A fitness behavior intelligent monitoring method and system based on the Internet of Things

Abstract

The present invention discloses a fitness behavior intelligent monitoring method and system based on the Internet of Things, which relates to the field of fitness monitoring technology. The method comprises: first classifying fitness equipment, calculating a comprehensive fitness standard according to fitness duration and movement standard in historical monitoring data, and then performing a second classification; constructing a two-dimensional classification matrix based on the two-category classification, setting a reference monitoring interval and a cycle length, and determining initial monitoring parameters of six types of equipment in the matrix; obtaining associated parts and standard motion data of each fitness equipment, constructing an associated part set, identifying and locating the parts of fitness personnel and equipment in a video, analyzing the motion speed and trajectory of the associated parts, and calculating a motion trajectory deviation value; calculating a speed mean, a trajectory deviation mean, a speed coincidence ratio and a unit fitness standard, thereby obtaining a unit comprehensive fitness standard and adjusting monitoring parameters; and realizing intelligent monitoring of fitness equipment classification, fitness personnel behavior monitoring, and dynamic adjustment of monitoring parameters based on the Internet of Things.

Description

Technical Field

[0001] The present invention relates to the field of fitness monitoring technology, and in particular to an Internet of Things-based fitness behavior intelligent monitoring method and system. Background Art

[0002] In today's society, with the improvement of people's living standards and the strengthening of health awareness, fitness has become an important part of many people's daily lives. However, traditional fitness methods often lack scientific guidance and personalized monitoring, resulting in poor fitness results and even sports injuries due to improper exercise methods. At the same time, the rapid development of Internet of Things technology has opened up new possibilities for intelligent monitoring of fitness behavior.

[0003] IoT technology connects various smart devices and sensors to the internet, building a vast and efficient monitoring network. In the fitness field, this technology can be applied to comprehensively monitor fitness equipment, fitness users, and the fitness environment. For example, wearable devices such as smart bracelets and smart watches can monitor users' heart rate, blood pressure, step count, and other physiological indicators in real time. Smart fitness equipment can also record users' exercise trajectory, speed, calories burned, and other exercise data.

[0004] However, existing fitness monitoring technologies still have some shortcomings. Traditional monitoring methods may use fixed monitoring intervals and cycle lengths, which cannot be dynamically adjusted according to the actual performance of fitness personnel. Secondly, most traditional monitoring methods do not conduct in-depth analysis of the related parts of fitness equipment and cannot accurately evaluate the movement standards of fitness personnel. Summary of the Invention

[0005] (1) Technical problems solved

[0006] In response to the technical problems in the background technology, the present invention proposes an intelligent fitness behavior monitoring method and system based on the Internet of Things. By obtaining the standard motion data of the associated parts of each fitness equipment and analyzing the actual motion trajectory and speed of the fitness person in real time; by calculating the unit comprehensive fitness standard degree of the current fitness person using the corresponding fitness equipment, the initial monitoring interval and the initial monitoring cycle length are adjusted in real time; thereby solving the technical problems recorded in the background technology.

[0007] (2) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] An intelligent fitness behavior monitoring method based on the Internet of Things, comprising:

[0010] The fitness equipment is first classified; a monitoring record sheet of each fitness equipment is obtained, and based on the fitness duration and movement standard in the historical monitoring data, the comprehensive fitness standard of each fitness equipment is calculated, and the fitness equipment is secondly classified;

[0011] Constructing a two-dimensional classification matrix based on the first and second categories of fitness equipment; setting a baseline monitoring interval and a baseline monitoring cycle length; and determining an initial monitoring interval and an initial monitoring cycle length for each of the six elements of the two-dimensional classification matrix corresponding to the equipment type;

[0012] Obtain the associated parts of each fitness device and the standard motion data of each associated part, and construct an associated part set for each fitness device; identify and locate the parts of the fitness person and fitness device in the detection video based on computer vision and deep learning algorithms; analyze the movement speed and trajectory of each associated part in the video, and calculate the motion trajectory deviation value of each associated part;

[0013] Calculate the mean speed of each associated part and the mean deviation of the motion trajectory; calculate the speed compliance ratio of each associated part based on the mean speed and the standard motion speed range; calculate the unit fitness standard of the associated part based on the speed compliance ratio and the mean deviation of the motion trajectory; combine the unit fitness standard of all associated parts to obtain the unit comprehensive fitness standard of the current fitness person using the corresponding fitness equipment, adjust the initial monitoring interval and the initial monitoring cycle length; calculate the fitness standard of the current fitness record when the fitness equipment is used.

