CN103425895A - Multi-sensor motion and posture monitoring and analyzing method and system - Google Patents
Multi-sensor motion and posture monitoring and analyzing method and system Download PDFInfo
- Publication number
- CN103425895A CN103425895A CN2013103821710A CN201310382171A CN103425895A CN 103425895 A CN103425895 A CN 103425895A CN 2013103821710 A CN2013103821710 A CN 2013103821710A CN 201310382171 A CN201310382171 A CN 201310382171A CN 103425895 A CN103425895 A CN 103425895A
- Authority
- CN
- China
- Prior art keywords
- data
- motion
- sensor
- attitude monitoring
- air pressure
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The invention relates to the field of measurement, in particular to a multi-sensor motion and posture monitoring and analyzing method and a system. In the multi-sensor motion and posture monitoring and analyzing method and the system, an entire solution of collecting relevant data via multiple sensors to perform data molding analysis and outputting a data analysis result is adopted, and a main body consists of multiple sensors and molding algorithms (a step-counting molding algorithm, a stair-climbing molding algorithm and a sleeping molding algorithm). The method and the system have the beneficial effects that multi-sensor information processed in an integrated way is synthesized via a solution of establishing the multi-sensor motion and posture monitoring and analyzing method and the system, and the multiple sensors are used for sensing motion and postures to analyze and record various daily health index data of motion data, sleeping quality and the like, can be used for reflecting the characteristics of motion and postures more completely and more accurately, and are mainly applied to intelligent wearable equipment to assist users in collecting and analyzing various body data indexes.
Description
Technical field
The present invention relates to fields of measurement, relate in particular to a kind of multisensor motion and attitude monitoring analytical approach and system.
Background technology
Along with social development, multisensor is paid close attention to widely at civil area, and the rise of Internet of Things makes the user more and more stronger to the demand of wearable equipment; Wearable equipment mainly depends on the motion of multisensor and the solution that attitude monitoring is analyzed, and only there is the calorie value that calculates moved how many steps or consumption in current passometer, can not show other more parameters, can not carry out monitoring and the analysis of system simultaneously.
Summary of the invention
For the defect existed in prior art or deficiency, technical matters to be solved by this invention is: provide a kind of and can be shown more many reference amounts, and can carry out the method and system of monitoring and the analysis of system.
The technical scheme that the present invention takes, for a kind of sensor movement and attitude monitoring analytical approach and system are provided, comprises the following steps
A, at least two kinds of sensor image data of use, described two kinds of sensors comprise baroceptor and voltage sensor;
B, the data that sensor is collected carry out the air pressure difference and voltage difference calculating, denoising, difference are processed and matching, after matching, are moved and the attitude monitoring modeling analysis;
Described motion and attitude monitoring modeling analysis comprise meter step modeling analysis method, for the effective amplitude data of exporting by sensor, and the walking quantity of the regular motion of judgement user;
Described motion and attitude monitoring modeling analysis comprise the building modeling analysis method of climbing, and for the data that sensor is collected, carry out the function processing;
Described motion and attitude monitoring modeling analysis comprise sleep modeling analysis method, for by the user, sleep front unlatching intelligent sensor stopwatch and wake up after close stopwatch and confirm whole sleep procedure consuming time and correlation time of point;
The data that C, motion and attitude monitoring analysis obtain afterwards output to terminal by the data output unit;
D, described terminal and cloud server carry out exchanges data.
As a further improvement on the present invention, described meter step modeling analysis method comprises the following steps:
The effective amplitude data of S20, collecting sensor output;
S21, by the effective amplitude data, draw oscillogram;
S22, analysis waveform figure;
S23, obtain regular data;
S24, by the walking quantity of the regular motion of regular data judgement user.
The described building modeling analysis method of climbing also comprises following processing function flow process
S100 ,Pa building detects and starts;
S200, the first pretreatment stage that raw data is carried out to the logic judgement, purpose is to get rid of the module-external disturbing factor;
S300, the second pretreatment stage that raw data is carried out to computing, purpose is that the processing by calculating and data obtains the total step number on current time and the same day;
Determine whether in S400, data cached analyzing and testing to exist and climb building;
S500 ,Pa building detects and finishes;
After described step S500, then clocked flip, repeating exactly described processing function flow process, described timing is that per second is processed once.
As a further improvement on the present invention, described the first pretreatment stage comprises the following steps
S1, judge whether more changed handling frequency;
S2, judge that whether the air pressure chip is available;
S3, judge the sensor atmospheric pressure value and temperature value whether effective;
The difference of S4, comparison current time and the last writing time;
Be judged as YES in step S1, enter S5 step by step: calculate inner threshold values, then enter step S2;
In step S2, S3, being judged as NO, is all not pass through, and enters described step S500;
In step S4, difference is larger, is dormancy awakening, enters S6 step by step: clear up data cachedly, then enter described step S500;
In step S4, difference is less, for by pre-service, enters described step S300.
