CN103079011B - Android smart phone-based multifunctional bodybuilder system - Google Patents
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Abstract
The invention relates to an Android smart phone-based multifunctional bodybuilder system, which comprises four modules, i.e. an exercise plan customization module, a real-time exercise monitoring module, an exercise statistical module and a sharing module, wherein the exercise plan customization module provides a user registration function, and customizes a corresponding exercise plan according to the specific situations of a registered user; the real-time exercise monitoring module can identify three exercise modes of deep squat, dumbbell lifting and running, and also can convert the exercise amount and the exercise intensity of the user into consumed energy, and display the consumed energy to the user; the exercise statistical module makes statistics on the previous data of the user every other time when the user takes exercises for a certain period of time, evaluates the exercise condition of the user in the previous period of time, judges whether a scheduled target is reached or not, gives a comprehensive evaluation, and displays a statistical result to the user; and the sharing module is connected to a network in ways of wireless fidelity (WIFI), general packet radio service (GPRS) and the like. A plurality of exercise modes are simulated, so that the problem of no time or no bodybuilding equipment for the exercises of contemporary people is mainly solved.
Description
Technical field
The invention belongs to mobile terminal applied technical field, be specifically related to a kind of multi-gym system based on Android intelligent.
Background technology
At present, the operating system of smart mobile phone is varied, and account for the maximum cell phone system of the market share be open mobile phone alliance (Open Handset Alliance---OHA) headed by Google within 2007, release provide based on increase income mobile phone operating system Android, Android of Linux platform the interface much having much characteristic.Mainly comprise: sensing system (Sensor), speech recognition technology (Recognizer Intent), Google Map and be used for developing the plug-in unit (Widget) of desktop.Built-in a lot of sensor interface in sensing system wherein, current Android phone generally all can a built-in very practical acceleration sensor, there is it, the function such as gravity sensing, walking direction just supported by mobile phone, automatically can change screen display layout in the horizontal screen of recognition screen, perpendicular screen direction in partial game or software.With Android be the intelligent mobile phone terminal platform of representative with its powerful characteristic, constantly expand and deepen the application of intelligent mobile phone terminal.
And in addition on the one hand, with the quickening pace of modern life and the increase of social pressures, in developed country and city, the health of people has become a problem that can not be ignored, according to interrelated data display, in China, the people of 15% is about had to be in health status, the people of 15% is in unhealthy status, and the people of 70% is sub-health state.Particularly IT white collar personage, body kinematics wretched insufficiency, brains is overdrawed, and adds permanent unhealthy emotion impact, causes the state in a healthy and balanced way of large quantities of crowd to be broken, be absorbed in sub-health state.There is no body-building apparatus simultaneously or have no time to go to fitness center to take exercises, therefore, the present invention is based on this and propose a multifunctional body-building software based on Android intelligent, the Colony Design Mobile phone application program that the work that is intended to is busy yet.Allow user after the work leisure, easily according to the health of individual, set the re-set target of oneself, then automatically produce the timetable of exercise plan, use the built-in Gravity accelerometer of smart mobile phone can realize some and simply take exercise, as simulation lift the dumbbell, deep-knee-bend, sit-ups, running etc., the change of gravity and acceleration when perception mobile phone moves in 3 dimension spaces, carry out, to momental statistics, calculating the energy of actual consumption, and then assessing health.Each exercise can be recorded in diary automatically, also achievements synchronously can be delivered microblogging, everybody etc. social network sites, exchange with good friend, what realize between user is mutual.
Summary of the invention
The present invention is directed to modern people due to busy life rhythm, there is no the time more than the spare time or do not have body-building apparatus to take exercises, provide a kind of multi-gym system based on Android phone, achieve some simple exercising waies, such as simulation lifts the dumbbell, deep-knee-bend, running etc.And the change of gravity and acceleration when being moved in 3 dimension spaces by mobile phone, carry out, to momental statistics, calculating the energy of actual consumption, and then assessing health.And record can be carried out to each exercise, share.
