CN116133590A - Motion physiological data statistical method, device, equipment, storage medium and chip - Google Patents
Motion physiological data statistical method, device, equipment, storage medium and chip Download PDFInfo
- Publication number
- CN116133590A CN116133590A CN202280004545.2A CN202280004545A CN116133590A CN 116133590 A CN116133590 A CN 116133590A CN 202280004545 A CN202280004545 A CN 202280004545A CN 116133590 A CN116133590 A CN 116133590A
- Authority
- CN
- China
- Prior art keywords
- heart rate
- user
- data
- rate data
- exercise
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000007619 statistical method Methods 0.000 title claims description 5
- 238000000034 method Methods 0.000 claims abstract description 56
- 230000000386 athletic effect Effects 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 10
- 238000001914 filtration Methods 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 abstract description 2
- 238000012545 processing Methods 0.000 description 11
- 238000004891 communication Methods 0.000 description 10
- 238000005516 engineering process Methods 0.000 description 9
- 230000001133 acceleration Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 230000005236 sound signal Effects 0.000 description 4
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 3
- 239000008280 blood Substances 0.000 description 3
- 210000004369 blood Anatomy 0.000 description 3
- 230000036772 blood pressure Effects 0.000 description 3
- 229910052760 oxygen Inorganic materials 0.000 description 3
- 239000001301 oxygen Substances 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 230000000284 resting effect Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013475 authorization Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Physics & Mathematics (AREA)
- Cardiology (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Physiology (AREA)
- Veterinary Medicine (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Dentistry (AREA)
- Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
Abstract
The disclosure relates to a method, a device, equipment, a storage medium and a chip for counting exercise physiological data, and relates to the field of exercise monitoring, wherein the method comprises the following steps: and determining that the user is in a motion state by detecting heart rate data of the user and counting physiological data of the user in the motion state under the condition that the heart rate data acquired at least two times in succession is larger than the reference heart rate data, so as to obtain the motion physiological data of the user. Through the technical scheme, whether the user is in a motion state or not can be automatically identified, automatic recording of physiological data in the motion state is achieved, statistics is carried out on the physiological data in the motion state, the problem of complex operation caused by recording through manual operation of the user can be solved, and the problem of inaccurate data recording caused by forgetting operation of the user is avoided.
Description
Technical Field
The disclosure relates to the field of motion monitoring, and in particular relates to a motion physiological data statistics method, a device, equipment, a storage medium and a chip.
Background
In the related technology, the related scientific and technological products of sports need the user to manually operate the equipment to record the starting time and the ending time of the sports, and the statistics analysis is carried out on the data of the sports, so that the operation is complicated. However, users often forget to trigger starting or ending actions for a variety of reasons, resulting in inaccurate athletic data statistics.
Disclosure of Invention
To overcome the problems in the related art, the present disclosure provides a sports physiological data statistics method, apparatus, electronic device, computer-readable storage medium, and chip.
According to a first aspect of embodiments of the present disclosure, there is provided a sports physiological data statistics method, applied to a terminal device, including:
detecting heart rate data of a user;
determining that the user is in a motion state when heart rate data acquired at least twice in succession are detected to be greater than reference heart rate data;
and counting the physiological data of the user in the motion state to obtain the motion physiological data of the user.
In an embodiment of the disclosure, the method further includes:
and determining that the user exits from the exercise state when the heart rate data acquired at least twice in succession is detected to be less than or equal to the reference heart rate data.
Optionally, the method further comprises:
acquiring heart rate data of the user in a specified time period in the latest preset days;
filtering heart rate data smaller than a heart rate lower limit threshold value and larger than a heart rate upper limit threshold value in heart rate data in a specified time period in the preset days to obtain preprocessed heart rate data;
and determining the reference heart rate data according to the preprocessed heart rate data.
Optionally, the counting the physiological data of the user in the exercise state to obtain exercise physiological data of the user includes:
determining the acquisition time of the first heart rate data in the heart rate data acquired at least twice continuously as the starting time of the motion state under the condition that the user is in the motion state;
under the condition that the user exits from the motion state, determining the collection time of the heart rate data which is more than the reference heart rate data last time before the user exits from the motion state as the ending time of the motion state;
and counting the physiological data of the user between the starting time and the ending time to obtain the exercise physiological data of the user.
In an embodiment of the present disclosure, the exercise physiological data is exercise heart rate data obtained by counting heart rate data of the user in the exercise state, and the method further includes:
determining a target heart rate ratio at the user according to the exercise heart rate data and a target heart rate corresponding to the user;
determining the exercise intensity of the user according to the target heart rate ratio;
determining corresponding suggested movement time according to the movement intensity;
and outputting the prompt information of the suggested exercise time to the user.
