Training feedback method based on intelligent training apparatus and intelligent training apparatus

Abstract

The embodiment of the invention relates to the technical field of intelligent training instruments, and discloses a training feedback method based on an intelligent training instrument. The method comprises the steps of controlling the intelligent training apparatus to present a mode selection interface, responding to a mode selection operation received by the mode selection interface, determining a target training mode and standard monitoring information corresponding to the target training mode, obtaining speed information, and sending the speed information to the intelligent training apparatus; the speed information is the speed when the user performs action training on the intelligent training apparatus entering the target training mode, and according to the speed information and the standard monitoring information, the training feedback information is presented, and the training feedback information is used for guiding the training state of the user. The training feedback information is given in real time, so that the training effect and the exercise safety under different strength training targets can be better ensured, and the user experience feeling can be improved.

Description

Technical Field

[0001] This application relates to the field of intelligent training equipment technology, and in particular to a training feedback method based on intelligent training equipment and an intelligent training equipment. Background Technology

[0002] When using smart training equipment for strength training, users place great importance on training effectiveness. Users can adjust their training plans based on feedback; for example, after training, the technology can monitor muscle soreness recovery time to determine the effectiveness. If the training effect does not meet expectations, users can increase or decrease the difficulty of their training plan. However, the training effectiveness evaluation methods provided by these technologies are lagging, unable to provide timely feedback and guidance to users' training. Furthermore, real-time monitoring of training effectiveness requires high levels of expertise, is difficult, and expensive. Summary of the Invention

[0003] One objective of this application is to provide a training feedback method and an intelligent training device based on intelligent training equipment, so as to solve the technical problem that related technologies cannot provide training feedback information in a timely manner.

[0004] In a first aspect, embodiments of this application provide a training feedback method based on an intelligent training device, comprising: controlling the intelligent training device to present a mode selection interface; responding to a mode selection operation received by the mode selection interface; determining a target training mode and standard monitoring information corresponding to the target training mode; acquiring speed information, wherein the speed information is the speed information of a user performing action training on the intelligent training device when entering the target training mode; and presenting training feedback information based on the speed information and the standard monitoring information, wherein the training feedback information is used to guide the user's training state.

[0005] Optionally, the standard monitoring information includes a standard speed range, and the step of presenting training feedback information based on the speed information and the standard monitoring information includes: determining whether the speed information deviates from the standard speed range; if it deviates, generating training feedback information to guide the user to perform the next action on the intelligent training device as a valid action; if it does not deviate, generating action count information, which is used to indicate that the action performed by the user on the intelligent training device is a valid action.

[0006] Optionally, determining whether the speed information deviates from the standard speed range includes: determining speed monitoring information corresponding to the target training mode based on the speed information, wherein the speed monitoring information is used to quantitatively represent the user's training state on the intelligent training device, and determining whether the speed monitoring information deviates from the standard speed range.

[0007] Optionally, the training feedback information includes weight increase information and training adjustment information. The generation of training feedback information includes: if the speed information is greater than the standard speed range, then weight increase information is generated, which is used to indicate increasing the weight of the intelligent training device; if the speed information is less than or equal to the standard speed range, then training adjustment information is generated, which is used to indicate decreasing the weight of the intelligent training device or to guide the user to increase the speed of the next training movement.

[0008] Optionally, the standard monitoring information further includes a speed loss percentage, and the training feedback information includes rest guidance information and training adjustment information. The generation of training feedback information includes: if the speed information is less than or equal to the standard speed range, then determining the real-time speed loss ratio; if the real-time speed loss ratio is less than the speed loss percentage, then generating training adjustment information, which is used to instruct the user to reduce the weight of the intelligent training device or guide the user to increase the speed of the next training movement; if the real-time speed loss ratio is greater than or equal to the speed loss percentage, then generating rest guidance information, which is used to guide the user to perform rest adjustments.

[0009] Optionally, determining the real-time speed loss ratio includes: obtaining historical speed indicators, which are the user's speed indicators over a past period of time, and calculating the real-time speed loss ratio based on the historical speed indicators and the speed information.

[0010] Optionally, the historical speed index includes historical single-action speed index for a single action and / or historical single-group speed index for each group of actions, and the speed information includes real-time single-action speed index for a single action and / or real-time single-group speed index for each group of actions. Calculating the real-time speed loss ratio based on the historical speed index and the speed information includes: obtaining a first difference between the real-time single-action speed index and the historical single-action speed index; if the first difference is negative, obtaining the absolute value of the ratio of the first difference to the historical single-action speed index to obtain the real-time speed loss ratio; and / or, obtaining a second difference between the real-time single-group speed index and the historical single-group speed index; if the second difference is negative, obtaining the absolute value of the ratio of the second difference to the historical single-group speed index to obtain the real-time speed loss ratio.

[0011] Optionally, the rest guidance information includes first rest information, second rest information, or training stop information. Generating rest guidance information includes: generating first rest information if the ratio of real-time speed loss of the real-time single-speed indicator to the real-time single-speed indicator is greater than or equal to the speed loss percentage, wherein the first rest information is used to instruct the user to rest for a first preset duration; and / or, generating second rest information if the ratio of real-time speed loss of the real-time single-group speed indicator to the real-time single-group speed indicator is greater than or equal to the speed loss percentage, wherein the second rest information is used to instruct the user to rest for a second preset duration; and / or, generating training stop information if the ratio of real-time speed loss of each real-time single-speed indicator to each real-time single-speed indicator is greater than or equal to the speed loss percentage within all preset groups, wherein the training stop information is used to instruct the user to end training.

[0012] Optionally, before determining the target training mode, the method further includes: acquiring motion configuration data, and controlling the intelligent training device to output an initial weight based on the motion configuration data, wherein the initial weight is used to assist the user in exercising on the intelligent training device.

[0013] Optionally, controlling the output of the initial weight of the intelligent training device according to the exercise configuration data includes: determining a preset ratio coefficient corresponding to the target training mode, wherein the exercise configuration data includes the maximum weight, determining the initial weight according to the preset maximum weight and the preset ratio coefficient, and controlling the output of the initial weight of the intelligent training device.

[0014] Optionally, the method further includes: obtaining the maximum weight applied by the user to the intelligent training device during a strength test; or, obtaining the user's historical training data and determining the user's maximum weight in the target training mode based on the historical training data and a preset strength learning model; or, obtaining the maximum weight corresponding to the user pre-stored locally by the intelligent training device; or, requesting a server communicatively connected to the intelligent training device to send the maximum weight corresponding to the user.

[0015] In a second aspect, embodiments of this application provide an intelligent training device, including an intelligent resistance output component and a controller. The intelligent resistance output component is used to output resistance, and the controller is electrically connected to the intelligent resistance output component to implement the above-mentioned training feedback method based on the intelligent training device.

[0016] Optionally, the intelligent resistance output component is a motor.

