Training cycle control method, apparatus, device, medium, and intelligent fitness equipment

Abstract

The application discloses a training period control method and device, equipment, medium and intelligent fitness equipment, and relates to the technical field of fitness. The method comprises the following steps: in the training process of a target training period, training characteristic data of each training stage in the target training period is acquired, the target training period comprises a plurality of training stages which are continuously progressive with a same reference training load; a training completion state of a current training stage is determined according to the training characteristic data of the current training stage; and a training progress is controlled to flow from the current training stage to a target training stage according to the training completion state, the target training stage comprising any one of the following: a next training stage located after the current training stage, the current training stage, and a previous training stage located before the current training stage. The application can realize adaptive flow control between stages based on actual training performance of a user, so that the training load is matched with the actual ability of the user, and the training effect of periodic training is improved.

Description

Technical Field

[0001] This application relates to the field of fitness technology, specifically to a training cycle control method, device, equipment, medium, and intelligent fitness equipment. Background Technology

[0002] Periodic training is becoming increasingly popular in strength training using fitness equipment. Periodic training involves dividing the training process into multiple progressively increasing training phases, each with a different training load. Strength improvement is achieved through these progressively varying loads. Currently, periodic training typically uses a statically configured phase transition scheme, with users executing each phase sequentially. However, different users exhibit varying training performance, and a single, fixed phase transition scheme may not be suitable for each user's actual ability, thus affecting the effectiveness of periodic training. Summary of the Invention

[0003] This application provides a training cycle control method, device, equipment, medium, and intelligent fitness equipment, which can realize inter-stage adaptive flow control based on the user's actual training performance, so that the training load matches the user's actual ability and improves the training effect of periodic training.

[0004] In a first aspect, embodiments of this application provide a training cycle control method, the method comprising: during the training process of a target training cycle, acquiring training feature data of each training stage in the target training cycle, wherein the target training cycle includes multiple training stages that are continuously progressively advanced with the same baseline training load; determining the training achievement status of the current training stage based on the training feature data of the current training stage; and controlling the training progress to flow from the current training stage to the target training stage based on the training achievement status; wherein the target training stage includes any one of the following: the next training stage in the target training cycle after the current training stage, the current training stage, and the previous training stage in the target training cycle before the current training stage.

[0005] In some embodiments, the training load of multiple training phases in the target training cycle is non-linearly progressive based on the same baseline training load.

[0006] In some embodiments, the training feature data includes training completion data and training quality data; wherein, the training completion data includes a completion rate; the training quality data is determined based on at least one of the following: movement speed data, movement rhythm data, movement amplitude data, and left-right balance data; wherein, the movement speed data is used to indicate the user's movement speed in a single movement or multiple movements, the movement rhythm data is used to indicate the periodicity of the user's movement during the execution of the movement, the movement amplitude data is used to indicate the amount of spatial position change achieved by the user during the execution of the movement, and the left-right balance data is used to indicate the degree of force balance between the left and right limbs in movements involving both limbs.

[0007] In some embodiments, the process of determining the training quality data includes: selecting any one dimension of data from the movement speed data, the movement rhythm data, the movement amplitude data, and the left-right balance data as the training quality data; or, selecting at least two dimensions of data from the movement speed data, the movement rhythm data, the movement amplitude data, and the left-right balance data, obtaining the weight values ​​corresponding to the at least two dimensions of data respectively, and performing a weighted summation of the at least two dimensions of data according to the weight values ​​corresponding to the at least two dimensions of data respectively to obtain the training quality data.

[0008] In some embodiments, determining the training achievement status of the current training stage based on the training feature data of the current training stage includes: determining a first data score corresponding to the training completion data; determining a second data score corresponding to the training quality data; determining a comprehensive training score based on the first data score and the second data score; and determining the training achievement status based on the comprehensive training score and state mapping data; wherein the state mapping data includes at least one set of correspondences between the comprehensive training score and the training achievement status.

[0009] In some embodiments, determining the comprehensive training score based on the first data score and the second data score includes: normalizing the first data score and the second data score to the same data interval; and fusing the normalized first data score and the normalized second data score to obtain the comprehensive training score.

[0010] In some embodiments, controlling the training progress to flow from the current training stage to the target training stage based on the training achievement status includes: when the training achievement status is a first state, controlling the training progress to flow from the current training stage to the next training stage; when the training achievement status is a second state, controlling the training progress to maintain the current training stage; and when the training achievement status is a third state, controlling the training progress to flow from the current training stage to the previous training stage; wherein the achievement status corresponding to the first state is better than the achievement status corresponding to the second state, and the achievement status corresponding to the second state is better than the achievement status corresponding to the third state.

[0011] In some embodiments, controlling the training progress to flow from the current training stage to the next training stage includes: determining whether a load progression condition is met based on the training quality data in the training feature data; if the load progression condition is met, controlling the training progress to flow from the current training stage to the next training stage, and adjusting the target training load of the next training stage to the initial training load of the next training stage; if the load progression condition is not met, controlling the training progress to flow from the current training stage to the next training stage, and adjusting the target training load of the next training stage to the target training load of the current training stage.

[0012] In some embodiments, the load progression condition includes left-right balance data being less than or equal to a balance threshold.

[0013] In some embodiments, the method further includes: pausing the training progress and entering a platform training phase when a training stagnation condition is met; wherein the platform training phase is used to interrupt the progressive mode of multiple training phases in the target training cycle.

[0014] In some embodiments, the training stagnation condition includes at least one of the following: the training feature data for N consecutive training phases meets a preset bottleneck condition, where N is a positive integer; the user fatigue data for M consecutive training phases meets a preset fatigue condition, where M is a positive integer.

[0015] In some embodiments, the user fatigue data includes at least one of the following: subjective fatigue feedback data and physiological fatigue monitoring data.

[0016] In some embodiments, the method further includes: after completing the target training cycle, determining the baseline training load for the next training cycle of the target training cycle based on the training achievement status of the reference training phase in the target training cycle and the baseline training load of the target training cycle.

[0017] In some embodiments, the reference training phase is the final training phase in the target training cycle.

[0018] In some embodiments, determining the baseline training load for the next training cycle based on the training achievement status of the reference training phase in the target training cycle and the baseline training load of the target training cycle includes: if the training achievement status of the reference training phase is a first state, increasing the baseline training load of the target training cycle by a first load ratio to obtain the baseline training load of the next training cycle; if the training achievement status of the reference training phase is a second state, using the baseline training load of the target training cycle as the baseline training load of the next training cycle; if the training achievement status of the reference training phase is a third state, decreasing the baseline training load of the target training cycle by a second load ratio to obtain the baseline training load of the next training cycle; wherein the achievement status corresponding to the first state is better than the achievement status corresponding to the second state, and the achievement status corresponding to the second state is better than the achievement status corresponding to the third state; both the first load ratio and the second load ratio are positive numbers.

[0019] Secondly, embodiments of this application provide a training cycle control device, the device comprising: a data acquisition module, configured to acquire training feature data of each training stage in the target training cycle during the training process of the target training cycle, the target training cycle comprising multiple training stages progressively progressing with the same baseline training load; a target achievement detection module, configured to determine the training achievement status of the current training stage based on the training feature data of the current training stage; and a transition control module, configured to control the training progress to transition from the current training stage to the target training stage based on the training achievement status; wherein the target training stage comprises any one of the following: the next training stage in the target training cycle after the current training stage, the current training stage, or the previous training stage in the target training cycle before the current training stage.

[0020] Thirdly, embodiments of this application provide an electronic device, including: a memory storing computer programs or instructions thereon; and a processor for executing the computer programs or instructions in the memory to implement the training cycle control method as described above.

[0021] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program or instructions stored thereon, wherein the computer program or instructions, when executed by a processor, implement the training cycle control method as described above.

[0022] Fifthly, embodiments of this application provide an intelligent fitness device, comprising: a device body; a sensing unit for collecting motion signals; and a training cycle control device as described above, or an electronic device as described above.

[0023] Sixthly, embodiments of this application provide a terminal device, including the training cycle control device as described above, or including the electronic device as described above.

[0024] In summary, the technical solution provided in this application includes multiple training stages that progressively advance based on the same baseline training load within the target training cycle. During the training process of the target training cycle, training feature data for each training stage is acquired, and the training achievement status of the current training stage is determined based on the training feature data. Based on this, the subsequent training progress flow is automatically decided, i.e., proceeding to the next training stage, repeating the current training stage, or reverting to the previous training stage. Compared to a single, fixed stage flow scheme, this application implements adaptive inter-stage flow control based on the user's actual training performance, ensuring that the training load matches the user's actual ability within the complete training cycle consisting of multiple training stages, thereby improving the training effect of periodic training. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0026] Figure 1 This is a flowchart of a training cycle control method provided in an embodiment of this application; Figure 2 This is a schematic diagram illustrating a method for dividing training achievement states according to an embodiment of this application; Figure 3 This is a flowchart of another training cycle control method provided in the embodiments of this application; Figure 4 This is a schematic diagram of a stage transition scheme provided in an embodiment of this application; Figure 5 This is a schematic diagram of a training cycle control device provided in an embodiment of this application; Figure 6 This is a schematic diagram of another training cycle control device provided in the embodiments of this application; Figure 7 This is a schematic diagram of a tension-velocity curve provided in an embodiment of this application; Figure 8 This is a schematic diagram of a length-tension curve provided in an embodiment of this application. Detailed Implementation

[0027] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified. Furthermore, "A and / or B" includes three cases: only A, only B, and a combination of A and B.

