CN110993058A - Motion guiding method and mobile terminal using same - Google Patents
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Abstract
The invention relates to the field of data processing, provides a motion guidance method and a mobile terminal using the same, and can provide a safer and more scientific motion guidance scheme for a sporter. The method comprises the following steps: determining a first athletic guidance program for the athlete prior to the athlete initiating a current exercise based on the athlete's daily athletic and physiological vital data; prompting the first exercise guidance program to the exerciser; when the sporter starts current sports, current sports data of the sporter are obtained from the wearable equipment, wherein the current sports data comprise sports parameters and physiological parameters of the sporter in the sports process; determining a second motion guidance program for the athlete based on the current motion data; prompting the second athletic guidance regime to the athlete. Compared with the prior art that the exercise guidance scheme can be provided only after the exercise starts, the technical scheme of the invention provides a safer and more scientific guidance scheme for the exerciser, and avoids the injury of the exercise to the maximum extent.
Description
Technical Field
The invention belongs to the field of data processing, and particularly relates to a motion guidance method and a mobile terminal using the same.
Background
With the development and progress of the times and the national emphasis on the health of the whole people, more and more people start to participate in exercise. Wearable devices (such as sports wristwatches) are increasingly popular among sports enthusiasts as miniature electronic devices that can be worn on a user to go out and do activities, and can record the motion data of daily motions of the user (such as motion trail, motion heart rate, motion pace, stride frequency, and the like). However, the form of the sports wristwatch is limited to the characteristics and requirements of miniaturization, light weight, low power consumption and the like, and the sports wristwatch has very limited processing capability and prompting (displaying) capability, so that the sports wristwatch cannot complete more complex data processing, analysis and corresponding prompting functions.
In view of the fact that sporters pay attention to various sports data in the process of sports and want to obtain scientific guidance more and more to achieve the purposes of scientific exercise, safe exercise and effective exercise, technical schemes for guiding the sporters to conduct scientific exercise and safe exercise in the process of sports after the sports data are transmitted to terminals such as mobile phone APP (application) from a sports wristwatch and the like and processed by the terminals have appeared at present.
However, the data of various physiological parameters are changed once the exerciser moves, and the physiological parameters are more greatly changed as the intensity of the exercise is continuously increased. For some athletes with chronic illness or who are not suitable for exercise at the time, drastic changes in physiological parameters can cause physical harm and, in severe cases, can also lead to disability. Therefore, the above-mentioned prior art solutions for guiding the exerciser to exercise still have the disadvantage of being unsafe.
Disclosure of Invention
In view of this, there is a need for providing a motion guidance method and a mobile terminal using the same, which can provide a safer and more scientific motion guidance scheme for an exerciser.
A motion guidance method is applied to a mobile terminal, and comprises the following steps:
determining a first athletic guidance program for an athlete prior to the athlete initiating a current workout based on the athlete's daily athletic and physiological vital data;
prompting the first athletic guidance program to the athlete;
when the athlete starts the current exercise, obtaining current exercise data of the athlete from a wearable device, wherein the current exercise data comprises exercise parameters and physiological parameters of the athlete in the process of exercise;
determining a second motion guidance program for the athlete based on the current motion data;
prompting the second athletic guidance regime to the athlete.
In a further embodiment, the determining a first athletic guidance program for the athlete prior to the athlete initiating a current athletic maneuver based on the athlete's daily athletic and physiological vital data comprises:
extracting daily exercise and physiological big data of the athlete from a database before the athlete starts current exercise;
inputting additional factors and daily movement and physiological big data of the sporter into a movement monitoring model subordinate to the sporter, and generating the first movement guidance scheme by the movement monitoring model, wherein the additional factors comprise current external environment data and recent pathological data of the sporter.
In a further embodiment, the second exercise guidance program is determined based on the obtained current exercise data, personal basic data, and preset standard data, wherein the personal basic data includes one or more of gender, age, height, weight, and health condition.
In a further embodiment, the preset standard data includes one or more of sports item, sports time, sports intensity and sports frequency, and the method further includes: and storing the acquired current motion data to a storage unit, and correcting the preset standard data according to the stage user motion data stored in the storage unit.
In a further embodiment, the first motion guidance program is prompted to the sporter by one or more of sound, text and image, and the second motion guidance program is prompted to the sporter by one or more of sound, display and vibration.
In a further embodiment, said obtaining current motion data of said athlete from a wearable device comprises: establishing a communication connection with the wearable device; acquiring the motion data from the wearable device at predetermined intervals.
In a further embodiment, the exercise guidance method further comprises: and when the acquired current exercise data meet a preset condition, broadcasting preset encouragement voice.
