A muscle fatigue early warning method, device, storage medium and electronic equipment

Abstract

The application relates to a muscle fatigue early warning method and device, a storage medium and an electronic device, and relates to the technical field of muscle fatigue monitoring. The method comprises the following steps: acquiring the fitness plan information of a target user this time; determining at least one muscle part to be monitored from each muscle part of the target user according to each planned fitness action and the corresponding planned number of times; acquiring the actual electromyographic signal of each muscle part to be monitored; if the actual electromyographic signal is abnormal, determining the corresponding muscle part to be monitored as an abnormal muscle part, and determining whether the abnormal muscle part has a muscle fatigue risk according to the actual fitness action completed by the target user currently and the corresponding actual number of times; and if it is determined that the abnormal muscle part has a muscle fatigue risk, issuing a muscle fatigue early warning for the abnormal muscle part. The application has the effect of improving the accuracy of muscle fatigue early warning.

Description

Technical Field

[0001] This application relates to the field of muscle fatigue monitoring technology, specifically to a muscle fatigue early warning method, device, storage medium, and electronic device. Background Technology

[0002] Muscle fatigue refers to the phenomenon where muscles, after sustained contraction or repetitive exercise, are unable to maintain normal contractile force or speed, leading to a decline in athletic performance. It is a temporary decline in muscle function, typically related to factors such as energy metabolism, neural control, and muscle fiber damage. Muscle fatigue warning systems, through bodily signals, physiological indicators, or technical means, issue alerts before muscles enter a state of fatigue, prompting timely adjustments to exercise intensity, rest, or intervention to avoid over-fatigue and injury. It is an important strategy for preventing sports injuries and optimizing training results. Muscle fatigue warning systems are also widely used in the fitness industry.

[0003] Currently, the common method for early warning of muscle fatigue in fitness enthusiasts is to collect electromyography (EMG) signals from their bodies, analyze the signal characteristics of the EMG signals, and determine whether there is a risk of muscle fatigue by using a preset fixed threshold. However, because the collection of EMG signals is easily affected by factors such as poor electrode contact, the EMG signals may deviate. The method of judging by a fixed threshold interferes with the accuracy of the determination of muscle fatigue risk, resulting in poor accuracy of muscle fatigue early warning. Summary of the Invention

[0004] To improve the accuracy of muscle fatigue early warning, this application provides a muscle fatigue early warning method, device, storage medium, and electronic device.

[0005] The first aspect of this application provides a muscle fatigue early warning method, specifically including:

[0006] Obtain the target user's current fitness plan information, which includes at least one planned fitness exercise and the corresponding number of repetitions.

[0007] Based on each of the planned fitness movements and the corresponding number of repetitions, at least one muscle group to be monitored is determined from each muscle group of the target user;

[0008] The actual electromyographic signals of each of the muscle sites to be monitored are obtained. If the actual electromyographic signals are abnormal, the corresponding muscle site to be monitored is identified as an abnormal muscle site. Based on the actual fitness movements and corresponding number of repetitions completed by the target user, it is determined whether the abnormal muscle site is at risk of muscle fatigue.

[0009] If it is determined that the abnormal muscle area is at risk of muscle fatigue, a muscle fatigue warning is issued for the abnormal muscle area.

[0010] By employing the aforementioned technical solution, after obtaining the target user's planned fitness movements and corresponding number of repetitions, the potential for muscle fatigue in various muscle groups is analyzed based on these movements and repetitions. This helps identify the muscle groups to be monitored, enabling targeted detection of muscle fatigue in the target user based on the fitness plan information. Furthermore, if abnormal electromyographic signals are observed, it indicates a potential risk of muscle fatigue in the affected muscle group, requiring further verification. Based on the target user's completed fitness movements and corresponding repetitions, the likelihood of muscle fatigue in the affected muscle group is analyzed, thus determining whether a muscle fatigue risk exists. If a risk of muscle fatigue exists, a muscle fatigue warning is issued for the affected muscle group to intervene in advance, thereby improving the accuracy of the warning.

[0011] In one implementation, determining at least one muscle group to be monitored from the target user's muscle groups based on each of the planned fitness movements and the corresponding planned repetitions specifically includes:

[0012] Based on multiple historical muscle sites that have experienced muscle fatigue during a single fitness exercise, at least one target muscle site is identified, wherein the target muscle site is a historical muscle site that is prone to inducing muscle fatigue.

[0013] Based on the range of number of times a single fitness exercise was completed when a historical user experienced muscle fatigue in a single target muscle area, at least one target number range is determined. The target number range is the range of number of times that easily induces muscle fatigue. The historical user and the target user are consistent in terms of fitness exercise and user profile.

[0014] A first weight is determined for each target muscle group, and a second weight is determined for the target number range corresponding to each target muscle group. The first weight represents the probability of muscle fatigue occurring in the target muscle group, and the second weight represents the probability of muscle fatigue occurring when the number of times a single fitness movement is completed is within the target number range.

[0015] Based on the first weight, second weight, planned fitness movements, and corresponding number of repetitions for each individual health movement, at least one muscle group to be monitored is determined from the muscle groups of the target user.

[0016] In one implementation, determining at least one muscle group to be monitored from the target user's muscle groups based on a first weight, a second weight, each planned fitness movement, and the corresponding number of planned repetitions for a single health movement specifically includes:

[0017] For a single planned fitness exercise, if there is a corresponding planned number of repetitions within the target repetition range corresponding to the target muscle group, then the corresponding target muscle group is identified as the key muscle group, and the corresponding target repetition range is identified as the key repetition range.

