A physical training examination method and system, and a cognitive ability training system

Abstract

The present application relates to a kind of physical training examination method, system and intelligence capacity training system.Solve the problem that traditional physical training examination statistical method cannot objectively and quantitatively examine trainee.Method includes the completion accuracy of each action based on training process data and evaluation standard, then according to personal basic information, the number of times of completing standard examination action, heart rate variation value and facial expression as examination result information, through accurate, quantitative statistics and analysis, obtain objective examination result, remove the subjective influence factor when examination, can be efficiently, comprehensively, objectively to trainee examination and ranking in large-scale examination.Intelligence capacity training system includes mechanical training device, relevant sensor module, man-machine interaction module, control module and statistical analysis system;Control module obtains examination result information, and statistical analysis system receives the examination result information of all trainees, the score of each person's examination situation is calculated, and is sorted according to score.

Description

TECHNICAL FIELD

[0001] The present application relates to a physical training examination method and related system, in particular to a physical training examination method, system and intelligent capacity training system. BACKGROUND

[0002] The physical training result examination is common in the army, military and sports colleges. The common physical training examination method is simple and rough. Whether the training action is based on the equipment or not, the number of specified actions is mainly used as the standard. The action completion standard and quality are only judged by the examiner, which is subjective. The speed, position and time of the action completion are not quantitatively included in the examination and evaluation range. Therefore, the large-scale examination and result statistics are mixed with human factors, which is difficult to reflect the objectivity. Moreover, the examination result does not reflect the individual basic physical quality factor, which is one-sided. SUMMARY

[0003] In order to solve the problem that the traditional physical training examination and statistics method cannot objectively and quantitatively examine the trainer, the present application provides a comprehensive, fair and objective physical training examination method including individual physical factors. The system for realizing the method and the intelligent capacity training system for realizing the physical training examination method are also provided.

[0004] The technical scheme of the present application provides a physical training examination and statistics method, which is characterized by comprising the following steps:

[0005] Step 1: Before the examination, the heart rate value A1 of the trainer is collected.

[0006] Step 2: The trainer is prompted to repeat the examination action according to the standard examination action curve.

[0007] Step 3: During the training, the facial expression of the trainer is collected according to the set frequency. The actual position of the trainer pulling the load is collected according to the set frequency, and the actual position s of the trainer pulling the load and the time curve s(t) are obtained.

[0008] The curve s(t) is analyzed to extract the examination standard parameters p and sigma. P is the completion accuracy of each action, and sigma is the completion deviation. The number N of the trainer completing the standard examination action is statistically calculated in real time based on the examination standard. The examination standard is p≥x and sigma≤y×Smax, x and y are set values, and Smax is the maximum distance of the load pulling in the standard examination action curve.

[0009] If and only if the trainer meets the examination standard each time, the trainer is considered to complete the standard examination action, and the number of times the trainer completes the standard examination action is added by 1; otherwise, the trainer is considered not to complete the standard examination action, and when the trainer does not complete the standard examination action for i consecutive times, the examination ends; wherein i is a natural number greater than 1;

[0010] Step 4, collecting the heart rate value A2 of the trainer at the end of the examination;

[0011] Step 5, taking the personal basic information of the trainer, the number of times N of completing the standard examination action, the heart rate change value ΔA, and the facial expression as the examination result information; wherein ΔA=A2-A1;

[0012] After completing the examination result information of all the trainers, the score of each person's examination is calculated, and the score is sorted.

[0013] Further, in step 3, the completion accuracy p of each action refers to the percentage of the actual position s of the trainer pulling up the load in each examination action and the time curve s(t) between the upper limit sH(t) curve and the lower limit sL(t) curve of the standard examination action curve.

[0014] The standard examination action curve is the highest position sH(t) curve and the lowest position sL(t) curve of the trainer pulling up and putting down the load with time.

[0015] Further, in step 3, the completion deviation σ is the deviation that cannot meet the standard examination action in the process of completing the examination action, which is calculated by the following formula:

[0016]

[0017] Wherein, sA(t) is a curve composed of points with the same distance from sH(t) and sL(t), sA(t)=[sH(t)+sL(t)] / 2, and n is the total number of time points in s(t) curve not between sH(t) curve and sL(t) curve.

