A smart horizontal bar system based on the HarmonyOS operating system

Abstract

This invention discloses a smart horizontal bar system based on the HarmonyOS operating system, comprising: a smart horizontal bar, a cloud platform, a remote terminal, and a local terminal; the smart horizontal bar is connected to the cloud platform, and the cloud platform is connected to both the remote terminal and the local terminal; the smart horizontal bar includes a horizontal bar and a smart horizontal bar terminal mounted on the horizontal bar; image data and infrared data are collected through the smart horizontal bar terminal, and counting is performed based on the image data and infrared data; the image data, infrared data, and calculation results are received and stored through the cloud platform, and the image data and infrared data are processed through big data analysis to generate an evaluation report; the remote terminal and the local terminal are equipped with the HarmonyOS operating system, and the evaluation reports are stored and viewed remotely and locally, respectively.

Description

Technical Field

[0001] This invention relates to the field of smart sports technology, and in particular to a smart horizontal bar system based on the HarmonyOS operating system. Background Technology

[0002] Pull-ups, a hanging exercise that relies on one's own strength to overcome one's own body weight, are the most basic method for strengthening the back. They are one of the optional events in the middle school and high school physical education entrance examinations and are an important reference standard and item for measuring students' physical fitness. Currently, school equipment and facilities are far from adequate. For example, the school's horizontal bar, parallel bars, and other gymnastics equipment are outdated and cannot be adjusted according to students' individual physical conditions during teaching, thus affecting the teaching effectiveness.

[0003] Currently, pull-up counting is often done manually or using machine vision. However, manual counting suffers from difficulties in establishing accurate counting standards, as the human eye perceives different images from different angles and positions, and subjective judgment plays a significant role. Existing methods using machine vision to identify pull-up movements and then analyzing, calculating, and judging based on the displacement ratio of a specific body part in the image have significant drawbacks. Correct and standardized pull-up counting uses the height of the chin over the bar as one count. If a displacement ratio of a specific body part is used for analysis and judgment, students may exploit this rule to make incorrect judgments. Cheating can manifest in various ways. For example, curling up a part of the body to achieve a larger ratio to the initial distance allows for cheating, non-standard, and incorrect calculations. Furthermore, the proper pull-up technique requires the examinee to grip the bar with a pronated grip and perform the pull-up motion. However, current standardized pull-up counting methods, which rely on image recognition technology, lack a proper grip check. Some methods use the head silhouette captured in the image to determine if the head is above the bar, but this method is inaccurate and prone to errors. Examinees can also extend their heads to clear the bar, but without any overall body movement, resulting in a non-standard pull-up motion and contributing to cheating during pull-up tests.

[0004] In summary, existing monobar systems have the following technical problems:

[0005] 1. Issues of impartiality. The accuracy of manual or machine counting methods is insufficient, and inconsistent evaluation standards lead to inaccurate counting and raise issues of impartiality in the evaluation.

[0006] 2. Issues with accuracy. Current methods rely on simple counting, lack standardized decomposition and analysis of the movements, and do not utilize cameras for data collection, making it impossible to conduct big data analysis on pull-up movements among a large number of students nationwide.

[0007] 3. Lack of a big data platform and data security issues. Currently available pull-up counters are not based on the HarmonyOS operating system, making cross-platform use impossible, and there is a risk of information leakage regarding national youth physical fitness test data. Adding cameras for data collection, if widely applied, could collect massive amounts of data, establishing a big data platform that would benefit the formulation of national youth sports policies.

[0008] 4. The single-bar counter is not intelligent. The current single-bar counter does not have an automatic recording system. From recording to background registration, everything is done manually. Data recording, transmission, and analysis are not done by machines. Summary of the Invention

[0009] To address the problems existing in the prior art, this invention provides a smart horizontal bar system based on the HarmonyOS operating system, and a smart sports horizontal bar system that incorporates the Huawei HarmonyOS operating system as a connector to solve the examination problems in smart sports.