[0029] The use of "applies to" or "configured to" in this application implies open and inclusive language, which does not exclude the applicability to or configuration to devices performing additional tasks or steps. Additionally, the use of "based on" also implies openness and inclusivity, because processes, steps, calculations, or other actions "based on" one or more of the stated conditions or values ​​may in practice be based on additional conditions or values ​​beyond those stated.

[0030] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0031] Please see Figure 1 , Figure 1 This is a flowchart of a training cycle control method provided in an embodiment of this application. Figure 1 As shown, the training cycle control method may include the following steps S100 to S300.

[0032] Step S100: During the training process of the target training cycle, acquire the training feature data of each training stage in the target training cycle; wherein, the target training cycle includes multiple training stages that are continuously progressive with the same baseline training load.

[0033] The target training cycle corresponds to a complete training plan with multiple training phases to achieve systematic improvement in strength and ability. The multiple training phases within the target training cycle are typically executed sequentially according to a preset order, with each phase configured with a different training load. Progressive overload stimulation is achieved through progressive changes in the training load. This application does not limit the specific type of training load; in practical applications, it can be flexibly set arbitrarily based on the type of training movement and requirements. For example, the training load may include, but is not limited to, at least one of the following: training repetitions, training weight, and training volume.

[0034] In this context, a training phase refers to a training organization unit in the time dimension. Its time granularity can be flexibly set according to the actual application scenario, such as including but not limited to days, half-weeks, weeks, months, or years. This application embodiment does not limit this. For example, in a periodic strength training scenario, one training phase can correspond to a week's training plan. This application embodiment also does not limit the number of training phases included in the target training cycle. In practical applications, it can be flexibly set according to needs, for example, the target training cycle may include 3, 4, 5, or 6 training phases.

[0035] The training load for multiple training phases within the target training cycle progresses continuously based on the same baseline training load. The baseline training load refers to the training load benchmark value set during the initialization of the target training cycle, serving as the sole original benchmark for the progression of training load across all training phases throughout the entire cycle. The training load for multiple training phases can be progressively increased based on the same baseline training load, following linear or non-linear fluctuation rules, etc.

[0036] For example, the target training cycle may include four progressively increasing training phases: adaptation, enhancement, peak, and recovery. The peak phase represents the peak of the training load throughout the cycle, while the adaptation phase has the lowest training load. The training load in the adaptation phase can be equal to the baseline training load of the target training cycle. The training load in the enhancement phase can increase from the adaptation phase load by a first progressive ratio, the peak phase load can increase from the adaptation phase load by a second progressive ratio, and the recovery phase load can increase from the adaptation phase load by a third progressive ratio. The second progressive ratio is greater than the first progressive ratio, and the third progressive ratio is less than the second progressive ratio. The third progressive ratio is greater than, less than, or equal to, the first progressive ratio (typically, the third progressive ratio is greater than the first progressive ratio). For example, the first progressive ratio might be 4% to 6%, the second progressive ratio 9% to 11%, and the third progressive ratio 6% to 8.5%. Based on this, a non-linear fluctuation curve of "low, medium, high, and reduced load" can be formed to promote supercompensation.

[0037] Training feature data refers to the set of data collected and recorded by sensors during training that reflects the user's training performance. These sensors can be built into smart fitness equipment, such as an IMU (Inertial Measurement Unit) or a motor resistance sensing module (e.g., a motor with a digital encoder and its associated resistance output mechanism, such as a cable traction system); or they can be other sensing modules independent of the smart fitness equipment, such as a camera used to capture the user's training movements and facial expressions. Taking an IMU as an example, the IMU includes an accelerometer and a gyroscope. The accelerometer measures acceleration information in three spatial directions (which can be mapped to data such as movement speed), and the gyroscope measures rotational angular velocity information (which can be mapped to data such as movement amplitude and movement trajectory). In response to the user's initiation of the target training cycle, training parameters for the target training cycle can be loaded, and the user can be guided through each training stage sequentially. During each training stage, the aforementioned sensing mechanism collects training feature data in real time.

[0038] Step S200: Determine the training achievement status of the current training stage based on the training feature data of the current training stage.

[0039] The current training phase refers to the training phase that the user is currently executing or has completed within the target training cycle (before transitioning to other training phases). The training achievement status of the current training phase refers to the classification result obtained after comprehensively evaluating the training feature data of the current training phase, indicating whether the user's training performance in the current training phase meets preset standards. For example, a comprehensive training score can be calculated based on the training feature data of the current training phase, and then the training achievement status can be divided based on the comprehensive training score and preset score ranges; alternatively, user performance levels can be divided separately for the training feature data of the current training phase, and then the training achievement status can be further divided based on the user performance levels corresponding to different training feature data.

[0040] This application does not limit the specific types of training achievement status classification. In practical applications, it can be arbitrarily and flexibly set according to the requirements of fine granularity and accuracy. For example, training achievement status can include achieving or not achieving, or it can include exceeding, achieving or not achieving, or it can include exceeding, achieving (slightly exceeding), achieving, achieving (slightly not achieving), not achieving, or it can be directly in the form of a score, etc.

[0041] Step S300: Based on the training achievement status, control the training progress to flow from the current training stage to the target training stage; wherein, the target training stage includes any one of the following: the next training stage after the current training stage in the target training cycle, the current training stage, and the previous training stage before the current training stage in the target training cycle.

[0042] The target training phase refers to the training phase that the user will subsequently execute after the training achievement status is determined. Target training phases typically include three types: the next training phase following the current training phase in the target training cycle, the current training phase, and the previous training phase preceding the current training phase in the target training cycle. Based on the training achievement status of the current training phase, the specific type of target training phase can be determined, and the training progress can be controlled to flow from the current training phase to the determined target training phase.

[0043] In this embodiment, when the training achievement status of the current training stage indicates that the user's training performance meets the advancement conditions, the training progress can be transferred to the next training stage, achieving cyclical progression. When the training achievement status of the current training stage indicates that the user's training performance meets the consolidation conditions, the same training plan as the current training stage is repeated in subsequent time units of the current training stage, achieving stage consolidation. When the training achievement status of the current training stage indicates that the user's training performance meets the rollback conditions, the training progress can be rolled back to the previous training stage, achieving load reduction. The three types of target training stages (i.e., the three directions of training progress) constitute a three-state closed-loop flow system of "advancement, consolidation, and rollback," enabling the flow path between multiple training stages to be adaptively adjusted according to the user's actual training performance.

[0044] In summary, the technical solution provided in this application includes multiple training stages that progressively advance based on the same baseline training load within the target training cycle. During the training process of the target training cycle, training feature data for each training stage is acquired, and the training achievement status of the current training stage is determined based on the training feature data. Based on this, the subsequent training progress flow is automatically decided, i.e., proceeding to the next training stage, repeating the current training stage, or reverting to the previous training stage. Compared to a single, fixed stage flow scheme, this application implements adaptive inter-stage flow control based on the user's actual training performance, ensuring that the training load matches the user's actual ability within the complete training cycle consisting of multiple training stages, thereby improving the training effect of periodic training.

[0045] In some embodiments, the training feature data described above may include, but is not limited to, training completion data and training quality data.

[0046] Training completion data is used to indicate the user's task completion level during the training phase. In this embodiment, training completion data can cover the full range of completion statistics from a single action to the entire training phase, forming a multi-level indicator system. Taking completion rate as an example, training completion data may include, but is not limited to, at least one of the following: single action completion rate, single action multi-set completion rate, multiple action multi-set completion rate, single lesson capacity completion rate, phase capacity completion rate, and total capacity completion rate across the entire fluctuation cycle. In practical applications, training completion data may also include actual completion amount, target completion amount, etc.

[0047] The single-action completion rate refers to the ratio of the actual number of times a user completes a specific action in a single execution during the training phase to the target number of times. It is used to quantify the degree of task achievement for a single action. The single-action multi-set completion rate refers to the ratio of the actual number of times a user completes multiple sets of repetitions of a specific action during the training phase to the target number of times. It is used to reflect the overall completion rate of the action across multiple scenarios. The multi-action multi-set completion rate refers to the ratio of the total number of times a user completes multiple sets of training for different action types during the training phase to the target total number of times. It is used to evaluate the overall completion rate in scenarios involving multiple combinations of actions.

[0048] Single-session course capacity completion rate refers to the ratio of the actual training capacity to the target training capacity in a single training session. Phase capacity completion rate refers to the ratio of the actual cumulative training capacity to the target phase training capacity within a training phase. Total capacity completion rate for the entire fluctuation cycle refers to the ratio of the actual cumulative total capacity to the total capacity of the target cycle within a complete target training cycle containing multiple training phases.