[0018] Further, in step 3, the facial expression of the trainer includes three states of relaxed, normal, and painful;

[0019] In step 5, the personal basic information of the trainer, the number of times N of completing the standard examination action, the heart rate change value ΔA, the number of times Th of relaxed facial expression, the number of times Tn of normal facial expression, and the number of times Tp of painful facial expression are taken as the examination result information.

[0020] Further, in step 3, x is equal to 0.95, and y is equal to 0.1.

[0021] Further, in step 5, the score is calculated by the following formula:

[0022]

[0023] wherein is the average value of the heart rate variation values ΔA of all trainers.

[0024] The application further provides a physical training examination and statistics system, which is characterized by comprising a memory and a processor, the memory stores a computer program, and the computer program is executed in the processor to realize the physical training examination and statistics method.

[0025] The application further provides a computer readable storage medium for storing a program, and the program is executed to realize the physical training examination and statistics method.

[0026] The application further provides an intelligence capacity training system, which is characterized by comprising an intelligence capacity training device and a statistics and analysis system.

[0027] The intelligence capacity training device comprises a mechanical training device, a motion information sensor module, a physiological information sensor module, a face information acquisition module, a facial expression recognition module, a man-machine interaction module and a control module.

[0028] The mechanical training device is a mechanical device for providing training load and contacting with the trainer.

[0029] The motion information sensor module is installed in the mechanical training device and is used for acquiring training load information and acquiring the actual position of the trainer pulling the load according to a set frequency.

[0030] The physiological information sensor module is used for being worn on the body of the trainer and is used for acquiring the heart rate of the trainer.

[0031] The face information acquisition module is installed on the mechanical training device and is used for acquiring the face information of the trainer in real time according to a set frequency.

[0032] The facial expression recognition module is installed on the mechanical training device and is used for recognizing the facial expression of the trainer according to the face information of the trainer sent by the face information acquisition module.

[0033] The man-machine interaction module is installed on the mechanical training device and is used for providing picture vision and sound hearing feedback, can be touched to input, and can receive the basic information input of the trainer.

[0034] The control module is installed on the mechanical training device and communicates with the motion information sensor module, the physiological information sensor module, the face information acquisition module, the facial expression recognition module, the human-computer interaction module and the statistical analysis system; the control module calculates the examination result information of the trainee based on the examination standard and the feedback data of the motion information sensor feedback, the physiological information sensor feedback and the feedback data of the facial expression recognition module of the trainee, and sends the examination result information of the trainee to the statistical analysis system;

[0035] The statistical analysis system receives the examination result information of all the trainees; after receiving the examination result information of all the trainees, the score Score of the examination of each person is calculated, and the persons are sorted according to the scores.

[0036] Further, the control module includes a memory and a processor, and the memory stores a computer program which, when executed by the processor, implements the following processes:

[0037] Step 1, before the examination starts, the physiological information sensor module is controlled to collect the heart rate value A1 of the trainee;

[0038] Step 2, the human-computer interaction module is controlled to prompt the trainee to repeatedly complete the examination action according to the standard examination action;

[0039] Step 3, during the training, the facial expression of the trainee is collected at a set frequency; and the actual position of the trainee pulling the load is collected at a set frequency to obtain the curve s(t) of the actual position s of the trainee pulling the load and time;

[0040] The curve s(t) is analyzed to extract the examination standard parameters p and σ, p is the completion accuracy of each action, and σ is the completion deviation; the number N of times of completing the standard examination action of the trainee is counted in real time based on the examination standard; wherein the examination standard is p≥x and σ≤y×Smax, x and y are set values, and Smax is the maximum distance of the load pulling in the standard examination action curve;

[0041] Only when each examination action of the trainee meets the examination standard, it is considered that the trainee has completed the standard examination action, and the number of times of completing the standard examination action of the trainee is increased by 1; otherwise, it is considered that the trainee has not completed the standard examination action, and when the trainee has not completed the standard examination action for i consecutive times, the examination is ended; wherein i is a natural number greater than 1;

[0042] Step 4, the physiological information sensor module is controlled to collect the heart rate value A2 of the trainee at the end of the examination;

[0043] Step 5, the personal basic information of the trainee, the number N of times of completing the standard examination action, the heart rate change value ΔA and the facial expression are sent to the statistical analysis system, wherein ΔA=A2-A1.

