing data of the human body moving along with the motion platform under 9 inclination modes of the motion platform, and then the individualized training prescription based on data driving is automatically generated. The invention can make up the blank that the personalized training prescription can not be generated in a self-adaptive manner in the current human body balance system.
Description
Technical Field
The invention relates to a balance training system, in particular to a human body balance training system based on a data-driven personalized prescription.
Background
Balance training is a specific task training that reduces muscle tension, stabilizes blood circulation, prevents osteoporosis, improves gastrointestinal tract excretory function, prevents joint stiffness, improves breathing, and the like, by training the patient's dynamic and static balance sensation.
The balance training device generally adopts two independent systems of a balance trainer and a balance measuring instrument. When the balance measuring instrument is used, a user stands on the instrument, the balance ability of the user is measured through sensory interaction tests of measuring the balance sense and the stability limit of the patient, and an approximate semi-quantitative basis is provided for balance training of the user.
However, even if semi-quantitative evaluations are known, the balance training devices currently on the market can essentially only provide fixed training tasks or pattern-specific training tasks, in other words, these devices can only provide single or statistically empirical training tasks. However, these training devices cannot generate a personalized training task from the result of the measurement balance, and cannot meet the requirements of different individuals: if the training strength is not enough, the training effect is greatly reduced; if the training intensity is too strong, the health of the user is excessively compromised. Therefore, how to solve the personalized balance training is a big problem of the design of the balance training device at present.
Disclosure of Invention
The invention aims to solve the problem of automatically generating a training task and further make up the blank that the existing balance training device cannot generate an individualized balance training task.
The invention is realized in such a way that a personalized human body balance training system is composed of a motion platform module (1), a signal acquisition module (2), a signal preprocessing module (3), an analysis module (4) and a personalized task generation module (5) based on data driving
The embodiment of the invention adopts the method that in the motion platform module (1), the method further comprises the following steps: the motion platform module is a mechanical device capable of estimating motion according to preset, an individual can stand or sit on the mechanical device, 9 inclination modes, 3 motion speeds and 3 amplitude intensities are prestored in the motion platform module, and in total, 81 different combined motion modes are totally stored
Further, the preset trajectory includes: left and right sinusoidal curves, front and rear sinusoidal curves, "8" -shaped curves, front and rear straight lines with random noise, "meter" -shaped trace curves, elliptic curves and any closed curve tracks; the human body can stand or sit on the motion platform to keep the body stable; 81 combined motion modes are preset, wherein the motion modes comprise 9 tilting modes, 3 amplitude intensity modes and 3 speed modes; wherein the 9 tilt modes include: a motion platform horizontal mode, a motion platform forward tilt mode, a motion platform backward tilt mode, a motion platform left tilt mode, a motion platform right tilt mode, a motion platform left front tilt mode, a motion platform left back tilt mode, a motion platform right front tilt mode, a motion platform right back tilt mode; the 3 amplitude intensity modes include: a small-amplitude mode, a medium-amplitude mode and a large-amplitude mode; the 3 speed modes include: a slow mode, a medium mode, and a fast mode;
the embodiment of the invention adopts a signal acquisition module (2) in the method, which further comprises the following steps: in the process that the human body moves along with the motion platform, the left wrist and the right wrist of the human body, and 7 areas of the left ankle, the right ankle, the neck and the waist and the abdomen of the human body are provided with acceleration sensors to form 7 pathsTime series Si(i＝1,2,3…,7)；
Furthermore, each acceleration signal synchronously starts to collect signals, the collection time is 1-15 minutes, and the sampling speed is 100-300 Hz;
the signal preprocessing module (3) in the method adopted by the embodiment of the invention further comprises the following steps: the module is responsible for respectively carrying out phase space reconstruction on 7 paths of acceleration signals per minute and completing denoising processing to obtain 7 high-dimensional phase space attractors A corresponding to the 7 paths of acceleration signals of the human body moving along with the motion platformi(i＝1,2,3…7)
Further, the phase space reconstruction adopts a Takens phase space reconstruction method, wherein the embedding dimension m and the delay time tau are determined by a correlation dimension and mutual information method;
furthermore, the denoising processing adopts a local manifold projection method or principal component analysis based on the phase space octave geometry to complete phase space denoising.
