An intelligent sports monitoring system and method for water sports

Abstract

The application belongs to the technical field of motion monitoring, and provides an intelligent motion monitoring system and method for water boat sports, which installs combined displacement sensors, inertial sensors and pressure sensing electronic fabric sensors on a hull, installs pressure sensors on paddles, and integrates a multi-modal sensor system in combination with a UAV monitoring system and a heart rate band. With the support of a deep learning algorithm, the application realizes a multi-modal intelligent motion monitoring system. The system can obtain kinematics, dynamics and physiological parameters of athletes in water boat sports in real time, comprehensively and accurately, and does not interfere with normal movement of the athletes. Through multi-dimensional data operation and coupling analysis, the application constructs the most real athlete data model, effectively guiding daily training of the athletes.

Description

Technical Field

[0001] This invention relates to the field of motion monitoring technology, and in particular to an intelligent motion monitoring system and method for water sports such as boating. Background Technology

[0002] With the booming development of water sports, the demand for personalized and efficient training for rowing athletes is increasing. Traditional training models aimed at improving competitive performance are facing significant challenges from digital transformation.

[0003] Currently, training in water sports primarily relies on the experience of coaches and guidance through remote observation of athletes. This approach is not only energy-intensive but also offers limited information, depending mainly on visual data. Therefore, despite existing technologies that assist in training, issues remain, including limitations in assessment dimensions, ineffective coupling of multi-sensor data analysis, and insufficient experimental data in real-world environments.

[0004] Therefore, there is an urgent need for a technical means to comprehensively and accurately monitor and quantify water sports in real time. Summary of the Invention

[0005] This invention provides an intelligent motion monitoring system and method for water sports, which addresses the shortcomings of existing motion monitoring methods in comprehensively and quantitatively analyzing water sports, and enables comprehensive, accurate, and real-time monitoring and quantitative analysis of water sports.

[0006] This invention provides an intelligent motion monitoring system for water sports, including:

[0007] Multimodal sensors are used to monitor data from the hull, the propellers of the hull, and the two athletes on the hull.

[0008] The multimodal sensor includes a combined navigation sensor, an inertial sensor, a pressure sensor, and a pressure-sensing electronic fabric sensor. The combined navigation sensor, inertial sensor, and pressure-sensing electronic fabric sensor are mounted on the hull, and the pressure sensor is mounted on the propeller blade.

[0009] The integrated navigation sensor is used to collect speed information of the hull in multiple directions in order to obtain the motion state of the hull.

[0010] The inertial sensor is used to collect acceleration information of the hull in multiple directions in order to analyze the heading information of the hull;

[0011] The pressure sensor is used to collect force data on the paddle surface in order to analyze the magnitude of the force applied by the athlete with each stroke, the duration of the force application, the degree of coordination between the front and rear paddles, and the stroke frequency.

[0012] The pressure-sensing electronic fabric sensor is used to collect the interface pressure between the athlete and the boat hull in order to analyze the athlete's exertion state.

[0013] The signal processor includes a near-end multi-channel signal acquisition module arranged around the multimodal sensor, which acquires data from the multimodal sensor and stores and transmits the data.

[0014] The integrated processing terminal is used to receive multi-channel data from the signal processor, perform back-end processing and extract key data from the multi-channel data through algorithm models, and use time calibration algorithms to calibrate the multi-channel data on the time axis. Finally, the processing results are converted into a visualized integrated interface.

[0015] An intelligent motion monitoring system for water sports, provided by the present invention, further includes:

[0016] The drone monitoring system is used to collect information on the athlete's technical movements.

[0017] Heart rate monitors are used to collect the athletes' heart rate information in order to analyze the training intensity during water sports.

[0018] According to the present invention, an intelligent motion monitoring system for water sports is provided, wherein the pressure-sensing electronic fabric sensor has piezoresistive fibers arranged in a crisscross pattern to form intersecting sensing points, constituting a sensing array, which is placed at the athlete's feet and knees and connected to the signal processor.

[0019] According to the present invention, an intelligent motion monitoring system for water sports is provided, wherein the multimodal sensor is further used to convert the collected data of the boat hull, paddle and athlete from physical signals into electrical signals;

[0020] The signal processor is further configured to, upon detecting the electrical signal, convert the electrical signal into a digital signal through filtering, amplification, and analog-to-digital conversion, and then transmit the digital signal to the integrated processing terminal.

[0021] According to the present invention, an intelligent motion monitoring system for water sports, specifically the integrated processing terminal is used for:

[0022] Based on the force data of the paddle surface collected by the pressure sensor, the entry and exit times of each paddle are determined.

[0023] The difference between the entry times of the front and rear propellers is determined based on the entry times of each propeller, and the difference between the exit times of the front and rear propellers is determined based on the exit times of each propeller.

[0024] The degree of coordination between the front and rear propellers is determined based on the time difference between their entry and exit from the water.

[0025] According to the present invention, an intelligent motion monitoring system for water sports, specifically boat sports, is provided, wherein the integrated processing terminal is further used for:

[0026] Based on the force data of the paddle surface collected by the pressure sensor, the entry time, exit time and time in the water of each paddle are determined.

[0027] The total number of strokes is determined based on the entry time, exit time, and time spent in the water for each paddle.

[0028] The paddling frequency is determined based on the total number of strokes and the total paddling time.

[0029] According to the present invention, an intelligent motion monitoring system for water sports, specifically the integrated processing terminal is used for:

[0030] Based on the velocity information of the hull in multiple directions collected by the combined navigation sensors, the effective acceleration, effective velocity, and effective displacement of each group of athletes are obtained;

[0031] The effective work of each group of athletes is determined based on their effective acceleration, effective velocity, and effective displacement.

[0032] Based on the force data of the paddle surface collected by the pressure sensor, the total work of each group of athletes is determined;

[0033] The work efficiency of each group of athletes is determined based on their effective work and total work.

[0034] According to the present invention, an intelligent motion monitoring system for water sports, specifically the integrated processing terminal is used for:

[0035] Based on the speed information of the hull in multiple directions collected by the combined navigation sensors;

[0036] Based on the speed information of the hull, the motion of the hull is divided into an acceleration phase, a stabilization phase, and a sprint phase.

[0037] Determine the number of strokes for the acceleration phase, the stabilization phase, and the sprint phase.

[0038] According to the present invention, an intelligent motion monitoring system for water sports, specifically the integrated processing terminal is used for:

[0039] Based on the coordination between the front and rear paddles, the paddling frequency, the work efficiency of each group of athletes, the athletes' technical movement information, and heart rate information, a multi-dimensional data synchronous display interface for athletes' training is generated based on time calibration algorithms and visualization technology. Furthermore, the fatigue level and exercise status of athletes are evaluated based on deep learning algorithms to obtain a comprehensive evaluation result of their athletic performance, which is convenient for coaches to refer to and guide intuitively.

[0040] This invention also provides an intelligent motion monitoring method for watercraft sports, comprising:

[0041] Multimodal sensors are used to monitor data from the hull, the hull's propellers, and the two athletes aboard the hull.

[0042] The multimodal sensor includes a combined navigation sensor, an inertial sensor, a pressure sensor, and a pressure-sensing electronic fabric sensor. The combined navigation sensor, inertial sensor, and pressure-sensing electronic fabric sensor are mounted on the hull, and the pressure sensor is mounted on the propeller blade.

[0043] The combined navigation sensor is used to collect speed information of the hull in multiple directions to obtain the motion state of the hull;

[0044] The inertial sensor is used to collect acceleration information of the hull in multiple directions in order to analyze the heading information of the hull;

[0045] The pressure sensor is used to collect force data on the paddle surface in order to analyze the magnitude of the force applied by the athlete with each stroke, the duration of the force application, the degree of coordination between the front and rear paddles, and the stroke frequency.

[0046] The pressure-sensing electronic fabric sensor is used to collect the interface pressure between the athlete and the boat hull in order to analyze the athlete's exertion state.

[0047] After acquiring data from the multimodal sensor using a near-end multichannel signal acquisition module located around the multimodal sensor in a signal processor, the data is stored and transmitted.

[0048] The integrated processing terminal receives multi-channel data transmitted by the signal processor, performs back-end processing and extracts key data from the multi-channel data through an algorithm model, and uses the integrated processing terminal to align the extracted key data to the same time axis through a time calibration algorithm, clarifying the spatiotemporal mapping relationship between the key data and the athlete's movement state. Finally, the processed data results are presented through visualization technology.

[0049] The intelligent motion monitoring system and method for water sports provided by this invention combines a displacement sensor, an inertial sensor, a pressure sensor, and a pressure-sensing electronic fabric sensor, and is supplemented by a deep learning algorithm to realize a multimodal intelligent motion monitoring system. It can comprehensively and accurately acquire and analyze the motion state of athletes during water sports, obtain real-time synchronous information on the kinematics and dynamics of the athletes' rowing, accurately extract effective motion indicators of athletes, and can be used to construct the most realistic athlete data model to effectively guide athletes' daily training. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0051] Figure 1 This is a schematic diagram of the intelligent motion monitoring system for watercraft sports provided by the present invention;

[0052] Figure 2 This is a schematic diagram of the processing flow of the intelligent motion monitoring system for water sports provided by the present invention;

[0053] Figure 3 This is a schematic diagram of multimodal sensor coupling in the intelligent motion monitoring system for watercraft sports provided by the present invention;

[0054] Figure 4 This is a schematic diagram of the force changes on the paddle surface during paddling in the intelligent motion monitoring system for water sports provided by this invention.

[0055] Figure 5 This is a schematic diagram of the working efficiency of the paddle in the intelligent motion monitoring system for watercraft sports provided by the present invention.

[0056] Figure 6 This is a schematic diagram of the speed change of the hull in the intelligent motion monitoring system for watercraft sports provided by the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0058] The following is combined Figure 1 The present invention describes an intelligent motion monitoring system for water sports, comprising:

[0059] Multimodal sensors are used to monitor data from the hull, the propellers of the hull, and the athletes on the hull.

[0060] The multimodal sensor includes a combined navigation sensor, an inertial sensor, a pressure sensor, and a pressure-sensing electronic fabric sensor. The combined navigation sensor, inertial sensor, and pressure-sensing electronic fabric sensor are mounted on the hull, and the pressure sensor is mounted on the propeller blade.

[0061] The integrated navigation sensor is used to collect speed information of the hull in multiple directions in order to obtain the motion state of the hull.

[0062] The inertial sensor is used to collect acceleration information of the hull in multiple directions in order to analyze the heading information of the hull;

[0063] The pressure sensor is used to collect force data on the paddle surface in order to analyze the magnitude of the force applied by the athlete with each stroke, the duration of the force application, the degree of coordination between the front and rear paddles, and the stroke frequency.

[0064] The pressure-sensing electronic fabric sensor is used to collect the interface pressure between the athlete and the boat hull in order to analyze the athlete's exertion state.

[0065] The signal processor includes a near-end multi-channel signal acquisition module arranged around the multimodal sensor, which acquires data from the multimodal sensor and stores and transmits the data.

[0066] The integrated processing terminal receives multi-channel data transmitted by a signal processor and performs backend processing and key data extraction on this multi-channel data using an algorithm model. The integrated processing terminal uses a time calibration algorithm to align the extracted key data to the same timeline, clarifying the spatiotemporal mapping relationship between the key data and the athlete's movement state, and finally converts the processed results into a visualized integrated interface.

[0067] The monitoring unit in this embodiment includes a hull and oars, and is used to install multimodal sensors. These multimodal sensors collect data on athletes performing rowing activities in still water as standard data. By collecting hull acceleration information in multiple directions, the motion state of the hull and athletes is obtained; by collecting the force on the oar surface, the magnitude of the force applied by each stroke and the duration of each stroke are analyzed; and by collecting the pressure information of the fore and aft oars and the duration of each stroke, the coordination between the fore and aft oarsmen and the stroke frequency in rowing activities are analyzed.

[0068] Different modal sensors, based on different mechanisms, simultaneously collect and transmit motion signals from each paddle, without interfering with each other. Multimodal sensors collect data from different dimensions, forming a network analysis of the athlete's overall paddling status. The data is transmitted wirelessly from the boat hull to a remote signal processor.

[0069] The integrated processing terminal includes terminal devices such as mobile phones, tablets, computers, and servers. This terminal uses a time calibration algorithm to uniformly process the collected multi-dimensional data, mapping the data to the same timeline. It then trains a data model based on a deep neural network, ultimately achieving data visualization. By coupling and statistically analyzing the aforementioned multimodal data, a water sports analysis model for athletes is constructed, thereby comprehensively evaluating their athletic performance and providing a scientific basis for optimizing training strategies.

[0070] The entire system can integrate various areas, including but not limited to outdoor and indoor venues, and can be applied to various water sports such as rowing, kayaking, and dragon boating.

[0071] This embodiment incorporates a hull sensor module and signal processor on the main body of the moving boat, and a pressure sensing module on the paddle surface, with the paddle force sensor positioned closer to the paddle surface. A combined navigation sensor controller is placed in the middle of the boat to collect information such as acceleration and yaw angle, ensuring the sensors are stable and secure. An acceleration sensor is placed at the front of the boat, and a flexible pressure sensor is placed inside the cabin. This allows for real-time acquisition of parameters such as hull angle, hull speed, paddle force, and pedal force. Centered on the boat, paddle, and operator, multimodal data during motion is collected. After signal processing, time axis alignment, and algorithm model analysis, the raw data is transformed into quantitative motion indicators and presented digitally to athletes and coaches, ultimately assisting in training and building personalized training models.

[0072] Furthermore, based on deep learning algorithms, motion models and champion models for water sports athletes can be established by coupling the kinematic and dynamic indicators of the athlete, paddle, and boat. The system includes a controller, a combined navigation sensor module, a paddle pressure sensor module, a signal processor, and a cloud server. The controller receives and analyzes the parameter information collected by the combined navigation sensor module and the pressure sensor module in real time, and sends it to the signal processor. The signal processor analyzes the parameter information from the combined navigation sensor module and the paddle pressure sensor module and uploads the analysis results to the cloud server. Finally, a time calibration algorithm aligns all parameter information to the same timeline, allowing coaches to observe specific parameter information during water sports training in real time and synchronously on a smart terminal. This system significantly improves the efficiency of water sports training, overcoming the technical shortcomings of existing technologies where coaches cannot grasp specific parameter information of athletes during rowing in real time, accurately, and intuitively, and are unable to adjust training methods in a timely manner based on training results.

[0073] This embodiment combines a displacement sensor, an inertial sensor, a pressure sensor, and a pressure-sensing electronic fabric sensor. By unifying the time axis of each parameter through a time calibration algorithm and supplementing it with a deep learning algorithm, a multimodal intelligent real-time motion monitoring system is realized. This system can comprehensively and accurately acquire and analyze the athlete's motion state during water sports, obtain the kinematic and dynamic information of the athlete's rowing in real time, and accurately extract the athlete's effective motion indicators. It can be used to construct the most realistic athlete data model and effectively guide the athlete's daily training.

[0074] Based on the above embodiments, this embodiment also includes a drone monitoring system for collecting the athlete's technical movement information.

[0075] The athlete's technical movement information includes the degree of completion of the movement, the stroke frequency, and the stroke rhythm.

[0076] Based on the above embodiments, this embodiment also includes a heart rate belt for collecting the athlete's heart rate information to analyze the training intensity during water sports.

[0077] When placing a heart rate monitor on an athlete to collect heart rate information, the monitor should be placed closer to the heart.

[0078] By collecting athletes' heart rate information through heart rate monitors, and analyzing the intensity of exercise training and the athletes' fatigue status, scientific training guidance can be provided.

[0079] Based on the above embodiments, in this embodiment, the piezoresistive fibers of the pressure-sensing electronic fabric sensor form intersecting sensing points in a crisscross pattern to constitute a sensing array, which is placed at the athlete's feet and knees and connected to the signal processor.

[0080] The sensor point density must be no less than 1000 points / square meter, the sensor response time no more than 20 milliseconds, and the lifespan greater than 500,000 cycles. The signal acquisition rate of the pressure-sensing electronic fabric sensor must be no less than 100 Hz.

[0081] Based on the above embodiments, the multimodal sensor in this embodiment is also used to convert the collected data of the boat hull, paddle and athlete from physical signals into electrical signals;

[0082] The signal processor is also used to convert the electrical signal into a digital signal after detecting the electrical signal through filtering, amplification and analog-to-digital conversion, and the digital signal finally reaches the integrated processing terminal through the data transmission and processing module.

[0083] like Figure 2 As shown, the processing flow of an intelligent motion monitoring system for water sports includes:

[0084] S01, uses a multimodal sensor to collect motion characteristic signals of the athlete while exercising in the water. After capturing the athlete's motion characteristic signals, the sensor converts the physical signals into electrical signals and sends them to the signal processing circuit;

[0085] S02, after detecting an electrical signal, the signal processor converts the analog signal into a valid digital signal through filtering, amplification, analog-to-digital conversion, etc., and further processes the valid digital signal and transmits the processed digital signal to the integrated processing terminal through wired or wireless communication.

[0086] S03, the integrated processing terminal receives multi-channel data transmitted by the signal processor, and performs time axis alignment, back-end processing and key data extraction through algorithm models, ultimately transforming it into a comprehensive interface that is visualized by athletes and coaches.

[0087] Based on the above embodiments, such as Figure 3 As shown, the integrated processing terminal in this embodiment is specifically used for:

[0088] Based on the force data of the paddle surface collected by the pressure sensor, the entry and exit times of each paddle are determined.

[0089] The difference between the entry times of the front and rear propellers is determined based on the entry times of each propeller, and the difference between the exit times of the front and rear propellers is determined based on the exit times of each propeller.

[0090] The degree of coordination between the front and rear propellers is determined based on the time difference between their entry and exit from the water.

[0091] By combining pressure sensors, changes in the force on the paddle surface during paddling can be obtained, such as... Figure 4As shown. Based on the changes in force on the front and rear propellers, the entry and exit times of the front and rear propellers can be determined. The force on the front and rear propellers increases when they enter the water and decreases when they exit the water. Based on this pattern, the entry and exit times of the front and rear propellers can be determined.

[0092] For example, in a 200-meter rowing test experiment, by analyzing the changes in the force on the paddle surface, it can be found that the average difference in water entry between the front and rear paddles is 0.05s, indicating good coordination between the front and rear paddlers.

[0093] The entry time differences of the front and rear paddles over a period of time can be calculated as a1, a2...an, and the exit time differences as b1, b2...bn. The sum of the entry time differences is calculated as A, and the sum of the exit time differences is calculated as B. The sum of the differences between the entry and exit time differences for each stroke is calculated as C, i.e., the sum of a1-b1, a2-b2...an-bn. The weighted sum of A, B, and C is taken as the coordination degree between the front and rear paddles.

[0094] Based on the above embodiments, such as Figure 3 As shown, the integrated processing terminal in this embodiment is also used for:

[0095] The total number of strokes is determined based on the entry time, exit time, and time spent in the water for each paddle.

[0096] The paddling frequency is determined based on the total number of strokes and the total paddling time.

[0097] The total number of strokes can be determined by dividing each stroke into segments based on the time each paddle enters the water, exits the water, and remains in the water. The stroke frequency is then obtained by dividing the total number of strokes by the total stroke time.

[0098] Based on the above embodiments, such as Figure 3 As shown, the integrated processing terminal in this embodiment is specifically used for:

[0099] Based on the velocity information of the hull in multiple directions collected by the combined navigation sensors, the effective acceleration, effective velocity, and effective displacement of each group of athletes are obtained;

[0100] The effective work of each group of athletes is determined based on their effective acceleration, effective velocity, and effective displacement.

[0101] Based on the force data of the paddle surface collected by the pressure sensor, the total work of each group of athletes is determined;

[0102] The work efficiency of each group of athletes is determined based on their effective work and total work.

[0103] By combining displacement and pressure sensors, the work efficiency of each athlete's stroke can be calculated, such as... Figure 5As shown. For example, in a 200-meter rowing test experiment, the work efficiency of the two athletes tended to stabilize, with each stroke efficiency exceeding 75%.

[0104] Based on the above embodiments, such as Figure 3 As shown, the integrated processing terminal in this embodiment is specifically used for:

[0105] Based on the speed information of the hull in multiple directions collected by the combined navigation sensors;

[0106] Based on the speed information of the hull, the motion of the hull is divided into an acceleration phase, a stabilization phase, and a sprint phase.

[0107] Determine the number of strokes for the acceleration phase, the stabilization phase, and the sprint phase.

[0108] By combining a combination of displacement sensors, the velocity changes of the ship's motion can be obtained, such as... Figure 6 As shown. For example, in a 200-meter rowing test experiment, the athlete paddled a total of 60 strokes. Approximately the first 14 strokes were the acceleration phase, during which the speed of the boat continuously increased; strokes 14-47 were the stabilization phase; strokes 48-60 were the sprint phase, during which the athlete made a final sprint, and the speed increased again.

[0109] Based on the above embodiments, the integrated processing terminal in this embodiment is specifically used for:

[0110] Based on the coordination between the front and rear paddles, the paddle frequency, the work efficiency of each stroke, the athlete's technical movement information, and heart rate information, a visualization graph is generated on the same time axis using a time calibration algorithm and uploaded to a visualization terminal. The athlete's fatigue level and exercise status are obtained using a deep learning algorithm.

[0111] The system employs a time calibration algorithm (such as Dynamic Time Warping, DTW) to synchronize multi-channel data, without specifying the exact type of the algorithm. By training the time calibration algorithm, the time axis of the multimodal data collected by each sensor is unified, ultimately generating a visual graph on the same time axis, thus demonstrating the correlation and consistency between the multi-dimensional collected data.

[0112] The deep learning algorithm can be a convolutional neural network; this embodiment does not limit the type of deep learning algorithm. The deep learning algorithm is trained to obtain the correlation between the coordination of the front and rear paddles, paddle frequency, the work efficiency of each paddle, the athlete's technical movement information, heart rate information, and the athlete's fatigue level.

[0113] Using a trained deep learning algorithm, the athlete's fatigue level can be determined based on the coordination between the front and rear paddles, the stroke frequency, the work efficiency of each paddle, the athlete's technical movement information, and heart rate information.

[0114] This invention also provides an intelligent motion monitoring method for watercraft sports, comprising:

[0115] Multimodal sensors are used to monitor data from the hull, the hull's propellers, and the two athletes aboard the hull.

[0116] The multimodal sensor includes a combined navigation sensor, an inertial sensor, a pressure sensor, and a pressure-sensing electronic fabric sensor. The combined navigation sensor, inertial sensor, and pressure-sensing electronic fabric sensor are mounted on the hull, and the pressure sensor is mounted on the propeller blade.

[0117] The combined navigation sensor is used to collect speed information of the hull in multiple directions to obtain the motion state of the hull;

[0118] The inertial sensor is used to collect acceleration information of the hull in multiple directions in order to analyze the heading information of the hull;

[0119] The pressure sensor is used to collect force data on the paddle surface in order to analyze the magnitude of the force applied by the athlete with each stroke, the duration of the force application, the degree of coordination between the front and rear paddles, and the stroke frequency.

[0120] The pressure-sensing electronic fabric sensor is used to collect the interface pressure between the athlete and the boat hull in order to analyze the athlete's exertion state.

[0121] After acquiring data from the multimodal sensor using a near-end multichannel signal acquisition module located around the multimodal sensor in a signal processor, the data is stored and transmitted.

[0122] The integrated processing terminal receives multi-channel data transmitted by the signal processor, performs back-end processing and extracts key data from the multi-channel data through an algorithm model, and uses the integrated processing terminal to align the extracted key data to the same time axis through a time calibration algorithm, clarifying the spatiotemporal mapping relationship between the key data and the athlete's movement state. Finally, the processed data results are presented through visualization technology.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent motion monitoring system for water sports, characterized in that, include: Multimodal sensors are used to monitor data from the hull, the propeller of the hull, and the two athletes on the hull; The multimodal sensor includes a combined navigation sensor, an inertial sensor, a pressure sensor, and a pressure-sensing electronic fabric sensor. The combined navigation sensor, inertial sensor, and pressure-sensing electronic fabric sensor are mounted on the hull, and the pressure sensor is mounted on the propeller blade. The integrated navigation sensor is used to collect speed information of the hull in multiple directions in order to obtain the motion state of the hull. The inertial sensor is used to collect acceleration information of the hull in multiple directions in order to analyze the heading information of the hull; The pressure sensor is used to collect force data on the paddle surface in order to analyze the magnitude of the force applied by the athlete with each stroke, the duration of the force application, the degree of coordination between the front and rear paddles, and the stroke frequency. The pressure-sensing electronic fabric sensor is used to collect the interface pressure between the athlete and the boat hull in order to analyze the athlete's exertion state. The signal processor includes a near-end multi-channel signal acquisition module arranged around the multimodal sensor, which acquires data from the multimodal sensor and stores and transmits the data. The integrated processing terminal is used to receive multi-channel data transmitted by the signal processor, and to perform back-end processing and key data extraction on the multi-channel data through an algorithm model. The integrated processing terminal uses a time calibration algorithm to align the extracted key data to the same time axis, clarify the spatiotemporal mapping relationship between the key data and the athlete's movement state, and finally present the processed data results through visualization technology. The pressure-sensing electronic fabric sensor has piezoresistive fibers arranged in a crisscross pattern to form intersecting sensing points, constituting a sensing array. It is placed on the athlete's feet and knees and connected to the signal processor. The integrated processing terminal is specifically used for: Based on the force data of the paddle surface collected by the pressure sensor, the entry and exit times of each paddle are determined. The difference between the entry times of the front and rear propellers is determined based on the entry times of each propeller, and the difference between the exit times of the front and rear propellers is determined based on the exit times of each propeller. The degree of coordination between the front and rear propellers is determined based on the time difference between their entry and exit from the water.

2. The intelligent motion monitoring system for watercraft sports according to claim 1, characterized in that, Also includes: The drone monitoring system is used to collect information on the athlete's technical movements. Heart rate monitors are used to collect the athletes' heart rate information in order to analyze the training intensity during water sports.

3. The intelligent motion monitoring system for water sports (boat-based sports) according to claim 1, characterized in that, The multimodal sensor is also used to convert the collected data from the hull, paddle, and athlete into electrical signals; The signal processor is further configured to, upon detecting the electrical signal, convert the electrical signal into a digital signal through filtering, amplification, and analog-to-digital conversion, and then transmit the digital signal to the integrated processing terminal.

4. The intelligent motion monitoring system for water sports (boat-based sports) according to claim 1, characterized in that, The integrated processing terminal is also used for: Based on the force data of the paddle surface collected by the pressure sensor, the entry time, exit time and time in the water of each paddle are determined. The total number of strokes is determined based on the entry time, exit time, and time spent in the water for each paddle. The paddling frequency is determined based on the total number of strokes and the total paddling time.

5. The intelligent motion monitoring system for water sports (boat-based sports) according to claim 2, characterized in that, The integrated processing terminal is specifically used for: Based on the velocity information of the hull in multiple directions collected by the integrated navigation sensors, the effective acceleration, effective velocity, and effective displacement of the hull are obtained. The effective work of each group of athletes is determined based on the effective acceleration, effective velocity, and effective displacement of the boat hull. Based on the force data of the paddle surface collected by the pressure sensor, the total work of each group of athletes is determined; The work efficiency of each group of athletes is determined based on their effective work and total work.

6. The intelligent motion monitoring system for water sports (boat-based sports) according to claim 1, characterized in that, The integrated processing terminal is specifically used for: Based on the speed information of the hull in multiple directions collected by the combined navigation sensors; Based on the speed information of the hull, the motion of the hull is divided into an acceleration phase, a stabilization phase, and a sprint phase. Determine the number of strokes for the acceleration phase, the stabilization phase, and the sprint phase.

7. The intelligent motion monitoring system for water sports (boat-based sports) according to claim 5, characterized in that, The integrated processing terminal is specifically used for: Based on the coordination between the front and rear paddles, the paddle frequency, the work efficiency of each athlete, the technical movement information and heart rate information of the athletes, a comprehensive visualization interface for the athletes' training process is generated using a time calibration algorithm. Furthermore, a deep learning algorithm is used to evaluate the fatigue level and athletic performance of the two athletes, thereby providing quantitative analysis results of athletic performance.

8. An intelligent motion monitoring method for water sports such as boating, characterized in that, The intelligent motion monitoring system for watercraft sports as described in any one of claims 1-7 includes: Multimodal sensors are used to monitor data from the hull, the hull's propellers, and the two athletes aboard the hull. The multimodal sensor includes a combined navigation sensor, an inertial sensor, a pressure sensor, and a pressure-sensing electronic fabric sensor. The combined navigation sensor, inertial sensor, and pressure-sensing electronic fabric sensor are mounted on the hull, and the pressure sensor is mounted on the propeller blade. The combined navigation sensor is used to collect speed information of the hull in multiple directions to obtain the motion state of the hull; The inertial sensor is used to collect acceleration information of the hull in multiple directions in order to analyze the heading information of the hull; The pressure sensor is used to collect force data on the paddle surface in order to analyze the magnitude of the force applied by the athlete with each stroke, the duration of the force application, the degree of coordination between the front and rear paddles, and the stroke frequency. The pressure-sensing electronic fabric sensor is used to collect the interface pressure between the athlete and the boat hull in order to analyze the athlete's exertion state. After acquiring data from the multimodal sensor using a near-end multichannel signal acquisition module located around the multimodal sensor in a signal processor, the data is stored and transmitted. The integrated processing terminal receives multi-channel data transmitted by the signal processor, performs back-end processing and extracts key data from the multi-channel data through an algorithm model, and uses the integrated processing terminal to align the extracted key data to the same time axis through a time calibration algorithm, clarifying the spatiotemporal mapping relationship between the key data and the athlete's movement state. Finally, the processed data results are presented through visualization technology.

Images