[0014] Specifically, all fitness durations t in the historical monitoring data are obtained from the monitoring record table of each fitness equipment. i And the corresponding fitness standard Fs i And combine the action standard data of all monitoring data of the same fitness equipment to obtain the comprehensive fitness standard CFs of each fitness equipment i , the expression is: Wherein, i represents the i-th historical monitoring data of each fitness device, and n represents the total number of historical monitoring data of each fitness device.

[0015] Specifically, the first classification is primary equipment, intermediate equipment and advanced equipment;

[0016] The second classification is: Comprehensive Fitness Standard CFs i Fitness equipment that is lower than the fitness standard threshold CFs0 is marked as error-prone equipment; the comprehensive fitness standard CFs i Fitness equipment that is not lower than the fitness standard threshold CFs0 is marked as non-error-prone equipment.

[0017] Specifically, the associated parts of each fitness equipment are obtained, and the associated part set {P1, P2…, P m}, where P j represents the jth associated part in the associated part set, and m represents the total number of associated parts in the associated part set;

[0018] Obtain standard motion data of each associated part, including motion trajectory and motion speed data.

[0019] Specifically, the real-time process video of the fitness personnel using the fitness equipment is automatically recorded according to the preset initial monitoring interval and initial monitoring cycle length;

[0020] Identify the associated parts in the associated parts set from the video data, track the associated parts in consecutive frames in the video, record their positions in each frame and analyze the motion trajectory; calculate the real-time motion speed of the associated parts based on the position changes of the associated parts in consecutive frames.

[0021] Furthermore, multiple repetitive cycle trainings are performed while using the fitness equipment, and the movement trajectory of the associated parts in each repetitive cycle training is recorded;

[0022] When a fitness person uses fitness equipment, the actual motion trajectory of the associated parts is aligned with the standard motion trajectory of the corresponding associated parts according to each complete repeated cycle training, and the Euclidean distance between the actual trajectory point and the corresponding standard trajectory point is calculated to obtain the motion trajectory deviation value of one repeated cycle training;

[0023] The alignment according to each complete repetitive cycle training is specifically performed by aligning the time of one repetitive cycle training in the standard motion trajectory with the time when the current fitness person actually completes one repetitive cycle training, adjusting the standard motion trajectory frame by frame with the same time ratio based on the time ratio, and then calculating it based on the Euclidean distance between the actual trajectory point and the corresponding standard trajectory point.

[0024] Specifically, based on the real-time monitoring of the movement speed of the relevant parts during the monitoring period, the speed average Rap is obtained; when the fitness person is monitored to have completed a repeated cycle training, the motion trajectory deviation value of the repeated cycle training is obtained, and the average operation is performed on the motion trajectory deviation values ​​of several previous repeated cycle trainings to obtain the motion trajectory deviation mean Mdt.

[0025] Furthermore, the speed compliance ratio Sir of each associated part is calculated based on the speed mean Rap of each associated part and the standard motion speed range [Rap1, Rap2]. Specifically, when the speed mean Rap of the associated part is within the standard motion speed range [Rap1, Rap2], the speed compliance ratio Sir is 1.

[0026] When the velocity mean Rap of the associated part is not within the standard motion speed range [Rap1, Rap2], if Rap < Rap1, the velocity compliance ratio Sir is If Rap>Rap2, the speed ratio Sir is

[0027] Furthermore, the unit fitness standard UFs of the associated parts is calculated, and the expression is: Wherein, Mdt0 represents the preset motion trajectory deviation threshold of the associated part;

[0028] Unit fitness standard UFs of all associated parts in the associated parts set j , calculate the unit comprehensive fitness standard UCFs of the current fitness personnel using the corresponding fitness equipment, the expression is: Among them, β j represents the preset weight coefficient of the jth associated part in the associated part set, and

[0029] Adjust the initial monitoring interval T3 to T3*UCFs, and adjust the initial monitoring period length T4 to

[0030] An intelligent fitness behavior monitoring system based on the Internet of Things, comprising:

[0031] The fitness equipment classification module is used to perform a first classification on the fitness equipment; obtain a monitoring record sheet for each fitness equipment, calculate the comprehensive fitness standard of each fitness equipment based on the fitness duration and movement standard in the historical monitoring data, and perform a second classification on the fitness equipment;

[0032] A monitoring parameter setting module is used to construct a two-dimensional classification matrix based on the first and second categories of fitness equipment; set a baseline monitoring interval and a baseline monitoring cycle length, and determine an initial monitoring interval and an initial monitoring cycle length for each equipment type corresponding to the six elements in the two-dimensional classification matrix;

