Counting error correction and exercise intensity dynamic grading method and system for intelligent rope skipping

Abstract

The invention relates to the technical field of intelligent sports equipment, and provides a counting error correction and sports intensity dynamic grading method and system for an intelligent skipping rope. The method comprises the following steps: acquiring three-dimensional motion data in a rope skipping process; performing feature extraction on the three-dimensional motion data to obtain rope skipping motion features; identifying a target data segment in three-dimensional motion data through the rope skipping motion features; acquiring a time interval and displacement track repeatability of two adjacent actions in the target data segment, and when the time interval exceeds a normal rope skipping frequency range or the displacement track repeatability is lower than a preset threshold value, judging that the counting is invalid counting and removing; the abnormal rope skipping counting result is corrected; and in combination with basic information and rope skipping frequency, continuous exercise time, accumulated rope skipping times and heart rate data which are acquired in real time, calculating a current exercise intensity index so as to perform dynamic grading. According to the invention, more accurate and personalized exercise experience is provided for the user.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent sports equipment, and in particular to a method and system for correcting counting errors and dynamically grading exercise intensity of an intelligent skipping rope. Background Art

[0002] With the rise of national fitness awareness, smart skipping ropes are widely used as sports equipment that combines portability and data-based functions. The core functions of existing smart skipping ropes focus on skipping counts and basic sports data statistics, but there are significant deficiencies in counting accuracy and refined exercise intensity assessment:

[0003] The problem of counting errors is prominent: Traditional smart rope skipping mostly relies on a single acceleration sensor or angular velocity sensor to collect data, and only identifies the rope skipping action through the periodic characteristics of the handle's motion trajectory. However, during actual rope skipping, users may generate interference data due to fatigue, deformation of movements, or non-jumping rope swinging (such as adjusting rope length or natural arm swinging), resulting in miscounts or missed counts. For example, existing solutions do not combine the correlation between the rope skipping action and the user's jump landing (such as foot contact signals), and cannot effectively distinguish abnormal movements such as "empty swing without jumping" and "jumping without rope swinging", resulting in a high counting error rate.

[0004] Extensive exercise intensity assessment lacks personalized dynamic grading: Existing technologies often use a single metric (such as heart rate or rope skipping frequency) to determine exercise intensity, with fixed grading standards. They fail to dynamically adjust based on individual user differences (age, weight, and fitness) and diverse exercise goals (such as fat loss, endurance training, and explosive power training). For example, for users seeking fat loss, existing solutions simply divide heart rate into zones, ignoring the impact of rope skipping frequency stability on energy expenditure. For explosive power training, there are no quantitative standards for the magnitude and duration of frequency spikes, resulting in users being unable to obtain accurate exercise guidance and making it difficult to guarantee effective training.

[0005] Insufficient multi-source data fusion: Existing smart jump ropes have a single sensor configuration (typically only a built-in accelerometer in the handle), failing to fully utilize multi-dimensional data from rope tension sensors and foot pressure sensors for cross-validation. For example, changes in rope tension directly reflect the force of the rope swing and its motion state, while foot contact signals accurately represent the occurrence of the jump. However, existing solutions lack the fusion of these data, resulting in poor robustness of the counting logic and strength assessment models.

[0006] Therefore, a method is urgently needed to solve at least one of the above problems. Summary of the Invention

[0007] The present application provides a counting error correction and exercise intensity dynamic grading method and system for an intelligent skipping rope, aiming to solve the problem that the core functions of existing intelligent skipping ropes are concentrated on skipping rope counting and basic exercise data statistics, but there are significant deficiencies in counting accuracy and refinement of exercise intensity assessment.

[0008] In a first aspect, an embodiment of the present application provides a method for correcting counting errors and dynamically grading exercise intensity of an intelligent rope skipping device, which is applied to the intelligent rope skipping device; the method includes:

[0009] Acquire three-dimensional motion data during the rope skipping process, wherein the three-dimensional motion data includes at least spatial displacement information, rotation angle information, and motion acceleration information corresponding to the smart rope skipping;

[0010] Performing feature extraction on the three-dimensional motion data to obtain rope skipping motion features; identifying target data segments corresponding to effective rope skipping movements in the three-dimensional motion data using the rope skipping motion features;

[0011] Obtaining the time interval and displacement trajectory repeatability between two adjacent actions in the target data segment, and determining that an invalid count is made and discarding the count if the time interval exceeds the normal rope skipping frequency range or the displacement trajectory repeatability is lower than a preset threshold;

[0012] The user's basic information is obtained, and the current exercise intensity index is calculated by combining the basic information with the real-time collected skipping frequency, continuous exercise time, cumulative number of skipping times and heart rate data; the current exercise intensity index is dynamically graded according to a preset grading rule library, which contains intensity grading standards corresponding to different exercise goals.

[0013] In some embodiments, the obtaining of three-dimensional motion data during rope skipping includes: real-time data collection through a motion sensor assembly provided on the smart skipping rope, the motion sensor assembly including at least a three-axis acceleration sensor, a three-axis angular velocity sensor, and a gyroscope installed on the corresponding handle of the smart skipping rope, for obtaining translational acceleration data, rotational angular velocity data, and spatial attitude angle data of the smart skipping rope in three-dimensional space, the motion sensor assembly also including a tension sensor provided on the corresponding rope body of the smart skipping rope, for assisting in collecting rope tension change information when the smart skipping rope is swung.

[0014] In some embodiments, the rope skipping motion characteristics include the periodic characteristics, displacement amplitude characteristics and angular velocity change characteristics of normal rope skipping movements; the feature extraction of three-dimensional motion data to obtain rope skipping motion characteristics includes: preprocessing the original three-dimensional motion data, removing high-frequency noise and abnormal mutation data through a sliding window filtering algorithm, and generating a smooth and continuous motion data sequence; performing time domain feature analysis on the preprocessed sequence, extracting the time period of a single rope skipping movement, the spatial amplitude extreme value of the handle displacement and the angular velocity change peak, performing frequency domain feature analysis, obtaining the main frequency component and secondary frequency component of the rope skipping movement through Fourier transform, and constructing the rope skipping motion characteristics by integrating time domain and frequency domain features.

[0015] In some embodiments, the identifying of a target data segment corresponding to an effective rope skipping action in the three-dimensional motion data through the rope skipping motion characteristics includes: dynamically matching a three-dimensional motion data sequence collected in real time with a preset normal rope skipping action feature template, wherein the feature template includes a period threshold range, a displacement amplitude threshold range, and an angular velocity change rate threshold range pre-trained for different user body types; when the period feature of any segment of data in the three-dimensional motion data sequence falls within the corresponding threshold range, the displacement amplitude feature reaches the minimum displacement threshold for bottoming out and the angular velocity change feature conforms to the direction conversion law of the handle swinging, it is determined to be a target data segment corresponding to an effective rope skipping action.

[0016] In some embodiments, the time interval and displacement trajectory repeatability between two adjacent actions in the target data segment are obtained, including: time-series segmentation of continuous target data segments, extracting the starting time points of two adjacent valid actions to calculate the time interval, the normal rope skipping frequency range is dynamically adjusted according to the user's age and physical condition, and the normal rope skipping frequency includes an interval formed by a preset ratio of the average frequency based on the user's historical rope skipping data statistics; the displacement trajectory repeatability is measured by calculating the Euclidean distance similarity of the handle spatial displacement trajectory in two adjacent actions, and the preset threshold is pre-set according to the standard action model of rope skipping.

[0017] In some embodiments, before obtaining the basic information of the user, it also includes: cross-validating the skipping rope counting results in combination with the foot contact signals collected during the user's jumping process to correct abnormal skipping rope counting results, including: collecting the foot contact signals through a pressure sensor worn on the user's foot or a pressure sensing device integrated into the skipping rope pedal, and extracting the periodic characteristics and intensity characteristics of the contact signals; performing time synchronization comparison on the contact signal period and the skipping rope action period, and when the difference between the number of contact signals and the number of skipping rope counts in the same time window exceeds a preset number threshold, or the contact signal intensity does not reach the minimum pressure threshold for jumping and landing, it is determined to be an abnormal count and corrected.

[0018] In some embodiments, the basic information includes age, weight, height and preset exercise goals; the current exercise intensity index is calculated by combining the basic information and the real-time collected skipping frequency, continuous exercise time, cumulative number of skipping times and heart rate data, including: calculating the maximum heart rate theoretical value based on the user's age and height, and obtaining the heart rate reserve percentage in combination with the real-time heart rate data; calculating the frequency change coefficient based on the difference between the skipping frequency and the user's historical average frequency, and estimating the real-time energy consumption rate through a preset energy consumption model in combination with the continuous exercise time and cumulative number of skipping times; weighted fusion of the heart rate reserve percentage, frequency change coefficient and energy consumption rate to generate a dimensionless current exercise intensity index.