[0010] To achieve the above technical objectives, the present invention provides the following technical solution: a smart horizontal bar system based on the HarmonyOS operating system, comprising:

[0011] Smart horizontal bar, cloud platform, remote terminal and local terminal;

[0012] The smart horizontal bar is connected to the cloud platform, and the cloud platform is connected to both a remote terminal and a local terminal.

[0013] The smart horizontal bar includes a horizontal bar and a smart horizontal bar terminal installed on the horizontal bar; image data and infrared data are collected through the smart horizontal bar terminal, and counting is performed based on the image data and infrared data;

[0014] The cloud platform receives and stores image data, infrared data, and calculation results, and processes the image data and infrared data through big data analysis to generate an evaluation report.

[0015] The remote and local terminals are equipped with the HarmonyOS operating system, and are used for remote and local storage and viewing of evaluation reports, respectively.

[0016] Optionally, the smart horizontal bar terminal includes an infrared sensor, a depth camera, a processor, and a pressure sensor;

[0017] The infrared sensor and pressure sensor are connected to the processor via signal amplifiers and signal converters; the depth camera is connected to the processor.

[0018] The pressure sensor is located inside the handle of the horizontal bar, the depth camera is located on the support of the horizontal bar, the processor is located on the horizontal bar, and the infrared transmitter and receiver of the infrared sensor are located opposite each other on both sides of the horizontal bar to form an infrared travel sensing area, wherein the upper and lower ends of the infrared travel sensing area are located on the upper and lower sides of the horizontal bar of the horizontal bar, respectively.

[0019] Optionally, the processor is also connected to a key input module, a storage module, a power module, a communication module, a voice module, and a display module.

[0020] Optionally, the processor is also connected to a height adjustment device, through which the height of the horizontal bar is adjusted.

[0021] Optionally, the processor is equipped with the HarmonyOS operating system, and performs counting based on the image data and infrared data; wherein the counting process includes:

[0022] When the pressure sensor generates a pressure signal, counting begins;

[0023] When the infrared data corresponding to the infrared blocking area of ​​the head in the infrared travel sensing area is blocked, and the infrared data corresponding to the infrared data corresponding to the unblocked area of ​​the head in the infrared travel sensing area is unblocked, the count result of the infrared data is 1.

[0024] The infrared blocking area at the top of the head is the area above the horizontal bar where the infrared travel sensing area is located, and the unblocked infrared area at the top of the head is the area below the horizontal bar where the infrared travel sensing area is located.

[0025] Artificial intelligence is used to process image data and generate corresponding counting results. The artificial intelligence analysis includes intelligent capture of human motion, detection of a single pull-up motion, and intelligent counting of pull-ups.

[0026] Optionally, in the processor, human motion intelligent capture includes: performing key point recognition on image data, extracting three-dimensional human skeleton data based on the key point recognition results, extracting three-dimensional human skeleton data frame by frame from the image data, and generating a sequence of three-dimensional human skeleton data.

[0027] Optionally, in the processor, the detection of a single pull-up motion includes:

[0028] Based on the three-dimensional human skeletal data sequence, the system identifies the free walking state and the body hanging state. The free walking state is generated by judging the vertical direction of the ankle joint in the three-dimensional human skeletal data sequence, and the movement of the ankle joint is fitted with a straight line as the ground baseline. The body hanging state is determined by whether the angle formed by the shoulder joint, elbow joint and wrist joint meets the requirement of 160-180 degrees and whether the vertical value of the ankle joint is greater than or equal to the vertical value of the shoulder joint.

[0029] When a physical state is generated, motion detection is performed. In the motion detection, when the angle formed by the shoulder joint, elbow joint and wrist joint in the human three-dimensional skeleton data sequence reaches 45 degrees, one valid pull-up is recorded.

[0030] After motion detection is completed, the body's hanging state is identified and motion detection is performed based on the human three-dimensional skeletal data sequence to achieve single pull-up motion detection.

[0031] Optionally, in the processor, the pull-up intelligent counting includes: after recording a valid pull-up, generating key element data by analyzing the three-dimensional skeletal data sequence of the human body, wherein the key element data includes the relative position of the chin and the bar, whether the body is using a wave or swing, and whether the body is in a hanging state with both elbows straight.

