CN109567813B - Motion state monitoring system based on footprint - Google Patents
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Abstract
The invention discloses a motion state monitoring system based on footprints, which comprises: evaluating the current motion state; a motion state statistic module; a motion mode feedback module; and an exercise plan arrangement module. The present application can evaluate the current motion state and the motion intensity through the data related to the footprints, can evaluate the frequency of the similar motion state in a short period and the suggestion of the motion mode, and can also carry out the motion planning of every day, every week and every month. The invention can effectively evaluate the motion state of the current user and give reasonable suggestions, so that the motion of the user is more regular and healthy, and the motion damage is reduced.
Description
Technical Field
The invention relates to a motion state monitoring system, in particular to a motion state monitoring system based on footprints.
Background
With the gradual improvement of health consciousness of people, sports become the primary choice for urban people to work, and a large number of sports products are developed towards high-end and intelligent directions. However, sports also has certain dangerousness, has certain requirements on physical ability and athletic ability of people, and often people can excessively estimate the ability of people, so that unexpected damage is brought to the body in sports; to reduce these unnecessary injuries, the state of the human body needs to be monitored in real time during the exercise.
The footprint images can be widely generated in various occasions, the footprint images are applied, the information contained in the footprint images is mined, and the motion state of people can be monitored.
Disclosure of Invention
The application provides a footprint-based exercise state monitoring system, which evaluates the current exercise state and exercise intensity according to footprint related data, can also monitor the frequency of similar exercise states in a short term and provides an exercise mode suggestion.
The first technical scheme of the application is as follows: a footprint-based athletic condition monitoring system, comprising:
the current motion state evaluation module judges which motion state the current motion belongs to based on the pace;
the motion state counting module is used for counting the time and the switching frequency of each motion state;
the motion mode feedback module is used for feeding back whether the motion mode is correct or not;
and the movement plan arrangement module gives a reasonable suggestion for the movement mode.
Further, the current motion state evaluation module specifically includes:
a) classifying the motion state;
b) according to the energy consumed by the activity item and the motion state, quantitatively stipulating the motion state;
R＝E/Eavg
wherein R is the amount of movement of a certain movement state, E is the energy consumed by the movement per hour, E is the amount of energy consumed by the movement per houravgCollecting energy consumed by the object in each hour when the object normally walks;
c) the motion state is determined based on the pace.
Further, the method for determining the motion state based on the pace comprises the following steps:
A. firstly, acquiring footprint data of the left foot or the right foot at intervals of delta t seconds, reducing the dimension of two-dimensional data into one-dimensional data according to a column limit principle, and defining the data set as a data set P ═ { P ═ P1,p2,...,pn}；
B. And then calculating a derivative set of the data set P according to the acquisition time sequence, wherein P' ═ P2-p1,p3-p2,...,pn-pn-1Taking P' as the motion state at the time interval;
C. let the homomorphic derivative statistics be PmWhen no status is recorded, Pm＝{}，PmP', perform a; otherwise, Pm＝PmUsing a normalized correlation function to evaluate the correlation of the current derivative with the derivative set, if the maximum value of the absolute value of the correlation is greater than delta, considering that the state is repeated, and executing D1, otherwise executing D2;
d, D.D1: calculating the repeated time t of the state according to the length of the derivative, and using the repeated time t to evaluate the pace speed, wherein the pace speed is 1/t, the pace speed is fed back, the derivative set is emptied, and the information acquisition of the next motion state is carried out;
d2: combining the two derivatives, judging the length of the set, assuming that the length of the set exceeds the specified length, and considering the pace to be 0, emptying the derivative set, and acquiring and evaluating the next data;
E. performing an estimation of the motion state once per determination of the pace;
F. if the footprint information is pressure related and the pace is 0, the pressure status for a certain period of time is represented by calculating the mean of D1.