In a further embodiment, the second exercise guidance program comprises prompting the user to increase the exercise parameter when the physiological parameter is below a preset value, and prompting the user to decrease the exercise parameter when the physiological parameter reaches a preset value.
In a further embodiment, the second motion guidance program comprises guidance suggestions for the user's motion frequency, and when the user's motion frequency reaches a preset value, the user is prompted not to exceed the preset motion frequency.
A mobile terminal comprising a communication unit for establishing a communication connection with a wearable device, a storage unit storing a computer program executable by the processing unit for implementing the steps of the method as described above, and a processing unit.
According to the technical scheme, on one hand, the motion guidance scheme is determined for the sporter before the sporter starts to move currently, and the sporter is prompted, so that the motion risk is prompted in advance, and the risk possibly generated when the sporter does not move properly but does not move forcibly is avoided; on the other hand, after the sporter starts to exercise, the exercise guidance scheme is determined for the sporter again based on the real-time exercise data acquired from the wearable device, and risks in the exercise process are avoided.
Drawings
FIG. 1 is a flow chart of a motion guidance method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of personal basic data for an athletic guidance method according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating determination criteria data provided by an embodiment of the present invention;
FIG. 4 is a diagram of standard data for one embodiment of the present invention;
fig. 5 is a schematic structural diagram of a mobile terminal according to an embodiment of the present invention;
the following detailed description will further illustrate the invention in conjunction with the above-described figures.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the system embodiments described below are merely illustrative, and the division of the modules or circuits is only one logical function division, and other division manners may be available in actual implementation. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. Several units or means recited in the system claims may also be implemented by one and the same unit or means in software or hardware. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
In order that the above objects, features and advantages of the present invention can be more clearly understood, a detailed description of the present invention will be given below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention, and the described embodiments are merely a subset of the embodiments of the present invention, rather than a complete embodiment. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
The embodiment of the invention provides a motion guidance method based on wearable devices such as a motion wristwatch and the like and a mobile terminal, which can provide a scientific motion guidance scheme for a sporter according to personal motion capability and motion level based on daily motion and physiological big data of the sporter and real-time motion data of the sporter, wherein the motion guidance scheme comprises suggestions of motion items, motion time, motion intensity and motion frequency. The exercise guidance scheme can comprise analysis and suggestion of single exercise and staged exercise, functions of virtual exercise coaches/partners are achieved, scientificity and safety of exercise are improved, interestingness of exercise is improved, and good habits of persisting exercise and exercise are developed for athletes.
The wearable device related to the embodiment of the invention can be a wearable article capable of recording the motion data of a wearer, including, but not limited to, a sports watch, a bracelet, a hanging chain, glasses and the like. The wearable device includes motion sensors capable of sensing wearer motion parameters, such as: the Inertial sensor comprises an acceleration sensor and an angular velocity sensor, and a single-axis, double-axis and three-axis combined Inertial Measurement Unit (IMU) and an Attitude and Heading Reference System (AHRS) of the two sensors, wherein the acceleration sensor is preferably a Micro-Electro-Mechanical System (MEMS) accelerometer, is a sensor for measuring Inertial force by using sensing mass, and generally comprises a standard mass block (sensing element) and a detection circuit, and the IMU mainly comprises three MEMS accelerometers, three gyros and a resolving circuit. In some embodiments, the motion sensor further comprises a Global Positioning System (GPS) sensor for sensing environmental location information such as height, longitude and latitude, orientation, etc. where the wearer is located. The motion parameters include, but are not limited to, pace, distance, altitude, motion trajectory, etc. In some embodiments, the motion sensor further comprises a physiological sensor capable of sensing a physiological parameter of the wearer including, but not limited to, heart rate, body temperature, respiration, blood oxygen concentration, electrocardiogram, and the like. For convenience of description, in the following embodiments, a sports watch is exemplified.
The mobile terminal related to the embodiment of the invention can be a portable terminal device such as a tablet personal computer, a smart phone and a palm computer.
Fig. 1 is a flowchart of an exercise guidance method according to a first embodiment of the present invention. The motion guidance method may be applied to a mobile terminal. The exercise guidance method mainly includes the following steps S101 to S105:
step S101: based on the daily athletic and physiological vital data of the athlete, a first athletic guidance program is determined for the athlete prior to the athlete initiating a current exercise.