[0018] Calculate the first product of the first weight of each key muscle group and the second weight of the corresponding target repetition range, and sum the first products corresponding to the same key muscle group based on the first products corresponding to each of the planned fitness movements to obtain the corresponding first summation result;

[0019] The first summation result is compared with a preset first threshold. If the first summation result exceeds the first threshold, the corresponding key muscle area is identified as the muscle area to be monitored.

[0020] In one implementation, determining whether the abnormal muscle area is at risk of muscle fatigue based on the actual fitness movements and corresponding repetitions currently completed by the target user specifically includes:

[0021] For a single actual fitness movement that the target user has completed, if there is a corresponding actual number of repetitions within the target number of repetitions for the target muscle part, then the corresponding target muscle part is determined as the reference muscle part, and the corresponding target number of repetitions is determined as the reference number of repetitions range.

[0022] Calculate the second product of the first weight of each reference muscle part and the second weight of the corresponding reference number range, and sum the second products corresponding to all actual fitness movements to obtain the second summation result;

[0023] If the second summation result is greater than the preset second threshold, then the second products of the same reference muscle part in the second products corresponding to all actual fitness movements are summed to obtain the corresponding third summation result;

[0024] If the third summation result exceeds the preset first threshold, the corresponding reference muscle part is determined as a risk muscle part. When the abnormal muscle part is the risk muscle part, it is determined that the abnormal muscle part has a risk of muscle fatigue.

[0025] If the abnormal muscle site is not a risky muscle site, it is determined that the abnormal muscle site does not pose a risk of muscle fatigue.

[0026] In one embodiment, the method further includes:

[0027] When there is no risk of muscle fatigue at the abnormal muscle site, if the abnormal muscle site is the reference muscle site, then the second product of the same abnormal muscle site in all actual fitness movements is summed to obtain the corresponding fourth summation result.

[0028] Calculate the difference between the first threshold and the fourth summation result. If the difference does not exceed the preset difference threshold, then the planned fitness movements that have not yet started are identified as remaining movements.

[0029] If there is an abnormal muscle part among the target muscle parts corresponding to the remaining movements, the corresponding remaining movements are determined as movements to be adjusted, and for a single movement to be adjusted, the third product of the first weight of the abnormal muscle part and the second weight of the corresponding target number range is calculated.

[0030] Select the smallest third product from each of the third products, and sum the fourth summation result with the smallest third product corresponding to each of the actions to be adjusted to obtain the final summation result;

[0031] If the final summation result does not exceed the first threshold, then based on the target number range corresponding to the single minimum third product, the appropriate number of the corresponding adjustment action is determined, and each appropriate number is sent to the target user's terminal.

[0032] In one embodiment, the method further includes:

[0033] If it is determined that there is no risk of muscle fatigue in the abnormal muscle area, then at least one area of ​​concern is identified based on the muscle areas where poor electrode contact has occurred in the past by users during exercise. The area of ​​concern is the muscle area where poor electrode contact is likely to occur when monitoring electromyographic signals.

[0034] Based on the historical fitness movements that caused poor electrode contact at a single site of concern, at least one corresponding movement of concern is determined, wherein the movement of concern is a historical fitness movement that is likely to cause poor electrode contact.

[0035] A first weight is determined for each of the parts to be concerned, and a second weight is determined for the action to be concerned corresponding to each part to be concerned;

[0036] Based on the first weight, the second weight, and each of the actual fitness movements, it is verified that there is no risk of muscle fatigue in the abnormal muscle areas.

[0037] In one implementation, the step of verifying whether the abnormal muscle area has a risk of muscle fatigue based on the first weight, the second weight, and each of the actual fitness movements specifically includes:

[0038] If there is at least one actual fitness movement among the movements corresponding to the area to be focused on, then the corresponding area to be focused on is determined as the key area to be focused on. When the abnormal muscle area is the key area to be focused on, the fourth product of the first weight of the abnormal muscle area and the second weight of the corresponding actual fitness movement is calculated.

[0039] Summing each of the fourth products yields the fifth summation result corresponding to the abnormal muscle region.

[0040] The fifth summation result is compared with the preset third threshold. If the fifth summation result exceeds the third threshold, it is determined that the verification of no muscle fatigue risk for the abnormal muscle area has passed.

[0041] A second aspect of this application provides a muscle fatigue early warning device, specifically comprising:

[0042] The information acquisition module is used to acquire the fitness plan information of the target user for this time. The fitness plan information includes at least one planned fitness movement and the corresponding number of planned movements.

[0043] The muscle group determination module is used to determine at least one muscle group to be monitored from each muscle group of the target user based on each of the planned fitness movements and the corresponding planned number of repetitions.

[0044] The risk assessment module is used to acquire the actual electromyographic signals of each of the muscle sites to be monitored. If the actual electromyographic signals are abnormal, the corresponding muscle site to be monitored is identified as an abnormal muscle site. Based on the actual fitness movements and corresponding number of repetitions completed by the target user, it is determined whether the abnormal muscle site is at risk of muscle fatigue.

[0045] The fatigue warning module is used to issue a muscle fatigue warning for the abnormal muscle area if it is determined that there is a risk of muscle fatigue in the abnormal muscle area.

[0046] By adopting the above technical solution, the information acquisition module obtains the target user's current fitness plan information. Then, the part determination module determines at least one muscle part to be monitored based on the planned fitness movements and the corresponding planned number of repetitions. Next, the risk assessment module determines whether there is a risk of muscle fatigue in the abnormal muscle part based on the actual fitness movements and the corresponding actual number of repetitions completed by the target user. Finally, when the fatigue warning module determines that there is a risk of muscle fatigue in the abnormal muscle part, it issues a muscle fatigue warning for the abnormal muscle part.

