CN105808959A - Motion detection system, motion detection terminal and cloud platform - Google Patents
Motion detection system, motion detection terminal and cloud platform Download PDFInfo
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Abstract
The invention discloses a motion detection system, a motion detection terminal and a cloud platform. The motion detection terminal is used for acquiring and storing feature data and/or physiological data of a user, and computing the matching degree with a stored motion strategy according to the feature data and/or the physiological data, comparing the physiological data with the motion strategy with the highest matching degree so as to evaluate whether the motion of the user is normal, wherein the feature data comprise gender, age or health state, the physiological data comprises an acceleration parameter, a breath parameter or a heart rate parameter; the cloud platform is used for storing the motion strategy provided by the motion detection terminal. The system and terminal disclosed by the invention are used for providing an ideal pace strategy for the user under the condition of different motion modes and different physical states; the pace strategy is safe, efficient, reliable and dynamic, and the step of computing the pace strategy is simplified.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a motion detection system, a motion detection terminal and a cloud platform.
Background
More and more fitness people are realizing the importance of aerobic exercise for health. Aerobic exercise can improve cardiovascular system, and has effects of activating body function, maintaining healthy weight, and preventing diseases. Even has the effects of improving sleep quality and improving memory. Therefore, most of the body-building people select aerobic exercise as the main exercise mode. However, during aerobic exercise, there are many false areas, contraindications and easily made mistakes. Typically, only professional coaches or instruments are available to discover or monitor the occurrence of these problems. Under the unconscious condition, the body builder often causes sports injury, can not achieve the negative effects of sports effect and the like. Therefore, it is highly desirable to invent a way to monitor and guide the exercise status of a fitness person according to the physiological parameters of the fitness person, such as age, sex, heart rate, and respiration.
Disclosure of Invention
In order to solve the technical defects that the exercise injury is caused by the over-exercise of the user or the exercise effect cannot be achieved due to insufficient exercise in the prior art, the invention provides a detection system which judges the exercise strategy suitable for the current user by collecting the characteristic data and/or the physiological data of the user and evaluates whether the exercise of the user is normal.
The invention provides a motion detection system, wherein a motion detection terminal is used for providing a motion mode, acquiring and storing characteristic data and/or physiological data of a user, calculating the matching degree of the characteristic data and/or the physiological data with a pre-stored motion strategy according to the motion mode selected by the user by utilizing the characteristic data and/or the physiological data, comparing the characteristic data and/or the physiological data with the motion strategy with the highest matching degree, and evaluating whether the motion of the user is normal or not, wherein the motion mode comprises a weight-losing motion mode or a cardio-pulmonary motion mode, the characteristic data comprises gender, age or a health state, and the physiological data comprises an acceleration parameter, a breathing parameter or a heart rate parameter; the cloud platform is used for providing a motion strategy for the motion detection terminal.
In the motion detection system, the motion detection terminal is further used for acquiring the geographic position of the current user and sending the geographic position to the cloud platform.
In the motion detection system, the cloud platform is further configured to acquire data acquired by the motion detection terminal, judge a commonly used motion mode of the same motion detection terminal user within a preset time period, and push a corresponding motion strategy to the motion detection terminal user.
In the motion detection system of the present invention, the motion detection terminal is further configured to acquire a geographic position of the current user.
The invention also provides a motion detection terminal, which comprises an interaction module, an acquisition module and a processing module, wherein,
the interaction module is used for providing and displaying a motion mode which can be selected by a user;
the acquisition module is used for acquiring characteristic data and/or physiological data of a user;
the processing module is used for storing the physiological data acquired by the acquisition module, calculating the matching degree of the characteristic data and/or the physiological data acquired by the acquisition module and a prestored motion strategy according to a motion mode selected by a user, comparing the physiological data with the motion strategy with the highest matching degree, and evaluating whether the motion of the user is normal or not, wherein the motion mode comprises a weight-losing motion mode or a cardio-pulmonary motion mode, the characteristic data comprises gender, age or a health state, and the physiological data comprises an acceleration parameter, a breathing parameter or a heart rate parameter.