In the embodiment of the present invention, the physiological big data of the athlete includes a huge amount of basic data of the athlete's heart rate, blood pressure, blood oxygen, sleep quality, body temperature, lung capacity, flexibility, and muscle strength, etc. monitored by the wearable device over a long period of time, and analysis results obtained by combining some intelligent algorithms (e.g., various algorithms for deep learning), etc. based on the huge amount of basic data, the daily exercise big data of the athlete includes the exercise frequency, the exercise amount, the exercise intensity, the exercise items, and the training plans of the athlete over a long period of time, and similar to the physiological big data of the athlete, the daily exercise big data of the athlete obviously also includes the analysis results of the daily exercise data of the athlete.
It should be noted that, when data analysis, especially big data analysis is performed, high-quality data is beneficial to improving the accuracy of an analysis result, so in the embodiment of the present invention, when daily movement and physiological data of an athlete are collected through a wearable device and transmitted to a mobile terminal such as a smart phone, and before the daily movement and physiological data of the athlete are finally stored locally or in a cloud server, the daily movement and physiological data of the athlete can be preprocessed, which mainly includes cleaning the data and converting the data with low quality into data meeting the data quality requirement. Specifically, the assignment to the empty packet may be to assign a value to the empty packet, remove a spurious value and a logical check, where the assignment to the empty packet mainly considers that when data is transmitted from the wearable device to the mobile terminal, packet dropping is likely to occur, and for this problem, the assignment to the empty packet is mainly to take an average value, a middle value or an adjacent interpolation of the data over a period of time, and the removal of the spurious value means that, considering that as a normal person, the value of the motion and physiological data has a reasonable value range, that is, a threshold value, and therefore, the data that exceeds the normal range should be deleted or corrected by checking whether the data is satisfactory, and the logical check means that the logically incorrect, unreasonable or contradictory motion and physiological are deleted or corrected by setting a mutual constraint and dependency relationship of the motion and physiological data used by the mobile terminal.
For the high efficiency of subsequent analysis of data, the data after preprocessing can be further characterized after the daily movement and physiological data of the sporter are preprocessed, and the data finally stored in a local or cloud server is the data after characterization. As an embodiment of the present invention, determining a first exercise guidance program for an athlete before the athlete starts current exercise based on the athlete' S daily exercise and physiological big data may be implemented by the following steps S1011 and S1012:
step S1011: before the athlete starts the current exercise, the athlete's daily exercise and physiological vital data are extracted from the database.
Daily movement and physiological big data of the sporter are transmitted to mobile terminals such as smart phones by equipment for monitoring the data, for example wearable equipment, and then are uploaded to a cloud server by the mobile terminals, and the cloud server stores the data in a database by personal identification of the sporter. Before the sporter starts current movement, the mobile terminal sends a request to the cloud server with the personal identification of the sporter as main information. The cloud server takes the personal identification of the sporter as an index, searches the daily movement and physiological big data of the sporter from the database and then transmits the daily movement and physiological big data back to the mobile terminal. Certainly, if the storage capacity of the mobile terminal is large enough, the daily exercise and physiological big data of the exerciser can also be stored in the local database of the mobile terminal, and only local extraction is performed when extraction is needed, so that the requirement on smooth network when a request is sent to the cloud server and the big data are obtained can be avoided.
Step S1012: and inputting additional factors and daily movement and physiological big data of the sporter into a movement monitoring model subordinate to the sporter, and generating a first movement guidance scheme by the movement monitoring model, wherein the additional factors comprise current external environment data, recent pathological data of the sporter and the like.
In the embodiment of the present invention, the motion monitoring model may be a model obtained through Machine Learning (ML), and the model takes additional factors and the daily motion and physiological data of the athlete as input and takes an optimal motion guidance scheme as output, wherein the ML algorithm includes a decision tree algorithm, a naive bayes algorithm, a support vector Machine algorithm, a random forest algorithm, a Boosting and Bagging algorithm, an association rule algorithm, and the like. Because Deep Learning (DL) solves many complex pattern recognition problems in the fields of search, data mining, recommendation, personalization and the like, a machine can imitate human activities such as audio-visual and thinking to the maximum extent, and therefore, in the embodiment of the invention, ML can be DL; when the network judgment is wrong, the artificial neural network algorithm can reduce the possibility of making the same mistake by learning, has strong generalization capability and nonlinear mapping capability, can perform model processing on a system with small information amount, and has the characteristics of high parallelism and high information transmission speed from the view point of functional simulation, so that in the embodiment of the invention, the ML algorithm can also be an artificial neural network algorithm; alternatively, deep learning and artificial neural network algorithms are combined. As for the specific process of obtaining the motion monitoring model, a complex data processing network (i.e., a preliminary motion monitoring model) including multiple levels is assumed, and a Loss Function (Loss Function) which is a standard for measuring whether the data processing network is good or not is determined according to actual needs, such as euclidean distance, cross entropy cost Function, and the like; then, the additional factors and the acquired massive daily movement and physiological data of a certain sporter are used as a training set to be input into a data processing network for training, whether the output result meets the requirements after the data processing network is processed is checked, for example, the value of a loss function is minimum, and the like; if the output result still does not meet the requirement after passing through the data processing network, optimizing the current data processing network, such as adjusting the parameter setting of the data processing network, until the output result after being processed by the data processing network meets the requirement, that is, the data processing network corresponding to the meeting requirement is used as the final motion monitoring model of the sporter.