[0044] Further, in step 3, the completion accuracy p of each action is the percentage of the actual position s of the trainer lifting the load and the time curve s(t) between the upper limit sH(t) curve and the lower limit sL(t) curve of the standard examination action curve; wherein the standard examination action curve is the highest position and the lowest position curve of the trainer lifting and lowering the load varying with time.

[0045] Further, in step 3, the completion deviation σ is the deviation that cannot meet the standard examination action during the completion of the examination action, which can be calculated by the following formula:

[0046]

[0047] Wherein, sA(t) is a curve composed of points with the same distance from sH(t) and sL(t), sA(t) = [sH(t) + sL(t)] / 2, and n is the total number of time points in the s(t) curve that are not between the sH(t) curve and the sL(t) curve.

[0048] Further, the facial expressions of the trainer in the above-mentioned step 3 include relaxed, normal, and painful three states.

[0049] In step 5, the personal basic information of the trainer, the number of times of completing the standard examination action N, the heart rate change value ΔA, the number of times of the relaxed facial expression Th, the number of times of the normal facial expression Tn, and the number of times of the painful facial expression Tp are taken as the examination result information.

[0050] Further, in the above-mentioned step 3, x is equal to 0.95, and y is equal to 0.1.

[0051] Further, in the above-mentioned step 5, the score is calculated by the following formula:

[0052]

[0053] Wherein is the average value of the heart rate change value ΔA of all trainers.

[0054] Further, the motion information sensor module includes sensors for collecting the actual position of the trainer lifting the load and the training load information; the sensors for collecting the actual position of the trainer lifting the load include angle displacement sensors for monitoring the angle, ultrasonic sensors, infrared sensors, pull wire displacement sensors, or laser sensors; the sensors for collecting the training load information of the trainer include pull pressure sensors or torque sensors.

[0055] Further, the physiological information sensor module includes a heart rate sensor worn on the wrist or a heart rate sensor worn on the upper body.

[0056] Further, the human-computer interaction module comprises a touch all-in-one machine and a loudspeaker; the touch all-in-one machine is used for providing picture vision, can perform touch operation input, and can receive basic information input of the trainer; and the loudspeaker is used for sound auditory feedback.

[0057] Further, in step 2, the touch all-in-one machine displays a standard examination action curve, and simultaneously outputs training instruction through the loudspeaker, so as to prompt the trainer to repeatedly complete the examination action according to the standard examination action.

[0058] Further, the control module is connected with the motion information sensor module through a line, and is connected with the physiological information sensor module through wireless communication; the wireless communication comprises but is not limited to Bluetooth and Wifi transmission modes; the control module is connected with the face information acquisition module, the facial expression recognition module and the human-computer interaction module through a line; and the control module is connected with the statistical analysis system through wireless communication.

[0059] The present application has the following advantages:

[0060] The present application discloses a strength training examination statistical method and system, and an intelligent capacity training system with the examination statistical function, which can record the speed, position, holding time factor, heart rate change value representing the basic physical quality of the trainer, and facial expression value representing the ease degree when the action is completed into the examination and evaluation range, obtains an objective examination result through accurate and quantitative statistics and analysis, removes the human subjective influence factor during the examination, can efficiently, comprehensively and objectively examine and rank the trainers in large-scale examination, and solves the disadvantages of the traditional training examination statistical method. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 The sH(t) curve, the sL(t) curve, the sA(t) curve and the s(t) curve generated by actual training are shown in the schematic diagram. DETAILED DESCRIPTION

[0062] In order to make the above objectives, characteristics and advantages of the present application more apparent, obvious and easy to understand, the specific embodiments of the present application are described in detail below with reference to the drawings in the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present application.

[0063] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced in other manners different from those described herein, and those skilled in the art can make similar generalizations without departing from the spirit and scope of the present application, so the present application is not limited to the specific embodiments disclosed below.