[0047] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when loaded and executed by a processor, performs the steps of the method described in any one of the first aspects.

[0048] A fourth aspect of this application provides an electronic device, specifically comprising:

[0049] A processor, a memory, and a computer program stored in the memory and capable of running on the processor, the processor being configured to load and execute the computer program stored in the memory to cause the electronic device to perform the method as described in any one of the first aspects.

[0050] In summary, this application includes at least one of the following beneficial technical effects: After obtaining the planned fitness movements and corresponding number of repetitions for the target user's current workout, based on the planned movements and repetitions, the potential for muscle fatigue in each muscle group is analyzed to determine the muscle groups to be monitored. This allows for targeted detection of muscle fatigue in the target user based on the fitness plan information. Furthermore, if abnormal electromyographic signals are observed, it indicates a potential risk of muscle fatigue in the abnormal muscle group, requiring further verification of the risk. Based on the actual fitness movements and repetitions completed by the target user, the probability of muscle fatigue in the abnormal muscle group is analyzed, thus determining whether a risk of muscle fatigue exists. If a risk of muscle fatigue exists, a muscle fatigue warning is issued for the abnormal muscle group to intervene in advance, thereby improving the accuracy of the muscle fatigue warning. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating a muscle fatigue early warning method provided in an embodiment of this application;

[0052] Figure 2 This is a schematic diagram of the structure of a muscle fatigue early warning device provided in an embodiment of this application;

[0053] Figure 3 This is a schematic diagram of another muscle fatigue early warning device provided in the embodiments of this application.

[0054] Explanation of reference numerals in the attached diagram: 11. Information acquisition module; 12. Part determination module; 13. Risk assessment module; 14. Fatigue warning module; 15. Fitness reminder module; 16. Risk verification module. Detailed Implementation

[0055] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0056] In the description of the embodiments of this application, words such as "exemplarily," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplarily," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.

[0057] In the description of the embodiments of this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, B existing alone, or A and B existing simultaneously. Furthermore, unless otherwise stated, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, 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 indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.

[0058] See Figure 1 This application discloses a flowchart of a muscle fatigue early warning method, which can be implemented using a computer program or run on a muscle fatigue early warning device based on the von Neumann architecture. The computer program can be integrated into an application or run as a standalone utility application, specifically including:

[0059] S101: Obtain the target user's current fitness plan information, which includes at least one planned fitness exercise and the corresponding number of repetitions.

[0060] Specifically, in this embodiment, the target user is the user monitoring muscle fatigue during this workout. The fitness plan information is the training plan information for the target user's workout. The fitness plan information includes at least one planned fitness exercise and its corresponding number of repetitions. The planned fitness exercise can be understood as the training movement or activity that the target user plans to complete during this workout. The number of repetitions for each planned fitness exercise can be understood as the number of times the target user plans to complete the corresponding planned fitness exercise. For example, if the planned fitness exercise is a squat, the corresponding number of repetitions is 40; if the planned fitness exercise is a pull-up, the corresponding number of repetitions is 20.

[0061] Furthermore, in this embodiment of the muscle fatigue early warning method, the executing entity is a server. The server is wirelessly connected to a terminal, which is the target user's smartphone or personal computer. The terminal has a client related to muscle fatigue early warning installed, and the server is the backend server of the client. Specifically, it can be an independent physical server or a cluster of multiple physical servers. The server is also wirelessly connected to a wearable electromyography (EMG) detection device worn by the target user. The wearable EMG detection device mainly monitors the EMG signals of the target user's body through electrodes. The wearable EMG detection device can be an armband / legband or a smart fitness garment. The target user wears at least one wearable EMG detection device during the workout. One implementation scenario is: when the target user starts exercising according to the fitness plan information, the client in the terminal sends an activation command to the server. The server activates the command, acquires the EMG signals of the target user's body during exercise through the wearable EMG detection device, and issues a muscle fatigue early warning based on the EMG signals. Another feasible way to obtain fitness plan information is: the target user sends the fitness plan information for this workout to the server through the terminal, and the server ultimately obtains the fitness plan information.

[0062] S102: Based on each planned fitness movement and the corresponding number of repetitions, identify at least one muscle group to be monitored from the target user's muscle groups.

[0063] Specifically, after determining the various planned fitness movements and their corresponding repetitions, it is necessary to identify at least one muscle group to be monitored from various muscle parts of the target user's body to achieve targeted muscle fatigue warning. One feasible implementation method is as follows: based on the first historical record of muscle fatigue warning, obtain multiple historical muscle groups that have experienced muscle fatigue when the fitness user performed a single fitness movement in the past, count the occurrence number of each individual historical muscle group in all historical muscle groups, and if the occurrence number exceeds a corresponding threshold, then the corresponding historical muscle group is identified as the target muscle group, that is, the historical muscle group that is prone to fatigue under a single fitness movement. Here, the first historical record includes, but is not limited to, historical muscle groups that have experienced muscle fatigue under a single fitness movement.

[0064] Furthermore, based on the second historical record of muscle fatigue warning, the range of number of times a historical user performed a single fitness exercise when muscle fatigue occurred in a specific target muscle group is obtained. The frequency of occurrence of each individual range within all ranges is calculated. If the frequency exceeds a corresponding frequency threshold, the corresponding range is determined as the target range for that target muscle group—that is, the range of numbers most likely to induce muscle fatigue in that target muscle group. Historical users and target users are consistent in their fitness exercises and user profiles. Specifically, all fitness exercises performed by historical users are identical to all planned fitness exercises performed by target users. Additionally, the user profile is a virtual user model abstracted from basic user information, including but not limited to the user's age and gender. The second historical record includes, but is not limited to, the range of number of times each fitness exercise was performed when a historical user experienced muscle fatigue.