In the motion detection terminal of the present invention, the motion detection terminal further includes a positioning module for detecting the geographic position of the current user.
In the motion detection terminal of the present invention, the motion detection terminal further includes a communication module for acquiring a motion policy.
In the motion detection terminal of the present invention, the evaluation result includes normal, near normal, or abnormal.
In the motion detection terminal of the present invention, the collection module comprises
The acceleration detection module is used for detecting the acceleration of the current user;
the breath detection module is used for detecting the breath parameters of the current user;
and the heart rate detection module is used for detecting the heart rate parameter of the current user.
In the motion detection terminal of the present invention, the processing module comprises
The pace matching strategy group module is used for storing the incidence relation between different motion strategies and user characteristic data and/or physiological data, wherein the characteristic data comprises physiological data, age, gender or health state;
the data storage module is used for storing the characteristic data and/or the physiological data of the user, which are acquired by the acquisition module;
and the speed matching module is used for calculating the matching degree with different motion strategies according to the data collected by the collecting module, comparing the physiological data with the motion strategy with the highest matching degree, and evaluating whether the motion of the user is normal or not.
The invention also provides a cloud platform, which comprises a data pool, a strategy pool, a data fusion and processing module and a model calculation module, wherein,
the data pool is used for receiving characteristic data and/or physiological data of a user during movement;
the data fusion and processing module is used for acquiring the environmental parameters of the geographic position of the user, and screening, combining and converting the environmental parameters and the characteristic data and/or the physiological data of the user during movement;
the computing module is used for computing according to the data screened, combined and converted by the data fusion and processing module to obtain a corresponding motion strategy;
and the strategy pool is used for storing the motion strategy calculated and obtained by the model calculation module.
In conclusion, the invention has the advantages that the reasonability of helping the user to conveniently control the self movement speed in aerobic exercise is included; the speed matching strategy is diversified, the strategy selection process is intelligent, and an ideal speed matching strategy under different motion modes and different body states can be provided for a user; the generation of the pace matching strategy is based on individual physiological data, large-scale group data and professional aerobic cardiopulmonary exercise expert knowledge, and has the characteristics of safety, effectiveness, reliability, dynamics and the like; and transferring the calculation burden of the motion detection terminal to the cloud platform through the pre-calculation process of the cloud platform, and simply calculating the speed matching strategy.
Drawings
FIG. 1 is a block diagram of a motion detection system according to the present invention;
fig. 2 is a block diagram schematically illustrating a structure of a motion detection terminal according to the present invention;
fig. 3 is a block diagram structural diagram of a cloud platform according to the present invention.
Detailed Description
The present invention will be described in further detail below with reference to specific embodiments and with reference to the attached drawings.
The invention provides a motion detection system which comprises a motion detection terminal 101 and a cloud platform 102.
Wherein,
the motion detection terminal 101 is configured to provide a motion mode, acquire and store feature data and/or physiological data of a user, calculate a matching degree with a pre-stored motion strategy according to the motion mode selected by the user by using the feature data and/or the physiological data, compare the feature data and/or the physiological data with the motion strategy with the highest matching degree, and evaluate whether the motion of the user is normal, where the motion mode includes a weight-loss motion mode or a cardiopulmonary motion mode, the feature data includes gender, age, or a health state, and the physiological data includes an acceleration parameter, a breathing parameter, or a heart rate parameter.
The cloud platform 200 is configured to provide a motion policy to the motion detection terminal.
Different exercise modes and exercise strategies suitable for different body state conditions are different. The diversification of the motion strategy needs to be obtained by calculation according to massive user data.
The motion detection terminal detects individual data, and the pre-stored motion strategy is generated through large-scale group data and professional aerobic cardiopulmonary motion expert knowledge, so that the method has the characteristics of safety, effectiveness, reliability, dynamics and the like. According to the invention, the motion detection terminal acquires the motion strategy from the cloud platform, and the motion detection terminal automatically acquires the optimal motion strategy by acquiring the user characteristic data and/or physiological data and judges the reasonability of the motion of the user. According to the method, the scientific motion strategy is obtained by calculating the massive individual data (obtained by the motion detection terminal in the invention) obtained by the cloud platform, so that the steps of directly sending the calculated motion strategy to the motion detection terminal and transferring the calculation burden of the massive data to the cloud platform through the pre-calculation process of the cloud platform are realized, and the calculation motion strategy is simplified. The motion detection terminal is transferred to the cloud platform, so that the energy consumption of the motion detection terminal is reduced.
