Activity amount detection method and device, electronic equipment and medium

Abstract

The invention relates to an activity amount detection method and device, electronic equipment and a medium. The activity amount detection method comprises the following steps: acquiring multiple pieces of motion information of a user, wherein parameters corresponding to the motion information are different; determining a plurality of exercise intensities according to the exercise information; and determining the current activity amount according to each exercise intensity. The current activity amount of the user is determined according to the motion information of the multiple dimensions, the problems that part of the motion information is inaccurate and the motion intensity of part of the dimensions cannot reflect the motion condition of the user are avoided, and therefore the accuracy of activity amount detection is improved.

Description

Technical Field

[0001] This disclosure relates to the field of information processing technology, and in particular to a method, apparatus, electronic device, and medium for detecting activity levels. Background Technology

[0002] Currently, electronic devices suffer from low accuracy in detecting user activity levels. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this disclosure provides an activity level detection method, apparatus, electronic device, and medium.

[0004] According to a first aspect of the present disclosure, an activity level detection method is provided, the activity level detection method comprising:

[0005] Multiple motion information pieces are acquired from the user, and each motion information piece corresponds to different parameters;

[0006] Based on the described motion information, multiple motion intensities are determined;

[0007] Determine the current activity level based on the described exercise intensity.

[0008] In some embodiments of this disclosure, the exercise information includes heart rate information, step count information, speed information, cadence information, and / or power information of the exercise device; determining multiple exercise intensities based on the exercise information includes:

[0009] The first exercise intensity is determined based on the heart rate information and / or the step count information;

[0010] The second exercise intensity is determined based on the speed information, the step frequency information, and / or the power information.

[0011] In some embodiments of this disclosure, determining the current activity level based on the respective exercise intensities includes:

[0012] The first motion intensity and the second motion intensity are filtered to obtain the target motion intensity;

[0013] Determine the target motion time corresponding to the target motion intensity;

[0014] The current activity level is determined based on the target exercise intensity and the target exercise time.

[0015] In some embodiments of this disclosure, the filtering process of the first motion intensity and the second motion intensity to obtain the target motion intensity includes:

[0016] By comparing the first exercise intensity and the second exercise intensity, a first comparison result is obtained;

[0017] If the first comparison result is within a first preset range, the average of the first motion intensity and the second motion intensity is determined to be the target motion intensity.

[0018] If the first comparison result is outside the first preset range, the larger of the first motion intensity and the second motion intensity is determined as the target motion intensity.

[0019] In some embodiments of this disclosure, determining the current activity level based on the target motion intensity and the target motion time includes:

[0020] The first exercise time is obtained by adding the exercise time at the exercise intensity level where the target exercise intensity is located to the target exercise time.

[0021] The first exercise time under different exercise intensity levels is normalized and summed to obtain the second exercise time;

[0022] The current activity level is determined based on the second exercise time and the first preset relationship;

[0023] The first preset relationship is used to characterize the relationship between exercise time and activity level.

[0024] In some embodiments of this disclosure, determining multiple motion intensities based on the motion information includes:

[0025] Obtaining the user's physiological information;

[0026] Based on the physiological information and each of the exercise information, a plurality of exercise intensities are determined.

[0027] In some embodiments of this disclosure, after determining the current activity level based on each of the stated exercise intensities, the activity level detection method further includes:

[0028] Get historical activity volume;

[0029] The target activity level is determined based on the historical activity level and the current activity level.

[0030] In some embodiments of this disclosure, determining the target activity level based on the historical activity level and the current activity level includes:

[0031] The target activity level is determined based on the historical activity level, the current activity level, and the second preset relationship.

[0032] In some embodiments of this disclosure, after determining the target activity level based on the historical activity level and the current activity level, the activity level detection method further includes:

[0033] The target activity level and the preset activity level are compared to obtain a second comparison result;

[0034] If the second comparison result is outside the second preset range, a first prompt message is sent to the user. The first prompt message is used to prompt the user to reach the preset amount of activity and the corresponding exercise time.

[0035] If the second comparison result is within the second preset range, a second prompt message is sent to the user, which is used to remind the user that the preset activity amount has been reached.

[0036] In some embodiments of this disclosure, before comparing the target activity level and the preset activity level to obtain a second comparison result, the activity level detection method further includes:

[0037] Obtaining the user's physiological information;

[0038] The preset activity level is determined based on the physiological information.

[0039] In some embodiments of this disclosure, before issuing the first prompt message to the user, the activity level detection method further includes:

[0040] Based on the target activity level and the preset activity level, determine the type of exercise and the corresponding exercise time required to achieve the preset activity level.

[0041] According to a second aspect of the present disclosure, an activity level detection device is provided, the activity level detection device comprising:

[0042] An acquisition module is configured to acquire multiple motion information of a user, each of which has different parameters.

[0043] A first determining module is configured to determine multiple motion intensities based on the motion information.

[0044] The second determining module is configured to determine the current activity level based on each of the said exercise intensities.