In the data analysis module (4) in the method, the embodiment of the invention further comprises: the module obtains the indexes of the balance adaptability of the human body under 9 inclination modes of the motion platform;
further, the real-time coupling index is the coupling matrix C between the human body and the motion platform module in the p-th inclination modep(n) derived from traces of the (n) and, further, coupling matrix Cp(n)＝{Cijp(n) is a time sequence Sip(n) (i ═ 1,2, …,7) for other time series Sjp(n) (j ═ 1,2, …,7) similarity coefficient Cijp(n) is;
the embodiment of the invention adopts a data-driven-based task generating module (5) in the method, which further comprises the following steps: establishing a historical database of the balance adaptability indexes of the individuals on the motion platform, and automatically giving personalized training prescriptions for the individuals under 9 inclination modes of the motion platform based on the balance adaptability indexes;
further, the database collects the motion platform, the left and right wrists of the human body, the left and right ankles and the neck of the human body of the people of 3 to 85 years old in the preset combined motion mode of the motion platformCorresponding 7 paths of acceleration data of 7 areas of the waist and abdomen, balance adaptive capacity indexes of different individuals under 9 different inclination angles, and after each training, balance adaptive capacity index b of the individual under 9 inclination modes of the motion platformp(n) (p ═ 1,2, …,9) into and updating the database;
further, the crowd balance adaptability index refers to: evenly dividing different inclination angle balance adaptability indexes of the platform into 9 grades;
further, the different motion modes refer to: the grades from 1 grade to 9 grades correspond to 9 different motion amplitude and speed combined mode tasks of the motion platform: level 1 is a slow-speed small-amplitude mode task, level 2 is a medium-speed small-amplitude mode task, level 3 is a fast small-amplitude mode task, level 4 is a slow-speed medium-amplitude mode task, level 5 is a medium-speed medium-amplitude mode task, level 6 is a fast medium-amplitude mode task, level 7 is a slow large-amplitude mode task, level 8 is a medium-speed large-amplitude mode task, and level 9 is a fast large-amplitude mode task;
further, the amplitude is determined by an amplitude angle, and the amplitude represents the angle between the maximum deviation position of the motion platform and the initial rest position; the amplitude angle of the small amplitude mode is 5-10 degrees, the amplitude angle range of the medium amplitude mode is 10-20 degrees, and the amplitude angle range of the large amplitude mode is 20-30 degrees;
furthermore, the speed is determined by the number of cycles completed per second, the range of the slow mode speed is 1-5 cycles completed per second, the range of the medium mode speed is 5-10 cycles completed per second, and the range of the fast mode task speed is 10-15 cycles completed per second;
further, the personalized training scheme is determined according to the individual historical records and is applied to the proportion w of 9 training tasks inclined in the directionp(n +1) (p ═ 1,2, …,9) is selected based on the individual's last balance adaptability index bp(n) (p ═ 1,2, …, 9);
further, the proportion of each training task inclined in the direction appearing in the next training is as follows:
wherein the ratio of each tilt direction occurring in the primary training is 1/9;
further, the ratio w when a specific direction is givenp<When 0.1, the moving speed of the inclined direction is increased by one grade on the original basis; when the ratio w of a specific directionp<When 0.05, the motion amplitude of the inclined direction is increased by one grade on the original basis; comparing the data with the balance adaptability indexes in the recorded population in the database to determine the initial motion amplitude and the initial motion speed of different inclination directions matched with the individual;
the invention has the following beneficial effects:
according to the method, firstly, the coupling degree between the human body and the motion platform is obtained by utilizing a nonlinear dynamics method, and then the overall balance energy index of the human body and the balance capability index of different parts of the human body in different motion states are obtained. The personalized balance training method is obtained according to the indexes, and the personalized training task based on data driving is the innovation of the invention.
The system is expected to be used in the balance training of sports athletes and rehabilitation patients, and adaptively adds or subtracts balance training tasks on the basis of ensuring the individual safety, thereby obviously increasing the human body balance training efficiency.
Drawings
Fig. 1 is a schematic diagram of the overall system structure of the present invention.
Detailed Description
The present invention will be described below with reference to specific examples, but the present invention is not limited thereto.
Firstly, the user firstly inputs own information into a database, such as height, weight, age, current physical condition and the like.