[0049] In this embodiment, the training completion data can comprehensively calculate the overall completion status of the training phase by considering dimensions such as training volume, load weight, and number of completed sets. For example, after calculating the completion rate of each level, a weighted sum or arithmetic mean can be performed on the completion rates of multiple levels or all levels to calculate comprehensive training completion data; alternatively, one level completion rate can be selected as the training completion data.

[0050] Training quality data is used to indicate the quality of a user's actions during the training phase and can be obtained by parsing and calculating the raw signals collected by sensors. In this embodiment, training quality data can be determined based on at least one of the following: action speed data, action rhythm data, action amplitude data, and left-right balance data.

[0051] Motion speed data is used to indicate the user's speed in a single action or multiple actions. It can be obtained by integrating or filtering the acceleration signal collected by the accelerometer and can be used to evaluate the speed of action execution and speed fluctuations.

[0052] Movement speed is a key parameter reflecting the force characteristics of the neuromuscular system. According to Hill's muscle contraction model, the concentric contraction speed of a muscle is inversely proportional to the generated active tension: the faster the speed, the lower the tension that a single muscle fiber can output; the slower the speed, the higher the tension output is allowed, such as... Figure 7As shown. This physiological characteristic determines that different training goals require different movement speeds. For example, explosive power training requires high-speed concentric contraction to preferentially recruit type II fast-twitch muscle fibers and improve motor unit synchronicity, while hypertrophy training is more suitable for moderately controllable speeds to evenly activate type I slow-twitch and type II fast-twitch muscle fibers, thereby obtaining sufficient mechanical tension duration. In addition, the velocity loss rate within a single training set, that is, the percentage of the difference between the first and last movement speeds within a set relative to the initial speed, is a core indicator that objectively reflects the decline in movement quality and the accumulation of neuromuscular fatigue: a velocity loss within a reasonable range indicates that the training load matches the current physical condition, while a sudden drop in speed indicates movement deformation, increased compensation, and abnormal joint load. In the embodiments of this application, movement speed data can be used to quantitatively evaluate the force efficiency of a single movement and the ability to maintain the quality of a single training set based on the above-mentioned tension-velocity curve principle and muscle fiber recruitment theory.

[0053] Motion rhythm data is used to indicate the periodic characteristics of a user's actions, including but not limited to motion cycle duration, motion cycle fluctuations, rhythm type switching, and rhythm consistency within a group. This data can be obtained by analyzing the time-series periodicity of accelerometer or gyroscope signals. For example, the standard deviation and expected value of multiple motion cycles can be calculated, and the coefficient of variation (standard deviation divided by expected value) can be used to measure rhythm stability. Simultaneously, the number of rhythm type switching events (such as constant speed, acceleration, and deceleration) within a group can be recorded to assess rhythm consistency within the group.

[0054] Movement rhythm can generally be broken down into four phases: eccentric contraction, peak contraction, concentric contraction, and bottom pause. From an exercise physiology perspective, the rhythm of each phase has a differentiated physiological impact on training quality: During the eccentric phase, muscles are passively lengthened under external force, generating higher mechanical tension than during the concentric phase. This phase is the primary stimulus for inducing controllable muscle micro-injuries and triggering subsequent muscle fiber repair and hypertrophy. During the peak contraction phase, muscles are in an isometric contraction state, and a brief static pause maximizes the electromyographic activation of the target muscle group and reduces compensatory leveraging by adjacent muscle groups. The concentric phase mainly reflects the active recruitment efficiency and force exertion speed of motor units. The bottom pause eliminates movement inertia and re-establishes neuromuscular control, ensuring that the initial force exertion of each repetition is in an effective force state. Inappropriate rhythm allocation, such as uncontrolled rapid descent during the eccentric phase or an excessively short peak contraction time, weakens mechanical tension stimulation and increases joint impact load. Movement rhythm data can be used to extract the movement cycle duration, inter-phase transition rate, and intra-set rhythm consistency of each phase, thereby evaluating the rhythm control quality of training movements based on mechanical tension theory and metabolic stress theory.

[0055] Motion amplitude data indicates the amount of spatial position change achieved by the user during the execution of an action, such as the range of joint angle changes and the distance of device displacement. For example, for rotational movements, angle changes can be obtained by integrating gyroscope signals; for translational movements, displacement information can be obtained by double integration of accelerometer signals. In practical applications, motion amplitude data can also include motion trajectory deviation, indicating the degree of deviation between the standard spatial trajectory achieved by the user during the execution of an action and the actual trajectory, such as the deviation between the range of joint angle changes and the standard range, and the deviation between the device displacement trajectory and the standard trajectory.

[0056] In resistance training, the range of motion directly determines the length-tension relationship of muscle contraction. For example... Figure 8 As shown, the peak of the curve corresponds to the optimal initial length, at which point the overlap of thick and thin filaments is optimal, the number of crossbridges is maximized, and the tension is greatest. When the length is shorter or longer than this optimal value, the number of crossbridges decreases, and the tension also decreases. Skeletal muscles can generate maximum active tension at the optimal initial length. Insufficient range of motion will prevent the target muscle group from completing sufficient eccentric stretching and concentric contraction within the optimal length range, weakening the effectiveness of mechanical tension stimulation. Conversely, a range of motion exceeding the safe range may lead to abnormal stress on the passive structures of the joints. Furthermore, the stability of the range of motion is an important indicator of the quality of neuromuscular control, which depends on the trunk stability platform provided by the pre-activation of the core muscles, joint proprioceptive feedback, and the coordinated contraction of agonist and antagonist muscles. When there is core fatigue or delayed proprioceptive feedback, the range of motion may exhibit unexpected trajectory deviations or stroke contractions. This instability not only reduces the effective stimulation of the target muscle group but also induces compensatory load concentration in adjacent joints through the transmission of the human kinetic chain, increasing the risk of sports injuries. In the embodiments of this application, the range of motion data can be used to evaluate the scientific validity and safety of training movements from two dimensions: maximizing mechanical tension and improving the quality of motor control.

[0057] Left-right balance data is used to indicate the degree of force balance between the left and right limbs in bilateral movements. For example, in a squat or bicep curl with dumbbells held in both hands, the peak acceleration / angular velocity, work done, or range of motion of the dumbbells on both sides is calculated to obtain the difference in force exertion between the left and right limbs. Typically, left-right balance data can be obtained through comparative analysis of sensor signals from both sides.

[0058] The essence of left-right balance is the manifestation of the bilateral coordinated control ability of the neuromuscular system. Although there are slight innate bilateral differences in the human body, significant left-right imbalance in resistance training can disrupt the symmetrical biomechanical structure of movements: the stronger limb bears excessive load while the weaker limb receives insufficient stimulation, leading to an unbalanced overall strength development. Simultaneously, to maintain movement execution in an unbalanced state, the trunk and core muscle groups will produce compensatory center of gravity shifts, which in turn cause asymmetrical load concentration in the spine, pelvis, and adjacent joints through kinetic chain transmission, increasing the risk of chronic strain injuries in areas such as the shoulders, knees, and lower back. From a neurocontrol perspective, left-right imbalance reflects the difference in recruitment efficiency and proprioceptive feedback sensitivity between the two sides; the weaker limb often exhibits problems with delayed neural mobilization and decreased accuracy in movement trajectory control. This application's embodiments, by setting left-right balance data and comparing and analyzing signals collected by sensors on both sides (such as bilateral IMUs, bilateral motor encoders, or bilateral skeletal point trajectories extracted by visual sensing modules), quantify the difference in force exertion and trajectory deviation between the two sides, thereby evaluating the quality and safety of training movements from the perspective of bilateral symmetry and kinetic chain integrity.

[0059] Based on this, the aforementioned data on movement speed, movement rhythm, movement amplitude, and left-right balance comprehensively characterize the quality features of resistance training movements from four independent and complementary perspectives: time dynamics, phase control, spatial trajectory, and bilateral symmetry.

[0060] It should be understood that embodiments of this application may provide an indicator data pool, which includes training quality data from multiple dimensions. In practical applications, a single dimension of data can be arbitrarily selected from the indicator data pool as training quality data, or multiple dimensions of data or a combination of multiple dimensions of data can be selected as training quality data.

[0061] Taking an indicator data pool that includes motion speed data, motion rhythm data, motion amplitude data, and left-right balance data as an example, in some embodiments, the process of determining training quality data may include: selecting any one dimension of data from the motion speed data, motion rhythm data, motion amplitude data, and left-right balance data as the training quality data; or, selecting at least two dimensions of data from the motion speed data, motion rhythm data, motion amplitude data, and left-right balance data, obtaining the weight values ​​corresponding to the at least two dimensions of data respectively, and performing a weighted summation of the at least two dimensions of data according to the weight values ​​corresponding to the at least two dimensions of data respectively to obtain the training quality data. The weight values ​​corresponding to multiple dimensions of data may be the same or different; the weight values ​​corresponding to each dimension of data may be preset or determined based on motion type and requirements, etc., and this application embodiment does not limit this.