[0033] The motion data acquisition module is used to obtain the associated parts of each fitness device and the standard motion data of each associated part, and to construct an associated part set for each fitness device; based on computer vision and deep learning algorithms, it identifies and locates the parts of the fitness person and fitness device in the detection video; analyzes the motion speed and motion trajectory of each associated part in the video, and calculates the motion trajectory deviation value of each associated part;

[0034] The standardization evaluation and monitoring adjustment module is used to calculate the speed mean and motion trajectory deviation mean of each associated part; calculate the speed compliance ratio of each associated part based on the speed mean and the standard motion speed range; calculate the unit fitness standard of the associated part based on the speed compliance ratio and the motion trajectory deviation mean; combine the unit fitness standard of all associated parts to obtain the unit comprehensive fitness standard of the current fitness person using the corresponding fitness equipment, adjust the initial monitoring interval and the initial monitoring cycle length; calculate the fitness standard of the current fitness record when the fitness equipment is used.

[0035] (3) Beneficial effects

[0036] The present invention provides a method and system for intelligently monitoring fitness behaviors based on the Internet of Things, which has the following beneficial effects:

[0037] 1. By classifying fitness equipment into primary, intermediate, and advanced categories and secondary categories (error-prone and non-error-prone), refined management of fitness equipment is achieved. The secondary category calculates the comprehensive fitness standard based on fitness duration and movement standard in historical monitoring data, identifying error-prone equipment and helping to strengthen monitoring of equipment with a high incidence of non-standard operation.

[0038] 2. The method of determining monitoring parameters based on the diagonal principle makes the allocation of monitoring resources more scientific and reasonable. For fitness equipment of different categories and characteristics, the monitoring interval and cycle length can be flexibly adjusted according to factors such as the difficulty of use and the degree of error. This can not only ensure the effective monitoring of fitness personnel while avoiding the waste of resources caused by excessive monitoring, but also strengthen the monitoring of key equipment and error-prone situations, thereby improving monitoring efficiency.

[0039] 3. It achieves precise monitoring of fitness personnel using fitness equipment. Through advanced computer vision and deep learning technologies, it can accurately identify fitness personnel and equipment, obtain real-time motion information of related parts, and compare it with standard motion data. This provides detailed and accurate data support for subsequent evaluation of fitness personnel's operational standards, helps to promptly detect irregular movements of fitness personnel when using equipment, and provides a strong basis for fitness guidance and safety assurance.

[0040] 4. It can comprehensively and objectively evaluate the degree of standardization of fitness personnel in using fitness equipment, and make the evaluation results more accurate and reliable by comprehensively considering the speed compliance ratio and the mean deviation of the motion trajectory; dynamically adjust the monitoring parameters according to the comprehensive fitness standard of the unit, realize the dynamic optimization allocation of monitoring resources, and improve the pertinence and effectiveness of monitoring; record the fitness standard of the current fitness record, which helps fitness personnel understand their own fitness situation and also provides data support for fitness coaches to develop more personalized fitness guidance plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of the steps of an Internet of Things-based fitness behavior intelligent monitoring method provided by the present invention;

[0042] Figure 2 This is a structural diagram of an Internet of Things-based fitness behavior intelligent monitoring system provided by the present invention. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0044] refer to Figure 1 The present invention provides a method for intelligently monitoring fitness behaviors based on the Internet of Things, comprising:

[0045] Step 1: Perform a first classification of the fitness equipment; obtain a monitoring record sheet for each fitness equipment, calculate the comprehensive fitness standard of each fitness equipment based on the fitness duration and movement standard in the historical monitoring data, and perform a second classification of the fitness equipment;

[0046] The step one includes the following steps:

[0047] Step 101: Obtain various types of fitness equipment in the gym, and obtain first classification results of all fitness equipment, specifically divided into elementary equipment, intermediate equipment, and advanced equipment;

[0048] Primary equipment refers to fitness equipment with simple functions and simple operation, which is mainly used for basic physical exercise, such as dumbbells, treadmills, fitness balls and other fitness equipment. These equipment are easy to use and safe, so fitness users who use these fitness equipment only need to be monitored at longer intervals.

[0049] Intermediate equipment refers to fitness equipment with diverse functions and adjustable parameters that can meet certain exercise needs but is relatively simple to operate, such as elliptical machines and rowing machines. These fitness equipment require certain operating skills and are generally safe, so fitness monitoring of fitness users at medium intervals is required.