Further, the motion state statistics module specifically includes:
step 1: preprocessing and denoising the footprint image data;
step 2: counting the time of each motion state;
and step 3: counting the switching frequency: on a given time period basis: acquiring a motion state after being preprocessed in a certain time period, then differentiating the motion state, and carrying out frequency statistics according to different differential values to obtain switching frequency statistics;
and 4, step 4: counting the time probability density of each motion state: and (4) counting the motion states at regular time every day, and taking the time-occupying ratio of each motion state as the state probability in each counting interval.
Furthermore, the motion mode feedback module specifically includes:
i. coordination and symmetry of the feedback motion;
evaluating the degree of exercise and judging the reasonableness;
assessment of impairment of the locomotor pattern to the body.
Further, the coordination and symmetry of the feedback motion are specifically:
A. under the premise of simultaneously providing footmark data of left and right feet, carrying out mirror image transformation on data vectors in any direction;
B. calculating data vectors of the left foot and the right foot in the same state in two directions by using a mode of calculating a state evaluation set;
C. and calculating the correlation degree of the data vectors in different directions, wherein the larger the correlation is, the better the harmony and the symmetry are, and the worse the harmony and the symmetry are.
Furthermore, the evaluation of the degree of exercise and the judgment of the rationality are specifically as follows:
counting the motion states of each day, and multiplying the quantized motion states by the normal walking energy consumption to obtain the energy consumption of each motion state so as to evaluate the energy consumption of each day; the exercise amount is within 300 kilocalories every day, the exercise amount is considered to be insufficient, the exercise amount is 300-600 kilocalories, the exercise amount is considered to be moderate, the body shaping purpose can be achieved by more than 600 kilocalories, the exercise amount is considered to be slightly higher than 2000, the appropriate control is needed, the exercise amount is considered to be too high by more than 3000, and the exercise amount is recommended to be reduced;
the switching frequency of each motion state is counted, the switching frequency of each motion state is too high, the switching frequency is considered to be too frequent, the metabolism is not favorable for stabilization, the more the cross-level motion switching frequency is, the more the motion switching frequency is, the static state is immediately entered after the high-energy motion for a long time, the condition that the muscle soreness is caused due to the negative effect on the relaxation of the motor nerves and organs of the human body is considered.
Furthermore, the damage degree of the exercise mode to the body is evaluated, specifically:
●, the larger the movement coordination, the lower the damage degree to the body, and the lower the coordination, the higher the damage degree, wherein, an evaluation model of y ═ aexp (x) + b is constructed, x is coordination, y is damage degree, a, b are constant coefficients;
●, the higher the movement amount exceeding the reasonable amount, the more damage to the body, here, construct the evaluation model of y ═ clog (x) + d, x is the statistics of energy consumption of movement state of each day, y is the damage degree, c, d are constant coefficients;
● the longer the running time of the whole body, the greater the damage degree to the knee, here, the construction y is k/(1+ e)-x+1/24) The average running time per day is 24x, y is the degree of damage, and k is a constant coefficient.
As a further step, the movement plan scheduling module: according to the energy consumed by daily exercise, the health, body shaping and athlete taking are divided into three states:
i. on the premise of giving a moving target, carrying out motion state statistics and motion mode feedback on a scheduled object within a period of time;
if the moving object has been reached, performing iii, otherwise performing iv;
iii, sorting according to the existing motion state, and directly making a motion plan with a period of N weeks;
judging the difference between the current motion amount and the motion level of the motion target, if the difference is 2 motion levels, executing v, and if not, executing vi;
v. lowering the target by one level;
vi, according to the existing motion state, performing weighted distribution according to the motion consumption of the target every day and the consumption ratio of the current stage every day, then randomly promoting part of the motion state under the condition of not influencing the motion state distribution every day, and making an N-week motion plan in advance according to the mode;
and vii, counting the motion state of completing N weeks, if the motion target is completed, adjusting the motion state which does not accord with the original plan, and making a motion plan with a longer period according to the counting result, otherwise, adjusting the motion state which does not accord with the original plan and continuing making the motion plan for N weeks according to the mode vi.