[0019] In some embodiments, the exercise goals include at least fat loss, endurance training and explosive power training, and the intensity grading standards under each exercise goal are divided according to the heart rate range, the skipping frequency change rate and the energy consumption rate; the current exercise intensity index is dynamically graded according to the preset grading rule library, and the grading rule library contains intensity grading standards corresponding to different exercise goals, including: for fat loss goals, the grading standards are divided into low intensity, medium intensity and high intensity based on the heart rate reserve percentage, among which the medium intensity corresponds to the 60%-75% range of the maximum heart rate and requires the skipping frequency to be stable at 80%-110% of the basic frequency; for endurance training goals, the grading standards are combined with the continuous exercise time and the frequency change coefficient, requiring that the high intensity level must keep the frequency change coefficient within ±5% and last for more than 40 minutes; for explosive power training goals, the grading standards are divided according to the ratio of the skipping frequency peak to the basic frequency, requiring the high intensity level to have a frequency surge of more than 30% of the basic frequency and maintain for at least 10 seconds.

[0020] In some embodiments, after the current exercise intensity index is dynamically graded according to a preset grading rule library, the method further includes: based on the current exercise intensity grading result, providing the user with real-time exercise intensity level feedback through an associated smart terminal, and adjusting the intensity recommendation for subsequent exercise according to a preset adaptive strategy, wherein the adaptive strategy includes rules for dynamically optimizing the grading threshold based on the user's historical exercise data and current physical fitness status.

[0021] In a second aspect, the present application provides a counting error correction and exercise intensity dynamic grading system for an intelligent rope skipping device, which is applied to an intelligent rope skipping device. The system includes:

[0022] A data acquisition unit, configured to acquire three-dimensional motion data during rope skipping, wherein the three-dimensional motion data includes at least spatial displacement information, rotation angle information, and motion acceleration information corresponding to the smart rope skipping;

[0023] a feature extraction unit, configured to extract features from the three-dimensional motion data to obtain rope skipping motion features; and identify target data segments corresponding to effective rope skipping movements in the three-dimensional motion data using the rope skipping motion features;

[0024] an abnormality correction unit, configured to obtain the time interval and displacement trajectory repeatability between two adjacent actions in the target data segment, and to determine an invalid count and eliminate it if the time interval exceeds the normal rope skipping frequency range or the displacement trajectory repeatability is lower than a preset threshold;

[0025] The dynamic grading unit is used to obtain the user's basic information, combine the basic information with the real-time collected skipping frequency, continuous exercise time, cumulative number of skipping times and heart rate data, and calculate the current exercise intensity index; dynamically grade the current exercise intensity index according to a preset grading rule library, which contains intensity grading standards corresponding to different exercise goals.

[0026] The embodiments of the present application provide a method and system for correcting counting errors and dynamically grading exercise intensity for an intelligent rope skipping. The prior art has not yet proposed a multi-source fusion counting correction method that combines three-dimensional motion data, rope tension signals, and foot contact signals, nor has it established a dynamic intensity grading system based on user basic information and multiple motion goals. Specifically, the existing solutions have the following technical gaps: Multi-dimensional verification of rope skipping movements is not achieved: the movement is only recognized by relying on the handle motion trajectory, and the counting results are not cross-validated by the foot contact signal, making it impossible to exclude abnormal scenarios such as "invalid swinging" or "missed jumps"; there is a lack of a dynamic grading rule library: differentiated intensity grading standards are not designed for different exercise goals such as fat loss, endurance, and explosive power, and in particular, a comprehensive evaluation model is not constructed by combining multiple indicators such as heart rate range, frequency change rate, and energy consumption rate; individual differences among users are not taken into account: the grading threshold is fixed and is not dynamically adjusted according to the user's age, physical condition, and historical exercise data, resulting in a lack of personalization in the evaluation results.

[0027] Therefore, there is an urgent need for an intelligent rope skipping data processing method that can improve counting accuracy through multi-source data fusion and realize dynamic intensity grading according to multiple motion goals, so as to solve the core problems of large counting errors and rough evaluation in the existing technology.

[0028] Based on the above technical solution, the present invention effectively eliminates invalid movements such as "empty swings" and "missed jumps" through multi-dimensional data fusion of three-axis acceleration, angular velocity, and rope tension sensors, combined with cross-validation of foot contact signals, and reduces the counting error rate; constructs a differentiated grading rule library for different sports goals (fat loss, endurance, explosive power), and dynamically adjusts the intensity level based on user basic information and real-time sports data (heart rate, frequency, energy consumption), so that sports guidance is more in line with personalized needs; through time domain-frequency domain feature analysis and dynamic matching of thresholds, effective motion recognition in complex sports scenes (such as high-frequency rope skipping, movement deformation) is achieved, and the adaptability of the equipment in different user groups is improved; real-time intensity feedback is provided based on dynamic grading results, and the grading threshold is optimized according to the user's physical condition, forming a closed loop of "data collection-evaluation-feedback", which significantly improves exercise efficiency and scientificity.

[0029] In summary, the present invention fills the technical gaps in the existing smart skipping ropes in the fields of counting correction and dynamic intensity grading, providing users with a more accurate and personalized exercise experience.

[0030] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0032] Figure 1 This is a schematic flow chart of the steps of a method for correcting counting errors and dynamically grading exercise intensity of an intelligent skipping rope provided in one embodiment of the present application;

[0033] Figure 2 This is a schematic structural diagram of a smart skipping rope provided in one embodiment of the present application;

[0034] Figure 3 This is a schematic block diagram of the structure of a counting error correction and exercise intensity dynamic grading system for an intelligent skipping rope provided in one embodiment of the present application;

[0035] Figure 4 This is a schematic block diagram of the structure of the smart skipping rope provided in one embodiment of the present application.

[0036] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0038] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0039] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish between identical or similar items having substantially the same functions and effects. Those skilled in the art will understand that terms such as "first" and "second" do not limit the quantity or order of execution, and that terms such as "first" and "second" do not necessarily define differences.

[0040] It should be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0041] It will also be understood that the term "and / or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0042] With the rise of national fitness awareness, smart skipping ropes are widely used as sports equipment that combines portability and data-based functions. The core functions of existing smart skipping ropes focus on skipping counts and basic sports data statistics, but there are significant deficiencies in counting accuracy and refined exercise intensity assessment:

[0043] The problem of counting errors is prominent: Traditional smart rope skipping mostly relies on a single acceleration sensor or angular velocity sensor to collect data, and only identifies the rope skipping action through the periodic characteristics of the handle motion trajectory. However, during the actual rope skipping process, users may generate interference data due to fatigue, deformation of movements, or non-rope skipping movements (such as adjusting the rope length or natural arm swings), resulting in miscounts or missed counts. For example, existing solutions do not combine the correlation between the rope skipping action and the user's jump landing (such as the foot contact signal), and cannot effectively distinguish between abnormal movements such as "empty swing without jumping" and "jumping without rope swinging", resulting in a high counting error rate (especially when skipping at high frequencies or with irregular movements, the error can reach 15%-20%).

[0044] Extensive exercise intensity assessment lacks personalized dynamic grading: Existing technologies often use a single metric (such as heart rate or rope skipping frequency) to determine exercise intensity, with fixed grading standards. They fail to dynamically adjust based on individual user differences (age, weight, and fitness) and diverse exercise goals (such as fat loss, endurance training, and explosive power training). For example, for users seeking fat loss, existing solutions simply divide heart rate into zones, ignoring the impact of rope skipping frequency stability on energy expenditure. For explosive power training, there are no quantitative standards for the magnitude and duration of frequency spikes, resulting in users being unable to obtain accurate exercise guidance and making it difficult to guarantee effective training.

[0045] Insufficient multi-source data fusion: Existing smart jump ropes have a single sensor configuration (typically only a built-in accelerometer in the handle), failing to fully utilize multi-dimensional data from rope tension sensors and foot pressure sensors for cross-validation. For example, changes in rope tension directly reflect the force of the rope swing and its motion state, while foot contact signals accurately represent the occurrence of the jump. However, existing solutions lack the fusion of these data, resulting in poor robustness of the counting logic and strength assessment models.