[0065] A first weight is determined for each target muscle group, and a second weight is determined for the target repetition range corresponding to each target muscle group, thus obtaining the first and second weights for a single fitness exercise. The first weight is the ratio of the number of occurrences of each target muscle group to the sum of the number of occurrences of all target muscle groups. The second weight is the ratio of the frequency of occurrence of a single target repetition range corresponding to a target muscle group to the sum of the frequencies of occurrence of all corresponding target repetition ranges. Finally, based on the first and second weights for a single fitness exercise, each planned fitness exercise, and the corresponding planned repetitions, the muscle group to be monitored is determined. In this embodiment, a feasible determination method is as follows:

[0066] For a single planned fitness exercise, if the planned number of repetitions for the target muscle group exists within the target repetition range, then the corresponding target muscle group is identified as a key muscle group, and the corresponding target repetition range is identified as a key repetition range. The first product of the first weight of each key muscle group and the second weight of the corresponding target repetition range is calculated, resulting in at least one first product for each planned fitness exercise. The larger the first product, the greater the likelihood of muscle fatigue in the key muscle group when the planned number of repetitions for the exercise is reached. Next, the first products corresponding to the same key muscle group across all planned fitness exercises are summed to obtain the first summation result for that key muscle group. The larger the first summation result, the greater the overall likelihood of muscle fatigue in the corresponding key muscle group when the target user follows the fitness plan. Furthermore, the first summation result is compared with a preset first threshold. If the first summation result exceeds the first threshold, it indicates a higher overall likelihood of muscle fatigue in the corresponding key muscle group when the target user follows the fitness plan, requiring targeted monitoring. Therefore, the corresponding key muscle group is identified as the muscle group to be monitored.

[0067] S103: Obtain the actual electromyographic signals of each muscle group to be monitored. If the actual electromyographic signals are abnormal, the corresponding muscle group to be monitored is identified as an abnormal muscle group. Based on the actual fitness movements and corresponding number of repetitions completed by the target user, determine whether there is a risk of muscle fatigue in the abnormal muscle group.

[0068] Specifically, after identifying the muscle sites to be monitored, the wearable electromyography (EMG) detection device on each muscle site collects the current actual EMG signal. Then, it is determined whether the actual EMG signal is abnormal. One feasible method is to preprocess the actual EMG signal, including filtering (removing high-frequency noise), amplification (enhancing signal strength), and rectification. Next, feature parameters are calculated based on the preprocessed results. If the feature parameters are not within the corresponding threshold range, the actual EMG signal is determined to be abnormal, and the corresponding muscle site may have a risk of muscle fatigue, requiring further verification. In this case, the corresponding muscle site is identified as an abnormal muscle site. The feature parameters can be the median frequency (MF) or the root mean square (RMS), both of which are used to assess muscle activity and fatigue levels. This is existing technology and will not be elaborated further.

[0069] Furthermore, based on the actual fitness movements and corresponding repetitions completed by the target user, it is determined whether there is a risk of muscle fatigue in the abnormal muscle group. One feasible implementation method is as follows: For a single actual fitness movement, if there are corresponding actual repetitions within the target repetition range for the target muscle group, then the corresponding target muscle group is determined as the reference muscle group, and the corresponding target repetition range is determined as the reference repetition range. Next, the second product of the first weight of each reference muscle group and the second weight of the corresponding reference repetition range is calculated. The second products corresponding to all actual fitness movements are summed to obtain a second summation result. The larger the second summation result, the greater the overall probability that the target user's body has experienced muscle fatigue at the current time of this fitness exercise. The second summation result is then compared with a preset second threshold. If the second summation result is greater than the second threshold, it indicates that the overall probability of the target user's body experiencing muscle fatigue is relatively high. Based on this, the second products corresponding to the same reference muscle group in the second products corresponding to all actual fitness movements are summed to obtain a third summation result for the corresponding reference muscle group. Finally, if the third summation exceeds the first threshold, it indicates that the target user's current health monitoring activity suggests a higher probability of muscle fatigue in the corresponding reference muscle area, thus identifying it as a risk muscle area. If the abnormal muscle area is indeed identified as a risk muscle area, then it is determined that this abnormal muscle area does indeed pose a risk of muscle fatigue; otherwise, it is determined that the abnormal muscle area does not pose a risk of muscle fatigue. It should be noted that the second threshold is greater than the first threshold.