The invention has the beneficial effects that: the current exercise state and exercise intensity are evaluated through the data related to the footprints, the frequency of the similar exercise state in a short term and the suggestion of the exercise mode can be evaluated, and daily, weekly and monthly exercise planning can be carried out. The invention can effectively evaluate the motion state of the current user and give reasonable suggestions, so that the motion of the user is more regular and healthy, and the motion damage is reduced.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in detail below with reference to specific embodiments.
The embodiment provides a motion state monitoring system based on footprint, which specifically includes:
providing a data module: and dynamic footprint data in the walking process, including but not limited to a pressure map, a dynamic track and the like, wherein the sampling rate of the footprint data of each foot is above 25 Hz.
a) The current motion state evaluation module:
i. classifying the motion states into 7 classes: sitting, standing still, normal walking, slow walking, fast walking, aerobic running and anaerobic running, wherein the state of riding the vehicle is considered as sitting except riding;
according to the energy consumption and the motion state of the activity item per hour, quantitatively specifying the motion state:
R＝E/Eavg
r is the amount of exercise in a certain state of motion, E is the energy (in kilocalories) consumed by the exercise per hour, EavgThe energy consumed by the object in normal walking per hour is collected (unit kilocalorie);
the following are the respective motion state consumption energy reference values:
A. sit, 0.3;
B. standing, 0.4;
C. normal walking, 1.0;
D. slow walking, 0.6;
E. fast walking, 2.2;
F. aerobic running, 2.6;
G. anaerobic run, 2.7.
The quantization method is that the energy consumed by normal walking is used as a reference value, the motion states of other activities are compared with the reference value, and 1 bit after a decimal point is rounded off to obtain the quantization condition of the motion states.
A pace-based motion state determination method:
A. firstly, acquiring footprint data of the left foot or the right foot at intervals of delta t seconds (in the process of running without oxygen, the speed limit is about 10 meters per second, the left foot and the right foot need to be respectively acquired more than 5 times, here, according to the Nyquist sampling rate, the state per second is considered to be evaluated 10 times, namely, the motion state is updated once per 0.1 second), and reducing the dimension of the two-dimensional data into one-dimensional data according to the column limit principle, wherein the two-dimensional data is defined as a data set P ═ { P ═ P { (one time of motion state is one second)1,p2,...,pn}；
B. And then calculating a derivative set of the data set P according to the acquisition time in sequence, wherein P' ═ P2-p1,p3-p2,...,pn-pn-1Taking P' as the motion state at the time interval;
C.Pmp', perform a; otherwise, Pm＝PmUsing a normalized correlation function to evaluate the correlation between the current derivative and the derivative set, if the maximum value of the absolute value of the correlation is greater than Δ (such as 0.8), regarding the state as repeated, and performing D1, otherwise performing D2;
d, D.D1: calculating the repeated time t of the state according to the length of the derivative, and using the repeated time t to evaluate the pace speed, wherein the pace speed is 1/t, the pace speed is fed back, the derivative set is emptied, and the information acquisition of the next motion state is carried out;
d2: combining the two derivatives, judging the length of the set, assuming that the length of the set exceeds the specified length, and considering the pace to be 0, emptying the derivative set, and acquiring and evaluating the next data;
E. and performing the estimation of the exercise state once every time the pace speed is determined, wherein for the adult with the medium physique, the adult with the moderate physique has the pace speed within delta a (such as 0.5) and is standing or sitting, the adult with the pace speed within 1-2 delta a is slow walking, the adult with the pace speed within 2-3 delta a is normal walking, the adult with the pace speed within 3-4 delta a is fast walking, the adult with the pace speed within 4-5 delta a is aerobic running, and the adult with the pace speed above 5 delta a is anaerobic running. Assuming that besides the footprint information, the posture information (lean, normal, fat, etc.) can be provided, the pace can be adjusted according to the posture condition, and the adjustment mode is as follows: under normal conditions, the step speed of (3-3.6) delta a is defined as fast walking by the obesity and the obesity without adjustment, the step speed of (3-4) delta a is defined as aerobic running, and the step speed of more than 4 delta a is defined as anaerobic running;
F. if the footprint information is related to pressure and the pace is 0, the pressure state of a certain time period can be represented by calculating the average value of D1, if the pressure value is lower than a certain threshold value, the current state is considered as sitting, otherwise, the state is static standing, and the threshold value is defined according to the sensitivity of the pressure sensor and the quantization bit number.
b) The motion state statistic module is used for:
the statistical object features here are the following:
i. the duration of a certain motion state for a certain period of time, such as the time until now, for normal walking today;
frequency of switching between different motion states, such as the number of times such state changes occur today by the time standing still to running without oxygen so far;
distribution of occurrence probability of different motion states within a certain time period, such as those periods of the day in which normal walking states have a greater probability distribution.