[0017] In a third aspect, embodiments of this application provide a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to execute the above-described training feedback method based on an intelligent training device.

[0018] The embodiments of this application can achieve the following technical effects: The method includes: responding to mode selection information received by the intelligent training device, determining the target training mode and the standard monitoring information corresponding to the target training mode, acquiring the speed information of a single movement performed by the user in the target training mode, determining speed information based on the speed information, and generating training feedback information based on the speed information and the standard monitoring information. The training feedback information is used to guide the user's training status. Therefore, the embodiments of this application can provide training feedback information in real time during the user's current training, which can better ensure the training effect and training safety under different strength training goals, and is conducive to improving the user's experience. At the same time, the embodiments of this application do not require the use of expensive professional equipment or the hiring of professional personnel to evaluate the training effect, which is conducive to saving costs. It can also be applied to the evaluation and feedback of training effect by the general population, which is conducive to expanding the scope of application. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating a training feedback method based on an intelligent training device provided in an embodiment of this application;

[0021] Figure 2 A schematic diagram illustrating the setting of five training modes based on the VBT method provided in the embodiments of this application;

[0022] Figure 3 A schematic diagram of the structure of a training feedback device based on an intelligent training instrument provided in an embodiment of this application;

[0023] Figure 4 This is a schematic diagram of the structure of an intelligent training device provided in an embodiment of this application;

[0024] Figure 5 A circuit block diagram of an intelligent training device provided in an embodiment of this application;

[0025] Figure 6 This application provides a schematic diagram of the structure of an electric motor according to an embodiment of the present application.

[0026] Figure 7 This is a schematic diagram of the structure of a controller provided in an embodiment of this application. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0028] It should be noted that, unless there is a conflict, the various features in the embodiments of this application can be combined with each other, all of which are within the protection scope of this application. Furthermore, although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than the module division in the device or the order in the flowchart. Moreover, the terms "first," "second," and "third" used in this application do not limit the data or execution order, but only distinguish identical or similar items with essentially the same function and effect.

[0029] Monitoring training effectiveness is a crucial aspect of the training process. In the short term, monitoring training effectiveness allows users to determine whether the current training intensity (volume, repetitions, weight, etc.) is appropriate. In the long term, users can clarify whether the current training plan can support the achievement of training goals. Therefore, in addition to the training effectiveness monitoring methods mentioned in the background section, related technologies also provide the following training effectiveness monitoring methods:

[0030] 1. Evaluate whether the user's various strength qualities have improved through regular strength tests.

[0031] 2. Real-time monitoring by professional personnel and equipment is used to evaluate whether the user's various strengths and qualities have improved.

[0032] Both of the above monitoring methods are lagging and cannot evaluate and guide users' training in real time. In addition, the monitoring costs of the two methods are high and they require a high level of expertise.

[0033] The embodiments of this application can provide training feedback information in real time, so as to guide the real-time adjustment of the weight of the intelligent training device and / or guide the user to adjust the training speed or take a rest.

[0034] The following embodiments of this application provide a training feedback method based on intelligent training equipment. Please refer to... Figure 1The training feedback method based on intelligent training equipment includes the following steps:

[0035] S11: Controls the intelligent training equipment to display the mode selection interface.

[0036] In this step, the mode selection interface is the interface through which the intelligent training device provides the user with multiple training modes to choose from. The intelligent training device responds to the user's input start command and presents the mode selection interface. In some embodiments, the intelligent training device provides physical buttons; these physical buttons respond to user input, generating a start command, and the intelligent training device responds to the start command and presents the mode selection interface. In some embodiments, the intelligent training device presents a main interface, which includes a mode selection button. This button responds to user input, generating a start command, and the intelligent training device responds to the start command and presents the mode selection interface. In some embodiments, the intelligent training device is equipped with a microphone for collecting the user's voice data. The intelligent training device parses the start command from the voice data and presents the mode selection interface based on the start command.

[0037] Users select different training modes on the mode selection interface based on their different training goals. Each training mode corresponds to a different training objective. For example, a training objective aimed at improving muscle strength and size corresponds to a strength-driven training mode, while a training objective aimed at improving explosive power corresponds to a speed-driven training mode.

[0038] This application's embodiments utilize a velocity-based strength training (VBT) method to construct various training modes. The VBT method leverages the strong correlation between velocity and percentage of maximum weight (%1RM), velocity and repetition count, and velocity and fatigue to develop, monitor, and adjust strength training loads. The VBT method is characterized by real-time performance, accuracy, and versatility. VBT-based training methods have various applications, such as establishing force-velocity curves, predicting 1RM (maximum weight), automatic adjustment, and evaluating training quality. Therefore, based on the VBT method, this application's embodiments can monitor velocity in different training modes to provide guidance on resistance, velocity, and rest for the next training session or set.

[0039] This application embodiment pre-sets multiple training modes and corresponding speed monitoring intervals and recommended force intervals based on the VBT method. In some embodiments, this application embodiment sets 5 training modes based on VBT force-velocity curves and extensive research results.

[0040] Please see Figure 2The embodiments of this application set up the following 5 training modes, namely absolute strength training mode, acceleration strength training mode, strength-speed training mode (strength-dominant training mode), speed-strength training mode (speed-dominant training mode) and starting strength training mode. The target training mode can be any of the above 5 training modes.

[0041] The %1RM corresponding to the absolute strength training mode is 80%1RM-100%1RM, where [80%, 100%] is the preset ratio range corresponding to the absolute strength training mode, and the standard speed range is (-∞, 0.50m / s). In the VBT method, %1RM (percentage of maximum repetition weight) represents the percentage of the load used in the current training session relative to the user's maximum repetition weight (1RM). For example, if a user's squat 1RM is 100 kg, training with 80 kg is equivalent to using 80%1RM.

[0042] The %1RM corresponding to the accelerated strength training mode is 60%1RM-80%1RM. [60%, 80%) is the preset ratio range corresponding to the accelerated strength training mode. The standard speed range for the lower limbs is (0.50m / s, 0.75m / s), and the standard speed range for the upper limbs is (0.40m / s, 0.60m / s).

[0043] The %1RM corresponding to the strength-speed training mode is 40%1RM-60%1RM. [40%, 60%) is the preset ratio range corresponding to the strength-speed training mode. The standard speed range for the lower limbs is (0.75m / s, 1.00m / s), and the standard speed range for the upper limbs is (0.60m / s, 0.85m / s).

[0044] The %1RM corresponding to the speed-strength training mode is 20%1RM-40%1RM. [20%, 40%) is the preset ratio range corresponding to the speed-strength training mode. The standard speed range for the lower limbs is (1.00m / s, 1.30m / s), and the standard speed range for the upper limbs is (0.85m / s, 1.10m / s).

[0045] The %1RM corresponding to the start of the strength training mode is <20%1RM, and [0%, 20%) is the preset ratio range corresponding to the start of the strength training mode. The standard speed range for the lower limbs is (1.30m / s, +∞), and the standard speed range for the upper limbs is (1.10m / s, +∞).

