Training state visualization method and strength training apparatus

Abstract

The embodiment of the invention relates to the technical field of strength training instruments, and discloses a training state visualization method and a strength training instrument. The method comprises the steps of obtaining target training data, determining a target training state according to the target training data, determining a target color block corresponding to the target training state and a target dynamic effect rate of the target color block, and controlling the strength training apparatus to present the target color block according to the target dynamic effect rate. According to the embodiment of the invention, the target color block can be selected for presentation according to the training state, and the presentation of the color can be adjusted according to the target dynamic effect rate, so that a strong dynamic atmosphere is created for the user visually, and the training enthusiasm and the user experience of the user are improved.

Description

Technical Field

[0001] This application relates to the field of strength training equipment technology, and in particular to a method for visualizing training status and a strength training equipment. Background Technology

[0002] With the increasing popularity of smart fitness, people's demand for home workouts has grown. People are now able to purchase strength training equipment to place at home for convenient daily exercise. This technology allows strength training equipment to provide voice prompts or play music to interact with users during training, thereby increasing their enthusiasm. However, the voice and music provided by this technology can only convey the user's training stage or merely create a training atmosphere; they have little relevance to the user's actual training state. Therefore, their effectiveness in regulating or enhancing user training enthusiasm is limited. Summary of the Invention

[0003] One objective of this application is to provide a method for visualizing training status and a strength training device to solve the technical problem that related technologies cannot effectively convey the user's training status through strength training devices.

[0004] In a first aspect, embodiments of this application provide a method for visualizing training states, applied to strength training equipment, comprising:

[0005] Acquire target training data, which is used to represent the user's training status on the strength training equipment;

[0006] The target training state is determined based on the target training data, and the target training state is the user's training state on the strength training equipment.

[0007] Determine the target color block corresponding to the target training state and the target motion rate of the target color block, wherein the target color block with the target motion rate is used to represent the user's training state;

[0008] The strength training device is controlled to display the target color block according to the target motion rate.

[0009] Optionally, the target training state is represented by a target training intensity or a target calorie consumption value, and determining the target training state based on the target training data includes:

[0010] If the target training data includes user physiological data, then a preset intensity model is selected based on the target training data to calculate the target training intensity. The preset intensity model is constrained by the user physiological data, which represents the user's physiological state when training on the strength training equipment.

[0011] If the target training data does not include user physiological data, then a preset calorie model is selected based on the target training data to calculate the target calorie consumption value.

[0012] Optionally, the target training data includes the user's local training data and training mode data collected by the strength training equipment, and the step of selecting a preset intensity model to calculate the target training intensity based on the target training data includes:

[0013] The target training mode is determined based on the training mode data, and the target training mode is the training mode selected by the user on the strength training equipment.

[0014] Determine the target weight set based on the target training mode;

[0015] The target weight set, the local training data, and the user physiological data are input into the preset intensity model and fused to obtain the target training intensity.

[0016] Optionally, determining the target weight set based on the target training mode includes:

[0017] Obtain a preset weight table, which includes weight sets corresponding to different training modes;

[0018] The weight set corresponding to the target training mode is determined as the target weight set based on the training mode data.

[0019] Optionally, the target weight set includes a first type of weight coefficient and a second type of weight coefficient, the preset intensity model includes training configuration parameters and physiological configuration parameters, and the step of inputting the target weight set, the local training data, and the user physiological data into the preset intensity model for fusion to obtain the target training intensity includes:

[0020] Calculate the local weighting value based on the first type of weighting coefficient, the training configuration parameters, and the local training data;

[0021] The physiological weighting value is calculated based on the second type of weighting coefficient, the physiological configuration parameters, and the user's physiological data;

[0022] The target training intensity is obtained by adding the local weighted value to the physiological weighted value.

[0023] Optionally, the training configuration parameters include maximum user strength and maximum user speed, the local training data includes current user strength and current user speed, the first type of weight coefficients includes strength weight coefficients and speed weight coefficients, and the calculation of the local weighted value based on the first type of weight coefficients, the training configuration parameters, and the local training data includes:

[0024] Calculate the first ratio between the current user strength and the maximum user strength;

[0025] Multiply the first ratio by the force weighting coefficient to obtain the force weighted value;

[0026] Calculate the second ratio between the current user speed and the maximum user speed;

[0027] Multiply the second ratio by the speed weighting coefficient to obtain the speed weighted value;

[0028] The force weighted value is added to the speed weighted value to obtain the local weighted value.

[0029] Optionally, the physiological configuration parameters include resting heart rate and maximum heart rate, the user physiological data includes target heart rate, the second type of weighting coefficient includes heart rate weighting coefficient, and the calculation of the physiological weighted value based on the second type of weighting coefficient, the physiological configuration parameters, and the user physiological data includes:

[0030] Calculate the first difference between the target heart rate and the resting heart rate;

[0031] Calculate the second difference between the maximum heart rate and the resting heart rate;

[0032] The third ratio of the first difference to the second difference;

[0033] The third ratio is multiplied by the heart rate weighting coefficient to obtain the physiological weighted value.

[0034] Optionally, the target training mode is one of the following training modes: standard mode, eccentric mode, isokinetic mode, and elastic mode.

[0035] The standard pattern is used to indicate that the resistance output by the strength training device during the cable pull-out phase is equal to the resistance output during the cable retraction phase.

[0036] The centrifugal mode is used to indicate that the resistance output by the strength training device during the cable pull-out phase is less than the resistance output during the cable retraction phase.

[0037] The constant speed mode indicates that the greater the user's speed, the greater the resistance output by the strength training device.

[0038] The elasticity mode indicates that the longer the rope of the strength training device is pulled out, the greater the output resistance.