The specific statistical method comprises the following steps:
i. data preprocessing and denoising: because the walking state of a person is not completely ideal in the walking process, the phenomenon of state jump and unreasonable phenomenon may occur in the evaluation process of the motion state, the evaluated motion state is changed into noise, and in order to acquire more objective motion data, the specific method is as follows:
A. the motion state in a certain time period is defined as 1-7, and the faster the speed is, the larger the label value is;
B. counting the duration from each occurrence to the end of each different state, if a certain state only lasts for a few sampling times (within 10 times of the sampling time), considering that the state is jumping, and modifying the state into a motion state which is closest to the state in the time dimension;
C. counting the change of each state, performing problem state evaluation according to a continuous change criterion of the states, considering that the motion body in the time period has problems if more than 4 state spans appear in the continuous motion states, and emptying the motion state of the part without counting.
Based on the temporal statistics of the motion state, on a given time period basis: acquiring a motion state after being preprocessed in a certain time period, and then performing time accumulation according to different motion states to acquire the time of all the motion states;
switching frequency statistics, on a given time period basis: acquiring a motion state after being preprocessed in a certain time period, then differentiating the motion state, and carrying out frequency statistics according to different differential values to obtain switching frequency statistics;
the time probability density statistics of the motion state, wherein the statistics and the updating of the probability density are carried out according to day as a unit, and the specific mode is as follows:
A. setting the minimum statistical unit of each day as hour, namely performing statistics of the exercise state once per hour;
B. in each statistical interval, the time-occupying ratio of the motion state is taken as the state probability, for example, at 7 to 8 points, there are two motion states, namely normal walking and jogging, the normal walking is 38 minutes in total, the jogging is 22 minutes, the probability of the normal walking is 63%, and the probability of the jogging is 37%.
c) A motion mode feedback module:
the main feedback points are as follows:
i. coordination and symmetry of motion:
A. under the premise of simultaneously providing footmark data of left and right feet, carrying out mirror image transformation on data vectors in any direction;
B. calculating data vectors of the left foot and the right foot in the same state in two directions by using a mode of calculating a state evaluation set;
C. and calculating the correlation degree of the data vectors in different directions, wherein the larger the correlation is, the better the harmony and the symmetry are, and the worse the harmony and the symmetry are.
Assessment of degree of exercise and rationality judgment:
A. counting the motion state every day, wherein the energy consumption of normal walking per hour is 300 kilocalories, and the energy consumption of other states can be obtained by multiplying the quantized motion state by the energy consumption of normal walking per hour, so that the energy consumption of one day is evaluated, wherein the energy consumption of sitting and standing is not listed in energy consumption statistics (non-motion energy consumption), the exercise amount of each day is within 300, the exercise amount is considered insufficient, the 300-600 exercise amounts are moderate (healthy), the body shaping purpose can be achieved by more than 600 (body shaping), the exercise amount of more than 2000 is considered slightly high and needs to be properly controlled, the exercise amount of more than 3000 is considered too high, and the exercise amount is recommended to be reduced;
B. the switching frequency of each motion state is counted, the higher the switching frequency of each motion state (the motion state is changed every 1 hour averagely), the more frequent the switching frequency is considered to be unfavorable for stabilizing metabolism, the more the cross-level motion switching frequency is, and the more the cross-level motion switching frequency is, the more the motion state is in a static state (from aerobic motion to sitting) immediately after long-time high-energy motion, the negative effect on relaxation of the motor nerves and organs of a human body is considered to be caused, and the muscle soreness and the like can be caused.