[0032] The key data elements are judged. If the chin is higher than the bar, the body does not use the wave or swing and the body is in a hanging state with both elbows extended, then count once; otherwise, do not count.

[0033] Based on the recorded valid pull-up results, key element data are generated and judged in sequence, and corresponding counting results are generated for image data.

[0034] Optionally, in the cloud platform, the process of processing the image data and infrared data through big data analysis includes:

[0035] Acquire infrared data, image data, keyframe images corresponding to counting results, and user appearance data;

[0036] The data is processed into data streams, and the results of the data stream processing are stored.

[0037] The data is processed in batches, and the batch processing results are stored.

[0038] The stored data is then used in various ways, including data visualization, recommendation algorithms, and machine learning, to generate big data application results and achieve big data analysis.

[0039] The present invention has the following technical effects:

[0040] 1. Machines replacing manual labor: Machines can replace manual counting, making it more accurate, convenient, and intelligent. This reduces the complexity of manual counting, allows students to monitor their pull-up counts at any time, improves form, and ensures proper exercise, reducing unnecessary injuries. As a mandatory subject in the middle school entrance examination's physical fitness test, machines will reduce errors caused by individual teacher differences. They will also free up teachers' time, allowing students to independently test their pull-ups.

[0041] 2. Posture judgment: The infrared beam sensor and wide-angle camera video image analysis technology are used to collect video data of students' pull-ups. The combination of the two is used to judge whether the movement meets the standard, making the judgment more accurate.

[0042] 3. Overall Examination System: The overall examination system includes a pull-up counter terminal, a wide-angle camera, a dedicated PAD for remote physical education examinations, a dedicated PAD for on-site physical education examinations, a cloud server, and WiFi and Bluetooth communication modules, providing services such as big data analysis and storage backup.

[0043] 4. HarmonyOS Operating System: The advantages of using HarmonyOS are: ① HarmonyOS is compatible with different smart terminals such as mobile phones, PCs, tablets, and watches. ② The advantages of HarmonyOS, which uses a Linux-based microkernel, are mainly high security, high reliability, high scalability, high maintainability, and support for distributed computing, ensuring system stability. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is an architecture diagram of a smart single-pole system provided in an embodiment of the present invention;

[0046] Figure 2 A basic structural diagram of the hardware design of the smart single bar terminal provided in the embodiments of the present invention;

[0047] Figure 3 A flowchart of big data analysis provided for embodiments of the present invention;

[0048] Figure 4 This is an overall flowchart of the intelligent single bar system provided in an embodiment of the present invention;

[0049] Figure 5This is a schematic diagram of a smart single bar structure provided in an embodiment of the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] like Figure 1 As shown, the smart horizontal bar system generally includes the following components: 1. Smart horizontal bar terminal, 2. Cloud platform, 3. Remote physical education examination PAD (remote terminal), and 4. Local physical education examination PAD (local terminal). Figure 4 As shown, the architecture diagram and overall flowchart of the intelligent single-pole system are presented.

[0052] The cloud platform consists of a cloud server, a smart horizontal bar examination service, and data visualization. The smart horizontal bar terminal communicates with the cloud platform via a network, specifically 5G or WiFi. The cloud server uses Huawei Cloud, providing the entire system with internet infrastructure services such as computing, storage, online backup, hosting, and bandwidth. The smart horizontal bar examination service provides the corresponding programs and interface for user interaction, input of student identity information and body data, and recording of pull-up data.

[0053] The remote physical education exam PAD, equipped with the domestically developed HarmonyOS operating system, is used for remote proctoring. The local physical education exam PAD, also equipped with the domestically developed HarmonyOS operating system, is used for on-site proctoring.