[0062] Furthermore, training quality data for the desired dimensions can be selected arbitrarily from the indicator data pool or according to preset filtering conditions to adapt to the differentiated judgment needs of different movement types, user habits, and training stages. For example, preset filtering conditions include the highest accuracy and / or discrimination in the current training scenario, which can be obtained based on data dispersion analysis, sensitivity analysis, and anti-interference analysis of all dimensions of data (movement speed data, movement rhythm data, movement amplitude data, left-right balance data, etc.) in the indicator data pool. Specifically, data dispersion analysis is used to assess the differences in the numerical distribution of each dimension of data across different training groups; sensitivity analysis is used to assess the responsiveness of each dimension of data to changes in the user's physical condition; and anti-interference analysis is used to assess the stability of each dimension of data in the presence of slight shaking or signal noise. Combining the results of these three analyses, the dimension data with the highest score can be selected to further determine the training quality data. For example, when a user performs dumbbell curls, the movement amplitude data has the highest dispersion, the highest sensitivity, and good anti-interference performance, so movement amplitude data can be selected as the training quality data.

[0063] The above embodiments use training quality data, including but not limited to at least one of the following: movement speed data, movement rhythm data, movement amplitude data, and left-right balance data. These data can be acquired using any one or more types of sensors. This application does not limit the content or acquisition method of the training quality data; in practical applications, it can be flexibly set according to the training scenario, movement type, accuracy requirements, and device form.

[0064] For example, training quality data can be collected based on an IMU. An IMU includes an accelerometer and a gyroscope. An accelerometer can be used to measure acceleration information in three directions in space (which can be mapped to motion velocity data, etc.), and a gyroscope can be used to measure rotational angular velocity information (which can be mapped to motion amplitude data, motion trajectory data, etc.).

[0065] For example, training quality data can be collected using visual sensors such as cameras and depth cameras. Visual sensors can be used to capture images or videos of the user's training movements, extracting motion posture data (such as the position of human skeletal points, changes in joint angles, etc.) from the captured images or videos, and further mapping this data into training quality data. In addition to being mapped to the four types of training quality data shown in the above embodiments, the motion posture data extracted from images or videos can also be mapped to other types, such as human motion posture tracking data, real-time skeletal point rendering, and joint range of motion analysis.

[0066] For example, training quality data can be collected based on a motor resistance sensing module. This module can include a motor with a digital encoder and a linked resistance output mechanism (such as a cable traction system). The encoder can be used to collect the motor's rotation angle, speed, and output torque in real time and map them to training quality data. The motor, as the resistance source, can dynamically adjust its output resistance according to control commands to replace traditional weightlifting equipment (such as dumbbells and barbells) for strength training. Typically, the encoder in a motor resistance sensing module collects data with higher accuracy than an IMU, and the resistance output can be digitally controlled to achieve more precise training load adjustment and training quality feedback.

[0067] In summary, the technical solution provided in this application divides training feature data into two dimensions: training completion data and training quality data. Training completion data reflects the user's basic execution ability from the perspective of task achievement, while training quality data reflects the user's motor control ability and physical condition from the perspective of action standardization. Combining training completion data and training quality data avoids the judgment bias caused by relying solely on a single dimension of data or a single indicator. This provides a comprehensive, multi-dimensional, and adaptive data foundation for accurately determining the training achievement status and deciding on the direction of training progress, thus improving the accuracy, reliability, and scenario adaptability of training achievement status determination.

[0068] In some embodiments, step S200 may include steps S210 to S240.

[0069] Step S210: Determine the first data score corresponding to the completed training data.

[0070] The first data score refers to the result of mapping the training completion data to a quantitative score. For example, the training completion data includes the completion rate, which can be directly used as the first data score. For instance, if the completion rate is 70%, the first data score is mapped to 0.7. Alternatively, the completion rate can be divided into intervals, with different intervals mapped to different first data scores. For instance, if the completion rate is between 70% and 100%, the first data score is mapped to 1, and if the completion rate is below 70%, the first data score is mapped to between 0 and 1 according to a linear ratio.

[0071] Step S220: Determine the second data score corresponding to the training quality data.

[0072] The second data score refers to the result of mapping training quality data to a quantitative score. In this embodiment, the training quality data can be divided into levels and assigned values ​​to map it to the second data score. The number of levels and the specific values ​​assigned can be arbitrarily and flexibly set according to requirements, and this embodiment does not limit this. For example, the training quality data can be divided into three levels (e.g., movement rhythm stability can be divided into stable, slightly unstable, and unstable levels; left and right limb movement balance can be divided into good balance, average balance, and poor balance levels; movement trajectory accuracy can be divided into accurate, slightly biased, and significantly biased levels), so that the second data score is assigned three different values, such as 1.8, 0.9, 0 or 1, 0.5, 0. When the training quality data includes multiple dimensions, the multiple dimensions can be divided into levels and assigned values, and then the assigned values ​​corresponding to the multiple dimensions can be weighted and summed or arithmetically averaged to determine the second data score.

[0073] Step S230: Determine the overall training score based on the first data score and the second data score.

[0074] The comprehensive training score refers to the quantitative result obtained by fusing the first data score and the second data score, which is used to comprehensively reflect the user's training performance. This application does not limit the method of fusing the first and second data scores; in practical applications, it can be flexibly set according to requirements. For example, the comprehensive training score can be determined by averaging, weighted summing, or taking the minimum value of the first and second data scores.

[0075] For example, taking weighted summation, weight values ​​can be set for the two dimensions of training completion data and training quality data, such as 25% and 75% respectively. Then, the first data score and the second data score are weighted and summed according to the weight values ​​to obtain the comprehensive training score. Alternatively, taking the minimum value as an example, to ensure that the overall training performance is not overestimated when a single dimension fails, the minimum value between the first data score and the second data score can be taken as the comprehensive training score. It should be understood that, to ensure the accuracy of the comprehensive training score, the first data score and the second data score can be normalized to the same data range (e.g., the normalized first data score and the normalized second data score are both [0, 1]), and then the normalized first data score and the normalized second data score are fused to obtain the comprehensive training score.

[0076] Step S240: Determine the training achievement status based on the comprehensive training score and state mapping data.

[0077] The state mapping data can be preset based on historical training data or expert experience. This state mapping data can include at least one set of correspondences between comprehensive training scores and training achievement states. This application embodiment does not limit the specific content of the state mapping data (i.e., the specific correspondence between comprehensive training scores and training achievement states). For example, when the comprehensive training score is greater than or equal to a first scoring threshold (e.g., the first scoring threshold is between 0.85 and 0.95), the corresponding training achievement state can be "exceeding the standard"; when the comprehensive training score is greater than or equal to a second scoring threshold (e.g., the second scoring threshold is between 0.7 and 0.8) and less than the first scoring threshold (e.g., the first scoring threshold is between 0.85 and 0.95), the corresponding training achievement state can be "meeting the standard" (slightly exceeding the standard); when the comprehensive training score is greater than or equal to a third scoring threshold (e.g., the third scoring threshold is between 0.5 and 0.6), the training achievement state can be "meeting the standard" (slightly exceeding the standard). When the training comprehensive score is greater than or equal to the fourth scoring threshold (e.g., between 0.3 and 0.5) and less than the third scoring threshold (e.g., between 0.5 and 0.6), the corresponding training achievement status can be considered "achieved" (slightly unsatisfactory). When the training comprehensive score is less than the fourth scoring threshold (e.g., between 0.3 and 0.5), the corresponding training achievement status can be considered "unsatisfactory." In a more refined and granular division of training achievement status, the training comprehensive score can be further divided into more intervals to map to more types of training achievement statuses.

[0078] This application embodiment can also classify user performance levels for training completion data and training quality data respectively, and then classify the training achievement status based on the user performance levels corresponding to the training completion data and training quality data respectively. For example, as shown in the example... Figure 2 As shown, training completion data can include completion rate (divided into incomplete, completed, and over-completed), and training quality data can include rhythm stability (divided into irregular rhythm, average rhythm, and stable rhythm), movement speed (divided into fast speed, slow speed, and appropriate speed), movement amplitude (divided into small amplitude, large amplitude, and appropriate amplitude), and left-right balance (divided into poor balance, average balance, and good balance). Based on the user performance levels corresponding to the training completion data and training quality data, the training achievement status can be further divided into training failure, achievement, and exceeding the target. It should be understood that... Figure 2This example only categorizes training achievement status into three types, but this does not constitute a limitation on the embodiments of this application. Taking the division of training completion data into P user performance levels (P being a positive integer) and training quality data into Q user performance levels (Q being a positive integer) as an example, the training achievement status can be divided into at most P×Q types. For example, based on... Figure 2 The user performance levels shown in the training completion data and training quality data can be divided into a maximum of 9 (3×3) types for the training achievement status.

[0079] In summary, the technical solution provided in this application quantifies training completion data and training quality data into a first data score and a second data score, respectively, and then merges them into a comprehensive training score. Based on state mapping data, the comprehensive training score is mapped to a training achievement state, achieving refined and multi-dimensional grading of user training performance. This application supports multiple types of training achievement state classifications, providing a more granular gradient control basis for subsequent stage transition decisions, enabling stage transitions to more accurately adapt to the user's actual training performance.

[0080] In some embodiments, step S300 may include steps S310 to S330.