In specific implementation, the cloud platform obtains user individual data, such as feature data and/or physiological data, through the motion detection terminal, and calculates massive individual data (obtained through the motion detection terminal in the invention) to obtain a scientific motion strategy. And the cloud platform sends the motion strategy obtained by calculation to the motion detection terminal so that the motion detection terminal can evaluate the motion of the user in real time. Meanwhile, the motion strategy prestored in the motion detection terminal can be updated through real-time communication with the cloud platform.
The motion strategies prestored in the motion detection terminal are ideal motion strategies under different motion modes and different body states. The motion detection terminal can also judge the matching degree with the recommended motion strategy according to the physiological data of the current user. It should be noted that the motion detection terminal described in the present invention is used for collecting user characteristic data and/or physiological data, and aims to determine a motion strategy suitable for the user according to the detected physiological data of the user. And in specific implementation, the motion detection terminal is optionally set as a bracelet, a user wears the bracelet to detect the physiological data of the user, the physiological data is sent to the cloud platform to obtain the motion state, the motion strategy suitable for the user is judged according to the motion state sent by the cloud platform, meanwhile, the matching degree of the physiological data and the motion strategy is optionally calculated, and the fitness of the motion strategy is reflected to the user by using visual numbers. It should be noted that the motion detection terminal includes but is not limited to a bracelet, and may also be a handheld terminal, a wearable device, and other devices.
The environmental parameters at different geographical locations are different and include climate temperature, climate humidity, barometric pressure, wind power level or altitude, etc. These factors have a relatively large influence on the movement of the user. Therefore, further, the motion detection terminal is further configured to acquire the geographic position of the user and send the geographic position to the cloud platform. The cloud platform is further used for judging the environmental parameters of the current user according to the received geographic position, and the environmental parameters comprise climate temperature, climate humidity, air pressure or wind power.
The cloud platform is further used for acquiring data acquired by the motion detection terminal, judging a common motion mode of the same motion detection terminal user within a preset time period, and pushing a corresponding motion strategy to the motion detection terminal user.
The cloud platform receives the physiological data of the multiple movements of the moving user, and aims to judge the movement state of the user according to the physiological data and send the movement state to the movement detection terminal. The acquired motion state can be the same according to the physiological data of each motion of the same user, namely the exercise of the user is kept unchanged. Therefore, the cloud platform can optionally send recommended motion information to the motion detection terminal user according to the motion state of the same user in a certain period of time, and the motion detection terminal user can optionally exercise according to the recommended motion information from the cloud platform.
The method for judging the motion phase comprises the following steps:
during exercise, the user may be in different exercise phases (e.g., start phase, warm-up phase, intermittence phase, recovery phase, relaxation phase, finish phase, etc.) with time, and the physiological data of the user may be different in different exercise phases, resulting in different strategy matching processes.
According to the motion and physiological data at each moment, the HMM method can be used to judge and predict the motion stage of the user.
As shown in fig. 2, the present invention also provides a motion detection terminal. The motion detection terminal comprises an interaction module 10, an acquisition module 20 and a processing module 30.
Wherein,
the interaction module 10 is used for providing and displaying motion modes which can be selected by a user;
the acquisition module 20 is used for acquiring characteristic data and/or physiological data of a user;
the processing module 30 is configured to store the physiological data acquired by the acquisition module, calculate a matching degree between the feature data and/or the physiological data acquired by the acquisition module and a pre-stored motion strategy according to a motion mode selected by a user, compare the physiological data with the motion strategy with the highest matching degree, and evaluate whether the motion of the user is normal, where the motion mode includes a weight-losing motion mode or a cardio-pulmonary motion mode, the feature data includes gender, age, or a health state, and the physiological data includes an acceleration parameter, a breathing parameter, or a heart rate parameter.
