Athletic training system and method for generating a training program

Abstract

To provide an athletic training system.SOLUTION: An athletic training system includes an athlete data repository, an adaptive algorithm, and at least one training unit. The athlete data repository includes biometric data and performance data of athletes. The adaptive algorithm has access to the athlete data repository and is trained on biometric data and performance data to generate athlete-specific training programs for use with the training unit. Each athlete-specific training program is adapted to biometric data and performance data of an individual athlete. The training unit includes adjustable athletic resistance equipment and a controller.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present invention relates generally to athletic training systems and methods, and more particularly to Includes sport-specific training and performance improvement programs, where applicable position-specific and athlete-specific training and performance improvement programs SYSTEMS, METHODS, APPARATUS AND COMPUTER PROGRAM PRODUCTS ADAPTED FOR PROVIDING SYSTEMS - Patent application Regarding.

[0002] [Related Applications] This application is a continuation of U.S. Provisional Patent Application No. 62 / 877,594, filed July 23, 2019. No. 6,239,993, the entire disclosure of which is incorporated herein by reference. Let's say. [Background technology]

[0003] Known athletic training units are used by users, athletes, coaches, or trainers. The force is provided by resistance from weights, bungee cords, hydraulic fluid, or air compression. It is necessary to judge the resistance and adjust the resistance for athletic training. Teams, athletes, and trainers should use their own personal know-how and methods that are not specific to their sport. A training routine is provided using various drills. The athlete then Routines that follow tacit knowledge and unquantifiable interpretations of athletes' strengths by experts Athletes often receive a program that includes vertical, agility, and drills. training without establishing baseline measures in the areas of laterality, speed, and horizontality. Coaches and trainers often develop athletes in these four areas. Without setting a baseline measure of leadership, athletes can Train your athlete. Athletic gains in four areas: improvement, progress, Progress is the validation of observed improvements using previously established metrics. Since it is not possible to do so, it becomes subjective.

[0004] Generalized training programs created by coaches and trainers for athletes Because these routines are not a one-size-fits-all training program, These programs are optimized to provide Any athletic improvement or gains that result are unpredictable and random, and therefore They are not guaranteed because they are not necessarily measured and validated for a particular athlete. Individualized to biological and physiologic profile and athletic abilities or goals Creating individualized plans for athletes is a challenge for many coaches and trainers. It is time-consuming, costly and inefficient for coaches and trainers. The company does not have access to a large amount of data from athletes and therefore By leveraging the overall availability of data, It is not possible to extrapolate data.

[0005] Athletes can achieve gains through traditional training programs However, it does not necessarily achieve the gains that meet each specific need. A leading athlete may want to improve his or her speed. Leap and Burst training program We have contracted with specialized training centers such as er Vertical Power, and VASH Achieve gains in all four areas (vertical, agility, speed, and horizontal). However, this athlete believes that verticality is a key factor in his performance as a hockey player. Verticality is not a major consideration as it does not have a significant effect on the performance. In this case, the coach will create a training program specifically for this athlete. Obviously, the latest enabling technologies in machine learning and artificial intelligence Without technological automation, this would not be possible for a large number of athletes.

[0006] Additionally, coaches and trainers in clubs, schools, universities and professional fitness centers Pre-planned training programs using the Athletes have no way to optimally calibrate the resistance they use. Resistance is individualized according to the program, but if the resistance is too high or too low, If resistance levels are adjusted manually, this must be done for each athlete. Even if a coach manually adjusts the resistance for each athlete, it is still a trial and error process. Therefore, it may still be an inaccurate process.

[0007] Similarly, it is used to extract resistance for athletes while performing drills. The accessories include the athlete's age, height, weight, gender, grip and hand size, waist and This should be optimized based on the size of the seat and seam, but Coaches rely on trial and error to determine the accessories for each individual athlete. Fine-tune and manually optimize drills and training to fit each athlete's specific goals and needs. The science of optimizing the sequence of resisted and non-resisted drills, rest periods, and accessories The mathematical approach significantly improves the results of the training process and allows for more applications. Allows sleet to train at any given time. Summary of the Invention

[0008] The athletic training system will have an athlete data repository and adaptive algorithms. and at least one training unit. The Athlete Data Repository contains athlete biometric and performance data. The fitting algorithm accesses the athlete data repository and Regarding biometric and performance data of the type stored in the data repository Multiple athlete specific training units used in conjunction with the training unit Generate a unique training program for each athlete. , adapted to individual athlete's biometric and performance data.