[0081] Step S310: If the training achievement status of the current training stage is in the first state, control the training progress to flow from the current training stage to the next training stage.

[0082] The first state refers to the classification of a user's training performance meeting the advanced conditions. Depending on the different types of training achievement states, the specific type of the first state may vary. For example, when the training achievement states include exceeding the target, meeting the target, or failing to meet the target, the first state may include exceeding the target; when the training achievement states include exceeding the target, meeting the target (slightly exceeding the target), meeting the target, meeting the target (slightly failing to meet the target), or failing to meet the target, the first state may include exceeding the target, meeting the target (slightly exceeding the target), or meeting the target. It should be understood that the first state may include one or more state types, and needs to be determined based on the specific classification of training achievement states and requirements.

[0083] When the training achievement status of the current training phase is at the first level, it can be determined that the user has demonstrated good physical condition and motor control ability in the current training phase, and is ready to move on to the next training phase to withstand the progressive training load. At this point, the training progress can be controlled to flow from the current training phase to the next training phase in the target training cycle, which is after the current training phase, thus achieving cycle progression.

[0084] Due to the refined and granular division of training achievement states, the first state may have multiple state types. For these multiple state types, when controlling the training progress to the next training stage, the target training load for the next training stage can be the same; for example, it can be the initial training load for the next training stage (i.e., the training load configured for the next training stage when the target training cycle begins). Alternatively, for these multiple state types, when controlling the training progress to the next training stage, the target training load for the next training stage may differ depending on the specific state type of the first state. For example, the target training load for the next training stage when the first state is "exceeding the target" is greater than the target training load when the first state is "achieving the target." Generally, for these multiple state types, the target training load for the next training stage can be positively correlated with the user's training performance corresponding to that state type; that is, the better the user's training performance corresponding to the state type of the first state, the larger the target training load can be for the next training stage.

[0085] Step S320: If the training achievement status of the current training stage is in the second state, control the training progress to maintain the current training stage.

[0086] The second state refers to the classification of user training performance that meets the consolidation conditions. It should be understood that the achievement status corresponding to the first state is superior to that corresponding to the second state; that is, the user training performance indicated by the second state is inferior to that indicated by the first state. Depending on the different types of training achievement states, the specific type of the second state may vary. For example, when the training achievement states include exceeding the target, meeting the target, or failing to meet the target, the second state may include meeting the target; when the training achievement states include exceeding the target, meeting the target (slightly exceeding the target), meeting the target, meeting the target (slightly failing to meet the target), or failing to meet the target, the second state may include meeting the target (slightly failing to meet the target). It should be understood that the second state may include one or more state types, and needs to be determined specifically based on the classification of training achievement states and requirements.

[0087] When the training achievement status of the current training phase is in the second state, it can be determined that the user's training performance in the current training phase is slightly insufficient. Forcing the user into the next training phase may lead to a mismatch between the training load and the user's ability. Therefore, at this point, the training progress can be controlled to repeat the same training plan as the current training phase in subsequent time units after the current training phase, i.e., maintaining the current training phase for consolidation training. During the repeated execution of the current training phase, the target training load of the current training phase can remain the same as the target training load of the previous round of the current training phase, or it can be fine-tuned according to needs.

[0088] Step S330: If the training achievement status of the current training stage is the third state, control the training progress to flow from the current training stage to the previous training stage.

[0089] The third state refers to the classification of user training performance that meets the rollback conditions. It should be understood that the achievement status corresponding to the second state is superior to that corresponding to the third state; that is, the user training performance indicated by the third state is inferior to that indicated by the second state. Depending on the different types of training achievement status classifications, the specific type of the third state may vary. For example, when training achievement status includes exceeding the target, meeting the target, or failing to meet the target, the third state may include failing to meet the target; when training achievement status includes exceeding the target, meeting the target (slightly exceeding the target), meeting the target, meeting the target (slightly failing to meet the target), or failing to meet the target, the third state may include failing to meet the target, or may include meeting the target (slightly failing to meet the target) or failing to meet the target. It should be understood that the third state may include one or more state types, and needs to be determined specifically based on the classification of training achievement status types and requirements.

[0090] When the training achievement status of the current training phase is in the third state, it can be determined that the user has experienced significant fatigue accumulation or a decline in movement quality within the current training phase, and the current target training load has exceeded the user's actual capacity. At this point, the training progress can be controlled to shift from the current training phase to the previous training phase within the target training cycle, achieving load reduction and rollback. After shifting to the previous training phase, the target training load when executing the previous training phase again can be the same as the target training load when executing the previous training phase, or it can be fine-tuned based on needs, such as reducing the target training load from the previous execution to provide more sufficient recovery and adaptation space.

[0091] It's important to note that for the initial training phase (the first training phase) in the target training cycle, since there is no preceding training phase, if the training achievement status of the initial training phase is in the third state, the initial training phase can be maintained. The target training load when executing the initial training phase again can be the same as the target training load of the previous execution, or it can be fine-tuned based on needs (such as adjustments). For the final training phase in the target training cycle, since there is no next training phase, if the training achievement status of the final training phase is in the first state, the training plan to complete the current target training cycle can be determined. However, due to the special nature of the final training phase, in practical applications, regardless of whether the training achievement status of the final training phase is in the first, second, or third state, the training plan to complete the current target training cycle can be determined without needing to execute a phase transition.

[0092] In summary, the technical solution provided in this application constructs a transition system between stages by mapping the training achievement status to three mutually exclusive training progress transition paths (advanced, consolidated, and regressed). When a user's training performance is good, the advanced transition is used to tap into their training potential; when a user's training performance is slightly insufficient, the consolidated transition is used to strengthen adaptation; and when a user experiences fatigue or insufficient ability, the regressed transition is used for proactive protection. The transition system provided in this application allows the transition path between stages to be adaptively adjusted according to the user's actual training performance, avoiding the training load mismatch problem caused by the one-size-fits-all stage advancement in static configuration schemes, and improving the dynamic adaptability of periodic training and the safety of long-term training.

[0093] In some embodiments, the above-mentioned control of the training progress from the current training stage to the next training stage may include: determining whether the load progression condition is met based on training quality data; if the load progression condition is met, controlling the training progress from the current training stage to the next training stage, and adjusting the target training load of the next training stage to the initial training load of the next training stage; if the load progression condition is not met, controlling the training progress from the current training stage to the next training stage, and adjusting the target training load of the next training stage to the target training load of the current training stage.

[0094] Load progression conditions refer to additional constraints that need to be met during the progression of training stages. These constraints can be used to prevent sports injuries caused by users forcibly advancing to higher levels when their movement quality is flawed. In this embodiment, the determination of load progression conditions can be based on training quality data, such as left-right balance data, or data on movement rhythm, amplitude and speed, etc. In practical applications, one or more dimensions of data from the training quality data can be used to determine whether the load progression conditions are met.

[0095] When the load progression condition is met, the training load can be increased normally according to the preset progression rules when transitioning to the next training phase. The initial training load of the next training phase (i.e., the training load configured for the next training phase when the target training cycle begins) is used as the target training load for the next training phase. When the load progression condition is not met, the progression permission of the current training load can be locked when transitioning to the next training phase. The target training load of the next training phase will remain the same as the target training load of the current training phase, and the training load will not be increased further, thereby achieving the correction constraint for movement quality.

[0096] For example, taking the determination of whether the load progression condition is met based on left-right balance data (such as the force deviation value of the left and right limbs) as an example, the load progression condition may include left-right balance data being less than or equal to a balance threshold. When the left-right balance data is less than or equal to the balance threshold, it can be determined that the load progression condition is met, and when moving to the next training stage, the initial training load of the next training stage is used as the target training load of the next training stage. When the left-right balance data is greater than the balance threshold, it indicates poor left-right balance, and it can be determined that the load progression condition is not met, and when moving to the next training stage, the target training load of the next training stage is maintained as the target training load of the current training stage. The specific value of the balance threshold is not limited in this application embodiment. In practical applications, it can be arbitrarily and flexibly set according to the type of movement, training stage, or needs, for example, set to between 12% and 20%.

[0097] In practical applications, in addition to the load progression condition, a load reduction condition can also be set to determine whether the training load is properly reduced during the rollback process in the training phase. This load reduction condition can also be set based on training quality data, which is not limited in this embodiment.

[0098] In summary, the technical solution provided in this application, by introducing a secondary verification of the load progression condition during the progressive transition in the training phase, achieves deviation correction constraints on movement quality. Based on this, even if a user's overall training performance meets the progressive requirements, if there are movement quality defects such as imbalance in the force exerted by the left and right limbs, the load progression will be locked during the progressive transition. This prevents the user from forcibly enduring a higher training load while injured or unbalanced, improving the safety of periodic training and reducing the risk of sports injuries.

[0099] In some embodiments, the above training cycle control method may further include step S000: when the training stagnation condition is met, suspend the training progress and enter the platform training stage; wherein, the platform training stage is used to interrupt the progressive mode of multiple training stages in the target training cycle.