Further, the motion detection terminal further comprises a positioning module for detecting the geographic position of the current user.
Further, the motion detection terminal further comprises a communication module for acquiring the motion strategy. .
Further, the evaluation result includes normal, near normal, or abnormal.
Further, the collection module comprises
The acceleration detection unit is used for detecting the acceleration of the current user;
the breath detection unit is used for detecting the breath parameters of the current user;
and the heart rate detection unit is used for detecting the heart rate parameter of the current user.
And a motion strategy library prestored in the motion detection terminal is provided by the cloud platform. Different motion modes and different body states correspond to different motion strategies. The motion strategy is related to a user's heart rate parameter, breathing parameter, speed parameter, gender, age, etc.
Further, the processing module comprises
The data storage unit 301 is configured to store the feature data and/or the physiological data of the user acquired by the acquisition module. And the data storage unit stores the data sent by the motion detection terminal.
A pace strategy group unit 302 for storing the association relationship between different motion strategies and user characteristic data and/or physiological data.
The motion strategy comprises a strategy and a formula for calculating the matching degree. The specific matching principle can be selected as follows:
fast acceleration, low heart rate acceleration, and if in the fat-reducing exercise mode, strategy 5 with slightly higher intensity is used.
The acceleration is fast, the heart rate acceleration is low, and if in the cardiopulmonary exercise mode, the strategy 6 with high intensity is used.
The acceleration is fast, and the heart rate acceleration rate is also fast, no matter what kind of motion state the user is in, gets into the moderate intensity strategy 4 who suits with the current motion. When various sports physiological indexes are changed, strategy adjustment is carried out, and the matching degree is calculated according to the corresponding calculation mode.
The acceleration is low, the heart rate acceleration is also low, the user is not bored in which motion state, the medium-low intensity strategy 3 adaptive to the current motion is entered, and the matching degree is calculated according to the corresponding calculation mode. When various sports physiological indexes are changed, strategy adjustment is carried out.
Low acceleration, fast heart rate acceleration, and if in the fat-reducing exercise mode, low intensity strategy 1 is used.
Low acceleration, fast heart rate acceleration, and slightly less intensive strategy 2 if in cardiopulmonary exercise mode.
In specific implementation, the above description is related to the motion policy and the physiological data of the user. In specific implementation, how to judge whether the acceleration of the user is high or low or whether the heart rate is accelerated quickly or slowly can be set according to a large amount of training. For example, the heart rate of the user in a static state is a certain range, and the value can be obtained according to the medical database; the speed of the heart rate acceleration and the acceleration of the user are obtained through a large amount of training.
And the speed matching unit 303 is configured to calculate matching degrees with different motion strategies according to the data acquired by the acquisition module, compare the physiological data with the motion strategy with the highest matching degree, and evaluate whether the motion of the user is normal. The motion strategy comprises an incidence relation between the strategy and a calculation model thereof, and the matching degree of the motion strategy is calculated by the speed matching unit after the speed matching strategy group unit judges the motion strategy suitable for the speed matching strategy according to the user characteristic data and/or the physiological data. The high matching degree indicates that the current movement of the user is normal, and the low matching degree indicates that the movement of the user is abnormal, so that in specific implementation, the evaluation is obtained by comparing the matching degree with a preset threshold.
In specific implementation, the matching degree calculation may be selected as follows:
strategy 1 (200-age) _ (60% -70%)
Strategy 2 (200-age) _ (70% -80%)
Strategy 3 (220-age) _ (60% -70%)
Strategy 4 (220-age) _ 70% -80%)
Strategy 5 (220-age-resting heart rate) _ (65% -75%) + resting heart rate
Strategy 6 (220-age-resting heart rate) + 75% -85%.
And the motion detection terminal calculates and judges the optimal motion strategy according to the matching degree of the collected characteristic data and/or physiological data of the user and the motion strategy.