[0050] Advanced equipment refers to fitness equipment with complex functions, combining multiple exercise modes, and requiring high physical fitness and skills, such as Smith machines, gantry racks, bench press racks, etc. These fitness equipment are difficult to use and require high physical fitness and skills, and require strict safety measures. Therefore, fitness users of these fitness equipment need to be monitored at short intervals.

[0051] The division of primary, intermediate and advanced equipment is pre-set by professional fitness trainers;

[0052] Step 102: Obtain a monitoring record sheet for each fitness device from the gym management system. The monitoring record sheet for each fitness device records historical monitoring data for the fitness device, including fitness duration, motion data of associated parts, and fitness standard data. The motion data of associated parts represents data such as the speed and trajectory of the exercised body part when the corresponding fitness device is used. The fitness standard represents the standard degree of exercise of the associated part during a complete use of the corresponding fitness device by the fitness user, and is specifically calculated by combining actual motion data of multiple associated parts with standard motion data. Each piece of monitoring data for the fitness device corresponds to one usage record.

[0053] Obtain all fitness durations t in the historical monitoring data from the monitoring record table of each fitness equipment i And the corresponding fitness standard Fs i And combine the action standard data of all monitoring data of the same fitness equipment to obtain the comprehensive fitness standard CFs of each fitness equipment i , the expression is: Wherein, i represents the i-th historical monitoring data of each fitness device, and n represents the total number of historical monitoring data of each fitness device;

[0054] Step 103: Perform the second classification on all fitness equipment. Specifically, a professional fitness coach pre-sets a fitness standard threshold CFs0 and classifies the comprehensive fitness standard CFs i Fitness equipment that is below the fitness standard threshold CFs0 is marked as error-prone equipment, indicating that fitness enthusiasts are mostly not operating these fitness equipment in a standard way, and it is necessary to strengthen the monitoring of these equipment;

[0055] Comprehensive Fitness Standard CFs i Fitness equipment with a fitness standard threshold CFs0 or higher is marked as non-error-prone equipment, indicating that most fitness personnel can use and operate these fitness equipment in a standardized manner, and there is no need to increase the monitoring of these equipment.

[0056] When using, combine the contents in steps 101 to 103:

[0057] By classifying fitness equipment into the first category (elementary, intermediate, and advanced equipment) and the second category (error-prone equipment and non-error-prone equipment), refined management of fitness equipment is achieved; the second classification calculates the comprehensive fitness standard based on the fitness duration and movement standard in the historical monitoring data, identifies error-prone equipment, and helps to increase the monitoring of equipment with more irregular operations.

[0058] Step 2: construct a two-dimensional classification matrix based on the first and second categories of fitness equipment; set a baseline monitoring interval and a baseline monitoring cycle length, and determine an initial monitoring interval and an initial monitoring cycle length for each equipment type corresponding to the six elements in the two-dimensional classification matrix;

[0059] The step 2 includes the following steps:

[0060] Step 201: Based on the first and second classifications of fitness equipment, construct a two-dimensional classification matrix of fitness equipment; obtain six elements in the two-dimensional classification matrix, namely, primary non-error-prone equipment, primary error-prone equipment, intermediate non-error-prone equipment, intermediate error-prone equipment, advanced non-error-prone equipment, and advanced error-prone equipment;

[0061] Step 202: Set a reference monitoring interval T1 and a reference monitoring cycle length T2, wherein the monitoring cycle length represents the actual monitoring time length, i.e., the real-time monitoring of the fitness person's fitness operations during this time period; the monitoring interval represents the time when the real-time monitoring of the fitness person's fitness operations is not performed; the specific monitoring process is to perform real-time monitoring of the fitness person's fitness operations for a monitoring cycle length, then stop for a monitoring interval, and then perform real-time monitoring of the fitness person's fitness operations again for a monitoring cycle length; the reference monitoring interval and the reference monitoring cycle length are fixed preset values;

[0062] Step 203: Based on the diagonal principle, the initial monitoring interval and the initial monitoring cycle length are determined for each of the six equipment types corresponding to the elements in the two-dimensional classification matrix. The diagonal principle indicates that the initial monitoring interval and the initial monitoring cycle length of several types of fitness equipment located on the diagonal in the two-dimensional classification matrix are the same. Specifically, the initial monitoring interval of the primary non-error-prone equipment is adjusted to twice the reference monitoring interval T1, i.e., 2T1, and the initial monitoring cycle length is adjusted to half the reference monitoring cycle length T2, i.e.,

[0063] The initial monitoring interval of primary error-prone instruments and intermediate non-error-prone instruments is adjusted to the reference monitoring interval T1, and the length of the initial monitoring cycle is adjusted to half of the length of the reference monitoring cycle T2, that is,