[0046] It is understandable that the standard speed range and preset ratio range of each training mode can be customized by the designer according to business needs or the characteristics of the training equipment, which will not be elaborated here.

[0047] This application's embodiments match corresponding speed indicators to be monitored for different training modes based on their characteristics. These speed indicators include Mean Centripetal Velocity (MCV), Peak Centripetal Velocity (PCV), and Velocity Loss Percentage (VL%). Generally, if the training goal is to improve muscle strength, MCV is more suitable. If the training goal is to improve explosive power, PCV is more suitable. Velocity loss percentage is related to accumulated training fatigue and rest recovery time.

[0048] In this embodiment, the MCV index is selected as the speed index for the absolute strength training mode, the accelerated strength training mode, and the strength-speed training mode, and the PCV index is selected as the speed index for the speed-strength training mode and the starting strength training mode.

[0049] It is understood that, in addition to using MCV and PCV metrics as speed metrics for various training modes, the embodiments of this application may also use average speed or instantaneous speed as speed metrics, without any specific limitation.

[0050] It is also understood that, in addition to the above five training modes, other training modes are also provided in the embodiments of this application.

[0051] It is also understood that the embodiments of this application can not only construct various training modes and speed monitoring intervals corresponding to each training mode based on the VBT algorithm, but also construct various other training modes based on other training algorithms.

[0052] S12: Response mode selection interface receives mode selection operation, determines target training mode and standard monitoring information corresponding to target training mode.

[0053] In this step, the intelligent training device is configured with multiple training modes, and the target training mode is the training mode selected by the user in the mode selection interface.

[0054] The response mode selection interface receives a mode selection operation, and the determination of the target training mode includes the following steps: responding to the mode selection interface receives a mode selection operation, obtaining mode selection information, parsing the target type identifier from the mode selection information, and determining the training mode corresponding to the target type identifier as the target training mode.

[0055] After determining the target training mode, the intelligent training equipment operates based on the exercise configuration data. This data is used to configure the intelligent training equipment to operate in a state that aligns with the training objectives. For example, the intelligent training equipment needs to output resistance with a specified weight, cooperate with the user to complete the target number of repetitions and sets, and monitor whether the user's exercise speed meets the set requirements within a standard speed range.

[0056] Exercise configuration data includes the standard speed range corresponding to the target training mode, the preset ratio range corresponding to the target training mode, and the maximum weight. It is understood that the exercise configuration data may include the target number of training sets and / or the target number of repetitions per training set linked to the target training mode, or it may not include the target number of training sets and / or the target number of repetitions per training set.

[0057] In some embodiments, when a user selects a target training mode on the smart training device, the smart training device calls the target number of training sets and / or the target number of training sessions for each training set as part of the exercise configuration data.

[0058] In some embodiments, after a user selects a target training mode on the smart training device, the user sets the target number of training sets and / or the target number of training sessions per training set on the smart training device. The target number of training sets and / or the target number of training sessions per training set are part of the exercise configuration data.

[0059] In some embodiments, the setup method is as follows: the user manually sets the target number of training sets and / or the target number of training sessions per training set on the display screen of the smart training device. In some embodiments, the setup method is as follows: the user operates a mobile terminal that is communicatively connected to the smart training device, launches a training APP installed on the mobile terminal, sets the target number of training sets and / or the target number of training sessions per training set on the training APP, and the training APP sends the target number of training sets and / or the target number of training sessions per training set to the smart training device through the mobile terminal, and the smart training device receives the target number of training sets and / or the target number of training sessions per training set.

[0060] In some embodiments, when a user selects a target training mode on the intelligent training device, the user does not need to set the target number of training groups and / or the target number of training sessions for each training group, and can directly start the intelligent training device.

[0061] To obtain the maximum weight, the embodiments of this application can employ the following four methods: obtaining the maximum weight applied by the user to the intelligent training device during the strength test; obtaining the user's historical training data and determining the user's maximum weight in the target training mode based on the historical training data and a preset strength learning model; obtaining the maximum weight pre-stored locally by the intelligent training device corresponding to the user; or requesting a server connected to the intelligent training device to send the maximum weight corresponding to the user. It is understood that those skilled in the art can also use other methods to obtain the maximum weight, which will not be elaborated here.

[0062] After obtaining the exercise configuration data, this embodiment of the application can control the intelligent training device to output the initial counterweight according to the exercise configuration data. The initial counterweight is used to assist the user in exercising on the intelligent training device.

[0063] In some embodiments, the present application can directly receive the initial weight set by the user and control the intelligent training device according to the initial weight.

[0064] In some embodiments, the present application can determine the initial weight based on the maximum weight. The present application determines the target proportion range corresponding to the target training mode, determines the initial weight based on the maximum weight and the target proportion range, and controls the intelligent training device to output the initial weight.

[0065] The target percentage range is a preset percentage range corresponding to the target training mode. For example, if the target training mode is absolute strength training mode, then the target percentage range is (80%, 100%). Similarly, if the target training mode is acceleration strength training mode, then the target percentage range is (60%, 80%).

[0066] The preset ratio range is defined by the minimum ratio value and the maximum ratio value. As mentioned earlier, if the target training mode is the absolute strength training mode, the minimum ratio value is 80% and the maximum ratio value is 100%.

[0067] There are various ways to determine the initial weight based on the maximum weight and the target proportion range. In some embodiments, this application selects the minimum proportion value within the target proportion range as the target proportion value, and multiplies the maximum weight by the target proportion value to obtain the initial weight. For example, if the target training mode is absolute strength training mode, the user's squat 1RM is 100 kg, and the minimum proportion value is 80%, then this application provides 80 kg as the initial weight. This approach uses the minimum strength in the target training mode as the initial weight at the beginning of the training phase, adopting a gradual approach to help the user transition from simple training intensity to more difficult training intensity, allowing the user's training state to gradually enter a better state, which is beneficial to improving the user experience.

[0068] In some embodiments, this application selects the median of the target ratio range as the target ratio value, and multiplies the maximum weight by the target ratio value to obtain the initial weight. For example, if the target training mode is an absolute strength training mode and the median of the target ratio range is 90%, then this application provides 90 kg as the initial weight.

[0069] In some embodiments, this application selects the maximum ratio value within the target ratio range as the target ratio value, and multiplies the maximum weight by the target ratio value to obtain the initial weight. For example, if the target training mode is an absolute strength training mode, the user's squat 1RM is 100 kg, and the maximum ratio value is 100%, then this application provides 100 kg as the initial weight.

[0070] Understandably, the target ratio can also be customized by the designer according to business needs, and is not limited to the three methods mentioned above.