[0054] like Figure 2As shown, the smart horizontal bar terminal is responsible for data acquisition and consists of several parts, including a processor chip, a wide-angle camera, an infrared beam sensor, a power module, and a communication module. Pressure sensors are located inside the handles on both sides. The pressure sensors and infrared beam sensors are connected to the processor chip via signal amplifiers and signal converters. The display module and button counting module are mounted on the horizontal bar and connected to the processing chip. The storage module and power module are also connected to the processor chip. The main functions implemented include: geolocation, precise positioning of the lever's location and number, voice input, height-based extension / retraction, infrared detection for counting, remote and on-site communication transmission, and a mechanical braking device. Sliding rods are installed at both ends of the horizontal bar, and telescopic rods are installed at the bottom of the sliding rods. A controller drives a motor to intelligently control the raising or lowering of the horizontal bar, meeting the needs of users of different heights, and automatically counting and broadcasting the count via voice prompts. In typical smart sports examination scenarios, the smart horizontal bar system can adopt the HarmonyOS operating system, and the dedicated examination PAD system can also use HarmonyOS. The backend cloud service system can use Huawei's Euler operating system. The basic components of hardware design, such as Figure 2 .

[0055] I. Detailed Implementation Method of Pull-up Counting

[0056] The pull-up technique employs a dual-mode judgment system of infrared sensor recognition and video image analysis to ensure accuracy. The "National Student Physical Fitness and Health Standards" clearly stipulates the assessment criteria for pull-ups, which should meet the following three conditions: ① The chin must be higher than the bar; ② The body cannot use any jiggling or swinging motions; ③ Both elbows must be straight during the hang.

[0057] When the pressure sensors located inside the handles on both sides receive a pressure signal, the infrared sensor and camera start working. The standard for determining whether the user's pull-up count is that the infrared sensor detects that the chin is above the bar, and the video image recognition part also determines that the chin is above the bar, then the count is incremented by 1.

[0058] (I) Infrared Sensor Identification Section

[0059] Working principle of active infrared sensors: The transmitter of an active infrared sensor emits a modulated infrared beam, which is received by an infrared receiver, thus forming a warning line composed of infrared beams. It should not alarm when obstructed by leaves, rain, small animals, snow, dust, or fog; however, it will alarm if obstructed by a person or an object of similar size.

[0060] The pull-up counter terminal is mounted on a horizontal bar support and has several pairs of infrared through-beam sensors spaced apart from top to bottom. The infrared emitter and receiver in each infrared through-beam sensor are distributed opposite each other on the left and right sides of the horizontal bar support. The distribution area of ​​the infrared emitter and receiver in the infrared through-beam sensor can sense the movement of the test subject's head when performing pull-ups. That is, all infrared through-beam sensors can form an infrared travel sensing range corresponding to the up and down movement of the test subject's head. The upper and lower ends of the infrared travel sensing range are located on the upper and lower sides of the horizontal bar in the horizontal bar support, respectively. The infrared emitter and receiver in all infrared through-beam sensors are connected to the counter terminal. The counter terminal can pre-set that when the test subject's entire head moves up over the horizontal bar on the horizontal bar support, the infrared light area that the head can block in the infrared travel sensing range is the high-position infrared light blocking area, and the infrared light area below the head that is not blocked at this time is defined as the high-position infrared light unblocked area. When the test subject's head is in a low position, the test subject's head will not block the infrared through-beam sensors in the infrared travel sensing range, that is, there will be no infrared light blocking area.

[0061] According to the above process, the infrared beam sensor is used to locate the user's head. When the chin is above the horizontal plane of the bar, the pull-up count is incremented by 1. However, this cannot determine whether the user's body is performing a standard pull-up. Therefore, it is necessary to combine the video analysis part for judgment. When there is a difference between the infrared and video analysis results, the infrared count result shall prevail, and the standard of the pull-up shall be analyzed in conjunction with the video analysis part.

[0062] (II) Video Image Analysis Section

[0063] Video image analysis utilizes computers to process, analyze, and understand images to identify targets and objects in various patterns. This invention employs a wide-angle 8-megapixel C-EP28WT305 camera for motion video capture and uses JavaScript for system development. AI motion recognition is affected by many factors during the recognition process, such as insufficient lighting, cluttered backgrounds, and numerous people, all of which can impact recognition accuracy. The accuracy of motion recognition is directly related to the algorithm; only when the key algorithm matches the motion validity judgment will the recognition accuracy improve. Therefore, to improve the accuracy of AI recognition, this invention's motion recognition system has undergone further algorithmic improvements.