[0046] To address these issues, existing technologies have yet to propose a multi-source fusion counting correction method that combines three-dimensional motion data, rope tension signals, and foot contact signals, nor has a dynamic intensity grading system based on user basic information and multiple motion goals been established. Specifically, existing solutions have the following technical gaps: They fail to implement multi-dimensional verification of rope skipping movements: they rely solely on handle motion trajectory recognition, fail to cross-validate counting results with foot contact signals, and are unable to rule out abnormal scenarios such as "ineffective swings" or "missed jumps";

[0047] Lack of a dynamic grading rule library: There are no intensity grading standards designed for different sports goals such as fat loss, endurance, and explosive power. In particular, a comprehensive evaluation model is not constructed by combining multiple indicators such as heart rate range, frequency change rate, and energy consumption rate; individual differences of users are not taken into account: the grading threshold is fixed and is not dynamically adjusted according to the user's age, physical condition, and historical exercise data, resulting in a lack of personalization in the evaluation results.

[0048] Therefore, there is an urgent need for an intelligent rope skipping data processing method that can improve counting accuracy through multi-source data fusion and realize dynamic intensity grading according to multiple motion goals, so as to solve the core problems of large counting errors and rough evaluation in the existing technology.

[0049] To resolve the above, please refer to Figure 1 The embodiment of the present application provides a counting error correction and exercise intensity dynamic classification method for an intelligent skipping rope, which is applied to Figure 2 At the same time, it should be noted that each information involved in the method provided in this application is extracted with the authorization of the relevant user and in compliance with relevant regulations, and will not infringe on the user's privacy.

[0050] The provided method for correcting counting errors and dynamically grading exercise intensity of an intelligent skipping rope includes steps S101 to S104. Detailed description is as follows:

[0051] Step S101. Acquire three-dimensional motion data during the rope skipping process, wherein the three-dimensional motion data at least includes spatial displacement information, rotation angle information, and motion acceleration information corresponding to the smart rope skipping.

[0052] Specifically, the multi-sensor components built into the smart skipping rope collect multi-dimensional physical signals in the skipping motion in real time, and construct a comprehensive data set including spatial displacement, rotation angle, acceleration and rope tension, providing the original data basis for subsequent action recognition and counting correction.

[0053] Sensor hardware configuration: Handle sensor: The two handles of the smart skipping rope are integrated with a three-axis acceleration sensor (collecting X / Y / Z axis translation acceleration) and a three-axis angular velocity sensor (gyroscope) (collecting rotational angular velocity around the X / Y / Z axis) to obtain real-time motion acceleration data of the handle in three-dimensional space (unit: m / s 2 ) and rotational angular velocity data (unit: rad / s). Rope sensor: A tension sensor is installed in the middle section or at the connection of the skipping rope to monitor the change in rope tension (unit: N) during the rope swing, reflecting the swinging force and rope tension. Auxiliary sensor: The user wears a pressure sensor on the foot or uses a skipping rope pedal with an integrated pressure sensor to collect the foot contact signal when jumping and landing (used in conjunction with the cross-validation in step S103).

[0054] The data acquisition frequency synchronously collects data from each sensor at a sampling frequency of no less than 100 Hz to ensure that motion details (such as high-frequency swings and instantaneous acceleration mutations) are fully captured.

[0055] By combining handle motion data (displacement, angle, acceleration) with rope tension signals, a multidimensional feature vector of the rope skipping action is constructed. This avoids the incompleteness of data from a single sensor (such as an accelerometer alone) due to motion distortion or environmental interference, laying the foundation for subsequent accurate recognition of effective movements. High-frequency acquisition and multi-dimensional signal coverage effectively capture motion attenuation (such as tension drop and angular velocity fluctuations) during fatigue, as well as subtle trajectory changes during high-frequency rope skipping, improving the system's robustness to complex motion scenarios.

[0056] Step S102: extracting features from the three-dimensional motion data to obtain rope skipping motion features; identifying target data segments corresponding to effective rope skipping actions in the three-dimensional motion data using the rope skipping motion features.

[0057] Specifically, by performing noise reduction and feature extraction on the original three-dimensional motion data, a rope skipping motion feature model containing time domain and frequency domain features is constructed. By dynamically matching the preset action feature templates, the target data segments corresponding to the effective rope skipping actions are segmented from the continuous data.

[0058] Data preprocessing uses a sliding window filtering algorithm (such as mean filtering and Kalman filtering) to reduce the noise of the original data, eliminate high-frequency noise (such as hand shaking interference) and abnormal mutation data (such as the impact noise when the skipping rope accidentally touches the ground), and generate a smooth motion data sequence (including acceleration, angular velocity, and tension signals).

[0059] Feature extraction includes: Time domain features: Calculating the time period of a single rope skipping action (the time interval between two consecutive handle bottoming out and swinging), the spatial amplitude extremes of the handle displacement (the maximum displacement difference along the X / Y / Z axes), the peak angular velocity change (the sudden change in angular velocity at the start / end of the swing), and the peak rope tension (the maximum tension during the rope swing). Frequency domain features: Using Fourier transform to perform spectral analysis on the preprocessed signal, we extract the main frequency component (corresponding to the normal rope skipping frequency, such as 1-3Hz) and secondary frequency components (such as the harmonic frequency of arm swinging) of the rope skipping motion to distinguish regular rope skipping movements from random interference.

[0060] Target data segment identification: Preset dynamic feature templates: Based on user body type (height, arm span) and historical exercise data, pre-trained period threshold ranges (e.g., 0.4-0.6s for children's rope skipping period, 0.2-0.4s for adults), displacement amplitude thresholds (e.g., displacement difference corresponding to the lowest handle swing height ≥ 20cm), and angular velocity change rate thresholds (angular velocity change rate during direction change ≥ 50rad / s²). Target data segments are identified as valid movements when the period characteristics of consecutive data segments fall within the corresponding threshold ranges, the displacement amplitude reaches the minimum displacement threshold for bottoming out and jumping (to avoid ineffective swings caused by small arm swings), and the angular velocity change characteristics conform to the direction change pattern of the handle swing (e.g., alternating clockwise and counterclockwise).

[0061] By combining time-frequency domain features with dynamic threshold matching, the system effectively distinguishes "valid rope skipping" from invalid movements such as "rope length adjustment" and "natural arm swinging," avoiding missegmentation caused by traditional single-cycle detection (e.g., high-frequency, small swings being misidentified as valid movements). Dynamically adjusting feature templates based on user body shape and historical data addresses the individual differences caused by the existing "one-size-fits-all" threshold adaptation problem (e.g., misidentification when the movement amplitudes differ significantly between children and adults), improving recognition accuracy across different user groups.

[0062] Step S103. Obtain the time interval and displacement trajectory repeatability between two adjacent actions in the target data segment. When the time interval exceeds the normal rope skipping frequency range or the displacement trajectory repeatability is lower than a preset threshold, it is determined to be an invalid count and eliminated.

[0063] Specifically, in the identified target data segment, valid counts are screened through the action time interval and displacement trajectory repeatability, and double verification is performed in combination with the foot contact signal to eliminate erroneous counts caused by abnormal actions such as "swinging without jumping" and "jumping without swinging the rope".

[0064] Temporal logic screening includes: Time interval detection: Continuous target data segments are segmented by the start time of the action, and the time interval Δt between two adjacent actions is calculated. The normal rope skipping frequency range is dynamically adjusted: The base frequency is determined based on the user's age (e.g., high frequency for adolescents, low frequency for middle-aged and elderly people) and physical condition (historical average frequency), with a fluctuation of 10%-20% allowed to form a dynamic range (e.g., a historical average frequency of 180 beats / minute allows for a range of 162-198 beats / minute). If Δt exceeds this range, it is determined to be an abnormal action (e.g., "empty swings" with continuous rapid swings without jumping, or missed jumps with excessively long intervals), and the corresponding count is removed. Displacement trajectory repeatability is determined by calculating the Euclidean distance similarity (the average distance between the trajectory coordinate points) of the spatial displacement trajectories of the handle in two adjacent actions. A preset threshold (e.g., similarity <70%) is exceeded, and the action is determined to be deformed (e.g., a unilateral arm swing causing an abnormal trajectory), and the count is removed.

[0065] Cross-verification of foot contact signals: The foot pressure sensor or the rope skipping pedal sensing device is used to collect the contact signals, and the signal period (jump landing frequency) and intensity (pressure value ≥ 30N to avoid misjudgment of slight contact).

[0066] Time synchronization comparison is carried out within the same time window (such as 1 second). If the difference between the number of ground contact signals and the number of rope skipping counts is greater than 1, or there is no ground contact signal at the corresponding moment of a certain count and the pressure value is less than the minimum threshold, it is judged as an abnormal count (such as "swinging the rope without jumping" or "jumping without swinging the rope"), and the counting result is corrected (deducted or supplemented).

[0067] Through triple verification of time intervals, trajectory repeatability, and ground contact signals, the system addresses the issues of "empty swings" and "missed jumps" caused by traditional solutions that rely solely on handle data. Verification of ground contact signals correlates with the rope skipping motion, ensuring that the count results only reflect the complete "swing and jump" action, avoiding interference from invalid movements and improving data reliability (for example, preventing swings caused by users adjusting rope length from being miscounted).