[0071] In some embodiments, once the intelligent training device has reached its maximum weight and initial counterweight, the user can directly train on the device without needing to set the number of training sets or repetitions for each set. The intelligent training device outputs resistance according to the initial counterweight, and the user exercises under the action of this resistance. The intelligent training device monitors the user's training status based on speed information from the target training mode and standard monitoring information.

[0072] Once the intelligent training device has reached its maximum weight and initial counterweight, it can also be configured with the target number of training sets and the target number of training sessions per set.

[0073] In some embodiments, the exercise configuration data includes the target number of training sets and the target number of training sessions contained in each target training set. In this application embodiment, the number of training sets bound to the target training mode is determined as the target number of training sets, and the number of training sessions bound to the target training mode is determined as the target number of training sessions.

[0074] The intelligent training device, responding to the determination of the target training mode, generates a sets / repetitions setting page. This page presents the number of training sets and repetitions bound to the target training mode to the user. After confirming the recommended number of sets and repetitions are appropriate, the user clicks the confirmation button on the settings page. The intelligent training device, responding to the confirmation button click, saves the recommended number of sets and repetitions as the target number of sets and target number of repetitions, respectively.

[0075] When a user selects the "Absolute Strength Training Mode" on the smart training device, this embodiment uses the MCV (Motor Capacity Value) index to monitor the user's movement process on the device. The standard speed range (e.g., the speed monitoring range) is 0.2 m / s to 0.5 m / s, the recommended weight range is 80% to 100% of 1RM, and the suggested number of repetitions is 3 to 5, with 4 to 10 sets. This embodiment saves the number of repetitions and sets as the target number of repetitions and sets.

[0076] In some embodiments, this application embodiment responds to the setting operation received by the intelligent training device and determines the target number of training sets and the target number of training sessions. The user can set the number of training sets and the number of training sessions on the set setting page, and then click the confirmation button. The intelligent training device responds to the click operation received by the confirmation button and saves the set number of training sets and the target number of training sessions as the target number of training sets and the target number of training sessions, respectively. For example, the user determines the target number of training sets between 4 and 10 sets, and the target number of training sessions between 3 and 5 sessions.

[0077] Standard monitoring information is monitoring information that matches the requirements of the target training mode. The standard monitoring information may be the same or different for different training modes. In some embodiments, the standard monitoring information is configured to include a standard speed range, which is designed by the designer according to the requirements of the training mode. In some embodiments, in order to reflect the diversity of the user's training state, the standard monitoring information is configured to include a standard speed range and a speed loss percentage. The speed loss percentage is customized by the designer according to business needs and is used to quantitatively represent the changes in the user's training state. When the user's speed loss is greater than or equal to the speed loss percentage, it indicates that the user has entered a fatigue state; when the user's speed loss is less than the speed loss percentage, it indicates that the user has not yet entered a fatigue state.

[0078] Each training mode corresponds to a specific standard speed range. This standard speed range is used to monitor whether the user's real-time single-shot speed metric or real-time single-set speed metric is within a predetermined range, thus indicating the user's fatigue level or training suitability. For example... Figure 2 As shown, if the target training mode is absolute strength training mode, the standard speed range is (-∞, 0.50m / s).

[0079] S13: Obtain speed information, which is the speed information of the user when performing movement training on the intelligent training device in the target training mode.

[0080] In this step, the speed information is used to represent the speed at which the user trains on the intelligent training device. For intelligent training devices where resistance is provided by a motor, the motor is equipped with an encoder, and in this embodiment, the speed information is calculated based on the encoded data collected by the encoder. For intelligent training devices where resistance is provided by other means, it is only necessary to be able to obtain the speed information of the intelligent training device during training.

[0081] S14: Present training feedback information based on speed information and standard monitoring information.

[0082] In this step, training feedback information is used to guide the user's training status. It is understood that the connotation of standard monitoring information differs. This embodiment of the application can provide real-time training feedback information during the user's current training, better ensuring training effectiveness and exercise safety under different strength training goals, and thus improving the user experience. At the same time, this embodiment of the application does not require the use of expensive professional equipment or hiring professionals to evaluate training effectiveness, which helps save costs. Furthermore, it is applicable to the evaluation and feedback of training effectiveness by the general population, thus expanding the scope of applicability.

[0083] This application embodiment can present training feedback information in various ways based on speed information and standard monitoring information. In some embodiments, the standard monitoring information includes a standard speed range, and presenting training feedback information based on speed information and standard monitoring information includes the following steps: determining whether the speed information deviates from the standard speed range; if it deviates, generating training feedback information to guide the user to perform the next action on the intelligent training device as a valid action; if it does not deviate, generating action count information, which is used to indicate that the action performed by the user on the intelligent training device is a valid action.

[0084] It is understood that, in some embodiments, the speed information here may be the real-time speed of the user performing movement training on the intelligent training device. In some embodiments, the speed information here may be speed monitoring information corresponding to the target training mode determined based on the speed information.

[0085] Determining whether speed information deviates from the standard speed range includes the following steps: determining the speed monitoring information corresponding to the target training mode based on the speed information. The speed monitoring information is used to quantitatively represent the user's training status on the intelligent training device, and determining whether the speed monitoring information deviates from the standard speed range.

[0086] For example, when a user selects the "Absolute Strength Training Mode" on a smart training device, this embodiment determines the MCV (Mean Change Volume) index corresponding to the "Absolute Strength Training Mode" as speed monitoring information based on speed information. The MCV index is used to monitor the user's movement process on the smart training device, where the standard speed range is 0.2 m / s to 0.5 m / s. If the MCV index of the user completing one movement is within the range of 0.2 m / s to 0.5 m / s, it indicates that the movement is valid, and this embodiment generates movement count information and counts it normally. If the MCV index of the user completing one movement is not within the range of 0.2 m / s to 0.5 m / s, it indicates that the movement is invalid, and this embodiment generates training feedback information.

[0087] Therefore, in the target training mode, this embodiment can convert speed information into speed monitoring information that matches the target training mode. If the target training mode is any of the training modes of absolute strength training mode, acceleration strength training mode, and strength-speed training mode, this embodiment converts speed information into MCV monitoring information. If the target training mode is any of the training modes of speed-strength training mode and activation strength training mode, this embodiment converts speed information into PCV monitoring information. In this way, this embodiment can monitor the user's training status in the target training mode with speed monitoring information that matches the target training mode, and can better guide the user to train in the target training mode in order to achieve the user's expected training goal.

[0088] It should be noted that the speed information mentioned in this application can refer to one or more real-time speed information directly obtained from the training movement, such as the instantaneous speed of one or more stroke positions corresponding to a certain training movement. This instantaneous speed can be directly compared with standard monitoring information, and training feedback information can be generated based on the comparison results. Speed ​​information can also refer to matching speed monitoring information obtained after converting one or more directly obtained real-time speed information. This speed monitoring information includes, but is not limited to, specific speed parameters or speed indices. The speed monitoring information obtained after speed information conversion is used to compare with standard monitoring information, and training feedback information is generated based on the comparison results. This application does not limit the specific method of comparing speed information directly with standard monitoring information, or comparing speed monitoring information obtained after speed information conversion with standard monitoring information.