[0064] Adjust the initial monitoring interval of intermediate error-prone instruments and advanced non-error-prone instruments to twice the baseline monitoring interval T1, i.e. 2T1, and adjust the initial monitoring cycle length to the baseline monitoring cycle length T2;

[0065] Adjust the initial monitoring interval of advanced error-prone instruments to the baseline monitoring interval T1, and adjust the initial monitoring cycle length to the baseline monitoring cycle length T2;

[0066] When using, combine the contents in steps 201 to 203:

[0067] The method of determining monitoring parameters based on the diagonal principle makes the allocation of monitoring resources more scientific and reasonable; for fitness equipment of different categories and characteristics, the monitoring interval and cycle length can be flexibly adjusted according to factors such as the difficulty of use and the degree of error. This can not only avoid waste of resources caused by excessive monitoring while ensuring effective monitoring of fitness personnel, but also strengthen monitoring of key equipment and error-prone situations to improve monitoring efficiency.

[0068] Step 3: Obtain the associated parts of each fitness device and the standard motion data of each associated part, and construct an associated part set for each fitness device; identify and locate the parts of the fitness person and the fitness device in the detection video based on computer vision and deep learning algorithms; analyze the movement speed and movement trajectory of each associated part in the video, and calculate the movement trajectory deviation value of each associated part;

[0069] The step three includes the following steps:

[0070] Step 301: Based on the official instruction manual of each fitness device, the structure, function, and correct usage of each fitness device are obtained, specifically including the associated parts of each fitness device, as well as the motion trajectory and motion speed data of the associated parts when using each fitness device, and recorded as standard motion data for each associated part. The associated parts of the fitness device are the specific body parts that directly interact with the device, bear the exercise load, or produce the exercise effect when exercising with the specific fitness device. For example, when performing arm curls with dumbbells, the biceps is a body part directly associated with the dumbbell because the dumbbell's weight and motion trajectory directly act on the biceps. Therefore, the front of the upper arm where the biceps is located is an associated part of the dumbbell.

[0071] Step 302: Obtain the associated parts of each fitness equipment and form an associated part set {P1, P2..., P m}, where P j represents the jth associated part in the associated part set, and m represents the total number of associated parts in the associated part set;

[0072] Step 303: Based on the binocular camera installed in the gym, computer vision and deep learning algorithms are used to identify and locate the fitness personnel in the picture, identify whether the fitness personnel are using fitness equipment, and periodically capture the fitness personnel's use of the fitness equipment; specifically:

[0073] The binocular camera is calibrated to obtain accurate spatial information, and the model is then trained to distinguish the interactive motion characteristics of the human body and the equipment. When a fitness person is identified using fitness equipment, the binocular camera's shooting function is activated, and the real-time process of the fitness person using the fitness equipment is automatically recorded according to the preset initial monitoring interval and initial monitoring cycle length.

[0074] The trained OpenPose deep learning model is used to identify the various parts of the fitness user in the recorded real-time video data. The fitness equipment used by the fitness user is identified based on the pre-trained fitness equipment recognition model. The OpenPose deep learning model is an open source computer vision model for human pose estimation. It can monitor and identify the body joints and limb contours of multiple people in images or videos in real time, and output the position of each part in the form of two-dimensional coordinates. The fitness equipment recognition model is pre-loaded with image data of various fitness equipment from multiple angles to implement a model for image-based fitness equipment type recognition.

[0075] Step 304: Based on the associated part set corresponding to the fitness equipment, identify each associated part in the associated part set from the video data, and obtain corresponding standard motion data of each associated part, including a standard motion speed range and a standard motion trajectory;

[0076] The motion trajectory and speed of each associated part are analyzed during the real-time recording of the fitness person using the fitness equipment. The motion trajectory is obtained by tracking the associated part in consecutive frames of the video and recording its position in each frame. The real-time motion speed of the associated part is calculated based on the position change of the associated part in consecutive frames.

[0077] During the use of fitness equipment, multiple repeated cycle trainings will be performed, and the motion trajectories of the associated parts in each repeated cycle training will be recorded; the actual motion trajectory of the associated parts when the fitness person uses the fitness equipment will be aligned with the standard motion trajectory of the corresponding associated parts according to each complete repeated cycle training, and the Euclidean distance between the actual trajectory point and the corresponding standard trajectory point will be calculated to obtain the motion trajectory deviation value of one repeated cycle training; the alignment according to each complete repeated cycle training is specifically performed by aligning the time of one repeated cycle training in the standard motion trajectory with the time when the current fitness person actually completes one repeated cycle training, adjusting the standard motion trajectory frame by frame with the same time ratio based on the time ratio, and then calculating it based on the Euclidean distance between the actual trajectory point and the corresponding standard trajectory point.