[0045] According to a third aspect of the present disclosure, an electronic device is provided, the electronic device comprising:

[0046] processor;

[0047] Memory used to store the processor's executable instructions;

[0048] The processor is configured to execute the activity detection method described above.

[0049] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein when instructions in the storage medium are executed by a processor of a terminal, the terminal is enabled to perform the activity detection method as described above.

[0050] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0051] This process acquires multiple motion data points from the user, each with different parameters, to obtain multi-dimensional motion information. Based on this multi-dimensional motion data, the intensity of each motion dimension is determined, providing a comprehensive reflection of the user's activity level. Furthermore, based on these multiple intensity dimensions, the current activity level is determined, reflecting the user's activity across these dimensions. By using multi-dimensional motion data to determine the user's current activity level, the accuracy of activity level detection is improved, avoiding inaccuracies in some motion data and ensuring that some intensity dimensions accurately reflect the user's activity.

[0052] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0053] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0054] Figure 1 This is a flowchart illustrating an activity level detection method according to an exemplary embodiment;

[0055] Figure 2 This is a flowchart illustrating an activity level detection method according to another exemplary embodiment;

[0056] Figure 3 This is a flowchart illustrating an activity level detection method according to another exemplary embodiment;

[0057] Figure 4 This is a flowchart illustrating an activity level detection method according to another exemplary embodiment;

[0058] Figure 5 This is a flowchart illustrating an activity level detection method according to another exemplary embodiment;

[0059] Figure 6 This is a schematic diagram illustrating a first preset relationship according to an exemplary embodiment;

[0060] Figure 7 This is a flowchart illustrating an activity level detection method according to another exemplary embodiment;

[0061] Figure 8 This is a block diagram of an activity detection device according to an exemplary embodiment;

[0062] Figure 9 This is a block diagram of an electronic device according to an exemplary embodiment.

[0063] In the picture:

[0064] 100 - Acquisition module; 200 - First determination module; 300 - Second determination module; 400 - Electronic device; 402 - Processing component; 404 - Memory; 406 - Power supply component; 408 - Multimedia component; 410 - Audio component; 412 - Input / output interface; 414 - Sensor component; 416 - Communication component; 420 - Processor. Detailed Implementation

[0065] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0066] Currently, with the development of electronic devices and increased user awareness of health, electronic devices can acquire users' exercise information and determine the corresponding activity level based on that information. Users can then determine whether to exercise based on the activity level displayed on their electronic devices to maintain their health.

[0067] In related technologies, an activity level detection method is provided, including: acquiring a user's heart rate, respiratory rate, and speed information; determining the maximal oxygen uptake (VO2 max) based on the heart rate, respiratory rate, and speed information; determining the user's excess oxygen consumption (EOC); determining the aerobic training effect based on the VO2 max and EOC; and determining the activity level based on the aerobic training effect. However, determining the VO2 max requires the user to undergo a rigorous exhaustion test. When it is difficult for the user to perform an exhaustion test, the accuracy of the VO2 max detection is low. Furthermore, since VO2 max cannot be detected in real-time throughout the day, activity level can only be detected for part of the time, leading to low accuracy in activity level detection. Additionally, if one or more of the heart rate, respiratory rate, and speed information have low accuracy, the accuracy of the VO2 max detection is low, resulting in low accuracy in activity level detection.

[0068] To address the aforementioned technical issues, this disclosure provides an activity level detection method. This method determines the intensity of movement across multiple dimensions by acquiring motion information from multiple dimensions, and then determines the current activity level based on these intensities. Since the current activity level is determined based on multi-dimensional motion intensity, users do not need to perform rigorous exhaustion tests, thus improving the accuracy of activity level detection. Furthermore, because the multi-dimensional motion intensity can be determined in real-time throughout the day, the current activity level can be detected in real-time, further improving the accuracy of activity level detection. Simultaneously, since the current activity level is determined by comprehensively considering the motion intensity across multiple dimensions, noise can be eliminated through filtering when the accuracy of one or more dimensions of motion information is low, thereby further improving the accuracy of activity level detection.

[0069] This disclosure provides an activity level detection method, such as... Figure 1 As shown, the method includes:

[0070] S100: Obtain multiple motion information from the user, with different parameters for each motion information.

[0071] S200: Determine multiple exercise intensities based on various exercise information.

[0072] S300. Determine the current activity level based on the intensity of each exercise.

[0073] In this embodiment, multiple motion information points of the user are acquired, each with different parameters, to obtain multi-dimensional motion information. Based on this multi-dimensional motion information, the motion intensity of each dimension is determined, which comprehensively reflects the user's motion status. Based on the multi-dimensional motion intensity, the current activity level is determined, which reflects the user's activity status across multiple dimensions. By determining the user's current activity level based on multi-dimensional motion information, inaccurate partial motion information and the inability of some dimensions of motion intensity to reflect the user's motion status are avoided, thereby improving the accuracy of activity level detection.