[0089] Training feedback information includes weight increase information and training adjustment information. Generating training feedback information involves the following steps: If the speed information is greater than the standard speed range, weight increase information is generated, indicating an increase in the weight of the intelligent training device. If the speed information is less than or equal to the standard speed range, training adjustment information is generated, indicating a decrease in the weight of the intelligent training device or guiding the user to increase the speed of the next training movement. It can be understood that the speed information here can be the real-time speed collected by the user during exercise on the intelligent training device, or it can be speed monitoring information determined based on the speed information to correspond to the target training mode.

[0090] For example, if the MCV (Mean Change Volume) of a user completing one action is greater than 0.5 m / s, this embodiment generates weight increase information. In some embodiments, the intelligent training device responds to the weight increase information by adding a preset unit weight to the current weight, resulting in an updated current weight. For example, if the preset unit weight is 2 kg, the current weight is 90 kg, and the updated current weight is 92 kg. In some embodiments, the user manually operates the intelligent training device to add the preset unit weight based on the weight increase information displayed on the device's screen or announced via voice.

[0091] The training adjustment information includes weight reduction information or speed increase information. Weight reduction information is used to instruct the user to reduce the weight of the intelligent training device, while speed increase information is used to guide the user to increase the speed of the next training movement. In some embodiments, the intelligent training device responds to the weight reduction information by subtracting a preset unit weight from the current weight to obtain an updated current weight. For example, if the preset unit weight is 2 kg, the current weight is 90 kg, and the updated current weight is 88 kg. This embodiment of the application, when the speed information deviates from the standard speed range, adjusts the weight of the intelligent training device in real time and flexibly according to the direction of the deviation, so that the adjusted weight can more effectively help the user achieve the training objectives corresponding to the target training mode.

[0092] In some embodiments, the user manually operates the intelligent training device to reduce a preset unit weight by receiving weight reduction information from the device's display screen or by having the weight reduction information broadcast via voice.

[0093] In some embodiments, the intelligent training device increases the response speed by adding a preset speed value to the current motor speed to obtain an updated speed. In some embodiments, the user manually increases the motor speed of the intelligent training device by receiving the speed increase information displayed on the device's screen or by having the speed increase information read aloud via voice. This embodiment of the application, when the speed information deviates from the standard speed range, adjusts the motor speed of the intelligent training device in real time and flexibly according to the direction of the deviation, so that the updated speed can more effectively help the user achieve the training objectives corresponding to the target training mode.

[0094] This application embodiment can compare speed information with its corresponding standard speed range, count effective movements to ensure that the counted movements meet the training objectives of the target training mode, and provide training feedback information for invalid movements so that the user can adjust the weight of the intelligent training device, or the intelligent training device can automatically adjust the weight or guide the user to adjust the speed. This ensures that the weight output by the intelligent training device can help the user perform movements that match the target training mode, or guide the user to adjust the training speed to a speed range that meets the requirements of effective training, ensuring that the training movements are effective, and thus enabling the user to effectively achieve the training objectives.

[0095] In some embodiments, the standard monitoring information further includes a speed loss percentage, and the training feedback information includes rest guidance information and training adjustment information. Generating the training feedback information includes: if the speed information is less than or equal to the standard speed range, determining the real-time speed loss ratio; if the real-time speed loss ratio is less than the speed loss percentage, generating training adjustment information to instruct the user to reduce the weight of the intelligent training device or guide the user to increase the speed of the next training movement; and if the real-time speed loss ratio is greater than or equal to the speed loss percentage, generating rest guidance information to guide the user to perform rest adjustments. It can be understood that the speed information here can be the real-time speed collected by the user during movement training on the intelligent training device, or the speed information here can be speed monitoring information determined based on the speed information to correspond to the target training mode.

[0096] Determining the real-time speed loss ratio involves the following steps: obtaining historical speed indicators, which are the user's speed indicators over past times; and calculating the real-time speed loss ratio based on the historical speed indicators and the speed information. It can be understood that the speed information here can be the real-time speed collected when the user is performing motion training on the intelligent training device, or it can be the speed monitoring information determined based on the speed information to correspond to the target training mode.

[0097] Calculating the real-time speed loss ratio based on historical speed indicators and speed information involves the following steps: Subtract the historical speed indicator from the current speed information to obtain the speed indicator difference. If the speed indicator difference is negative, calculate the absolute value of the ratio of the speed indicator difference to the historical speed indicator to obtain the real-time speed loss ratio. For example, if the real-time speed indicator is d... i The historical speed index is d i-1 If the speed information d i Compared with historical speed index d i-1 Speed ​​index difference Δd i If the value is negative, then the real-time speed loss ratio = |d i -d i-1 | / d i-1 In reality, when the detected speed is greater than the historical speed index, there is no speed loss and it does not fall under the category of speed loss.

[0098] Historical speed metrics include historical single-action speed metrics for individual actions and / or historical single-group speed metrics for each group of actions. Speed ​​information includes real-time single-action speed metrics for individual actions and / or real-time single-group speed metrics for each group of actions. Real-time single-action speed metrics are calculated based on one or more speed information points within a single action, and real-time single-group speed metrics are calculated based on one or more speed information points within each group of actions.

[0099] If the target training mode is any of the following training modes: absolute strength training mode, acceleration strength training mode, and strength-speed training mode, then both the real-time single-shot speed index and the real-time single-set speed index are MCV indices. The real-time single-set speed index is the sum of all real-time single-shot speed indices within the set divided by the number of training sessions.

[0100] If the target training mode is either the speed-strength training mode or the starting strength training mode, then both the real-time single-shot speed index and the real-time single-set speed index are PCV indices. The real-time single-set speed index is the sum of all real-time single-shot speed indices within the set divided by the number of training sessions.

[0101] In some embodiments, calculating the real-time speed loss ratio based on historical speed indicators and speed information includes the following steps: obtaining a first difference between the real-time single speed indicator and the historical single speed indicator; if the first difference is negative, obtaining the absolute value of the ratio of the first difference to the historical single speed indicator to obtain the real-time speed loss ratio.

[0102] In some embodiments, calculating the real-time speed loss ratio based on historical speed indicators and speed information includes the following steps: obtaining a second difference between a real-time single-group speed indicator and a historical single-group speed indicator; if the second difference is negative, obtaining the absolute value of the ratio of the second difference to the historical single-group speed indicator to obtain the real-time speed loss ratio.

[0103] Rest reminders include first rest information, second rest information, or stop training information.

[0104] In some embodiments, generating rest guidance information includes the following steps: if the ratio of real-time speed loss to historical speed loss is greater than or equal to the percentage of speed loss, then first rest information is generated. The first rest information is used to instruct the user to rest for a first preset duration. The first preset duration is customized by the designer based on business experience.