[0039] Optionally, the target training data includes training time and rest time, the preset calorie model includes the target weight set on the strength training equipment and the calorie value per unit of rest, and the step of selecting the preset calorie model based on the target training data to calculate the target calorie consumption value includes:

[0040] Determine the unit training calorie value corresponding to the target weight;

[0041] Calculate the training calorie consumption value based on the training time and the unit training calorie value;

[0042] Calculate the rest calorie consumption value based on the rest time and the unit rest calorie value;

[0043] The target calorie expenditure value is obtained by adding the training calorie expenditure value to the rest calorie expenditure value.

[0044] In a second aspect, embodiments of this application provide a strength training device, including: a memory and a processor, the memory being connected to the processor, the processor being configured to execute one or more computer programs stored in the memory, and the processor, when executing the one or more computer programs, enabling the strength training device to implement the aforementioned method for visualizing training states.

[0045] In a third aspect, embodiments of this application provide a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the above-described visualization method for training states.

[0046] The embodiments of this application can achieve the following technical effects: the embodiments of this application can select target color blocks for presentation according to the training state, and can also adjust the presentation of target color blocks according to the target motion rate, thereby creating a strong dynamic atmosphere for users visually, which is conducive to improving users' training enthusiasm and user experience. Attached Figure Description

[0047] 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.

[0048] Figure 1 This is a schematic diagram of the structure of a strength training device provided in an embodiment of this application;

[0049] Figure 2aA circuit block diagram of a strength training device provided in this application embodiment;

[0050] Figure 2b This is a schematic diagram of the structure of the motor and winding assembly provided in the embodiments of this application;

[0051] Figure 3 This is a schematic diagram of the communication architecture between the strength training equipment and the smart wearable device provided in the embodiments of this application;

[0052] Figure 4 A flowchart illustrating a method for visualizing training states provided in an embodiment of this application;

[0053] Figure 5 A schematic diagram showing a first state in yellow during the medium-intensity training phase, provided for an embodiment of this application;

[0054] Figure 6 This is a schematic diagram illustrating the second state, represented by yellow, during the medium-intensity training phase, as provided in this application embodiment.

[0055] Figure 7 A schematic diagram of the structure of a training state visualization device provided in an embodiment of this application;

[0056] Figure 8 This is a schematic diagram of the structure of a controller provided in an embodiment of this application. Detailed Implementation

[0057] 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.

[0058] 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.

[0059] The relevant technology involved conducting a user survey questionnaire regarding users' willingness to "go to the gym" and "work out at home." The questionnaire asked: "Would you rather "go to the gym" or "work out at home"? Out of 51 users surveyed, 45 (88.2%) preferred "going to the gym," while 6 (11.8%) preferred "working out at home." Reasons for users' reluctance to work out at home included: a dull and uninspiring workout atmosphere at home, lack of encouragement and supervision from friends, and limited range of exercises.

[0060] As mentioned in the background section, the strength training equipment provided by the relevant technology only enhances the training atmosphere through auditory means, such as adding voice prompts and background music, but cannot convey the atmosphere of real-time training status through visual means.

[0061] This application embodiment can not only select appropriate colors to display according to the training status, but also adjust the color display according to the motion effect rate, thereby creating a strong dynamic atmosphere for users and improving their training enthusiasm and user experience.

[0062] The following embodiments of this application provide a strength training device, wherein the strength training device employs any suitable resistance output method. This strength training device includes weight-type fitness equipment, dynamic spring-type fitness equipment, pneumatic fitness equipment, hydraulic fitness equipment, electromagnetic fitness equipment, and motor-resistance fitness equipment, etc. Weight-type fitness equipment provides resistance by attaching weight plates of different weights; dynamic spring-type fitness equipment utilizes the elastic deformation of springs to provide resistance; pneumatic fitness equipment compresses gas to provide resistance; hydraulic fitness equipment compresses liquid to provide resistance; electromagnetic fitness equipment uses electromagnetic control to provide resistance; and motor-resistance fitness equipment uses motor control to provide resistance.

[0063] To facilitate understanding of the training feedback method based on strength training equipment provided in the embodiments of this application, the following embodiments of this application provide a strength training equipment. It is understood that the training feedback method based on strength training equipment provided below can be applied to various types of strength training equipment and is not limited to the strength training equipment provided in the following embodiments.

[0064] Please see Figure 1 The strength training equipment 100 includes a frame 11, a motor 12, a winding assembly 13, a display screen 14, and a controller 15.

[0065] The rack 11 is used to support various components. The rack 11 can be constructed into any suitable structure, and the material of the rack 11 can be steel or carbon fiber, etc.

[0066] The frame 11 includes a motor housing 111, in which a motor 12 is mounted. The output shaft of the motor 12 is connected to a winding assembly 13, and the motor 12 can drive the winding assembly 13 to move. The motor 12 can be equipped with various sensors to collect its operating data. For example, the motor 12 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 resistance. The current sensor can collect the motor's operating current to monitor the motor's load and resistance output status.

[0067] Please refer to the following: Figure 2a and Figure 2b The winding assembly 13 includes a winding wheel 131 and a rope 132. The output shaft of the motor 12 is connected to the winding wheel 131. The winding wheel 131 has a groove, and all the rope 132 is wound in the same groove. The first end of the rope 132 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 131. The motor 12 can drive the winding wheel 131 to rotate with the rope 132. When the output shaft of the motor 12 moves in a first circumferential direction, the winding wheel 131 can wind the rope 132 into the groove. When the output shaft of the motor 12 moves in a second circumferential direction, the winding wheel 131 can disengage the rope 132 from the groove. The first circumferential direction is opposite to the second circumferential direction.

[0068] The display screen 14 is used to display relevant training information. The display screen 14 can be a touch screen. For example, the display screen 14 can display the user's real-time training data or historical data, and can also receive the user's control commands.

[0069] The controller 15 can store and analyze training data. The controller 15 analyzes the training data according to the preset training model. For example, the preset training model is configured with training type, trainee physiological parameters, training data, training plan, etc. The preset training model compares and calculates the training data and stores the calculation results (including matching items and difference items) locally on the controller 15. At the same time, it uploads the training data or analysis results to the server and feeds them back to the user through the display screen to inform the user of the training status.