[0100] Training plateau conditions refer to the conditions used to determine whether a user has entered a training plateau (i.e., training performance stagnates for a long period and cannot make progress). This application does not limit the specific content of the training plateau conditions; in practical applications, they can be flexibly set according to needs. In some embodiments, training plateau conditions may include, but are not limited to, at least one of the following: training feature data for N consecutive training stages meets a preset bottleneck condition, where N is a positive integer; user fatigue data for M consecutive training stages meets a preset fatigue condition, where M is a positive integer. The values ​​of N and M can be the same (e.g., both 2) or different (e.g., N is 2 and M is 3, or N is 3 and M is 2, etc.).

[0101] Preset bottleneck conditions refer to data standards used to determine training plateaus from the perspective of training feature data. The determination of preset bottleneck conditions can be based on the aforementioned training completion data and / or training quality data. Taking the simultaneous use of training completion data and training quality data as an example, preset bottleneck conditions can include: within N consecutive training phases, both training completion data and training quality data fail to meet preset completion conditions, meaning both training completion data and training quality data indicate poor user training performance. For example, training completion data includes weekly volume completion rate, and preset completion conditions include a completion rate threshold; preset quality data includes left-right balance data, and preset quality conditions include a balance threshold. When the weekly volume completion rate is less than the completion rate threshold and the left-right balance data is greater than the balance threshold for N consecutive training phases, the preset bottleneck condition can be determined to be met. In practical applications, the determination of preset bottleneck conditions can also be based on other completion rate parameters in the training completion data and / or other dimensions of data in the training quality data, such as single action completion rate, action rhythm data, etc.

[0102] Preset fatigue conditions refer to data standards used to determine training plateaus from the perspective of user physiological sensations. The determination of preset fatigue conditions can be based on user fatigue data. This application does not limit the specific type of user fatigue data; exemplarily, user fatigue data may include, but is not limited to, at least one of the following: subjective fatigue feedback data and physiological fatigue monitoring data. Subjective fatigue feedback data can be obtained based on the user's subjective rating of the RPE (Rating of Perceived Exertion) scale; physiological fatigue monitoring data may include, but is not limited to, at least one of the user's sleep data and heart rate data. Taking user fatigue data including subjective fatigue feedback data as an example, preset fatigue conditions may include the percentage of times subjective fatigue feedback data is greater than or equal to a preset feedback threshold (e.g., 7 or 8 points) within M consecutive training phases exceeding a preset proportion threshold (e.g., 75%, 80%, or 90%).

[0103] The platform training phase refers to an independent consolidation phase inserted into the normal progressive flow of the target training cycle. It is used to interrupt the progressive load path of the target training cycle (i.e., the progressive pattern of multiple training phases) when the training load stimulus reaches adaptive saturation, and to rebuild the positive response relationship between training load and physiological adaptation. After the platform training phase is completed, one can choose to return to the training phase before the interruption of the target training cycle and continue the phase flow, or one can choose to restart the target training cycle. In the embodiments of this application, the training plan for the platform training phase can be completely independent of the training plans for multiple training phases in the target training cycle, or it can be adaptively adjusted based on the training plan of any one training phase in the target training cycle. For example, when the training stagnation condition is currently met, adaptive adjustments can be made based on the training plan of the current training phase or the next training phase. The adjustments may include, but are not limited to, at least one of the following: training load, course difficulty, training method, and training intensity.

[0104] For example, when making adaptive adjustments based on the training plan of the current training phase, the training method ratio (such as increasing the proportion of supersets and compound sets), course difficulty and movement difficulty, and controlling the training load within the consolidation range can be adjusted to obtain the training plan for the plateau training phase, thereby helping users recover and adapt in a safer way; or, when making adaptive adjustments based on the training plan of the next training phase, the training volume, movement rhythm requirements, etc., in the next training phase can be adjusted to obtain the training plan for the plateau training phase, thereby stimulating the breakthrough of the plateau by breaking the training inertia.

[0105] In summary, the technical solution provided in this application introduces a platform training phase as an independent fallback module. When the system detects that a user has entered a plateau, it automatically pauses the normal cyclic progression and performs consolidation training in the platform training phase. This avoids sports injuries and diminishing training benefits caused by forcibly advancing to a higher level when the user is fatigued or has reached adaptive saturation, and improves the long-term adaptability of cyclical training and the ability to break through plateaus.

[0106] In some embodiments, the training cycle control method may further include step S400: after completing the target training cycle, determining the benchmark training load for the next training cycle of the target training cycle based on the training achievement status of the reference training phase in the target training cycle and the benchmark training load of the target training cycle.

[0107] A reference training phase is a benchmark training load used to calibrate the next training cycle. A reference training phase can be any one or more training phases within the target training cycle; for example, it could be the last training phase (the final training phase) of the target training cycle, or any training phase within the target training cycle.

[0108] After completing the target training cycle, the baseline training load for the next training cycle can be determined based on the training achievement status of the reference training phase and the baseline training load of the target training cycle. For example, based on the different types of training achievement status in the reference training phase, the baseline training load of the target training cycle can be adjusted upwards, downwards, or kept unchanged to obtain the baseline training load for the next training cycle.

[0109] For example, when the training achievement status in the reference training phase is in the first state, a first load percentage (which can be a positive number, such as 4% to 8%) can be added to the baseline training load of the target training cycle to obtain the baseline training load for the next training cycle. When the training achievement status in the reference training phase is in the second state, the baseline training load of the target training cycle can be directly used as the baseline training load for the next training cycle to maintain the current baseline training load. When the training achievement status in the reference training phase is in the third state, a second load percentage (which can be a positive number, such as 4% to 8%, and can be equal to, greater than, or less than the first load percentage) can be reduced from the baseline training load of the target training cycle to obtain the baseline training load for the next training cycle. The achievement status corresponding to the first state is better than that corresponding to the second state, and the achievement status corresponding to the second state is better than that corresponding to the third state. For further descriptions of the first, second, and third states, please refer to the above embodiments, which will not be repeated here.

[0110] It's important to note that in practice, different training cycles can operate independently. The baseline training load for each cycle can be calibrated based on the user's strength assessment data (such as 1RM load assessment data). Users can periodically re-perform strength assessments to correct for baseline drift that may occur during long-term training.

[0111] In summary, the technical solution provided in this application automatically calibrates the baseline training load for the next training cycle after the entire target training cycle has been completed, based on the training achievement status of the reference training phase within the target training cycle and the baseline training load of the target training cycle. Based on this, users do not need to manually intervene in cycle planning and load adjustment throughout the entire process; cycle initialization and load evolution can be automatically completed based on long-term training performance. This allows periodic training to continuously and dynamically adapt to changes in the user's abilities, improving the adaptability and user engagement of long-term strength training.

[0112] The technical solution provided in the embodiments of this application will be described below with an example.

[0113] Please see Figure 3 , Figure 3 This is a flowchart of another training cycle control method provided in an embodiment of this application. For example... Figure 3 As shown, the training cycle control method may include the following steps S301 to S308.

[0114] Step S301: Determine the baseline training load for the target training cycle based on the strength assessment data.

[0115] Before the start of the target training cycle, the baseline training load Z1 of the target training cycle can be determined based on the user's previous strength assessment data (such as 1RM load assessment data). This load serves as the benchmark for the progressive load of multiple training phases in the target training cycle, and the training plan for the target training cycle is initialized.

[0116] Step S302: During the training process of the target training cycle, acquire the training feature data of each training stage.

[0117] The training load for multiple training phases within the target training cycle is progressively increased based on the same baseline training load. Training plans for the corresponding training phases can be loaded sequentially to guide the user through each phase. During each training phase, training feature data, such as training completion data and training quality data, are collected in real time.

[0118] Step S303: Determine the training achievement status of the current training stage based on the training feature data of the current training stage.

[0119] Training achievement status can be categorized as exceeding the standard, meeting the standard (slightly exceeding the standard), meeting the standard, meeting the standard (slightly failing to meet the standard), or failing to meet the standard.

[0120] Step S304: If the training achievement status of the current training stage is in the first state, control the training progress to flow from the current training stage to the next training stage.

[0121] The first state can include, for example, exceeding the standard, meeting the standard (slightly exceeding the standard), or meeting the standard.

[0122] Step S305: If the training achievement status of the current training stage is in the second state, control the training progress to maintain the current training stage.

[0123] The second state can include, for example, meeting the standard (minor non-compliance).

[0124] Step S306: If the training achievement status of the current training stage is the third state, control the training progress to flow from the current training stage to the previous training stage.

[0125] The third state may include, for example, failure to meet the standard.

[0126] Step S307: If the training stagnation condition is met, pause the training progress and enter the platform training phase.

[0127] Step S308: After completing the target training cycle, determine the baseline training load for the next training cycle based on the training achievement status of the reference training phase in the target training cycle and the baseline training load of the target training cycle.