In an embodiment of the disclosure, the determining, according to the exercise heart rate data and the target heart rate corresponding to the user, the target heart rate ratio at the user and the target heart rate corresponding to the user includes:
acquiring user information of the user;
determining a fat burning heart rate corresponding to the user as the target heart rate according to the user information;
acquiring heart rate acquisition times and times of reaching the target heart rate in a specified duration from the exercise heart rate data;
the target heart rate ratio is determined based on the number of heart rate acquisitions and the number of times the target heart rate is reached.
In an embodiment of the present disclosure, the determining, according to the target heart rate ratio, the exercise intensity of the user includes:
determining a heart rate ratio range to which the target heart rate ratio belongs;
and determining the exercise intensity level corresponding to the heart rate ratio range to which the target heart rate ratio belongs according to the corresponding relation between the heart rate ratio range and the exercise intensity level.
In an embodiment of the present disclosure, the determining that the user is in a motion state when detecting that heart rate data acquired at least two consecutive times is greater than reference heart rate data includes:
acquiring step counting data of the terminal equipment within a preset duration under the condition that heart rate data acquired at least twice continuously are detected to be larger than reference heart rate data;
and under the condition that the step counting data is larger than a step number threshold value, determining that the user is in a motion state.
In an embodiment of the present disclosure, the determining that the user is in a motion state when detecting that heart rate data acquired at least two consecutive times is greater than reference heart rate data includes:
acquiring motion sensor parameters of the terminal equipment under the condition that heart rate data acquired at least twice continuously are detected to be larger than reference heart rate data;
and determining that the user is in a motion state under the condition that the motion sensor parameter is larger than the corresponding parameter threshold value.
In a third aspect of the present disclosure, there is provided an electronic device comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the method of the first aspect.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the exercise physiological data statistics method provided by the first aspect of the present disclosure.
According to a fifth aspect of embodiments of the present disclosure, there is provided a chip comprising a processor and an interface; the processor is configured to read instructions to perform the method of the first aspect of the present disclosure.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects:
according to the technical scheme, through detecting heart rate data of the user, under the condition that at least two continuous collection heart rate data are detected to be larger than the reference heart rate data, the user is determined to be in a motion state, and physiological data of the user in the motion state are counted, so that the motion physiological data of the user are obtained. Through the technical scheme, whether the user is in the motion state or not can be automatically identified, and the physiological data in the motion state is counted, so that the problem of complex operation caused by the fact that the user needs to manually operate to record in the related technology can be solved, the automatic recording of the physiological data in the motion state is realized, the problem of inaccurate data recording caused by forgetting operation of the user is avoided, and the effectiveness of the data is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
FIG. 1 is a flow chart illustrating a method of athletic physiological data statistics, according to an example embodiment;
FIG. 2 is a flowchart illustrating another method of athletic physiological data statistics, according to an example embodiment;
FIG. 3 is a flowchart illustrating yet another method of athletic physiological data statistics, according to an example embodiment;
FIG. 4 is a flowchart illustrating yet another method of athletic physiological data statistics, according to an example embodiment;
FIG. 5 is a flowchart illustrating yet another method of athletic physiological data statistics, according to an example embodiment;
FIG. 6 is a block diagram of an athletic physiological data statistics device, according to an example embodiment;
fig. 7 is a block diagram illustrating a sports physiological data statistics device, according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
It should be noted that, all actions for acquiring signals, information or data in the present application are performed under the condition of conforming to the corresponding data protection rule policy of the country of the location and obtaining the authorization given by the owner of the corresponding device.
Before introducing the exercise physiological data statistics method provided by the embodiment of the present disclosure, an application scenario related to the embodiment of the present disclosure is first described, where the exercise physiological data statistics method provided by the embodiment of the present disclosure may be applied to a terminal device, where the terminal device may be a wearable device, and the wearable device includes, but is not limited to, a smart bracelet, a smart watch, and the like, and one or more sensors for detecting physiological data are provided on the wearable device, where the physiological data may include heart rate data, blood oxygen data, blood pressure data, and the like, and when the wearable device is worn by a user, the above sensors provided on the wearable device may contact with a designated part of the user, so that the wearable device may detect the above one or more physiological data of the user, and perform the exercise physiological data statistics method provided by the embodiment of the present disclosure based on the detected physiological data. Or the terminal device may be a mobile terminal such as a mobile phone, a tablet computer, a notebook computer, etc., and the mobile terminal may be wirelessly connected with the wearable device (for example, bluetooth), so that the mobile terminal may detect physiological data of a user through the wirelessly connected wearable device, and after the wearable device detects the physiological data of the user, may acquire the physiological data from the wearable device based on the wireless connection, and execute the exercise physiological data statistical method provided by the embodiment of the present disclosure based on the acquired physiological data. The following describes a sports physiological data statistical method provided by an embodiment of the present disclosure.