[0064] Embodiment 1

[0065] The embodiment discloses a physical training examination statistical method, which is realized through the following steps:

[0066] Step 1, before the examination, the heart rate value A1 of the trainee is collected; the value can be collected through a related heart rate sensor worn on the body of the trainee.

[0067] Step 2, according to the standard examination action curve, an action instruction is sent to the trainee, so that the trainee repeatedly completes the examination action according to the instruction; the standard examination action curve is a highest position and a lowest position curve of the trainee required to pull up and put down the load with time changing; as shown in the sH(t) curve and the sL(t) curve, which are the standard examination action curves. The sH(t) curve is the highest position curve of the trainee required to pull up and put down the load with time changing, and the sL(t) curve is the lowest position curve of the trainee required to pull up and put down the load with time changing. Figure 1

[0068] Step 3, in the training process, the facial expression of the trainee is collected in real time according to the set frequency; the collection can be realized through a corresponding sensor, such as a camera. In the embodiment, the facial expression is divided into three categories, including relaxed, normal and painful, which can be recognized through a corresponding sensor.

[0069] At the same time, in the training process, the actual position of the trainee pulling up the load is collected according to the set frequency, and the curve s(t) of the actual position s of the trainee pulling up the load with time is obtained, which can be realized based on a corresponding sensor.

[0070] The curve s(t) is analyzed, and the examination standard parameters p and σ are extracted, p is the completion accuracy of each action, and σ is the completion deviation; the number N of times of the trainee completing the standard examination action is statistically calculated in real time based on the examination standard; the examination standard is p≥x and σ≤y×Smax, wherein x and y are set values, x is equal to 0.95 and y is equal to 0.1 in the embodiment. In other embodiments, the values can be determined according to the difficulty requirement of the actual examination. When the requirement of the examination selection is high, a larger x value within 1 and a smaller y value greater than 0 should be selected. Smax is the maximum distance of the load pulling up in the standard examination action curve.

[0071] The completion accuracy p of each action refers to the percentage of the s(t) curve of the actual position s of the trainee pulling up the load with time between the upper limit sH(t) curve and the lower limit sL(t) curve of the standard examination action curve in each examination action. Figure 1 The s(t) curve actually produced in the training is also indicated in the figure.

[0072] The completion deviation σ is the deviation that cannot meet the standard examination action in the process of completing the examination action, which is calculated by the following formula:​

[0073]

[0074] Where sA(t) is the curve formed by points equidistant from sH(t) and sL(t), sA(t) = [sH(t) + sL(t)] / 2, and n is the total number of time points in s(t) that are not between sH(t) and sL(t).

[0075] It should be noted that this invention collects the actual position of the trainee pulling up the load at a set frequency. The sampling frequency can be 10Hz-1000Hz; this embodiment uses a sampling frequency of 50Hz, meaning it collects data every 20ms. The specific calculation process for the deviation σ is as follows: The s(t) curve of an assessment action is recorded. This curve consists of a set of points representing the actual position of the pulled-up load, spaced 20ms apart, as shown below. Figure 1 As shown. Let n be the number of points in the s(t) curve that are not between sH(t) and sL(t) (the number of points n is related to the sampling frequency). According to the formula for calculating σ, the s(t) of the n points... i ) and sA(t i The result of subtracting the two numbers and taking the root mean square is σ. The magnitude of n is related to the sampling frequency.

[0076] If a trainee meets the assessment standard in every assessment action, the trainee is considered to have completed the standard assessment action, and the number of times the trainee has completed the standard assessment action is incremented by 1; otherwise, the trainee is considered to have failed to complete the standard assessment action. The assessment ends when the trainee fails to complete the standard assessment action i times consecutively; where i is a natural number greater than 1.

[0077] Step 4: Collect the trainee's heart rate value A2 at the end of the assessment; similarly, it can be collected based on the relevant heart rate sensor worn on the trainee's body.

[0078] Step 5: Collect the trainee's basic personal information, the number of times the standard assessment movement was completed (N), the heart rate change value (ΔA), the number of times the facial expression was relaxed (Th), the number of times the facial expression was normal (Tn), and the number of times the facial expression was painful (Tp) as the assessment result information. Where ΔA = A2 - A1;

[0079] After completing the assessment results for all trainees, calculate a score for each person and rank them according to their scores.