[0070] During exercise, the user first sets the resistance on the display screen 14. Then, the user stands on the frame 11, holding the rope 132 with both hands. The fitness equipment 100 controls the operation of the motor 12 according to the resistance. Under the control of the motor 12, the winding wheel 131 pulls the rope 132 to generate resistance, allowing the user to complete the training under the action of resistance.

[0071] In some embodiments, please refer to Figure 3The strength training device 100 can wirelessly connect to the smart wearable device 200, including Bluetooth or Wi-Fi connections. The smart wearable device 200 collects the user's physiological data and transmits it to the strength training device 100. For example, if the user's physiological data is heart rate data, the smart wearable device 200 can collect the user's heart rate data during training and transmit it to the strength training device 100 via Bluetooth. The strength training device 100 then calculates the user's training status based on the heart rate data.

[0072] It is understood that the training status visualization method provided in this application embodiment is not only applicable to the strength training equipment provided in the above embodiments, but also applicable to other types of strength training equipment.

[0073] The following embodiments of this application provide a method for visualizing training states. Please refer to... Figure 4 The visualization method for training status includes the following steps:

[0074] S41: Obtain target training data.

[0075] In this step, the target training data is used to represent the user's training status on the strength training equipment. In some embodiments, the target training data includes user physiological data, such as the user's target heart rate or respiratory rate during strength training.

[0076] In some embodiments, the target training data includes the user's local training data, which is the user's speed and force collected by the strength training machine when the user is training on the strength training machine. The user speed is the speed at which the user pulls the rope of the strength training machine, for example, the speed at which the user pulls the rope of the strength training machine, and the user force is the tension of the user pulling the rope of the strength training machine.

[0077] In some embodiments, the target training data includes training mode data, which is data on the training mode selected by the user on the strength training equipment, and is used to represent the training mode selected by the user.

[0078] In some embodiments, the strength training equipment is equipped with multiple training modes. These modes include standard mode, eccentric mode, isokinetic mode, and elastic mode. Standard mode indicates that the resistance output by the strength training equipment during the cable pull-out phase is equal to the resistance output during the cable retrieval phase. Standard mode aims to increase the user's muscle strength and endurance. For example, if a user sets the strength training equipment to standard mode and sets the weight to 10kg, then: Standard mode requires the resistance output by the strength training equipment during both the cable pull-out and retrieval phases to be equal to the force corresponding to 10kg.

[0079] Eccentric mode indicates that the resistance output of a strength training machine during the cable pull-out phase is less than the resistance output during the cable retrieval phase. Eccentric mode aims to enhance muscle stimulation and improve control. For example, if a user sets the strength training machine to eccentric mode, sets the weight to 10kg, and the eccentric ratio to 0.7, then: eccentric mode requires the strength training machine to output a force equivalent to 7kg of resistance during the cable pull-out phase and a force equivalent to 10kg of resistance during the cable retrieval phase.

[0080] Constant speed mode indicates that the greater the user's speed, the greater the resistance output of the strength training equipment. This is designed to enhance explosive power and reduce joint stress. For example, when a user sets the strength training equipment to work in constant speed mode, the greater the user's speed, the greater the resistance output of the strength training equipment; and the less the user's speed, the less the resistance output of the strength training equipment.

[0081] The elastic mode indicates that the longer the cable of a strength training machine is pulled out, the greater the output resistance. It is designed to enhance peak stimulation and improve stability and control. For example, when a user sets a strength training machine to work in elastic mode, the longer the cable is pulled out, the greater the output resistance of the strength training machine, and the shorter the cable is pulled out, the less the output resistance of the strength training machine.

[0082] In some embodiments, the target training data includes training time and rest time. Training time is the time the user trains on the strength training machine, and rest time is the time the user pauses training. In this embodiment, while the strength training machine is in training mode, the system records the first time the user pulls the rope out of the machine, the second time the machine retracts all the rope, subtracts the first time from the second time to obtain the training time, and then records the third time the user pulls the rope out of the machine again. Subtracting the second time from the third time yields the rest time.

[0083] S42: Determine the target training state based on the target training data. The target training state is the user's training state on the strength training equipment.

[0084] In this step, the target training state is represented by the target training intensity or the target calorie expenditure. Target training intensity refers to the amount of force or training load the user exerts while using the strength training equipment. Generally, the higher the target training intensity, the more strenuous the training; conversely, the lower the target training intensity, the easier the training. Target calorie expenditure is the amount of calories burned by the user while using the strength training equipment. Generally, the higher the target calorie expenditure, the more strenuous the training; conversely, the lower the target calorie expenditure, the easier the training.

[0085] S43: Determine the target color block and the target motion rate of the target color block corresponding to the target training state. The target color block with the target motion rate is used to represent the user's training state.

[0086] In this step, the target color block is the color block that matches the target training state. A color block is a colored area, and different target color blocks correspond to different training states. The target motion effect rate is the motion effect rate that matches the target training state. The motion effect rate is the speed at which the color block moves on the display screen. The unit of the motion effect rate is ms / m, where "ms / m" represents the time required for the color block to move 1 meter, and the unit of this time is milliseconds. This application embodiment provides a color motion effect mapping table; please refer to Table 1:

[0087] Table 1

[0088]

[0089] As shown in Table 1, for a motion rate of 5500 ms / m, it takes 5500 milliseconds for the color block to move within a 1-meter range. Therefore, the motion rate of the color block is 5500 ms / m. Similarly, for a motion rate of 3400 ms / m, it takes 3400 milliseconds for the color block to move within a 1-meter range. Therefore, the motion rate of the color block is 3400 ms / m. Table 1 shows that the higher the motion rate, the slower the movement of the color block on the display screen, resulting in a slower movement effect. Conversely, the lower the motion rate, the faster the movement of the color block on the display screen, resulting in a faster movement effect.

