A quantitative analysis and feedback method for sports dance training rhythm synchronization

Abstract

The application discloses a kind of sports dance training rhythm synchronism quantitative analysis and feedback method, it is related to sports dance training data analysis technical field, comprising the following steps: training data multimodal acquisition, 12 camera 16 key joint data are collected, 48kHz directional microphone audio is collected, wrist heart rate instrument is collected heart rate;Preprocessing, action data filter noise completion is removed exception, audio is extracted feature and is removed noise, heart rate is smoothed, unified time sequence;Rhythm characteristic bidirectional extraction, audio is extracted beat cycle and stress, action is extracted force and action cycle;Synchronism quantization, calculate time difference, sequence consistency, identify synchronism decline section;Analysis result, grade, mark weak link, calculate progress amplitude;Generation feedback, AR and voice prompt, adjust plan and push APP.This method solves the problem of traditional training synchronism subjective fuzzy;Real-time multi-sensory feedback helps immediate adjustment, individualized report and plan fit demand.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sports dance training data analysis, and particularly relates to a quantitative analysis and feedback method for rhythm synchronicity in sports dance training. BACKGROUND

[0002] In sports dance training, rhythm synchronicity is a core indicator for measuring training quality, and directly affects the fluency and watchability of dance performance. In the traditional training mode, the judgment of rhythm synchronicity highly depends on the subjective experience of the coach. The coach can only give vague evaluations such as "action is slightly fast" and "rhythm is not followed" by observing the matching situation of dancer's action and music with naked eyes, and cannot quantify the time difference between action and music beat, the consistency of action sequence and beat sequence, and other key dimensions, which leads to the difficulty of dancers to accurately locate the problem and low training efficiency. For example, the deviation of dancer's hip joint force moment and music accent may reach 120 ms, but the coach can only perceive that "the force moment is not right", and cannot specify the specific value of the deviation, and the dancer still has difficulty in achieving ideal synchronization state after repeated adjustment.

[0003] The existing training auxiliary means have the problems of single data collection and limited analysis dimensions. Some auxiliary tools only collect action data through a camera, or only analyze music beat through an audio device, without realizing the fusion and linkage of multi-modal data, ignoring the correlation between training intensity and rhythm adaptation state reflected by the dancer's heart rate, and not considering the synchronicity of muscle force details and music rhythm. When the dancer appears fatigue due to too high training intensity (heart rate exceeding 150 times per minute), the action synchronicity will decrease significantly, but the existing tools cannot correlate and analyze the fatigue factor and synchronicity deviation, and only attribute it to "insufficient rhythm perception", which leads to poor pertinence of improvement suggestions. At the same time, for different styles of sports dance such as waltz and tango, their beat characteristics (such as 3 / 4 beat and 4 / 4 beat, and cut rhythm) have significant differences, but the existing tools mostly adopt a unified quantification standard, which cannot adapt to the rhythm requirements of different styles, and the analysis accuracy is limited.

[0004] The synchronization analysis in the multi-dancer cooperative training scene is a short board of the prior art. In double or multi-person sports dancing (such as double waltz and group Latin), the motion synchronization between dancers is as important as the synchronization of individual dancers and music, but the existing tools can only evaluate the synchronization of each dancer with the music, cannot calculate the time difference of the same action between dancers, the intra-class similarity of the action sequence, and cannot mark the weak dancer combination. For example, in double training, the time difference of the turning action of dancer A and dancer B reaches 60 ms, which cannot be effectively identified, resulting in that the cooperative training is long-term in the "relying on tacit understanding" stage, and it is difficult to improve the cooperative efficiency through accurate analysis. In addition, the existing feedback mode is mainly to generate a simple report after training, lacks real-time interactive feedback, dancers cannot adjust the action in time during training, and the improvement suggestions in the report are mostly general templates, without generating personalized schemes combined with the historical training data of dancers, which is difficult to support long-term training improvement. SUMMARY

[0005] The present application provides a sports dance training rhythm synchronization quantitative analysis and feedback method to solve the problems mentioned in the prior art.

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme: a sports dance training rhythm synchronization quantitative analysis and feedback method, comprising the following steps:

[0007] Step 1: Training data multi-modal acquisition, 12 camera optical motion capture system is used to collect dancer's limb motion data, focusing on capturing real-time angle, motion speed and force moment of 16 key joints; 48kHz sampling rate directional microphone is used to collect training music audio data, and music beat interval, tempo value and accent position are recorded synchronously;

[0008] Step 2: Multi-modal data preprocessing, 5th order Butterworth low-pass filter is used for motion data to filter out noise, missing data is completed by linear interpolation, and abnormal values are removed by 3σ criterion; short-time Fourier transform is used for audio data to extract frequency spectrum characteristics; sliding average filter is used for heart rate data to smooth fluctuations;

[0009] Step 3: Rhythm feature bidirectional extraction, music beat cycle is identified from audio data by autocorrelation algorithm, dynamic time warping algorithm is used to match music accent moment, and beat intensity weight in each measure is calculated; dancer's key action force moment is extracted from motion data by peak detection algorithm, action cycle is calculated, and limb coordination degree is analyzed by joint angle change rate;

[0010] Step 4: Synchronization multi-dimension quantification, action and music time difference is calculated; cosine similarity algorithm is used to calculate action sequence and music beat sequence; sliding window is used to calculate synchronization similarity change rate;

[0011] Step 5: Quantitative result hierarchical analysis, according to time difference, consistency and dynamic trend data, the synchronization is divided into four levels of excellent, good, medium and poor; the weak link is marked by the heat map; the progress of the synchronization is calculated by comparing the historical training data, and the progress trend curve is generated;

[0012] Step 6: personalized feedback generation, real-time feedback through AR glasses superimposed beat prompt line, with voice prompt; detailed report includes synchronization level, weak link list, historical comparison chart and improvement suggestion; automatically adjust the training plan according to the weak link.

[0013] Further, it further includes a motion and music synchronization stability evaluation step, which calculates a synchronization stability coefficient by the formula wherein is the synchronization stability coefficient, t1 is the starting time of the evaluation period, is the time difference between the motion and the music beat at time t; this step calculates t1=0s, t2=120s for a complete dance segment lasting 1-3 minutes in training, and when <0.6, the time interval of stability decline is automatically located.