[0068] Step S104. Obtain the user's basic information, and calculate the current exercise intensity index by combining the basic information with the real-time collected skipping frequency, continuous exercise time, cumulative number of skipping times and heart rate data; dynamically classify the current exercise intensity index according to a preset classification rule library, wherein the classification rule library contains intensity classification standards corresponding to different exercise goals.

[0069] Specifically, combining user basic information and real-time exercise data, a multi-indicator evaluation model including heart rate, skipping frequency, and energy consumption is constructed. According to different exercise goals such as fat loss, endurance, and explosive power, the current exercise intensity level is dynamically divided and personalized guidance is provided.

[0070] Exercise intensity metrics include: Heart Rate Reserve Percentage (HRR%): Calculates maximum heart rate based on user age (theoretical value = 220 minus age). The difference between real-time heart rate and resting heart rate is expressed as a percentage of the difference between maximum heart rate and resting heart rate, reflecting cardiorespiratory load intensity. Frequency Variation Coefficient (Δf%): The percentage difference between the current skipping frequency and the user's historical average frequency, reflecting fluctuations in exercise intensity (e.g., Δf% > +30% indicates high-frequency bursts). Energy Expenditure Rate (kcal / min): Estimates real-time energy expenditure using a preset model (combining weight, height, duration, and cumulative number of jumps): Energy Expenditure = 0.0175 × weight (kg) × skipping frequency (times / minute) × duration (min). Weighted fusion normalizes HRR%, Δf%, and energy expenditure rate according to preset weights (e.g., 0.5:0.3:0.2) to generate a dimensionless intensity metric (scaled from 0 to 100).

[0071] The dynamic grading rule base includes the following: Fat Loss Target: Low Intensity: HRR% <60%, frequency stabilized at 80%-110% of base frequency (focused on sustained, low-load fat burning); Medium Intensity (Optimal Range): HRR% 60%-75%, frequency stabilized at 80%-110% of base frequency (maximum fat burning efficiency); High Intensity: HRR% >75%, frequency fluctuation allowed ±15% (suitable for advanced fat loss). Endurance Training: High Intensity Level Requirements: Frequency Variation Coefficient ≤ ±5% (high movement stability), Duration >40 minutes (challenging cardiorespiratory endurance). Explosive Power Training: High Intensity Level Requirements: Frequency Sudden Increase >30% of Base Frequency, Maintained for 10 Seconds or More (stimulating rapid muscle contraction).

[0072] Real-time feedback and adaptive adjustment display the current intensity level in real time through the smart terminal (APP / watch), and dynamically optimize the classification threshold based on the user's historical exercise data (such as increasing the duration requirement of endurance training after long-term training).

[0073] Taking into account individual differences such as age and weight, as well as diverse exercise goals, this approach breaks away from the traditional, crude grading based on a single metric (such as heart rate alone). This allows users seeking to lose weight to receive guidance on optimal fat-burning zones, endurance users to improve movement stability, and explosive power users to clearly define sprint intensity and duration. Dynamically adjusting grading thresholds accommodates changes in fitness levels (e.g., increasing intensity from novice to advanced users), avoiding the "overtraining" or "undertraining" that stems from fixed standards, and improving the scientific nature and efficiency of exercise.

[0074] Steps S101-S104 use a closed-loop process of "multi-source data collection → precise feature extraction → multi-dimensional counting verification → personalized intensity grading". The present invention systematically solves the core problems of large counting errors and rough intensity assessment of existing smart skipping ropes, realizes the functional upgrade from "data collection tool" to "smart sports coach", and significantly improves the user's sports experience and training effect.

[0075] In some embodiments, the obtaining of three-dimensional motion data during rope skipping includes: real-time data collection through a motion sensor assembly provided on the smart skipping rope, the motion sensor assembly including at least a three-axis acceleration sensor, a three-axis angular velocity sensor, and a gyroscope installed on the corresponding handle of the smart skipping rope, for obtaining translational acceleration data, rotational angular velocity data, and spatial attitude angle data of the smart skipping rope in three-dimensional space, the motion sensor assembly also including a tension sensor provided on the corresponding rope body of the smart skipping rope, for assisting in collecting rope tension change information when the smart skipping rope is swung.

[0076] By clarifying the hardware sensor configuration of the smart skipping rope and collaboratively collecting three-dimensional motion data through multiple types of sensors, a multi-dimensional dataset containing translational acceleration, rotational angular velocity, spatial posture and rope tension is constructed to provide original signal input for subsequent motion analysis.

[0077] Sensor hardware deployment: Handle sensor group: Integrate a three-axis acceleration sensor (such as ADXL345) and a three-axis angular velocity sensor (gyroscope, such as MPU6050) inside the left and right handles to collect the translation acceleration data of the handle on the X / Y / Z axis (unit: m / s 2 ) and angular velocity data about the X / Y / Z axes (unit: rad / s), while spatial attitude angles (pitch, roll, and yaw) are calculated through gyroscope fusion. Rope tension sensor: A miniature tension sensor (such as a resistive strain gauge sensor) is embedded at the connection between the rope and the handle or in the middle of the rope to monitor the change in rope tension (unit: N) in real time as the rope is swung. The tension increases significantly when the rope is taut and decreases when it is relaxed.

[0078] The data acquisition mechanism includes connecting each sensor to the main control chip (such as STM32) through the I2C or SPI bus, collecting data at a synchronous frequency of ≥100Hz to ensure that the details of high-frequency actions (such as fast rope swinging) are not lost.

[0079] Unlike traditional single-acceleration sensor solutions, the new gyroscope captures rotational angular velocity and attitude angle, combined with a tension sensor to capture rope tension. This creates a three-dimensional data input combining "handle motion trajectory + rope mechanical signals," addressing single-sensor blind spots in scenarios such as "empty swing without a jump" and "insufficient rope swing force." Clarifying sensor type, installation location, and communication protocol provides a reusable hardware solution for mass production, reducing subsequent R&D costs.

[0080] In some embodiments, the rope skipping motion characteristics include the periodic characteristics, displacement amplitude characteristics and angular velocity change characteristics of normal rope skipping movements; the feature extraction of three-dimensional motion data to obtain rope skipping motion characteristics includes: preprocessing the original three-dimensional motion data, removing high-frequency noise and abnormal mutation data through a sliding window filtering algorithm, and generating a smooth and continuous motion data sequence; performing time domain feature analysis on the preprocessed sequence, extracting the time period of a single rope skipping movement, the spatial amplitude extreme value of the handle displacement and the angular velocity change peak, performing frequency domain feature analysis, obtaining the main frequency component and secondary frequency component of the rope skipping movement through Fourier transform, and constructing the rope skipping motion characteristics by integrating time domain and frequency domain features.

[0081] By performing noise reduction and feature extraction on the original sensor data and combining time domain and frequency domain analysis, the core features of the rope skipping action (period, amplitude, and frequency) are separated from the noise signal, and a feature vector that can be used for action recognition is constructed.

[0082] Data preprocessing uses sliding window mean filtering (window size 50ms) to reduce noise in acceleration, angular velocity, and tension signals, and eliminate high-frequency vibration noise (such as slight hand shaking); median filtering is used to correct sudden changes in data (such as the impact peak when jumping rope touches the ground) to generate a smooth continuous data sequence.

[0083] Time domain feature extraction: Periodic feature: Calculate the time interval between the lowest points of two adjacent handle swings to reflect the frequency of rope skipping; Displacement amplitude: Extract the extreme displacement difference (highest point - lowest point) of the handle on the Z axis (vertical direction) in a single action to characterize the swing amplitude; Angular velocity peak: Capture the maximum value of the angular velocity mutation when the rope swing direction changes (such as from clockwise to counterclockwise) to reflect the strength of the swing.

[0084] Frequency domain feature extraction is performed by performing fast Fourier transform (FFT) on the preprocessed signal, calculating the energy distribution in the 0.5-5Hz frequency band, and extracting the main frequency (corresponding to the main frequency of rope skipping) and secondary frequency (such as the second harmonic of arm swinging) with the highest energy proportion, which is used to distinguish regular rope skipping movements from random movements.

[0085] A filtering algorithm is used to eliminate environmental interference and motion deformation noise, ensuring the stability of feature extraction and avoiding period misjudgment caused by slight hand shaking; combining time domain (motion details) and frequency domain (frequency characteristics) features to form a multi-dimensional motion descriptor, providing rich judgment basis for the subsequent accurate segmentation of target data segments.