[0070] In one embodiment, when there is no risk of muscle fatigue at the abnormal muscle site, if the abnormal muscle site is a reference muscle site, then the second products of the same abnormal muscle site in all actual fitness movements are summed to obtain a corresponding fourth summation result. The larger the fourth summation result, the greater the likelihood that the target user's current abnormal muscle site will experience muscle fatigue. The difference between the first threshold and the fourth summation result is calculated. If the difference does not exceed a preset difference threshold, it means that although the fourth summation result is less than the first threshold, the difference is small. When the target user performs subsequent fitness movements, the abnormal muscle site is still prone to muscle fatigue. Therefore, the planned fitness movements that have not yet been started are identified as remaining movements. Further, if there are abnormal muscle sites among the target muscle sites corresponding to the remaining movements, then the corresponding remaining movements are identified as movements to be adjusted. For a single exercise to be adjusted, the third product of the first weight of the abnormal muscle group and the second weight of the corresponding target repetition range is calculated. The larger the third product, the more likely the abnormal muscle group is to experience muscle fatigue when the number of repetitions of the exercise to be adjusted falls within the corresponding target repetition range. The smallest third product is selected from all the third products, and the fourth summation result is summed with the smallest third product corresponding to each exercise to be adjusted to obtain the final summation result. The larger the final summation result, the greater the possibility of muscle fatigue in the abnormal muscle group when completing the remaining exercises in this workout. If the final summation result does not exceed the first threshold, it means that the possibility of muscle fatigue in the abnormal muscle group is small when completing the corresponding remaining exercises according to the target repetition range corresponding to the smallest third product. Therefore, to avoid muscle fatigue in the abnormal muscle group during subsequent workouts, the appropriate number of repetitions for the corresponding exercise to be adjusted is determined based on the target repetition range corresponding to the individual smallest third product. Specifically, the maximum value in the target repetition range can be determined as the appropriate number of repetitions. Finally, the appropriate number of repetitions for each exercise to be adjusted is sent to the target user's terminal, so that the target user can adjust the number of repetitions of each exercise to be adjusted in a timely manner during subsequent workouts.

[0071] In other embodiments, the target user's current target fitness movement is obtained. For this target fitness movement, the weight product of a first weight for each target muscle part and a second weight for each corresponding target repetition range is calculated. The weight products are summed to obtain the summation result for this target fitness movement. If the summation result is greater than a preset threshold, it indicates that there is a high probability of muscle fatigue when performing the target fitness movement, and special attention needs to be paid to the standard posture. Then, the actual posture of the target user when performing the target fitness movement is obtained, and the similarity between the actual posture and the standard posture of the target fitness movement is calculated. If the similarity is not greater than a preset similarity threshold, it indicates that the target user's posture when performing the target fitness movement is relatively non-standard, which is likely to accelerate muscle fatigue. Then, a non-standard posture reminder message is sent to the target user's terminal. The similarity threshold is determined from a preset threshold matching table based on the summation result. The larger the summation result, the larger the similarity threshold, and the higher the requirement for the standardization of the target fitness movement. The threshold matching table includes different summation result ranges and corresponding similarity thresholds, all set based on human experience. For example, the table includes summation result ranges of 0.6-1 with a corresponding similarity threshold of 0.8; summation result ranges of 1-1.4 with a corresponding similarity threshold of 0.9; and if the summation result is within the range of 0.6-1, the similarity threshold is 0.8. It should be noted that the actual posture of the target fitness movement is acquired in real time using a preset infrared sensor. Alternatively, a feasible method for calculating similarity is to map both the actual posture and the standard posture to a high-dimensional vector space to obtain corresponding vectors, and then calculate the cosine similarity between the two vectors.

[0072] S104: If it is determined that there is a risk of muscle fatigue in an abnormal muscle area, a muscle fatigue warning will be issued for the abnormal muscle area.

[0073] Specifically, if it is determined that there is a risk of muscle fatigue in an abnormal muscle area, it means that the abnormal muscle area is more likely to experience muscle fatigue. In this case, a muscle fatigue warning is sent to the target user's terminal for the abnormal muscle area, thereby reminding the target user to intervene in time to avoid muscle fatigue or even muscle damage.

[0074] In other embodiments, if it is determined that the abnormal muscle area does not pose a risk of muscle fatigue, then based on the historical record of poor electrode contact, the muscle areas where poor electrode contact has occurred during exercise by historical users are obtained. The first frequency of recurrence of a single muscle area among all muscle areas is counted. If the first frequency exceeds a corresponding frequency threshold, the corresponding muscle area is identified as a site of concern, i.e., a muscle area where electrode contact is prone to occur when monitoring electromyographic signals. Next, based on the above historical record, the historical exercise movements that triggered poor electrode contact at a single site of concern are obtained. The second frequency of recurrence of a single historical exercise movement among all historical exercise movements is counted. If the second frequency exceeds a corresponding frequency threshold, the corresponding historical exercise movement is identified as the exercise of concern for that site of concern, i.e., a historical exercise movement prone to triggering poor electrode contact. The historical record includes, but is not limited to, the muscle areas where poor electrode contact has occurred and the corresponding exercise movements.

[0075] A first weight is determined for each muscle group to be monitored, and a second weight is determined for the corresponding exercise to be monitored for each muscle group. The first weight is the ratio of the first frequency of each muscle group to the sum of the first frequencies of all muscle groups to be monitored. The second weight is the ratio of the second frequency of a single exercise to the sum of the second frequencies of all corresponding exercises to the muscle group to be monitored. Finally, based on the first and second weights and the actual fitness exercises, it is verified that there is no risk of muscle fatigue in the abnormal muscle groups. One feasible implementation method is as follows:

[0076] If at least one actual exercise is present among the various exercises corresponding to the area of ​​interest, then the corresponding area of ​​interest is designated as a key area of ​​interest. When an abnormal muscle area is a key area of ​​interest, the fourth product of the first weight of the abnormal muscle area and the second weight of each corresponding exercise is calculated. The larger the fourth product, the greater the likelihood that the exercise will cause poor electrode contact at the abnormal muscle area. The fourth products are summed to obtain a fifth sum. The larger the fifth sum, the greater the likelihood of poor electrode contact at the abnormal muscle area of ​​the current target user. Finally, if the fifth sum is greater than a preset third threshold, it indicates that the previous target user's abnormal muscle area is likely to have poor electrode contact, and the corresponding actual electromyography (EMG) signal is likely to have errors. This suggests that the actual EMG signal at the abnormal muscle area is not actually abnormal, implying that there is no risk of muscle fatigue at the abnormal muscle area. Therefore, the check that there is no risk of muscle fatigue at the abnormal muscle area passes, thus improving the accuracy of muscle fatigue warning.