The motion strategy is related to heart rate parameters, breathing parameters and speed parameters of a user, wherein the speed parameters comprise acceleration and are acquired by an acceleration detection unit; the heart rate parameter is obtained by a heart rate detection unit. In specific implementation, the motion mode can be obtained by a man-machine interaction module optionally. And meanwhile, the age of the motion detection terminal user can be selectively obtained from an interactive module or synchronized with cloud platform data.
The invention also provides a cloud platform 200, which comprises a data pool 201, a strategy pool 202, a data fusion and processing module 203 and a computing module 204.
Wherein,
the data pool is used for receiving characteristic data and/or physiological data of a user during movement;
the data fusion and processing module is used for acquiring the environmental parameters of the geographic position of the user, and screening, combining and converting the environmental parameters and the characteristic data and/or the physiological data of the user during movement;
and the computing module is used for computing according to the data screened, combined and converted by the data fusion and processing module to obtain a corresponding motion strategy.
The purpose of the calculation module is to calculate a scientific motion strategy, which is a standard motion strategy given by an expert. The invention screens the sports crowd through the computing module and searches the sports strategy used by the crowd with good sports effect.
In specific implementation, parameters in the motion strategy are optimized through calculation methods such as regression and the like, so that the motion strategy is suitable for use requirements of different individuals or crowds. The model parameter training for motion phase judgment can use EM (effective man-machine) and Baum-Welch algorithm for optional calculation by using HMM model parameters, and the invention is not described in detail herein.
The calculation module is used for calculating a plurality of characteristic data transmitted by the data fusion stage according to age, gender, heart rate, respiratory rate, speed, acceleration, heart rate acceleration, motion mode, resting heart rate, physical fitness, frequent motion, temperature, humidity, air pressure, motion duration, motion stage, average oxygen uptake, motion strategy, feedback information and the like.
The calculation module specifically and optionally calculates a decision tree for performing motion strategy matching according to the physiological data and the motion data through a decision tree generation algorithm by using the corresponding relation between the accumulated physiological data and the labeled motion strategy. The adopted algorithm is a decision tree generation algorithm, and optionally, ID3, GBDT and other methods are utilized.
And the strategy pool is used for storing the motion strategy calculated and obtained by the model calculation module.
The processing process of the cloud platform by using the data fusion and processing module can be selected as follows:
information such as air pressure, temperature and humidity of the position of the user is obtained by using the Internet. Using the history of the user's motor physiological data, additional tags and indicators are counted, calculated and used: whether the patient is frequently moving, the physical quality is strong or weak, the type of the patient, the resting heart rate, the highest heart rate, the lowest heart rate, the stable breathing rate, the highest breathing rate, the lowest breathing rate, the average oxygen uptake and the like. It is to be understood that the present invention includes, but is not limited to, the types described above.
And acquiring keywords related to the movement and living habits of the user and the like by utilizing the personal social contact account number associated with the human-computer interaction interface module by the user. Clustering and grouping individual data, and analyzing and extracting group characteristics. The process of data fusion is not limited to the above range, and may be incorporated into the process of association with personal motion data. Methods available for the fusion process: data capture, data filtering, data statistics, data mining, data table association, caching, dumping and the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A motion detection system is characterized by comprising a motion detection terminal and a cloud platform, wherein,
the motion detection terminal is used for providing a motion mode, acquiring and storing feature data and/or physiological data of a user, calculating the matching degree of the feature data and/or the physiological data with a pre-stored motion strategy according to the motion mode selected by the user by using the feature data and/or the physiological data, comparing the feature data and/or the physiological data with the motion strategy with the highest matching degree, and evaluating whether the motion of the user is normal or not, wherein the motion mode comprises a weight-losing motion mode or a heart-lung motion mode, the feature data comprises gender, age or a health state, and the physiological data comprises an acceleration parameter, a breathing parameter or a heart rate parameter;
the cloud platform is used for providing a motion strategy for the motion detection terminal.
2. The motion detection system according to claim 1, wherein the cloud platform is further configured to obtain data collected by the motion detection terminal, determine a common motion mode of the same motion detection terminal user within a preset time period, and push a corresponding motion policy to the motion detection terminal user.