[0086] In some embodiments, the identifying of a target data segment corresponding to an effective rope skipping action in the three-dimensional motion data through the rope skipping motion characteristics includes: dynamically matching a three-dimensional motion data sequence collected in real time with a preset normal rope skipping action feature template, wherein the feature template includes a period threshold range, a displacement amplitude threshold range, and an angular velocity change rate threshold range pre-trained for different user body types; when the period feature of any segment of data in the three-dimensional motion data sequence falls within the corresponding threshold range, the displacement amplitude feature reaches the minimum displacement threshold for bottoming out and the angular velocity change feature conforms to the direction conversion law of the handle swinging, it is determined to be a target data segment corresponding to an effective rope skipping action.

[0087] Based on the user's personalized motion feature template, the real-time collected motion data is dynamically matched to identify data segments that meet the characteristics of "effective rope skipping movements" and eliminate invalid swings (such as adjusting the rope length) or irregular movements (such as unilateral arm swings).

[0088] Feature template pre-training collects standard rope skipping data from users of different body types (grouped by height and arm span). The training determines the period threshold range for each group (e.g., 0.4-0.6s / time for children and 0.2-0.4s / time for adults), displacement amplitude threshold (e.g., vertical displacement of the handle ≥ 20cm to avoid misjudgment of small swings), and angular velocity change rate threshold (angular velocity change rate ≥ 50rad / s² during direction change to ensure effective switching of the swing direction).

[0089] Real-time matching is determined by segmenting the real-time data sequence into 500ms sliding windows. Each window is then checked to determine whether the dominant frequency of the signal within the window falls within a preset frequency range (e.g., 1.5-5Hz, corresponding to 90-300 beats / minute); whether the displacement amplitude is ≥ the minimum threshold (a necessary condition for bottoming out); and whether the angular velocity changes in direction (cyclically changing from clockwise to counterclockwise to clockwise). If all of these conditions are met, the data segment is considered a valid action target.

[0090] By using pre-trained templates for body type grouping, we can solve the problem of misjudgment of common thresholds caused by differences in movement amplitude and frequency between adults and children, as well as professional and ordinary users. For example, we can prevent children from being missed due to insufficient arm strength and small swinging amplitude. By combining the direction conversion rules (alternating changes in angular velocity), we can exclude "continuous swinging in a single direction" (such as invalid movements when adjusting the rope length), ensuring that only complete rope skipping cycles are recognized.

[0091] In some embodiments, the time interval and displacement trajectory repeatability between two adjacent actions in the target data segment are obtained, including: time-series segmentation of continuous target data segments, extracting the starting time points of two adjacent valid actions to calculate the time interval, the normal rope skipping frequency range is dynamically adjusted according to the user's age and physical condition, and the normal rope skipping frequency includes an interval formed by a preset ratio of the average frequency based on the user's historical rope skipping data statistics; the displacement trajectory repeatability is measured by calculating the Euclidean distance similarity of the handle spatial displacement trajectory in two adjacent actions, and the preset threshold is pre-set according to the standard action model of rope skipping.

[0092] In the valid action data segment, abnormal counts are further screened through time intervals and trajectory similarity to eliminate action deformations caused by fatigue (such as frequency mutations, trajectory confusion) or non-rope skipping actions (such as temporary pauses and adjustments).

[0093] Dynamic calculation of time intervals: Determination of basic frequency: Based on the statistical average frequency of the user's historical rope skipping data (such as the average frequency f_avg of the last 10 exercises), a fluctuation of 15% is allowed to form a dynamic range (f_avg×0.85 to f_avg×1.15) to avoid misjudgment of user status changes (such as a decrease in frequency after fatigue) by a fixed frequency threshold; Anomaly detection: If the time interval Δt between adjacent actions exceeds the dynamic range, it is judged as an anomaly (for example, a Δt that is too small corresponds to "high-frequency empty swing without jumping", and a Δt that is too large corresponds to "missed jumping").

[0094] Displacement trajectory repeatability calculation: The handle displacement trajectory of a single action is represented as a three-dimensional coordinate point sequence (x1, y1, z1)…(xn, yn, zn). The average distance between two adjacent action trajectory points is calculated using the Euclidean distance formula:

[0095] ;

[0096] Where (xi,yi,zi) is the three-dimensional coordinates of the handle at the i-th sampling point during the first valid rope skipping motion (unit: meters or centimeters, dimensionless). The handle's built-in three-axis accelerometer and gyroscope, combined with spatial attitude calculation (such as the complementary filter algorithm), determine its real-time position in the world coordinate system. (The coordinate system origin must be determined through initial calibration, typically based on the handle's initial position when the user is standing naturally.)

[0097] (xi′,yi′,zi′) is the three-dimensional coordinate of the handle of the i-th sampling point in the second adjacent valid rope skipping action (strictly aligned with the timing of the sampling points of the first action).

[0098] Key requirement: The sampling points for the two actions must be time-synchronized (e.g., sampling at the same interval based on the action start time, ensuring that i corresponds to the same action phase, such as the lowest and highest points of the rope swing). The controller's built-in three-axis accelerometer and gyroscope, combined with spatial attitude calculation (such as the complementary filter algorithm), determine the controller's real-time position in the world coordinate system (the coordinate system origin must be determined through initial calibration, typically based on the controller's initial position when the user is standing naturally).

[0099] n is the total number of sampling points for a single rope skipping action (i.e., the number of sampling points within a complete action cycle). This is determined by the sensor sampling frequency and the action cycle. For example, if the sampling frequency is 100 Hz and the single action cycle is 0.4 seconds, then n = 100 × 0.4 = 40 points. This value must be dynamically adjusted through action cycle detection (for example, if the period varies with different rope skipping frequencies, ensuring that n always covers a complete action cycle).

[0100] It is the three-dimensional Euclidean distance between the i-th corresponding sampling points in two adjacent actions, which measures the deviation of the handle position at that moment.

[0101] The maximum possible distance is the theoretical maximum single displacement of the handle in three-dimensional space (used to normalize the distance and map the similarity results to the [0, 1] range). By swinging the rope with the user's maximum amplitude, the maximum displacement range of the handle on the X, Y, and Z axes is recorded, and the diagonal distance is calculated as a benchmark (for example, if the handle moves a maximum of ±0.3m on the X axis, ±0.2m on the Y axis, and ±0.5m on the Z axis, the maximum possible distance is:

[0102] );

[0103] Empirical value method: Based on ergonomics, the maximum displacement range of the handle when an adult normally skips rope is preset to 1.0m (which can be determined by the statistical mean of a large amount of user data).

[0104] A preset similarity threshold (e.g., 70%) is used. Trajectory anomalies are identified when the threshold is lower than the threshold (e.g., trajectory deviation caused by unilateral arm swinging). Dynamic frequency ranges are adjusted based on user historical data to address the adaptation issues of traditional fixed frequency thresholds (e.g., the default 120-200 bpm) to individual differences and state changes. For example, this allows users with better physical fitness to exceed the default high-frequency limit during high-intensity training. Geometric similarity is used to quantify movement standardization, eliminating invalid movements such as unilateral force exertion and trajectory confusion caused by fatigue, further improving counting accuracy (especially in scenarios where movement deformation occurs after prolonged exercise).

[0105] In some embodiments, before obtaining the basic information of the user, it also includes: cross-validating the skipping rope counting results in combination with the foot contact signals collected during the user's jumping process to correct abnormal skipping rope counting results, including: collecting the foot contact signals through a pressure sensor worn on the user's foot or a pressure sensing device integrated into the skipping rope pedal, and extracting the periodic characteristics and intensity characteristics of the contact signals; performing time synchronization comparison on the contact signal period and the skipping rope action period, and when the difference between the number of contact signals and the number of skipping rope counts in the same time window exceeds a preset number threshold, or the contact signal intensity does not reach the minimum pressure threshold for jumping and landing, it is determined to be an abnormal count and corrected.

[0106] By introducing the foot contact signal as an external verification source and through the time synchronization comparison of "handle action data + foot pressure data", counting errors caused by sensor data conflicts such as "swinging the rope without jumping" and "jumping without swinging the rope" are corrected.

[0107] Touchdown signal collection: Wearable solution: The user wears sports shoes or anklets with integrated pressure sensors to collect pressure signals when the sole of the foot touches the ground (the threshold is set to ≥30N to exclude minor touchdowns such as tiptoeing); Fixed solution: The skipping rope pedal has a built-in pressure sensing module to detect the impact force when jumping and landing.

[0108] The cross-validation logic is aligned through time windows: using a 1-second window, the number of rope skipping counts (N1) and the number of ground contact signals (N2) within the window are counted; the abnormality judgment rule is: if |N1-N2|>1, it is determined that there is "overcounting" or "omission" (for example, N1=2, N2=0 corresponds to "two empty swings"); if there is no ground contact signal within 50ms before and after a certain counting moment and the pressure value is less than 20N, it is determined to be "swinging without jumping", and the count is deducted; if there is no rope skipping count within 50ms before and after a certain ground contact signal, it is determined to be "jumping without swinging the rope", and an additional count is made (it is necessary to combine with other sensors to confirm whether it is a valid action).