As a further improvement on the present invention, described the second pretreatment stage comprises the following steps
S10, utilize computing formula to carry out the numerical evaluation of height above sea level temperature and air pressure;
Wherein:
Be 1 unit standard atmospheric pressure, equal 101325
, 1013.25
,
To take the sea level elevation that rice is unit,
Be this height with
Air pressure for unit;
The step number of S11, the current barometric information of buffer memory and per second;
S12, record the current time and the same day total step number.
As a further improvement on the present invention, in the data cached analyzing and testing of described step S400, decision method is: when the air pressure fall reaches the high air pressure difference of first floor of this Altitude Regions, and at gas
In the process of drops, step number also changes, and regards as and has climbed first floor.
As a further improvement on the present invention, described sleep modeling analysis method is further comprising the steps of:
S31, cloud server are received passometer stopwatch data;
S32, cloud server judge whether the stopwatch time period satisfies condition;
S33, cloud server obtain minute mobility data in the stopwatch time period;
S34, cloud server are processed minute mobility data and are generated the sleep data;
S35, cloud server are preserved the sleep data;
S36, passometer stop;
In step S32, what do not satisfy condition directly enters step S36.
As a further improvement on the present invention, described denoising is that the raw data to collecting is carried out the analyzing and processing process, and it is that the erratic scattergram that voltage difference is formed carries out the data processing that described difference is processed, and forms the oscillogram of rule after processing.
As a further improvement on the present invention, the step of described matching comprises:
After S40, meter step start, adjacent twice efficiently sampling section is calculated as a step;
Do not detect effective section in S41, certain hour, the meter EOS;
S42, three axles are done above processing simultaneously, and getting in three axles matter of fundamental importance step number is actual meter step number.
A kind of system of using described multisensor motion and attitude monitoring analytical approach, comprise
Data acquisition unit, used a plurality of sensor image data;
Data analysis unit, the data analysis that described data acquisition unit is collected;
The data output unit, exported the data after described data analysis unit analysis;
Terminal, for receiving data, showing data and resolution data;
Cloud server, for receiving data, resolution data and save data;
Carry out exchanges data between terminal and cloud server.
The invention has the beneficial effects as follows: by building the solution of a kind of multisensor motion and attitude monitoring method and system, the multi-sensor information of integrated processing is synthesized, by multisensor paratonic movement and attitude, carry out every health indicator data such as the analytic record exercise data of every day, sleep quality, multisensor can be more perfect, the feature of reflection motion more accurately and attitude, be mainly used on intelligent wearable device, with the every data target of assisting users collection analysis health.
The accompanying drawing explanation
Fig. 1 is the process flow diagram of multisensor motion of the present invention and attitude monitoring method and system;
Fig. 2 is Timing Processing function process flow diagram in multisensor motion of the present invention and attitude monitoring method and system;
Fig. 3 is the first pretreatment stage process flow diagram in multisensor motion of the present invention and attitude monitoring method and system;
Fig. 4 is multisensor motion of the present invention and attitude monitoring method and system the second pretreatment stage process flow diagram;
Fig. 5 is the process flow diagram of multisensor motion of the present invention and attitude monitoring method and system sleep modeling analysis method;
Fig. 6 is the rule oscillogram that multisensor motion of the present invention and attitude monitoring method and system voltage difference form;
Fig. 7 is the rule oscillogram of one section of multisensor of the present invention motion and attitude monitoring method and system X-axis;
Fig. 8 is multisensor motion of the present invention and attitude monitoring method and system wrist waveform sensor figure;
Fig. 9 is multisensor motion of the present invention and attitude monitoring method and system waistband waveform sensor figure;
Figure 10 is multisensor motion of the present invention and attitude monitoring method and system ankle waveform sensor figure;
Figure 11 is the process flow diagram of multisensor motion of the present invention and attitude monitoring method and system meter step modeling analysis method;
Numeral wherein: 1, data acquisition unit; 2, data analysis unit; 3, data output unit; 4, terminal; 5, cloud server; 6, air pressure difference; 7, voltage difference; 8, denoising; 9, difference is calculated; 10, matching; 11, motion and attitude monitoring modeling analysis; 12, meter step modeling analysis method; 13,Pa building modeling analysis method; 14, sleep modeling analysis method.
Embodiment
Below in conjunction with accompanying drawing explanation and embodiment, the present invention is further described.
As shown in Figure 1, multisensor motion of the present invention and attitude monitoring method and system, collect related data by multisensor, carry out Modeling analysis, and the total solution of output data results, main body forms (meter step modeling algorithm ，Pa building modeling algorithm, sleep modeling algorithm) by multisensor+modeling algorithm.