Fig. 1 is a flowchart illustrating a sports physiological data statistics method according to an exemplary embodiment, and as shown in fig. 1, the sports physiological data statistics method is used in a terminal device, which may be the wearable device or the mobile terminal, and the method includes the following steps.
In step S101, heart rate data of a user is detected.
Where heart rate generally refers to the number of beats per minute of a normal person in a resting state, also referred to as resting heart rate, typically 60-100 beats per minute, the heart rate data may be a numerical value of heart rate in beats per minute. Individual differences may also occur due to age, sex, or other physiological factors.
In step S102, in case it is detected that the heart rate data acquired at least two consecutive times are each larger than the reference heart rate data, it is determined that the user is in a motion state.
For example, the reference heart rate data may be understood as a reference heart rate value of the human body in a non-moving state, which may be understood as a heart rate threshold value for judging whether the human body is in a moving state, and in case that the heart rate exceeds the reference heart rate value, the human body may be considered to be in a moving state.
In one implementation, the reference heart rate data may be a predetermined reference heart rate value, which may be suitable for a large part of the population, and the reference heart rate data may be pre-calculated based on experimental data and stored in the terminal device. In the embodiment of the present disclosure, different reference heart rate data may be set for different groups, for example, different reference heart rate data may be set according to different age ranges, so as to obtain multiple reference heart rate data corresponding to multiple age ranges, and after determining the age range of the current user, the corresponding reference heart rate data is selected to execute step S102.
Alternatively, in another implementation, corresponding reference heart rate data may be set for each user, and different reference heart rate values may be set for different users as the user's reference heart rate data due to differences in heart rates of the different individuals. For example, the reference heart rate data of the user may be determined from heart rate data of the user over a specified period of time in a preceding certain number of days, for example, an average value of heart rate data over a specified period of time of the days is acquired as the reference heart rate data of the user.
It will be appreciated that in order to avoid false positives, it may be determined that the user is in motion in the event that at least two consecutive acquisitions of heart rate data are detected that are both greater than the reference heart rate data. That is, it is necessary that the heart rate data acquired at two or more consecutive times is greater than the reference heart rate data to determine that the user is in motion, less than two times, or two times that are not consecutive to determine that the user is in motion.
In the embodiment of the present disclosure, the number of times of continuously satisfying the condition may be increased as required, and the more the number of continuous times, the more accurate the judgment is, for example, the user is determined to be in a motion state when the heart rate data acquired for three continuous times or five continuous times is set to be greater than the reference heart rate data.
In step S103, the physiological data of the user in the exercise state is counted to obtain exercise physiological data of the user.
According to the determination method in step S102, it may be determined whether the user is in a motion state, and based on physiological data between a start time and an end time of the motion state, the physiological data may include at least one of heart rate data, blood oxygen data, and blood pressure data, that is, may be used as the motion physiological data.
According to the technical scheme, through detecting heart rate data of the user, under the condition that at least two continuous collection heart rate data are detected to be larger than the reference heart rate data, the user is determined to be in a motion state, and physiological data of the user in the motion state are counted, so that the motion physiological data of the user are obtained. Through the technical scheme, whether the user is in the motion state or not can be automatically identified, and the physiological data in the motion state is counted, so that the problem of complex operation caused by the fact that the user needs to manually operate to record in the related technology can be solved, the automatic recording of the physiological data in the motion state is realized, the problem of inaccurate data recording caused by forgetting operation of the user is avoided, and the effectiveness of the data is improved.
Fig. 2 is a flowchart illustrating another exercise physiological data statistics method according to an exemplary embodiment, as shown in fig. 2, for use in a terminal device, which may be the wearable device or the mobile terminal described above, the method comprising the following steps.
Step S201, acquiring heart rate data of the user in a specified time period in a latest preset day.
For example, heart rate data is acquired for a specified period of time, such as a heart rate value of between 9:00 am and 9:00 pm, for the last 7 days or half a month of the user.
Step S202, determining the reference heart rate data according to the heart rate data in the appointed time period in the preset days.
For example, this step S202 may include the steps of:
firstly, filtering heart rate data smaller than a heart rate lower limit threshold value and larger than a heart rate upper limit threshold value in heart rate data in a specified time period in the preset days to obtain preprocessed heart rate data. Next, the reference heart rate data is determined from the preprocessed heart rate data.
Illustratively, in one possible implementation, taking the example that the preset number of days is 7 days, after the heart rate values of each minute between 9:00 am and 9:00 pm in the last 7 days are obtained, data of more than 100 times/min (min) and less than 40 times/min of the heart rate values are deleted, and the remaining heart rate values are averaged as the reference heart rate data.