[0105] For example, the standard speed range is 0.2 m / s to 0.5 m / s, the real-time single-run speed index is 0.18 m / s, and the historical single-run speed index is 0.2 m / s. Since the real-time single-run speed index deviates from the standard speed range, this embodiment of the application needs to calculate the real-time speed loss ratio to determine whether a first rest information needs to be generated. The real-time speed loss ratio is 10% = |0.18 - 0.2| / 0.2. Because the real-time speed loss ratio equals a speed loss percentage of 10%, this embodiment of the application generates a first rest information message indicating that the user needs to rest for 5 minutes. For example, the first rest information message might be: "Dear user, you have entered a state of fatigue and it is recommended to rest for 5 minutes."

[0106] This application embodiment monitors the user's motion status by comparing the real-time speed loss ratio of the real-time single speed index with the historical single speed index. This is applicable not only to situations where the user sets a target number of training sessions and sets on the smart training device, or when the smart training device recommends a target number of training sessions and sets, but also to situations where the user can operate the smart training device directly without setting a target number of training sessions and sets.

[0107] In some embodiments, generating rest guidance information includes the following steps: if the ratio of real-time speed loss of a single set of speed indicators to the real-time speed loss of a single set of historical speed indicators is greater than or equal to the percentage of speed loss, then generating second rest information, which is used to instruct the user to rest for a second preset duration.

[0108] For example, in this embodiment, the real-time single-shot velocity indices within a group are summed to obtain a total indices for the group. Then, the total indices for the group are divided by the number of training iterations to obtain the real-time single-group velocity indices. Using this method, this embodiment obtains a real-time single-group velocity indices of 0.18 m / s, a historical single-group velocity indices of 0.2 m / s, and a real-time velocity loss ratio of 10% = |0.18 - 0.2| / 0.2. Therefore, this embodiment generates a second rest message indicating that the user needs to rest for 10 minutes. For example, the second rest message might be: "Dear user, you have entered a relatively fatigued state and it is recommended to rest for 10 minutes."

[0109] In some embodiments, generating rest guidance information includes the following steps: within all preset groups, if the ratio of real-time speed loss of each real-time single speed indicator to each historical single speed indicator is greater than or equal to the speed loss percentage, then training stop information is generated, which is used to instruct the user to end training.

[0110] For example, if the ratio of the real-time speed loss between the second real-time single-set speed index and the first real-time single-set speed index (which is a historical single-set speed index relative to the second real-time single-set speed index) is greater than 10%, the ratio of the real-time speed loss between the third real-time single-set speed index and the second real-time single-set speed index is greater than 10%, and the ratio of the real-time speed loss between the fourth real-time single-set speed index and the third real-time single-set speed index is greater than 10%, this indicates that the user's training state is gradually declining and fatigue is gradually increasing. This embodiment of the application generates stop training information to instruct the user to end training, thereby preventing injury due to excessive fatigue or overload. This embodiment of the application can combine past exercise states with current real-time exercise states to generate rest guidance information, thus improving the real-time generation of rest guidance information and providing scientific guidance for users to train healthily and scientifically, avoiding injury due to excessive fatigue or overload.

[0111] It should be noted that in the above embodiments, there is no necessarily a certain order between the steps. Those skilled in the art can understand from the description of the embodiments of this application that the above steps may have different execution orders in different embodiments, that is, they may be executed in parallel or in turn, etc.

[0112] As another aspect of the embodiments of this application, this application provides a training feedback device based on an intelligent training device. The training feedback device based on the intelligent training device can be a software module, which includes several instructions stored in a memory. A processor can access the memory, call the instructions, and execute them to complete the training feedback method based on the intelligent training device described in the various embodiments above.

[0113] In some embodiments, the training feedback device based on the intelligent training apparatus can also be constructed from hardware components. For example, the training feedback device based on the intelligent training apparatus can be constructed from one or more chips, which can work in coordination to complete the training feedback method based on the intelligent training apparatus described in the various embodiments above. As another example, the training feedback device based on the intelligent training apparatus can also be constructed from various logic devices, such as general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), microcontrollers, ARM (Acorn RISC Machine) or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination of these components.

[0114] Please see Figure 3 The training feedback device 300 based on intelligent training equipment includes: an interface presentation module 31, a mode determination module 32, a speed acquisition module 33, and a training feedback module 34.

[0115] The interface presentation module 31 controls the presentation mode selection interface of the intelligent training device. The mode determination module 32 responds to the mode selection operation received by the mode selection interface, determines the target training mode and the corresponding standard monitoring information. The speed acquisition module 33 acquires speed information, which is the speed information of the user when performing movement training on the intelligent training device in the target training mode. The training feedback module 34 presents training feedback information based on the speed information and the standard monitoring information, which is used to guide the user's training status.

[0116] This application embodiment can provide real-time training feedback during the user's current training, better ensuring training effectiveness and safety under different strength training goals, and improving the user experience. Furthermore, this application embodiment eliminates the need for costly professional equipment or hiring professionals to evaluate training effectiveness, thus saving costs. It is also applicable to the general population for evaluating training effectiveness, expanding its applicability.

[0117] In some embodiments, the standard monitoring information includes a standard speed range. The training feedback module 34 is specifically used to: determine whether the speed information deviates from the standard speed range; if it deviates, generate training feedback information to guide the user to perform the next action on the intelligent training device as a valid action; if it does not deviate, generate action count information to indicate that the action performed by the user on the intelligent training device is a valid action.

[0118] In some embodiments, the training feedback module 34 is further specifically used to: determine speed monitoring information corresponding to the target training mode based on speed information, wherein the speed monitoring information is used to quantitatively represent the user's training status on the intelligent training device, and to determine whether the speed monitoring information deviates from the standard speed range.

[0119] In some embodiments, the training feedback information includes weight increase information and training adjustment information. The training feedback module 34 is further specifically used to: generate weight increase information if the speed information is greater than the standard speed range, the weight increase information is used to indicate the increase of the weight of the intelligent training device; generate training adjustment information if the speed information is less than or equal to the standard speed range, the training adjustment information is used to indicate the decrease of the weight of the intelligent training device or to guide the user to increase the speed of the next training movement.

[0120] In some embodiments, the standard monitoring information also includes a percentage of speed loss, and the training feedback information includes rest guidance information and training adjustment information. The training feedback module 34 is further specifically used to: determine the real-time speed loss ratio if the speed information is less than or equal to the standard speed range; generate training adjustment information if the real-time speed loss ratio is less than the percentage of speed loss; the training adjustment information is used to instruct the user to reduce the weight of the intelligent training device or guide the user to increase the speed of the next training movement if the real-time speed loss ratio is greater than or equal to the percentage of speed loss; and generate rest guidance information to guide the user to make rest adjustments.