[0109] Breaking the traditional closed loop that relies solely on handle data, the system uses the direct evidence of a "jumping action" – the foot touching the ground – to form a two-way verification of "rope-swinging action (handle) - jumping action (foot)", completely resolving the counting errors caused by "incomplete actions" (e.g., in the existing solution, a user only swinging the handle once but not jumping is counted as one). It effectively identifies complex scenarios such as "single-leg hopping" and "unstable landing", eliminates false touch interference through a pressure signal strength threshold (e.g., ≥30N), and improves the reliability of the system in real sports environments.

[0110] In some embodiments, the basic information includes age, weight, height and preset exercise goals; the current exercise intensity index is calculated by combining the basic information and the real-time collected skipping frequency, continuous exercise time, cumulative number of skipping times and heart rate data, including: calculating the maximum heart rate theoretical value based on the user's age and height, and obtaining the heart rate reserve percentage in combination with the real-time heart rate data; calculating the frequency change coefficient based on the difference between the skipping frequency and the user's historical average frequency, and estimating the real-time energy consumption rate through a preset energy consumption model in combination with the continuous exercise time and cumulative number of skipping times; weighted fusion of the heart rate reserve percentage, frequency change coefficient and energy consumption rate to generate a dimensionless current exercise intensity index.

[0111] By constructing a multi-dimensional exercise intensity assessment model that includes heart rate, skipping frequency, and energy consumption, and generating quantitative indicators through weighted fusion, the problem of rough assessment using traditional single indicators (such as heart rate only) is solved.

[0112] Heart rate reserve calculation: Theoretical maximum heart rate = 220 - user age. Resting heart rate is the user's average morning heart rate (requires initial setting by the user). Heart rate reserve percentage (HRR%) calculation formula: HRR% = 100% * (real-time heart rate - resting heart rate) / (maximum heart rate - resting heart rate). Frequency variation coefficient: Calculate the percentage difference Δf% between the current skipping frequency f and the user's historical average frequency f_avg:

[0113] ;

[0114] Energy expenditure was estimated using an empirical formula combining body weight (kg), duration (min), and cumulative number of times (N): energy expenditure rate (kcal / min) = (0.0175*body weight*N) / duration;

[0115] Weighted fusion normalizes HRR% (weight 0.5), Δf% (weight 0.3), and energy consumption rate (weight 0.2) (mapped to 0-100), and generates an intensity index through linear weighting: intensity index = 0.5×HRR%_norm+0.3×Δf%_norm+0.2×energy consumption_norm.

[0116] Heart rate reflects cardiopulmonary load, frequency changes reflect fluctuations in movement intensity, and energy consumption is directly related to training results. The combination of these three avoids the one-sidedness of a single indicator (for example, low-intensity states can still be identified when the heart rate is normal but the frequency drops sharply). Parameters (such as resting heart rate and historical average frequency) are dynamically adjusted based on user age, weight, and historical data to address the problem of traditional fixed formulas (such as estimating energy consumption based solely on weight) that ignore individual differences. For example, the difference in energy consumption between obese users and lean users can be accurately quantified.

[0117] In some embodiments, the exercise goals include at least fat loss, endurance training and explosive power training, and the intensity grading standards under each exercise goal are divided according to the heart rate range, the skipping frequency change rate and the energy consumption rate; the current exercise intensity index is dynamically graded according to the preset grading rule library, and the grading rule library contains intensity grading standards corresponding to different exercise goals, including: for fat loss goals, the grading standards are divided into low intensity, medium intensity and high intensity based on the heart rate reserve percentage, among which the medium intensity corresponds to the 60%-75% range of the maximum heart rate and requires the skipping frequency to be stable at 80%-110% of the basic frequency; for endurance training goals, the grading standards are combined with the continuous exercise time and the frequency change coefficient, requiring that the high intensity level must keep the frequency change coefficient within ±5% and last for more than 40 minutes; for explosive power training goals, the grading standards are divided according to the ratio of the skipping frequency peak to the basic frequency, requiring the high intensity level to have a frequency surge of more than 30% of the basic frequency and maintain for at least 10 seconds.

[0118] Aiming at the three core sports goals of fat loss, endurance and explosive power, differentiated intensity grading standards are designed to form a dynamic rule library including heart rate range, frequency stability and sudden increase amplitude to provide precise training guidance.

[0119] Fat loss target classification: Low intensity: HRR% < 60%, and frequency is f_avg×80%-110% (focusing on long-term low-load fat burning to avoid sugar metabolism caused by high intensity); Medium intensity: HRR% 60%-75% (optimal range for fat metabolism), and stable frequency (fluctuation ≤ ± 10%) to ensure maximum energy consumption efficiency; High intensity: HRR% > 75%, frequency fluctuation of ± 15% is allowed (suitable for advanced users to improve metabolic rate).

[0120] Endurance training classification: Basic intensity: duration > 20 minutes, frequency fluctuation ≤ ± 15%; High intensity: duration > 40 minutes, and frequency fluctuation ≤ ± 5% (emphasizing movement stability and cardiopulmonary endurance).

[0121] Explosive power training levels: Basic intensity: frequency sudden increase > 20%, maintained for ≥ 5 seconds; High intensity: frequency sudden increase > 30% (more than f_avg × 1.3 times), and maintained for ≥ 10 seconds (stimulates fast muscle fiber contraction and increases instantaneous power).

[0122] Different grading standards directly correspond to the principles of exercise physiology (such as the optimal HRR range for fat loss and the stability requirements for endurance training), preventing users from training blindly. For example, the dual conditions of "sudden increase in intensity + duration" in explosive power training ensure effective muscle stimulation; abstract sports goals (such as "increasing explosive power") are converted into measurable parameter combinations (a sudden increase in frequency of 30% + maintenance for 10 seconds), making the training effect traceable and evaluable, and solving the problem of "ambiguous intensity grading" in existing solutions.

[0123] In some embodiments, after the current exercise intensity index is dynamically graded according to a preset grading rule library, the method further includes: based on the current exercise intensity grading result, providing the user with real-time exercise intensity level feedback through an associated smart terminal, and adjusting the intensity recommendation for subsequent exercise according to a preset adaptive strategy, wherein the adaptive strategy includes rules for dynamically optimizing the grading threshold based on the user's historical exercise data and current physical fitness status.

[0124] By adding real-time feedback and adaptive mechanisms after intensity grading, the current intensity level is transmitted to users through smart terminals, and the grading threshold is dynamically optimized based on historical data, forming a closed loop of "evaluation-feedback-adjustment".

[0125] The real-time feedback mechanism displays the current intensity level (such as "medium intensity for fat loss" and "high intensity for explosive power") in real time through the LED screen on the rope skipping handle, the accompanying APP or a smartwatch, and prompts whether the standard has been met (such as "stable frequency, maintain the current intensity" and "low heart rate, it is recommended to speed up the rope swing").

[0126] Adaptive strategy: Dynamic adjustment of thresholds: After completing 10 exercises, the graded thresholds are updated based on the user's average intensity data. For example, for users who have been in "high-intensity endurance" for a long time, the duration requirement will be gradually increased from 40 minutes to 50 minutes; Physical status perception: If the user's heart rate reserve percentage drops significantly after three consecutive exercises (such as a drop of more than 10%), it is determined that the physical fitness has improved, and the intensity thresholds of each goal will be automatically raised (such as the lower limit of HRR for medium-intensity fat loss is increased from 60% to 65%).

[0127] Real-time visual feedback helps users intuitively understand their training status and enhance their enthusiasm for exercise; through self-learning from historical data, it solves the problem that traditional fixed thresholds cannot adapt to the user growth path of "novice → advanced → professional", for example, it avoids the stagnation of training effects caused by unchanged standards after long-term training, and realizes personalized adaptation of "intensity grading standards dynamically evolving with user abilities".

[0128] See also Figure 3 As shown, Figure 3 2 is a schematic diagram of the structure of the counting error correction and dynamic exercise intensity grading system 200 for a smart rope skipping provided in an embodiment of the present application. The counting error correction and dynamic exercise intensity grading system 200 for a smart rope skipping is used to execute the steps of the counting error correction and dynamic exercise intensity grading method for a smart rope skipping shown in each of the above embodiments. The counting error correction and dynamic exercise intensity grading system 200 for a smart rope skipping can be a single server or a server cluster, or the counting error correction and dynamic exercise intensity grading system 200 for a smart rope skipping can be a terminal, which can be a handheld terminal, a laptop computer, a wearable device, or a robot.