After the reference heart rate data is obtained, the following steps S203 to S206 are performed. In addition, the reference heart rate data may be updated periodically, for example, daily, such as the current date of 4 months 28 days, the reference heart rate data used in performing steps S203 to S206 on 4 months 28 days being determined based on the heart rate data of 4 months 21 days to 4 months 27 days, and the reference heart rate data used in performing steps S203 to S206 on 4 months 29 days being updated to the reference heart rate data determined based on the heart rate data of 4 months 22 days to 4 months 28 days.
In step S203, heart rate data of the user is detected.
Step S203 can refer to step S101, and will not be described again.
In step S204, in case it is detected that the heart rate data acquired at least two consecutive times are both larger than the reference heart rate data, it is determined that the user is in a motion state.
Step S204 can refer to step S102, and will not be described again.
In step S205, in case it is detected that the heart rate data collected at least two consecutive times are less than or equal to the reference heart rate data, it is determined that the user exits the exercise state.
For example, similarly to the method of judging whether or not in the motion state, whether or not the heart rate data is less than or equal to the reference heart rate data may be detected a plurality of times in succession in order to avoid erroneous judgment. For example, it may be set that the user exits the exercise state in the case where the heart rate data acquired three times in succession or five times in succession is smaller than or equal to the reference heart rate data, for example.
Step S206, counting the physiological data of the user in the exercise state to obtain exercise physiological data of the user.
In an implementation of the disclosure, fig. 3 is a flowchart of another exercise physiological data statistics method according to yet another exemplary embodiment, as shown in fig. 3, step S206 may include the following steps:
in step S2061, when it is determined that the user is in the exercise state, the first heart rate data acquisition time in the at least two consecutive heart rate data is determined as the start time of the exercise state.
In step S2062, when it is determined that the user is in the exercise state, the last time the heart rate data is acquired, which is greater than the reference heart rate data, before the user is in the exercise state is determined as the end time of the exercise state.
In step S2063, the physiological data of the user from the start time to the end time is counted to obtain the exercise physiological data of the user.
Wherein the physiological data may include at least one of heart rate data, blood oxygen data, blood pressure data.
By way of example, the exercise time of the user may be guided based on the obtained exercise physiological data, and exercise heart rate data obtained by counting heart rate data of the user in an exercise state will be described below as an example.
FIG. 4 is a flowchart illustrating yet another method of athletic physiological data statistics, as illustrated in FIG. 4, in accordance with an exemplary embodiment, the method may further include:
step S104, determining a target heart rate ratio value of the user according to the exercise heart rate data and the target heart rate corresponding to the user. Illustratively, in one embodiment, as shown in FIG. 5, the method comprises the steps of:
step S1041, obtaining user information of the user.
Step S1042, determining the fat burning heart rate corresponding to the user as the target heart rate according to the user information.
Step S1043, obtaining the heart rate acquisition times in the specified duration and the times of reaching the target heart rate from the exercise heart rate data.
Step S1044, determining the target heart rate ratio based on the heart rate acquisition number and the number of times the target heart rate is reached.
For example, the user information may include an age of the user, the age may be input into the terminal device by the user, and the method for determining the fat burning heart rate corresponding to the user based on the age information may include:
fat burning heart rate range = (220-age) ×60% to (220-age) ×80%
The target heart rate may be at an upper limit of the range of fat burning heart rates, i.e. the target heart rate may be a (220-age) x 80% heart rate.
For example, when the user is 30 years old, the range of the corresponding fat burning heart rate is = (220-30) ×60% - (220-30) ×80% = 114-152 times/min. The target heart rate for that user is 152 beats/min.
It will be appreciated that while heart rate is measured in beats/min, it is not necessary to collect a full minute to obtain heart rate data, and it is generally possible to record a number of beats of 10 seconds times 6, or a number of beats of 15 seconds times 4, etc. Thus heart rate data may be collected multiple times within a specified period of time, for example, the specified period of time may be 5 minutes, heart rate data may be collected every 10 seconds, such that the heart rate collection number within 5 minutes is 30, and assuming the user has 25 heart rate data exceeding the target heart rate 152 times/min, the target heart rate ratio = 25 +.30 = 83.3%.
Step S105, determining the exercise intensity of the user according to the target heart rate ratio. Illustratively, the steps may include:
firstly, determining a heart rate ratio range to which the target heart rate ratio belongs;
and secondly, determining the exercise intensity level corresponding to the heart rate ratio range to which the target heart rate ratio belongs according to the corresponding relation between the heart rate ratio range and the exercise intensity level.
Step S106, corresponding suggested movement time is determined according to the movement intensity.
Step S107, outputting the prompt information of the suggested exercise time to the user.
For example, the correspondence of different target heart rate values to heart rate ratio ranges may be as shown in table 1, and the correspondence shown in table 1 may be pre-established.