[0121] In some embodiments, the training feedback module 34 is specifically used to: obtain historical speed indicators, which are the speed indicators of the user in the past time, and calculate the real-time speed loss ratio based on the historical speed indicators and speed information.

[0122] In some embodiments, the historical speed index includes the historical single-action speed index for a single action and / or the historical single-group speed index for each group of actions. The speed information includes the real-time single-action speed index for a single action and / or the real-time single-group speed index for each group of actions. The training feedback module 34 is specifically used to: calculate a first difference between the real-time single-action speed index and the historical single-action speed index; if the first difference is negative, calculate the absolute value of the ratio of the first difference to the historical single-action speed index to obtain the real-time speed loss ratio; and / or, calculate a second difference between the real-time single-group speed index and the historical single-group speed index; if the second difference is negative, calculate the absolute value of the ratio of the second difference to the historical single-group speed index to obtain the real-time speed loss ratio.

[0123] In some embodiments, the rest guidance information includes first rest information, second rest information, or stop training information. The training feedback module 34 is specifically configured to: generate first rest information if the ratio of real-time single-speed indicator to historical single-speed indicator's real-time speed loss is greater than or equal to the speed loss percentage; the first rest information is used to instruct the user to rest for a first preset duration; and / or, generate second rest information if the ratio of real-time single-group speed indicator to historical single-group speed indicator's real-time speed loss is greater than or equal to the speed loss percentage; the second rest information is used to instruct the user to rest for a second preset duration; and / or, within all preset groups, generate stop training information if the ratio of real-time single-speed indicator to historical single-speed indicator's real-time speed loss is greater than or equal to the speed loss percentage; the stop training information is used to instruct the user to end training.

[0124] In some embodiments, before determining the target training mode, the mode determination module 32 is further configured to: acquire exercise configuration data, and control the intelligent training device to output an initial weight based on the exercise configuration data, wherein the initial weight is used to assist the user in exercising on the intelligent training device.

[0125] In some embodiments, the mode determination module 32 is specifically used to: determine the target ratio range corresponding to the target training mode, the motion configuration data includes the maximum weight, determine the initial counterweight according to the maximum weight and the target ratio range, and control the intelligent training device to output the initial counterweight.

[0126] The mode determination module 32 is also specifically used for: obtaining the maximum weight applied by the user to the intelligent training device during the strength test operation; or, obtaining the user's historical training data and determining the user's maximum weight in the target training mode based on the historical training data and the preset strength learning model; or, obtaining the maximum weight corresponding to the user pre-stored locally by the intelligent training device; or, requesting the server connected to the intelligent training device to send the maximum weight corresponding to the user.

[0127] The following embodiments of this application provide an intelligent training device that applies the above-described training feedback method. The intelligent training device includes an intelligent resistance output component and a controller. The intelligent resistance output component is used to output resistance, and the controller is electrically connected to the intelligent resistance output component to implement the above-described training feedback method based on the intelligent training device.

[0128] The intelligent resistance output component employs an intelligent counterweight adjustment method to intelligently output resistance. In some embodiments, the intelligent resistance output component is a motor, and the controller determines speed information based on the motor's rotation data. In some embodiments, the intelligent resistance output component is a pneumatic resistance output component, which includes an air compression mechanism and a resistance transmission mechanism. The air compression mechanism, controlled by the controller, compresses air into high-pressure gas, and the resistance transmission mechanism outputs resistance based on the high-pressure gas. In some embodiments, the intelligent resistance output component is an electromagnetic resistance output component, which includes an electromagnet mechanism and a resistance transmission mechanism. The electromagnet mechanism, controlled by the controller, outputs electromagnetic force, and the resistance transmission mechanism outputs resistance based on the electromagnetic force.

[0129] Please refer to the following: Figure 4 and Figure 5 The intelligent training device 400 includes a frame 44, a motor 42, a winding assembly 43, a display screen 44, and a controller 45.

[0130] The frame 41 is used to support various components. The frame 41 can be constructed into any suitable structure, and the material of the frame 41 can be steel or carbon fiber, etc.

[0131] The motor 42 is installed inside the housing 411 of the frame 41, and the output shaft of the motor 42 is connected to the winding assembly 43, allowing the motor 42 to drive the winding assembly 43 to move. The motor 42 can be equipped with various sensors to collect its operating data. For example, the motor 42 is equipped with an encoder and a current sensor. The encoder can record the rotational position of the motor rotor in real time to calculate the motor speed; based on the motor speed, the speed information of the intelligent training device in various training modes can be calculated. The current sensor can collect the motor's operating current to monitor the motor's load and resistance output status.

[0132] Please see Figure 6 The winding assembly 43 includes a winding wheel 431 and a rope 432. The output shaft of the motor 42 is connected to the winding wheel 431. The winding wheel 431 has a groove, and all the rope 432 is wound in the same groove. The first end of the rope 432 is used for the user to apply tension, and the portion of the rope away from the first end is wound in the groove of the winding wheel 431. The motor 42 can drive the winding wheel 431 to rotate with the rope 432. When the output shaft of the motor 42 moves in a first circumferential direction, the winding wheel 431 can wind the rope 432 into the groove. When the output shaft of the motor 42 moves in a second circumferential direction, the winding wheel 431 can disengage the rope 432 from the groove. The first circumferential direction is opposite to the second circumferential direction.

[0133] The display screen 44 is used to display relevant motion information. The display screen 44 can be a touchscreen; for example, it can display the user's real-time speed dataset or historical data, and can also receive user control commands. Understandably, some intelligent training equipment may not require the display screen 44.

[0134] The controller 45 can store and analyze speed datasets. The controller 45 analyzes the speed datasets based on a preset motion model. For example, the preset motion model is configured with motion type, exerciser physiological parameters, speed dataset, exercise plan, etc. The preset motion model compares and calculates the speed datasets and stores the calculation results (including matching items and difference items) locally on the controller 45. It also uploads the speed datasets or analysis results to the server and provides feedback to the user through the display screen to inform the user of the training status.

[0135] During exercise, the user first sets the resistance on the display screen 44. Then, the user stands on the frame 41, holding the ropes 432 with both hands. The fitness equipment 400 controls the operation of the motor 42 according to the resistance. Under the control of the motor 42, the winding wheel 431 pulls the ropes 432 to generate resistance, allowing the user to complete the training under the action of resistance.

[0136] It is understood that the training feedback method provided in this application embodiment is not only applicable to the intelligent training devices provided in the above embodiments, but also applicable to other types of intelligent training devices.

[0137] It should be noted that the above-described training feedback device based on intelligent training equipment can execute the training feedback method based on intelligent training equipment provided in the embodiments of this application, and has the corresponding functional modules and beneficial effects of the method. Technical details not described in detail in the embodiments of the training feedback device based on intelligent training equipment can be found in the training feedback method based on intelligent training equipment provided in the embodiments of this application.