[0090] As shown in Table 1, different training states correspond to different colored blocks and different motion rate. During the warm-up phase of training, the user's training state is relaxed, with low training intensity and low calorie consumption. Therefore, this embodiment selects blue as the target color block and uses a slower motion rate as the target motion rate. During the low-intensity phase of training, the user's training state is relatively relaxed, with low training intensity or moderate calorie consumption. Therefore, this embodiment selects green as the target color block and uses a slow motion rate as the target motion rate. During the medium-intensity phase of training, the user's training state is more strenuous, with medium training intensity or moderate calorie consumption. Therefore, this embodiment selects yellow as the target color block and uses a medium-speed motion rate as the target motion rate. During the high-intensity training phase, the user is in a strenuous training state, with high training intensity or high calorie consumption. Therefore, this embodiment selects orange as the target color block and uses a fast-moving motion effect rate as the target motion effect rate. During the high-intensity training phase, the user is in a very strenuous training state, with high training intensity or high calorie consumption. Therefore, this embodiment selects red as the target color block and uses a motion effect rate with a fast flashing movement effect as the target motion effect rate.

[0091] Determining the target color block and target motion rate corresponding to the target training state includes the following steps: obtaining a color-motion mapping table, which includes colors and motion rates corresponding to different training states; and determining the color and motion rate corresponding to the target training state in the color-motion mapping table as the target color block and target motion rate, respectively. For example, if the target training state is a relatively relaxed state, then the target color block is green, and the target motion rate is a slow motion rate.

[0092] S44: Control the force training equipment to display the target color block according to the target motion rate.

[0093] In this step, in some embodiments, the background color of the display screen of the strength training equipment is the color of the target color block, and it is presented according to the target motion rate. Please refer to... Figure 5 and Figure 6During the medium-intensity training phase, the target color block is yellow, and the target motion rate is 4100ms / m-3400ms / m. In this embodiment, the training interface 51 of the display screen is controlled to display yellow. At time t1, the yellow area 52 is located in the upper right corner of the training interface 51. At time t2, this embodiment controls the yellow area 51 to train according to the target motion rate, so that the yellow area 52 is located in the upper center of the training interface 51. When the training phase switches from medium-intensity to high-intensity, this embodiment changes the yellow color to orange within a specified duration.

[0094] In some embodiments, the strength training equipment is equipped with a light strip that can display different colors. In this application embodiment, the light strip is controlled to display target color blocks according to the target motion rate.

[0095] The embodiments of this application can select target color blocks for presentation based on the training status, and can also adjust the color presentation according to the target motion rate, thereby creating a strong dynamic atmosphere for users and improving their training enthusiasm and user experience.

[0096] As mentioned above, the target training state is represented by the target training intensity or the target calorie consumption value. In some embodiments, determining the target training state based on the target training data includes the following steps:

[0097] S421: If the target training data includes user physiological data, then select the preset intensity model to calculate the target training intensity based on the target training data.

[0098] S422: If the target training data does not include user physiological data, then select the preset calorie model based on the target training data to calculate the target calorie consumption value.

[0099] In S421, user physiological data is used to represent the user's physiological state when training on the strength training equipment. The user physiological data includes target heart rate or respiratory rate, etc.

[0100] In some embodiments, a user wears a smart wearable device while training on a strength training device, and the smart wearable device can collect the user's target heart rate. The strength training device communicates with the smart wearable device via Bluetooth, wherein the smart wearable device can transmit the target heart rate to the strength training device, and therefore the strength training device saves the target heart rate as the user's physiological data.

[0101] In some embodiments, the strength training device is equipped with a heart rate sensor, which is located on the grip of the strength training device. When the user holds the grip during training, the heart rate sensor can detect the pulse in the user's palm, thereby obtaining the user's heart rate data. The strength training device saves the heart rate data as the user's physiological data.

[0102] The preset intensity model is a model that designers build in advance to calculate the target training intensity. Understandably, designers can build the preset intensity model based on engineering experience and in combination with elements that can reflect training intensity, such as the force output by the user on the strength training equipment, the speed at which the user trains on the strength training equipment, the user's heart rate during training and rest, the user's facial expressions during training, and the user's breathing rate on the strength training equipment.

[0103] The preset intensity model is constrained by the user's physiological data. When the strength training device parses the user's physiological data from the target training data, the device calls the preset intensity model and inputs the target training data into it for processing, thereby obtaining the target training intensity. User physiological data can more effectively and directly reflect the user's training load. The embodiments of this application, by combining user physiological data, can accurately and reliably derive the target training intensity.

[0104] In S422, in some embodiments, the user does not wear a smart wearable device during training, and the strength training device is unable to acquire the user's physiological data. Therefore, the strength training device is unable to package the user's physiological data into the target training data. In some embodiments, the strength training device does not have a heart rate acquisition function, or, although the strength training device has a heart rate acquisition function, the user cannot correctly place their palm at the heart rate acquisition position, causing the strength training device to be unable to acquire the user's physiological data. Therefore, the strength training device is unable to package the user's physiological data into the target training data.

[0105] The preset calorie model is a model pre-built by the designer to calculate the target calorie consumption value. Understandably, the designer can construct the preset calorie model based on engineering experience and elements that reflect calorie consumption, such as the user's breathing rate on the strength training equipment, the user's training duration on the equipment, the user's habitual calorie consumption per unit during training, and the user's rest duration after pausing training. When the strength training equipment cannot parse the user's physiological data from the target training data, it calls the preset calorie model and processes the target training data to obtain the target calorie consumption value.

[0106] In the absence of user physiological data, this application embodiment can call a preset calorie model to calculate the target calorie consumption value, thereby reflecting the user's target training state. This ensures that the strength training device can reliably determine the target training state in both scenarios with and without heart rate data, and thus reliably present the target color block according to the target motion rate.