It should be noted that, considering that some external environmental factors (e.g., weather, air index, etc.) may affect the movement of the sporter, the sporter is ill recently and sometimes unsuitable for the movement even if recovered, and the harm caused by the movement due to the disadvantage of these additional factors is often easily overlooked by the sporter. Therefore, unlike the common data model building process which rarely considers the additional factors, the invention also takes the additional factors as training set data when training the motion monitoring model. In this way, when the first exercise guidance scheme is generated, the additional factors, the daily exercise and physiological data of the exerciser are input into the exercise monitoring model belonging to the exerciser, and the result output by the exercise monitoring model is ideal.
Step S102: the first athletic guidance program is prompted to the athlete.
In the embodiment of the invention, the first exercise guidance scheme can prompt the sporter in a picture, text and/or sound mode, and comprises the steps of giving a conclusion of unsuitable exercise when physiological parameters are analyzed to be unsuitable to exercise and showing related reason analysis to remind the sporter of exercise safety; when suitable for sports, the athlete is reminded of some cautions, including specific sports parameters, such as sports time, sports pace, sports heart rate interval recommendations, and the like.
Step S103: when the athlete starts to exercise, obtaining current exercise data of the athlete from the wearable device, wherein the current exercise data comprises exercise parameters and physiological parameters of the athlete during the exercise.
In the embodiment of the present invention, the exercise parameters of the athlete during the exercise process include, but are not limited to, pace/speed, exercise time, exercise step frequency, exercise stride, distance, height, exercise trajectory, etc., and the physiological parameters of the athlete during the exercise process include, but are not limited to, heart rate, body temperature, respiration, blood oxygen concentration, electrocardiogram, etc.
In some embodiments, at least some of the recorded motion data is displayed in real-time on a display unit of the wearable device, such as pace, heart rate, altitude, etc. motion data.
Each recorded movement data is stored in a memory unit of the wearable device. The stored athletic data may include a time identifier, and/or an activity type identifier, etc.
Specifically, in one embodiment, the steps may include: establishing a communication connection with a wearable device; upon establishment of a communication connection with the wearable device, current motion data is obtained from the wearable device. The communication connection can be Bluetooth connection, and when the mobile terminal is successfully paired with the wearable device, the mobile terminal automatically acquires motion data stored in the wearable device from the wearable device; in other embodiments, the communication connection is a wired connection, and when the mobile terminal and the wearable device are connected by the communication connection line, the mobile terminal establishes a communication connection with the wearable device, and the mobile terminal automatically acquires current motion data stored by the wearable device from the wearable device. In some embodiments, the wearable device automatically transmits current motion data to the mobile terminal upon establishment of a communication connection between the mobile terminal and the wearable device.
In order to acquire real-time motion data, the mobile terminal may be configured to automatically acquire the real-time motion data from the wearable device every preset time period, wherein the preset time period may be 2 minutes, 3 minutes, 4 minutes, 5 minutes, 6 minutes, 7 minutes, 8 minutes, 9 minutes, 10 minutes, and the like. In some embodiments, the preset duration may allow the athlete to set his or her preferences.
Step S104: a second motion guidance program is determined for the athlete based on the athlete's current motion data.
In some embodiments, the second exercise guidance program is determined based on the obtained current exercise data, personal basic data, and preset standard data, wherein the personal basic data includes one or more of gender, age, height, weight, and health condition. Please refer to fig. 2, which is a personal basic data table according to an embodiment of the present invention. The mobile terminal may be configured to allow the user to select to enter his personal basic data. In some embodiments, the mobile terminal may store personal essential data of a plurality of users, the acquired current motion data may include a user identification, and the mobile terminal may be configured to read the corresponding personal essential data stored in the memory according to the user identification in the acquired motion data. In some embodiments, the mobile terminal may access the corresponding personal basic data in the memory according to the personal information input by the user when the motion guidance method is started. In some embodiments, the mobile terminal allows the user to input personal basic data for setting the user when the motion guidance method is initiated. The personal basic data stored in the memory may also be updated based on user input.