3. The motion detection system of claim 1, wherein the motion detection terminal is further configured to collect a geographic location of a current user.
4. A motion detection terminal is characterized by comprising an interaction module, an acquisition module and a processing module, wherein,
the interaction module is used for providing and displaying a motion mode which can be selected by a user;
the acquisition module is used for acquiring characteristic data and/or physiological data of a user;
the processing module is used for storing the physiological data acquired by the acquisition module, calculating the matching degree of the characteristic data and/or the physiological data acquired by the acquisition module and a prestored motion strategy according to a motion mode selected by a user, comparing the physiological data with the motion strategy with the highest matching degree, and evaluating whether the motion of the user is normal or not, wherein the motion mode comprises a weight-losing motion mode or a cardio-pulmonary motion mode, the characteristic data comprises gender, age or a health state, and the physiological data comprises an acceleration parameter, a breathing parameter or a heart rate parameter.
5. The motion detection terminal of claim 4, further comprising
And the positioning module is used for detecting the geographic position of the current user.
6. The motion detection terminal of claim 1, further comprising a communication module for obtaining a motion policy.
7. The motion detection terminal of claim 1, wherein the evaluation result includes normal, near normal, or abnormal.
8. The motion detection terminal of claim 1, wherein the acquisition module comprises
The acceleration detection unit is used for detecting the acceleration of the current user;
the breath detection unit is used for detecting the breath parameters of the current user;
and the heart rate detection unit is used for detecting the heart rate parameter of the current user.
9. The motion detection module of claim 1, wherein the processing module comprises
The pace matching strategy group unit is used for storing the incidence relation between different motion strategies and user characteristic data and/or physiological data;
the data storage unit is used for storing the characteristic data and/or the physiological data of the user, which are acquired by the acquisition module;
and the speed matching unit is used for calculating the matching degree with different motion strategies according to the data collected by the collection module, comparing the physiological data with the motion strategy with the highest matching degree, and evaluating whether the motion of the user is normal or not.
10. A cloud platform is characterized by comprising a data pool, a strategy pool, a data fusion and processing module and a computing module, wherein,
the data pool is used for receiving characteristic data and/or physiological data of a user during movement;
the data fusion and processing module is used for acquiring the environmental parameters of the geographic position of the user, and screening, combining and converting the environmental parameters and the characteristic data and/or the physiological data of the user during movement;
the computing module is used for computing according to the data screened, combined and converted by the data fusion and processing module to obtain a corresponding motion strategy;
and the strategy pool is used for storing the motion strategy calculated and obtained by the model calculation module.
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Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106474717A (en) * | 2016-10-18 | 2017-03-08 | 江西博瑞彤芸科技有限公司 | Data processing method based on user movement state |
| CN107169269A (en) * | 2017-04-24 | 2017-09-15 | 广东健凯医疗有限公司 | Fitness campaign capability evaluation system and method |