[0014] Further, it further includes a multi-dancer collaborative synchronization analysis step, which, on the basis of single-person synchronization quantification, increases the motion synchronization evaluation between dancers; adopts motion capture system to simultaneously collect key joint data of training dancers, calculates the time difference between the same motion of any two dancers, and calculates the maximum value of in each measure; the clustering algorithm is used to classify the motion sequences of multiple dancers; a multi-dancer collaborative heat map is generated to mark the weak dancer combination in collaboration.

[0015] Further, it further includes a music style adaptability optimization step, which optimizes feature extraction and quantification standards according to the rhythm characteristic differences of different sports dance styles; in waltz style, the music beat is 3 / 4 beat, the emphasis is on the first beat, the emphasis beat weight is set to 1.2, the non-emphasis beat weight is set to 0.6, and the motion period needs to match the 3-beat rhythm; in tango style, the music contains split rhythm, split beat detection is added; in lumbang style, the music tempo is slow, the matching degree of motion amplitude and music emotion is emphasized.

[0016] Further, it further includes a dynamic synchronization adaptation ability evaluation step, which calculates a synchronization adaptation rate by the formula wherein is the synchronization adaptation rate, S(t) is the synchronization similarity between the motion and the music at time t, and t is the training time; this step calculates , draw the synchronization adaptation rate curve, when 3 consecutive 10s When < 0.02, it is determined that the adaptation ability is weak, and the reason is analyzed.

[0017] Further, it further includes a training intensity and synchronization correlation analysis step, which combines heart rate data and synchronization quantization results to establish an intensity and synchronization model; the heart rate is divided into three intervals of low intensity, medium intensity and high intensity, and the synchronization scores in each interval are counted; the trend of synchronization with intensity is analyzed, and if the score in the high intensity interval decreases by more than 15%, it is determined that the intensity affects the synchronization; and an intensity and synchronization correlation report is generated, marking the optimal training intensity interval.

[0018] Further, it further includes a motion detail synchronization optimization step, which, on the basis of key joint motion quantization, increases the synchronization analysis of muscle force and music rhythm; an electromyographic sensor is used to collect the electromyographic signal of the core muscle of the dancer, extract the muscle activation time M, and calculate the time difference between M and the music beat time T0 An integral algorithm is used to calculate the muscle activation intensity I, and the matching degree of I and the music beat intensity is analyzed.

[0019] Further, it further includes a multi-dimensional synchronization comprehensive scoring step, which calculates the comprehensive synchronization score by the formula , wherein is the comprehensive score, is the time difference weight, is the consistency weight, is the stability weight is the time difference standardized score, is the consistency standardized score, is the stability standardized score.

[0020] Further, it further includes a real-time feedback interaction optimization step, which, on the basis of AR glasses prompts, increases the tactile feedback device; when the synchronization deviation is ≤50ms, the tactile feedback intensity is low; the deviation is 50-100ms, and the deviation is >100ms, the tactile feedback intensity is high, helping the dancer to perceive the synchronization state through tactile perception; and the voice prompt content is optimized, and the prompt is adjusted in detail according to the dancer training stage.

[0021] Further, it further includes a training effect prediction and plan iteration step, which uses an LSTM neural network model to predict the synchronization improvement potential of the dancer based on historical training data; according to the prediction result, the training plan is iterated, if the potential is high, the advanced task is increased, if the potential is medium, the current training intensity is maintained and the weak link training is optimized, and if the potential is low, the training method is adjusted; a training effect evaluation report is generated every month.

[0022] Compared with the prior art, the beneficial effects of the present application are:

[0023] This invention completely solves the problem of subjective ambiguity in rhythm synchronization judgment in traditional training by acquiring and quantitatively analyzing multimodal data. The system simultaneously collects multi-dimensional data such as movement, audio, and heart rate, and quantifies synchronization from multiple dimensions such as time difference, consistency, and stability. This allows dancers to clearly understand the details of the deviation between their movements and the music beat, as well as the degree of matching between the movement sequence and the beat sequence. No longer relying on subjective judgment, it accurately locates training weaknesses and significantly improves the targeting of training.

[0024] The feedback mechanism of this invention combines real-time performance with personalization, providing dancers with end-to-end training support. During training, multi-sensory real-time feedback is achieved through AR glasses, voice, and haptic devices, allowing dancers to instantly perceive synchronization deviations and adjust their movements to avoid incorrect practice. The detailed report generated after training not only includes the synchronization level but also marks specific weaknesses (such as hip joint movement deviations in a certain section or problems with the adaptation of a certain type of movement cycle). It also provides customized improvement suggestions and targeted training tasks based on historical data, while automatically adjusting subsequent training plans to ensure accurate training direction and avoid ineffective repetition.

[0025] This invention is adaptable to different training scenarios and needs, expanding the scope of application of synchronization analysis. For collaborative training of multiple dancers, it can quantitatively analyze the synchronization among dancers, mark weak combinations in coordination, and generate collaborative training tasks to improve the overall coordination of multi-person dance. For different styles of ballroom dance, it can automatically identify the music style and call the corresponding quantitative standards to ensure the accuracy of the analysis. It can also correlate training intensity with synchronization to find the optimal training intensity range for dancers, avoiding a decline in synchronization due to fatigue. At the same time, through training effect prediction and plan iteration, it matches the dancer's progress rhythm, reduces ineffective training, enhances the dancer's sense of accomplishment in training, and provides strong support for long-term improvement of rhythm synchronization in ballroom dance. Attached Figure Description

[0026] Figure 1 This is a schematic block diagram of a quantitative analysis and feedback method for rhythm synchronization in sports dance training proposed in this invention.