Before training, the measuring device is first worn and placed in a specific area. The signal acquisition modules are respectively arranged on the motion platform, the left wrist, the right wrist, the left ankle and the right neck of the human bodyAnd 7 waist and abdomen regions, the device is a 6-axis acceleration sensor, the acquisition time is 1-15 minutes, the sampling speed is 100-300 Hz, and further a 7-path time sequence S is formedi(i＝1,2,3……7)；
In the use process, the human body can stand or sit on the motion platform to keep the body stable.
The motion platform has a plurality of preset tracks and 81 combined motion modes.
The preset trajectory includes: left and right sinusoidal curves, front and rear sinusoidal curves, "8" -shaped curves, front and rear straight lines with random noise, "meter" -shaped trace curves, elliptic curves and any closed curve tracks;
the preset 81 combined motion modes comprise: 9 tilt modes, 3 amplitude intensity modes and 3 velocity modes; wherein the 9 tilt modes include: a motion platform horizontal mode, a motion platform forward tilt mode, a motion platform backward tilt mode, a motion platform left tilt mode, a motion platform right tilt mode, a motion platform left front tilt mode, a motion platform left back tilt mode, a motion platform right front tilt mode, a motion platform right back tilt mode; the 3 amplitude intensity modes include: a small-amplitude mode, a medium-amplitude mode and a large-amplitude mode; the 3 speed modes include: a slow mode, a medium mode, and a fast mode;
the amplitude is determined by an amplitude angle, and the amplitude represents the angle between the maximum deviation position and the initial rest position of the motion platform; the amplitude angle of the small amplitude mode is 5-10 degrees, the amplitude angle range of the medium amplitude mode is 10-20 degrees, and the amplitude angle range of the large amplitude mode is 20-30 degrees;
the speed is determined by the number of cycles completed per second, the range of the slow mode speed is 1-5 cycles completed per second, the range of the medium mode speed is 5-10 cycles completed per second, and the range of the fast mode task speed is 10-15 cycles completed per second;
if the user uses the device for the first time, the track of the motion platform needs to be selected, and the proportion of the 9 inclination modes is 1/9; the amplitude intensity pattern is: the small amplitude mode, the speed mode is the slow mode, and the result is used as a reference for selecting the platform motion mode.
The signal preprocessing module adopts a Takens phase space reconstruction method, wherein the embedding dimension m and the delay time tau are determined by a correlation dimension and mutual information method; and the phase space denoising is finished by adopting a local manifold projection method or principal component analysis based on the phase space octave geometry.
The signal analysis module calculates the balance adaptability index b of the human body moving along with the motion platform module under 9 inclination modes of the motion platformp(n) (p ═ 1,2, …,9) and the overall balance adaptability index B (n) of the human body.
bp(n) (p ═ 1,2, …,9) is the coupling matrix C by the human body to the motion platform modulep(n) derived from traces of a coupling matrix Cp(n)＝{Cijp(n) is a time sequence Sip(n) (i ═ 1,2, …,7) for other time series Sjp(n) (j ═ 1,2, …,7) similarity coefficient Cijp(n) is;
phase space coupling index CijThe method is specifically realized by the following steps:
1, local manifold structure of phase space A: in this embodiment, the prediction mode is obtained by using a local shape preserving structure method, and first, local linearization is performed on all points in the phase space a, where a phase point x is any phase pointiThe peripheral 3 points with the nearest euclidean distance represent:
wherein, WipIs a phase point xiPhase point x within neighborhood point groupipWeight coefficient of (d):
dipis a phase separation point xiAnd xipEuclidean distance, di1Is a phase separation point xiAnd xipMinimum Euclidean distance
2, obtaining a predicted phase space Aij: the prediction method is to apply a certain phase space AiAny one of phase points x iniRespectively applied to other phase spaces A according to own local manifold characteristic structurej(i ≠ j), the corresponding predicted phase point is obtained:
traversing all phase points in Ai, all predicted phase points xjComposition AiTo AjPredicted phase space A ofij。
3, obtaining a phase space AjOpposite space AiCoupling index Cij
Predicting the phase space AijCorresponding time series SijAnd the original reconstruction phase space AjCorresponding time series S of acceleration signalsjPerforming pairwise correlation analysis to obtain correlation coefficient CijAs a phase space AjOpposite space AiAnd (4) degree of coupling.
When a user uses the training system for the first time, an initial personalized training task needs to be determined according to the first training, and the overall balance adaptability index B of the individual needs to be compared with the crowd in the database.