Assessment of impairment of the locomotor pattern to the body:
●, the larger the movement coordination, the lower the damage degree to the body, and the lower the coordination, the higher the damage degree, wherein, an evaluation model of y ═ aexp (x) + b is constructed, x is coordination, y is damage degree, a, b are constant coefficients;
●, the higher the movement amount exceeding the reasonable amount, the more damage to the body, here, construct the evaluation model of y ═ clog (x) + d, x is the statistics of energy consumption of movement state of each day, y is the damage degree, c, d are constant coefficients;
● the longer the running time of the whole body, the greater the damage degree to the knee, here, the construction y is k/(1+ e)-x+1/24) The average running time per day is 24x, y is the degree of damage, and k is a constant coefficient.
d) An exercise plan arrangement module:
the motion targets are here defined as: health, body shaping, and athletes (more than 2000 kilocalories of daily exercise consumption capacity), with sequentially increased exercise amount.
The arrangement method comprises the following steps:
i. on the premise of giving a moving target, performing at least one-circle motion state statistics and motion mode feedback on a scheduled object;
if the moving object has been reached, performing iii, otherwise performing iv;
iii, sorting the existing motion states (motion frequency, motion consumption per day and motion state distribution frequency per hour) and directly making a motion plan with a period of 1 week;
judging the difference between the current motion amount and the motion level of the motion target, if the difference is 2 motion levels, executing v, and if not, executing vi;
v. lowering the target by one level;
vi, according to the existing motion states (motion frequency, daily motion consumption and hourly motion state distribution), performing weighted distribution according to the target daily motion consumption and the current daily consumption ratio, and then randomly promoting part of the motion states under the condition of not influencing the daily motion state distribution, wherein for example, the original motion states from 7 points to 8 points are normal walking, and the current motion states are changed into fast walking, and a 1-week motion plan is made in advance according to the mode;
and vii, counting the movement state of completing a week, if the movement target is completed, adjusting the movement state which does not accord with the original plan, and making a movement plan for half a month according to the counting result, otherwise, adjusting the movement state which does not accord with the original plan and continuing making a movement plan for 1 week according to the mode vi.
The application realizes that:
a) evaluating and classifying the motion state based on the short-time footprint data;
b) counting the motion state of a certain time period through the real-time motion state;
c) based on the statistical motion state, a more reasonable motion mode is given.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be able to cover the technical solutions and the inventive concepts of the present invention within the technical scope of the present invention.
Claims (6)
1. A footprint-based athletic condition monitoring system, comprising:
the current motion state evaluation module judges which motion state the current motion belongs to based on the pace;
the motion state counting module is used for counting the time and the switching frequency of each motion state;
the motion mode feedback module is used for judging whether the motion mode has a problem and feeding back the motion mode;
the movement plan arrangement module gives a rationality suggestion for the movement mode;
the current motion state evaluation module specifically comprises:
a) classifying the motion state;
b) according to the energy consumed by the activity item and the motion state, quantitatively stipulating the motion state;
R＝E/Eavg
wherein R is the amount of movement of a certain movement state, E is the energy consumed by the movement per hour, E is the amount of energy consumed by the movement per houravgCollecting energy consumed by the object in each hour when the object normally walks;
c) determining a motion state based on the pace;
the method for judging the motion state based on the pace speed comprises the following steps:
A. firstly, acquiring footprint data of the left foot or the right foot at intervals of delta t seconds, reducing the dimension of two-dimensional data into one-dimensional data according to a column limit principle, and defining the data set as a data set P ═ { P ═ P1,p2,...,pn}；
B. And then calculating a derivative set of the data set P according to the acquisition time sequence, wherein P' ═ P2-p1,p3-p2,...,pn-pn-1Taking P' as the motion state at the time interval;