As shown in Figure 1, the technical scheme that the present invention takes, for a kind of sensor movement and attitude monitoring analytical approach and system are provided, comprises the following steps
A, at least two kinds of sensor image data of use, described two kinds of sensors comprise baroceptor and voltage sensor;
B, the data that sensor is collected are carried out air pressure difference 6 and voltage difference 7 calculating, denoising 8, difference processing 9 and matching 10, after matching, moved and attitude monitoring analysis 11,
Described motion and attitude monitoring modeling analysis 11 comprise meter step modeling analysis method 12, for the effective amplitude data of exporting by sensor, and the walking quantity of the regular motion of judgement user;
Described motion and attitude monitoring modeling analysis 11 comprise the building modeling analysis method 13 of climbing, and for the data that sensor is collected, carry out the function processing; In the certain hour scope, the user is by the mode of walking stair or climbing slope, makes oneself sea level elevation promote the height of a floor, the counter statistics that sensor is corresponding cumulative 1;
Described motion and attitude monitoring modeling analysis 11 comprise sleep modeling analysis method 14, for by the user, sleep front unlatching intelligent sensor stopwatch and wake up after close stopwatch and confirm whole sleep procedure consuming time and correlation time of point; Sleep modeling analysis method 14: by the user sleep front unlatching intelligent sensor stopwatch and wake up after close stopwatch and confirm whole sleep procedure consuming time and correlation time of point; After opening the intelligent sensor stop watch function, in whole sleep procedure, when intelligent sensor minute mobility (movable amplitude in a minute and motion frequency.Movable amplitude: sensor activity produces the efficiently sampling section, and this movable amplitude is effective.Motion frequency: when the efficiently sampling section number that sensor activity produces)<10 lasting N minute (N tentative 5), think that the user enters sleep state; Other situations think that the user is in non-sleep state.After the user enters sleep state, when intelligent sensor minute mobility >=10 and continue M minute when (M suspends 2), think that the user awakens from sleep, take minute is minimum time unit, (several seconds) passometer mobility of a bit of time in needing only certain minute > 10, this minute counts non-sleep state;
The data that C, motion and attitude monitoring are analyzed 11 rear acquisitions output to terminal by the data output unit;
D, described terminal and cloud server carry out exchanges data.
Described meter step modeling analysis method 12 comprises the following steps:
The effective amplitude data of S20, the output of collection intelligent sensor;
S21, by the effective amplitude data, draw oscillogram;
S22, analysis waveform figure;
S23, obtain regular data;
S24, by the walking quantity of the regular motion of regular data judgement user;
Meter step modeling analysis method 12 is mainly exported the effective amplitude data by intelligent sensor, obtain final data after analyzing the oscillogram by the effective amplitude data formation, judge user's walking quantity, comprise normal gait, the two kinds of motion states of running, accidental non-rule moves does not carry out processing.
Meter step modeling analysis method 12 functions realize principle:
1, normal gait or running all belong to regular motion, at the X of intelligent sensor, and Y, on Z tri-axles, acceleration change also can present corresponding rule;
2, in order not lose step number, calculate X simultaneously, Y, Z tri-axle step numbers, getting the step number maximal value is actual step number;
3, intelligent sensor is worn on respectively wrist, waistband, ankle, sample frequency 50Hz.
As shown in Figure 2, the described building modeling analysis method 13 of climbing also comprises following processing function flow process
S100 ,Pa building detects and starts;
S200, the first pretreatment stage that raw data is carried out to the logic judgement, purpose is to get rid of the module-external disturbing factor;
S300, the second pretreatment stage that raw data is carried out to computing, purpose is that the processing by calculating and data obtains the total step number on current time and the same day;
Determine whether in S400, data cached analyzing and testing to exist and climb building;
S500 ,Pa building detects and finishes;
After described step S500, then clocked flip, repeating exactly described processing function flow process, described timing is that per second is processed once.
As shown in Figure 3, described the first pretreatment stage comprises the following steps
S1, judge whether more changed handling frequency;
S2, judge that whether the air pressure chip is available;
S3, judge the sensor atmospheric pressure value and temperature value whether effective; The normal atmospheric pressure value that the air pressure crude sampling value of sensor obtains after chip algorithm is processed, the normal temperature value that the temperature crude sampling value of sensor obtains after chip algorithm is processed;
The difference of S4, comparison current time and the last writing time;
Be judged as YES in step S1, enter S5 step by step: calculate inner threshold values, then enter step S2;
In step S2, S3, being judged as NO, is all not pass through, and enters described step S500;
In step S4, difference is larger, is dormancy awakening, enters S6 step by step: clear up data cachedly, then enter described step S500;
In step S4, difference is less, for by pre-service, enters described step S300.