Innovative significance of the present invention has lower cost with other body building method comparing, without the need to the body-building equipment that other is valuable, spend one's leisure the object that just can reach and take exercises, and the exercise plan of interesting man-machine interface, hommization, mutual more dull exercise between user add interest, thus reach residence white silk in happy effect, user is allowed to improve health gradually in carefree process, final solution inferior health problem.
Multi-gym system based on Android phone comprises following four modules: (1) exercise plan customized module: the function that this module provides user's registration, and according to a corresponding exercise plan of the concrete condition customization of registered user, and there is prompting function.(2) Real-Time Monitoring module is tempered: this module can identify deep-knee-bend, lifts the dumbbell, three kinds of motion modes of running.The amount of exercise of user and exercise intensity can also be converted to the energy of consumption, and present to user, be recorded in the database of user simultaneously, generate exercise journal.(3) statistical module is tempered: often through exercise after a while, this module can be added up the data before user, the exercise regime of user is is for the previous period evaluated and tested, judge whether reach scheduled target, provide a comprehensive evaluation, and statistics is presented to user, be written in customer data base, accordingly for user revises exercise time table.(4) sharing module: this module is connected to network by modes such as WIFI, GPRS, can by achievements synchronized update recent for user to microblogging, everybody etc. social network sites, exchange experience with good friends.
Exercise Real-Time Monitoring module recited above can identify deep-knee-bend, lifts the dumbbell, three kinds of motion modes of running, and the motion mode that wherein lifts the dumbbell has two kinds of recognition modes: (1) lifts the dumbbell 1: curved act of crooking one's arm, is held in the palm of the hand by mobile phone, and main screen outwardly.Arm naturally droops, and before handheld mobile phone bends up to shoulder, upper arm is tried not mobile, then puts down gently, then constantly repeats above-mentioned action.In time carrying out dumbbell pattern 1, what amplitude was larger is Z axis, X-axis and Y-axis then relative amplitude less (mobile phone is put in horizontal table top, vertical with screen orientation with desktop level is X-axis, and consistent with screen orientation is Y-axis, with vertical be Z axis).So using Z axis as monitoring axle, vector, as auxiliary judgment, arranges two threshold values: high threshold is set to 15m/s2, threshold ones is set to-5m/s2.Do not stop to judge its data, if break through high threshold a period of time to fall suddenly ground threshold value instantaneously and maintain a period of time once, then using this motion as valid data, count value increases.(2) 2 are lifted the dumbbell: horizontal side is lifted, and mobile phone is held in the palm of the hand, and main screen outwardly.Arm naturally droops, handheld mobile phone by arm side lift to shoulder level, palm down, then puts down gently, then constantly repeats above-mentioned action.In time carrying out dumbbell pattern 2, X-axis and Z axis relatively have feature, when X-axis peaks, Z axis touches the bottom simultaneously, according to this feature, using X-axis as monitoring axle, Z axis helps auxiliary judgment, and arranging high threshold is 15m/s2, and threshold ones is 4.5m/s2.The algorithm the same with pattern 1 is used to identify.
Deep-knee-bend motion mode has a kind of recognition mode: (1) deep-knee-bend pattern: mobile phone is put into pocket or hand held, pauses preferably no more than one second, then stand up when squatting down, and realizes once counting.During owing to carrying out deep-knee-bend, the direction of mobile phone is uncertain, so just can not with any axle as monitoring axle, thus can only use the synthesis of three axles and vector (
) judge, still use identical algorithm, the high-low threshold value of vector is set to 12m/s2,8m/s2 respectively.If vector breaks through high threshold, fall ground threshold value after a period of time and maintain a period of time once, then using this motion as valid data, count value increases.