[0074] For example, exercise information may include heart rate information, step count information, speed information, cadence information, and / or power information of the exercise device, with corresponding parameters such as heart rate parameter, step count parameter, speed parameter, cadence parameter, and / or power parameter. Specifically, heart rate information can be calculated based on information detected by the photoplethysmography (PPG) module of the electronic device; step count information can be calculated based on information detected by the accelerometer of the electronic device; speed information can be calculated based on information detected by the Global Positioning System (GPS) module or accelerometer of the electronic device; cadence information can be calculated based on information detected by the accelerometer of the electronic device; and power information can be calculated based on information detected by sensors built into the exercise device. The exercise device may be, for example, a treadmill, an elliptical trainer, or a rowing machine.

[0075] In one embodiment, such as Figure 2 As shown, in step S200, determining multiple motion intensities based on various motion information can be done in the following way:

[0076] S210. Determine the first exercise intensity based on heart rate information and / or step count information.

[0077] S220. Determine the second motion intensity based on speed information, step frequency information, and / or power information.

[0078] In this embodiment, since heart rate information and step count information have certain similarities in reflecting exercise intensity and their errors are similar, heart rate information and step count information can be used as one dimension to determine exercise intensity. A first exercise intensity is determined based on heart rate information and / or step count information to determine exercise intensity from one dimension. Since speed information, cadence information, and power information have certain similarities in reflecting exercise intensity and can reflect oxygen uptake or energy consumption, speed information, cadence information, and power information can be used as one dimension to determine exercise intensity. A second exercise intensity is determined based on speed information, cadence information, and / or power information to determine exercise intensity from another dimension. By determining two exercise intensities based on two sets of exercise information, the two exercise intensities can comprehensively reflect the user's exercise status, thereby improving the accuracy of activity level detection. Furthermore, since there are only two exercise intensities, the calculation of exercise intensity and activity level is simple, thereby reducing the complexity of activity level detection and the power consumption of electronic devices.

[0079] In one embodiment, determining the multiple motion intensities based on the motion information in step S200 can also be done in the following way:

[0080] Based on each piece of motion information, determine the corresponding exercise intensity.

[0081] In this embodiment, since each exercise intensity corresponds to one exercise information, the current activity level can comprehensively reflect the user's activity status, thereby improving the accuracy of activity level detection.

[0082] In one embodiment, such as Figure 3 As shown, the determination of the current activity level based on various exercise intensities in step S300 can be achieved in the following way:

[0083] S310. Filter the first motion intensity and the second motion intensity to obtain the target motion intensity.

[0084] S320. Determine the target motion time corresponding to the target motion intensity.

[0085] S330. Determine the current activity level based on the target exercise intensity and target exercise time.

[0086] In this embodiment, due to potential errors in motion information, there may be discrepancies between the first and second motion intensities, necessitating the elimination of noise in both intensities. Furthermore, in some motion scenarios, one motion intensity may accurately reflect the motion while the other fails to do so, requiring filtering out the inaccurate intensities. For example, in yoga practice, the first motion intensity may reflect the yoga activity, while the second may not. Filtering the first and second motion intensities eliminates noise or filters one of them to obtain the target motion intensity. Since activity level depends not only on motion intensity but also on exercise time, a target exercise time corresponding to the target motion intensity is determined. Based on the target motion intensity and target exercise time, the current activity level is determined to ascertain the user's activity status. By filtering the first and second motion intensities to determine the current activity level, the influence of motion information errors and the motion scenario on the current activity level is reduced, thereby improving the accuracy of activity level detection.

[0087] In one embodiment, when there are more than two exercise intensities, the determination of the current activity level based on each exercise intensity in step S300 can also be determined in the following way:

[0088] The target motion intensity is obtained by filtering each motion intensity.

[0089] Determine the target motion time corresponding to the target motion intensity.

[0090] Determine the current activity level based on the target exercise intensity and target exercise duration.

[0091] In this embodiment, since the target motion intensity is obtained by filtering multiple motion intensities, the influence of motion information errors and motion scene on the current activity level is reduced, thereby improving the accuracy of activity level detection.

[0092] In one embodiment, such as Figure 4 As shown, in step S310, the first motion intensity and the second motion intensity are filtered to obtain the target motion intensity, which is determined in the following way:

[0093] S311. Compare the first exercise intensity and the second exercise intensity to obtain the first comparison result.

[0094] S312. If the first comparison result is within the first preset range, the average value of the first motion intensity and the second motion intensity is determined to be the target motion intensity.

[0095] S313. If the first comparison result is outside the first preset range, determine the larger of the first motion intensity and the second motion intensity as the target motion intensity.

[0096] In this embodiment, a first motion intensity and a second motion intensity are compared to obtain a first comparison result, which determines the difference in motion intensity between the two dimensions. If the first comparison result is within a first preset range, both motion intensities are applicable to the current motion scenario and have only a small difference due to error. The average of the first and second motion intensities is determined as the target motion intensity to eliminate noise. If the first comparison result is outside the first preset range, one motion intensity is not applicable to the current motion scenario, resulting in a large difference between the two motion intensities. The larger of the first and second motion intensities is determined as the target motion intensity to filter out the smaller motion intensity. By comparing the first and second motion intensities to determine the target motion intensity in a corresponding manner, the target motion intensity can accurately reflect the user's motion status, thereby improving the accuracy of activity level detection.