[0128] Taking a target training cycle comprising four progressively increasing training phases—adaptation phase W1, improvement phase W2, peak phase W3, and recovery phase W4—as an example, the training load for adaptation phase W1 can be equal to the baseline training load Z1 of the target training cycle. The training load Z2 for improvement phase W2 can be increased by a% based on the training load Z1 for adaptation phase W1 (i.e., Z2 = Z1 + a%). The training load Z3 for peak phase W3 can be increased by b% based on the training load Z1 for adaptation phase W1 (i.e., Z3 = Z1 + b%). The training load Z4 for recovery phase W4 can be increased by c% based on the training load Z1 for adaptation phase W1 (i.e., Z4 = Z1 + c%). Here, b% (the second progressive ratio) is greater than a% (the first progressive ratio), and c% (the third progressive ratio) is less than b% (the second progressive ratio). c% (the third progressive ratio) is greater than, less than, or equal to a% (the first progressive ratio). For example, a% is 4% to 6%, b% is 9% to 11%, and c% is 6% to 8.5%.

[0129] Please see Figure 4 , Figure 4 This is a schematic diagram of a stage transition scheme provided in an embodiment of this application. For example... Figure 4 As shown, for each training phase, if the training achievement status of that phase is exceeding the target, achieving the target (slightly exceeding the target), or achieving the target, the trainee can move from that phase to the next phase and adjust the training load accordingly. If the training achievement status of that phase is achieving the target (slightly failing to achieve the target), the phase can be repeated to maintain the training load. If the training achievement status of that phase is failing to achieve the target, the trainee can revert to the previous phase and adjust the training load accordingly. Specifically, for the adaptation phase W1, since it does not have a previous phase, when its achievement status is failing to achieve the target, W1 can be repeated, but the training load can be reduced. For the recovery phase W4, since it is the final training phase of the target training cycle, regardless of its achievement status, the target training cycle can be terminated, and the baseline training load for the next training cycle can be determined based on its achievement status and the baseline training load Z1 of the target training cycle. Figure 4In this example, a% is 5.5%, b% is 10.2%, and c% is 7%, but this does not constitute a limitation on the embodiments of this application. In actual applications, other proportional values ​​can also be set.

[0130] It should be noted that the technical solutions provided in the embodiments of this application may have various alternative, extended or modified solutions in practical applications, and other solutions that can be obtained by those skilled in the art from the content disclosed in the embodiments of this application are all within the protection scope of this application. For example, training completion data can also include training duration completion rate, movement coverage, etc.; various thresholds can be replaced with user-personalized thresholds, interval-level adaptive thresholds, etc.; the weight progression ratio of training phases can be dynamically adapted based on user ability level, training years, etc.; training plans can be divided into muscle groups, with upper limbs, lower limbs, push-pull muscle groups, etc., each running an independent adaptive flow system; support for user-defined upper and lower limits for training load adjustment, locking phase flow direction, movement quality correction constraints, and training load for platform training phases, etc.; advance notice of subsequent phase flows to users; adaptation to diverse fitness training modes such as supersets, compound sets, circuit training, and endurance training; prompting users with phase flow direction, current training phase training plan, and training reports for each training phase through terminal devices and / or smart fitness equipment; retaining users' long-term training data, indicator performance, flow adaptation records, etc., for subsequent self-learning iterative optimization of the selection model, judgment thresholds, and progression rules.

[0131] Please see Figure 5 , Figure 5 This is a schematic diagram of a training cycle control device provided in an embodiment of this application. This training cycle control device can be used to implement the above-described training cycle control method. The training cycle control device can be an electronic device or can be installed within an electronic device. Figure 5 As shown, the training cycle control device 500 may include: a data acquisition module 510, a pass detection module 520, and a flow control module 530.

[0132] The data acquisition module 510 is used to: acquire training feature data of each training stage in the target training period during the training process of the target training period, wherein the target training period includes multiple training stages that are continuously progressive with the same baseline training load; the achievement detection module 520 is used to: determine the training achievement status of the current training stage based on the training feature data of the current training stage; the flow control module 530 is used to: control the training progress to flow from the current training stage to the target training stage based on the training achievement status; wherein the target training stage includes any one of the following: the next training stage after the current training stage in the target training period, the current training stage, or the previous training stage before the current training stage in the target training period.

[0133] In some embodiments, the training load of multiple training phases in the target training cycle is nonlinearly progressive based on the same baseline training load.

[0134] In some embodiments, the training feature data includes training completion data and training quality data; wherein, training completion data includes completion rate; training quality data is determined based on at least one of the following: movement speed data, movement rhythm data, movement amplitude data, and left-right balance data; wherein, movement speed data is used to indicate the user's movement speed in a single movement or multiple movements, movement rhythm data is used to indicate the periodicity of the user's movement during the execution of the movement, movement amplitude data is used to indicate the amount of spatial position change achieved by the user during the execution of the movement, and left-right balance data is used to indicate the degree of force balance between the left and right limbs in movements involving both limbs.

[0135] In some embodiments, the data acquisition module 510 is further configured to: select any one dimension of data from motion speed data, motion rhythm data, motion amplitude data, and left-right balance data as training quality data; or, select at least two dimensions of data from motion speed data, motion rhythm data, motion amplitude data, and left-right balance data, obtain the weight values ​​corresponding to the at least two dimensions of data respectively, and perform a weighted summation of the at least two dimensions of data according to the weight values ​​corresponding to the at least two dimensions of data respectively to obtain training quality data.

[0136] In some embodiments, the above-mentioned achievement detection module 520 is further configured to: determine a first data score corresponding to the training completion data; determine a second data score corresponding to the training quality data; determine a comprehensive training score based on the first data score and the second data score; and determine a training achievement status based on the comprehensive training score and state mapping data; wherein the state mapping data includes at least one set of correspondences between the comprehensive training score and the training achievement status.

[0137] In some embodiments, the above-mentioned compliance detection module 520 is further configured to: normalize the first data score and the second data score to the same data range; and fuse the normalized first data score and the normalized second data score to obtain a training comprehensive score.

[0138] In some embodiments, the above-mentioned flow control module 530 is further configured to: control the training progress to flow from the current training stage to the next training stage when the training achievement state is a first state; control the training progress to maintain the current training stage when the training achievement state is a second state; and control the training progress to flow from the current training stage to the previous training stage when the training achievement state is a third state; wherein the achievement status corresponding to the first state is better than the achievement status corresponding to the second state, and the achievement status corresponding to the second state is better than the achievement status corresponding to the third state.

[0139] In some embodiments, the above-mentioned flow control module 530 is further configured to: determine whether the load progression condition is met based on the training quality data; if the load progression condition is met, control the training progress to flow from the current training stage to the next training stage, and adjust the target training load of the next training stage to the initial training load of the next training stage; if the load progression condition is not met, control the training progress to flow from the current training stage to the next training stage, and adjust the target training load of the next training stage to the target training load of the current training stage.

[0140] In some embodiments, the load progression condition includes left and right balance data being less than or equal to a balance threshold.

[0141] In some embodiments, such as Figure 6 As shown, the training cycle control device 500 may further include a platform entry module 540, which is used to pause the training progress and enter the platform training stage when the training stagnation condition is met; wherein, the platform training stage is used to interrupt the progressive mode of multiple training stages in the target training cycle.

[0142] In some embodiments, the training stagnation condition includes at least one of the following: the training feature data of N consecutive training stages meet the preset bottleneck condition, where N is a positive integer; the user fatigue data of M consecutive training stages meet the preset fatigue condition, where M is a positive integer.

[0143] In some embodiments, user fatigue data includes at least one of the following: subjective fatigue feedback data and physiological fatigue monitoring data.

[0144] In some embodiments, such as Figure 6 As shown, the training cycle control device 500 further includes a load calibration module 550, which is used to determine the benchmark training load for the next training cycle after the target training cycle is completed, based on the training achievement status of the reference training phase in the target training cycle and the benchmark training load of the target training cycle.

[0145] In some embodiments, the reference training phase is the last training phase in the target training cycle.

[0146] In some embodiments, such as Figure 6 As shown, the load calibration module 550 is further configured to: when the training achievement status of the reference training phase is in the first state, increase the first load ratio based on the baseline training load of the target training cycle to obtain the baseline training load of the next training cycle; when the training achievement status of the reference training phase is in the second state, use the baseline training load of the target training cycle as the baseline training load of the next training cycle; when the training achievement status of the reference training phase is in the third state, decrease the second load ratio based on the baseline training load of the target training cycle to obtain the baseline training load of the next training cycle; wherein, the achievement status corresponding to the first state is better than the achievement status corresponding to the second state, and the achievement status corresponding to the second state is better than the achievement status corresponding to the third state; both the first load ratio and the second load ratio are positive numbers.

[0147] For further description of the steps performed by each module in the above-mentioned training cycle control device and their beneficial effects, please refer to the embodiments of the above-mentioned training cycle control method. Their technical concepts are the same or similar, and will not be elaborated here.

[0148] This application also provides a computer-readable storage medium storing a computer program or instructions. When the computer program or instructions are executed by a processor, they implement the above-described training cycle control method and have all the beneficial effects of the above-described training cycle control method. Further details will not be elaborated here.

[0149] This application also provides a computer program product, including a computer program or instructions. When the computer program or instructions are executed by a processor, they implement the above-described training cycle control method and have all the beneficial effects of the above-described training cycle control method. Further details will not be elaborated here.