[0078] When using, combine the contents in steps 301 to 304:

[0079] It has achieved precise monitoring of the process of fitness personnel using fitness equipment. Through advanced computer vision and deep learning technology, it can accurately identify fitness personnel and equipment, obtain motion information of related parts in real time, and compare it with standard motion data, providing detailed and accurate data support for subsequent evaluation of the degree of operational standardization of fitness personnel, helping to promptly discover irregular movements of fitness personnel when using equipment, and providing a strong basis for fitness guidance and safety assurance.

[0080] Step 4. Calculate the mean speed of each associated part and the mean deviation of the motion trajectory; calculate the speed compliance ratio of each associated part based on the mean speed and the standard motion speed range; calculate the unit fitness standard of the associated part based on the speed compliance ratio and the mean deviation of the motion trajectory; combine the unit fitness standard of all associated parts to obtain the unit comprehensive fitness standard of the current fitness person using the corresponding fitness equipment, adjust the initial monitoring interval and the initial monitoring cycle length; calculate the fitness standard of the current fitness record when the fitness equipment is used.

[0081] The step 4 includes the following steps:

[0082] Step 401: Based on the real-time monitoring of the movement speed of the associated parts during the monitoring period, a speed average Rap is obtained; when the fitness person completes a repetitive cycle training, a motion trajectory deviation value of the repetitive cycle training is obtained, and the average is calculated with the motion trajectory deviation values ​​of several previous repetitive cycle trainings to obtain a motion trajectory deviation average;

[0083] If the fitness person has not completed a repeated circuit training at the end of a monitoring period, then based on the ratio of the average time of the fitness person's previous completed several repeated circuit trainings to the time of the fitness person's current repeated circuit training at the end of the current monitoring period, in the standard motion trajectory after the last alignment, a portion of the standard motion trajectory of the corresponding time period is obtained based on the time ratio, and the portion is taken from the beginning of the standard motion trajectory backwards, and the motion trajectory deviation value is calculated between the portion of the standard motion trajectory and the portion of the repeated circuit training that is not completed currently; similarly, after the start of a monitoring period, the same analysis is performed when the time when the fitness person completes the repeated circuit training for the first time is monitored, but when obtaining the portion of the standard motion trajectory of the corresponding time period based on the time ratio, the portion is taken from the end of the standard motion trajectory forwards;

[0084] Step 402: Calculate the speed compliance ratio Sir of each associated part based on the speed average Rap of each associated part and the standard motion speed range [Rap1, Rap2]. Specifically, when the speed average Rap of the associated part is within the standard motion speed range [Rap1, Rap2], the speed compliance ratio is 1.

[0085] When the velocity mean Rap of the associated part is not within the standard motion speed range [Rap1, Rap2], if Rap < Rap1, the velocity compliance ratio Sir is If Rap>Rap2, the speed ratio Sir is

[0086] Step 403: Calculate the unit fitness standard UFs of the associated part based on the speed compliance ratio Sir of each associated part and the mean value of the motion trajectory deviation Mdt. The expression is:

[0087] Wherein, Mdt0 represents the preset motion trajectory deviation threshold of the associated part;

[0088] Step 404: Combine the unit fitness standards UFs of all associated parts in the associated part set j , we can get the unit comprehensive fitness standard degree UCFs of the current fitness personnel using the corresponding fitness equipment, which is expressed as: Among them, β j represents the weight coefficient of the jth associated part in the associated part set, which is pre-set by a professional fitness coach, and

[0089] Based on the unit comprehensive fitness standard UCFs, the initial monitoring interval T3 and the initial monitoring cycle length T4 of the current fitness equipment are adjusted. The initial monitoring interval T3 is adjusted to T3*UCFs, and the initial monitoring cycle length T4 is adjusted to

[0090] When the fitness person completes the use of the current fitness equipment, the fitness standard Fs of the current fitness record is obtained by calculating the average of all unit comprehensive fitness standards and recorded in the monitoring record table of the current fitness equipment.

[0091] When using, combine the contents in steps 401 to 404:

[0092] It can comprehensively and objectively evaluate the degree of standardization of fitness personnel's use of fitness equipment, and make the evaluation results more accurate and reliable by comprehensively considering the speed compliance ratio and the mean value of motion trajectory deviation; dynamically adjust the monitoring parameters according to the unit's comprehensive fitness standard, realize the dynamic optimization allocation of monitoring resources, and improve the pertinence and effectiveness of monitoring; record the fitness standard of the current fitness record, which helps fitness personnel understand their own fitness situation, and also provides data support for fitness coaches to develop more personalized fitness guidance plans.