[0097] For example, the comparison of the first exercise intensity and the second exercise intensity in step S311 to obtain the first comparison result can be either by subtracting the first exercise intensity and the second exercise intensity and taking the difference as the first comparison result, or by comparing the first exercise intensity and the second exercise intensity and taking the ratio as the first comparison result.

[0098] For example, since the first and second exercise intensities are determined in different ways, the values ​​of the first and second exercise intensities are different. When the difference in exercise intensities is used as the first comparison result, the first preset range can be expressed as -a1 to a1, where a1 is a positive number. When the ratio of exercise intensities is used as the first comparison result, the first preset range can be expressed as a2 to a3, where a2 and a3 are positive numbers, and a2 < 1 and a3 > 1.

[0099] For example, in addition to determining the average of the first and second motion intensities as the target motion intensity, the first and second motion intensities can also be multiplied by their corresponding correction coefficients and then summed to obtain the target motion intensity.

[0100] In one embodiment, when the number of motion intensities is greater than two, the filtering process performed on each motion intensity in the above steps yields the target motion intensity determined in the following manner:

[0101] By comparing each exercise intensity in pairs, multiple first comparison results are obtained.

[0102] If all the first comparison results are within the first preset range, the average value of each exercise intensity is determined as the target exercise intensity.

[0103] If at least the first comparison result is outside the first preset range, the maximum motion intensity among all motion intensities is determined as the target motion intensity.

[0104] In this embodiment, each motion intensity is compared pairwise to obtain multiple first comparison results, thereby determining the difference in motion intensity between each pairwise dimension. If all first comparison results are within a first preset range, each motion intensity is applicable to the current motion scenario and exhibits only minor differences due to error; the average of all motion intensities is then determined as the target motion intensity to eliminate noise. If at least one first comparison result is outside the first preset range, one or more motion intensities are not applicable to the current motion scenario, resulting in significant differences between at least two motion intensities; the largest of all motion intensities is then determined as the target motion intensity to filter out smaller motion intensities. By comparing each motion intensity to determine the target motion intensity in a corresponding manner, the target motion intensity accurately reflects the user's motion status, thereby improving the accuracy of activity level detection.

[0105] In one embodiment, such as Figure 5 As shown, in step S330, the current activity level is determined based on the target exercise intensity and target exercise time in the following way:

[0106] S331. Add the target exercise time to the exercise time at the exercise intensity level where the target exercise intensity is located to obtain the first exercise time.

[0107] S332. Normalize and sum the first exercise time under different exercise intensity levels to obtain the second exercise time.

[0108] S333. Determine the current activity level based on the second exercise time and the first preset relationship.

[0109] The first preset relationship is used to characterize the relationship between exercise time and activity level.

[0110] In this embodiment, since the target exercise intensity varies over a wide range, directly determining the current activity level based on the target exercise intensity would involve a large computational burden. The target exercise intensity is categorized into corresponding exercise intensity levels, and the target exercise time is added to each level to obtain the accumulated first exercise time for that level. Since different exercise intensity levels correspond to different activity levels, simply summing the first exercise times for each level would lead to a large error in the current activity level. Therefore, the first exercise times for different exercise intensity levels are normalized and summed, then converted to a single exercise intensity level and summed again to obtain the second exercise time. Using a first preset relationship, the second exercise time is converted to determine the current activity level. By categorizing and converting the target exercise intensity to determine the current activity level, the current activity level can reflect the user's activity under different target exercise intensities and corresponding target exercise times, thereby improving the accuracy of activity level detection.

[0111] For example, in step S331, adding the target exercise time to the exercise time at the target exercise intensity level to obtain the first exercise time can be done by pre-setting different exercise intensity ranges as different exercise intensity levels. For example, when the exercise intensity range is b1 to b2, the exercise intensity level is low; when the exercise intensity range is b2 to b3, the exercise intensity level is medium; and when the exercise intensity range is above b3, the exercise intensity level is high. Then, the exercise intensity range where the target exercise intensity is located is determined, and the exercise time at the corresponding exercise intensity level is added to the target exercise time to obtain the first exercise time.

[0112] For example, in step S332, normalizing and summing the first exercise times at different exercise intensity levels to obtain the second exercise time can be achieved by retaining the first exercise time at one exercise intensity level and multiplying the first exercise times at other exercise intensity levels by the corresponding exercise coefficient. Then, the first exercise times multiplied by the exercise coefficient and the retained first exercise times are summed to obtain the second exercise time.

[0113] For example, in step S333, determining the current activity level based on the second exercise time and the first preset relationship can be achieved by substituting the second exercise time into the first preset relationship to obtain the current activity level. Figure 6 As shown, the first preset relationship can be non-linear to avoid users blindly exercising and affecting their health, and to prevent the current activity level from increasing indefinitely and becoming inconsistent with the user's actual activity level. Here, the horizontal axis t represents time in minutes, and the vertical axis A represents the current activity level. The current activity level can be the user's activity level for the day.