[0138] Please see Figure 7 , Figure 7 This is a schematic diagram of a controller provided in an embodiment of this application. The controller can be applied to intelligent training equipment. The controller 700 includes one or more processors 71 and a memory 72. The memory 72 is connected to one or more processors 71, for example, via a bus.

[0139] Processor 71 is configured to support the controller in performing the corresponding functions in the methods described in the above method embodiments. The processor may be a central processing unit (CPU), a network processor (NP), a hardware chip, or any combination thereof. The aforementioned hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The aforementioned PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a general-purpose array logic (GAL), or any combination thereof.

[0140] Memory 72 is used to store program code, etc. Memory may include volatile memory (VM), such as random access memory (RAM); memory may also include non-volatile memory (NVM), such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD); memory may also include combinations of the above types of memory.

[0141] The memory 72 can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the training feedback method based on intelligent training equipment in the embodiments of this application. The processor executes the non-volatile software programs, instructions, and modules stored in the memory to perform various functional applications and data processing of the training feedback method and training feedback device based on intelligent training equipment, thereby realizing the functions of each module or unit of the training feedback method and training feedback device based on intelligent training equipment provided in the above method embodiments.

[0142] The memory 72 may include a program storage area and a data storage area, wherein the program storage area may store the operating system and applications required for at least one function. The data storage area may store data created based on the use of the training feedback device based on the intelligent training apparatus. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the training feedback device based on the intelligent training apparatus via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0143] The one or more modules are stored in the memory. When executed by the one or more processors, they perform the training feedback method based on the intelligent training device in any of the above method embodiments. For example, they perform the method steps described in the above method embodiments to realize the functions of the modules described in the above device embodiments.

[0144] This application also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a computer, cause the computer to perform the method described in the foregoing embodiments.

[0145] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0146] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.

Claims

1. A training feedback method based on intelligent training equipment, characterized in that, include: Control the intelligent training device to display a mode selection interface; In response to the mode selection operation received by the mode selection interface, the target training mode and the standard monitoring information corresponding to the target training mode are determined. Acquire speed information, which is the speed information of the user when performing action training on the intelligent training device that has entered the target training mode; Training feedback information is presented based on the speed information and the standard monitoring information, and the training feedback information is used to guide the user's training status.

2. The training feedback method according to claim 1, characterized in that, The standard monitoring information includes a standard speed range, and the presentation of training feedback information based on the speed information and the standard monitoring information includes: Determine whether the speed information deviates from the standard speed range; If the deviation is found, training feedback information is generated to guide the user to perform the next action on the intelligent training device as a valid action. If there is no deviation, action count information is generated, which is used to indicate that the action performed by the user on the intelligent training device is a valid action.

3. The training feedback method according to claim 2, characterized in that, The determination of whether the speed information deviates from the standard speed range includes: Based on the speed information, speed monitoring information corresponding to the target training mode is determined, and the speed monitoring information is used to quantitatively represent the user's training status on the intelligent training device. Determine whether the speed monitoring information deviates from the standard speed range.

4. The training feedback method according to claim 2, characterized in that, The training feedback information includes weight increase information and training adjustment information, and the generation of training feedback information includes: If the speed information is greater than the standard speed range, then weight increase information is generated, which is used to indicate the addition of weight to the intelligent training device; If the speed information is less than or equal to the standard speed range, training adjustment information is generated. The training adjustment information is used to indicate the reduction of the weight of the intelligent training device or to guide the user to increase the speed of the next training movement.

5. The training feedback method according to claim 2, characterized in that, The standard monitoring information also includes the percentage of speed loss, the training feedback information includes rest guidance information and training adjustment information, and the generated training feedback information includes: If the speed information is less than or equal to the standard speed range, then the real-time speed loss ratio is determined; If the real-time speed loss ratio is less than the speed loss percentage, training adjustment information is generated. The training adjustment information is used to indicate the reduction of the weight of the intelligent training device or to guide the user to increase the speed of the next training movement. If the real-time speed loss ratio is greater than or equal to the speed loss percentage, rest guidance information is generated to guide the user to take a rest.

6. The training feedback method according to claim 5, characterized in that, The determination of the real-time velocity loss ratio includes: Obtain historical speed metrics, which are the user's speed metrics over a past period of time; The real-time speed loss ratio is calculated based on the historical speed index and the speed information.

7. The training feedback method according to claim 6, characterized in that, The historical speed indicators include historical single-action speed indicators for a single action and / or historical single-group speed indicators for each group of actions. The speed information includes real-time single-action speed indicators for a single action and / or real-time single-group speed indicators for each group of actions. Calculating the real-time speed loss ratio based on the historical speed indicators and the speed information includes: Calculate the first difference between the real-time single-shot speed index and the historical single-shot speed index. If the first difference is negative, calculate the absolute value of the ratio of the first difference to the historical single-shot speed index to obtain the real-time speed loss ratio; and / or, Calculate the second difference between the real-time single-group speed index and the historical single-group speed index. If the second difference is negative, calculate the absolute value of the ratio of the second difference to the historical single-group speed index to obtain the real-time speed loss ratio.

8. The training feedback method according to claim 5, characterized in that, The rest guidance information includes first rest information, second rest information, or training stop information. Generating rest guidance information includes: If the ratio of the real-time single-speed index to the historical single-speed index in terms of real-time speed loss is greater than or equal to the speed loss percentage, then first rest information is generated, which is used to instruct the user to rest for a first preset duration; and / or, If the ratio of the real-time speed loss of the real-time single-group speed index to the real-time speed loss of the historical single-group speed index is greater than or equal to the speed loss percentage, then a second rest information is generated, which is used to indicate to the user a second preset rest duration; and / or, Within all preset groups, if the ratio of real-time speed loss of each real-time single speed indicator to the real-time speed loss of each historical single speed indicator is greater than or equal to the percentage of speed loss, a stop training message is generated, which is used to instruct the user to end the training.

9. The training feedback method according to any one of claims 1 to 8, characterized in that, Before determining the target training mode, the following is also included: Obtain sports configuration data; The intelligent training device is controlled to output an initial weight based on the exercise configuration data. The initial weight is used to assist the user in exercising on the intelligent training device.

10. The training feedback method according to claim 9, characterized in that, The step of controlling the intelligent training device to output the initial weight based on the motion configuration data includes: Determine the target ratio range corresponding to the target training mode, wherein the exercise configuration data includes the maximum weight; The initial counterweight is determined based on the maximum weight and the target proportion range; Control the intelligent training device to output the initial counterweight.

11. An intelligent training device, characterized in that, include: Intelligent resistance output component, used to output resistance; The controller is electrically connected to the intelligent resistance output component and is used to implement the training feedback method based on the intelligent training device as described in any one of claims 1-10.

12. The intelligent training device according to claim 11, characterized in that, The intelligent resistance output component is a motor, and the controller determines the speed information based on the rotation data of the motor.

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