[0077] The implementation principle of a muscle fatigue early warning method according to an embodiment of this application is as follows: After obtaining the planned fitness movements and corresponding number of repetitions for the target user's current workout, the method analyzes the possible occurrence of muscle fatigue in various muscle groups based on the planned movements and number of repetitions, thereby identifying the muscle groups to be monitored. Based on the fitness plan information, targeted detection of muscle fatigue in the target user is achieved. Furthermore, if abnormal electromyographic signals are observed, it indicates that the abnormal muscle group may be at risk of muscle fatigue, requiring further verification of the existence of this risk. Based on the actual fitness movements and corresponding number of repetitions completed by the target user, the method analyzes the probability of muscle fatigue in the abnormal muscle group given the completed fitness content, thereby determining whether the abnormal muscle group is at risk of muscle fatigue. If a risk of muscle fatigue exists, a muscle fatigue early warning is issued for the abnormal muscle group to intervene in advance, thus improving the accuracy of the muscle fatigue early warning.

[0078] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0079] Please see Figure 2 This is a schematic diagram of the muscle fatigue early warning device provided in an embodiment of this application. This device can be implemented as all or part of a whole through software, hardware, or a combination of both. The device includes an information acquisition module 11, a location determination module 12, a risk assessment module 13, and a fatigue early warning module 14.

[0080] The information acquisition module 11 is used to acquire the target user's current fitness plan information, which includes at least one planned fitness movement and the corresponding number of repetitions.

[0081] The muscle group determination module 12 is used to determine at least one muscle group to be monitored from each muscle group of the target user based on each planned fitness movement and the corresponding planned number of repetitions.

[0082] The risk assessment module 13 is used to acquire the actual electromyographic signals of each muscle group to be monitored. If the actual electromyographic signal is abnormal, the corresponding muscle group to be monitored is identified as an abnormal muscle group. Based on the actual fitness movements and corresponding number of repetitions completed by the target user, it is determined whether there is a risk of muscle fatigue in the abnormal muscle group.

[0083] The fatigue warning module 14 is used to issue a muscle fatigue warning for abnormal muscle areas if it is determined that there is a risk of muscle fatigue in the abnormal muscle areas.

[0084] Optional, the part determination module 12 is specifically used for:

[0085] Based on multiple historical muscle groups that have experienced muscle fatigue during a single fitness exercise, identify at least one target muscle group. The target muscle group is the historical muscle group that is prone to inducing muscle fatigue.

[0086] Based on the range of repetitions of a single fitness exercise when historical users experienced muscle fatigue in a single target muscle group, at least one corresponding target repetition range is determined. The target repetition range is the range of repetitions that are likely to induce muscle fatigue. Historical users and target users are consistent in terms of fitness exercises and user profiles.

[0087] A first weight is determined for each target muscle group, and a second weight is determined for the target number of repetitions for each target muscle group. The first weight represents the likelihood of muscle fatigue occurring in the target muscle group, and the second weight represents the likelihood of muscle fatigue occurring when the number of repetitions of a single fitness exercise is within the target number of repetitions.

[0088] Based on the first weight, second weight, planned fitness movements, and corresponding number of repetitions for each individual health movement, at least one muscle group to be monitored is identified from the target user's muscle groups.

[0089] Optional, the part determination module 12 is specifically used for:

[0090] For a single planned fitness exercise, if there are corresponding planned repetitions within the target repetition range for the target muscle group, then the corresponding target muscle group is identified as the key muscle group, and the corresponding target repetition range is identified as the key repetition range.

[0091] Calculate the first product of the first weight of each key muscle group and the second weight of the corresponding target repetition range, and sum the first products corresponding to the same key muscle group based on the first products corresponding to each planned fitness movement to obtain the corresponding first summation result;

[0092] The first summation result is compared with a preset first threshold. If the first summation result exceeds the first threshold, the corresponding key muscle area is identified as the muscle area to be monitored.

[0093] Optional, risk assessment module 13, specifically used for:

[0094] For a single actual fitness movement that the target user has already completed, if there is a corresponding actual number of repetitions within the target repetition range for the target muscle part, then the corresponding target muscle part is determined as the reference muscle part, and the corresponding target repetition range is determined as the reference repetition range.

[0095] Calculate the second product of the first weight of each reference muscle part and the second weight of the corresponding reference repetition range, and sum the second products corresponding to all actual fitness movements to obtain the second summation result;

[0096] If the second summation result is greater than the preset second threshold, then the second products of the same reference muscle part in the second products corresponding to all actual fitness movements are summed to obtain the corresponding third summation result;

[0097] If the third summation result exceeds the preset first threshold, the corresponding reference muscle part is identified as a risk muscle part. When the abnormal muscle part is a risk muscle part, it is determined that the abnormal muscle part has a risk of muscle fatigue.

[0098] When the abnormal muscle site is not a high-risk muscle site, it is determined that there is no risk of muscle fatigue in the abnormal muscle site.

[0099] Optional, such as Figure 3 As shown, the device also includes a fitness reminder module 15, specifically used for:

[0100] When there is no risk of muscle fatigue in the abnormal muscle area, if the abnormal muscle area is a reference muscle area, the second product of the same abnormal muscle area in all actual fitness movements is summed to obtain the corresponding fourth summation result.