C. let the homomorphic derivative statistics be PmWhen no status is recorded, Pm＝{}，PmP', perform a; otherwise, Pm＝PmUsing a normalized correlation function to evaluate the correlation of the current derivative with the derivative set, if the maximum value of the absolute value of the correlation is greater than delta, considering that the state is repeated, and executing D1, otherwise executing D2;
d, D.D1: calculating the repeated time t of the state according to the length of the derivative, and using the repeated time t to evaluate the pace speed, wherein the pace speed is 1/t, the pace speed is fed back, the derivative set is emptied, and the information acquisition of the next motion state is carried out;
d2: combining the two derivatives, judging the length of the set, assuming that the length of the set exceeds the specified length, and considering the pace to be 0, emptying the derivative set, and acquiring and evaluating the next data;
E. performing an estimation of the motion state once per determination of the pace;
F. if the footprint information is related to pressure and the pace is 0, representing the pressure state of a certain time period by calculating the mean value of D1;
the motion state statistical module is specifically as follows:
step 1: preprocessing and denoising the footprint image data;
step 2: counting the time of each motion state;
and step 3: counting the switching frequency: on a given time period basis: acquiring a motion state after being preprocessed in a certain time period, then differentiating the motion state, and carrying out frequency statistics according to different differential values to obtain switching frequency statistics;
and 4, step 4: counting the time probability density of each motion state: and (4) counting the motion states at regular time every day, and taking the time-occupying ratio of each motion state as the state probability in each counting interval.
2. The footprint-based exercise state monitoring system according to claim 1, wherein the exercise mode feedback module is specifically:
i. coordination and symmetry of the feedback motion;
evaluating the degree of exercise and judging the reasonableness;
assessment of impairment of the locomotor pattern to the body.
3. The system for monitoring the motion state based on the footprint according to claim 2, wherein the coordination and symmetry of the feedback motion are specifically:
A. under the premise of simultaneously providing footmark data of left and right feet, carrying out mirror image transformation on data vectors in any direction;
B. calculating data vectors of the left foot and the right foot in the same state in two directions by using a mode of calculating a state evaluation set;
C. and calculating the correlation degree of the data vectors in different directions, wherein the larger the correlation is, the better the harmony and the symmetry are, and the worse the harmony and the symmetry are.
4. The system for monitoring the motion state based on the footprint according to claim 2, characterized in that the evaluation of the degree of motion and the judgment of the reasonableness are specifically as follows:
counting the motion states of each day, and multiplying the quantized motion states by the normal walking energy consumption to obtain the energy consumption of each motion state, so as to evaluate the energy consumption of each day, divide the energy consumption into different grades and further evaluate whether the motion is reasonable;
the switching frequency of each motion state is counted, the switching frequency of each motion state is too high, the switching frequency is considered to be too frequent, the metabolism is not favorable for stabilization, the more the cross-level motion switching frequency is, the more the motion switching frequency is, the static state is immediately entered after the high-energy motion for a long time, the condition that the muscle soreness is caused due to the negative effect on the relaxation of the motor nerves and organs of the human body is considered.
5. The system for monitoring the motion state based on the footprint as claimed in claim 2, wherein the degree of damage of the motion mode to the body is evaluated by:
the larger the motor coordination is, the lower the damage degree to the body is considered, and the lower the coordination is, the higher the damage degree is, wherein an evaluation model of y ═ aexp (x) + b is constructed, x is the coordination, y is the damage degree, and a and b are constant coefficients;
building an evaluation model of y ═ clog (x) + d, wherein x is a daily exercise state energy consumption statistic value, y is the damage degree, and c and d are constant coefficients;
the longer the overall running time, the greater the degree of damage to the knee, where y is constructed as k/(1+ e)-x+1/24) The average running time per day is 24x, y is the degree of damage, and k is a constant coefficient.
