Intelligent sports monitoring system and method for water boat sports

Abstract

The invention belongs to the technical field of motion monitoring, and provides an intelligent motion monitoring system and method oriented to water boat sports, a combined displacement sensor, an inertial sensor and a pressure sensing electronic fabric sensor are mounted on a boat body, a pressure sensor is mounted on a paddle, and an unmanned aerial vehicle monitoring system and a heart rate band are combined to monitor the motion of the boat. A multi-modal sensor system is integrated. Under the support of a deep learning algorithm, the invention realizes a multi-modal intelligent motion monitoring system. The system can comprehensively and accurately obtain the kinematics, dynamics and physiology parameters of the athlete in the water boat sports in real time, and does not interfere with the normal sports of the athlete. Through multi-dimensional data operation and coupling analysis, the most real athlete data model is constructed, and daily training of athletes is effectively guided.

Description

Technical Field

[0001] The present invention relates to the technical field of motion monitoring, and in particular to an intelligent motion monitoring system and method for watercraft sports. Background Art

[0002] With the booming development of water sports, the demand for personalized and efficient training for boat athletes is growing. The traditional training model aimed at improving competitive level is facing major challenges of digital transformation.

[0003] At present, the training of water sports mainly relies on the experience of coaches and the guidance of athletes through remote observation. This method is not only energy-consuming, but also the dimension of information acquisition is relatively single, mainly relying on visual information. Therefore, although there are some auxiliary training methods in the existing technology, there are still problems such as limited evaluation dimensions, ineffective coupling of multi-sensor data analysis, and insufficient experimental data in real environments.

[0004] Therefore, there is an urgent need for a technical means that can comprehensively and accurately monitor and quantitatively analyze water sports in real time. Summary of the invention

[0005] The present invention provides an intelligent motion monitoring system and method for water boat sports, which are used to solve the defect that existing motion monitoring means are difficult to comprehensively and quantitatively analyze water sports, and realize comprehensive, accurate and real-time monitoring and quantitative analysis of water sports.

[0006] The present invention provides an intelligent motion monitoring system for watercraft sports, comprising:

[0007] A multimodal sensor for monitoring data of a boat, an oar of the boat, and two athletes on the boat;

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

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

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

[0011] The pressure sensor is used to collect force data of the paddle surface to analyze the force applied by each paddle, the force application time, the coordination degree of the front and rear paddles, and the paddling frequency of the athlete;

[0012] The pressure sensing electronic fabric sensor is used to collect the interface pressure between the athlete and the boat body to analyze the athlete's force state;

[0013] A signal processor, comprising a proximal multi-channel signal acquisition module arranged around the multi-modal sensor, storing and transmitting the data after the proximal multi-channel signal acquisition module acquires the data of the multi-modal sensor;

[0014] The integrated processing terminal is used to receive multi-channel data from the signal processor, perform back-end processing on the multi-channel data and extract key data through the algorithm model, and use the time calibration algorithm to realize the calibration processing of the multi-channel data on the time axis, and finally convert the processing results into a visual integrated interface.

[0015] According to the present invention, an intelligent motion monitoring system for watercraft sports also includes:

[0016] A drone monitoring system for collecting technical movement information of the athletes;

[0017] The heart rate belt is used to collect the athlete's heart rate information to analyze the training intensity during water sports.

[0018] According to an intelligent sports monitoring system for watercraft sports provided by the present invention, the piezoresistive fibers of the pressure-sensing electronic textile sensor form cross-sensing points in a horizontal and vertical staggered manner to form a sensor array, which is placed at the feet and knees of the athlete and connected to the signal processor.

[0019] According to the intelligent sports monitoring system for watercraft sports provided by the present invention, the multimodal sensor is also used to convert the collected data of the boat body, oars and athletes from physical signals into electrical signals;

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

[0021] According to an intelligent motion monitoring system for watercraft sports provided by the present invention, the comprehensive processing terminal is specifically used for:

[0022] Determine the water entry and exit time of each paddle according to the force data of the paddle surface collected by the pressure sensor;

[0023] Determine the water entry time difference between the front and rear paddles according to the water entry time of each paddle, and determine the water exit time difference between the front and rear paddles according to the water exit time of each paddle;

[0024] The degree of coordination between the front and rear paddles is determined based on the time difference between the front and rear paddles entering the water and exiting the water.

[0025] According to the intelligent motion monitoring system for watercraft sports provided by the present invention, the comprehensive processing terminal is also used for:

[0026] Determine the time when each paddle enters the water, the time when each paddle exits the water, and the time in the water according to the force data of the paddle surface collected by the pressure sensor;

[0027] Determine the total number of strokes based on the time in the water, out of the water, and in the water for each paddle;

[0028] Determine your stroke rate based on the total number of strokes and the total time you stroke.

[0029] According to an intelligent motion monitoring system for watercraft sports provided by the present invention, the comprehensive processing terminal is specifically used for:

[0030] According to the speed information of the boat body in multiple directions collected by the combined navigation sensor, the effective acceleration, effective speed and effective displacement of each group of athletes are obtained;

[0031] Determining the effective work of each group of athletes based on the effective acceleration, effective velocity and effective displacement of each group of athletes;

[0032] Determine the total work of each group of athletes based on the force data of the paddle surface collected by the pressure sensor;

[0033] Based on the effective work and total work of each group of athletes, the work efficiency of each group of athletes is determined.

[0034] According to an intelligent motion monitoring system for watercraft sports provided by the present invention, the comprehensive processing terminal is specifically used for:

[0035] Speed ​​information of the hull in multiple directions collected by the combined navigation sensor;

[0036] According to the speed information of the boat body, the movement of the boat body is divided into an acceleration phase, a stabilization phase and a sprint phase;

[0037] The number of strokes during the acceleration phase, the steady state phase, and the sprint phase are determined.

[0038] According to an intelligent motion monitoring system for watercraft sports provided by the present invention, the comprehensive processing terminal is specifically used for:

[0039] According to the coordination degree of the front and rear oars, the rowing frequency, the work efficiency of each group of athletes, the athletes' technical movement information and heart rate information, a multi-dimensional data synchronization display interface for the athletes' training is generated based on the time calibration algorithm and visualization technology; further, the athletes' fatigue level and exercise status are evaluated based on the deep learning algorithm to obtain a comprehensive evaluation result of the sports performance, which is convenient for the coach to intuitively refer to and guide.

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

[0041] Using multimodal sensors to monitor the data of a boat, the paddles of the boat and two athletes on the boat,

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

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

[0044] Using the inertial sensor to collect acceleration information of the hull in multiple directions to analyze the heading information of the hull;

[0045] Using the pressure sensor to collect force data on the paddle surface to analyze the force applied by each paddle, the force application time, the coordination degree of the front and rear paddles, and the paddling frequency of the athlete;

[0046] Using the pressure sensing electronic fabric sensor to collect the interface pressure between the athlete and the boat body to analyze the athlete's force state;

[0047] After collecting data from the multimodal sensor using a proximal multi-channel signal acquisition module arranged around the multimodal sensor in a signal processor, the data is stored and transmitted;

[0048] An integrated processing terminal is used to receive multi-channel data transmitted by a signal processor, and the multi-channel data is subjected to back-end processing and key data extraction through an algorithm model. The integrated processing terminal is used to align the extracted key data to the same time axis through a time calibration algorithm, and the spatiotemporal mapping relationship between the key data and the athlete's motion state is clarified, and finally the processed data results are presented through visualization technology.

[0049] The intelligent motion monitoring system and method for water boat sports provided by the present invention realizes a multimodal intelligent motion monitoring system by combining a combined displacement sensor, an inertial sensor, a pressure sensor and a pressure-sensing electronic fabric sensor, and supplemented by a deep learning algorithm. The system can comprehensively and accurately obtain and analyze the motion state of athletes during water sports, obtain the synchronous information of the kinematics and dynamics of the athletes' rowing in real time, and accurately extract the effective motion indicators of the athletes. It can be used to construct the most realistic athlete data model and effectively guide the athletes' daily training. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

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

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

[0053] Figure 3 It is a schematic diagram of multi-modal sensor coupling in an intelligent motion monitoring system for watercraft sports provided by the present invention;

[0054] Figure 4 It is a schematic diagram of force changes on the paddle surface when paddling in the intelligent sports monitoring system for watercraft sports provided by the present invention;

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

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

[0057] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Combine the following Figure 1 The present invention is an intelligent motion monitoring system for watercraft sports, comprising:

[0059] A multimodal sensor for monitoring the boat, the oars of the boat, and the data of the athletes on the boat;

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

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

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

[0063] The pressure sensor is used to collect force data of the paddle surface to analyze the force applied by each paddle, the force application time, the coordination degree of the front and rear paddles, and the paddling frequency of the athlete;

[0064] The pressure sensing electronic fabric sensor is used to collect the interface pressure between the athlete and the boat body to analyze the athlete's force state;

[0065] A signal processor, comprising a proximal multi-channel signal acquisition module arranged around the multi-modal sensor, storing and transmitting the data after the proximal multi-channel signal acquisition module acquires the data of the multi-modal sensor;

[0066] The integrated processing terminal is used to receive the multi-channel data transmitted by the signal processor, and perform back-end processing and key data extraction on the multi-channel data through the algorithm model. The integrated processing terminal aligns the extracted key data to the same time axis through the time calibration algorithm, clarifies the time-space mapping relationship between the key data and the athlete's motion state, and finally converts the processed results into a visual integrated interface.

[0067] The monitoring body in this embodiment includes a boat body and a paddle, which are used to install a multimodal sensor. Through the multimodal sensor, the data of athletes performing boat sports are collected in a still water environment as standard data. By collecting the acceleration information of the boat body in multiple directions, the movement state of the boat body and the athlete is obtained; by collecting the force on the paddle surface, the force applied by each paddle and the time information of each paddle are analyzed; by collecting the pressure information of the front and rear paddles and the time information of each paddle, the coordination degree and paddling frequency of the front and rear paddle athletes in boat sports are analyzed.

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

[0069] The comprehensive processing terminal includes terminal devices such as mobile phones, tablet computers, computers and servers. The comprehensive processing terminal uniformly processes the collected multi-dimensional data through a time calibration algorithm, maps the multi-dimensional data to the same time axis, and trains the data model based on a deep neural network to finally achieve data visualization. By coupling the above multi-modal data and performing statistical analysis, an athlete's water sports analysis model is constructed, thereby comprehensively evaluating the athlete's sports status and providing a scientific basis for optimizing training strategies.

[0070] The entire system can integrate multiple areas including but not limited to outdoor venues and indoor venues, and can be applied to various watercraft sports such as rowing, canoeing, and dragon boat racing.

[0071] In this embodiment, a hull sensor combination module and a signal processor are set on the main body of the mobile boat, and a pressure sensor module is set on the paddle surface. The sensor for collecting the paddle force should be 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 deflection angle, and it is necessary to ensure that the sensor is stable and not loose. An acceleration sensor is placed at the front of the boat, and a flexible pressure sensor is placed in the cabin. Parameter information such as hull angle, hull speed, paddle surface force, and pedal force can be collected in real time. With the boat, paddle, and person as the center, multimodal data in motion is collected. After signal processing, time axis alignment, and algorithm model analysis, the relevant raw data is converted into sports quantitative indicators and presented to athletes and coaches in digital form, finally completing training assistance and building a personalized training model.

[0072] It is also possible to establish a sports model and a champion model for water athletes by coupling the kinematic and dynamic indicators of people, oars and boats based on the deep learning algorithm model. The system includes a controller, a combined navigation sensor module, a paddle pressure sensing module, a signal processor and a cloud server. The controller can receive and analyze the parameter information collected by the combined navigation sensor module and the pressure sensing module in real time, and send it to the signal processor; the signal processor is used to analyze the parameter information of the combined navigation sensor module and the paddle pressure sensing module, and upload the analysis results to the cloud server. Finally, all parameter information is aligned to the same time axis through the time calibration algorithm, so that the coach can observe the specific parameter information of the water sports training process in real time and synchronously on the smart terminal. The system greatly improves the efficiency of water sports training, overcomes the technical defects in the prior art that the coach cannot grasp the specific parameter information of the athlete in the rowing process in real time, accurately and intuitively, and it is difficult to adjust the training method in time according to the training effect.

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

[0074] On the basis of the above embodiment, this embodiment also includes a drone monitoring system for collecting the technical movement information of the athlete.

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

[0076] On the basis of the above embodiment, this embodiment further includes a heart rate belt for collecting the heart rate information of the athlete to analyze the training intensity during water sports.

[0077] When placing a heart rate belt on an athlete, the belt that collects heart rate information should be closer to the human heart.

[0078] By collecting the athlete's heart rate information through a heart rate belt, the intensity of exercise training and the athlete's fatigue state can be analyzed, and scientific training guidance can be given.

[0079] On the basis of the above-mentioned embodiment, the piezoresistive fibers of the pressure-sensing electronic textile sensor in this embodiment form cross-sensing points in a horizontal and vertical staggered manner to form a sensing array, which is placed at the athlete's feet and knees and connected to the signal processor.

[0080] The density of sensing points is required to be no less than 1,000 per square meter, the sensor response time is no more than 20 milliseconds, and the service life is greater than 500,000 times. The signal acquisition rate of the pressure sensing electronic fabric sensor is no less than 100 Hz.

[0081] On the basis of the above embodiment, the multimodal sensor in this embodiment is further used to convert the collected data of the boat, oars and athletes from physical signals into electrical signals;

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

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

[0084] S01, collecting motion characteristic signals of athletes in water sports through multimodal sensors. After the sensors capture the motion characteristic signals of athletes, they convert the physical signals into electrical signals and transmit them to the signal processing circuit;

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

[0086] S03, the integrated processing terminal receives the multi-channel data transmitted by the signal processor, and performs time axis alignment, back-end processing and key data extraction through the algorithm model, and finally converts it into a visual integrated interface for athletes and coaches.

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

[0088] Determine the water entry and exit time of each paddle according to the force data of the paddle surface collected by the pressure sensor;

[0089] Determine the water entry time difference between the front and rear paddles according to the water entry time of each paddle, and determine the water exit time difference between the front and rear paddles according to the water exit time of each paddle;

[0090] The degree of coordination between the front and rear paddles is determined based on the time difference between the front and rear paddles entering the water and exiting the water.

[0091] Combined with the pressure sensor, the force changes on the paddle surface during paddling can be obtained, such as Figure 4As shown. According to the force changes of the front and rear paddles, the entry and exit times of the front and rear paddles are determined. The force on the front and rear paddles increases when they enter the water, and decreases when they exit the water. Based on this rule, the entry and exit times of the front and rear paddles 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 learned that the average water entry difference between the front and rear paddles is 0.05s, and the front and rear paddlers cooperate well.

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

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

[0095] Determine the total number of strokes based on the time in the water, out of the water, and in the water for each paddle;

[0096] Determine your stroke rate based on the total number of strokes and the total time you stroke.

[0097] The total number of strokes can be determined by dividing the time of each stroke by the time it takes each paddle to enter the water, exit the water, and stay in the water. The total number of strokes is divided by the total stroke time to get the stroke frequency.

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

[0099] According to the speed information of the boat body in multiple directions collected by the combined navigation sensor, the effective acceleration, effective speed and effective displacement of each group of athletes are obtained;

[0100] Determining the effective work of each group of athletes based on the effective acceleration, effective velocity and effective displacement of each group of athletes;

[0101] Determine the total work of each group of athletes based on the force data of the paddle surface collected by the pressure sensor;

[0102] Based on the effective work and total work of each group of athletes, the work efficiency of each group of athletes is determined.

[0103] By combining the displacement sensor and the pressure sensor, the work efficiency of each stroke of each group of athletes can be calculated, such as Figure 5For example, in a 200-meter rowing test experiment, the work efficiency of the two athletes tended to be stable, with each stroke efficiency above 75%.

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

[0105] Speed ​​information of the hull in multiple directions collected by the combined navigation sensor;

[0106] According to the speed information of the boat body, the movement of the boat body is divided into an acceleration phase, a stabilization phase and a sprint phase;

[0107] The number of strokes during the acceleration phase, the steady state phase, and the sprint phase are determined.

[0108] Combined with the combined displacement sensor, the speed change of the hull motion can be obtained, such as Figure 6 For example, in a 200-meter rowing test, the athlete rowed a total of 60 strokes. The first 14 strokes were the acceleration phase, and the boat speed continued to increase; 14-47 strokes were the stable phase; 48-60 strokes were the sprint phase, and the athlete made the final sprint, and the speed increased again.

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

[0110] According to the coordination degree of the front and rear oars, the rowing frequency, the work efficiency of each oar, the technical movement information and the heart rate information of the athlete, a visualization graph is generated on the same time axis based on a time calibration algorithm and uploaded to a visualization terminal, and the fatigue degree and exercise state of the athlete are obtained based on a deep learning algorithm.

[0111] The system uses a time calibration algorithm (such as dynamic time warping, Dynamic Time Warping, DTW) to synchronize the multi-channel data, and the specific type of the time calibration algorithm is not limited. By training the time calibration algorithm, the time axis of the multimodal data collected by each sensor is unified, and finally a visualization graph is generated under the same time axis to obtain the correlation and consistency between the multi-dimensional collected data.

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

[0113] Using the trained deep learning algorithm, the athlete’s fatigue level can be obtained based on the coordination between the front and rear oars, the rowing frequency, the work efficiency of each oar, the athlete’s technical movement information and heart rate information.

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

[0115] Using multimodal sensors to monitor the data of a boat, the paddles of the boat and two athletes on the boat,

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

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

[0118] Using the inertial sensor to collect acceleration information of the hull in multiple directions to analyze the heading information of the hull;

[0119] Using the pressure sensor to collect force data on the paddle surface to analyze the force applied by each paddle, the force application time, the coordination degree of the front and rear paddles, and the paddling frequency of the athlete;

[0120] Using the pressure sensing electronic fabric sensor to collect the interface pressure between the athlete and the boat body to analyze the athlete's force state;

[0121] After collecting data from the multimodal sensor using a proximal multi-channel signal acquisition module arranged around the multimodal sensor in a signal processor, the data is stored and transmitted;

[0122] An integrated processing terminal is used to receive multi-channel data transmitted by a signal processor, and the multi-channel data is subjected to back-end processing and key data extraction through an algorithm model. The integrated processing terminal is used to align the extracted key data to the same time axis through a time calibration algorithm, and the spatiotemporal mapping relationship between the key data and the athlete's motion state is clarified, and 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, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent motion monitoring system for watercraft sports, characterized in that: include: A multimodal sensor for monitoring data of a boat, an oar of the boat, and two athletes on the boat; The multimodal sensor comprises a combined navigation sensor, an inertial sensor, a pressure sensor and a pressure-sensing electronic fabric sensor, wherein the combined navigation sensor, the inertial sensor and the pressure-sensing electronic fabric sensor are mounted on the hull, and the pressure sensor is mounted on the 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 to analyze the heading information of the hull; The pressure sensor is used to collect force data of the paddle surface to analyze the force applied by each paddle, the force application time, the coordination degree of the front and rear paddles, and the paddling frequency of the athlete; The pressure sensing electronic fabric sensor is used to collect the interface pressure between the athlete and the boat body to analyze the athlete's force state; A signal processor, comprising a proximal multi-channel signal acquisition module arranged around the multi-modal sensor, storing and transmitting the data after the proximal multi-channel signal acquisition module acquires the data of the multi-modal sensor; The integrated processing terminal is used to receive the multi-channel data transmitted by the signal processor, and perform back-end processing and key data extraction on the multi-channel data through the algorithm model. The integrated processing terminal aligns the extracted key data to the same time axis through the time calibration algorithm, clarifies the spatiotemporal mapping relationship between the key data and the athlete's motion state, and finally presents the processed data results through visualization technology.

2. The intelligent motion monitoring system for watercraft sports according to claim 1 is characterized in that: Also includes: A drone monitoring system for collecting technical movement information of the athletes; The heart rate belt is used to collect the athlete's heart rate information to analyze the training intensity during water sports.

3. The intelligent motion monitoring system for watercraft sports according to claim 1 is characterized in that: The piezoresistive fibers of the pressure-sensing electronic textile sensor are arranged in a horizontal and vertical interlaced manner to form cross-sensing points, forming a sensing array, which is placed at the athlete's feet and knees and connected to the signal processor.

4. The intelligent motion monitoring system for watercraft sports according to claim 1 is characterized in that: The multimodal sensor is also used to convert the collected data of the boat, oars and athletes from physical signals into electrical signals; The signal processor is also used to convert the electrical signal into a digital signal through filtering, amplification and analog-to-digital conversion after detecting the electrical signal, and transmit the digital signal to the integrated processing terminal.

5. The intelligent motion monitoring system for watercraft sports according to claim 1 is characterized in that: The comprehensive processing terminal is specifically used for: Determine the water entry and exit time of each paddle according to the force data of the paddle surface collected by the pressure sensor; Determine the water entry time difference between the front and rear paddles according to the water entry time of each paddle, and determine the water exit time difference between the front and rear paddles according to the water exit time of each paddle; The degree of coordination between the front and rear paddles is determined based on the time difference between the front and rear paddles entering the water and exiting the water.

6. The intelligent motion monitoring system for watercraft sports according to claim 1, characterized in that: The integrated processing terminal is also used for: Determine the time when each paddle enters the water, the time when each paddle exits the water, and the time in the water according to the force data of the paddle surface collected by the pressure sensor; Determine the total number of strokes based on the time in the water, out of the water, and in the water for each paddle; Determine your stroke rate based on the total number of strokes and the total time you stroke.

7. The intelligent motion monitoring system for watercraft sports according to claim 2, characterized in that: The comprehensive processing terminal is specifically used for: Acquire the effective acceleration, effective speed and effective displacement of the hull according to the speed information of the hull in multiple directions collected by the combined navigation sensor; Determining the effective work of each group of athletes based on the effective acceleration, effective velocity and effective displacement of the boat; Determine the total work of each group of athletes based on the force data of the paddle surface collected by the pressure sensor; Based on the effective work and total work of each group of athletes, the work efficiency of each group of athletes is determined.

8. The intelligent motion monitoring system for watercraft sports according to claim 1, characterized in that: The comprehensive processing terminal is specifically used for: Speed ​​information of the hull in multiple directions collected by the combined navigation sensor; According to the speed information of the boat body, the movement of the boat body is divided into an acceleration phase, a stabilization phase and a sprint phase; The number of strokes during the acceleration phase, the steady state phase, and the sprint phase are determined.

9. The intelligent motion monitoring system for watercraft sports according to claim 7, characterized in that: The comprehensive processing terminal is specifically used for: According to the coordination degree of the front and rear oars, the rowing frequency, the work efficiency of each athlete, the technical movement information and the heart rate information of the athletes, a time calibration algorithm is used to generate a comprehensive visualization interface of the athletes' training process, and a deep learning algorithm is used to evaluate the fatigue level and athletic performance of the two athletes, thereby providing quantitative analysis results of the athletic performance.

10. An intelligent motion monitoring method for watercraft sports, characterized in that: include: Using multimodal sensors to monitor the data of the boat, the paddles of the boat and the two athletes on the boat, The multimodal sensor comprises a combined navigation sensor, an inertial sensor, a pressure sensor and a pressure-sensing electronic fabric sensor, wherein the combined navigation sensor, the inertial sensor and the pressure-sensing electronic fabric sensor are mounted on the hull, and the pressure sensor is mounted on the blade; Using the combined navigation sensor to collect speed information of the hull in multiple directions to obtain the motion state of the hull; Using the inertial sensor to collect acceleration information of the hull in multiple directions to analyze the heading information of the hull; Using the pressure sensor to collect force data on the paddle surface to analyze the force applied by each paddle, the force application time, the coordination degree of the front and rear paddles, and the paddling frequency of the athlete; Using the pressure sensing electronic fabric sensor to collect the interface pressure between the athlete and the boat body to analyze the athlete's force state; After collecting data of the multimodal sensor using a proximal multi-channel signal acquisition module arranged around the multimodal sensor in a signal processor, the data is stored and transmitted; An integrated processing terminal is used to receive multi-channel data transmitted by a signal processor, and the multi-channel data is subjected to back-end processing and key data extraction through an algorithm model. The integrated processing terminal is used to align the extracted key data to the same time axis through a time calibration algorithm, and the spatiotemporal mapping relationship between the key data and the athlete's motion state is clarified, and finally the processed data results are presented through visualization technology.

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