[0101] Calculate the difference between the first threshold and the fourth summation result. If the difference does not exceed the preset difference threshold, then the planned fitness movements that have not yet started are identified as remaining movements.

[0102] If there are abnormal muscle parts in each target muscle part corresponding to the remaining movements, the corresponding remaining movements are identified as movements to be adjusted, and for a single movement to be adjusted, the third product of the first weight of the abnormal muscle part and the second weight of the corresponding range of target repetitions is calculated.

[0103] Select the smallest third product from each third product, and sum the fourth summation result with the smallest third product corresponding to each action to be adjusted to obtain the final summation result;

[0104] If the final summation does not exceed the first threshold, then based on the target number range corresponding to a single least third product, the appropriate number of the corresponding action to be adjusted is determined, and each appropriate number is sent to the target user's terminal.

[0105] Optionally, the device also includes a risk verification module 16, specifically used for:

[0106] If it is determined that there is no risk of muscle fatigue in the abnormal muscle area, then based on the muscle areas where poor electrode contact has occurred in the past users during exercise, at least one area to be concerned is identified. The area to be concerned is the muscle area where poor electrode contact is likely to occur when monitoring electromyographic signals.

[0107] Based on the historical fitness movements that cause poor electrode contact at a single site of concern, at least one corresponding movement of concern is identified. The movement of concern is a historical fitness movement that is likely to cause poor electrode contact.

[0108] Determine the first weight for each area to be monitored, and determine the second weight for the action to be monitored corresponding to each area to be monitored;

[0109] Based on the first weight, the second weight, and each actual fitness movement, it is verified that there is no risk of muscle fatigue in abnormal muscle areas.

[0110] Optional, risk verification module 16, specifically used for:

[0111] If there is at least one actual fitness movement among the movements corresponding to the area to be focused on, then the corresponding area to be focused on is identified as the key area to be focused on. When the abnormal muscle area is the key area to be focused on, the fourth product of the first weight of the abnormal muscle area and the second weight of the corresponding actual fitness movement is calculated.

[0112] Summing each of the fourth products yields the fifth summation result corresponding to the abnormal muscle location;

[0113] The fifth summation result is compared with the preset third threshold. If the fifth summation result exceeds the third threshold, the verification that there is no risk of muscle fatigue in the abnormal muscle area is passed.

[0114] It should be noted that the muscle fatigue early warning device provided in the above embodiments is only illustrated by the division of the above functional modules when executing the muscle fatigue early warning method. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the muscle fatigue early warning device and the muscle fatigue early warning method embodiment provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiment, which will not be repeated here.

[0115] This application also discloses a computer-readable storage medium, which stores a computer program, wherein when the computer program is executed by a processor, it implements a muscle fatigue early warning method according to the above embodiments.

[0116] The computer program can be stored in a computer-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain middleware. The computer-readable medium includes any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.

[0117] The above-described muscle fatigue early warning method is stored in the computer-readable storage medium and loaded and executed on the processor to facilitate the storage and application of the method.

[0118] This application also discloses an electronic device in which a computer program is stored in a computer-readable storage medium. When the computer program is loaded and executed by a processor, it implements the above-mentioned muscle fatigue early warning method.

[0119] The electronic device can be a desktop computer, a laptop computer, or a cloud server, and includes, but is not limited to, a processor and a memory. For example, the electronic device may also include input / output devices, network access devices, and buses.

[0120] The processor can be a central processing unit (CPU). Of course, depending on the actual use, it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc., and this application does not limit it.

[0121] The memory can be an internal storage unit of an electronic device, such as a hard disk or RAM, or an external storage device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD), or flash memory card (FC) equipped on the electronic device. Furthermore, the memory can be a combination of an internal storage unit and an external storage device. The memory is used to store computer programs and other programs and data required by the electronic device. The memory can also be used to temporarily store data that has been output or will be output. This application does not limit this.

[0122] In this electronic device, a muscle fatigue early warning method according to the above embodiment is stored in the memory of the electronic device and loaded and executed on the processor of the electronic device for convenient use.

[0123] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. This application 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 described in this disclosure. The specification and embodiments are considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for early warning of muscle fatigue, characterized in that, The method includes: Obtain the target user's current fitness plan information, which includes at least one planned fitness exercise and the corresponding number of repetitions. Based on each of the planned fitness movements and the corresponding number of planned repetitions, at least one muscle group to be monitored is determined from each muscle group of the target user, including: determining at least one target muscle group based on multiple historical muscle groups that have experienced muscle fatigue under a single fitness movement, wherein the target muscle group is a historical muscle group that is prone to inducing muscle fatigue. Based on the range of number of times a single fitness exercise was completed when a historical user experienced muscle fatigue in a single target muscle area, at least one target number range is determined. The target number range is the range of number of times that easily induces muscle fatigue. The historical user and the target user are consistent in terms of fitness exercise and user profile. A first weight is determined for each target muscle group, and a second weight is determined for the target number range corresponding to each target muscle group. The first weight represents the probability of muscle fatigue occurring in the target muscle group, and the second weight represents the probability of muscle fatigue occurring when the number of times a single fitness movement is completed is within the target number range. Based on the first weight, second weight, each planned fitness movement, and the corresponding planned number of repetitions for a single health movement, at least one muscle group to be monitored is determined from each muscle group of the target user, including: for a single planned fitness movement, if there is a corresponding planned number of repetitions within the target number range corresponding to the target muscle group, then the corresponding target muscle group is determined as the key muscle group, and the corresponding target number range is determined as the key number range. Calculate the first product of the first weight of each key muscle group and the second weight of the corresponding target repetition range, and sum the first products corresponding to the same key muscle group based on the first products corresponding to each of the planned fitness movements to obtain the corresponding first summation result; The first summation result is compared with a preset first threshold. If the first summation result exceeds the first threshold, the corresponding key muscle area is identified as the muscle area to be monitored. The actual electromyographic signals of each of the muscle sites to be monitored are obtained. If the actual electromyographic signals are abnormal, the corresponding muscle site to be monitored is identified as an abnormal muscle site. Based on the actual fitness movements and corresponding number of repetitions completed by the target user, it is determined whether the abnormal muscle site is at risk of muscle fatigue. If it is determined that the abnormal muscle area is at risk of muscle fatigue, a muscle fatigue warning is issued for the abnormal muscle area.