The obtained current sports data may further comprise a sports type identification, wherein the sports type identification is used for distinguishing sports categories, the sports categories comprising mid-distance running and/or marathon running and/or indoor and outdoor sprinting and/or vigorous walking and/or swimming and/or mountain climbing and/or cycling and/or cross country running and/or hiking and/or cross country and/or other more sports types. And the second motion guidance scheme determines a motion guidance suggestion corresponding to the current motion type according to the motion type identification. The preset standard data may be previously stored in the memory of the mobile terminal. The preset standard data comprises the corresponding relation between the motion parameters (one or more of motion items, motion time, motion intensity and motion frequency) and the physiological parameters and the personal basic parameters. The second exercise guidance plan is determined according to the corresponding relation among the personal basic data, the real-time exercise data and the standard data.
The preset standard data can be determined based on exercise data learning training obtained by big data analysis. In some embodiments, the preset standard data may be continuously updated according to the acquired current motion data. For example, in some embodiments, the motion data acquired by the mobile terminal from the wearable device may be stored in the memory of the mobile terminal each time it is moved. When accumulating the motion data of the preset time period, the mobile terminal may be configured to learn to correct the preset standard data according to the motion data of the preset time period. The preset time period may be one month, one quarter, one half year, one year, etc. In some embodiments, the mobile terminal may be configured to provide user-selected autonomously-initiated-modification-criteria data, and when the user autonomously initiates modification-criteria data (e.g., triggers a button characterizing "modification-criteria data"), the mobile terminal modifies the criteria data stored in the memory according to a preset algorithm.
In some embodiments, the exercise guidance plan may also be obtained by performing analysis calculation based on the acquired exercise data and the personal basic data based on a preset algorithm. The preset algorithm can be obtained by performing statistical fitting modeling on the motion data acquired by the big data. Similarly, the preset algorithm can be continuously modified and updated according to the acquired motion data. For example, in some embodiments, the motion data acquired by the mobile terminal from the wearable device may be stored in the memory of the mobile terminal each time it is moved. When accumulating the motion data for the preset time period, the mobile terminal may be configured to learn to correct the algorithm according to the motion data for the preset time period. The preset time period may be one month, one quarter, one half year, one year, etc. In some embodiments, the mobile terminal may be configured to provide for user selection of an autonomous initiation to modify the algorithm, and when the user autonomously initiates modification of the algorithm (e.g., triggering a button characterizing "modify criteria data"), the mobile terminal modifies the criteria data stored in the memory according to a predetermined algorithm.
Fig. 3 is a schematic diagram illustrating an embodiment of determining standard data according to a preset algorithm. The user type is determined based on personal basic data such as the user's gender, age, height, weight, user health, and other factors including, but not limited to, age of the sport and goals of the sport, and then the criteria data is determined based on the user type. And if the user has diseases, determining the exercise contraindication of the user according to the diseases of the user. The standard data comprises sport items, sport time, sport intensity, sport frequency and sport contraindications. And accumulating and storing the motion data of the user to obtain stage motion data of the user, updating the user type according to the stage motion data, and updating the standard data according to the updated user type. For example, after the user moves for a period of time, the movement period changes, and the corresponding user type and standard data change. In some embodiments, users are also allowed to update their personal basic data, and after the personal basic data is updated, their user type and standard data are also updated correspondingly. Fig. 4 is a schematic diagram of standard data corresponding to a user according to an embodiment of the present invention. The standard data includes frequency, intensity, mileage, and time of each run. An exercise guidance program is generated based on the standard data. For example, when the real-time motion data value of the user is lower than the recommended range, the user is guided to increase the motion parameters so as to guide the scientific motion of the user; and when the real-time motion data value of the user is higher than the recommended range, guiding the user to reduce the motion parameters so as to guide the user to move safely. For example, in some scenarios, when the intensity of running does not reach the standard range, the user may be prompted to increase the exercise intensity, such as increasing the pace frequency, the stride, etc.; if the running intensity exceeds the recommended range, the user is prompted to reduce the exercise intensity so as to avoid exercise injury caused by excessive exercise.