| CN107680650A (en) * | 2017-09-22 | 2018-02-09 | 上海斐讯数据通信技术有限公司 | A kind of intelligence generates the method and system of professional running training plan |
| CN108769198A (en) * | 2018-05-29 | 2018-11-06 | 百度在线网络技术（北京）有限公司 | Method and apparatus for pushed information |
| CN109192267A (en) * | 2018-08-09 | 2019-01-11 | 深圳狗尾草智能科技有限公司 | Virtual robot is accompanied in movement |
| CN110226801A (en) * | 2017-04-12 | 2019-09-13 | 佛山市丈量科技有限公司 | A kind of Human Physiology safety monitoring and feedback system |
| CN113457108A (en) * | 2021-07-07 | 2021-10-01 | 首都体育学院 | Cognitive characterization-based exercise performance improving method and device |
| CN113762336A (en) * | 2021-07-28 | 2021-12-07 | 南京慕白科技有限公司 | Fitness guidance method and system |
| CN113892077A (en) * | 2019-06-01 | 2022-01-04 | 苹果公司 | Multi-modal activity tracking user interface |
| CN114788952A (en) * | 2021-01-26 | 2022-07-26 | 华为技术有限公司 | A competition guidance method, device, portable device and server |
| WO2023115436A1 (en) * | 2021-12-23 | 2023-06-29 | 广东高驰运动科技股份有限公司 | Method and apparatus for estimating equivalent level pace for cross-country running, and device and medium |
| US12036018B2 (en) | 2016-09-22 | 2024-07-16 | Apple Inc. | Workout monitor interface |
| US12080421B2 (en) | 2013-12-04 | 2024-09-03 | Apple Inc. | Wellness aggregator |
| US12186645B2 (en) | 2022-06-05 | 2025-01-07 | Apple Inc. | User interfaces for physical activity information |
| US12197716B2 (en) | 2022-06-05 | 2025-01-14 | Apple Inc. | Physical activity information user interfaces |
| US12224051B2 (en) | 2019-05-06 | 2025-02-11 | Apple Inc. | Activity trends and workouts |
| US12239884B2 (en) | 2021-05-15 | 2025-03-04 | Apple Inc. | User interfaces for group workouts |
| US12243444B2 (en) | 2015-08-20 | 2025-03-04 | Apple Inc. | Exercised-based watch face and complications |
| US12274918B2 (en) | 2016-06-11 | 2025-04-15 | Apple Inc. | Activity and workout updates |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101822888A (en) * | 2010-04-24 | 2010-09-08 | 山东汇祥健身器材有限公司 | Electric running machine provided with exercise effect evaluation system |
| CN102357284A (en) * | 2011-10-18 | 2012-02-22 | 中国科学院合肥物质科学研究院 | Intelligent running machine |
| CN103892814A (en) * | 2014-03-31 | 2014-07-02 | 无锡首康科技有限公司 | Treadmill control method for exercise rehabilitation therapy system |
| CN103961839A (en) * | 2013-01-29 | 2014-08-06 | 北京知康优美科技有限公司 | Internet of Things based intelligent treadmill and control method thereof |
| CN104657816A (en) * | 2015-01-26 | 2015-05-27 | 合肥博谐电子科技有限公司 | Intelligent human health evaluation and promotion service management system |
| CN105031875A (en) * | 2015-06-25 | 2015-11-11 | 上海济子医药科技有限公司 | Cloud platform system for remote rehabilitation training machine |
| CN105169620A (en) * | 2015-08-26 | 2015-12-23 | 中国科学院合肥物质科学研究院 | Intelligent fat-reducing treadmill and body building instruction method thereof |
| CN105233485A (en) * | 2015-10-30 | 2016-01-13 | 山西睿智健科技有限公司 | A bodybuilding-assisting motion mode correction system and method |
| CN105321135A (en) * | 2014-07-04 | 2016-02-10 | 北京大学第三医院 | Personalized exercise prescription design method and system |
-
2016
- 2016-03-16 CN CN201610149694.4A patent/CN105808959A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101822888A (en) * | 2010-04-24 | 2010-09-08 | 山东汇祥健身器材有限公司 | Electric running machine provided with exercise effect evaluation system |
| CN102357284A (en) * | 2011-10-18 | 2012-02-22 | 中国科学院合肥物质科学研究院 | Intelligent running machine |
| CN103961839A (en) * | 2013-01-29 | 2014-08-06 | 北京知康优美科技有限公司 | Internet of Things based intelligent treadmill and control method thereof |
| CN103892814A (en) * | 2014-03-31 | 2014-07-02 | 无锡首康科技有限公司 | Treadmill control method for exercise rehabilitation therapy system |