[0080] The score is calculated using the following formula:

[0081]

[0082] in This represents the average heart rate change ΔA for all trainees.

[0083] The embodiment also discloses a physical training examination and statistics system, comprising a memory and a processor, and the memory stores a computer program which, when executed in the processor, realizes the physical training examination and statistics method.

[0084] The embodiment also provides a computer readable storage medium for storing a program which, when executed, realizes the steps of the physical training examination and statistics method. In some possible implementation manners, the application can also be implemented in the form of a program product which comprises program codes for causing a terminal device to perform the steps of various exemplary embodiments of the application described in the method part of the specification when the program product is run on the terminal device.

[0085] The program product for realizing the method can adopt a portable compact disc read-only memory (CD-ROM) and comprises program codes and can be run on a terminal device such as a personal computer. However, the program product of the application is not limited to this, and in the application, the computer readable storage medium can be any tangible medium containing or storing a program which can be used by or in combination with an instruction execution system, device or apparatus.

[0086] The program product can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or apparatus, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0087] Embodiment 2

[0088] The embodiment discloses an intelligence capacity training system which can realize the physical training examination and statistics method in embodiment 1. The intelligence capacity training system comprises a mechanical training device, a motion information sensor module, a physiological information sensor module, a face information acquisition module, a facial expression recognition module, a man-machine interaction module, a control module and a statistical analysis system; the mechanical training device, the motion information sensor module, the physiological information sensor module, the face information acquisition module, the facial expression recognition module, the man-machine interaction module and the control module constitute an intelligence capacity training device.

[0089] The mechanical training device is a mechanical device for providing training load and contacting with the trainee. A motion information sensor module is installed in the mechanical training device, and the module includes a sensor for collecting the actual position of the training load pulled by the trainee. The sensor for collecting the actual position of the training load pulled by the trainee includes but is not limited to an angular displacement sensor for monitoring the angle, an ultrasonic sensor, an infrared sensor, a pull wire displacement sensor, and a laser sensor for monitoring the linear displacement. The sensor for monitoring the training load includes but is not limited to a tension and compression force sensor and a torque sensor. A physiological information sensor module is worn on the body of the trainee for collecting the heart rate of the trainee, including but not limited to a heart rate sensor worn on the wrist and a heart rate sensor worn on the upper body. A facial information collection module is a camera installed on the mechanical training device, which can collect the training value facial information of the trainee in real time during the training of the trainee, and identify the current expression of the trainee according to a facial expression recognition module. The identified expression includes three states: relaxed, normal, and painful. A human-computer interaction module is installed on the mechanical training device, including a touch all-in-one machine and a loudspeaker, which provides picture vision and sound hearing output, can perform touch operation input, and can receive basic information input of the trainee. A control module is installed on the mechanical device, including a microprocessor and an interface board. The control module is connected with the motion information sensor module through a line, connected with the physiological information sensor module through wireless communication, the wireless communication includes but is not limited to Bluetooth and Wifi transmission modes, connected with the facial information collection module, the facial expression recognition module, and the human-computer interaction module through a line, and connected with a statistical analysis system through wireless communication, including but not limited to Bluetooth and Wifi transmission modes. The control module outputs a standard examination motion curve to the human-computer interaction module, receives data of the motion information sensor module, the physiological information sensor module, the facial information collection module, and the facial expression recognition module, calculates the examination result of the trainee based on the examination standard and according to the feedback data of the motion information sensor, the physiological information sensor, and the facial information collection module.

[0090] The standard examination motion curve is displayed through the touch all-in-one machine, and the sound prompt is played by the loudspeaker.

[0091] The touch all-in-one machine displays the standard examination motion curve and the actual position-time curve of the load pulled by the trainee, as shown in Figure 1 The standard examination motion curve is a highest position and lowest position curve of the load pulled and lowered by the trainee with time, and the actual position s of the load pulled by the trainee should be between the highest actual position sH and the lowest actual position sL curve. At the same time, the loudspeaker plays the voice prompts of "pull up", "maintain", "lower", and "relax" according to the different situations of the load position going up, maintaining at the highest point, going down, and resting at the lowest point.