[0107] Target training data includes local training data and training mode data collected from users by strength training equipment. The calculation of target training intensity based on the target training data and a preset intensity model involves the following steps:

[0108] S4211: Determine the target training mode based on the training mode data. The target training mode is the training mode selected by the user on the strength training equipment.

[0109] S4212: Determine the target weight set based on the target training mode.

[0110] S4213: Input the target weight set, local training data and user physiological data into the preset intensity model for fusion to obtain the target training intensity.

[0111] In S4211, the training mode data includes a target mode identifier, with one training mode corresponding to one mode identifier. In this embodiment, the target mode identifier is parsed from the training mode data, and the training mode corresponding to the target mode identifier is determined to be the target training mode. As mentioned above, the training modes include standard mode, eccentric mode, isokinetic mode, and elastic mode, and the target training mode is one of the following training modes: standard mode, eccentric mode, isokinetic mode, and elastic mode.

[0112] For example, mode identifiers include standard mode identifiers, eccentric mode identifiers, isokinetic mode identifiers, and elastic mode identifiers. When a user selects eccentric mode on a strength training device, the device responds to the user's mode selection and generates training mode data, which includes the eccentric mode identifier. During the calculation of the target training intensity, the device parses the eccentric mode identifier from the training mode data and determines the eccentric mode as the target training mode based on the identifier.

[0113] In S4212, users have different training needs when selecting different training modes on strength training equipment. As mentioned above, the standard mode aims to increase the user's muscle strength and endurance, the eccentric mode aims to strengthen muscle stimulation and improve control, the isokinetic mode aims to enhance explosive power and reduce joint pressure, and the elastic mode aims to strengthen peak stimulation and improve stability and control. Therefore, this application embodiment configures different weight sets for different training modes so that the target training intensity can be calculated more accurately.

[0114] Determining the target weight set based on the target training mode includes the following steps: obtaining a preset weight table, which includes weight sets corresponding to different training modes, and determining the weight set corresponding to the target training mode as the target weight set based on the training mode data.

[0115] Please refer to Table 2. The preset weight table is shown in Table 2:

[0116] Table 2

[0117] Training mode Weight set Weighting coefficient Standard mode First weight set α=0.4, β=0.4, γ=0.2 Centrifugation mode Second weight set α=0.3, β=0.5, γ=0.2 Constant speed mode Third weight set α=0.3, β=0.2, γ=0.5 Elastic Mode Fourth weight set α=0.4, β=0.4, γ=0.2

[0118] As shown in Table 2, different training modes correspond to different weight sets. The weight set includes multiple weight coefficients. The preset strength model is generated by multiple constraint factors. The weight coefficients are used to highlight the influence of constraint factors that are strongly correlated with the target training strength in the target training mode, and to weaken the influence of constraint factors that are weakly correlated with the target training strength.

[0119] In S4213, the target weight set includes a first type of weight coefficient and a second type of weight coefficient. The first type of weight coefficient is used to weight the local training data, and the second type of weight coefficient is used to weight the user's physiological data. The preset intensity model includes training configuration parameters and physiological configuration parameters. The training configuration parameters are parameters that are set in advance in the preset intensity model and associated with the local training data, while the physiological configuration parameters are parameters that are set in advance in the preset intensity model and associated with the user's physiological data.

[0120] The target training intensity is obtained by fusing the target weight set, local training data, and user physiological data into a preset intensity model, including the following steps:

[0121] S42131: Calculate the local weighting value based on the first type of weight coefficients, training configuration parameters, and local training data.

[0122] S42132: Calculate the physiological weighted value based on the second type of weight coefficient, physiological configuration parameters, and user physiological data.

[0123] S42133: Add the local weighted value to the physiological weighted value to obtain the target training intensity.

[0124] In S42131, the first type of weighting coefficients includes force weighting coefficients and velocity weighting coefficients. The force weighting coefficients are used to weight constraint factors associated with force, and the velocity weighting coefficients are used to weight constraint factors associated with velocity.

[0125] Training configuration parameters include maximum user force and maximum user speed. Maximum user force is the maximum force a user can exert on the strength training device, and maximum user speed is the maximum speed a user can achieve while training on the device. In some embodiments, the strength training device derives the maximum user force or maximum speed based on the user's historical training data. In some embodiments, the user enters their maximum user force or maximum user speed on the strength training device. In some embodiments, the strength training device prompts the user to participate in a strength test, during which the user exerts their maximum force or maximum speed, and the device saves this maximum force or maximum speed as the maximum user force or maximum user speed.

[0126] Local training data includes the current user's strength and current user speed. The current user's strength is the force the user is currently applying on the strength training equipment, and the current user speed is the speed the user is currently using while training on the strength training equipment.

[0127] The calculation of the local weighted value based on the first type of weight coefficient, training configuration parameters, and local training data includes the following steps: obtaining a first ratio of the current user's strength to the maximum user's strength, multiplying the first ratio by the strength weight coefficient to obtain the strength weighted value, obtaining a second ratio of the current user's speed to the maximum user's speed, multiplying the second ratio by the speed weight coefficient to obtain the speed weighted value, and adding the strength weighted value and the speed weighted value to obtain the local weighted value.

[0128] In S42132, the second type of weighting coefficient includes a heart rate weighting coefficient, which is used to weight the constraint factors associated with heart rate. Physiological configuration parameters include resting heart rate and maximum heart rate; the resting heart rate is the user's heart rate at rest, and the maximum heart rate is the highest heart rate the user has exhibited over a past period. In some embodiments, the user enters their resting heart rate and maximum heart rate on the strength training device. In some embodiments, the strength training device derives the resting heart rate and maximum heart rate based on the user's historical heart rate data over a past period. User physiological data includes a target heart rate, which is the heart rate the user currently experiences while training on the strength training device.