[0114] In one embodiment, the determination of multiple motion intensities based on various motion information in step S200 can also be achieved in the following manner:

[0115] Obtain the user's physiological information.

[0116] Multiple exercise intensities are determined based on physiological and exercise information.

[0117] In this embodiment, since different users have different physical conditions, the exercise intensity may vary in the same exercise scenario. Therefore, it is necessary to combine the user's physiological information to determine the exercise intensity. The user's physiological information is acquired, and multiple exercise intensities are determined based on this physiological information and various exercise information. By combining physiological and exercise information to determine the exercise intensity, the exercise intensity can correspond to each user's physical condition, thereby improving the accuracy of activity level detection.

[0118] For example, physiological information may include age, height, weight, gender, resting heart rate, etc.

[0119] For example, the determination of multiple exercise intensities based on physiological information and various exercise information in the above steps is made in the following way:

[0120] The first exercise intensity is determined based on heart rate and / or step count information, as well as physiological information.

[0121] The second exercise intensity is determined based on speed information, cadence information and / or power information, as well as physiological information.

[0122] In one embodiment, after determining the current activity level based on each exercise intensity in step S300, the activity level detection method further includes:

[0123] Get historical activity volume.

[0124] Determine the target activity level based on historical and current activity levels.

[0125] In this embodiment, since the current activity level represents the user's activity level for that day, it cannot reflect whether the user's activity level over a period of time is sufficient. Therefore, it is necessary to combine historical activity levels to determine the target activity level. Historical activity levels are obtained, and the target activity level is determined based on both historical and current activity levels. By combining historical and current activity levels to determine the target activity level, the target activity level can reflect whether the user's activity level over a period of time is sufficient and the impact of the current activity level on the user's health, thereby improving the accuracy of activity level detection.

[0126] For example, historical activity can be the sum of activity over the past 7 days excluding current activity.

[0127] In one embodiment, the determination of the target activity level based on historical and current activity levels in the above steps can be achieved as follows:

[0128] The target activity level is determined based on historical activity levels, current activity levels, and the second preset relationship.

[0129] In this embodiment, by substituting historical activity levels and current activity levels into a second preset relationship, the target activity level can be quickly determined, thereby reducing the complexity of activity level detection.

[0130] For example, the step of determining the target activity level based on historical activity level, current activity level, and a second preset relationship in the above steps can be achieved by substituting the historical and current activity levels into the second preset relationship to obtain the target activity level. The second preset relationship can be non-linear to avoid users engaging in blind exercise that could negatively impact their health, and to prevent the target activity level from increasing indefinitely and becoming inconsistent with the user's actual activity level.

[0131] For example, the relationship between target activity level and exercise habits, exercise type, minimum activity level, and maximum activity level can be shown in Table 1:

[0132] Table 1:

[0133]

[0134] For example, the determination of the target activity level based on historical and current activity levels in the above steps can also be determined in the following way:

[0135] Use the average of historical activity levels and current activity levels as the target activity level.

[0136] In one embodiment, after determining the target activity level based on historical and current activity levels in the above steps, the activity level detection method further includes:

[0137] Compare the target activity level with the preset activity level to obtain a second comparison result.

[0138] If the second comparison result is outside the second preset range, a first prompt message is sent to the user. The first prompt message is used to prompt the user to reach the preset activity level and the corresponding exercise time.

[0139] If the second comparison result is within the second preset range, a second prompt message is sent to the user, which is used to remind the user that the preset activity amount has been reached.

[0140] In this embodiment, since users may not be able to determine whether their activity level has met expectations based on the target activity level, it is necessary to compare the target activity level with a preset activity level to provide prompts to the user. The target activity level and the preset activity level are compared to determine the difference, resulting in a second comparison result. If the second comparison result is outside a second preset range, the user's activity level has not met expectations, and a first prompt message is issued to the user, indicating the type of exercise and corresponding exercise time required to reach the preset activity level. If the second comparison result is within the second preset range, the user's activity level has met expectations, and a second prompt message is issued to the user, indicating that the preset activity level has been reached. By comparing the target activity level and the preset activity level to provide prompts, users can determine whether they need to exercise to maintain their health, thereby improving the user experience.

[0141] For example, the comparison between the target activity level and the preset activity level in the above steps to obtain the second comparison result can be obtained by subtracting the target activity level from the preset activity level and using the difference as the second comparison result, or by comparing the target activity level with the preset activity level and using the ratio as the second comparison result.

[0142] For example, when the difference in activity levels is used as the second comparison result, the second preset range can be expressed as ≥0. When the ratio of activity levels is used as the second comparison result, the second preset range can be expressed as ≥1.

[0143] In one embodiment, before obtaining the second comparison result by comparing the target activity level and the preset activity level in the above steps, the activity level detection method further includes:

[0144] Obtain the user's physiological information.

[0145] Determine the preset activity level based on physiological information.