TABLE 1
| Exercise intensity level | Target heart rate ratio | Suggesting exercise time |
| Highest strength | 90％-100％ | <5min |
| High strength | 80％-90％ | 2-10min |
| Medium strength | 70％-80％ | 10-40min |
| Low strength | 60％-70％ | 40-80min |
| Minimum strength of | 50％-60％ | 20-40min |
Taking the example that the user age is 30 years as described in step S104, in the case where the target heart rate ratio=66.7% described above, it is possible to determine that the current exercise intensity level is high intensity. At this time, a prompt message may be output to the user, where the prompt message may include the current exercise intensity level of the user and a prompt suggesting that the exercise time is 2-10 minutes. The prompt information may include at least one of an image, text, and voice, for example, in the case that the terminal device is a wearable device, the prompt information may be output through a display screen of the wearable device, or the prompt information may be broadcasted by outputting voice information. Under the condition that the terminal equipment is mobile equipment, the prompting information can be output by a display screen of the mobile equipment, or can be broadcast through outputting voice information, or can be sent to the wearing equipment bound with the mobile equipment through wireless connection such as Bluetooth, and the prompting information can be output by the display screen of the wearing equipment or be broadcast through outputting the voice information.
It should be noted that, since there may be an increase in heart rate, such as tension, caused by the non-exercise condition, further, in an embodiment, in the case where the heart rate data acquired at least two consecutive times is detected to be greater than the reference heart rate data in step S102 or S204, determining that the user is in an exercise state may include the following steps:
firstly, under the condition that heart rate data acquired at least twice continuously are detected to be larger than reference heart rate data, step counting data of the terminal equipment in preset duration are acquired.
Secondly, in the case that the step counting data is larger than the step number threshold value, the user is determined to be in a motion state.
Namely, under the condition that heart rate data collected twice or more continuously are larger than reference heart rate data, whether the step data are larger than a step number threshold value is combined to carry out comprehensive judgment, and under the condition that the two data meet the condition, the user is determined to be in a motion state. Illustratively, the person walks normally for about 110-116 steps a minute, and the step number threshold may take any value in the range of 110-116.
In another embodiment, in the case where it is detected that the heart rate data collected at least two consecutive times are greater than the reference heart rate data in step S102 or S204, determining that the user is in a motion state includes:
firstly, under the condition that heart rate data acquired at least twice successively are detected to be larger than reference heart rate data, acquiring motion sensor parameters of the terminal equipment.
Secondly, in case the motion sensor parameter is greater than the corresponding parameter threshold, it is determined that the user is in motion.
Namely, under the condition that heart rate data collected twice or more continuously are larger than reference heart rate data, comprehensive judgment is needed by combining whether the motion sensor parameters are larger than corresponding parameter thresholds, and under the condition that the two parameters meet the condition, the user is determined to be in a motion state. The motion sensor may be, for example, an acceleration sensor or a gyroscope. Taking an acceleration sensor as an example, the corresponding parameter threshold value may be an acceleration threshold value.
Fig. 6 is a block diagram illustrating a sports physiological data statistics device, according to an exemplary embodiment. Referring to fig. 6, the exercise physiological data statistics apparatus 600 includes a detection module 601, a state identification module 602, and a statistics module 603.
A detection module 601 configured to detect heart rate data of a user;
a state identification module 602 configured to determine that the user is in a state of motion if it is detected that the heart rate data acquired at least two consecutive times is greater than the reference heart rate data;
a statistics module 603 configured to count heart rate data of the user while in the exercise state.
In an embodiment of the present disclosure, the state identification module 602 may be further configured to:
in the event that at least two consecutive acquisitions of heart rate data are detected that are each less than or equal to the baseline heart rate data, it is determined that the user is exiting the exercise state.
In an embodiment of the present disclosure, the exercise physiological data statistics apparatus 600 may further include:
a data acquisition module configured to acquire heart rate data of the user over a specified period of time in a recent preset number of days;
a data determination module configured to determine the baseline heart rate data from heart rate data over a specified period of time in the preset number of days.
In an embodiment of the disclosure, the data determining module is configured to:
filtering heart rate data smaller than a heart rate lower limit threshold value and larger than a heart rate upper limit threshold value in heart rate data in a specified time period in the preset days to obtain preprocessed heart rate data;
the reference heart rate data is determined from the preprocessed heart rate data.
In an embodiment of the present disclosure, the statistics module 603 is configured to:
under the condition that the user is in a motion state, determining the acquisition time of first heart rate data in the heart rate data acquired at least two times continuously as the starting time of the motion state;
under the condition that the user exits the exercise state, determining the collection time of the heart rate data which is more than the reference heart rate data last time before the user exits the exercise state as the ending time of the exercise state;
and counting the physiological data of the user between the starting time and the ending time to obtain the exercise physiological data of the user.