[0129] The physiological weighted value is calculated based on the second type of weighting coefficient, physiological configuration parameters, and user physiological data, including the following steps: obtaining the first difference between the target heart rate and the resting heart rate, obtaining the second difference between the maximum heart rate and the resting heart rate, taking the third ratio of the first difference to the second difference, and multiplying the third ratio by the heart rate weighting coefficient to obtain the physiological weighted value.

[0130] In S42133, in this embodiment of the application, the local weighted value and the physiological weighted value are added together to obtain the target training intensity. In this way, this embodiment of the application can fuse user physiological data with local training data to obtain an accurate and reliable target training intensity.

[0131] For example, embodiments of this application provide the following formula for calculating the target training intensity, as shown below:

[0132] M = N + K;

[0133] N = α * (target heart rate - resting heart rate) / (maximum heart rate - resting heart rate);

[0134] K = β * Current user strength / Maximum user strength + γ * Current user speed / Maximum user speed;

[0135] Where M is the target training intensity, N is the physiological weighted value, K is the local weighted value, α is the heart rate weighting coefficient, β is the strength weighting coefficient, and γ is the speed weighting coefficient.

[0136] When the target training mode is the standard mode, α = 0.4, β = 0.4, and γ = 0.2. Since the standard mode aims to increase the user's muscle strength and endurance, this embodiment of the application increases the proportion of heart rate and strength and decreases the proportion of speed. Therefore, α and β are both larger than γ, and the target training intensity calculated in this way is more in line with the characteristics of the standard mode, and the target training intensity is more accurate.

[0137] When the target training mode is eccentric mode, α = 0.3, β = 0.5, γ = 0.2. Since the eccentric mode aims to enhance muscle stimulation and improve control, this embodiment of the application increases the proportion of strength and reduces the proportion of speed and heart rate. Therefore, β is larger than α and γ. The target training intensity calculated in this way is more in line with the characteristics of the eccentric mode, and the target training intensity is more accurate.

[0138] When the target training mode is constant speed mode, α = 0.3, β = 0.2, γ = 0.5. Since constant speed mode aims to enhance explosive power and reduce joint pressure, this embodiment of the application increases the proportion of speed and reduces the proportion of heart rate and strength. Therefore, γ is larger than α and β. The target training intensity calculated in this way is more in line with the characteristics of constant speed mode, and the target training intensity is more accurate.

[0139] When the target training mode is elastic mode, α = 0.4, β = 0.4, γ = 0.2. Since elastic mode aims to enhance peak stimulation and improve stability and control, this embodiment of the application increases the proportion of heart rate and strength and decreases the proportion of speed. Therefore, α and β are both larger than γ. The target training intensity calculated in this way is more in line with the characteristics of elastic mode, and the target training intensity is more accurate.

[0140] As mentioned above, the embodiments of this application can not only calculate the target training intensity to reflect the user's target training status when heart rate data is available, but also calculate the target calorie consumption value to reflect the user's target training status when heart rate data is unavailable.

[0141] In some embodiments, the target training data includes training time and rest time, where training time is the time the user is in a training state and rest time is the time the user is in a rest state.

[0142] In some embodiments, the training time is equal to the first time from when the ropes of the strength training machine are first pulled out until all the ropes are retrieved. The rest time is the second time from when all the ropes are retrieved until they are pulled out again.

[0143] In some embodiments, the strength training device determines the time from when all the ropes are retrieved to when they are pulled out again as the third time. If the third time is less than a preset threshold, the third time is added to the first time to obtain the training time, and the rest time is 0. If the third time is greater than or equal to the preset threshold, the preset threshold is added to the first time to obtain the training time, and the rest time is the third time minus the preset threshold.

[0144] For example, a user pulls the rope on a strength training machine for 8 minutes, rests for 1 minute, pulls the rope again for 3 minutes, rests for 5 minutes, pulls the rope again for 8 minutes, and then rests to end the training.

[0145] The training time t1 = 8 + 1 + 3 + 2 + 8 = 22 minutes, where the preset threshold is 2 minutes. The first "1 minute" refers to the third time of the first training session, which is less than 2 minutes. Therefore, in this embodiment, 1 minute is included in the calculation of the training time. The second "5 minutes" refers to the third time of the second training session, which is greater than 2 minutes. Therefore, in this embodiment, 2 minutes is included in the calculation of the training time.

[0146] The rest time t2 = 0 + (5 - 2) = 3 minutes, where the first "0" is used to indicate that the first "1 minute" is less than the preset threshold of 2 minutes, and "5 - 2" is used to indicate that the second "5 minutes" is greater than the preset threshold of 2 minutes.

[0147] This embodiment of the application takes into account that the user's physical state is still in an equivalent "training state" for two minutes from pulling the rope to stopping pulling the rope. Therefore, this embodiment of the application needs to include the calorie consumption value of the user in the two minutes from pulling the rope to stopping pulling the rope into the calorie consumption value of the training state. Therefore, this embodiment of the application needs to use the above method to calculate the training time and rest time, so as to accurately and reliably calculate the target calorie consumption value.

[0148] The preset calorie model includes the target weight set on the strength training equipment and the rest-time calorie value. For example, if a user sets a weight of 20kg on the strength training equipment, the target weight is 20kg. The rest-time calorie value represents the calorie expenditure per unit of time during the rest phase. This rest-time calorie value is customized by the designer based on engineering experience; for example, it might be 2 kcal / min.

[0149] The steps for selecting a preset calorie model to calculate the target calorie consumption value based on the target training data are as follows: determine the unit training calorie value corresponding to the target weight, calculate the training calorie consumption value based on the training time and the unit training calorie value, calculate the rest calorie consumption value based on the rest time and the unit rest calorie value, and add the training calorie consumption value and the rest calorie consumption value to obtain the target calorie consumption value.