As shown in Figure 4, described the second pretreatment stage comprises the following steps
S10, utilize computing formula to carry out the numerical evaluation of height above sea level temperature and air pressure; The effect of the second pretreatment stage is by data smoothing processing (to the analyzing and processing process of raw data) typing buffer area, determines whether in data cached analyzing and testing to exist and climbs building.
In the certain hour scope, the user is by the mode of walking stair or climbing slope, makes oneself sea level elevation promote the height of a floor, the statistics that sensor is corresponding cumulative 1; The height of this floor is a fixed value, is 3 meters; Time range is also restricted, is 60 seconds at present.That is to say, the user has climbed first floor within 60 seconds, sensor De Pa building statistical value cumulative 1.If the actual story height of climbing building of user is 4 meters, rather than setting value (3 meters), the user has climbed three floors (12 meters) so, and the statistical value of sensor can add up 4.The sensor device built-in chip, can detected temperatures and air pressure original value, can be converted into temperature and the air pressure of everyday concept by computing formula; Its relation is as follows
Wherein:
Be 1 unit standard atmospheric pressure, equal 101325
, 1013.25
,
To take the sea level elevation that rice is unit,
Be this height with
Air pressure for unit;
By above analysis, when the air pressure fall reaches the high air pressure difference of first floor of this Altitude Regions, and in the process descended at air pressure, step number also changes, and can regard as and climb first floor.And the judgement of air pressure fall needs air pressure to compare with reference to base value.The air pressure fall is judged equally also needs correlative value and base value relatively, only relies on a correlative value, can not well eliminate go off daily De Pa building error count phenomenon, and the atmospheric pressure value that therefore changes a N continuous cycle into all compares with base value.The consistance that the selective dependency of N descends in air pressure.When in air pressure decline process, the less air pressure rise phenomenon that occurs, N is smaller value 3.If more air pressure rise phenomenon occurs, N is higher value 5.
Also need to add up used time and the total step number of whole air pressure change simultaneously, need to meet ordinary person's the actual building scene of climbing.Climb building used time and step number lowest threshold and be the healthy and strong man that grows up and climbed rapidly step number used and the time of first floor, requiring at present is minimum 6 steps and 6 seconds.
In order to eliminate and to take elevator or staircase error count phenomenon better, when the air pressure fall acquires a certain degree (the air pressure difference that is at present 2 meters left sides), need to judge immediately in this scope and whether exist step number to change.If step number is unchanged, to regard as and take elevator, air pressure is invalid with reference to base value.
The step number of S11, the current barometric information of buffer memory and per second;
S12, record the current time and the same day total step number.
In the data cached analyzing and testing of described step S400, decision method is: when the air pressure fall reaches the high air pressure difference of first floor of this Altitude Regions, and in the process descended at air pressure, step number also changes, and regards as and has climbed first floor.
As shown in Figure 5, described sleep modeling analysis method 14 is further comprising the steps of:
S31, cloud server are received passometer stopwatch data;
S32, cloud server judge whether the stopwatch time period satisfies condition;
S33, cloud server obtain minute mobility data in the stopwatch time period;
S34, cloud server are processed minute mobility data and are generated the sleep data;
S35, cloud server are preserved the sleep data;
S36, passometer stop;
In step S32, what do not satisfy condition directly enters step S36.
Described denoising 8 is that the raw data to collecting is carried out the analyzing and processing process, and it is that the erratic scattergram that voltage difference is formed carries out the data processing that described difference is processed, and forms the oscillogram of rule after processing.
As shown in figure 11, the step of described matching 10 comprises:
After S40, meter step start, adjacent twice efficiently sampling section is calculated as a step;
Do not detect effective section in S41, certain hour, the meter EOS;
S42, three axles are done above processing simultaneously, and getting in three axles matter of fundamental importance step number is actual meter step number;
At first, collect certain axle output valve, this value is carried out to the data smoothing processing, then judge whether in meter step state, be judged as YES, whether form again the new judgement of effective section, be judged as YES, then carry out the effectively satisfied meter step of section condition criterion, be judged as YES, for this axle meter step number increases, get each axle meter step number maximal value for actual meter step number, judge and finish;
Whether, in meter step state, be judged as NO, preserve effective section that this axle forms, carry out effectively section and whether meet meter step condition, if not, finish to judge, in this way, this axle meter step state zero clearing effectively section storage configuration and corresponding data are set, counting this axle meter step number increases;
Form the new judgement of effective section whether, be judged as NO, carry out effectively section and form whether overtime judgement, be judged as NO, finish to judge, be judged as YES, eliminate this axle meter step state, finish to judge;
In effectively section meets meter step condition criterion, be judged as NO, finish to judge.
A kind of system of using described multisensor motion and attitude monitoring analytical approach, comprise
Terminal 4, for receiving data, showing data and resolution data;
Carry out exchanges data between terminal 4 and cloud server 5.