[0064] The image processing and analysis of the human body gripping the horizontal bar consists of three parts: intelligent human motion capture, single pull-up motion detection, and intelligent pull-up counting.

[0065] 1. Intelligent human motion capture

[0066] The skeletal structure of the human body determines the geometry of human movement; therefore, three-dimensional human skeletal data can be used to effectively describe human movements. Three-dimensional skeletal data can be acquired through motion capture systems, but early motion capture systems required specific acquisition environments and the application of reflective markers to key points on the human body, making the acquisition process complex and expensive. In recent years, with the rapid development of computer vision technology, intelligent capture of human movements based on video can be achieved through human skeletal estimation algorithms. Therefore, this invention utilizes the OpenPose framework, an open-source project from Carnegie Mellon University, to achieve multi-person body key point recognition. It extracts three-dimensional human skeletal data from ordinary RGB video, and after frame-by-frame motion capture of the images in the video, it obtains the sequence of three-dimensional human skeletal data for the entire pull-up process, which can be used to identify the effective movements of multiple people performing pull-ups.

[0067] 2. Single pull-up motion detection

[0068] Long videos typically contain multiple pull-up movements. Before intelligently evaluating pull-ups, the timing of each individual pull-up should be determined to identify its start and end times, allowing for the extraction of the individual pull-up video. This invention detects individual pull-up movements based on the rhythmic up-and-down movement of the body, especially the head, during the pull-up. By inputting the sequence of key head positions during the pull-up into a CAS search algorithm, the detection of individual pull-up movements can be achieved.

[0069] The specific steps are as follows: 1. Place the camera at the middle of the horizontal bar support, with the camera's field of view directly within the lens; 2. Identify two states: the body walking freely under the horizontal bar and the body hanging on the horizontal bar. When walking freely, the ankle joint y-axis value should fluctuate slightly around a straight line. The system automatically identifies this as the baseline of the body on the ground to distinguish the hanging state. When hanging, the shoulder-elbow-wrist angle should be close to 160-180 degrees, and the ankle joint y-axis value should be greater than or equal to the rise of the shoulder joint y-axis value. Once this condition is met, the test is triggered; 3. Identify the effectiveness of the pull-up movement. The sequence of key head points during the pull-up is input into the CAS search algorithm. Key head points include: eyebrows, eyes, nose, mouth, jaw, and neck. The CAS search algorithm determines a valid pull-up when the jaw crosses the top edge of the bar. When a pull-up occurs, its validity is checked. When the shoulder-elbow-wrist angle gradually approaches 45 degrees, which satisfies the condition that the jaw crosses the top edge of the bar, the system records one valid pull-up. When the shoulder-elbow-wrist angle approaches 160-180 degrees again, and the ankle joint y-axis value is greater than or equal to the increase in the shoulder joint y-axis value, a second pull-up is triggered.

[0070] 3. Pull-up intelligent counting

[0071] After recording one valid pull-up, the pull-up is evaluated. If the evaluation is satisfactory, the technology increments by 1. The three key elements of pull-ups are evaluated by analyzing human skeletal data. This is done by analyzing the generation of key points in the human skeleton and calculating the angle between the lines connecting these key points. The key points in the human skeleton include: upper limbs (shoulders, elbows, wrists, fingers); trunk (cervical vertebrae, thoracic vertebrae, lumbar vertebrae, pelvis, lower ribs); and lower limbs (hips, knees, ankles, toes). The key points and their connections are judged, and a three-dimensional vector [α, β, γ] is output based on the changes in the key points and their connections: α = 0 indicates that the chin is below the bar, α = 1 indicates that the chin is above the bar; β = 0 indicates that the body is using a wave or swing, β = 1 indicates that the body is not using a wave or swing; γ = 0 indicates that both elbows are not straight when hanging, γ = 1 indicates that both elbows are straight when hanging. If α=1∧β=1∧γ=1, then the pull-up is considered successful, and the pull-up count is incremented by 1. Otherwise, the pull-up is considered unsuccessful, and the count remains unchanged. By performing the above operations on single pull-up segments in the original video, intelligent evaluation and counting of pull-up movements can be achieved.

[0072] Integrating AI motion recognition into teaching allows for the assessment of whether a student's pull-up movements meet the standards through artificial intelligence analysis. This invention applies AI motion recognition technology to physical education teaching and examinations. In addition to analyzing and recognizing movements, it also integrates big data evaluation and quantification functions, enabling applications such as movement standardization analysis, exercise data analysis, and exercise prescriptions for adolescents.

[0073] II. Big Data Analysis

[0074] like Figure 3 As shown, this invention has big data analysis capabilities, creating forms for each user to collect various information such as height, weight, arm span, number of pull-ups, duration, etc. Through the widespread and extensive use of this invention, and through big data analysis of the dataset, the true pull-up performance level of middle school students can be determined, providing relevant data references for the formulation of related training plans.

[0075] (I) Data Acquisition

[0076] Data acquisition, based on the wide application of this invention, involves capturing images of the pull-up process, keyframes of the movement, and user physique data such as height and weight. Additionally, pull-up data from middle school students who have not used this invention can also be collected. Based on the characteristics of the data source, it can be categorized into internal and external data, structured and unstructured data, and variable and immutable data.

[0077] (ii) Data storage

[0078] Data storage utilizes the HDFS distributed file system, whose basic components are Name Node and Data Node. The Name Node manages the file system's metadata, while the Data Node stores the actual file data. HDFS can store data across multiple independent machines connected by a computer network, forming multiple autonomous processing units that manage the data accordingly, thus providing high fault tolerance, high throughput, and high reliability for batch data storage and processing.

[0079] (III) Data Processing

[0080] Data processing is the process of cleaning and transforming data according to relevant data standards and technical specifications to provide target data for data mining and development. Data cleaning specifically includes noise removal, missing value handling, redundancy elimination, and data type conversion. Specific methods include statistical methods, clustering-based methods, classification-based methods, distance-based methods, and association rule-based methods.

[0081] Based on the processed data, pull-up data can be categorized and summarized to meet various data analysis needs, and can also serve as a predictive tool.

[0082] III. Overall Process of the Intelligent Single Bar System

[0083] like Figure 4 As shown, in the first step, the user authenticates their identity using RFID or their student ID or exam number to enter the system. The system then begins to work, and the user information is confirmed remotely or locally by the Pad before proceeding to the next step.

[0084] The second step involves the user gripping the horizontal bar with both hands, initiating the smart horizontal bar counting process. The infrared photoelectric sensor and camera then begin working, counting according to the specific procedures described above.

[0085] The third step is to transmit the data collected by the camera and infrared sensor to the cloud platform, where the cloud platform stores the collected data.

[0086] The fourth step involves conducting big data analysis according to the big data analysis process described above, generating an evaluation report that includes an assessment of the standard of pull-up movements and a prediction of the potential for improvement in the number of pull-ups.

[0087] The fifth step is to send the evaluation report back to the remote or local tablet, completing the entire system process.

[0088] like Figure 5As shown, rope 1 serves to secure the horizontal bar. The main body 2 of the horizontal bar is a metal bar with sufficient strength. The pull-up counter terminal 3 is the terminal of the pull-up counter. The wide-angle camera 4 is a domestically produced camera, model C-EP28WT305, with a 100° (diagonal) ultra-wide-angle, distortion-free, high-definition 800W pixel high-definition lens.

[0089] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A smart horizontal bar system based on a HongMeng operating system, characterized in that, include: Smart horizontal bar, cloud platform, remote terminal and local terminal; The smart horizontal bar is connected to the cloud platform, and the cloud platform is connected to both a remote terminal and a local terminal. The smart horizontal bar includes a horizontal bar and a smart horizontal bar terminal installed on the horizontal bar; the smart horizontal bar terminal is equipped with the HarmonyOS operating system, and collects image data and infrared data through the smart horizontal bar terminal, and counts based on the image data and infrared data; wherein, the image data is continuous video frame data containing the entire process of human pull-up, and the infrared data is head position occlusion status data. The cloud platform receives and stores image data, infrared data, and calculation results, and processes the image data and infrared data through big data analysis to generate an evaluation report. The remote terminal and the local terminal are equipped with the HarmonyOS operating system, which is used for remote and local storage and viewing of evaluation reports, respectively. The intelligent single-bar terminal includes an infrared sensor, a depth camera, a processor, and a pressure sensor; The infrared sensor and pressure sensor are connected to the processor via signal amplifiers and signal converters; the depth camera is connected to the processor. The pressure sensor is located inside the handle of the horizontal bar, the depth camera is located on the support of the horizontal bar, the processor is located on the horizontal bar, and the infrared transmitter and receiver of the infrared sensor are located opposite each other on both sides of the horizontal bar to form an infrared travel sensing area, wherein the upper and lower ends of the infrared travel sensing area are located on the upper and lower sides of the horizontal bar of the horizontal bar, respectively. The processor is equipped with the HarmonyOS operating system and performs counting based on the image data and infrared data; wherein the counting process includes: When the pressure sensor generates a pressure signal, counting begins; When the infrared data corresponding to the infrared blocking area of ​​the head in the infrared travel sensing area is blocked, and the infrared data corresponding to the infrared data corresponding to the unblocked area of ​​the head in the infrared travel sensing area is unblocked, the count result of the infrared data is 1. The infrared blocking area at the top of the head is the area above the horizontal bar where the infrared travel sensing area is located, and the unblocked infrared area at the top of the head is the area below the horizontal bar where the infrared travel sensing area is located. Artificial intelligence is used to process image data and generate corresponding counting results. The artificial intelligence analysis includes intelligent capture of human motion, detection of a single pull-up motion, and intelligent counting of pull-ups. In the processor, the detection of a single pull-up motion includes: Based on the three-dimensional human skeletal data sequence, the system identifies the free walking state and the body hanging state. The free walking state is generated by judging the vertical direction of the ankle joint in the three-dimensional human skeletal data sequence, and the movement of the ankle joint is fitted with a straight line as the ground baseline. The body hanging state is determined by whether the angle formed by the shoulder joint, elbow joint and wrist joint meets the requirement of 160-180 degrees and whether the vertical value of the ankle joint is greater than or equal to the vertical value of the shoulder joint. When a physical state is generated, motion detection is performed. In the motion detection, when the angle formed by the shoulder joint, elbow joint and wrist joint in the human three-dimensional skeleton data sequence reaches 45 degrees, one valid pull-up is recorded. After motion detection is completed, the body's hanging state is identified and motion detection is performed based on the human three-dimensional skeletal data sequence to achieve single pull-up motion detection.

2. The system according to claim 1, characterized in that: The processor is also connected to a key input module, a storage module, a power module, a communication module, a voice module, and a display module.

3. The system according to claim 1, characterized in that: The processor is also connected to a height adjustment device, through which the height of the horizontal bar is adjusted.

4. The system according to claim 1, characterized in that: In the processor, intelligent human motion capture includes: identifying key points in image data, extracting three-dimensional human skeleton data based on the key point identification results, extracting three-dimensional human skeleton data frame by frame from the image data, and generating a sequence of three-dimensional human skeleton data.

5. The system according to claim 1, characterized in that: In the processor, the intelligent pull-up counting includes: after recording a valid pull-up, generating key element data by analyzing the three-dimensional skeletal data sequence of the human body, wherein the key element data includes the relative position of the chin and the bar, whether the body is using a wave or swing, and whether the body is in a hanging state with both elbows straight. The key data elements are judged. If the chin is higher than the bar, the body does not use the wave or swing and the body is in a hanging state with both elbows extended, then count once; otherwise, do not count. Based on the recorded valid pull-up results, key element data are generated and judged in sequence, and corresponding counting results are generated for image data.

6. The system according to claim 1, characterized in that: In the cloud platform, the process of processing the image data and infrared data through big data analysis includes: Acquire infrared data, image data, keyframe images corresponding to counting results, and user appearance data; The data is processed into data streams, and the results of the data stream processing are stored. The data is processed in batches, and the batch processing results are stored. The stored data is then used in various ways, including data visualization, recommendation algorithms, and machine learning, to generate big data application results and achieve big data analysis.

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