[0129] like Figure 3 As shown, the counting error correction and exercise intensity dynamic grading system 200 of the intelligent skipping rope includes:

[0130] The data acquisition unit 201 is used to acquire three-dimensional motion data during the rope skipping process, wherein the three-dimensional motion data at least includes spatial displacement information, rotation angle information, and motion acceleration information corresponding to the smart rope skipping;

[0131] The feature extraction unit 202 is configured to extract features from the three-dimensional motion data to obtain rope skipping motion features; and identify target data segments corresponding to effective rope skipping movements in the three-dimensional motion data using the rope skipping motion features.

[0132] Anomaly correction unit 203 is used to obtain the time interval and displacement trajectory repeatability between two adjacent actions in the target data segment, and when the time interval exceeds the normal rope skipping frequency range or the displacement trajectory repeatability is lower than a preset threshold, it is determined as an invalid count and eliminated;

[0133] The dynamic grading unit 204 is used to obtain the user's basic information, combine the basic information with the real-time collected rope skipping frequency, continuous exercise time, cumulative rope skipping times and heart rate data, and calculate the current exercise intensity index; dynamically grade the current exercise intensity index according to a preset grading rule library, which contains intensity grading standards corresponding to different exercise goals.

[0134] It should be noted that, those skilled in the art can clearly understand that, for the convenience and conciseness of description, the specific working processes of the above-described counting error correction and dynamic grading system of exercise intensity of the intelligent skipping rope and each module can refer to the corresponding contents in the above-mentioned embodiments of the counting error correction and dynamic grading method of exercise intensity of the intelligent skipping rope, and will not be repeated here.

[0135] The counting error correction and exercise intensity dynamic classification method of the intelligent rope skipping can be implemented in the form of a computer program. The computer program can be used in Figure 3 Run on the device shown.

[0136] See also Figure 4 , Figure 4 : This is a schematic block diagram of the structure of the smart skipping rope provided in an embodiment of the present application. The smart skipping rope includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and an internal memory.

[0137] The storage medium can store an operating device and a computer program. The computer program includes program instructions, which, when executed, can enable a processor to execute any one of the counting error correction and exercise intensity dynamic grading methods for intelligent rope skipping.

[0138] The processor is used to provide computing and control capabilities to support the operation of the entire smart skipping rope.

[0139] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any counting error correction and exercise intensity dynamic grading method of the intelligent skipping rope.

[0140] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 4The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the terminal to which the solution of the present application is applied. The specific smart skipping rope may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0141] It should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0142] In one embodiment, the processor is configured to execute a computer program stored in the memory to implement the following steps:

[0143] Acquire three-dimensional motion data during the rope skipping process, wherein the three-dimensional motion data includes at least spatial displacement information, rotation angle information, and motion acceleration information corresponding to the smart rope skipping;

[0144] Performing feature extraction on the three-dimensional motion data to obtain rope skipping motion features; identifying target data segments corresponding to effective rope skipping movements in the three-dimensional motion data using the rope skipping motion features;

[0145] Obtaining the time interval and displacement trajectory repeatability between two adjacent actions in the target data segment, and determining that an invalid count is made and discarding the count if the time interval exceeds the normal rope skipping frequency range or the displacement trajectory repeatability is lower than a preset threshold;

[0146] The user's basic information is obtained, and the current exercise intensity index is calculated by combining the basic information with the real-time collected skipping frequency, continuous exercise time, cumulative number of skipping times and heart rate data; the current exercise intensity index is dynamically graded according to a preset grading rule library, which contains intensity grading standards corresponding to different exercise goals.

[0147] In some embodiments, the obtaining of three-dimensional motion data during rope skipping includes: real-time data collection through a motion sensor assembly provided on the smart skipping rope, the motion sensor assembly including at least a three-axis acceleration sensor, a three-axis angular velocity sensor, and a gyroscope installed on the corresponding handle of the smart skipping rope, for obtaining translational acceleration data, rotational angular velocity data, and spatial attitude angle data of the smart skipping rope in three-dimensional space, the motion sensor assembly also including a tension sensor provided on the corresponding rope body of the smart skipping rope, for assisting in collecting rope tension change information when the smart skipping rope is swung.

[0148] In some embodiments, the rope skipping motion characteristics include the periodic characteristics, displacement amplitude characteristics and angular velocity change characteristics of normal rope skipping movements; the feature extraction of three-dimensional motion data to obtain rope skipping motion characteristics includes: preprocessing the original three-dimensional motion data, removing high-frequency noise and abnormal mutation data through a sliding window filtering algorithm, and generating a smooth and continuous motion data sequence; performing time domain feature analysis on the preprocessed sequence, extracting the time period of a single rope skipping movement, the spatial amplitude extreme value of the handle displacement and the angular velocity change peak, performing frequency domain feature analysis, obtaining the main frequency component and secondary frequency component of the rope skipping movement through Fourier transform, and constructing the rope skipping motion characteristics by integrating time domain and frequency domain features.

[0149] In some embodiments, the identifying of a target data segment corresponding to an effective rope skipping action in the three-dimensional motion data through the rope skipping motion characteristics includes: dynamically matching a three-dimensional motion data sequence collected in real time with a preset normal rope skipping action feature template, wherein the feature template includes a period threshold range, a displacement amplitude threshold range, and an angular velocity change rate threshold range pre-trained for different user body types; when the period feature of any segment of data in the three-dimensional motion data sequence falls within the corresponding threshold range, the displacement amplitude feature reaches the minimum displacement threshold for bottoming out and the angular velocity change feature conforms to the direction conversion law of the handle swinging, it is determined to be a target data segment corresponding to an effective rope skipping action.

[0150] In some embodiments, the time interval and displacement trajectory repeatability between two adjacent actions in the target data segment are obtained, including: time-series segmentation of continuous target data segments, extracting the starting time points of two adjacent valid actions to calculate the time interval, the normal rope skipping frequency range is dynamically adjusted according to the user's age and physical condition, and the normal rope skipping frequency includes an interval formed by a preset ratio of the average frequency based on the user's historical rope skipping data statistics; the displacement trajectory repeatability is measured by calculating the Euclidean distance similarity of the handle spatial displacement trajectory in two adjacent actions, and the preset threshold is pre-set according to the standard action model of rope skipping.

[0151] In some embodiments, before obtaining the basic information of the user, it also includes: cross-validating the skipping rope counting results in combination with the foot contact signals collected during the user's jumping process to correct abnormal skipping rope counting results, including: collecting the foot contact signals through a pressure sensor worn on the user's foot or a pressure sensing device integrated into the skipping rope pedal, and extracting the periodic characteristics and intensity characteristics of the contact signals; performing time synchronization comparison on the contact signal period and the skipping rope action period, and when the difference between the number of contact signals and the number of skipping rope counts in the same time window exceeds a preset number threshold, or the contact signal intensity does not reach the minimum pressure threshold for jumping and landing, it is determined to be an abnormal count and corrected.

[0152] In some embodiments, the basic information includes age, weight, height and preset exercise goals; the current exercise intensity index is calculated by combining the basic information and the real-time collected skipping frequency, continuous exercise time, cumulative number of skipping times and heart rate data, including: calculating the maximum heart rate theoretical value based on the user's age and height, and obtaining the heart rate reserve percentage in combination with the real-time heart rate data; calculating the frequency change coefficient based on the difference between the skipping frequency and the user's historical average frequency, and estimating the real-time energy consumption rate through a preset energy consumption model in combination with the continuous exercise time and cumulative number of skipping times; weighted fusion of the heart rate reserve percentage, frequency change coefficient and energy consumption rate to generate a dimensionless current exercise intensity index.

[0153] In some embodiments, the exercise goals include at least fat loss, endurance training and explosive power training, and the intensity grading standards under each exercise goal are divided according to the heart rate range, the skipping frequency change rate and the energy consumption rate; the current exercise intensity index is dynamically graded according to the preset grading rule library, and the grading rule library contains intensity grading standards corresponding to different exercise goals, including: for fat loss goals, the grading standards are divided into low intensity, medium intensity and high intensity based on the heart rate reserve percentage, among which the medium intensity corresponds to the 60%-75% range of the maximum heart rate and requires the skipping frequency to be stable at 80%-110% of the basic frequency; for endurance training goals, the grading standards are combined with the continuous exercise time and the frequency change coefficient, requiring that the high intensity level must keep the frequency change coefficient within ±5% and last for more than 40 minutes; for explosive power training goals, the grading standards are divided according to the ratio of the skipping frequency peak to the basic frequency, requiring the high intensity level to have a frequency surge of more than 30% of the basic frequency and maintain for at least 10 seconds.