The hardware of described motion and attitude monitoring module is former
Reason: U=Q4 π kd/ ε s, wherein: U means the voltage between two-plate, and Q is deposited the volume electric charge by capacitor plate, and d means distance, and ε is that specific inductive capacity k is that electrostatic force constant s is over against area; Voltage U between two-plate to apart from d, be directly proportional, and linear relationship.
F=ma, firmly larger to a direction, the acceleration a of generation is larger, and spring leaf is the closer to rightabout pole plate, and the absolute value of voltage difference vd is larger, and acceleration a and voltage difference vd exist linear relationship.
As shown in Figure 6, denoising, difference realize principle: the analyzing and processing process of raw data:
Relative crest, its two adjacent sampling point value of certain sampled point all are less than it;
Relative trough, its two adjacent sampling point value of certain sampled point all are greater than it;
Valid peak, at first meet relative crest condition, and secondly this crest and the absolute difference of previous effective trough and the absolute difference of a rear effective trough all are greater than certain setting value amp_min;
Effectively trough, at first meet relative trough condition, and secondly this trough and the absolute difference of previous Valid peak and the absolute difference of a rear Valid peak all are greater than certain setting value amp_min;
Valid peak is established a capital certain property delayed really with effective trough, and determining of a Valid peak must wait until that could confirm appears in next effectively trough;
Effective amplitude, the absolute difference of adjacent Valid peak and effective trough;
The efficiently sampling section, in regular data division, from Valid peak to effective trough, or is an efficiently sampling section from effective trough to Valid peak;
Efficiently sampling value percentage, hits ratio in hits and a rear efficiently sampling section in hits ratio or previous efficiently sampling section in hits and previous efficiently sampling section in a rear efficiently sampling section;
Downward efficiently sampling value number, (s1, s2 the sampled value set from absolute crest to absolute trough ... si-1, si ... sn), if a rear sampled value si is less than previous sampled value si-1, counting increases, and the sampled value of back must all be less than si, counting could increase;
Efficiently sampling value number upwards, (s1, s2 the sampled value set from absolute trough to absolute crest ... si-1, si ... sn), if a rear sampled value si is greater than previous sampled value si-1, counting increases, and the sampled value of back must all be greater than si, counting could increase;
This is exactly the oscillogram that a voltage difference forms in fact, is exactly from erratic scattergram, through data, processes, and forms the oscillogram of rule after processing.
Normal walking rule: amplitude is greater than certain value; Adjacent crest or trough spacing are more or less the same; In certain interval fluctuation.
As shown in Figure 7, Fig. 7 is the one piece of data of x axle, and by above analysis, we are converted to the some set of sampled data the set of line segment; The data of each axle are divided into regular data and irregular data two parts:
Define regular data division and must meet two pacing itemss:
1. effective amplitude is greater than certain setting value, and this setting value is 35;
2. effective amplitude can repeat and multiplicity is greater than certain setting value within a certain period of time;
Meet above 2 points, can think that this segment data is the data that meet certain rule.In like manner the discontented data that are enough at 2 are all thought for irregular data.
Intelligent sensor is worn on respectively to wrist, and waistband, find after the data analysis of ankle
As shown in Figure 8, the sensor of wrist, the level land walking, detect Y-axis efficiently sampling section maximum.
As shown in Figure 9, the sensor of waistband, the level land walking, detect X-axis efficiently sampling section maximum.
As shown in figure 10, the ankle sensor, detect X-axis efficiently sampling section maximum.
The above analysis, can design following algorithm:
Continuous some groups of regular waveforms appear in any one axle, think and have started to walk.One group of waveform here refers to continuous 2～4 pairs of Wave crest and wave troughs, namely 4～8 continuous effective waveform segments.Rule, similar on the waveform graph of two groups of front and back.Simplified characterization, we to the sampling section of two groups of waveform A and B be labeled as respectively (a1, a2, a3, a4 ...) and (b1, b2, b3, b4 ...), S means the sampling number of certain sampling section, M means the amplitude of certain sampling section.A is similar to B will meet the following conditions:
A) the sampling section number of A equates with the sampling section number of B, and in every group of waveform, the sampling section of ascendant trend and the sampling section of downtrending account for half;
B) S (a1): S (b1), S (a2): S (b2), S (a3): S (b3), all approach the 1:1 ratio, pusher successively, ratio value can be set as floating threshold;
C) M (a1): M (b1), M (a2): M (b2), M (a3): M (b3), all approach the 1:1 ratio, pusher successively, ratio value can be set as floating threshold.
1., after the meter step starts, adjacent twice efficiently sampling section is calculated as a step;
2. do not detect effective section in certain hour, the meter EOS;
3. three axles are done above processing simultaneously, and getting in three axles matter of fundamental importance step number is actual meter step number.