In the embodiment of the present disclosure, the exercise physiological data is exercise heart rate data obtained by counting heart rate data of the user in the exercise state, and the exercise physiological data counting device 600 may further include:
a computing module configured to determine a target heart rate ratio at the user based on the exercise heart rate data and a target heart rate corresponding to the user;
a exercise intensity determination module configured to determine an exercise intensity at which the user is located based on the target heart rate ratio;
a movement time determination module configured to determine a corresponding suggested movement time based on the movement intensity;
and the output module is configured to output prompt information of the suggested exercise time to the user.
In an embodiment of the disclosure, a computing module is configured to:
acquiring user information of the user;
determining a fat burning heart rate corresponding to the user as the target heart rate according to the user information;
acquiring heart rate acquisition times and times of reaching the target heart rate in a specified duration from the exercise heart rate data;
the target heart rate ratio is determined based on the number of heart rate acquisitions and the number of times the target heart rate is reached.
In an embodiment of the present disclosure, the exercise intensity determination module is configured to:
determining a heart rate ratio range to which the target heart rate ratio belongs;
and determining the exercise intensity level corresponding to the heart rate ratio range to which the target heart rate ratio belongs according to the corresponding relation between the heart rate ratio range and the exercise intensity level.
In the disclosed embodiment, the state identification module 602 is configured to:
acquiring step counting data of the terminal equipment within a preset duration under the condition that heart rate data acquired at least twice continuously are detected to be larger than reference heart rate data;
and determining that the user is in a motion state when the step counting data is greater than a step number threshold.
In the disclosed embodiment, the state identification module 602 is configured to:
acquiring motion sensor parameters of the terminal equipment under the condition that heart rate data acquired at least twice continuously are detected to be larger than reference heart rate data;
and determining that the user is in a motion state when the motion sensor parameter is greater than the corresponding parameter threshold.
According to the technical scheme, through detecting heart rate data of the user, under the condition that at least two continuous collection heart rate data are detected to be larger than the reference heart rate data, the user is determined to be in a motion state, and physiological data of the user in the motion state are counted, so that the motion physiological data of the user are obtained. Through the technical scheme, whether the user is in the motion state or not can be automatically identified, and the physiological data in the motion state is counted, so that the problem of complex operation caused by the fact that the user needs to manually operate to record in the related technology can be solved, the automatic recording of the physiological data in the motion state is realized, the problem of inaccurate data recording caused by forgetting operation of the user is avoided, and the effectiveness of the data is improved.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
The present disclosure also provides a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the exercise physiological data statistics method provided by the present disclosure.
Fig. 7 is a block diagram illustrating an athletic physiological data statistics device 700, according to an example embodiment. For example, apparatus 700 may be an electronic device such as a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, exercise device, personal digital assistant, or the like.
Referring to fig. 7, an apparatus 700 may include one or more of the following components: a processing component 702, a memory 704, a power component 706, a multimedia component 708, an audio component 710, an input/output (I/O) interface 712, a sensor component 714, and a communication component 716.
The processing component 702 generally controls overall operation of the apparatus 700, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 702 may include one or more processors 720 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 702 can include one or more modules that facilitate interaction between the processing component 702 and other components. For example, the processing component 702 may include a multimedia module to facilitate interaction between the multimedia component 708 and the processing component 702.
The memory 704 is configured to store various types of data to support operations at the apparatus 700. Examples of such data include instructions for any application or method operating on the apparatus 700, contact data, phonebook data, messages, pictures, videos, and the like. The memory 704 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
The power component 706 provides power to the various components of the device 700. Power component 706 can include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for device 700.
The multimedia component 708 includes a screen between the device 700 and the user that provides an output interface. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 708 includes a front-facing camera and/or a rear-facing camera. The front-facing camera and/or the rear-facing camera may receive external multimedia data when the apparatus 700 is in an operational mode, such as a photographing mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
The audio component 710 is configured to output and/or input audio signals. For example, the audio component 710 includes a Microphone (MIC) configured to receive external audio signals when the device 700 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 704 or transmitted via the communication component 716. In some embodiments, the audio component 710 further includes a speaker for outputting audio signals.
The I/O interface 712 provides an interface between the processing component 702 and peripheral interface modules, which may be a keyboard, click wheel, buttons, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.
The sensor assembly 714 includes one or more sensors for providing status assessment of various aspects of the apparatus 700. For example, the sensor assembly 714 may detect an on/off state of the device 700, a relative positioning of the components, such as a display and keypad of the device 700, a change in position of the device 700 or a component of the device 700, the presence or absence of user contact with the device 700, an orientation or acceleration/deceleration of the device 700, and a change in temperature of the device 700. The sensor assembly 714 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 714 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 714 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 716 is configured to facilitate communication between the apparatus 700 and other devices in a wired or wireless manner. The apparatus 700 may access a wireless network based on a communication standard, such as WiFi,2G or 3G, or a combination thereof. In one exemplary embodiment, the communication component 716 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 716 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 700 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for performing the above-described exercise physiological data statistics method.