[0027] Figure 2 Line graph showing the changes in multi-dimensional indicators of synchronicity at different training stages;

[0028] Figure 3 A bar chart comparing synchronization errors between dancers during duo waltz training;

[0029] Figure 4 A scatter plot showing the correlation between training intensity and synchronization score;

[0030] Figure 5 A line graph showing the historical progress of the overall synchronicity score. Detailed Implementation

[0031] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0032] In the description of the present application, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the purpose of facilitating the description of the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0033] In addition, the terms "first", "second" are only for descriptive purpose, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited. In addition, the terms "mounting", "connecting", "connection" should be broadly understood, for example, it can be fixed connection, or detachable connection, or integral connection; it can be mechanical connection, or electrical connection; it can be direct connection, or indirect connection through intermediate medium, or communication between two elements. For those of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances, and the present application will be further described in detail with reference to the drawings.

[0034] Referring to Figures 1 to 5 A quantitative analysis and feedback method for sports dance training rhythm synchronization, comprising the following steps:

[0035] Step 1: Multimodal data collection of training data, 12-camera optical motion capture system (frame rate 60 fps, spatial positioning accuracy ±0.5 mm, covering a 10 m x 10 m training area) is used to collect dancer's limb movement data, focusing on capturing the real-time angles (range 0°-180°), movement speed (0-5 m / s) and force moment of 16 key joints such as hip joint, knee joint and shoulder joint; 48 kHz sampling rate directional microphone is used to collect training music audio data, and music beat interval (50-200 ms), tempo value (60-180 BPM) and accent position (1, 3 beats per bar) are recorded synchronously; wrist heart rate monitor (sampling interval 1 s, accuracy ±1 / min) is used to collect dancer's heart rate data, which is associated with action intensity and rhythm adaptation state, all data are synchronized through time stamp, and synchronization error is ≤10 ms;

[0036] Step 2: Multimodal data preprocessing, 5th order Butterworth low-pass filter (cutoff frequency 10 Hz) is used for action data to filter out capture noise, and linear interpolation is used to complete the missing data (missing time ≤200 ms) caused by occlusion, and abnormal threshold (±15° beyond normal motion range is judged as abnormal) is set for joint angle, and 3σ criterion is used to remove outliers; Short-time Fourier transform (window length 20 ms, overlap rate 50%) is used for audio data to extract spectral features, and environmental noise is eliminated (signal-to-noise ratio is improved to ≥30 dB); Sliding average filter (window size 5 s) is used for heart rate data to smooth fluctuations and improve data stability, and all preprocessed data are converted to 100 Hz sampling rate time series;

[0037] Step 3: Rhythm feature extraction, music beat period T (unit: ms) is identified from audio data by autocorrelation algorithm, dynamic time warping algorithm is used to match music accent moment T0 (unit: s), and intensity weight of each beat in each bar (accent beat weight 1.0, non-accent beat weight 0.7) is calculated; The dancer's key action force moment A (unit: s) is extracted from the action data by peak detection algorithm, and the action period (unit: ms, time interval between adjacent two similar forces), joint angle change rate is used to analyze limb coordination C (0-1, the closer to 1, the higher the coordination), and the time corresponding relationship between action features and music features is established;

[0038] Step 4: Synchronization multi-dimension quantification, calculate action-music time difference Δt (unit: ms, |A-T0|), statistics the average value of Δt in each measure (≤50ms is excellent, 50-100ms is good, 100-150ms is medium, >150ms is poor); calculate the consistency of action sequence and music beat sequence S (0-1, ≥0.8 is high consistency, 0.6-0.8 is medium consistency, <0.6 is low consistency) by using cosine similarity algorithm; analyze the dynamic trend of synchronization with music change, calculate the synchronization similarity change rate by using sliding window (window size 3 measures), identify the music paragraph with weak adaptation (change rate < -0.1 is judged as weak paragraph);

[0039] Step 5: Quantification result hierarchical analysis, according to time difference, consistency and dynamic trend data, divide the synchronization into four levels of excellent (comprehensive score ≥90 points), good (80-89 points), medium (60-79 points) and poor (<60 points); mark the weak links by using heat map, such as "hip joint action in the 2nd-3rd measure is more than 120ms different from the heavy beat" and "turning action period is 20% deviated from the music tempo"; compare the historical training data (the last 10 training), calculate the synchronization progress amplitude (≥10% is significant progress, 5-10% is general progress, <5% is no progress), and generate progress trend curve;

[0040] Step 6: Personalized feedback generation, real-time feedback is superimposed on the beat prompt line (red for the current beat, green for the action should reach position) through AR glasses (resolution 1920x1080, field of view 80°), and is matched with voice prompt ("action is a little fast, 50ms delay is better"); detailed report includes synchronization level, weak link list, historical comparison chart and improvement suggestion (such as "for the 2nd-3rd measure, it is suggested to practice 20 times slowly first, and focus on the timing of hip joint force"); automatically adjust the training plan according to the weak link, generate targeted task (such as "10 minutes of heavy beat action alignment training every day"), and push to the dancer training APP.

[0041] In the application, the action-music synchronization stability evaluation step is also included, which is calculated by the formula to calculate the synchronization stability coefficient, wherein is the synchronization stability coefficient (dimensionless, 0-1, ≥0.8 is high stability, 0.6-0.8 is medium stability, <0.6 is low stability), t1 is the starting time of the evaluation period (unit: s), t2 is the end time of the evaluation period (unit: s), is the time difference between action and music beat at t moment (unit: ms); this step is for a complete dance segment of 1-3 minutes in training, t1=0s, t2=120s (2 minute segment) are selected for calculation, when When <0.6, the time interval (such as t=40-60s) of automatic positioning stability decline is analyzed, the music features (such as tempo mutation, accent shift) and action features (such as complex turning, fast pace) in the interval are analyzed, and the "weak stability interval special training suggestion" is added in the feedback report, such as "t=40-60s music tempo increases from 120BPM to 130BPM, it is suggested to first segment the action in the interval and gradually improve the tempo adaptation ability".