Condition is: a minute mobility is less than 10 and while continuing 5 minutes, thinks that the user enters sleep state; Other situations think that the user is in non-sleep state;
Minute mobility is more than or equal to 10 and while continuing 2 minutes, thinks user's awakening from sleep,
As long as in certain minute, (several seconds) passometer mobility of a bit of time is greater than 10, this minute counts non-sleep state.
Above content is in conjunction with concrete preferred implementation further description made for the present invention, can not assert that specific embodiment of the invention is confined to these explanations.For the general technical staff of the technical field of the invention, without departing from the inventive concept of the premise, can also make some simple deduction or replace, all should be considered as belonging to protection scope of the present invention.
Claims (10)
1. a multisensor moves and the attitude monitoring analytical approach, it is characterized in that: comprise the following steps
Use at least two kinds of sensor image data, described two kinds of sensors comprise baroceptor and voltage sensor;
The data that sensor is collected carry out air pressure difference and voltage difference calculating, denoising,
Difference is processed and matching, after matching, is moved and the attitude monitoring modeling analysis;
Described motion and attitude monitoring modeling analysis comprise meter step modeling analysis method, for the effective amplitude data of exporting by sensor, and the walking quantity of the regular motion of judgement user;
Described motion and attitude monitoring modeling analysis comprise the building modeling analysis method of climbing, and for the data that sensor is collected, carry out the function processing;
Described motion and attitude monitoring modeling analysis comprise sleep modeling analysis method, for by the user, sleep front unlatching intelligent sensor stopwatch and wake up after close stopwatch and confirm whole sleep procedure consuming time and correlation time of point;
The data that motion and attitude monitoring analysis obtain afterwards output to terminal by the data output unit;
Described terminal and cloud server carry out exchanges data.
2. multisensor according to claim 1 moves and the attitude monitoring analytical approach, it is characterized in that: described meter step modeling analysis method comprises the following steps:
The effective amplitude data of S20, collecting sensor output;
S21, by the effective amplitude data, draw oscillogram;
S22, analysis waveform figure;
S23, obtain regular data;
S24, by the walking quantity of the regular motion of regular data judgement user.
3. multisensor according to claim 1 moves and the attitude monitoring analytical approach, and it is characterized in that: the described building modeling analysis method of climbing also comprises following processing function flow process
S100 ,Pa building detects and starts;
S200, the first pretreatment stage that raw data is carried out to the logic judgement, purpose is to get rid of the module-external disturbing factor;
S300, the second pretreatment stage that raw data is carried out to computing, purpose is that the processing by calculating and data obtains the total step number on current time and the same day;
Determine whether in S400, data cached analyzing and testing to exist and climb building;
S500 ,Pa building detects and finishes;
After described step S500, then clocked flip, repeating exactly described processing function flow process, described timing is that per second is processed once.
4. multisensor according to claim 3 moves and the attitude monitoring analytical approach, and its feature exists
In: described the first pretreatment stage comprises the following steps
S1, judge whether more changed handling frequency;
S2, judge that whether the air pressure chip is available;
S3, judge the sensor atmospheric pressure value and temperature value whether effective;
The difference of S4, comparison current time and the last writing time;
Be judged as YES in step S1, enter S5 step by step: calculate inner threshold values, then enter step S2;
Be judged as NO in step S2, S3, for not passing through, enter described step S500;
In step S4, difference is larger, is dormancy awakening, enters S6 step by step: clear up data cachedly, then enter described step S500;
In step S4, difference is less, for by the first pretreatment stage, enters described step S300.
5. multisensor according to claim 3 moves and the attitude monitoring analytical approach, and it is characterized in that: described the second pretreatment stage comprises the following steps
S10, utilize computing formula to carry out the numerical evaluation of height above sea level temperature and air pressure;
Wherein:
Be 1 unit standard atmospheric pressure, equal 101325
, 1013.25
,
To take the sea level elevation that rice is unit,
Be this height with
Air pressure for unit;
The step number of S11, the current barometric information of buffer memory and per second;
S12, record the current time and the same day total step number.
6. multisensor according to claim 3 moves and the attitude monitoring analytical approach, it is characterized in that: in the data cached analyzing and testing of described step S400, decision method is: when the air pressure fall reaches the high air pressure difference of first floor of this Altitude Regions, and in the process descended at air pressure, step number also changes, and regards as and has climbed first floor.
7. multisensor according to claim 1 moves and the attitude monitoring analytical approach, and it is characterized in that: described sleep modeling analysis method comprises the following steps:
S31, cloud server are received passometer stopwatch data;
S32, cloud server judge whether the stopwatch time period satisfies condition;
S33, cloud server obtain minute mobility data in the stopwatch time period;
S34, cloud server are processed minute mobility data and are generated the sleep data;
S35, cloud server are preserved the sleep data;
S36, passometer stop;
In step S32, what do not satisfy condition directly enters step S36.