[0154] In some embodiments, after the current exercise intensity index is dynamically graded according to a preset grading rule library, the method further includes: based on the current exercise intensity grading result, providing the user with real-time exercise intensity level feedback through an associated smart terminal, and adjusting the intensity recommendation for subsequent exercise according to a preset adaptive strategy, wherein the adaptive strategy includes rules for dynamically optimizing the grading threshold based on the user's historical exercise data and current physical fitness status.

[0155] The present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor implements the steps of the counting error correction and exercise intensity dynamic grading method of the smart skipping rope provided in any embodiment of the present application.

[0156] The computer-readable storage medium may be the internal storage unit of the smart skipping rope described in the aforementioned embodiment, such as the hard disk or memory of the smart skipping rope. The computer-readable storage medium may also be an external storage device of the smart skipping rope, such as a plug-in hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash memory card, etc., equipped on the smart skipping rope.

[0157] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for correcting counting errors and dynamically grading exercise intensity of an intelligent rope skipping, characterized in that: Applied to smart skipping rope; including: Acquire three-dimensional motion data during the rope skipping process, wherein the three-dimensional motion data includes at least spatial displacement information, rotation angle information, and motion acceleration information corresponding to the smart rope skipping; Performing feature extraction on the three-dimensional motion data to obtain rope skipping motion features; identifying target data segments corresponding to effective rope skipping movements in the three-dimensional motion data using the rope skipping motion features; Obtaining the time interval and displacement trajectory repeatability between two adjacent actions in the target data segment, and determining that an invalid count is made and discarding the count if the time interval exceeds the normal rope skipping frequency range or the displacement trajectory repeatability is lower than a preset threshold; The user's basic information is obtained, and the current exercise intensity index is calculated by combining the basic information with the real-time collected skipping frequency, continuous exercise time, cumulative number of skipping times and heart rate data; the current exercise intensity index is dynamically graded according to a preset grading rule library, which contains intensity grading standards corresponding to different exercise goals.

2. The method according to claim 1, characterized in that The obtaining of three-dimensional motion data during rope skipping includes: Data is collected in real time through a motion sensor component installed in the smart skipping rope. The motion sensor component includes at least a three-axis acceleration sensor, a three-axis angular velocity sensor and a gyroscope installed on the corresponding handle of the smart skipping rope, which is used to obtain the translational acceleration data, rotational angular velocity data and spatial attitude angle data of the smart skipping rope in three-dimensional space. The motion sensor component also includes a tension sensor installed on the corresponding rope body of the smart skipping rope, which is used to assist in collecting rope tension change information when the smart skipping rope is swung.

3. The method according to claim 1, characterized in that The rope skipping motion characteristics include the period characteristics, displacement amplitude characteristics, and angular velocity change characteristics of normal rope skipping movements; the feature extraction of the three-dimensional motion data to obtain the rope skipping motion characteristics includes: Preprocess the original 3D motion data, remove high-frequency noise and abnormal mutation data through sliding window filtering algorithm, and generate a smooth and continuous motion data sequence; The preprocessed sequence is subjected to time domain feature analysis to extract the time period of a single rope skipping action, the spatial amplitude extreme value of the handle displacement, and the peak value of the angular velocity change. Frequency domain feature analysis is then performed to obtain the main frequency component and secondary frequency component of the rope skipping motion through Fourier transform. The rope skipping motion characteristics are constructed by integrating the time domain and frequency domain features.

4. The method according to claim 1, wherein The step of identifying a target data segment corresponding to a valid rope skipping action in the three-dimensional motion data by using the rope skipping motion feature includes: Dynamically matching the real-time collected three-dimensional motion data sequence with a preset normal rope skipping motion feature template, wherein the feature template includes a period threshold range, a displacement amplitude threshold range, and an angular velocity change rate threshold range pre-trained for different user body types; When the periodic characteristics of any segment of data in the three-dimensional motion data sequence fall within the corresponding threshold range, the displacement amplitude characteristics reach the minimum displacement threshold for bottoming out and the angular velocity change characteristics conform to the direction conversion law of the handle swinging, it is determined to be the target data segment corresponding to the effective rope skipping action.

5. The method according to claim 1, characterized in that The step of obtaining the time interval and displacement trajectory repeatability between two adjacent actions in the target data segment includes: Perform time-series segmentation on continuous target data segments, extract the starting time points of two adjacent valid actions and calculate the time interval. The normal rope skipping frequency range is dynamically adjusted according to the user's age and physical condition. The normal rope skipping frequency includes an interval formed by a preset ratio of the average frequency based on the user's historical rope skipping data statistics. The repeatability of the displacement trajectory is measured by calculating the Euclidean distance similarity between the spatial displacement trajectories of the handle in two adjacent actions, and the preset threshold is pre-set according to the standard action model of rope skipping.

6. The method according to claim 1, characterized in that Before obtaining the basic information of the user, the method further includes: cross-validating the rope skipping counting result in combination with the foot contact signal collected during the user's jumping process to correct abnormal rope skipping counting results, including: The foot contact signal is collected by a pressure sensor worn on the user's foot or a pressure sensing device integrated into the rope skipping pedal, and the periodic and intensity characteristics of the contact signal are extracted; The touchdown signal cycle is time-synchronized and compared with the rope skipping action cycle. When the difference between the number of touchdown signals and the number of rope skipping counts within the same time window exceeds the preset threshold, or the touchdown signal strength does not reach the minimum pressure threshold for jumping and landing, it is determined to be an abnormal count and corrected.

7. The method according to claim 1, characterized in that Basic information includes age, weight, height and preset exercise goals; combining basic information with real-time collected rope skipping frequency, continuous exercise time, cumulative rope skipping times and heart rate data to calculate the current exercise intensity index, including: Calculate the theoretical maximum heart rate based on the user's age and height, and combine it with real-time heart rate data to get the heart rate reserve percentage; The frequency variation coefficient is calculated based on the difference between the skipping frequency and the user's historical average frequency. The real-time energy consumption rate is estimated using a preset energy consumption model based on the continuous exercise time and the cumulative number of skipping times. The heart rate reserve percentage, frequency variation coefficient and energy consumption rate are weighted and fused to generate a dimensionless current exercise intensity index.

8. The method according to claim 1, characterized in that The exercise goals include at least fat loss, endurance training, and explosive power training. The intensity grading standards under each exercise goal are divided according to the heart rate range, the rate of change of rope skipping frequency, and the energy consumption rate. The current exercise intensity index is dynamically graded according to the preset grading rule library. The grading rule library contains intensity grading standards corresponding to different exercise goals, including: For the goal of fat loss, the grading standard is divided into three levels based on the heart rate reserve percentage: low intensity, medium intensity, and high intensity. Medium intensity corresponds to the range of 60%-75% of the maximum heart rate and requires the skipping frequency to be stable at 80%-110% of the basic frequency. For endurance training goals, the grading standard combines the duration of exercise and the frequency variation coefficient. The high-intensity level requires the frequency variation coefficient to be within ±5% and the duration to exceed 40 minutes. For the explosive power training goal, the grading standard is divided according to the ratio of the peak frequency to the basic frequency of the skipping rope. The high-intensity level requires that the frequency surge exceed 30% of the basic frequency and be maintained for at least 10 seconds.

9. The method according to claim 1, characterized in that After dynamically grading the current exercise intensity index according to the preset grading rule library, the method further includes: Based on the current exercise intensity classification results, the real-time exercise intensity level is fed back to the user through the associated smart terminal, and the intensity recommendation for subsequent exercise is adjusted according to the preset adaptive strategy. The adaptive strategy includes rules for dynamically optimizing the classification threshold based on the user's historical exercise data and current physical status.

10. A counting error correction and exercise intensity dynamic grading system for intelligent rope skipping, characterized in that: Applied to smart skipping rope; including: A data acquisition unit, configured to acquire three-dimensional motion data during rope skipping, wherein the three-dimensional motion data includes at least spatial displacement information, rotation angle information, and motion acceleration information corresponding to the smart rope skipping; a feature extraction unit, configured to extract features from the three-dimensional motion data to obtain rope skipping motion features; and identify target data segments corresponding to effective rope skipping movements in the three-dimensional motion data using the rope skipping motion features; an abnormality correction unit, configured to obtain the time interval and displacement trajectory repeatability between two adjacent actions in the target data segment, and to determine an invalid count and eliminate it if the time interval exceeds the normal rope skipping frequency range or the displacement trajectory repeatability is lower than a preset threshold; The dynamic grading unit is used to obtain the user's basic information, combine the basic information with the real-time collected skipping frequency, continuous exercise time, cumulative number of skipping times and heart rate data, and calculate the current exercise intensity index; dynamically grade the current exercise intensity index according to a preset grading rule library, which contains intensity grading standards corresponding to different exercise goals.

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