[0042] In the present application, a multi-dancer coordination synchronization analysis step is also included, which adds inter-dancer action synchronization evaluation on the basis of single-person synchronization quantification; key joint data of 2-5 dancers training coordinately are collected simultaneously by using an action capture system, and the time difference of the same action between any two dancers is calculated (unit: ms, the action force time difference of dancer i and dancer j), the maximum value in each measure is counted (≤30ms for excellent coordination, 30-50ms for good coordination, and >50ms for poor coordination); a clustering algorithm is used to classify the action sequences of multiple dancers, and the intra-class similarity is calculated (≥0.85 for high coordination, 0.7-0.85 for medium coordination, and <0.7 for low coordination); a multi-dancer coordination heat map is generated, and the dancer combinations with weak coordination (such as "dancer A and dancer B have poor coordination in turning action") are marked with colors; coordination training tasks are added in the feedback, such as "15 minutes of two-person mirror action practice every day, focusing on the force time alignment when turning"; and coordination training demonstration videos (recorded by professional dancers, with key synchronization points marked) are pushed.

[0043] In the present application, a music style adaptability optimization step is also included, which optimizes the feature extraction and quantification standard according to the rhythm feature differences of different sports dance styles (such as waltz, tango, and rumba); in the waltz style, the music beat is 3 / 4 beat, the accent is in the first beat, the accent beat weight is set to 1.2, the non-accent beat weight is set to 0.6, and the action period needs to match the 3-beat rhythm ); in the tango style, the music contains split rhythm, split beat detection is added (identified by spectrum energy mutation), and the time difference threshold between action and split beat is relaxed to ≤70ms; in the rumba style, the music tempo is slow (60-90BPM), and the matching degree of action amplitude and music emotion is focused on (through correlation analysis of joint angle change range and music volume change, a correlation coefficient ≥0.7 is high matching); this step automatically identifies the music style in the data preprocessing stage (through tempo, time signature, and spectrum feature matching style database), calls the corresponding quantification standard, improves the accuracy of synchronization analysis of different styles of dance, and adds style-specific improvement suggestions in the feedback, such as "in rumba training, it is suggested to increase the hip joint action amplitude to improve the matching degree with music emotion".

[0044] In the present application, a dynamic synchronization adaptation capacity evaluation step is also included, which calculates a synchronization adaptation rate by the formula wherein is the synchronization adaptation rate (dimensionless, unit s⁻¹, >0 for adaptation improvement, =0 for adaptation stability, <0 for adaptation decline), S(t) is the synchronization similarity between action and music at time t (0-1), t is the training time (unit s); this step takes 5 minutes as an evaluation period, and calculates once every 10 s , and draws a synchronization adaptation rate curve, when <0.02 for three consecutive 10 s, it is determined that the adaptation capacity is weak, and the reasons are analyzed (such as dancer fatigue, music complexity improvement); if it is caused by fatigue, the feedback suggests “shorten the single training time to 30 minutes, and increase the rest interval (rest for 2 minutes every 15 minutes)”; if it is caused by music complexity improvement, low complexity transition music (tempo fluctuation ≤5 BPM, no complex rhythm change) is pushed in the feedback, and it is suggested to first train the adaptation capacity through transition music, and then gradually increase the music complexity.

[0045] In the present application, a training intensity and synchronization correlation analysis step is also included, which combines heart rate data and synchronization quantization results to establish an intensity-synchronization model; the heart rate is divided into low intensity (≤120 times / minute), medium intensity (121-150 times / minute), and high intensity (>150 times / minute) three intervals, and the synchronization scores in each interval are counted (such as low intensity interval score 85 points, medium intensity 82 points, and high intensity 70 points); the trend of synchronization with intensity is analyzed, if the high intensity interval score decreases by more than 15%, it is determined that the intensity affects the synchronization, and the feedback suggests “adjust the training intensity distribution, reduce the high intensity training time proportion from 40% to 25%, and increase the medium and low intensity synchronization consolidation training”; at the same time, an intensity-synchronization correlation report is generated, which marks the optimal training intensity interval (such as “the optimal intensity of the dancer is 121-150 times / minute, and the synchronization score is the highest”), helps the dancer to reasonably plan the training intensity, and reduces the synchronization decline caused by excessive fatigue.

[0046] In the present application, a motion detail synchronization optimization step is also included, which increases the synchronization analysis of muscle force and music rhythm on the basis of key joint motion quantization; an electromyographic sensor (sampling rate 1000 Hz, resolution 12 bits) is used to collect the electromyographic signals of 8 core muscles of the dancer's thigh quadriceps and calf gastrocnemius, extract the muscle activation time M (unit s), and calculate the time difference between M and the music beat time T0 (Unit: ms, ≤30ms is excellent, 30-50ms is good, >50ms is poor); Muscle activation intensity I (unit: mV·s, positively correlated with movement force) is calculated using an integral algorithm, and the matching degree between I and the intensity of the music beat is analyzed (correlation coefficient ≥0.6 is high matching); suggestions for improving muscle exertion are added to the feedback, such as "quadriceps activation time is 25ms behind the beat, it is recommended to exert force earlier, and you can feel the rhythm of force exertion by practicing in front of a slow-motion mirror", and at the same time, electromyography-beat synchronization training animation is pushed (the correspondence between muscle activation and beat is marked).

[0047] This invention also includes a multi-dimensional synchronicity comprehensive scoring step, which is achieved through a formula. Calculate the overall synchronization score, where The overall score is 0-100 points. The time difference weight is 0.4. This is the consistency weight (value 0.3). This is the stability weight (value 0.3). Standardized score for time difference (0-100 points, 100 points for Δt≤50ms, 25 points deducted for every additional 50ms). The consistency standardization score is 0-100 points, with 100 points for S≥0.8 and 15 points deducted for every 0.1 decrease in S. Standardized score for stability (0-100 points) A score of ≥0.8 earns 100 points, and each decrease of 0.1 deducts 15 points. This step automatically summarizes data from each dimension to calculate the score, classifies the dancers into levels based on their scores, and matches them with corresponding training programs (90-100 points push advanced skills training, 80-89 points push consolidation training, 60-79 points push basic synchronization training, and <60 points push introductory rhythm perception training). At the same time, it generates a comprehensive score history curve to intuitively show the dancer's synchronization improvement trajectory and enhance the sense of accomplishment in training.