The second exercise guidance program includes prompting the user to increase the exercise parameter when the physiological parameter is below a preset value, and prompting the user to decrease the exercise parameter when the physiological parameter reaches the preset value. For example, while the athlete is running, when the user's heart rate is low, the user may be prompted to increase pace or stride frequency or stride length to enhance cardio-pulmonary exercise; when the heart rate of the user is too high, the user may overload to exercise at the moment, which is unfavorable for health, and at the moment, the user can be prompted to reduce pace or pace frequency or stride, discomfort caused by exercise intensity is relieved, and the athlete is guided to exercise safely. In a real gymnasium, a fitness coach can make reasonable strength exercise fully according to individual endurance or health conditions and exercise targets. The embodiment of the invention can simulate a fitness coach, remind the user to continuously insist to achieve the aim of scientific sports when the sports object of the sporter is not reached, and guide the user to safely exercise only because the user slows down the exercise intensity when the user does overload sports.
In some embodiments, the second motion guidance program includes guidance suggestions for the frequency of motion of the user. At present, the wrist watch and the mobile terminal do not guide the movement frequency of the user, and excessive movement can cause fatigue accumulation and movement damage. In an embodiment of the present invention, the exercise frequency of the user may be guided according to the exercise time, the exercise item, and the exercise intensity of the user. For example, the user runs for 1 hour each day, and all are in high intensity exercise, have run for 5 consecutive days, the second exercise guidance program determines that the user is suitable to exercise 3-5 times per week, then day 6, the second exercise guidance program will remind the user that it is a break day today, and no exercise is recommended.
In some embodiments, in order to increase interest and improve morale of the exerciser, the exercise guidance method further includes: and when the acquired exercise data meet a preset condition, broadcasting a preset voice to encourage the sporter to continue exercising. For example, when the user exercises for a preset time, the exercise parameter is obviously reduced, and the physiological parameter value is also obviously lower than the preset numerical range, it can be judged that the athlete has a tendency to give up exercising, and at this time, encouraging voice or encouraging music, etc. can be broadcasted. The motivational voice may be "oil," you have successfully consumed XX calories, victory is at hand, oil! ", or to simulate the voice of a real sports coach, etc. The motivational music may be music or songs with exciting melodies or lyrics that stimulate a person's motor mood.
Step S105: prompting the second athletic guidance regime to the athlete.
The second motion guidance scheme can be prompted to the sporter through the mobile terminal in one or more modes of sound, display and vibration. For example, the broadcast is performed by a preset voice. In some embodiments, the preset voice may be customized by the athlete, for example, by making or setting his own voice as the preset voice. In some embodiments, the prompting may also be made by outputting a preset audio sound, such as a sharp ring or beep to prompt the user to increase the speed, a slow ring to prompt the user to slow down, etc. In some embodiments, the user may also be prompted by flashing an indicator light, such as a double flashing light to prompt the user to increase speed, a long and soft light to prompt the user to decrease speed, etc. In some embodiments, the user may be prompted to increase the speed by a vibration, such as a high frequency vibration, a low frequency vibration, etc. In some embodiments, a combination of two or more prompting methods may also be used to prompt the athlete, such as a combination of voice and vibration.
In some embodiments, the second exercise guidance plan may also be saved in the memory of the mobile terminal, and when the exercise is finished, the comparison analysis may be performed according to the exercise guidance plan of the user and the actual exercise data of the user, so as to provide a further exercise guidance suggestion to guide the exerciser to perform the next exercise plan. In some embodiments, the second motion guidance program may also be output to other devices, such as other mobile terminals or cloud servers. In some embodiments, the second motion guidance program may also be transmitted back to the wearable device over a communication connection between the mobile terminal and the wearable device, prompting the athlete with the wearable device for the motion guidance program.
It should be noted that the first exercise guidance scheme and the second exercise guidance scheme of the present invention may be stored in the mobile terminal local or in the cloud server, and before starting exercise next time, serve as a reference for providing the athlete with the pre-exercise guidance scheme.
In another embodiment of the invention, the mobile terminal can transmit the motion related information of the sporter, such as various physiological data during motion and positioning information of the sporter, to the people closely related to the sporter in real time, so that the people closely related to the sporter can rescue the sporter in time when the sporter has an emergency.
Fig. 5 is a schematic structural diagram of a mobile terminal according to an embodiment of the present invention.
In some embodiments, the mobile terminal 3 may be, but is not limited to, any user terminal capable of data processing, such as a laptop, a tablet, a smart phone, and a Personal Digital Assistant (PDA). The mobile terminal 3 includes a memory 30 and a processor 32. The memory 30 may be used to store computer programs and/or modules, and the processor 32 may implement various functions of the mobile terminal 3 by executing or otherwise executing the computer programs and/or modules stored in the memory 30, as well as by invoking data stored in the memory 30. The memory 30 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a motion-accompanying program) required for at least one function, and the like; the storage data area may store data created according to the use of the mobile terminal 3, such as exercise data, personal basic data, exercise guide plans, preset standard data, and the like. Further, the memory 30 may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The Processor 32 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. The processor 32 is the control center of the mobile terminal and connects the various parts of the entire mobile terminal 3 using various interfaces and lines.