In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as memory 704 including instructions executable by processor 720 of apparatus 700 to perform the athletic physiological data statistics method described above. For example, the non-transitory computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
The apparatus may be a stand-alone electronic device or may be part of a stand-alone electronic device, for example, in one embodiment, the apparatus may be an integrated circuit (Integrated Circuit, IC) or a chip, where the integrated circuit may be an IC or may be a collection of ICs; the chip may include, but is not limited to, the following: GPU (Graphics Processing Unit, graphics processor), CPU (Central Processing Unit ), FPGA (Field Programmable Gate Array, programmable logic array), DSP (Digital Signal Processor ), ASIC (Application Specific Integrated Circuit, application specific integrated circuit), SOC (System on Chip, SOC, system on Chip or System on Chip), etc. The integrated circuit or chip may be configured to execute executable instructions (or code) to implement the exercise physiological data statistics method described above. The executable instructions may be stored on the integrated circuit or chip or may be retrieved from another device or apparatus, such as the integrated circuit or chip including a processor, memory, and interface for communicating with other devices. The executable instructions may be stored in the processor, which when executed by the processor, implement the exercise physiological data statistics method described above; alternatively, the integrated circuit or chip may receive executable instructions via the interface and transmit them to the processor for execution to implement the exercise physiological data statistics method described above.
In another exemplary embodiment, a computer program product is also provided, comprising a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-described sports physiological data statistics method when executed by the programmable apparatus.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (13)
1. A sports physiological data statistical method is applied to terminal equipment and comprises the following steps:
detecting heart rate data of a user;
determining that the user is in a motion state when heart rate data acquired at least twice in succession are detected to be greater than reference heart rate data;
and counting the physiological data of the user in the motion state to obtain the motion physiological data of the user.
2. The method of claim 1, wherein the method further comprises:
and determining that the user exits from the exercise state when the heart rate data acquired at least twice in succession is detected to be less than or equal to the reference heart rate data.
3. The method of claim 1, wherein the method further comprises:
acquiring heart rate data of the user in a specified time period in the latest preset days;
filtering heart rate data smaller than a heart rate lower limit threshold value and larger than a heart rate upper limit threshold value in heart rate data in a specified time period in the preset days to obtain preprocessed heart rate data;
and determining the reference heart rate data according to the preprocessed heart rate data.
4. The method of claim 1, wherein the counting physiological data of the user while in the exercise state to obtain exercise physiological data of the user comprises:
determining the acquisition time of the first heart rate data in the heart rate data acquired at least twice continuously as the starting time of the motion state under the condition that the user is in the motion state;
under the condition that the user exits from the motion state, determining the collection time of the heart rate data which is more than the reference heart rate data last time before the user exits from the motion state as the ending time of the motion state;
and counting the physiological data of the user between the starting time and the ending time to obtain the exercise physiological data of the user.
5. The method of claim 1, wherein the exercise physiological data is exercise heart rate data from statistics of heart rate data of the user while in the exercise state, the method further comprising:
determining a target heart rate ratio at the user according to the exercise heart rate data and a target heart rate corresponding to the user;
determining the exercise intensity of the user according to the target heart rate ratio;
determining corresponding suggested movement time according to the movement intensity;
and outputting the prompt information of the suggested exercise time to the user.
6. The method of claim 5, wherein the determining a target heart rate ratio at the user and a target heart rate corresponding to the user from the athletic heart rate data and a target heart rate corresponding to the user comprises:
acquiring user information of the user;
determining a fat burning heart rate corresponding to the user as the target heart rate according to the user information;
acquiring heart rate acquisition times and times of reaching the target heart rate in a specified duration from the exercise heart rate data;
the target heart rate ratio is determined based on the number of heart rate acquisitions and the number of times the target heart rate is reached.
7. The method of claim 5, wherein the determining the intensity of motion in which the user is located from the target heart rate ratio value comprises:
determining a heart rate ratio range to which the target heart rate ratio belongs;
and determining the exercise intensity level corresponding to the heart rate ratio range to which the target heart rate ratio belongs according to the corresponding relation between the heart rate ratio range and the exercise intensity level.
8. The method of claim 1, wherein the determining that the user is in motion if it is detected that the heart rate data acquired at least two consecutive times is greater than the baseline heart rate data comprises:
acquiring step counting data of the terminal equipment within a preset duration under the condition that heart rate data acquired at least twice continuously are detected to be larger than reference heart rate data;
and under the condition that the step counting data is larger than a step number threshold value, determining that the user is in a motion state.