[0150] For example, embodiments of this application provide the following formula for calculating the target calorie consumption value, as shown below:

[0151] Q = R + W;

[0152] R = t1 * L;

[0153] W = t² * 2Kcal / min;

[0154] Where Q is the target calorie expenditure, R is the training calorie expenditure, W is the rest calorie expenditure, t1 is the training time, L is the unit training calorie value corresponding to the target weight, and t2 is the rest time, with a unit rest calorie value of 2 kcal / min.

[0155] This application's embodiments match different unit training calorie values ​​with different weights. For example, for low weight (0kg-10kg), the corresponding unit training calorie value is 3.5 kcal / min. For medium weight (10kg-25kg), the corresponding unit training calorie value is 5.5 kcal / min. For high weight (25kg-60kg), the corresponding unit training calorie value is 7.5 kcal / min. If the target weight is 20kg, then the unit training calorie value is 5.5 kcal / min, and R = t1 * 7.5 kcal / min.

[0156] The embodiments of this application can not only calculate the training calorie consumption value generated during the training process, but also calculate the rest calorie consumption value after training rest. By adding the training calorie consumption value and the rest calorie consumption value, the calorie consumption value generated by the user throughout the entire training process (training and rest) can be obtained accurately and reliably, so as to present a more accurate target color block to represent the user's true training state.

[0157] 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.

[0158] As another aspect of the embodiments of this application, this application provides a visualization device for training states. The visualization device for training states 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 visualization method for training states described in the various embodiments above.

[0159] In some embodiments, the training state visualization device can also be constructed from hardware devices. For example, the training state visualization device can be constructed from one or more chips, which can work together to complete the training state visualization method described in the various embodiments above. As another example, the training state visualization device 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 (ArcRISC Machine) or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination of these components.

[0160] Please see Figure 7 The training state visualization device 700 includes a training data acquisition module 71, a training state determination module 72, a color rate determination module 73, and a color presentation module 74.

[0161] The training data acquisition module 71 is used to acquire target training data, which represents the user's training status on the strength training equipment. The training status determination module 72 is used to determine the target training status based on the target training data; the target training status is the user's training status on the strength training equipment. The color rate determination module 73 is used to determine the target color block and the target motion rate of the target color block corresponding to the target training status; the target color block with the target motion rate represents the user's training status. The color presentation module 74 is used to control the strength training equipment to present the target color block according to the target motion rate.

[0162] In some embodiments, the target training state is represented by the target training intensity or the target calorie consumption value. The color rate determination module 73 is specifically used to: if the target training data includes user physiological data, then select a preset intensity model to calculate the target training intensity based on the target training data. The preset intensity model is constrained by the user physiological data, which represents the user's physiological state when training on the strength training equipment. If the target training data does not include user physiological data, then select a preset calorie model to calculate the target calorie consumption value based on the target training data.

[0163] In some embodiments, the target training data includes local training data and training mode data collected by the user from the strength training device. The color rate determination module 73 is further specifically used to: determine the target training mode based on the training mode data, wherein the target training mode is the training mode selected by the user on the strength training device; determine the target weight set based on the target training mode; and input the target weight set, local training data, and user physiological data into a preset intensity model for fusion to obtain the target training intensity.

[0164] In some embodiments, the color rate determination module 73 is further specifically used to: obtain a preset weight table, the preset weight table including weight sets corresponding to different training modes, and determine the weight set corresponding to the target training mode as the target weight set based on the training mode data.

[0165] In some embodiments, the target weight set includes a first type of weight coefficient and a second type of weight coefficient, the preset intensity model includes training configuration parameters and physiological configuration parameters, and the color rate determination module 73 is further specifically used to: calculate a local weight value based on the first type of weight coefficient, the training configuration parameters and the local training data, calculate a physiological weight value based on the second type of weight coefficient, the physiological configuration parameters and the user's physiological data, and add the local weight value and the physiological weight value to obtain the target training intensity.

[0166] In some embodiments, the training configuration parameters include maximum user strength and maximum user speed, the local training data includes current user strength and current user speed, the first type of weight coefficient includes strength weight coefficient and speed weight coefficient, and the color rate determination module 73 is further specifically used for: calculating a first ratio of current user strength to maximum user strength, multiplying the first ratio by the strength weight coefficient to obtain a strength weighted value, calculating a second ratio of current user speed to maximum user speed, multiplying the second ratio by the speed weight coefficient to obtain a speed weighted value, and adding the strength weighted value and the speed weighted value to obtain a local weighted value.

[0167] In some embodiments, the physiological configuration parameters include resting heart rate and maximum heart rate, the user physiological data includes target heart rate, the second type of weighting coefficient includes heart rate weighting coefficient, and the color rate determination module 73 is further specifically used to: calculate a first difference between target heart rate and resting heart rate, calculate a second difference between maximum heart rate and resting heart rate, calculate a third ratio of the first difference to the second difference, and multiply the third ratio by the heart rate weighting coefficient to obtain a physiological weighted value.

[0168] In some embodiments, the target training mode is one of the following: standard mode, eccentric mode, isokinetic mode, and elastic mode. Standard mode indicates that the resistance output by the strength training device during the cable pull-out phase is equal to the resistance output during the cable retraction phase. Eccentric mode indicates that the resistance output by the strength training device during the cable pull-out phase is less than the resistance output during the cable retraction phase. Isokinetic mode indicates that the greater the user's speed, the greater the resistance output by the strength training device. Elastic mode indicates that the longer the cable of the strength training device is pulled out, the greater the output resistance.

[0169] In some embodiments, the target training data includes training time and rest time, the preset calorie model includes the target weight set on the strength training equipment and the unit rest calorie value, and the color rate determination module 73 is further specifically used to: determine the unit training calorie value corresponding to the target weight, calculate the training calorie consumption value based on the training time and the unit training calorie value, calculate the rest calorie consumption value based on the rest time and the unit rest calorie value, and add the training calorie consumption value and the rest calorie consumption value to obtain the target calorie consumption value.