8. multisensor according to claim 1 moves and the attitude monitoring analytical approach, it is characterized in that: described denoising is that the raw data to collecting is carried out the analyzing and processing process, it is that the erratic scattergram that voltage difference is formed carries out the data processing that described difference is processed, and forms the oscillogram of rule after processing.
9. multisensor according to claim 1 moves and the attitude monitoring analytical approach, and it is characterized in that: the step of described matching comprises:
After S40, meter step start, adjacent twice efficiently sampling section is calculated as a step;
Do not detect effective section in S41, certain hour, the meter EOS;
S42, three axles are done above processing simultaneously, and getting in three axles matter of fundamental importance step number is actual meter step number.
One kind use multisensor motion as claimed in claim 1 and attitude monitoring analytical approach be
System, is characterized in that: comprise
Data acquisition unit, used a plurality of sensor image data;
Data analysis unit, the data analysis that described data acquisition unit is collected;
The data output unit, exported the data after described data analysis unit analysis;
Terminal, for receiving data, showing data and resolution data;
Cloud server, for receiving data, resolution data and save data;
Carry out exchanges data between terminal and cloud server.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2013103821710A CN103425895A (en) | 2013-08-28 | 2013-08-28 | Multi-sensor motion and posture monitoring and analyzing method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2013103821710A CN103425895A (en) | 2013-08-28 | 2013-08-28 | Multi-sensor motion and posture monitoring and analyzing method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN103425895A true CN103425895A (en) | 2013-12-04 |
Family
ID=49650624
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2013103821710A Pending CN103425895A (en) | 2013-08-28 | 2013-08-28 | Multi-sensor motion and posture monitoring and analyzing method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103425895A (en) |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103941856A (en) * | 2014-02-19 | 2014-07-23 | 联想(北京)有限公司 | Information processing method and electronic equipment |
| CN104331796A (en) * | 2014-11-04 | 2015-02-04 | 北京握奇智能科技有限公司 | Wearable device and working method thereof |
| WO2015127724A1 (en) * | 2014-02-28 | 2015-09-03 | 惠州Tcl移动通信有限公司 | Method and system for automatically prompting user to sleep, and wearable device |
| CN105045372A (en) * | 2015-01-30 | 2015-11-11 | 腾讯科技（深圳）有限公司 | Method and equipment controlling wearable device |
| CN105953813A (en) * | 2016-06-21 | 2016-09-21 | 紫光股份有限公司 | Ankle type step counter based on single-shaft micro-mechanical accelerometer |
| CN106017504A (en) * | 2016-07-04 | 2016-10-12 | 紫光股份有限公司 | Step counting device based on ankle movement speed detection |
| CN106462665A (en) * | 2014-05-30 | 2017-02-22 | 微软技术许可有限责任公司 | Adaptive lifestyle metric estimation |
| CN109009137A (en) * | 2017-06-09 | 2018-12-18 | 富士通株式会社 | Obtain the method, apparatus and electronic equipment of gait parameter |
| CN110514243A (en) * | 2019-08-20 | 2019-11-29 | 重庆零壹空间科技集团有限公司 | A kind of carrier rocket collecting method, system, terminal device and medium |
| WO2021092873A1 (en) * | 2019-11-13 | 2021-05-20 | 北京数字联盟网络科技有限公司 | Method for determining floor of terminal device |
| CN113008230A (en) * | 2021-02-26 | 2021-06-22 | 广州偶游网络科技有限公司 | Intelligent wearable device and posture orientation recognition method and device thereof |
| CN113884101A (en) * | 2021-09-29 | 2022-01-04 | 歌尔科技有限公司 | Step counting method of wearable device, wearable device and medium |
-
2013
- 2013-08-28 CN CN2013103821710A patent/CN103425895A/en active Pending
Non-Patent Citations (7)
| Title |
|---|
| 张超: ""http://www.zhihu.com/question/21482786，Fitbit Flex如何设置睡眠模式"", 《知乎》 * |
| 无: ""http://tech.qq.com/a/20130705/015233.htm，大麦健康CEO张其林：穿戴设备互联带来更加人性化体验"", 《腾讯科技》 * |
| 曹耀峰: "《超深高酸性气田钻井工程技术与实践》", 28 February 2011 * |
| 李鸥等: "《实战Android应用开发》", 31 August 2012, 清华大学出版社 * |
| 王超文: ""http://www.ifanr.com/313602，大麦健康做计步器：本质上是做社交，专注于配件"", 《爱范儿》 * |
| 石欣等: ""一种基于压力传感器的人体运动识别方法研究"", 《仪器仪表学报》 * |
| 苏丽娜等: ""基于加速度传感器的计步器***"", 《测控技术》 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103941856A (en) * | 2014-02-19 | 2014-07-23 | 联想(北京)有限公司 | Information processing method and electronic equipment |
