CN105760643A - Exercise guidance method and terminal equipment - Google Patents

Abstract

The invention provides an exercise guidance method and terminal equipment. The exercise guidance method comprises the following steps: the exercise amount of a user up to now is calculated according to an exercise type and exercise intensity, and an exercise guidance scheme is generated according to the exercise amount obtained through calculation and an exercise target preset by the user. According to the exercise guidance method and the terminal equipment, the exercise amount is calculated on the basis of the exercise type and the exercise intensity of the user, and next exercise of the user is guided on the basis of the calculated exercise amount.

Description

A kind of method of exercise guidance and terminal unit

Technical field

The present invention relates to mobile healthy and portable medical field, particularly to method and the terminal unit of a kind of exercise guidance.

Background technology

Currently, with respect to the metering of body movement amount, mainly there is several ways:

Mode 1, reacting body movement amount by step number, this mode cannot react the information such as intensity when there are these step numbers, as cannot be distinguished by running and quantity of motion difference slowly, cannot react the quantity of motion of the less type of sports of step number simultaneously, as by bike etc..

Mode 2, reacting body movement amount by the activity time, its problem is similar with step number, it is impossible to the movement differential of reaction varying strength, runs and slow step same time, and its quantity of motion exists very big-difference.

Mode 3, by energy consumption react body movement amount, patent CN1211049C is proposed a kind of calorie consumed by counting user and weighs momental method, and energy consumption is relevant to basal metabolic rate, the basal metabolic rate of different people is different, adopt energy consumption to weigh quantity of motion and there is obvious personalization so that the quantity of motion between different people cannot be carried out comparing.

But, in above-mentioned several ways, about motion mode instruct and suggestion, it is common that utilize user profile and moving target to generate short-term or long-term motion scheme or perhaps exercise program for user, it is impossible to instruct according to the completed quantity of motion of user.The groundwork of this exercise guidance mode is to provide the user exercise program, does not have concrete directive significance.Patent CN1211049C proposes a kind of next step quantity of motion guidance program based on energy consumption, based on the principle of the energy balance taken in consume, generates next step quantity of motion of user.This scheme is it is considered that the balance of energy, it does not have consider the human body needs to motion itself, and such as intensity and motility in order to maintain the health of cardiovascular system, muscle and skeleton, somagenic need a certain amount of motion of guarantee, these do not account in such scheme.

Based on carrying out sports metering all there is limitation by step number, movement time, calorie at present, and the aspect that instructs moved carries out based on the angle of the energy balance taken in and consume, do not account for health itself to momental demand, the present invention proposes a kind of momental accurate count, provides next step exercise guidance to complete based on type of sports and exercise intensity.

Summary of the invention

The technical problem to be solved in the present invention is to provide method and the terminal unit of a kind of exercise guidance, based on type of sports and exercise intensity, the quantity of motion of user is measured, and based on this quantity of motion, next step activity of user is instructed.

In order to solve the problems referred to above, a kind of method that the invention provides exercise guidance, including:

The quantity of motion of user in preset time period is added up according to type of sports and exercise intensity；

The moving target that the described quantity of motion obtained by statistics and described user are pre-set, generates the exercise guidance scheme of described user.

Wherein, described exercise guidance method also includes:

Obtaining the physical sign parameters of described user, described physical sign parameters at least includes: the personal information of user, health status and moving target；

Physical sign parameters according to described user generates described user moving target in preset time period.

Wherein, described add up the momental step of user in preset time period according to type of sports and exercise intensity and include:

Obtain the exercise data of described user movement in a preset time period；

Temporal signatures and the frequency domain character of described exercise data is obtained from the exercise data of described user movement；

Temporal signatures according to described exercise data and frequency domain character, it is determined that the type of sports of described user and exercise intensity in described preset time period, and add up the quantity of motion of described user in preset time period according to described type of sports and exercise intensity.

Wherein, after the described type of sports determining user in described preset time period and exercise intensity, described add up the momental step of user in preset time period according to type of sports and exercise intensity and also include:

Judge whether described user remains static according to described type of sports；If so, the time that described user is static is then added up.