2. The muscle fatigue early warning method according to claim 1, characterized in that, The step of determining whether the abnormal muscle area is at risk of muscle fatigue based on the actual fitness movements and corresponding repetitions completed by the target user specifically includes: For a single actual fitness movement that the target user has completed, if there is a corresponding actual number of repetitions within the target number of repetitions for the target muscle part, then the corresponding target muscle part is determined as the reference muscle part, and the corresponding target number of repetitions is determined as the reference number of repetitions range. Calculate the second product of the first weight of each reference muscle part and the second weight of the corresponding reference number range, and sum the second products corresponding to all actual fitness movements to obtain the second summation result; If the second summation result is greater than the preset second threshold, then the second products of the same reference muscle part in the second products corresponding to all actual fitness movements are summed to obtain the corresponding third summation result; If the third summation result exceeds the preset first threshold, the corresponding reference muscle part is determined as a risk muscle part. When the abnormal muscle part is the risk muscle part, it is determined that the abnormal muscle part has a risk of muscle fatigue. When the abnormal muscle site is not a risky muscle site, it is determined that the abnormal muscle site does not pose a risk of muscle fatigue.

3. The muscle fatigue early warning method according to claim 2, characterized in that, The method further includes: When there is no risk of muscle fatigue at the abnormal muscle site, if the abnormal muscle site is the reference muscle site, then the second product of the same abnormal muscle site in all actual fitness movements is summed to obtain the corresponding fourth summation result. Calculate the difference between the first threshold and the fourth summation result. If the difference does not exceed the preset difference threshold, then the planned fitness movements that have not yet started are identified as remaining movements. If there is an abnormal muscle part among the target muscle parts corresponding to the remaining movements, the corresponding remaining movements are determined as movements to be adjusted, and for a single movement to be adjusted, the third product of the first weight of the abnormal muscle part and the second weight of the corresponding target number range is calculated. Select the smallest third product from each of the third products, and sum the fourth summation result with the smallest third product corresponding to each of the actions to be adjusted to obtain the final summation result; If the final summation result does not exceed the first threshold, then based on the target number range corresponding to the single minimum third product, the appropriate number of the corresponding adjustment action is determined, and each appropriate number is sent to the target user's terminal.

4. The muscle fatigue early warning method according to claim 1, characterized in that, The method further includes: If it is determined that there is no risk of muscle fatigue in the abnormal muscle area, then at least one area of ​​concern is identified based on the muscle areas where poor electrode contact has occurred in the past by users during exercise. The area of ​​concern is the muscle area where poor electrode contact is likely to occur when monitoring electromyographic signals. Based on the historical fitness movements that caused poor electrode contact at a single site of concern, at least one corresponding movement of concern is determined, wherein the movement of concern is a historical fitness movement that is likely to cause poor electrode contact. A first weight is determined for each of the parts to be concerned, and a second weight is determined for the action to be concerned corresponding to each part to be concerned; Based on the first weight, the second weight, and each of the actual fitness movements, it is verified that there is no risk of muscle fatigue in the abnormal muscle areas.

5. The muscle fatigue early warning method according to claim 4, characterized in that, The step of verifying whether the abnormal muscle area has a risk of muscle fatigue based on the first weight, the second weight, and each of the actual fitness movements specifically includes: If there is at least one actual fitness movement among the movements corresponding to the area to be focused on, then the corresponding area to be focused on is determined as the key area to be focused on. When the abnormal muscle area is the key area to be focused on, the fourth product of the first weight of the abnormal muscle area and the second weight of the corresponding actual fitness movement is calculated. Summing each of the fourth products yields the fifth summation result corresponding to the abnormal muscle region. The fifth summation result is compared with the preset third threshold. If the fifth summation result exceeds the third threshold, it is determined that the verification of no muscle fatigue risk for the abnormal muscle area has passed.

6. A muscle fatigue early warning device, used to implement the muscle fatigue early warning method according to any one of claims 1 to 5, characterized in that, include: The information acquisition module (11) is used to acquire the fitness plan information of the target user for this time. The fitness plan information includes at least one planned fitness movement and the corresponding number of planned movements. The part determination module (12) is used to determine at least one muscle part to be monitored from each muscle part of the target user according to each of the planned fitness movements and the corresponding planned number of times; The risk assessment module (13) is used to acquire the actual electromyographic signals of each of the muscle sites to be monitored. If the actual electromyographic signals are abnormal, the corresponding muscle site to be monitored is identified as an abnormal muscle site. Based on the actual fitness movements and the corresponding number of times that the target user has completed, the module determines whether the abnormal muscle site is at risk of muscle fatigue. The fatigue warning module (14) is used to issue a muscle fatigue warning for the abnormal muscle area if it is determined that there is a risk of muscle fatigue in the abnormal muscle area.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it implements the method of any one of claims 1-5.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor loads and executes the computer program, it implements the method of any one of claims 1-5.

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