[0150] This application also provides an electronic device, including a memory and a processor. The memory stores a computer program or instructions; the processor executes the computer program or instructions in the memory to implement the steps of the training cycle control method described above. This electronic device has all the beneficial effects of the training cycle control method described above, which will not be elaborated further here.

[0151] Computer-readable storage media can be, for example, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof, without specific limitation herein. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0152] In some embodiments of this application, a computer-readable storage medium may be any tangible medium that contains or stores a program that may be used or combined with an instruction execution system, apparatus, or device.

[0153] The aforementioned computer-readable storage medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0154] Computer program code for performing operations of some embodiments of this application can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network (including a Local Area Network (LAN) or a Wide Area Network (WAN)), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0155] This application also provides an intelligent fitness device, which may include: a device body; a sensing unit for collecting motion signals; and the aforementioned training cycle control device or electronic device. This intelligent fitness device possesses all the beneficial effects of the aforementioned training cycle control device or electronic device, which will not be elaborated upon here. The intelligent fitness device can be implemented as an intelligent dumbbell, intelligent barbell, intelligent kettlebell, or comprehensive strength training device, etc., and this application does not limit this. The sensing unit in the intelligent fitness device may be a built-in sensor (such as an IMU, motor resistance sensing module, etc.) to collect motion signals and map them to the aforementioned training feature data; or, the intelligent fitness device may also be connected to an external sensor (such as a camera) to obtain the aforementioned training feature data; thereby, the intelligent fitness device can execute the aforementioned training cycle control method.

[0156] This application also provides a terminal device, which may include the aforementioned training cycle control device or electronic device. This terminal device possesses all the beneficial effects of the aforementioned training cycle control device or electronic device, which will not be elaborated upon here. The terminal device can be a mobile phone, tablet computer, smart wearable device, smart speaker, or smart TV, etc., and this application does not limit its implementation. The terminal device may have built-in sensors (such as cameras), connect to external sensors (such as cameras), and / or connect to smart fitness equipment to obtain the aforementioned training feature data, thereby executing the aforementioned training cycle control method. The terminal device and the smart fitness equipment can be connected via a hard wire or via a network (such as wired network, wireless network, Bluetooth, etc.), and this application does not limit their connection.

[0157] It should be noted that, in the data processing stage, the technical solution of this application has strictly limited the scope of data collection to the minimum necessary to achieve the technical objectives, preventing the acquisition of irrelevant information. For any user information to be collected, the data subject will be clearly informed and their consent obtained. Furthermore, technologies such as encrypted storage and access control are employed to strengthen data security and ensure the security and compliance of the entire data processing process. The technical model and decision-making mechanism are based on objective technical parameters and do not introduce unnecessary parameters such as gender or age that may lead to discrimination, resolutely eliminating algorithmic discrimination and upholding public order and good morals. In addition, the specification fully describes the technical implementation methods, application scenarios, and compliance protection details. The claims are consistent with the content of the specification, key compliance designs are clear and verifiable, and the overall technical design is guided by the protection of public interests and adherence to social ethics, without any circumstances that harm public interests or violate public order and good morals.

[0158] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0159] The above provides a detailed description of a training cycle control method, apparatus, device, medium, and intelligent fitness equipment provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A training cycle control method, characterized in that, The method includes: During the training process of the target training cycle, training feature data of each training stage in the target training cycle are acquired; wherein, the target training cycle includes multiple training stages that are continuously progressively advanced with the same baseline training load; Based on the training feature data of the current training phase, determine the training achievement status of the current training phase; Based on the training achievement status, the training progress is controlled to flow from the current training stage to the target training stage; wherein, the target training stage includes any one of the following: the next training stage in the target training cycle after the current training stage, the current training stage, and the previous training stage in the target training cycle before the current training stage.

2. The method according to claim 1, characterized in that, The training load of multiple training phases in the target training cycle is nonlinearly progressive based on the same baseline training load.

3. The method according to claim 1, characterized in that, The training feature data includes training completion data and training quality data; wherein... The training completion data includes the completion rate; The training quality data is determined based on at least one of the following: movement speed data, movement rhythm data, movement amplitude data, and left-right balance data; The process for determining the training quality data includes: Choose any one dimension of data from the action speed data, the action rhythm data, the action amplitude data, and the left-right balance data as the training quality data; or, Select at least two dimensions of data from the motion speed data, the motion rhythm data, the motion amplitude data, and the left-right balance data; obtain the weight values ​​corresponding to the at least two dimensions of data respectively; and perform a weighted summation of the at least two dimensions of data according to the weight values ​​corresponding to the at least two dimensions of data respectively to obtain the training quality data.

4. The method according to claim 3, characterized in that, Determining the training achievement status of the current training stage based on the training feature data of the current training stage includes: Determine the first data score corresponding to the training completion data; Determine the second data score corresponding to the training quality data; A comprehensive training score is determined based on the first data score and the second data score. The training achievement status is determined based on the comprehensive training score and the state mapping data; wherein the state mapping data includes at least one set of correspondences between the comprehensive training score and the training achievement status.

5. The method according to claim 4, characterized in that, The step of determining the comprehensive training score based on the first data score and the second data score includes: Normalize the first data score and the second data score to the same data range; The normalized scores of the first data and the normalized scores of the second data are fused to obtain the comprehensive training score.

6. The method according to claim 1, characterized in that, The step of controlling the training progress to transition from the current training stage to the target training stage based on the training achievement status includes: When the training achievement status is in the first state, the training progress is controlled to flow from the current training stage to the next training stage; When the training achievement status is in the second state, the training progress is controlled to maintain the current training stage; When the training achievement status is in the third state, the training progress is controlled to flow from the current training stage to the previous training stage; The compliance status corresponding to the first state is better than that corresponding to the second state, and the compliance status corresponding to the second state is better than that corresponding to the third state.

7. The method according to claim 6, characterized in that, The control of the training progress from the current training stage to the next training stage includes: Based on the training quality data in the training feature data, determine whether the load progression condition is met; If the load progression condition is met, the training progress is controlled to flow from the current training stage to the next training stage, and the target training load of the next training stage is adjusted to the initial training load of the next training stage. If the load progression condition is not met, the training progress is controlled to flow from the current training stage to the next training stage, and the target training load of the next training stage is adjusted to the target training load of the current training stage.

8. The method according to claim 7, characterized in that, The load progression condition includes left and right balance data being less than or equal to the balance threshold.

9. The method according to claim 1, characterized in that, The method further includes: If the training stagnation condition is met, the training progress is paused and the platform training phase is entered; wherein, the platform training phase is used to interrupt the progressive mode of multiple training phases in the target training cycle.

10. The method according to claim 9, characterized in that, The training stagnation condition includes at least one of the following: the training feature data for N consecutive training phases satisfies a preset bottleneck condition, where N is a positive integer; User fatigue data for M consecutive training phases meet preset fatigue conditions, where M is a positive integer.

11. The method according to claim 10, characterized in that, The user fatigue data includes at least one of the following: subjective fatigue feedback data and physiological fatigue monitoring data.

12. The method according to claim 1, characterized in that, The method further includes: After completing the target training cycle, the baseline training load for the next training cycle is determined based on the training achievement status of the reference training phase in the target training cycle and the baseline training load of the target training cycle.

13. The method according to claim 12, characterized in that, The reference training phase is the final training phase in the target training cycle.

14. The method according to claim 12, characterized in that, The step of determining the baseline training load for the next training cycle based on the training achievement status of the reference training phase in the target training cycle and the baseline training load of the target training cycle includes: When the training achievement status in the reference training phase is in the first state, the first load ratio is added to the baseline training load of the target training cycle to obtain the baseline training load of the next training cycle. If the training achievement status in the reference training phase is the second state, the benchmark training load of the target training cycle is used as the benchmark training load of the next training cycle. If the training achievement status in the reference training phase is the third state, the second load ratio is reduced based on the baseline training load of the target training cycle to obtain the baseline training load of the next training cycle. Wherein, the compliance status corresponding to the first state is better than that corresponding to the second state, and the compliance status corresponding to the second state is better than that corresponding to the third state; both the first load ratio and the second load ratio are positive numbers.

15. A training cycle control device, characterized in that, The device includes: The data acquisition module is used to acquire training feature data of each training stage in the target training cycle during the training process; wherein, the target training cycle includes multiple training stages that are continuously progressively advanced with the same baseline training load; The achievement detection module is used to determine the training achievement status of the current training stage based on the training feature data of the current training stage. The flow control module is used to control the training progress to flow from the current training stage to the target training stage according to the training achievement status; wherein, the target training stage includes any one of the following: the next training stage after the current training stage in the target training cycle, the current training stage, and the previous training stage before the current training stage in the target training cycle.

16. An electronic device, characterized in that, include: A memory on which computer programs or instructions are stored; A processor for executing the computer program or instructions in the memory to implement the training cycle control method as described in any one of claims 1 to 14.

17. A computer-readable storage medium, characterized in that, It stores computer programs or instructions, which, when executed by a processor, implement the training cycle control method as described in any one of claims 1 to 14.

18. A smart fitness device, characterized in that, include: The instrument itself; The sensing unit is used to collect motion signals; as well as, The training cycle control device as described in claim 15, or the electronic device as described in claim 16.

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