The database collects corresponding 7-path acceleration data of 7 areas of a motion platform, the left wrist, the right wrist, the left ankle, the neck and the waist and abdomen of a person in a combined motion mode preset by the motion platform for people in the age range of 3 to 85 years, and balance adaptability indexes of different individuals under 9 different inclination angles are stored; evenly dividing different inclination angle balance adaptability indexes of the platform into 9 grades according to the overall crowd balance adaptability index;
the movement pattern was also evenly divided into 9 classes as an initial personalized training prescription: the grades from 1 grade to 9 grades correspond to 9 different motion amplitude and speed combined mode tasks of the motion platform: level 1 is a slow-speed small-amplitude mode task, level 2 is a medium-speed small-amplitude mode task, level 3 is a fast small-amplitude mode task, level 4 is a slow-speed medium-amplitude mode task, level 5 is a medium-speed medium-amplitude mode task, level 6 is a fast medium-amplitude mode task, level 7 is a slow large-amplitude mode task, level 8 is a medium-speed large-amplitude mode task, and level 9 is a fast large-amplitude mode task;
in each training process, the personalized training scheme is determined according to the individual historical records and is used for training the proportion w of training tasks inclined to 9 directionsp(n +1) (p ═ 1,2, …,9) is selected based on the individual's last balance adaptability index bp(n) (p ═ 1,2, …, 9);
further, the proportion of each training task inclined in the direction appearing in the next training is as follows:
wherein the ratio of each tilt direction occurring in the initial training of the initial training prescription is 1/9;
further, the ratio w when a specific direction is givenp<When 0.1, the moving speed of the inclined direction is increased by one grade on the original basis; when the ratio w of a specific directionp<At 0.05, the amplitude of the movement in the oblique direction is increased by one level from the original amplitude.
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 (4)
1. The utility model provides a personalized human balance training system, comprises motion platform module (1), signal acquisition module (2), signal preprocessing module (3), analysis module (4) and individualized task generating module (5) based on data drive, its characterized in that:
the motion platform module (1) is a mechanical device which can move according to a preset track, an individual can stand or sit on the mechanical device, and the motion platform module prestores 9 inclination modes, 3 motion speeds and 3 amplitude intensities, wherein the total number of the motion modes is 81 different combination motion modes;
the motion platform module and four limbs and core areas of the human body are provided with acceleration sensors, the signal acquisition module (2) is responsible for recording 7 corresponding paths of acceleration signals of 7 areas of the motion platform, the left and right wrists of the human body, the left and right ankles and the neck of the human body and the waist and abdomen in real time in the process that the human body keeps balance along with the motion of the motion platform module, and a 7-path time sequence is formed
S0i(n)(i＝1,2,3,4,5,6,7)；
The signal preprocessing module (3) is responsible for performing phase space reconstruction on the 7 paths of acceleration signals per minute and completing denoising processing to obtain a denoised 7-path time sequence Si(n)(i＝1,2,3,4,5,6,7)；
The data analysis module (4) is responsible for respectively obtaining the balance adaptability indexes of the human body in 9 inclination modes of the motion platform, bp (n) (p is 1,2,3,4,5,6,7,8 and 9) and the overall balance adaptability index B of the human body; the index is obtained from the trace of a coupling matrix C p between the human body and the motion platform module, where the coupling matrix C p (n) { Cij p (n) } is composed of a similarity coefficient Cij p (n) between a path of time sequence Si p (n) (i ═ 1,2,3,4,5,6,7) and other time sequence Sj p (n) (j ═ 1,2,3,4,5,6,7), where n denotes the number of times of training, p denotes different tilt modes of the motion platform, and i and j denote two paths of signals;
the personalized task generation module (5) based on data driving is responsible for enabling the individual to move on the motion platformAutomatically giving an individualized training prescription based on the balance adaptability index in 9 inclination modes, wherein a balance adaptability index historical database of an individual on a motion platform is established; the database collects corresponding 7-path acceleration data of 7 areas of a motion platform, the left wrist, the right wrist, the left ankle, the neck and the waist and abdomen of a person in a combined motion mode preset by the motion platform for people in the age range of 3 to 85 years, and balance adaptability indexes of different individuals under 9 different inclination angles are stored; evenly dividing different inclination angle balance adaptability indexes of the platform into 9 grades according to the crowd overall balance