The mobile terminal 3 further comprises at least one power supply means 35, at least one display means 36, at least one input means 37, at least one communication means 38 and at least one communication bus. Wherein the communication bus is used for realizing connection communication among the components. The power supply device 35 is used for supplying power to the mobile terminal 3. The display device 36 is used for displaying the motion data and the analysis result. The input and output device 37 is used to allow a user to interact with the electronic apparatus to input specified information, such as a recording manner, a presentation manner, and the like of data. The communication device 38 is used to communicate with the wearable apparatus to obtain the motion data of the athlete.
The power supply device 35 may be a built-in or external rechargeable or non-rechargeable power supply. It will be appreciated that in some embodiments, the power supply device 35 may include a charging power supply, and/or a power interface through which power is drawn directly from other power supply devices or mains electricity.
The Display device 36 may be a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) Display, an Organic Light-Emitting Diode (OLED) Display, or other suitable Display.
The communication device 38 may be a wired communication device or a wireless communication device. The wired communication device includes a communication port, such as a Universal Serial Bus (USB), Mini USB, microsusb, Type-C, Lighting interface, etc. The Wireless communication device may employ any type of Wireless communication system, such as bluetooth, Wireless Fidelity (WiFi), cellular technology, and the like.
The exercise guidance method provided by the embodiment of the invention can timely and effectively remind the exerciser of increasing or weakening the exercise intensity before and during the exercise of the exerciser, can effectively ensure the exercise purpose, can ensure scientific exercise and safe exercise, avoids the occurrence of exercise injury and even sudden death, and provides driving protection for the exercise of the exerciser. Simultaneously, whether according to the present motion level of sportsman lags behind standard motion data, for the sportsman provides various refuels, encourages the words, increase the accompany nature and the interest of motion for the sportsman is unlikely to alone the motion and feels boring and tasteless, nourishes the good custom of persisting the motion and taking exercise more easily, promotes healthy level. Meanwhile, the preset standard data and the standard algorithm can carry out self-learning and updating correction by utilizing the stored motion data, and the preset standard data and the standard algorithm are updated in time to match with the equal-level motion ability of the sporter so as to more scientifically and effectively guide the motion.
In some embodiments, the wearable device or mobile terminal integrated module/unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the exercise guidance method according to the above embodiment may also be implemented by a computer program that can be stored in a computer-readable storage medium and instructs related hardware to implement the steps of the exercise guidance method according to the above embodiment when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
In the embodiments provided in the present invention, it should be understood that the disclosed method and apparatus can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. Several of the means recited in the apparatus claims may also be embodied by one and the same means or system in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A motion guidance method is applied to a mobile terminal, and is characterized by comprising the following steps:
determining a first athletic guidance program for an athlete prior to the athlete initiating a current workout based on the athlete's daily athletic and physiological vital data;
prompting the first athletic guidance program to the athlete;
when the athlete starts the current exercise, obtaining current exercise data of the athlete from a wearable device, wherein the current exercise data comprises exercise parameters and physiological parameters of the athlete in the process of exercise;
determining a second motion guidance program for the athlete based on the current motion data;
prompting the second athletic guidance regime to the athlete.
2. The athletic guidance method of claim 1, wherein the determining a first athletic guidance regime for the athlete prior to the athlete initiating a current workout based on the athlete's daily athletic and physiological vital data comprises:
extracting daily exercise and physiological big data of the athlete from a database before the athlete starts current exercise;
inputting additional factors and daily movement and physiological big data of the sporter into a movement monitoring model subordinate to the sporter, and generating the first movement guidance scheme by the movement monitoring model, wherein the additional factors comprise current external environment data and recent pathological data of the sporter.
3. The exercise guidance method of claim 1, wherein the second exercise guidance program is determined based on the obtained current exercise data, personal basic data, and preset standard data, wherein the personal basic data includes one or more of gender, age, height, weight, and health condition.
4. The exercise guidance method of claim 3, wherein the preset standard data includes one or more of exercise items, exercise time, exercise intensity, and exercise frequency, and the method further comprises: and storing the acquired current motion data to a storage unit, and correcting the preset standard data according to the stage user motion data stored in the storage unit.
5. The exercise guidance method of claim 1, wherein the first exercise guidance program is presented to the exerciser by one or more of voice, text and image, and the second exercise guidance program is presented to the exerciser by one or more of voice, display and vibration.