6. The footprint-based athletic condition monitoring system of claim 4, wherein the athletic schedule module: according to the energy consumed by daily exercise, the health, body shaping and athlete taking are divided into three states:
i. on the premise of giving a moving target, carrying out motion state statistics and motion mode feedback on a scheduled object within a period of time;
if the moving object has been reached, performing iii, otherwise performing iv;
iii, sorting according to the existing motion state, and directly making a motion plan with a period of N weeks;
judging the difference between the current motion amount and the motion level of the motion target, if the difference is 2 motion levels, executing v, and if not, executing vi;
v. lowering the target by one level;
vi, according to the existing motion state, performing weighted distribution according to the motion consumption of the target every day and the consumption ratio of the current stage every day, then randomly promoting part of the motion state under the condition of not influencing the motion state distribution every day, and making an N-week motion plan in advance according to the mode;
and vii, counting the motion state of completing N weeks, if the motion target is completed, adjusting the motion state which does not accord with the original plan, and making a motion plan with a longer period according to the counting result, otherwise, adjusting the motion state which does not accord with the original plan and continuing making the motion plan for N weeks according to the mode vi.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104473650A (en) * | 2014-12-25 | 2015-04-01 | 中国科学院合肥物质科学研究院 | Movement energy consumption monitoring shoes based on flexible force sensor and monitoring method of movement energy consumption monitoring shoes |
| CN105760643A (en) * | 2014-12-16 | 2016-07-13 | 中国移动通信集团公司 | Exercise guidance method and terminal equipment |
| CN105962942A (en) * | 2016-04-22 | 2016-09-28 | 北京小米移动软件有限公司 | Motion state determining method and device |
| CN106418899A (en) * | 2015-08-06 | 2017-02-22 | 跑动（厦门）信息科技有限公司 | Method of calculating walking or running power via intelligent shoe pad or intelligent shoe |
| US20170056722A1 (en) * | 2015-08-26 | 2017-03-02 | Google Inc. | Upsampling sensors to auto-detect a fitness activity |
| CN106861166A (en) * | 2017-02-23 | 2017-06-20 | 佛山市量脑科技有限公司 | A kind of sport intellect shoe-pad |
| CN106963388A (en) * | 2017-04-12 | 2017-07-21 | 佛山市量脑科技有限公司 | A kind of reponse system of Intelligent insole |
| CN107157485A (en) * | 2017-06-13 | 2017-09-15 | 西安科技大学 | A kind of intellectual monitoring shoe-pad and its intelligent monitor system |
| US20170278419A1 (en) * | 2013-08-26 | 2017-09-28 | John Andrew Wells | Biometric data gathering |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9005129B2 (en) * | 2012-06-22 | 2015-04-14 | Fitbit, Inc. | Wearable heart rate monitor |
| US11561126B2 (en) * | 2015-11-20 | 2023-01-24 | PhysioWave, Inc. | Scale-based user-physiological heuristic systems |
-
2017
- 2017-09-29 CN CN201710904204.1A patent/CN109567813B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170278419A1 (en) * | 2013-08-26 | 2017-09-28 | John Andrew Wells | Biometric data gathering |
| CN105760643A (en) * | 2014-12-16 | 2016-07-13 | 中国移动通信集团公司 | Exercise guidance method and terminal equipment |
| CN104473650A (en) * | 2014-12-25 | 2015-04-01 | 中国科学院合肥物质科学研究院 | Movement energy consumption monitoring shoes based on flexible force sensor and monitoring method of movement energy consumption monitoring shoes |
| CN106418899A (en) * | 2015-08-06 | 2017-02-22 | 跑动（厦门）信息科技有限公司 | Method of calculating walking or running power via intelligent shoe pad or intelligent shoe |
| US20170056722A1 (en) * | 2015-08-26 | 2017-03-02 | Google Inc. | Upsampling sensors to auto-detect a fitness activity |
| CN105962942A (en) * | 2016-04-22 | 2016-09-28 | 北京小米移动软件有限公司 | Motion state determining method and device |
| CN106861166A (en) * | 2017-02-23 | 2017-06-20 | 佛山市量脑科技有限公司 | A kind of sport intellect shoe-pad |
| CN106963388A (en) * | 2017-04-12 | 2017-07-21 | 佛山市量脑科技有限公司 | A kind of reponse system of Intelligent insole |
| CN107157485A (en) * | 2017-06-13 | 2017-09-15 | 西安科技大学 | A kind of intellectual monitoring shoe-pad and its intelligent monitor system |
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