Road-work mode has a kind of recognition mode: (1) running modes: empocketed by mobile phone, then starts to run.Have employed a kind of new algorithm here: severe degree and the distance of being inferred running by the amplitude of its vector and frequency.When running according to acceleration estimation, the formula of the distance of motion per second is as follows:
distance?=（aixs_amp?*?step_length?*?count）/?aixs_scale
Wherein, distance is distance of running per second, unit m.Aixs_amp is acceleration a=
maximum in this 1 second deducts minimum value, i.e. the amplitude of acceleration, unit m/s2.Step_length is the step-length of user, unit cm.Count refers to that in one second, acceleration a changes to the number of times of minimum from peak, can be understood as per second in run to obtain step number.Aixs_scale is a proportionality coefficient, and value can be 0.03-0.05.According to formula above, just a timer can be set in a program, call the function once calculating distance each second, constantly add up, just can the real-time display distance of running.Detailed process is as follows: select interface to select motor pattern in exercise.After setting in motion, open the Gravity accelerometer of embedded in mobile phone, constantly read data, call motion recognition function, if meet this pattern, counter increases, until user arrives amount of exercise or manually presses end movement buttons.
Beneficial effect of the present invention:
The multi-gym system based on Android phone that the present invention proposes is by solving emphatically to the simulation of multiple exercising way the problem that contemporary people has no time or body-building apparatus is taken exercises.The present invention take smart mobile phone as platform development application program, not only can make full use of the powerful computing capability of mobile phone self and various software and hardware resources, provide intelligent, hommization application, development and production cost can also be reduced, expedite product go on the market.User only needs to download and install this application program, can not produce any electronic waste.The wasting of resources and environmental pollution can not be caused.The invention provides good operating experience, advocate green, environmental protection life idea, there are certain economical, societal benefits.
Accompanying drawing explanation
Fig. 1 is this system construction drawing;
The flow chart of Fig. 2 motion mode recognizer 1;
The flow chart of Fig. 3 motion mode recognizer 2.
Embodiment
Below in conjunction with accompanying drawing, this system is described.As shown in Figure 1, this system mainly contains following module:
(1) exercise plan customized module: when user registers, require that user answers some simple questions, the general level of the health of user is analyzed from problem, again according to the data inputted before user (comprising height and weight, work and rest custom, health etc.), automatically be the targeted exercise plan of a science of customization, generate exercise time table, and have exercise quarter-bell and temper prompting function.
(2) temper Real-Time Monitoring module: the multiple preset motor pattern of system customization: comprise lift the dumbbell, run, deep-knee-bend etc. is multiple.When often kind of motion mode runs, carry out dynamic sampling to the numerical value of Gravity accelerometer, to data analysis, if meet this motor pattern, this time action is effectively, counts amount of exercise.Motion each time terminates to analyze this motion afterwards.According to certain algorithm, amount of exercise and exercise intensity are converted into the energy of consumption, in units of calorie, present to user, and be recorded in the database of user, generate exercise journal.
(3) statistical module is tempered: often through exercise after a while, system can be added up the data before user, to exercise for the previous period evaluate and test, judge whether reach scheduled target, to exercise during this period of time to an overall merit, with the interesting interface (as utilized the animation of various vivid and interesting to show the state of active user) of close friend, rationally user presented to by chart (various exercise statistical form, block diagram) intuitively, be written in customer data base, and user modifies to exercise time table targetedly.
(4) sharing module: system is connected to network by modes such as WIFI, GPRS, can by achievements synchronized update recent for user to microblogging, everybody etc. social network sites, exchange experience with good friends.
Exercise Real-Time Monitoring module recited above can identify deep-knee-bend, lifts the dumbbell, three kinds of motion modes of running, and the motion mode that wherein lifts the dumbbell has two kinds of recognition modes: (1) lifts the dumbbell pattern 1: curved act of crooking one's arm, is held in the palm of the hand by mobile phone, and main screen outwardly.Arm naturally droops, and before handheld mobile phone bends up to shoulder, upper arm is tried not mobile, then puts down gently, then constantly repeats above-mentioned action.In time carrying out dumbbell pattern 1, what amplitude was larger is Z axis, X-axis and Y-axis then relative amplitude less (mobile phone is put in horizontal table top, vertical with screen orientation with desktop level is X-axis, and consistent with screen orientation is Y-axis, with vertical be Z axis).So using Z axis as monitoring axle, vector, as auxiliary judgment, arranges two threshold values: high threshold is set to 15m/s2, threshold ones is set to-5m/s2.Do not stop to judge its data, if break through high threshold a period of time to fall suddenly ground threshold value instantaneously and maintain a period of time once, then using this motion as valid data, count value increases, and its algorithm flow chart is as shown in Figure 2.(2) lift the dumbbell pattern 2: horizontal side is lifted, and mobile phone is held in the palm of the hand, and main screen outwardly.Arm naturally droops, handheld mobile phone by arm side lift to shoulder level, palm down, then puts down gently, then constantly repeats above-mentioned action.In time carrying out dumbbell pattern 2, X-axis and Z axis relatively have feature, when X-axis peaks, Z axis touches the bottom simultaneously, according to this feature, using X-axis as monitoring axle, Z axis helps auxiliary judgment, and arranging high threshold is 15m/s2, and threshold ones is 4.5m/s2.Use the algorithm the same with pattern 1 to identify, flow chart as shown in Figure 2.