6. The motion guidance method of claim 1, wherein the obtaining current motion data of the athlete from a wearable device comprises: establishing a communication connection with the wearable device; acquiring the motion data from the wearable device at predetermined intervals.
7. The exercise guidance method of claim 1, further comprising: and when the acquired current exercise data meet a preset condition, broadcasting preset encouragement voice.
8. The exercise guidance method of claim 1, wherein the second exercise guidance program comprises prompting the user to increase the exercise parameter when the physiological parameter is below a preset value, and prompting the user to decrease the exercise parameter when the physiological parameter reaches a preset value.
9. The athletic guidance method of claim 8, wherein the second athletic guidance program includes guidance advice for the user's exercise frequency, and the user is prompted not to exceed the preset exercise frequency when the user's exercise frequency reaches a preset value.
10. A mobile terminal comprising a communication unit for establishing a communication connection with a wearable device, a storage unit and a processing unit, characterized in that the storage unit stores a computer program executable by the processing unit for implementing the steps of the method of any of claims 1 to 9.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111564197A (en) * | 2020-04-14 | 2020-08-21 | 南京学立安教育科技有限公司 | Intelligent analysis system and method for physical exercise |
| CN111589092A (en) * | 2020-05-29 | 2020-08-28 | 安徽华米智能科技有限公司 | Exercise guidance method and device |
| CN114067952A (en) * | 2021-10-14 | 2022-02-18 | 西安理工大学 | Motion risk prediction method based on DEMATEL and Bayesian network |
| CN116959665A (en) * | 2023-09-21 | 2023-10-27 | 深圳市爱保护科技有限公司 | Motion monitoring method, device, equipment and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105117597A (en) * | 2015-08-20 | 2015-12-02 | 邵哲 | Method and device for making exercise program |
| CN107256330A (en) * | 2017-05-22 | 2017-10-17 | 深圳市见康云科技有限公司 | Recommendation method, device and the computer-readable recording medium of a kind of motion scheme |
| CN107910043A (en) * | 2017-11-29 | 2018-04-13 | 佛山市神风航空科技有限公司 | A kind of Sport Administration platform |
| CN108364674A (en) * | 2018-02-22 | 2018-08-03 | 国家体育总局体育科学研究所 | A kind of intelligent body-building guidance method and system using exercise prescription |
| CN108479044A (en) * | 2018-04-10 | 2018-09-04 | 四川斐讯信息技术有限公司 | A kind of movement technique and system of wearable device |
| CN110176288A (en) * | 2019-04-28 | 2019-08-27 | 深圳市龙腾飞通讯装备技术有限公司 | Move adjoint method and the mobile terminal using this method |
-
2019
- 2019-12-27 CN CN201911380252.0A patent/CN110993058A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105117597A (en) * | 2015-08-20 | 2015-12-02 | 邵哲 | Method and device for making exercise program |
| CN107256330A (en) * | 2017-05-22 | 2017-10-17 | 深圳市见康云科技有限公司 | Recommendation method, device and the computer-readable recording medium of a kind of motion scheme |
| CN107910043A (en) * | 2017-11-29 | 2018-04-13 | 佛山市神风航空科技有限公司 | A kind of Sport Administration platform |
| CN108364674A (en) * | 2018-02-22 | 2018-08-03 | 国家体育总局体育科学研究所 | A kind of intelligent body-building guidance method and system using exercise prescription |
| CN108479044A (en) * | 2018-04-10 | 2018-09-04 | 四川斐讯信息技术有限公司 | A kind of movement technique and system of wearable device |
| CN110176288A (en) * | 2019-04-28 | 2019-08-27 | 深圳市龙腾飞通讯装备技术有限公司 | Move adjoint method and the mobile terminal using this method |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111564197A (en) * | 2020-04-14 | 2020-08-21 | 南京学立安教育科技有限公司 | Intelligent analysis system and method for physical exercise |
| CN111589092A (en) * | 2020-05-29 | 2020-08-28 | 安徽华米智能科技有限公司 | Exercise guidance method and device |
| CN114067952A (en) * | 2021-10-14 | 2022-02-18 | 西安理工大学 | Motion risk prediction method based on DEMATEL and Bayesian network |
| CN116959665A (en) * | 2023-09-21 | 2023-10-27 | 深圳市爱保护科技有限公司 | Motion monitoring method, device, equipment and storage medium |
| CN116959665B (en) * | 2023-09-21 | 2023-12-19 | 深圳市爱保护科技有限公司 | Motion monitoring method, device, equipment and storage medium |
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