| CN105321135A (en) * | 2014-07-04 | 2016-02-10 | 北京大学第三医院 | Personalized exercise prescription design method and system |
| CN104657816A (en) * | 2015-01-26 | 2015-05-27 | 合肥博谐电子科技有限公司 | Intelligent human health evaluation and promotion service management system |
| CN105031875A (en) * | 2015-06-25 | 2015-11-11 | 上海济子医药科技有限公司 | Cloud platform system for remote rehabilitation training machine |
| CN105169620A (en) * | 2015-08-26 | 2015-12-23 | 中国科学院合肥物质科学研究院 | Intelligent fat-reducing treadmill and body building instruction method thereof |
| CN105233485A (en) * | 2015-10-30 | 2016-01-13 | 山西睿智健科技有限公司 | A bodybuilding-assisting motion mode correction system and method |
Non-Patent Citations (2)
| Title |
|---|
| 刘洋: ""智能化健身器械的研究与应用设计"", 《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》 * |
| 陈庆合: "《大学体育教程》", 31 May 2015, 中国铁道出版社 * |
Cited By (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US12094604B2 (en) | 2013-12-04 | 2024-09-17 | Apple Inc. | Wellness aggregator |
| US12080421B2 (en) | 2013-12-04 | 2024-09-03 | Apple Inc. | Wellness aggregator |
| US12243444B2 (en) | 2015-08-20 | 2025-03-04 | Apple Inc. | Exercised-based watch face and complications |
| US12274918B2 (en) | 2016-06-11 | 2025-04-15 | Apple Inc. | Activity and workout updates |
| US12036018B2 (en) | 2016-09-22 | 2024-07-16 | Apple Inc. | Workout monitor interface |
| CN106474717A (en) * | 2016-10-18 | 2017-03-08 | 江西博瑞彤芸科技有限公司 | Data processing method based on user movement state |
| CN106474717B (en) * | 2016-10-18 | 2018-10-19 | 江西博瑞彤芸科技有限公司 | Data processing method based on user movement state |
| CN110226801A (en) * | 2017-04-12 | 2019-09-13 | 佛山市丈量科技有限公司 | A kind of Human Physiology safety monitoring and feedback system |
| CN110226801B (en) * | 2017-04-12 | 2021-10-12 | 姚艳飞 | Human physiological safety monitoring and feedback system |
| CN107169269A (en) * | 2017-04-24 | 2017-09-15 | 广东健凯医疗有限公司 | Fitness campaign capability evaluation system and method |
| WO2019056606A1 (en) * | 2017-09-22 | 2019-03-28 | 上海斐讯数据通信技术有限公司 | Method and system for intelligently generating professional running training plan |
| CN107680650A (en) * | 2017-09-22 | 2018-02-09 | 上海斐讯数据通信技术有限公司 | A kind of intelligence generates the method and system of professional running training plan |
| CN108769198B (en) * | 2018-05-29 | 2021-11-12 | 百度在线网络技术（北京）有限公司 | Method and device for pushing information |
| CN108769198A (en) * | 2018-05-29 | 2018-11-06 | 百度在线网络技术（北京）有限公司 | Method and apparatus for pushed information |
| CN109192267A (en) * | 2018-08-09 | 2019-01-11 | 深圳狗尾草智能科技有限公司 | Virtual robot is accompanied in movement |
| US12224051B2 (en) | 2019-05-06 | 2025-02-11 | Apple Inc. | Activity trends and workouts |
| CN113892077A (en) * | 2019-06-01 | 2022-01-04 | 苹果公司 | Multi-modal activity tracking user interface |
| CN114788952A (en) * | 2021-01-26 | 2022-07-26 | 华为技术有限公司 | A competition guidance method, device, portable device and server |
| US12239884B2 (en) | 2021-05-15 | 2025-03-04 | Apple Inc. | User interfaces for group workouts |
| CN113457108A (en) * | 2021-07-07 | 2021-10-01 | 首都体育学院 | Cognitive characterization-based exercise performance improving method and device |
| CN113762336A (en) * | 2021-07-28 | 2021-12-07 | 南京慕白科技有限公司 | Fitness guidance method and system |
| WO2023115436A1 (en) * | 2021-12-23 | 2023-06-29 | 广东高驰运动科技股份有限公司 | Method and apparatus for estimating equivalent level pace for cross-country running, and device and medium |
| US12186645B2 (en) | 2022-06-05 | 2025-01-07 | Apple Inc. | User interfaces for physical activity information |
| US12197716B2 (en) | 2022-06-05 | 2025-01-14 | Apple Inc. | Physical activity information user interfaces |
| US12194366B2 (en) | 2022-06-05 | 2025-01-14 | Apple Inc. | User interfaces for physical activity information |
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