[0092] The control module in the training process, real-time statistics trainer complete standard assessment action times N, each action of the completion accuracy p and the completion deviation σ.

[0093] Wherein, each action of the completion accuracy p refers to each standard assessment action, the actual position s of the trainer to pull the load and the time curve s(t) between the upper limit sH(t) curve and the lower limit sL(t) curve of the standard assessment action curve, the higher p indicates that the training action is more in line with the assessment action requirements, and the action is more standard.

[0094] The completion deviation σ refers to the deviation that cannot meet the standard assessment action in the completion action process, and the larger σ indicates that the actual action and the standard assessment action requirement deviation is larger, which can be calculated according to the following formula:

[0095]

[0096] Wherein, sA(t) is a curve composed of points with the same distance from sH(t) and sL(t), sA(t) = [sH(t) + sL(t)] / 2, and n is the total number of time points in s(t) that are not between sH(t) and sL(t).

[0097] Based on the above intelligent capacity training system, the following process is used to realize the strength training assessment statistics:

[0098] (1) The trainer wears the physiological information sensor module, prepares on the mechanical training device, inputs the personal basic information on the touch all-in-one machine, and clicks the start assessment button on the touch all-in-one machine.

[0099] (2) The control module controls the physiological information sensor module to record the heart rate value A1 of the trainer at the beginning.

[0100] (3) The man-machine interaction module displays a continuous and repeated standard assessment action curve, and the speaker plays a voice prompt, and the trainer repeats the assessment action according to the prompt.

[0101] (4) In the training process, the control module controls the facial information acquisition module to start collecting the facial expression of the trainee in real time according to the set frequency; the sampling frequency is less than or equal to 1 Hz, and the facial expression recognition module identifies the trainee's expression as which state among relaxed, normal, and painful. At the same time, the motion information sensor module collects the actual position of the trainee lifting the load according to the set frequency; the actual position s of the trainee lifting the load and the time curve s(t) are obtained; the control module counts the number of times N of the trainee completing the standard examination action, and only when the trainee completes the standard examination action each time p≥0.95 and σ≤0.1×Smax, it is considered that the trainee completes the standard examination action according to the requirements, and the number of times of the trainee completing the standard examination action is added by 1. When the trainee does not complete the standard examination action according to the requirements for 3 times in a row, the examination is ended. Wherein Smax is the maximum distance of the load lifting in the standard examination action curve.

[0102] (5) The control module controls the physiological information sensor module to record the heart rate value A2 of the trainee at the end of the examination.

[0103] (6) The control module sends the personal basic information of the trainee, the number of times N of completing the standard examination action, the heart rate change value ΔA, the number of times Th of the relaxed facial expression, the number of times Tn of the normal facial expression, and the number of times Tp of the painful facial expression to the statistical analysis system, wherein ΔA=A2-A1.

[0104] (7) The statistical analysis system receives the examination result information of the trainee sent by all the intelligent capacity training devices. After receiving the examination result information of all the trainees, the score Score of each person's examination is calculated, and the scores are sorted according to the scores. Wherein the score Score of each person is:

[0105]

[0106] Wherein is the average value of the heart rate change value ΔA of all the trainees.

Claims

1. A statistical method for physical fitness training assessment, characterized in that, Includes the following steps: Step 1: Before the assessment begins, collect the trainee's heart rate value A1; Step 2: Instruct the trainee to repeat the assessment movements according to the standard assessment movement curve; Step 3: During the training process, collect the trainee's facial expressions at a set frequency; and collect the actual position of the trainee pulling up the load at a set frequency to obtain the curve s(t) of the actual position s of the trainee pulling up the load versus time. Analyze the curve s(t) and extract the assessment standard parameters p and σ, where p is the accuracy rate of each action and σ is the deviation. The number of times N that trainees complete the standard assessment action is statistically analyzed in real time based on the assessment criteria; where the assessment criteria are p≥x and σ≤y×Smax, x equals 0.95, y equals 0.1, and Smax is the maximum distance the load is pulled up in the standard assessment action curve; If a trainee meets the assessment standard in every assessment action, the trainee is considered to have completed the standard assessment action, and the number of times the trainee has completed the standard assessment action is incremented by 1; otherwise, the trainee is considered to have failed to complete the standard assessment action. The assessment ends when the trainee fails to complete the standard assessment action i times consecutively; where i is a natural number greater than 1. Step 4: Collect the trainee's heart rate value A2 at the end of the assessment; Step 5: Collect the trainee's basic personal information, the number of times the standard assessment movement was completed (N), the heart rate change value (ΔA), and facial expression as the assessment result information; where ΔA = A2 - A1; After completing the assessment results for all trainees, calculate the score for each person's assessment and rank them according to the scores; In step 3, the accuracy p of each action refers to the percentage of the actual position s of the trainee pulling up the load versus time curve s(t) in each assessment action between the upper limit sH(t) curve and the lower limit sL(t) curve of the standard assessment action curve; the standard assessment action curve is the highest position sH(t) curve and the lowest position sL(t) curve of the trainee pulling up and putting down the load that change over time. The completion deviation σ is the deviation that fails to meet the standard assessment action during the completion of the assessment action, and is calculated using the following formula: Where sA(t) is the curve formed by points equidistant from sH(t) and sL(t), sA(t) = [sH(t) + sL(t)] / 2, and n is the total number of time points in the s(t) curve that are not between the sH(t) and sL(t) curves; In step 3, the trainee's facial expressions include three states: relaxed, normal, and painful. In step 5, the trainee's basic personal information, the number of times the standard assessment movement was completed (N), the heart rate change value (ΔA), the number of times the facial expression was relaxed (Th), the number of times it was normal (Tn), and the number of times it was painful (Tp) are used as assessment result information. The score is calculated using the following formula: in This represents the average heart rate change ΔA for all trainees.

2. A physical fitness training assessment and statistics system, characterized in that: It includes a memory and a processor. The memory stores a computer program, which, when executed in the processor, implements the physical fitness training assessment statistical method as described in claim 1.

3. A computer-readable storage medium, characterized in that: Used to store programs, which, when executed, implement the physical fitness training assessment statistics method as described in claim 1.

4. An intelligent strength training system, characterized in that: Including intelligent strength training equipment and statistical analysis systems; The intelligent strength training equipment includes a mechanical training device, a motion information sensor module, a physiological information sensor module, a facial information acquisition module, a facial expression recognition module, a human-computer interaction module, and a control module. Mechanical training devices are mechanical devices that provide training loads and come into contact with the trainee; The motion information sensor module is installed inside the mechanical training device to collect training load information and to collect the actual position of the trainee pulling up the load at a set frequency. The physiological information sensor module is worn on the trainee's body to collect the trainee's heart rate; The facial information acquisition module is installed on the mechanical training device to collect the trainee's facial information in real time at a set frequency; The facial expression recognition module is installed on the mechanical training device to recognize the trainee's facial expressions based on the facial information sent by the facial information acquisition module. The human-computer interaction module is installed on the mechanical training device to provide visual and auditory feedback, enable touch operation input, and receive basic information input from the trainee; The control module is installed on the mechanical training device and communicates with the motion information sensor module, physiological information sensor module, facial information acquisition module, facial expression recognition module, human-computer interaction module and statistical analysis system. The control module calculates the trainee's assessment results based on the assessment criteria and the feedback data from the trainee's motion information sensor, physiological information sensor and facial expression recognition module. The trainees' assessment results will be sent to the statistical analysis system. The statistical analysis system receives assessment results from all trainees; After receiving the assessment results of all trainees, a score is calculated for each person's assessment, and the trainees are ranked according to their scores.