| CN103941856B (en) * | 2014-02-19 | 2017-12-26 | 联想(北京)有限公司 | A kind of information processing method and electronic equipment |
| WO2015127724A1 (en) * | 2014-02-28 | 2015-09-03 | 惠州Tcl移动通信有限公司 | Method and system for automatically prompting user to sleep, and wearable device |
| CN106462665A (en) * | 2014-05-30 | 2017-02-22 | 微软技术许可有限责任公司 | Adaptive lifestyle metric estimation |
| CN106462665B (en) * | 2014-05-30 | 2020-01-14 | 微软技术许可有限责任公司 | Wearable electronic device and method of estimating lifestyle metrics |
| CN104331796A (en) * | 2014-11-04 | 2015-02-04 | 北京握奇智能科技有限公司 | Wearable device and working method thereof |
| CN105045372A (en) * | 2015-01-30 | 2015-11-11 | 腾讯科技（深圳）有限公司 | Method and equipment controlling wearable device |
| CN105953813A (en) * | 2016-06-21 | 2016-09-21 | 紫光股份有限公司 | Ankle type step counter based on single-shaft micro-mechanical accelerometer |
| CN106017504A (en) * | 2016-07-04 | 2016-10-12 | 紫光股份有限公司 | Step counting device based on ankle movement speed detection |
| CN109009137A (en) * | 2017-06-09 | 2018-12-18 | 富士通株式会社 | Obtain the method, apparatus and electronic equipment of gait parameter |
| CN110514243A (en) * | 2019-08-20 | 2019-11-29 | 重庆零壹空间科技集团有限公司 | A kind of carrier rocket collecting method, system, terminal device and medium |
| WO2021092873A1 (en) * | 2019-11-13 | 2021-05-20 | 北京数字联盟网络科技有限公司 | Method for determining floor of terminal device |
| CN113008230A (en) * | 2021-02-26 | 2021-06-22 | 广州偶游网络科技有限公司 | Intelligent wearable device and posture orientation recognition method and device thereof |
| CN113008230B (en) * | 2021-02-26 | 2024-04-02 | 广州市偶家科技有限公司 | Intelligent wearable device and gesture direction recognition method and device thereof |
| CN113884101A (en) * | 2021-09-29 | 2022-01-04 | 歌尔科技有限公司 | Step counting method of wearable device, wearable device and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103425895A (en) | Multi-sensor motion and posture monitoring and analyzing method and system | |
| CN106289309B (en) | Step-recording method and device based on 3-axis acceleration sensor | |
| CN102292025B (en) | Determining energy expenditure of a user | |
| CN106096662A (en) | Human motion state identification based on acceleration transducer | |
| CN105877757A (en) | Multi-sensor integrated human motion posture capturing and recognizing device | |
| CN102445214B (en) | Step counting method based on 3D (three-dimensional) gravity sensor of intelligent mobile phone | |
| JP6134872B1 (en) | Device, method and system for counting the number of cycles of periodic motion of a subject | |
| CN104567912B (en) | Method for realizing pedometer on Android mobile phone | |
| CN104457751A (en) | Method and system for recognizing indoor and outdoor scenes | |
| Muscillo et al. | An adaptive Kalman-based Bayes estimation technique to classify locomotor activities in young and elderly adults through accelerometers | |
| EP3090685A1 (en) | Pedometer and method for analyzing motion data | |
| Liu et al. | A benchmark database and baseline evaluation for fall detection based on wearable sensors for the Internet of medical things platform | |
| CN104586402A (en) | Feature extracting method for body activities | |
| CN111415752A (en) | Hand-foot-and-mouth disease prediction method integrating meteorological factors and search indexes | |
| CN103675463A (en) | Liquid dielectric constant measuring system self-adapting to scale and precision | |
| CN107209050A (en) | For the apparatus and method for the change for monitoring user's weight | |
| CN116484307A (en) | Cloud computing-based intelligent ring remote control method | |
| CN108279019A (en) | A kind of step-recording method, device and intelligent wearable device | |
| Carneiro et al. | Accelerometer-based methods for energy expenditure using the smartphone | |
| Sundaravadivel et al. | Smart-walk: An intelligent physiological monitoring system for smart families | |
| CN106500718A (en) | Step number assay method | |
| WO2020228157A1 (en) | Precise step counting method using human body capacitance | |
| Kommuri et al. | Development of real time environment monitoring system using with MSP430 | |
| CN110260883A (en) | The energy saving step-recording method and pedometer of intelligent electronic device | |
| Bonroy et al. | Ambulatory monitoring of physical activity based on knee flexion/extension measured by inductive sensor technology |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20131204 |