9. The method of claim 1, wherein the determining that the user is in motion if it is detected that the heart rate data acquired at least two consecutive times is greater than the baseline heart rate data comprises:
acquiring motion sensor parameters of the terminal equipment under the condition that heart rate data acquired at least twice continuously are detected to be larger than reference heart rate data;
and determining that the user is in a motion state under the condition that the motion sensor parameter is larger than the corresponding parameter threshold value.
10. A sports physiological data statistics apparatus for use with a terminal device, the apparatus comprising:
a detection module configured to detect heart rate data of a user;
a state identification module configured to determine that the user is in a state of motion if it is detected that the heart rate data acquired at least two consecutive times are both greater than the reference heart rate data;
and the statistics module is configured to count heart rate data of the user in the motion state.
11. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1-9.
12. A computer readable storage medium having stored thereon computer program instructions which when executed by a processor implement the steps of the method of any of claims 1-9.
13. A chip comprising a processor and an interface; the processor is configured to read instructions to perform the method of any one of claims 1-9.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/CN2022/093149 WO2023220894A1 (en) | 2022-05-16 | 2022-05-16 | Statistical method and apparatus for motion physiological data, device, storage medium and chip |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116133590A true CN116133590A (en) | 2023-05-16 |
Family
ID=86303172
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202280004545.2A Pending CN116133590A (en) | 2022-05-16 | 2022-05-16 | Motion physiological data statistical method, device, equipment, storage medium and chip |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN116133590A (en) |
| WO (1) | WO2023220894A1 (en) |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11185241B2 (en) * | 2014-03-05 | 2021-11-30 | Whoop, Inc. | Continuous heart rate monitoring and interpretation |
| CN107094203A (en) * | 2017-04-26 | 2017-08-25 | 北京小米移动软件有限公司 | Message treatment method, device and computer-readable recording medium |
| CN110045994B (en) * | 2018-01-12 | 2022-09-16 | Oppo广东移动通信有限公司 | Application program processing method and device, electronic equipment and computer readable storage medium |
| CN108852333A (en) * | 2018-05-14 | 2018-11-23 | 四川斐讯信息技术有限公司 | A kind of heart rate monitoring method and system based on intelligent wearable device |
| CN110096195A (en) * | 2019-04-29 | 2019-08-06 | 努比亚技术有限公司 | Motion icon display methods, wearable device and computer readable storage medium |
| CN112967801A (en) * | 2021-01-28 | 2021-06-15 | 安徽华米健康科技有限公司 | PAI value processing method, PAI value processing device, PAI value processing equipment and storage medium |
| CN114469040A (en) * | 2022-03-08 | 2022-05-13 | 中国科学技术大学 | Heart rate detection method and device, storage medium and electronic equipment |
-
2022
- 2022-05-16 WO PCT/CN2022/093149 patent/WO2023220894A1/en unknown
- 2022-05-16 CN CN202280004545.2A patent/CN116133590A/en active Pending
Also Published As
| Publication number | Publication date |
|---|---|
| WO2023220894A1 (en) | 2023-11-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104850995B (en) | Operation execution method and device | |
| EP3188094A1 (en) | Method and device for classification model training | |
| EP3162284A1 (en) | Communication method, apparatus and system for wearable device | |
| US9924090B2 (en) | Method and device for acquiring iris image | |
| WO2020258556A1 (en) | Prompting method and device, and medium | |
| EP3024211B1 (en) | Method and device for announcing voice call | |
| CN113887474B (en) | Respiration rate detection method and device, electronic device and storage medium | |
| CN106599858B (en) | Fingerprint identification method and device and electronic equipment | |
| CN110706784A (en) | Calorie intake amount calculation method, device, system, apparatus, and storage medium | |
| CN116133590A (en) | Motion physiological data statistical method, device, equipment, storage medium and chip | |
| CN107677363B (en) | Noise prompting method and intelligent terminal | |
| CN111539617A (en) | Data processing method and device, electronic equipment, interaction system and storage medium | |
| CN107896277B (en) | Method and device for setting alarm clock reminding mode and computer readable storage medium | |
| CN112462997B (en) | Content display method and device, electronic equipment and storage medium | |
| CN105959486B (en) | Notification method and device | |
| CN112070483B (en) | Reminding method, reminding device and storage medium | |
| CN114343597A (en) | Physiological parameter monitoring method and device, keyboard, electronic equipment and storage medium | |
| CN112925466B (en) | Touch control method and device | |
| CN113952578A (en) | Sleep awakening method and device for insomnia treatment | |
| CN107122286B (en) | Method and device for counting event information | |
| CN110290271B (en) | Reminding method, reminding device and medium | |
| CN114563012B (en) | Step counting method, device, equipment and storage medium | |
| CN106959758B (en) | Gait monitoring method and device | |
| CN112704471B (en) | Control method of wearable device, wearable device and storage medium | |
| CN112833907B (en) | Step counting method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |