Swimming motion parameter real-time monitoring device and system based on acoustic feedback

Abstract

The invention relates to the technical field of exercise monitoring, in particular to a swimming exercise parameter real-time monitoring device based on acoustic feedback, which comprises an ear-hanging main body made of a skin-friendly anti-corrosion and anti-fouling material and used for being worn by athletes in an ear-hanging manner; the sensor module comprises an inertia measurement unit, a magnetometer, a water pressure sensor, a temperature sensor, a heart rate sensor and a biological impedance measurement unit; and the voice synthesizer is used for generating voice information from the parameters processed by the data operation processing and storage module and performing audio output through the bone conduction loudspeaker. Through the bone conduction acoustic technology, the limitation of traditional visual feedback under water is solved, and athletes can obtain motion parameters without interrupting actions. In addition, a multi-modal sensor is combined with a machine learning algorithm, synchronous analysis of multi-dimensional data such as motion postures, physiological indexes and training efficiency is achieved, and a basis is provided for scientific training.

Description

Technical Field

[0001] The present invention relates to the technical field of motion monitoring, and in particular to a device and system for real-time monitoring of swimming motion parameters based on acoustic feedback. Background Art

[0002] Swimming performance monitoring is the process of collecting, analyzing, and evaluating a swimmer's swimming data. It helps swimmers understand their performance, improve training effectiveness, and prevent sports injuries. By monitoring and analyzing swimming performance data, coaches can develop personalized training plans for each swimmer. Based on individual fitness, technical characteristics, and training goals, they can rationally arrange training intensity, distance, and content, improving the relevance and effectiveness of training.

[0003] Traditional devices rely on visual displays (such as waterproof watches and swimming pool monitors). However, light refraction and air bubble interference in underwater environments make visual information difficult to discern, forcing athletes to frequently look up to check data, disrupting their swimming rhythm. Some devices store data locally and synchronize it ashore, or due to bandwidth limitations of underwater wireless communications, training data feedback is delayed, making real-time adjustments impossible. Summary of the Invention

[0004] In response to the deficiencies of the prior art, the present invention provides a real-time monitoring device and system for swimming motion parameters based on acoustic feedback, which solves the problem that traditional equipment relies on visual display, resulting in inconvenience in viewing during exercise.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a real-time monitoring device for swimming motion parameters based on acoustic feedback, comprising:

[0006] An ear-hook body, made of skin-friendly, corrosion-resistant, and anti-fouling material, and intended for athletes to wear on their ears;

[0007] A sensor module is used to collect multiple data during swimming, and the sensor module includes an inertial measurement unit, a magnetometer, a water pressure sensor, a temperature sensor, a heart rate sensor, and other sensors;

[0008] A data processing and storage module, which is used to process and perform pattern recognition on the data collected by the sensor module and write the processing results to non-volatile storage. The data processing and storage module can also be used to store user-defined audio data. The data processing and storage module has a real-time signal processing unit and a machine learning analysis unit;

[0009] A speech synthesizer and a bone conduction speaker, wherein the speech synthesizer is used to generate speech information from the parameters processed by the data processing module and output audio through the bone conduction speaker;

[0010] A wireless communication module supporting multiple personal wireless local area network protocols for data transmission;

[0011] A wireless charging coil and a mobile power module, the mobile power module being a high-energy-density battery and having a charge-discharge management system, and the wireless charging coil being used for wireless charging of the mobile power module.

[0012] Preferably, the ear-hanging main body is connected with an earphone body at both ends, and the bone conduction speaker is close to the inner wall of one side of the earphone body.

[0013] Preferably, the wireless charging coil is fixedly connected to the middle position of the inner wall of one side of the earphone body, and the data operation processing and storage module, the wireless communication module, the sensor module and the mobile power module are distributed in a ring shape around the wireless charging coil, the voice synthesizer is fixedly connected to the outer wall of the other side of the earphone body, and the bone conduction speaker is fixedly connected with the voice synthesizer.

[0014] Preferably, the sensor module further comprises a bioimpedance measurement unit for calorie consumption calculation.

[0015] Preferably, the data operation processing and storage module adopts a distributed architecture, including a terminal computing unit and a cloud server, the terminal computing unit is used for data processing and recording during exercise, and the cloud server is used for deep analysis and storage of data after exercise; the voice synthesizer supports priority interrupt management of background music and voice information, and preferentially broadcasts exercise parameter prompts.

[0016] Preferably, an acoustic feedback-based real-time monitoring system for swimming exercise parameters comprises:

[0017] A cloud server, in communication connection with the monitoring device, for receiving, storing data and performing deep analysis;

[0018] A mobile terminal application, which can be in direct communication connection with the device of the application, or in communication connection with the cloud server, for data visualization, training plan generation and social sharing;

[0019] An edge computing unit: deployed locally on the device, for performing real-time noise filtering and feature extraction of sensor data;

[0020] The cloud server adopts an adaptive data compression transmission protocol to realize efficient data transmission between the monitoring device and the cloud.

[0021] Preferably, a real-time monitoring method for swimming exercise parameters comprises the following steps:

[0022] Swimming exercise data is collected by the sensor module;

[0023] Adaptive filtering algorithm is used to eliminate water environment interference;

[0024] The processed data is classified by using an LSTM neural network;

[0025] Real-time broadcast of motion parameters through bone conduction speakers, and management of the priority of background music and voice prompts;

[0026] Based on the random forest algorithm, the motion performance is predicted, and the SWOLF index and calorie consumption data are output.

[0027] The application provides a swimming motion parameter real-time monitoring device and system based on acoustic feedback.

[0028] The application has the following beneficial effects:

[0029] 1、The application solves the limitations of traditional visual feedback under water through bone conduction acoustic technology, so that athletes, swimming enthusiasts and users can obtain motion parameters without interrupting their actions. In addition, the combination of multi-modal sensors and machine learning algorithms realizes the synchronous analysis of multi-dimensional data such as motion posture, physiological indicators and training efficiency, providing a basis for scientific training. Furthermore, the combination of acoustic feedback priority management and adaptive algorithms meets the interactive needs of underwater motion scenes and improves the convenience of user operation. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 The application is a monitoring device structure schematic diagram;

[0031] Figure 2 The application is a monitoring device structure explosion diagram.

[0032] 1, ear-hanging main body;2, earphone body;3, wireless charging coil;4, data operation processing and storage module;5, wireless communication module;6, sensor module;7, mobile power module;8, voice synthesizer;9, bone conduction speaker. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0034] Embodiment one:

[0035] As Figure 1-2As shown, an embodiment of the present invention provides a real-time monitoring device for swimming motion parameters based on acoustic feedback, comprising:

[0036] The earhook body 1 is made of a skin-friendly, corrosion-resistant, and anti-fouling material, and is designed for athletes to wear over their ears. The earphone body 2 is connected to both ends of the earhook body 1, and the bone conduction speaker 9 is closely attached to the inner wall of the earphone body 2. Made of skin-friendly, corrosion-resistant, and anti-fouling material, the earhook body 1 has a soft and delicate touch, and does not cause noticeable friction or pressure when in contact with the ear skin.

[0037] Sensor module 6 is used to collect multidimensional data during swimming. It includes an inertial measurement unit (IMU), a magnetometer, a water pressure sensor, a temperature sensor, and a heart rate sensor. This microsensor array, manufactured using MEMS technology, integrates these components into a single chip. The sensor surface is coated with a 500nm thick nano-hydrophobic coating, which, combined with the multi-layer sealing design of the main structure, achieves IPX8 waterproofing.

[0038] Data processing and storage module 4, which is used to process and perform pattern recognition on the data collected by the sensor module 6 and write the processing results into non-volatile storage. The data processing and storage module 4 can also be used to store user-defined audio data. The data processing and storage module 4 has a real-time signal processing unit and a machine learning analysis unit;

[0039] A speech synthesizer 8 and a bone conduction speaker 9, wherein the speech synthesizer 8 is used to generate speech information from the parameters processed by the data operation and storage module 4 and output the audio through the bone conduction speaker 9;

[0040] Wireless communication module 5, which supports multiple personal wireless local area network protocols for data transmission;

[0041] The wireless charging coil 3 and the mobile power module 7, the mobile power module 7 is a high-energy-density battery and has a charge and discharge management system. The energy density reaches 600Wh / kg, supports 2-hour fast charging, and can work continuously for 8 hours when fully charged. The wireless charging coil 3 is used to wirelessly charge the mobile power module 7.

[0042] The wireless charging coil 3 is fixedly connected to the middle position of the inner wall of one side of the earphone body 2, and the data calculation processing and storage module 4, wireless communication module 5, sensor module 6 and mobile power module 7 are distributed in a ring around the wireless charging coil 3. The speech synthesizer 8 is fixedly connected to the outer wall of the other side of the earphone body 2, and the bone conduction speaker 9 is fixedly connected to the speech synthesizer 8.

[0043] The sensor module 6 also includes a bioimpedance measurement unit for calorie consumption calculation; the data calculation processing and storage module 4 adopts a distributed architecture, including a terminal computing unit and a cloud server. The terminal computing unit is used for data processing and recording during exercise, and the cloud server is used for in-depth analysis and storage of data after exercise; the speech synthesizer 8 supports priority interrupt management of background music and voice information, and gives priority to broadcasting exercise parameter prompts.

[0044] Example 2:

[0045] An embodiment of the present invention provides a real-time monitoring system for swimming motion parameters based on acoustic feedback, comprising:

[0046] A cloud server, in communication with the monitoring device, for receiving and storing data and performing in-depth analysis;

[0047] A mobile application can be directly connected to the headset body (2) and can also be connected to the cloud server for data visualization, training plan generation and social sharing;

[0048] Edge computing unit: deployed locally on the device, performing real-time noise filtering and feature extraction of sensor data;

[0049] The cloud server adopts an adaptive data compression transmission protocol to achieve efficient data transmission between the monitoring device and the cloud.

[0050] The cloud server is used to perform the following functions:

[0051] In-depth data analysis based on distributed processing architecture, including sports performance evaluation and training trend prediction;

[0052] Iterative optimization of machine learning models to support updates to swimming style classification models and athletic performance prediction models;

[0053] Multi-user data management and permission control enable cloud storage and sharing of training data.

[0054] The mobile application communicates with the cloud server and includes the following modules:

[0055] Data visualization module: displays SWOLF index, calorie consumption, exercise efficiency and other parameters in the form of charts;

[0056] Training Plan Generator: Automatically generates personalized plans based on user physical data and training goals;

[0057] Social sharing platform: supports social sharing of training data, performance rankings and training logs;

[0058] User interaction module: receives configuration information such as training goals and body parameters input by the user.

[0059] The formula for calculating the SWOLF index is: SWOLF = stroke number + time seconds, wherein the time is the time consumed for completing a single swimming. The SWOLF index is used for evaluating the swimming efficiency, and the lower the SWOLF value is, the higher the stroke efficiency is. By comparing the SWOLF indexes in different training stages, the stroke technology and rhythm can be optimized.

[0060] The formula for calculating the calorie consumption is: C = k(v 3 )t + m*BMR; wherein k is a motion coefficient (k = 0.012 for freestyle and k = 0.015 for breaststroke), v is the swimming speed (m / s), t is the time (s), m is the weight (kg), and BMR is the basal metabolic rate (kcal / h), which is calculated by the Mifflin-StJeor equation, such as male BMR = 10m + 6.25h - 5a + 5, h is the height (cm), and a is the age (years).

[0061] The formula for calculating the motion efficiency is: η = (D / (S*E)) * 100%; wherein D is the swimming distance (m), S is the stroke number, and E is the calorie consumption. The motion efficiency can reflect the swimming distance under the unit energy consumption, and is used for comparing the efficiency difference of different swimming postures or training methods, thereby providing a quantitative basis for technical optimization.

[0062] Example Three:

[0063] The embodiment of the present application provides a real-time monitoring method for swimming motion parameters, which comprises the following steps:

[0064] The motion data is synchronously collected by the sensor module 1 at a sampling rate of 100 Hz, including but not limited to acceleration ±16g, angular velocity ±2000° / s, water pressure 0-10m water depth, etc.

[0065] The adaptive filtering algorithm dynamically adjusts the filtering parameters according to the underwater environment, adopts the combination of Kalman filtering and wavelet transform, and eliminates the flow noise and motion artifacts.

[0066] The stroke cycle characteristics are extracted by the short-time Fourier transform, and the swimming posture conversion nodes are recognized based on threshold detection.

[0067] The processed feature vectors are classified by using the LSTM neural network.

[0068] According to the training target and the real-time data, a feedback strategy is generated, the motion parameters are broadcast in real time through the bone conduction speaker, and the priorities of the background music and the voice prompt are managed.

[0069] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A real-time monitoring device for swimming parameters based on acoustic feedback, characterized by: include: An ear-hanging main body (1), the ear-hanging main body (1) is made of a skin-friendly, anti-corrosion and anti-fouling material and is used for athletes to wear in an ear-hanging manner; A sensor module (6), the sensor module (6) is used to collect multiple data during swimming, the sensor module (6) includes an inertial measurement unit, a magnetometer, a water pressure sensor and a temperature sensor, a heart rate sensor and a bioimpedance measurement unit; A data operation processing and storage module (4), the data operation processing and storage module (4) is used to process and perform pattern recognition on the data collected by the sensor module (6), and write the processing results into a non-volatile storage, the data processing and storage module (4) can also be used to store user-defined audio data, and the data operation processing and storage module (4) has a real-time signal processing unit and a machine learning analysis unit; A speech synthesizer (8) and a bone conduction speaker (9), wherein the speech synthesizer (8) is used to generate speech information from parameters processed by the data operation and storage module (4) and output the information in audio through the bone conduction speaker (9); A wireless communication module (5), the wireless communication module (5) supporting a variety of personal wireless local area network protocols for data transmission; A wireless charging coil (3) and a mobile power module (7), wherein the mobile power module (7) is a high-energy-density battery and is equipped with a charge and discharge management system, and the wireless charging coil (3) is used to wirelessly charge the mobile power module (7); Both ends of the ear-hook main body (1) are connected to the earphone body (2), and the bone conduction speaker (9) is closely attached to the inner wall of one side of the earphone body (2).

2. The device for real-time monitoring of swimming parameters based on acoustic feedback according to claim 1, characterized in that: The wireless charging coil (3) is fixedly connected to the middle position of the inner wall of one side of the earphone body (2), and the data operation processing and storage module (4), the wireless communication module (5), the sensor module (6) and the mobile power module (7) are distributed in a ring shape around the wireless charging coil (3). The speech synthesizer (8) is fixedly connected to the outer wall of the other side of the earphone body (2), and the bone conduction speaker (9) is fixedly connected to the speech synthesizer (8).

3. The real-time monitoring device for swimming parameters based on acoustic feedback according to claim 1, characterized in that: The data operation processing and storage module (4) adopts a distributed architecture, including a terminal computing unit and a cloud server. The terminal computing unit is used for data processing and recording during exercise, and the cloud server is used for in-depth analysis and storage of data after exercise. The speech synthesizer (8) supports priority interrupt management of background music and voice information, and gives priority to broadcasting exercise parameter prompts.

4. A real-time monitoring system for swimming parameters based on acoustic feedback, characterized by: include: a cloud server, in communication with the monitoring device, for receiving and storing data and performing in-depth analysis; A mobile application can be directly connected to the headset body (2) and can also be connected to the cloud server for data visualization, training plan generation and social sharing; Edge computing unit: deployed locally on the device, performing real-time noise filtering and feature extraction of sensor data; The cloud server adopts an adaptive data compression transmission protocol to achieve efficient data transmission between the monitoring device and the cloud.

5. A method for real-time monitoring of swimming parameters, applied to the device according to any one of claims 1 to 4, characterized in that: The following steps are involved: Collecting swimming motion data through sensor module (1); Adaptive filtering algorithm is used to eliminate water environment interference; Use LSTM neural network to classify swimming styles on the processed data; Real-time broadcast of exercise parameters through bone conduction speakers, and management of the priority of background music and voice prompts; Predict athletic performance based on the random forest algorithm and output SWOLF index and calorie consumption data.

Images