5. The intelligent strength training system according to claim 4, characterized in that: The control module includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, performs the following processes: Step 1: Before the assessment begins, control the physiological information sensor module to collect the trainee's heart rate value A1; Step 2: Control the human-computer interaction module to prompt the trainee to repeat the assessment actions according to the standard assessment actions; Step 3: During the training process, control the facial information acquisition module to collect the trainee's facial expressions at a set frequency; The motion information sensor module is controlled to collect the actual position of the trainee pulling up the load at a set frequency, and the curve s(t) of the actual position s of the trainee pulling up the load versus time is obtained. Analyze the curve s(t) and extract the assessment standard parameters p and σ, where p is the accuracy rate of each action and σ is the deviation. The number of times N that trainees complete the standard assessment action is statistically analyzed in real time based on the assessment criteria; where the assessment criteria are p≥x and σ≤y×Smax, x and y are set values, and Smax is the maximum distance the load is pulled up in the standard assessment action curve; If a trainee meets the assessment standard in every assessment action, the trainee is considered to have completed the standard assessment action, and the number of times the trainee has completed the standard assessment action is incremented by 1; otherwise, the trainee is considered to have failed to complete the standard assessment action. The assessment ends when the trainee fails to complete the standard assessment action i times consecutively; where i is a natural number greater than 1. Step 4: Control the physiological information sensor module to collect the trainee's heart rate value A2 at the end of the assessment; Step 5: Send the trainee's basic personal information, the number of times the standard assessment action was completed (N), the heart rate change value (ΔA), and facial expression to the statistical analysis system, where ΔA = A2 - A1.

6. The intelligent strength training system according to claim 5, characterized in that: In step 3, the accuracy p of each action refers to the percentage of the trainee's actual position s pulling up the load versus time s(t) curve in each assessment action between the upper limit sH(t) curve and the lower limit sL(t) curve of the standard assessment action curve; where the standard assessment action curve is the curve of the highest and lowest position required for the trainee to pull up and put down the load as time changes.

7. The intelligent strength training system according to claim 6, characterized in that: In step 3, the completion deviation σ is the deviation that fails to meet the standard assessment action during the completion of the assessment action, and is calculated using the following formula: Wherein, sA(t) is the curve formed by points equidistant from sH(t) and sL(t), sA(t) = [sH(t) + sL(t)] / 2, and n is the total number of time points in the s(t) curve that are not between the sH(t) and sL(t) curves.

8. The intelligent strength training system according to claim 7, characterized in that: In step 3, the trainee's facial expressions include three states: relaxed, normal, and painful. In step 5, the trainee's basic personal information, the number of times the standard assessment movement was completed (N), the heart rate change value (ΔA), the number of times the facial expression was relaxed (Th), the number of times the facial expression was normal (Tn), and the number of times the facial expression was painful (Tp) are used as assessment result information.

9. The intelligent strength training system according to claim 8, characterized in that: In step 3, x equals 0.95 and y equals 0.

1.

10. The intelligent strength training system according to claim 9, characterized in that: In step 5, the score is calculated using the following formula: in This represents the average heart rate change ΔA for all trainees.

11. The intelligent strength training system according to any one of claims 4-10, characterized in that: The motion information sensor module includes sensors for collecting information on the actual position of the trainee pulling up the load and the training load. Sensors used to collect the actual position of the trainee pulling up the load include angular displacement sensors that monitor angles, ultrasonic sensors, infrared sensors, wire displacement sensors, or laser sensors that monitor linear displacement. Sensors used to collect training load information from trainees include tension / compression sensors or torque sensors.

12. The intelligent strength training system according to claim 11, characterized in that: The physiological information sensor module includes a heart rate sensor worn on the wrist or on the upper body.

13. The intelligent strength training system according to claim 12, characterized in that: The human-computer interaction module includes a touch screen all-in-one machine and a speaker; the touch screen all-in-one machine provides visual information, enables touch input, and can receive basic information input from the trainee; the speaker provides auditory feedback. In step 2, the touch screen all-in-one machine displays the standard assessment action curve and simultaneously outputs training guidance instructions through the speaker, prompting the trainee to repeat the assessment actions according to the standard assessment actions.

14. The intelligent strength training system according to claim 13, characterized in that: The control module is connected to the motion information sensor module via a line, and to the physiological information sensor module via wireless communication; wireless communication includes, but is not limited to, Bluetooth and Wi-Fi transmission methods. It is connected to the facial information acquisition module, facial expression recognition module, and human-computer interaction module via lines; and to the statistical analysis system via wireless communication.

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