[0146] In this embodiment, since different users have different physical conditions, the activities required to maintain good health vary. Therefore, it is necessary to determine the preset activity level using physiological information. The system acquires the user's physiological information and determines the preset activity level based on this information. By automatically determining the preset activity level based on physiological information, the preset activity level can correspond to each user's physical condition, thereby improving the user experience.

[0147] In one embodiment, before issuing the first prompt to the user in the above steps, the activity level detection method further includes:

[0148] Based on the target activity level and the preset activity level, determine the type of exercise and the corresponding exercise time required to achieve the preset activity level.

[0149] In this embodiment, the amount of activity the user still needs to perform is determined based on the target activity level and the preset activity level. Based on the remaining activity level, the type of exercise and the corresponding exercise time required to reach the preset activity level are determined. By determining the type of exercise and the corresponding exercise time based on the target activity level and the preset activity level, the user can exercise according to the first prompt information to maintain good health, thereby improving the user experience.

[0150] For example, the step of determining the type of exercise and corresponding exercise time required to reach the preset activity level based on the target activity level and the preset activity level can be achieved by determining the activity level difference between the target activity level and the preset activity level. This involves determining the user's previous historical exercise types and then determining the exercise time corresponding to those historical exercise types based on the activity level difference.

[0151] This disclosure provides an activity level detection method, such as... Figure 7 As shown, the method includes:

[0152] S400 acquires the user's physiological information and multiple motion information.

[0153] S410. Determine the first exercise intensity based on heart rate information, step count information, and physiological information.

[0154] S420. Determine the second exercise intensity based on speed information, cadence information or power information, and physiological information.

[0155] S430. Compare the first exercise intensity and the second exercise intensity to obtain the first comparison result.

[0156] S440. If the first comparison result is within the first preset range, determine the average of the first motion intensity and the second motion intensity as the target motion intensity.

[0157] S450. If the first comparison result is outside the first preset range, determine the larger of the first motion intensity and the second motion intensity as the target motion intensity.

[0158] S460. Determine the target motion time corresponding to the target motion intensity.

[0159] S470. Add the target exercise time to the exercise time at the exercise intensity level where the target exercise intensity is located to obtain the first exercise time.

[0160] S480. Normalize and sum the first exercise time under different exercise intensity levels to obtain the second exercise time.

[0161] S490. Determine the current activity level based on the second exercise time and the first preset relationship.

[0162] S500: Obtain historical activity volume.

[0163] S510. Determine the target activity level based on historical and current activity levels.

[0164] S520. Compare the target activity level with the preset activity level to obtain the second comparison result.

[0165] S530, if the second comparison result is outside the second preset range, issue a first prompt message to the user.

[0166] S540, if the second comparison result is within the second preset range, issue a second prompt message to the user.

[0167] In this embodiment, the user's physiological information and multiple exercise information are acquired to determine the user's physical condition and exercise information in multiple dimensions. Based on heart rate or step count information and physiological information, a first exercise intensity is determined to determine the exercise intensity in one dimension. Based on speed information, cadence information, or power information and physiological information, a second exercise intensity is determined to determine the exercise intensity in another dimension. The first and second exercise intensities are compared to obtain a first comparison result, which determines the difference in exercise intensity between the two dimensions. If the first comparison result is within a first preset range, both the first and second exercise intensities are applicable to the current exercise scenario, and the average of the first and second exercise intensities is determined as the target exercise intensity to eliminate noise in the first and second exercise intensities. If the first comparison result is outside the first preset range, one of the first and second exercise intensities is not applicable to the current exercise scenario, and the larger of the first and second exercise intensities is determined as the target exercise intensity to avoid affecting the determination of the current activity level. The target exercise time corresponding to the target exercise intensity is determined, and the target exercise time is accumulated to the exercise time at the corresponding exercise intensity level to obtain the first exercise time. The first exercise time at different exercise intensity levels is normalized and summed to obtain the second exercise time, which is then summed at the same exercise intensity level. The second exercise time is substituted into a first preset relationship to obtain the current activity level. Historical activity levels are obtained, and the target activity level is determined by combining historical and current activity levels, ensuring that the target activity level reflects the user's activity over a period of time. The target activity level and the preset activity level are compared to obtain a second comparison result, which determines whether the user's activity level meets expectations. If the second comparison result is outside the second preset range, the user's activity level does not meet expectations, and a first prompt message is issued to the user. If the second comparison result is within the second preset range, the user's activity level meets expectations, and a second prompt message is issued to the user. By determining the user's current activity level based on multiple dimensions of exercise information, inaccurate exercise information and the inability of some dimensions of exercise intensity to reflect the user's exercise level are avoided, thereby improving the accuracy of activity level detection.

[0168] In one exemplary embodiment, an activity level detection device is provided for implementing the method described above. (Reference) Figure 8 As shown, the activity level detection device may include an acquisition module 100, a first determination module 200, and a second determination module 300. During the implementation of the above method,

[0169] The acquisition module 100 is configured to acquire multiple motion information of the user.

[0170] The first determining module 200 is configured to determine multiple motion intensities based on various motion information.