[0048] In the present application, a real-time feedback interaction optimization step is also included, which increases the tactile feedback device (worn on the wrist, vibration frequency consistent with the music beat, and intensity positively correlated with the synchronization deviation) based on the AR glasses prompt; when the synchronization deviation is less than or equal to 50 ms, the tactile feedback intensity is low (vibration amplitude 0.1 mm); when the deviation is 50-100 ms, the tactile feedback intensity is medium (0.2 mm); and when the deviation is greater than 100 ms, the tactile feedback intensity is high (0.3 mm), helping the dancer to perceive the synchronization state through tactile perception, especially in noisy music or obstructed vision scenarios to improve feedback effectiveness; at the same time, the voice prompt content is optimized, and the prompt is adjusted according to the dancer's training stage (the junior stage prompts "left foot forward, align with the first beat", and the senior stage prompts "hip joint force lag, adjust 0.03s"); the feedback interaction confirmation function is added, and the dancer can pause / continue feedback or switch feedback mode (AR+voice / tactile alone mode) through gestures (such as waving hands), improving training autonomy.

[0049] In the present application, a training effect prediction and plan iteration step is also included, which uses an LSTM neural network model to predict the synchronization improvement potential in the next month (such as "predicted to improve the comprehensive score from 75 points to 85 points in 2 weeks") based on the dancer's historical training data (synchronization score, weak link, training duration of the last 30 training sessions); according to the prediction result, the training plan is iterated, if the potential is high (improvement ≥10 points), the advanced task is added (such as "learn to synchronize complex rhythm type movements"); if the potential is medium (5-10 points), the current training intensity is maintained and the weak link training is optimized; if the potential is low (<5 points), the training method is adjusted (such as "change the music style and use more familiar rhythm types to improve interest"); a training effect evaluation report is generated every month to compare the predicted value with the actual value (deviation ≤5 points is accurate prediction), optimize the neural network model parameters, and improve the prediction accuracy to ensure that the training plan always fits the dancer's progress pace and reduces ineffective training.

[0050] The specific embodiments of the present application are further illustrated by the following two examples:

[0051] Example 1: Single Lengba Training Rhythm Synchronization Analysis Scene for Junior Dancers

[0052] This example is for a junior Lengba dancer (training duration of 6 months, mastered basic steps but rhythm synchronization is unstable), who conducts 45-minute single Lengba training in a 10m x 10m dance training room, aiming to solve the problems of large deviation between hip joint movement and music accent, synchronization decline after high-intensity training, and inability to accurately locate weak links, and the specific implementation process is as follows:

[0053] I. System deployment and parameter configuration

[0054] Multi-modal acquisition device layout: The motion capture system uses 12 OptiTrack Prime 17W cameras, which are distributed in a ring around the training room (height 2.5 m, interval 2 m). The frame rate is set to 60 fps, and the spatial positioning accuracy is ±0.5 mm. The key joints of the dancer are marked, including the hip joint, knee joint, ankle joint, shoulder joint, and elbow joint. The joint angle acquisition range is 0°-180°, and the motion speed acquisition range is 0-3 m / s (the lumbago action speed is slower). Two 48 kHz directional microphones are fixed on the front and back walls of the training room (3 m away from the dancer), respectively, to collect lumbago training music (tempo 75 BPM, 4 / 4 beat, emphasis on the first beat), with an audio sampling interval of 2.1 ms. The dancer wears a heart rate monitor (sampling interval 1 s, accuracy ±1 / min), which is synchronized with the training APP. All devices are synchronized by timestamp, and the synchronization error is calibrated to 8 ms.

[0055] Core parameter preset: In the data preprocessing stage, the action data is filtered by a 5th order Butterworth low-pass filter (cutoff frequency 10 Hz), and the threshold for completing missing data by linear interpolation is set to 200 ms (lumbago action is continuous, and the occlusion time is short). The abnormal joint angle threshold is set to ±15° of the normal motion range (for example, the normal motion range of the hip joint is 0°-120°, and the abnormal value is determined as <-15° or >135°). The audio data short-time Fourier transform window length is 20 ms, the overlap rate is 50%, and the signal-to-noise ratio is improved by 35 dB. The heart rate data sliding average filter window is 5 s, and the smoothed heart rate interval is divided into: low intensity ≤120 beats / min, medium intensity 121-150 beats / min, and high intensity >150 beats / min. The AR glasses use Microsoft HoloLens 2 (resolution 1920x1080, field of view 80°), the voice prompt volume is 60 dB, and the vibration amplitude of the tactile feedback device (worn on the left wrist) is classified as: low 0.1 mm, medium 0.2 mm, and high 0.3 mm.

[0056] II. Core training process and formula application

[0057] Data acquisition and preprocessing: After the start of training, the motion capture system collects the joint data of the dancer's lumbago basic steps (cucaracha, fan-shaped step, and alemana) in real time. At the 5th minute, the dancer does the fan-shaped step, and the hip joint is occluded by the skirt for 150 ms, which is completed by linear interpolation (the angle deviation after completion is <2°). The microphone collects music audio, and after short-time Fourier transform, it filters out the air conditioner noise in the training room (the signal-to-noise ratio is improved from 22 dB to 36 dB). The heart rate monitor collects data, and after sliding average, the initial heart rate is displayed as 85 beats / min (low intensity). After preprocessing, all data are converted into time series with a sampling rate of 100 Hz and stored in the local server.

[0058] Rhythm feature extraction and synchronicity quantification: From the audio data, the salsa music beat cycle T = 800 ms (75 BPM = 60000 ms / 75 = 800 ms) is identified by the autocorrelation algorithm, and the accent time T0 (e.g. 10s, 10.8s, 11.6s) is matched by the dynamic time warping algorithm, with accent beat weight 1.0 and non-accent beat weight 0.7. From the motion data, the hip joint force time A (e.g. 10.05s, 10.83s, 11.62s) is extracted by the peak detection algorithm, and the motion cycle = 780 ms (close to T = 800 ms) is calculated, and the joint angle change rate analysis limb coordination C = 0.75 (medium coordination). Synchronicity quantification shows: the average value of action-music time difference Δt is 65 ms (good), the consistency S of action sequence and music beat sequence is 0.72 (medium consistency), and the sliding window (3 bars) calculation of synchronization similarity change rate is -0.05 (not up to the standard of weak section).

[0059] Synchronization stability and dynamic adaptability evaluation: The training is carried out for 20 minutes, and the complete salsa segment at t1 = 600 s (10 minutes) and t2 = 720 s (12 minutes) is selected to calculate the synchronization stability coefficient. Given that the average value of Δt(t) in this period is 60 ms, the maximum is 80 ms, and the minimum is 40 ms, substitute into the formula The integral result is the time integral of Δt(t), assuming that Δt(t) is 60 ms on average, the integral is substituted to get (high stability). When the training is 30 minutes, the dancer's heart rate rises to 155 beats per minute (high intensity), and the synchronization adaptation rate is calculated every 10 s, and the continuous three times are -0.025, -0.03, and -0.028 (all < -0.02), which is judged as weak adaptability, and the reason is fatigue, and the feedback suggestion is "shorten the single training to 30 minutes, rest for 2 minutes and continue, and the subsequent high-intensity training proportion is reduced to 25%".

[0060] Personalized feedback and plan adjustment: In real-time feedback, the AR glasses superimpose the current beat line (1st beat) in red and the action should reach the position line in green, showing "hip joint force lag 20 ms, align with the 1st beat"; the vibration amplitude of the tactile feedback device is 0.2 mm (medium intensity); the voice prompt is "action is slightly slow, 20 ms delay is better". After the training, a report is generated: the synchronization level is good (82 points), the weak links are "15-18 small section fan-shaped step hip joint and strong beat error 85 ms" and "high intensity interval (heart rate > 150 times / min) synchronization score decreased by 18%"; compared with the last 10 training, the progress is 8% (general progress); automatically adjust the training plan, generate "10 minutes of strong beat hip joint alignment training every day" and "high-intensity training time controlled within 10 minutes" tasks, and push them to the dancer APP.

[0061] III. Training effect data representation

[0062] Table 1: Comparison of key indicators of single lombard training in example 1

[0063]

[0064] In table 1: the data reflects the adaptability of the invention to the initial single lombard training: the synchronization comprehensive score is improved from 65 points to 82 points, which is due to the accurate positioning of the problem by multi-dimensional quantitative analysis; the weak link positioning only takes 5 minutes, which is much shorter than the traditional 30 minutes, allowing the dancer to quickly focus on the hip joint deviation problem; the real-time feedback delay is 25 ms, which avoids the "wrong training" caused by finding the problem after traditional training; the decrease amplitude of high-intensity synchronization is reduced from 35% to 18%, because the system timely identifies fatigue and adjusts the training intensity; the progress amplitude is 12% after 1 week, which is much higher than the traditional 5%, proving the effectiveness of the personalized training plan, and solving the pain points of unstable rhythm synchronization and difficult problem positioning for junior dancers.

[0065] Example 2: Intermediate dancer's double waltz coordination training rhythm synchronization analysis scene

[0066] This embodiment is aimed at two intermediate waltz dancers (partners trained for 8 months, mastered rotation, swing and other movements, and have coordination turning deviation problem), who conduct 60 minutes of double waltz training in a 12m x 12m training room, to solve the problems of large coordination synchronization error between dancers, inaccurate waltz 3 / 4 beat rhythm adaptation, and fuzzy comprehensive synchronization score, and the specific implementation process is as follows:

[0067] I. System deployment and parameter configuration

[0068] Multi-device collaborative acquisition layout: The motion capture system uses 16 OptiTrack Prime41 cameras (frame rate 60fps, accuracy ±0.5mm), covering a two-person training area (12m x 12m), marking the hip joints, shoulder joints, ankle joints, etc. of two dancers (Dancer A, Dancer B), focusing on monitoring the shoulder joint angle (0°-360°) in the rotating action; 3 48kHz directional microphones are placed in a triangular shape (2-4m away from the two-person combination), collecting waltz music (tempo 120BPM, 3 / 4 beat, emphasis on the first beat); both dancers wear heart rate monitors (sampling interval 1s); the downhole-ground communication module uses a 5G private network (transmission rate 100Mbps, delay 20ms) to ensure synchronous transmission of two-person data; AR glasses are worn by the two dancers, displaying a two-person action synchronization prompt line (blue for Dancer A's action, pink for Dancer B's action).

[0069] Style adaptation and scoring parameter preset: In the music style adaptation stage, the system automatically identifies the waltz 3 / 4 beat characteristics, sets the emphasis beat weight to 1.2, the non-emphasis beat weight to 0.6, and the action period ; Comprehensive scoring parameters: w1=0.4 (time difference weight), w2=0.3 (consistency weight), w3=0.3 (stability weight), (time difference standard score) ≥90 points corresponds to Δt≤30ms, (consistency standard score) ≥90 points corresponds to S≥0.85, (stability standard score) ≥90 points corresponds to ≥0.85; In multi-dancer collaborative evaluation, (time difference between dancers) excellent threshold ≤30ms, intra-class similarity high collaboration threshold ≥0.85.

[0070] II. Core collaborative process and formula application

[0071] Multi-dancer data acquisition and collaborative analysis: After the training starts, the motion capture system synchronously collects the waltz basic action (right turn, left turn, cross step) data of Dancer A and Dancer B, and at the 15th minute, the right turn action is calculated. Dancer A's shoulder joint force moment A1=450.2s, Dancer B's shoulder joint force moment A2=450.05s =150ms-50ms=100ms? Corrected to =|450.2-450.05|=150ms (>50ms, poor collaboration); using K-means clustering algorithm to classify the two-person action sequence, intra-class similarity 0.72 (medium collaboration); generate a collaborative heat map, mark "Dancer A and Dancer B right turn action collaboration difference", AR glasses display "Dancer B's action is 150ms ahead, adjust the force moment to 450.2s".

[0072] Overall score and plan iteration: At the 30-minute training, the overall synchronization score is calculated. It is known = 85 points (Δt average 40 ms), = 80 points (S = 0.82), = 88 points ( = 0.88), into the formula points (good); compared with the last 10 collaborative training, the overall score improved from 75 points to 84.4 points, an improvement of 12.5% (significant progress). The training effect prediction module is based on the LSTM model, and the input is the last 30 training data (scores 75-84.4, weak links are right turn coordination, training duration 60 minutes / time). The prediction module predicts that the overall score in the next two weeks can reach 90 points (excellent); after plan iteration, the task of "15 minutes of daily two-person right turn mirror practice, focusing on aligning the shoulder joint force moment" is added, and the waltz music tempo is adjusted from 120 BPM to 125 BPM, increasing the difficulty of rhythm adaptation.

[0073] Dynamic adaptation and feedback optimization: At the 45-minute training, dancer A's heart rate was 145 beats per minute (moderate intensity), and dancer B's heart rate was 152 beats per minute (high intensity). The synchronization adaptation rate , dancer A's continuous 3 times = 0.01, 0.005, 0.008 (> 0, adaptation improved), dancer B's continuous 3 times = -0.022, -0.025, -0.021 (< -0.02, adaptation decreased), the analysis reason is that dancer B has been training at high intensity for too long, and the feedback suggestion is "dancer B rest for 3 minutes, and the subsequent high-intensity training proportion is reduced to 20%"; the tactile feedback device vibrates 0.3 mm (high) for dancer B, prompting "motion synchronization is decreasing, adjust intensity".

[0074] III. Data representation of collaborative training effect

[0075] Table 2: Comparison of key indicators of two-person waltz collaborative training in Example 2

[0076]

[0077] In Table 2: The data reflects the adaptability of the invention to two-person waltz cooperative training: the inter-dancer synchronization error is reduced from the traditional 120ms to 45ms, the intra-class similarity is improved from 0.65 to 0.83, solving the problem of low efficiency of "fitting by tacit understanding"; the comprehensive score is improved by 12.5%, far exceeding the traditional 4%, proving the effectiveness of quantitative analysis and personalized plan; the cooperative weakness positioning only takes 3 minutes, which is greatly shortened compared with the traditional 45 minutes, allowing the couple to quickly focus on the right turning action coordination problem; the predicted score reaches 90 points after plan iteration, providing a clear goal for subsequent training and avoiding blind practice. Overall, the system effectively improves the precision and efficiency of two-person cooperative training, and promotes intermediate dancers from "knowing how to dance" to "dancing well".

[0078] Referring to Figure 2 : The figure intuitively presents the long-term progress trend of the dancer's synchronization multidimensional indicators. With the increase of training times, the action-music time difference continues to shorten from 95ms to 35ms, indicating that the time alignment accuracy of action and rhythm has been significantly improved; the consistency and stability coefficients increase from about 0.6 to more than 0.85, reflecting that the matching degree of action sequence and music rhythm and the persistence of synchronization state are both greatly optimized. The chart can help dancers and coaches clearly see the improvement rhythm of each dimension and judge the effectiveness of the training plan, such as the fastest decline in time difference during the 10th-15th training, which can analyze the advantages of the training method (such as special alignment exercises) at this stage, providing a basis for subsequent training adjustment.

[0079] Referring to Figure 3 : The figure clearly reflects the advantages of the invention in two-person cooperative training, and the synchronization error of all action types is greatly reduced, especially for complex actions such as right turn and left turn rotation, the error is reduced from more than 100ms to about 30ms, reaching the excellent coordination standard (≤30ms). Traditional tacit understanding relies on dancer's subjective feeling, with large and unstable error, while the invention accurately solves the weak points of coordination by quantifying the action time difference between dancers and generating targeted coordination tasks (such as right turn rotation force alignment exercise). For example, the error of swing step is reduced from 120ms to 35ms, making the couple's actions more unified and improving the smoothness of dance performance, while shortening the coordination training period and avoiding long-term ineffective grinding.

[0080] Referring to Figure 4The figure visually displays the effect of training intensity on synchronization through scatter points and trend lines. When the heart rate is 120-140 times / minute (medium intensity), the synchronization score is the highest (85-88 points), and the dancer is full of energy, can ensure the quality of movement, and can accurately perceive the rhythm; when the heart rate is <120 times / minute (low intensity), the score is lower (78-85 points), because the movement intensity is insufficient and the rhythm perception is not sensitive; when the heart rate is >150 times / minute (high intensity), the score decreases significantly (62-75 points), because fatigue causes movement deformation and deviation of the force application time. The chart helps the dancer find the optimal training intensity interval and avoid blindly increasing the intensity, such as reducing the proportion of high-intensity training from 40% to 25%, increasing the medium-intensity synchronization consolidation training, and improving the overall training efficiency.

[0081] Referring to Figure 5 The figure reflects the training effect prediction and historical progress tracking function of the present application. The actual score steadily increases from 65 points in the first week to 85 points in the sixth week, showing an overall upward trend, with a slight decrease in the fourth week (73 points), which can be analyzed in combination with the training record (such as the complexity of the music increasing that week); the deviation between the predicted score and the actual score is small (≤2 points), indicating that the prediction model is accurate and can provide clear progress expectations for the dancer. For example, the prediction of 88 points in the sixth week encourages the dancer to continue training according to the plan; at the same time, the chart helps the coach verify the effectiveness of the training plan, such as the rapid increase in scores from the third to the fifth week due to the addition of heavy beat special training, which can be continued in the future to enhance the dancer's training confidence and sense of purpose.

[0082] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can make equivalent substitutions or changes to the technical solutions and inventive concepts of the present application within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A quantitative analysis and feedback method for the rhythm synchronization of ballroom dance training, characterized in that, Includes the following steps: Step 1: Multimodal acquisition of training data. A 12-camera optical motion capture system was used, with a frame rate of 60fps and a spatial positioning accuracy of ±0.5mm, covering a 10m×10m training area. Dancer limb movement data was collected, focusing on capturing the real-time angles, movement speeds, and force exertion moments of 16 key joints. Training music audio data was collected using a 48kHz sampling rate directional microphone. Heart rate data of the dancers was collected through a wrist heart rate monitor, with a sampling interval of 1s and an accuracy of ±1 time / minute. The data was correlated with the intensity of movement and rhythm adaptation status, and the music beat interval, tempo value, and accent position were recorded simultaneously. All data were synchronized via timestamps, with a synchronization error of ≤10ms. Step 2: Multimodal data preprocessing. For motion data, a 5th-order Butterworth low-pass filter is used to filter out noise, missing data is completed by linear interpolation, and outliers are removed using the 3σ criterion. For audio data, short-time Fourier transform is used to extract spectral features. For heart rate data, moving average filtering is used to smooth fluctuations. Step 3: Bidirectional extraction of rhythm features. The music beat cycle is identified from the audio data using an autocorrelation algorithm, and the music accent time is matched using a dynamic time warping algorithm. The intensity weight of each beat in each measure is calculated. The key moments of the dancer's movements are extracted from the motion data using a peak detection algorithm. The movement cycle is calculated, and the limb coordination is analyzed using the rate of change of joint angles. Step 4: Quantify synchronization in multiple dimensions, calculate the time difference between action and music; use the cosine similarity algorithm to calculate the action sequence and music beat sequence; calculate the rate of change of synchronization similarity through a sliding window; Step 5: Quantify the results and perform stratified analysis. Based on time difference, consistency, and dynamic trend data, the synchronicity is divided into four levels: excellent, good, medium, and poor. Weak links are marked using a heatmap. By comparing with historical training data, the improvement in synchronicity is calculated, and a progress trend curve is generated. Step 6: Personalized feedback generation, real-time feedback is provided by overlaying beat cue lines on AR glasses, along with voice prompts; The detailed report includes synchronization level, a list of weaknesses, historical comparison charts, and improvement suggestions; the training plan is automatically adjusted based on the weaknesses.

2. The quantitative analysis and feedback method for rhythm synchronization in sports dance training according to claim 1, characterized in that, It also includes a step for evaluating the stability of motion and music synchronization, which is achieved through a formula. Calculate the synchronization stability coefficient, where The metric is the synchronization stability coefficient, and t1 is the start time of the evaluation period. Let t be the time difference between the movement and the musical beat; this step is for a complete dance segment lasting 1-3 minutes in training, using t1=0s and t2=120s for calculation. When <0.6, the time interval during which the stability of automatic positioning decreases.

3. The quantitative analysis and feedback method for rhythm synchronization in sports dance training according to claim 1, characterized in that, It also includes a multi-dancer collaboration synchronization analysis step, which, based on the quantification of individual synchronization, adds an assessment of the synchronization of movements between dancers; using a motion capture system to simultaneously collect key joint data of the trained dancers, and calculating the time difference of similar movements between any two dancers. Statistics within each section The maximum value; a clustering algorithm is used to classify the movement sequences of multiple dancers; a multi-dancer collaboration heatmap is generated, and dancer combinations with weak collaboration are marked with colors.

4. The quantitative analysis and feedback method for rhythm synchronization in sports dance training according to claim 1, characterized in that, It also includes a music style adaptation optimization step, which optimizes feature extraction and quantification standards based on the rhythmic characteristics of different ballroom dance styles. In the waltz style, the music time is 3 / 4 time, with the accent on the first beat. The weight of the accented beat is set to 1.2, and the weight of the non-accented beat is 0.

6. The movement cycle needs to match a 3-beat rhythm. In the tango style, the music contains syncopated rhythms, so syncopated beat detection is added. In the rumba style, the music tempo is relatively slow, so the focus is on quantifying the matching degree between the movement amplitude and the music emotion.

5. The quantitative analysis and feedback method for rhythm synchronization in sports dance training according to claim 1, characterized in that, It also includes a dynamic synchronization adaptation capability assessment step, which is achieved through a formula. Calculate the synchronization fitness rate, where S(t) represents the synchronization similarity between the action and the music at time t, where t is the training time. This step has an evaluation cycle of 5 minutes, calculated every 10 seconds. Plot the synchronization fitness curve. When there are three consecutive 10-second intervals... When the value is less than -0.02, it is determined to be a sign of weak adaptability. The reasons should be analyzed.

6. The quantitative analysis and feedback method for rhythm synchronization in sports dance training according to claim 1, characterized in that, It also includes a training intensity and synchronization correlation analysis step, which combines heart rate data and synchronization quantification results to establish an intensity and synchronization model; the heart rate is divided into three intervals: low intensity, medium intensity, and high intensity, and the synchronization score in each interval is calculated. Analyze the trend of synchronicity with intensity. If the score in the high-intensity range drops by more than 15%, it is determined that intensity affects synchronicity. At the same time, generate an intensity-synchronicity correlation report.

7. The quantitative analysis and feedback method for rhythm synchronization in sports dance training according to claim 1, characterized in that, It also includes a step to optimize the synchronization of movement details. This step, based on the quantification of key joint movements, adds an analysis of the synchronization between muscle exertion and musical rhythm; it uses electromyography (EMG) sensors to collect EMG signals from the dancer's core muscles, extracts the muscle activation moment M, and calculates the time difference between M and the musical beat moment T0. .

8. The quantitative analysis and feedback method for rhythm synchronization in sports dance training according to claim 1, characterized in that, It also includes a multi-dimensional synchronicity comprehensive scoring step, which is achieved through a formula. Calculate the overall synchronization score, where For the overall score, Weighted by time difference, For consistency weight, Stability weights Standardized score for time difference, For consistency standardization score, The stability score is standardized.

9. The quantitative analysis and feedback method for rhythm synchronization in sports dance training according to claim 1, characterized in that, It also includes a real-time feedback interaction optimization step, which adds a haptic feedback device to the AR glasses prompts; when the synchronization deviation is ≤50ms, the haptic feedback intensity is low; when the deviation is 50-100ms, it is medium; when the deviation is >100ms, it is high, to help dancers perceive the synchronization status through touch; at the same time, the voice prompt content is optimized.

10. A quantitative analysis and feedback method for rhythm synchronization in sports dance training according to claim 1, characterized in that, It also includes a training effect prediction and iterative planning step, which uses an LSTM neural network model to improve potential based on the synchronization of dancers’ historical training data; it iterates the training plan according to the prediction results, and if the potential is high, it adds advanced tasks. If the potential is moderate, maintain the current training intensity and optimize training for weak areas; if the potential is low, adjust the training methods. Monthly training effectiveness evaluation reports are generated.

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