[0170] It should be noted that the above-mentioned training state visualization device can execute the training state visualization method 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 training state visualization device embodiments can be found in the training state visualization method provided in the embodiments of this application.

[0171] Please see Figure 8 , Figure 8This is a schematic diagram of a controller provided in an embodiment of this application. The controller 800 includes one or more processors 81 and a memory 82. The memory 82 is connected to one or more processors 81, for example, via a bus.

[0172] Processor 81 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.

[0173] Memory 82 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.

[0174] The memory 82 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 state visualization method in the embodiments of this application. The processor executes the various functional applications and data processing of the training state visualization method and the training state visualization device by running the non-volatile software programs, instructions, and modules stored in the memory, that is, it realizes the functions of each module or unit of the training state visualization method and the training state visualization device provided in the above method embodiments.

[0175] The memory 82 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 visualization device during training. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the visualization device during training via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0176] The one or more modules are stored in the memory. When executed by the one or more processors, they perform the visualization method of the training state 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.

[0177] 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.

[0178] 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.

[0179] 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 method of visualizing a training status applied to a strength training machine, characterized in that, The method comprises: acquiring target training data, the target training data being used to represent a training condition of a user on the strength training equipment; determining a target training state according to the target training data, the target training state being a training state of the user on the strength training equipment; determining a target color block corresponding to the target training state and a target dynamic effect rate of the target color block, the target color block with the target dynamic effect rate being used to represent the training state of the user; controlling the strength training equipment to present the target color block according to the target dynamic effect rate.

2. The method of claim 1, wherein, The target training state is represented by a target training intensity or a target heat consumption value, and the determining of the target training state according to the target training data comprises: if the target training data comprises user physiological data, selecting a preset intensity model according to the target training data to calculate the target training intensity, the preset intensity model being constrained by the user physiological data, the user physiological data being used to represent a physiological state of the user when training on the strength training equipment; if the target training data does not comprise user physiological data, selecting a preset heat model according to the target training data to calculate the target heat consumption value.

3. The method of claim 2, wherein, The target training data comprises local training data and training mode data of the user collected by the strength training equipment, and the selecting of the preset intensity model according to the target training data to calculate the target training intensity comprises: determining a target training mode according to the training mode data, the target training mode being a training mode selected by the user on the strength training equipment; determining a target weight set according to the target training mode; inputting the target weight set, the local training data and the user physiological data into the preset intensity model for fusion to obtain the target training intensity.

4. The method of claim 3, wherein, The determining of the target weight set according to the target training mode comprises: acquiring a preset weight table, the preset weight table comprising weight sets corresponding to different training modes; determining a weight set corresponding to the target training mode as the target weight set according to the training mode data.

5. The method of claim 3, wherein, The target weight set comprises first-type weight coefficients and second-type weight coefficients, the preset intensity model comprises training configuration parameters and physiological configuration parameters, and the inputting of the target weight set, the local training data and the user physiological data into the preset intensity model for fusion to obtain the target training intensity comprises: calculating a local weighting value according to the first-type weight coefficients, the training configuration parameters and the local training data; calculating a physiological weighting value according to the second-type weight coefficients, the physiological configuration parameters and the user physiological data; adding the local weighting value and the physiological weighting value to obtain the target training intensity.

6. The method of claim 5, wherein, The training configuration parameters comprise maximum user strength and maximum user speed, the local training data comprise current user strength and current user speed, the first-type weight coefficients comprise strength weight coefficients and speed weight coefficients, and the calculating of the local weighting value according to the first-type weight coefficients, the training configuration parameters and the local training data comprises: obtaining a first ratio of the current user strength and the maximum user strength; multiplying the first ratio and the strength weight coefficient to obtain a strength weighted value; obtaining a second ratio of the current user speed and the maximum user speed; multiplying the second ratio and the speed weight coefficient to obtain a speed weighted value; adding the strength weighted value and the speed weighted value to obtain a local weighted value.

7. The method of claim 5, wherein, the physiological configuration parameters include a resting heart rate and a maximum heart rate, the user physiological data include a target heart rate, the second type weight coefficient includes a heart rate weight coefficient, and the calculating a physiological weighted value according to the second type weight coefficient, the physiological configuration parameters and the user physiological data includes: obtaining a first difference between the target heart rate and the resting heart rate; obtaining a second difference between the maximum heart rate and the resting heart rate; obtaining a third ratio of the first difference and the second difference; multiplying the third ratio and the heart rate weight coefficient to obtain a physiological weighted value.

8. The method of claim 3, wherein, the target training mode is one of a standard mode, a centrifugal mode, a constant speed mode and an elastic mode; the standard mode is used to represent that the resistance output by the strength training equipment in the rope pulling stage is equal to the resistance output in the rope returning stage; the centrifugal mode is used to represent that the resistance output by the strength training equipment in the rope pulling stage is less than the resistance output in the rope returning stage; the constant speed mode is used to represent that the greater the speed of the user is, the greater the resistance output by the strength training equipment is; the elastic mode is used to represent that the longer the length of the rope of the strength training equipment is pulled out, the greater the resistance output is.

9. The method according to any one of claims 2 to 8, characterized in that, the target training data include a training time and a rest time, the preset heat model includes a target weight set by the strength training equipment and a unit rest heat value, and the calculating a target heat consumption value according to the target training data selected from the preset heat model includes: determining a unit training heat value corresponding to the target weight; calculating a training heat consumption value according to the training time and the unit training heat value; calculating a rest heat consumption value according to the rest time and the unit rest heat value; adding the training heat consumption value and the rest heat consumption value to obtain a target heat consumption value.

10. A strength training apparatus characterized by, comprises: comprises a memory and a processor, the memory is connected to the processor, the processor is used to execute one or more computer programs stored in the memory, and the processor makes the strength training equipment realize the visualization method of the training state according to any one of claims 1-9 when executing the one or more computer programs.

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