[0093] refer to Figure 2 The present invention also provides a fitness behavior intelligent monitoring system based on the Internet of Things, comprising:

[0094] The fitness equipment classification module is used to perform a first classification on the fitness equipment; obtain a monitoring record sheet for each fitness equipment, calculate the comprehensive fitness standard of each fitness equipment based on the fitness duration and movement standard in the historical monitoring data, and perform a second classification on the fitness equipment;

[0095] A monitoring parameter setting module is used to construct a two-dimensional classification matrix based on the first and second categories of fitness equipment; set a baseline monitoring interval and a baseline monitoring cycle length, and determine an initial monitoring interval and an initial monitoring cycle length for each equipment type corresponding to the six elements in the two-dimensional classification matrix;

[0096] The motion data acquisition module is used to obtain the associated parts of each fitness device and the standard motion data of each associated part, and to construct an associated part set for each fitness device; based on computer vision and deep learning algorithms, it identifies and locates the parts of the fitness person and fitness device in the detection video; analyzes the motion speed and motion trajectory of each associated part in the video, and calculates the motion trajectory deviation value of each associated part;

[0097] The standardization evaluation and monitoring adjustment module is used to calculate the speed mean and motion trajectory deviation mean of each associated part; calculate the speed compliance ratio of each associated part based on the speed mean and the standard motion speed range; calculate the unit fitness standard of the associated part based on the speed compliance ratio and the motion trajectory deviation mean; combine the unit fitness standard of all associated parts to obtain the unit comprehensive fitness standard of the current fitness person using the corresponding fitness equipment, adjust the initial monitoring interval and the initial monitoring cycle length; calculate the fitness standard of the current fitness record when the fitness equipment is used.

[0098] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer storage medium or transmitted via a computer storage medium.

[0099] Computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. Computer storage media can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0100] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A fitness behavior intelligent monitoring method based on the Internet of Things, characterized by: The steps include: Performing a first classification on the fitness equipment; obtaining a monitoring record sheet for each fitness equipment, calculating a comprehensive fitness standard for each fitness equipment based on the fitness duration and movement standard in the historical monitoring data, and performing a second classification on the fitness equipment; The first classification is elementary equipment, intermediate equipment and advanced equipment; The second category is: Below fitness threshold fitness equipment is marked as error-prone equipment; the comprehensive fitness standard Not less than the fitness standard threshold of fitness equipment is marked as non-error-prone equipment; Constructing a two-dimensional classification matrix based on the first and second categories of fitness equipment; setting a baseline monitoring interval and a baseline monitoring cycle length; and determining an initial monitoring interval and an initial monitoring cycle length for each of the six elements of the two-dimensional classification matrix corresponding to the equipment type; Obtain the associated parts of each fitness device and the standard motion data of each associated part, and construct an associated part set for each fitness device; identify and locate the parts of the fitness person and fitness device in the detection video based on computer vision and deep learning algorithms; analyze the movement speed and trajectory of each associated part in the video, and calculate the motion trajectory deviation value of each associated part; Calculate the mean speed of each associated part and the mean deviation of the motion trajectory; calculate the speed compliance ratio of each associated part based on the mean speed and the standard motion speed range; calculate the unit fitness standard of the associated part based on the speed compliance ratio and the mean deviation of the motion trajectory; combine the unit fitness standard of all associated parts to obtain the unit comprehensive fitness standard of the current fitness person using the corresponding fitness equipment, adjust the initial monitoring interval and the initial monitoring cycle length; calculate the fitness standard of the current fitness record when the fitness equipment is used.

2. The method for intelligently monitoring fitness behaviors based on the Internet of Things according to claim 1, wherein: Get all fitness durations in historical monitoring data from the monitoring record table of each fitness equipment And the corresponding fitness standards And combine the action standard data of all monitoring data of the same fitness equipment to obtain the comprehensive fitness standard of each fitness equipment , the expression is: ,in, Indicates the first Historical monitoring data, Indicates the total number of historical monitoring data of each fitness equipment.

3. The method for intelligently monitoring fitness behaviors based on the Internet of Things according to claim 1, wherein: Get the associated parts of each fitness equipment and form the associated parts set of the corresponding fitness equipment ,in, Indicates the first Related parts, Indicates the total number of associated parts in the associated part concentration; Obtain standard motion data of each associated part, including motion trajectory and motion speed data.