Deep-knee-bend motion mode has a kind of recognition mode: (1) deep-knee-bend pattern: mobile phone is put into pocket or hand held, pauses preferably no more than one second, then stand up when squatting down, and realizes once counting.During owing to carrying out deep-knee-bend, the direction of mobile phone is uncertain, so just can not with any axle as monitoring axle, thus can only use the synthesis of three axles and vector (
) judge, still use identical algorithm, the high-low threshold value of vector as shown in Figure 2, is set to 12m/s2,8m/s2 by algorithm flow chart respectively.If vector breaks through high threshold, fall ground threshold value after a period of time and maintain a period of time once, then using this motion as valid data, count value increases.
Road-work mode has a kind of recognition mode: (1) running modes: empocketed by mobile phone, then starts to run.Have employed a kind of new algorithm here: severe degree and the distance of being inferred running by the amplitude of its vector and frequency, algorithm flow chart as shown in Figure 3.When running according to acceleration estimation, the formula of the distance of motion per second is as follows:
distance?=（aixs_amp?*?step_length?*?count）/?aixs_scale
Wherein, distance is distance of running per second, unit m.Aixs_amp is acceleration a=
maximum in this 1 second deducts minimum value, i.e. the amplitude of acceleration, unit m/s2.Step_length is the step-length of user, unit cm.Count refers to that in one second, acceleration a changes to the number of times of minimum from peak, can be understood as per second in run to obtain step number.Aixs_scale is a proportionality coefficient, and value can be 0.03-0.05.According to formula above, just a timer can be set in a program, call the function once calculating distance each second, constantly add up, just can the real-time display distance of running.Detailed process is as follows: select interface to select motor pattern in exercise.After setting in motion, open the Gravity accelerometer of embedded in mobile phone, constantly read data, call motion recognition function, if meet this pattern, counter increases, until user arrives amount of exercise or manually presses end movement buttons.
Claims (1)
1. based on the multi-gym system of Android intelligent, it is characterized in that this system comprises following four modules: (1) exercise plan customized module: the function that this module provides user's registration, and according to a corresponding exercise plan of the concrete condition customization of registered user, and there is prompting function; (2) Real-Time Monitoring module is tempered: this module can identify deep-knee-bend, lifts the dumbbell, three kinds of motion modes of running; The amount of exercise of user and exercise intensity can also be converted to the energy of consumption, and present to user, be recorded in the database of user simultaneously, generate exercise journal; (3) statistical module is tempered: often through exercise after a while, this module can be added up the data before user, the exercise regime of user is is for the previous period evaluated and tested, judge whether reach scheduled target, provide a comprehensive evaluation, and statistics is presented to user, be written in customer data base, accordingly for user revises exercise time table; (4) sharing module: this module is connected to network by WIFI, GPRS mode, can by achievements synchronized update recent for user to microblogging, everybody etc. social network sites, exchange experience with good friends;
The described motion mode that lifts the dumbbell has two kinds of recognition modes: (1) first lifts the dumbbell pattern: curved act of crooking one's arm, is held in the palm of the hand by mobile phone, and main screen outwardly; Arm naturally droops, and before handheld mobile phone bends up to shoulder, upper arm is tried not mobile, then puts down gently, then constantly repeats above-mentioned action; When carry out first lift the dumbbell pattern time, what amplitude was larger is Z axis, and X-axis and Y-axis then relative amplitude are less; Using Z axis as monitoring axle, vector, as auxiliary judgment, arranges two threshold values: high threshold is set to 15m/s