[0171] The second determining module 300 is configured to determine the current activity level based on various exercise intensities.

[0172] In one exemplary embodiment, an activity level detection device is provided, wherein a first determining module 200 is configured to:

[0173] Determine the first exercise intensity based on heart rate and / or step count information.

[0174] The second exercise intensity is determined based on speed information, cadence information, and / or power information.

[0175] In one exemplary embodiment, an activity level detection device is provided, wherein a second determining module 300 is configured to:

[0176] The first and second motion intensities are filtered to obtain the target motion intensity.

[0177] Determine the target motion time corresponding to the target motion intensity.

[0178] Determine the current activity level based on the target exercise intensity and target exercise duration.

[0179] In one exemplary embodiment, an activity level detection device is provided, wherein a second determining module 300 is configured to:

[0180] The first exercise intensity and the second exercise intensity are compared to obtain the first comparison result.

[0181] If the first comparison result is within a first preset range, the average of the first motion intensity and the second motion intensity is determined as the target motion intensity.

[0182] If the first comparison result is outside the first preset range, the larger of the first and second motion intensities is determined as the target motion intensity.

[0183] In one exemplary embodiment, an activity level detection device is provided, wherein a second determining module 300 is configured to:

[0184] The first exercise time is obtained by adding the exercise time at the exercise intensity level corresponding to the target exercise intensity.

[0185] The first exercise time under different exercise intensity levels is normalized and summed to obtain the second exercise time.

[0186] The current activity level is determined based on the second exercise time and the first preset relationship.

[0187] In one exemplary embodiment, an activity level detection device is provided, wherein an acquisition module 100 is configured to:

[0188] Obtain the user's physiological information.

[0189] In one exemplary embodiment, an activity level detection device is provided, wherein a first determining module 200 is configured to:

[0190] Multiple exercise intensities are determined based on physiological and exercise information.

[0191] In one exemplary embodiment, an activity level detection device is provided, wherein an acquisition module 100 is configured to:

[0192] Get historical activity volume.

[0193] In one exemplary embodiment, an activity level detection device is provided, wherein a second determining module 300 is configured to:

[0194] Determine the target activity level based on historical and current activity levels.

[0195] In one exemplary embodiment, an activity level detection device is provided, wherein a second determining module 300 is configured to:

[0196] The target activity level is determined based on historical activity levels, current activity levels, and the second preset relationship.

[0197] In one exemplary embodiment, an activity level detection device is provided, the device further comprising:

[0198] The processing module is configured to compare the target activity level with the preset activity level to obtain a second comparison result.

[0199] If the second comparison result is outside the second preset range, a first prompt message is sent to the user.

[0200] If the second comparison result is within the second preset range, a second prompt message is sent to the user.

[0201] In one exemplary embodiment, an activity level detection device is provided, wherein a second determining module 300 is configured to:

[0202] Determine the preset activity level based on physiological information.

[0203] In one exemplary embodiment, an activity level detection device is provided, wherein a second determining module 300 is configured to:

[0204] Based on the target activity level and the preset activity level, determine the type of exercise and the corresponding exercise time required to achieve the preset activity level.

[0205] In one exemplary embodiment, an electronic device is provided, such as a mobile phone, a laptop computer, a tablet computer, and a wearable device.

[0206] refer to Figure 9 As shown, the electronic device 400 may include one or more of the following components: processing component 402, memory 404, power supply component 406, multimedia component 408, audio component 410, input / output (I / O) interface 412, sensor component 414, and communication component 416.

[0207] Processing component 402 typically controls the overall operation of electronic device 400, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 402 may include one or more processors 420 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 402 may include one or more modules to facilitate interaction between processing component 402 and other components. For example, processing component 402 may include a multimedia module to facilitate interaction between multimedia component 408 and processing component 402.

[0208] Memory 404 is configured to store various types of data to support the operation of electronic device 400. Examples of this data include instructions for any application or method operating on electronic device 400, contact data, phonebook data, messages, pictures, videos, etc. Memory 404 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, 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 storage, flash memory, magnetic disk, or optical disk.

[0209] Power supply component 406 provides power to various components of electronic device 400. Power supply component 406 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 400.

[0210] Multimedia component 408 includes a screen that provides an output interface between electronic device 400 and user. 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 touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 408 includes a front-facing camera module and / or a rear-facing camera module. When electronic device 400 is in an operating mode, such as shooting mode or video mode, the front-facing camera module and / or rear-facing camera module may receive external multimedia data. Each front-facing camera module and rear-facing camera module may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0211] Audio component 410 is configured to output and / or input audio signals. For example, audio component 410 includes a microphone (MIC) configured to receive external audio signals when electronic device 400 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 404 or transmitted via communication component 416. In some embodiments, audio component 410 also includes a speaker for outputting audio signals.