4. The method for intelligently monitoring fitness behaviors based on the Internet of Things according to claim 3, wherein: Automatically record the real-time process video of fitness personnel using fitness equipment according to the preset initial monitoring interval and initial monitoring cycle length; Identify the associated parts in the associated parts set from the video data, track the associated parts in consecutive frames in the video, record their positions in each frame and analyze the motion trajectory; calculate the real-time motion speed of the associated parts based on the position changes of the associated parts in consecutive frames.

5. The method for intelligently monitoring fitness behaviors based on the Internet of Things according to claim 4, wherein: Perform multiple repetitive cycle training sessions using fitness equipment, and record the movement trajectory of the associated parts during each repetitive cycle training session; When a fitness person uses fitness equipment, the actual motion trajectory of the associated parts is aligned with the standard motion trajectory of the corresponding associated parts according to each complete repeated cycle training, and the Euclidean distance between the actual trajectory point and the corresponding standard trajectory point is calculated to obtain the motion trajectory deviation value of one repeated cycle training; The alignment according to each complete repetitive cycle training is specifically performed by aligning the time of one repetitive cycle training in the standard motion trajectory with the time when the current fitness person actually completes one repetitive cycle training, adjusting the standard motion trajectory frame by frame with the same time ratio based on the time ratio, and then calculating it based on the Euclidean distance between the actual trajectory point and the corresponding standard trajectory point.

6. The method for intelligently monitoring fitness behaviors based on the Internet of Things according to claim 5, characterized in that: Based on real-time monitoring of the movement speed of the associated parts during the monitoring period, the speed average is obtained When a fitness person completes a repetitive cycle training, the motion trajectory deviation value of the repetitive cycle training is obtained, and the mean value of the motion trajectory deviation is calculated by comparing it with the motion trajectory deviation values ​​of several previous repetitive cycle trainings. .

7. The method for intelligently monitoring fitness behaviors based on the Internet of Things according to claim 6, wherein: Based on the average velocity of each associated part , and standard motion speed range Calculate the velocity coincidence ratio of each associated part , specifically: when the velocity mean of the associated parts In the standard movement speed range If the speed is within The value of is 1; When the average velocity of the associated parts Not within the standard movement speed range Internally, if , then the speed complies with the ratio The value of ;like , then the speed complies with the ratio The value of .

8. The method for intelligently monitoring fitness behaviors based on the Internet of Things according to claim 7, wherein: Calculate the unit fitness standard of the associated parts , the expression is: ;in, Indicates the motion trajectory deviation threshold of the preset associated part; Combine the unit fitness standards of all related parts in the related parts set , calculate the unit comprehensive fitness standard of the current fitness personnel using the corresponding fitness equipment , the expression is: ;in, Indicates the concentration of related parts The preset weight coefficients of the associated parts, and ; Set the initial monitoring interval Adjust to , the initial monitoring period length Adjust to .

9. An Internet of Things-based fitness behavior intelligent monitoring system, used to implement the method described in any one of claims 1 to 8, characterized in that: include: A fitness equipment classification module is used to perform a first classification on the fitness equipment; obtain a monitoring record sheet for each fitness equipment, calculate the comprehensive fitness standard of each fitness equipment based on the fitness duration and movement standard in the historical monitoring data, and perform a second classification on the fitness equipment; A monitoring parameter setting module is used to construct a two-dimensional classification matrix based on the first and second categories of fitness equipment; set a baseline monitoring interval and a baseline monitoring cycle length, and determine an initial monitoring interval and an initial monitoring cycle length for each equipment type corresponding to the six elements in the two-dimensional classification matrix; The motion data acquisition module is used to obtain the associated parts of each fitness device and the standard motion data of each associated part, and to construct an associated part set for each fitness device; based on computer vision and deep learning algorithms, it identifies and locates the parts of the fitness person and fitness device in the detection video; analyzes the motion speed and motion trajectory of each associated part in the video, and calculates the motion trajectory deviation value of each associated part; The standardization evaluation and monitoring adjustment module is used to calculate the speed mean and motion trajectory deviation mean of each associated part; calculate the speed compliance ratio of each associated part based on the speed mean and the standard motion speed range; calculate the unit fitness standard of the associated part based on the speed compliance ratio and the motion trajectory deviation mean; combine the unit fitness standard of all associated parts to obtain the unit comprehensive fitness standard of the current fitness person using the corresponding fitness equipment, adjust the initial monitoring interval and the initial monitoring cycle length; calculate the fitness standard of the current fitness record when the fitness equipment is used.

Images