2, threshold ones is set to-5m/s
2; Do not stop to judge its data, drop to below threshold ones instantaneously suddenly if break through high threshold a period of time and maintain a period of time, then moving this as valid data, count value increases; (2) second lift the dumbbell pattern: horizontal side is lifted, and mobile phone is held in the palm of the hand, and main screen outwardly; Arm naturally droops, handheld mobile phone by arm side lift to shoulder level, palm down, then puts down gently, then constantly repeats above-mentioned action; When carry out second lift the dumbbell pattern time, when X-axis peaks, Z axis touches the bottom simultaneously, according to this feature, using X-axis as monitoring axle, Z axis help auxiliary judgment, arranging high threshold is 15m/s
2, threshold ones is 4.5m/s
2; To use and the first the same algorithm of pattern that lifts the dumbbell identifies;
Deep-knee-bend motion mode has a kind of recognition mode: mobile phone is put into pocket or hand held, and pausing when squatting down is no more than one second, then stands up, and realizes once counting; Use the synthesis of three axles and vector (
) judge, the high-low threshold value of vector is set to 12m/s respectively
2, 8m/s
2; If vector break through high threshold, fall after a period of time ground threshold value below and maintain a period of time, then using this motion as valid data, count value increase;
Road-work mode has a kind of recognition mode: empocketed by mobile phone, then starts to run; Have employed a kind of new algorithm here: severe degree and the distance of being inferred running by the amplitude of its vector and frequency, range formula is wherein as follows:
distance?=（aixs_amp?*?step_length?*?count）/?aixs_scale
Wherein, distance is distance of running per second, unit m; Aixs_amp is acceleration a=
maximum in this 1 second deducts minimum value, i.e. the amplitude of acceleration, unit m/s
2; Step_length is the step-length of user, unit cm; Count refers to that in one second, acceleration a changes to the number of times of minimum from peak, is interpreted as the step number of middle race per second; Aixs_scale is a proportionality coefficient, and value is 0.03-0.05.
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| CN103386181B (en) * | 2013-07-25 | 2016-06-15 | Tcl通讯（宁波）有限公司 | A kind of rope skipping method of counting of mobile terminal and this terminal |
| CN104144201A (en) * | 2013-12-09 | 2014-11-12 | 腾讯科技（深圳）有限公司 | Sport monitoring method, device and system |
| CN105491213B (en) * | 2014-09-17 | 2020-07-31 | 南京中兴软件有限责任公司 | Intelligent terminal-based travel service method and device |
| CN106156462A (en) * | 2015-04-01 | 2016-11-23 | 时云医疗科技（上海）有限公司 | A kind of behavioural information method for pushing based on sign data and system |
| CN105487656B (en) * | 2015-11-24 | 2018-09-18 | 小米科技有限责任公司 | Exercise data recording method and device |
| WO2017214926A1 (en) * | 2016-06-16 | 2017-12-21 | 深圳市屹石科技股份有限公司 | Execution device and method for fitness program visualization |
| CN106594797A (en) * | 2017-01-24 | 2017-04-26 | 青岛工学院 | Intelligent electronic smoking quitting lighter for assisting in smoking quitting and use method |
| CN107213595A (en) * | 2017-06-29 | 2017-09-29 | 沈建伟 | Belt type deep-squatting pulling-force exerciser |
| CN107213596A (en) * | 2017-06-29 | 2017-09-29 | 沈建伟 | Backpack deep-squatting pulling-force exerciser material |
| WO2019009084A1 (en) * | 2017-07-05 | 2019-01-10 | ソニー株式会社 | Information processing device, information processing method, and program |
| CN111450483A (en) * | 2020-04-01 | 2020-07-28 | 随机漫步(上海)体育科技有限公司 | Method for assisting bicycle training, readable storage medium and electronic device |
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