adaptability index B, and taking the grades as the reference of an initial personalized training task; the grades from 1 grade to 9 grades correspond to 9 different motion amplitude and speed combined mode tasks of the motion platform: level 1 is a slow-speed small-amplitude mode task, level 2 is a medium-speed small-amplitude mode task, level 3 is a fast small-amplitude mode task, level 4 is a slow-speed medium-amplitude mode task, level 5 is a medium-speed medium-amplitude mode task, level 6 is a fast medium-amplitude mode task, level 7 is a slow large-amplitude mode task, level 8 is a medium-speed large-amplitude mode task, and level 9 is a fast large-amplitude mode task; the amplitude is determined by an amplitude angle, and the amplitude represents the angle between the maximum deviation position and the initial rest position of the motion platform; the amplitude angle of the small amplitude mode is 5-10 degrees, the amplitude angle range of the medium amplitude mode is 10-20 degrees, and the amplitude angle range of the large amplitude mode is 20-30 degrees; the range of the slow mode speed is 1-5 periodic movements per second, the range of the medium mode speed is 5-10 periodic movements per second, and the range of the fast mode task speed is 10-15 periodic movements per second; after each training, the balance adaptability index b of the individual in 9 inclination modes of the motion platformp(n) (p ═ 1,2,3,4,5,6,7,8,9) into and updating the database; the personalized training scheme is determined according to the individual historical records and is used for training the proportion w of training tasks inclined in 9 directionsp(n +1) (p ═ 1,2,3,4,5,6,7,8,9) is selected based on the individual's last balance adaptability index bp(n) (p is 1,2,3,4,5,6,7,8,9), and the proportion of each training task with a slant direction appearing in the next training is:
wherein the ratio of each tilt direction occurring in the primary training is 1/9;
when the ratio w of a specific directionp<When 0.1, the moving speed of the inclined direction is increased by one grade on the original basis; when the ratio w of a specific directionp<When 0.05, the motion amplitude of the inclined direction is increased by one grade on the original basis; and comparing the data with the balance adaptability indexes in the recorded population in the database to determine the initial motion amplitude and the initial motion speed of the individual in different inclination directions.
2. The system of claim 1, wherein the motion platform module comprises: the platform can do preset track motion; the preset trajectory includes: left and right sinusoidal curves, front and rear sinusoidal curves, "8" -shaped curves, front and rear straight lines with random noise, "meter" -shaped trace curves, elliptic curves and any closed curve tracks; the human body can stand or sit on the motion platform to keep the body stable; 81 combined motion modes are preset, wherein the motion modes comprise 9 tilting modes, 3 amplitude intensity modes and 3 speed modes; wherein the 9 tilt modes include: a motion platform horizontal mode, a motion platform forward tilt mode, a motion platform backward tilt mode, a motion platform left tilt mode, a motion platform right tilt mode, a motion platform left front tilt mode, a motion platform left back tilt mode, a motion platform right front tilt mode, a motion platform right back tilt mode; the 3 amplitude intensity modes include: a small-amplitude mode, a medium-amplitude mode and a large-amplitude mode; the 3 speed modes include: slow mode, medium mode, fast mode.
3. The system according to claim 1, wherein the signal acquisition module comprises: in the process that the human body moves along with the motion platform, 7 paths of corresponding acceleration signals of 7 areas of the motion platform, the left wrist, the right wrist, the left ankle, the right ankle, the neck and the waist and abdomen of the human body are obtained; each acceleration signal synchronously starts to collect signals, the collection time is 1 minute to 15 minutes, and the sampling speed is 100Hz to 300 Hz.
4. The system according to claim 1, wherein the signal preprocessing module comprises: adopting a Takens phase space reconstruction method, wherein the embedding dimension m and the delay time tau are determined by a correlation dimension and mutual information method; and the phase space denoising is finished by adopting a local manifold projection method or principal component analysis based on the phase space octave geometry.
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| CN106419836A (en) * | 2015-08-04 | 2017-02-22 | 詹宝珠 | Balance analysis and evaluation device and analysis and evaluation method thereof |
| CN106510721A (en) * | 2016-12-12 | 2017-03-22 | 施则威 | Walking balance evaluating method and device and walking balance monitoring method and system |
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| CN105105756A (en) * | 2015-07-24 | 2015-12-02 | 重庆三峡学院 | Portable type balance capacity and gait analysis sensing device |
| CN106419836A (en) * | 2015-08-04 | 2017-02-22 | 詹宝珠 | Balance analysis and evaluation device and analysis and evaluation method thereof |
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