[0212] I / O interface 412 provides an interface between processing component 402 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0213] Sensor assembly 414 includes one or more sensors for providing state assessments of various aspects of electronic device 400. For example, sensor assembly 414 may detect the on / off state of electronic device 400, the relative positioning of components such as the display and keypad of electronic device 400, changes in position of electronic device 400 or a component of electronic device 400, the presence or absence of user contact with electronic device 400, orientation or acceleration / deceleration of electronic device 400, and temperature changes of electronic device 400. Sensor assembly 414 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 414 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 414 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0214] Communication component 416 is configured to facilitate wired or wireless communication between electronic device 400 and other terminals. Electronic device 400 can access wireless networks based on communication standards, such as WiFi, 2G, 3G, 4G, 5G, or combinations thereof. In one exemplary embodiment, communication component 416 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 416 also includes a near-field communication (NFC) module to facilitate short-range communication. 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.

[0215] In an exemplary embodiment, the electronic device 400 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing terminals (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods shown in the above embodiments or combinations thereof.

[0216] In one exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 404 including instructions, which can be executed by a processor 420 of an electronic device 400 to perform the methods shown in the embodiments or combinations thereof. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage terminal, etc. When the instructions in the storage medium are executed by the processor of the terminal, the terminal is able to perform the methods shown in the embodiments or combinations thereof.

[0217] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0218] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0219] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0220] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0221] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for detecting activity levels, characterized in that, The activity level detection method includes: Multiple motion information pieces are acquired from the user, and each motion information piece corresponds to different parameters; Based on the described motion information, multiple motion intensities are determined; Determine the current activity level based on the described exercise intensity.

2. The activity level detection method according to claim 1, characterized in that, The exercise information includes heart rate information, step count information, speed information, cadence information and / or power information of the exercise device; The determination of multiple motion intensities based on the motion information includes: The first exercise intensity is determined based on the heart rate information and / or the step count information; The second exercise intensity is determined based on the speed information, the step frequency information, and / or the power information.

3. The activity level detection method according to claim 2, characterized in that, Determining the current activity level based on the respective exercise intensities includes: The first motion intensity and the second motion intensity are filtered to obtain the target motion intensity; Determine the target motion time corresponding to the target motion intensity; The current activity level is determined based on the target exercise intensity and the target exercise time.

4. The activity level detection method according to claim 3, characterized in that, The step of filtering the first motion intensity and the second motion intensity to obtain the target motion intensity includes: By comparing the first exercise intensity and the second exercise intensity, a first comparison result is obtained; If the first comparison result is within a first preset range, the average of the first motion intensity and the second motion intensity is determined to be the target motion intensity. If the first comparison result is outside the first preset range, the larger of the first motion intensity and the second motion intensity is determined as the target motion intensity.

5. The activity level detection method according to claim 3, characterized in that, Determining the current activity level based on the target exercise intensity and the target exercise time includes: The first exercise time is obtained by adding the exercise time at the exercise intensity level where the target exercise intensity is located to the target exercise time. The first exercise time under different exercise intensity levels is normalized and summed to obtain the second exercise time; The current activity level is determined based on the second exercise time and the first preset relationship; The first preset relationship is used to characterize the relationship between exercise time and activity level.

6. The activity level detection method according to claim 1, characterized in that, The determination of multiple motion intensities based on the motion information includes: Obtaining the user's physiological information; Based on the physiological information and each of the exercise information, a plurality of exercise intensities are determined.

7. The activity level detection method according to any one of claims 1 to 6, characterized in that, After determining the current activity level based on each of the described exercise intensities, the activity level detection method further includes: Get historical activity volume; The target activity level is determined based on the historical activity level and the current activity level.

8. The activity level detection method according to claim 7, characterized in that, Determining the target activity level based on the historical activity level and the current activity level includes: The target activity level is determined based on the historical activity level, the current activity level, and the second preset relationship.

9. The activity level detection method according to claim 7, characterized in that, After determining the target activity level based on the historical activity level and the current activity level, the activity level detection method further includes: The target activity level and the preset activity level are compared to obtain a second comparison result; If the second comparison result is outside the second preset range, a first prompt message is sent to the user. The first prompt message is used to prompt the user to reach the preset amount of activity and the corresponding exercise time. If the second comparison result is within the second preset range, a second prompt message is sent to the user, which is used to remind the user that the preset activity amount has been reached.

10. The activity level detection method according to claim 9, characterized in that, Before comparing the target activity level and the preset activity level to obtain the second comparison result, the activity level detection method further includes: Obtaining the user's physiological information; The preset activity level is determined based on the physiological information.

11. The activity level detection method according to claim 9, characterized in that, Before issuing the first notification to the user, the activity level detection method further includes: Based on the target activity level and the preset activity level, determine the type of exercise and the corresponding exercise time required to achieve the preset activity level.

12. An activity level detection device, characterized in that, The activity level detection device includes: An acquisition module is configured to acquire multiple motion information of a user, each of which has different parameters. A first determining module is configured to determine multiple motion intensities based on the motion information. The second determining module is configured to determine the current activity level based on each of the said exercise intensities.

13. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to perform the activity detection method as described in any one of claims 1 to 11.

14. A non-transitory computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the terminal, the terminal is able to perform the activity detection method as described in any one of claims 1 to 11.

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