Wherein, before adding up described user static time, described add up the momental step of user in preset time period according to type of sports and exercise intensity and also include:

Judge whether described user is the state that sitting is motionless that is within default quiescent time according to described type of sports, if so, add up the number of times that the sitting of described user is motionless；Otherwise, the step of the described user of described statistics static time is entered.

Wherein, the quantity of motion of described user includes: described user complete movement time, described user complete the number of times that exercise intensity, described user accumulative quiescent time and described user's sitting are motionless.

Wherein, the described moving target by adding up the described quantity of motion that obtains and described user pre-sets, generate the exercise guidance scheme of described user, specifically include:

By adding up the completed quantity of motion of described user that obtains with the moving target that user pre-sets compared with, generate and the exercise guidance scheme completed for the follow-up needs of described user or the actionless situation corresponding exercise guidance scheme of offer for described user are provided to described user.

Additionally, present invention also offers a kind of terminal unit, including:

Statistical module, for adding up the quantity of motion of user in preset time period according to type of sports and exercise intensity；

Instruct module, for the moving target that the described quantity of motion obtained by statistics and described user are pre-set, generate the exercise guidance scheme of described user.

Wherein, described terminal unit also includes:

Acquisition module, for obtaining the physical sign parameters of described user, described physical sign parameters at least includes: the personal information of user, health status and moving target；

Generation module, generates described user moving target in preset time period for the physical sign parameters according to described user.

Wherein, described statistical module specifically for:

Obtain the exercise data of described user movement in a preset time period；Temporal signatures and the frequency domain character of described exercise data is obtained from the exercise data of described user movement；Temporal signatures according to described exercise data and frequency domain character, it is determined that the type of sports of described user and exercise intensity in described preset time period, and add up the quantity of motion of described user in preset time period according to described type of sports and exercise intensity.

Wherein, described statistical module is additionally operable to:

Judge whether described user remains static according to described type of sports；If so, the time that described user is static is then added up.

Wherein, described statistical module also particularly useful for:

Judge whether described user is the state that sitting is motionless that is within default quiescent time according to described type of sports, if so, add up the number of times that the sitting of described user is motionless；Otherwise add up the time that described user is static.

Wherein, described instruct module specifically for:

By adding up the completed quantity of motion of described user that obtains with the moving target that user pre-sets compared with, generate and the exercise guidance scheme completed for the follow-up needs of described user or the actionless situation corresponding exercise guidance scheme of offer for described user are provided to described user.

The technique scheme of the present invention at least has the advantages that

The method of the exercise guidance of the embodiment of the present invention and terminal unit, the completed quantity of motion in a preset time period of counting user is carried out based on type of sports and exercise intensity, compared with the preset moving target generated by the physical sign parameters of user, the quantity of motion instructing user to be not fully complete further, meets the motion requirement of user；Go back the time that counting user is static simultaneously, the time that user is static is also made further guidance, instruct more comprehensive.

Accompanying drawing explanation

Fig. 1 represents the flow chart of exercise guidance method in the embodiment of the present invention；

Fig. 2 represents the composition frame chart of terminal unit in the embodiment of the present invention；

Fig. 3 represents the flow chart of exercise guidance in the embodiment of the present invention；

Fig. 4 represents the flow chart of quantity of motion statistics in the embodiment of the present invention；

Fig. 5 represents the principle composition diagram of the embodiment one of terminal unit in the embodiment of the present invention；

Fig. 6 represents the algorithm flow chart of the embodiment one of terminal unit identification type of sports in the embodiment of the present invention；

Fig. 7 represents the workflow diagram of terminal unit in the embodiment of the present invention.

Detailed description of the invention

For making the technical problem to be solved in the present invention, technical scheme and advantage clearly, it is described in detail below in conjunction with the accompanying drawings and the specific embodiments.

The present invention is directed to and carry out sports metering all there is limitation by step number, movement time, calorie at present, and the aspect that instructs moved carries out based on the angle of the energy balance taken in and consume, do not account for the health problem to momental demand itself, it is provided that a kind of method of exercise guidance and terminal unit.

As it is shown in figure 1, a kind of method embodiments providing exercise guidance, including:

Step S11: add up the quantity of motion of user in preset time period according to type of sports and exercise intensity；

Step S12: the moving target that the described quantity of motion obtained by statistics and described user are pre-set, generates the exercise guidance scheme of described user.

In the above embodiment of the present invention, the quantity of motion in counting user one preset time period is carried out based on type of sports and exercise intensity, compared with the moving target pre-set by user, generate the exercise guidance scheme of user, the quantity of motion instructing user to be not fully complete further, meets the motion requirement of user.

Further, the generation step of the moving target that user pre-sets includes:

Obtaining the physical sign parameters of described user, described physical sign parameters at least includes: the personal information (height, body weight, age etc.) of user, health status and moving target；

Physical sign parameters according to described user generates described user moving target in preset time period.

In order to make what above-mentioned steps stated to become apparent from, further in conjunction with Fig. 3 explanation, i.e. above-mentioned steps can be classified as three steps: quantity of motion statistics, moving target generate and exercise guidance, and three concrete steps are analyzed as follows:

1. quantity of motion statistics: gather the multiple inertial data of user, quantity of motion is added up based on type of sports and intensity, wherein quantity of motion includes the completed movement time of user, intensity and user static time and the motionless number of times of sitting, further counting user every day, motion performance weekly；

2. moving target generates: generate moving target daily or weekly according to the individual essential information of user, physical condition and moving target etc.；

3. exercise guidance: compare with the moving target previously generated finally by the completed quantity of motion of counting user, the exercise guidance scheme that the formulation follow-up needs of user complete or the actionless situation for described user provide corresponding exercise guidance scheme.

Such as: a user being in a good state of health wants the purpose reaching weight-reducing, first the present embodiment can obtain the height of user, body weight, age and want to reach the information etc. of weight-reducing target, this programme can formulate a set of moving target for user according to these information of user, it is simultaneously based on the counting user quantity of motion daily or weekly such as the type of sports of user, intensity, the quantity of motion that prompting user is not fully complete, instructs user actively and scientifically removes to reach the moving target of fat-reducing.

Further, described count on current time according to type of sports and exercise intensity till the momental step of user include:

Obtain the exercise data of described user movement in a preset time period；

Temporal signatures and the frequency domain character of described exercise data is obtained from the exercise data of described user movement；

Temporal signatures according to described exercise data and frequency domain character, it is determined that the type of sports of described user and exercise intensity in described preset time period, and add up the quantity of motion of described user in preset time period according to described type of sports and exercise intensity.

After determining type of sports and the exercise intensity of user in described preset time period, also needing to determine whether whether described type of sports is static, if being judged as static, then adding up the described static time；Further, it is also possible to according to judging whether user is that sitting is motionless in default quiescent time, within as static in regulation user more than 60 minutes, may determine that to be that a sitting is motionless, the number of times that if so, then counting user sitting is motionless.To sum up, namely the quantity of motion of described user includes: described user complete movement time, described user complete the number of times that exercise intensity, described user accumulative quiescent time and described user's sitting are motionless.

Specifically, as shown in Figure 4, one or more inertial datas of user (acceleration, gyroscope, earth magnetism etc.) are obtained；Feature extraction is carried out based on type of sports, the large, medium and small of exercise intensity.Additionally, also need whether the type of sports determining whether user is static, if resting state, the then static time of accumulative user, and determine whether that sitting is motionless further, if sitting is motionless, then the number of times that statistics sitting is motionless.By the type of sports of counting user, intensity, quiescent time, the motionless number of times of sitting, complete momental statistics in user one preset time period.

In order to better realize the momental purpose of above-mentioned statistics, present embodiments provide a specific embodiment in conjunction with practical application:

Embodiment one

A kind of quantity of motion statistical module following the tracks of equipment for non-high-end activity, as a portable meter walks equipment, the sample rate of its acceleration transducer is 25Hz, and portable equipment only can buffer memory 3 axle acceleration data of 5 seconds.

Specifically as it is shown in figure 5, the terminal unit in the present embodiment mainly includes an acceleration transducer LIS3DH, a computing unit MSP430, the GPRS module WS6318 of a transmission, a power supply and a LCD MODULE.

Specifically, the recognizer flow process of type of sports and intensity is as shown in Figure 6:

First extracting Short-time Window feature, wherein short-time characteristic includes: variance, maximin, Sample Entropy, local binary patterns etc..

3-axis acceleration initial data respectively xi, yi, zi that in the present embodiment, acceleration transducer gathers, first calculates three axle quadratic sum ai of acceleration,

a_i=x_i ²+y_i ²+z_i ²(1)

Further, obtain the value of variance Std,

$\mathrm{Std}=\frac{1}{n}{\mathrm{\Σ}}_{i=1}^n{(a_i-m)}^2---\left(2\right)$

Wherein, ai is the quadratic sum signal of 3-axis acceleration, and n is the signal number in window, and m is the average of the 3-axis acceleration quadratic sum in window.Here the length of window is 5 seconds, therefore n=25*5=125.

$m=\frac{1}{n}{\mathrm{\Σ}}_{i=1}^n{a}_i---\left(3\right)$

Further, obtain the difference of minimax,

MaxMin=max (a_i)-min(a_i)(4)

Further, obtain Sample Entropy,

In the present embodiment, if the quadratic sum data of 3-axis acceleration are a (1), a (2) ..., a (n) is n point altogether.

One group of m n dimensional vector n is formed: from Xm (1) to Xm (n-m) by sequence number consecutive order,

Wherein: Xm (i)=[a (i), a (i+1) ..., a (i+m-1)] (i=1～n-m).These vectors represent the value starting m a of continuous print from i-th point.

Distance d [Xm (i) between definition vector Xm (i) and Xm (j), Xm (j)] be that in both corresponding elements, difference is maximum one, that is: d [Xm (i), Xm (j)]=max (| Xm (i+k)-Xm (j+k) |).Wherein k=0～m-1；I, j=1～n-m, j ≠ i.

Given threshold value r, the value to each i≤n-m, add up the ratio of the d [Xm (i), Xm (the j)] number (being called template matching number) less than r and this number and distance sum n-m-1, be denoted as $B_r^m\left(i\right)=n^m\left(i\right)/(n-$ $m-1).$

Then its meansigma methods to all i is sought $B^m\left(r\right)={(n-m)}^{-1}\·{\mathrm{\Σ}}_{i=1}^{n-m}{B}_r^m\left(i\right).$

Wherein, in the present embodiment, Sample Entropy takes 1 rank Sample Entropy.

Further, obtain local binary patterns,

Local binary patterns b (i) is defined as:

$b\left(i\right)=\left\{\begin{array}{cc}3& \mathrm{if\; a}(i-1)>\mathrm{\α}\left(i\right)\mathrm{and\; a}(i+1)>\mathrm{\α}\left(i\right)\\ 2& \mathrm{if\; a}(i-1)>\mathrm{\α}\left(i\right)\mathrm{and\; a}(i+1)\≤a\left(i\right)\\ 1& \mathrm{if\; a}(i-1)\≤a\left(i\right)\mathrm{and\; a}(i+1)>\mathrm{\α}\left(i\right)\\ 0& \mathrm{if\; a}(i-1)\≤a\left(i\right)\mathrm{and\; a}(i+1)\≤a\left(i\right)\end{array}\right.$

After obtaining b (i), it being asked statistics with histogram, obtain local binary patterns rectangular histogram h (k), k=0～3, this rectangular histogram reflects the complexity of the signal in window.

Wherein, h (k)=num (b (i)==k), the i.e. number of the b (i) point equal to k in window.

To sum up, after obtaining characteristic vector, the feature of window time long is carried out statistics with histogram, then carries out the judgement of Activity Type by methods such as arest neighbors, amplitude information according to 3-axis acceleration quadratic sum carries out the judgement of intensity, obtains the information such as Activity Type, activity intensity, this active duration.

Before judgement, it is thus necessary to determine that the class center of each classification.First organize raw acceleration data for each class of activity collection more, and calculate feature histogram vector V during its correspondence long, calculate the average of many stack features vector V of the different class of activity as class center.

I-th Lei Lei center: C_i=mean (V_j),V_jIt it is the characteristic vector of the i-th class；

After obtaining class center, it is possible to utilize arest neighbors rule to needing the vector judged to carry out classification judgement.

The similarity calculating current signature vector V and n Ge Lei center is S_i=sim (V, C_i), i=1 ... n, when the similarity of V and Ck is the highest, V just belongs to kth class.

V ∈ kth class, ifS_k==max{S_i, i=1 ... n}.

It should be noted that, the definition mode of similarity S

Similarity sim (the H of rectangular histogram H1 and H2₁,H₂) it is defined as: the quantity of H1 and H2 coincidence and the sum (total number of H1 and H2 is identical) divided by H1.

$\mathrm{sim}(H_1,H_2)=\frac{{\mathrm{\Σ}}_{i=1}^{M_{a11}}\min (H_1\left(i\right),H_2\left(i\right))}{{\mathrm{\Σ}}_{i=1}^{M_{\mathrm{all}}}{H}_1\left(i\right)}$

From the equations above it can be seen that sim (H₁,H₁)=1, similarity is the number between 0～1, and the similarity of two duplicate characteristic vectors is 1.The similarity of characteristic vector V to be judged and class center Ci is more big, and the probability that V belongs to the i-th class is more high.

Other measuring similarity mode can certainly be adopted, such as the similarity based on Euclidean distance, the measuring similarity etc. based on COS distance.

In the above embodiment of the present invention, specifically illustrate the algorithm identifying type of sports and intensity, completed quantity of motion is added up based on type of sports and intensity, completed quantity of motion is compared with moving target, difference according to both provides the quantity of motion that follow-up needs complete to instruct, being simultaneous for the actionless situation of user and provide corresponding guidance, user completes corresponding quantity of motion according to guidance, reaches the moving target such as building body or weight-reducing.

Additionally, as in figure 2 it is shown, the embodiment of the present invention additionally provides a kind of terminal unit, it is characterised in that including:

Acquisition module 21, for obtaining the physical sign parameters of described user, described physical sign parameters at least includes: the personal information of user, health status and moving target；

Generation module 22, generates described user moving target in preset time period for the physical sign parameters according to described user；

Statistical module 23, for adding up the quantity of motion of user in preset time period according to type of sports and exercise intensity；

Instruct module 24, for the moving target that the described quantity of motion obtained by statistics and described user are pre-set, generate the exercise guidance scheme of described user.

In the above embodiment of the present invention, as it is shown in fig. 7, user can pass through to wear the equipment comprising acceleration transducer and/or the inertial sensor such as gyroscope, earth magnetism, such as meter step terminal, mobile phone etc.；Terminal unit (relevant background server can be coordinated) is utilized to complete analysis and the statistics of User Activity situation, the activity of the user obtained to current time.Terminal unit can have an inputting interface, and user is by the essential information of its inputting interface input individual, and terminal unit generates moving target daily or weekly based on the information of input.The moving target that the quantity of motion obtained by terminal unit statistics and terminal unit are generated, generates the exercise guidance scheme of user.

Further, described statistical module 23 specifically for:

Obtain the exercise data of described user movement in a preset time period；The temporal signatures and the frequency domain character that obtain described user movement is extracted from the exercise data of described user movement；Temporal signatures according to described user movement and frequency domain character, it is determined that the type of sports of described user and exercise intensity in described preset time period, and add up the quantity of motion of described user in preset time period according to described type of sports and exercise intensity.

After determining type of sports and the exercise intensity of user in described preset time period, also need to determine whether whether described user remains static；If being judged as static, then add up the described static time.Further, judge whether described user is the state that sitting is motionless that is within default quiescent time according to described type of sports, if so, add up the number of times that the sitting of described user is motionless；Otherwise add up the time that described user is static.

Equally, in order to better show how above-mentioned terminal unit instructs user to go motion, a specific embodiment is provided again:

Embodiment two

Having completed motion list in the present embodiment is Ai, and Motion Recognition is Ti respectively, and exercise intensity is Li.

If the moderate strength aerobatic exercise time tm (3-5.9METs converts here as 4METs) of statistics

Tm=t*l/4；

Big intensity aerobatic exercise time th (>=6METs, converts here as 8METs)

Th=t*l/8；

Sitting is motionless: static continuously was that 1 sitting is motionless more than 60 minutes.

Further, moving target generation module in the present embodiment, it is possible to specifically measure division by the motion of children and adolescents, adult and old people, as:

The quantity of motion of Children and teenager:

1. should carry out the physical exertion of more than 60min every day；

2. should include the aerobic physical exertion of moderate strength and two strength grades of big intensity simultaneously；The physical exertion of big intensity should weekly at least for 3 days；

3. the activity carrying out strong muscle and skeleton at least 3 days weekly.

The quantity of motion of adult's health diseases prevention:

1. be more than or equal to the activity of 30 minute/day moderate strengths；

2. the moderate strength of at least 150min (2.5h) weekly, or at least the big intensity aerobic of 75min (1.5h) is movable weekly, or the combination aerobic carrying out identical time moderate strength and big intensity is movable.

The quantity of motion of old people:

1. old people advises that every day carries out the body movement of 30-60 minute moderate strength；If carried out the exercise of big intensity, the time can reduce by half；

2. old people also should do some exercises helping to maintain and improving balanced capacity, and other also have the moving target such as chronic diseases management, fat-reducing.

Further, in the present embodiment, the generation module according to moving target is compared with completed motion list Ai, carries out exercise guidance further: the quantity of motion=moving target also needed-completed quantity of motion.

Exercise intensity table

In conjunction with exercise intensity table as above, in the present embodiment, if being moving target citing be more than or equal to the activity of 30 minute/day moderate strengths, the quantity of motion then also needed: 4METs moderate strength aerobatic exercise 3 minutes, or the big intensity aerobatic exercise of 8METs 1.5 minutes, owing to aerobic exercise suggestion continues more than 10 minutes, therefore, you have also needed to 10 minutes moderate strengths or the aerobatic exercise of big intensity.

For moderate strength aerobatic exercise in 15 minutes, the motion recommending user in the present embodiment is: you can carry out the middling speed walking of 5,000 ms/h in 15 minutes, or 20 minutes bicycles at a slow speed, or housework activity in 18 minutes, or table tennis exercise in 15 minutes, or tennis exercise in 12 minutes.

Secondly, if it is longer to add up quiescent time in user sky, and having 1 sitting motionless, in the present embodiment, terminal unit then advises that user reduces quiescent time, increase activity, it is to avoid sitting is motionless.

Again specifically: during such as August 22 to 12 noon, terminal unit is added up the quantity of motion of certain user and is: " you have carried out bicycle 20 minutes at a slow speed；You have completed moderate strength aerobatic exercise 15 minutes；You are accumulative static 170 minutes, and motionless 1 time of sitting, the time is 85 minutes.”

Terminal unit is for the completed quantity of motion of certain user, then corresponding guiding opinion is: " quantity of motion that you have also needed: 4METs moderate strength aerobatic exercise 15 minutes; or the big intensity aerobatic exercise of 8METs 7.5 minutes; owing to aerobic exercise suggestion continues more than 10 minutes; therefore, you have also needed to 10 minutes moderate strengths or big intensity aerobatic exercise in 10 minutes；

You can carry out the middling speed walking of 5,000 ms/h in 15 minutes, or 20 minutes bicycles at a slow speed, or housework activity in 18 minutes, or table tennis exercise in 15 minutes, or tennis exercise in 12 minutes；

You are longer for accumulative quiescent time, and have 1 sitting motionless, it is proposed that you reduce quiescent time, increase body movement, it is to avoid sitting is motionless.”

In above-described embodiment, instruct according to exercise program and completed quantity of motion, the quantity of motion that the prompting follow-up needs of user complete, and mode and the time of multiple common motion are provided, facilitating understandable, guiding opinion also contemplates the impact that transfixion team is healthy simultaneously, more comprehensively.

The method of the exercise guidance of foregoing invention embodiment and terminal unit, terminal unit carrys out the completed quantity of motion in a preset time period of counting user based on type of sports and exercise intensity, compared with the preset moving target generated by the physical sign parameters of user, the quantity of motion instructing user to be not fully complete further, meets the motion requirement of user；Go back the time that counting user is static simultaneously, the time that user is static is also made further guidance, apply more comprehensive.

The above is the preferred embodiment of the present invention; it should be pointed out that, for those skilled in the art, under the premise without departing from principle of the present invention; can also making some improvements and modifications, these improvements and modifications also should be regarded as protection scope of the present invention.

Claims (13)

1. the method for an exercise guidance, it is characterised in that including:

The quantity of motion of user in preset time period is added up according to type of sports and exercise intensity；

The moving target that the described quantity of motion obtained by statistics and described user are pre-set, generates the exercise guidance scheme of described user.

2. method according to claim 1, it is characterised in that described method also includes:

Obtaining the physical sign parameters of described user, described physical sign parameters at least includes: the personal information of user, health status and moving target；

Physical sign parameters according to described user generates described user moving target in preset time period.

3. method according to claim 1, it is characterised in that described add up the momental step of user in preset time period according to type of sports and exercise intensity and include:

Obtain the exercise data of described user movement in a preset time period；

Temporal signatures and the frequency domain character of described exercise data is obtained from the exercise data of described user movement；

Temporal signatures according to described exercise data and frequency domain character, it is determined that the type of sports of described user and exercise intensity in described preset time period, and add up the quantity of motion of described user in preset time period according to described type of sports and exercise intensity.

4. method according to claim 3, it is characterised in that after the described type of sports determining user in described preset time period and exercise intensity, described add up the momental step of user in preset time period according to type of sports and exercise intensity and also includes:

Judge whether described user remains static according to described type of sports；If so, the time that described user is static is then added up.

5. method according to claim 4, it is characterised in that before adding up described user static time, described add up the momental step of user in preset time period according to type of sports and exercise intensity and also includes:

Judge whether described user is the state that sitting is motionless that is within default quiescent time according to described type of sports, if so, add up the number of times that the sitting of described user is motionless；Otherwise, the step of the described user of described statistics static time is entered.

6. method according to claim 5, it is characterised in that the quantity of motion of described user includes: described user complete movement time, described user complete the number of times that exercise intensity, described user accumulative quiescent time and described user's sitting are motionless.

7. method according to claim 4, it is characterised in that the described moving target by adding up the described quantity of motion that obtains and described user pre-sets, generates the exercise guidance scheme of described user, specifically includes:

By adding up the completed quantity of motion of described user that obtains with the moving target that user pre-sets compared with, generate and the exercise guidance scheme completed for the follow-up needs of described user or the actionless situation corresponding exercise guidance scheme of offer for described user are provided to described user.

8. a terminal unit, it is characterised in that including:

Statistical module, for adding up the quantity of motion of user in preset time period according to type of sports and exercise intensity；

Instruct module, for the moving target that the described quantity of motion obtained by statistics and described user are pre-set, generate the exercise guidance scheme of described user.

9. terminal unit according to claim 8, it is characterised in that described terminal unit also includes:

Acquisition module, for obtaining the physical sign parameters of described user, described physical sign parameters at least includes: the personal information of user, health status and moving target；

Generation module, generates described user moving target in preset time period for the physical sign parameters according to described user.

10. terminal unit according to claim 8, it is characterised in that described statistical module specifically for:

Obtain the exercise data of described user movement in a preset time period；Temporal signatures and the frequency domain character of described exercise data is obtained from the exercise data of described user movement；Temporal signatures according to described exercise data and frequency domain character, it is determined that the type of sports of described user and exercise intensity in described preset time period, and add up the quantity of motion of described user in preset time period according to described type of sports and exercise intensity.

11. terminal unit according to claim 10, it is characterised in that described statistical module is additionally operable to:

Judge whether described user remains static according to described type of sports；If so, the time that described user is static is then added up.

12. terminal unit according to claim 11, it is characterised in that described statistical module also particularly useful for:

Judge whether described user is the state that sitting is motionless that is within default quiescent time according to described type of sports, if so, add up the number of times that the sitting of described user is motionless；Otherwise add up the time that described user is static.

13. terminal unit according to claim 11, it is characterised in that described instruct module specifically for:

By adding up the completed quantity of motion of described user that obtains with the moving target that user pre-sets compared with, generate and the exercise guidance scheme completed for the follow-up needs of described user or the actionless situation corresponding exercise guidance scheme of offer for described user are provided to described user.