[0009] The training unit comprises electronically adjustable athletic resistance equipment and a control The controller includes a training program specific to the plurality of athletes. receiving at least one athlete-specific training program from The resistance level of the adjustable resistance equipment can be adjusted according to the athlete's specific training program. The controller is also configured to adjust the received angle. the athlete's use of the training unit in accordance with the athlete's specific training program The adaptive algorithm is configured to record performance data of the individual accessing an athlete's biometric and performance data to Generate a sleet-specific training program.

[0010] The training unit may be configured to provide an interface for at least one mobile device of a user. The mobile device may further comprise a training interface for the athlete. a training program specific to the athlete received from the adaptive algorithm; The mobile device communicates a program to the training unit. receiving the performance data recorded by the controller, and The adaptive algorithm may communicate the recorded puff Performance data may be communicated to the athlete data repository.

[0011] In some embodiments, the adaptive algorithm comprises a plurality of adaptive algorithms. For example, the adaptive algorithm may include multiple machine learning models. The learning model is based on the biometric data of a given athlete and the performance data of other athletes. a rating prediction model that receives the data as input and generates a rating training program. The plurality of machine learning models may be adapted to evaluate the performance of the computer during use of an evaluation training program. A drill routine that receives performance data recorded by the troller as input The drill routine model may further include a drill routine model. During the use of an athlete-specific training program previously generated by The performance data recorded by the controller may also be received as input.

[0012] In some embodiments, the training unit comprises a vertical jump station, a water In some embodiments, the training includes a speed station, a treadmill, agility station, or both. The training unit has multiple vertical jump stations and multiple horizontal agility and speed stations. The training unit further comprises a training station responsive to the controller. The adjustable device may further include a motor for adjusting the resistance level.

[0013] A computer that generates athlete-specific training programs adapted to individual athletes. A computer-implemented method is also provided, the method comprising: a first step of: Individual athletes and additional athletes with comparable biometric and performance data Stores biometric and performance data from athletes in an athlete data repository In a second step, the individual athletes are assigned to a training unit. The training unit includes an electronically adjustable athletic resistance and a controller, the controller including a plurality of athlete-specific training programs. Receive an athlete-specific training program from the training program and Adjust the resistance level of the adjustable resistance equipment according to your specific training program and training the athlete in accordance with the athlete's specific training program. The device is configured to record performance data of the use of the sleet.

[0014] The third step is to identify the individual athlete and the tracker being accessed. The adaptive algorithm provides an identifier of the processing unit. The system uses biometric and performance data of the type stored in the athlete data repository. Multiple units are trained on the sensor data and used with the training units. In the fourth step, a specific training program for the athlete is generated. The adaptive algorithm accesses the athlete data repository and At least one athletic tracker adapted to the student's biological and performance data In a fifth step, the athlete generates a specific training program. A unique training program is provided to the training unit.

[0015] The training unit may be an interface for at least one mobile device of a user. In this case, the training unit may further include a user interface. The steps include: connecting the individual athlete's mobile device to the training unit; and integrating the athlete-specific training program with the training unit. The step of providing a unit with a unique training program for the athlete includes providing the mobile device with the unique training program for the athlete. a training program from the adaptive algorithm; The method may further include communicating a training program to the training unit.

[0016] The method further comprises the step of: the mobile device receiving the puff recorded by the controller; receiving performance data and communicating said performance data to said adaptive algorithm; The method may further include the step of:

[0017] The adaptive algorithm may include multiple adaptive algorithms, such as multiple trained machine learning models. The adaptive algorithm may include an adaptive algorithm that is based on the individual athlete's biometrics. access information and / or performance data for that athlete, Generate a training program.

[0018] The present invention provides a method for training athletes in at least four athletic disciplines, including vertical, speed, and horizontal. Adaptive athletics including hardware and software adapted to enhance gain in The adaptive athletic training system includes: a sports training unit having several stations, each station comprising: It can accommodate one athlete at a time, allowing multiple athletes to train simultaneously. Some stations allow for training at least vertically. The remaining stations include a jumping platform for skiing, while the remaining stations are dedicated to at least three other disciplines: training horizontality, agility, and speed. The idea is not limited to these areas, but can also be used for general fitness goals and endurance, etc. It can be expanded to train other physical attributes. Each station has a Athlete Pairing for designed and personalized training routines and drills The performance-based resistance components are attached to the sports training unit. , a rechargeable battery, and a cradle to hold several interfacing IoT devices. The sports training unit may include Bluetooth or internet Connected to the database via the internet, the data collected by the sports training unit is and then artificial training to adapt each individual athlete's training to the sport. used by AI, machine learning (ML), or other computer software The sports training unit can be water resistant. can.

[0019] The sports training unit includes various sensors and sensors such as accelerometers, proximators, and sensors, gyroscopes, ultrasound, LiDAR, and cameras, computer software This can include software, databases, and mobile applications to help athletes improve their sports. Recommend a training program specific to the skill and position. Athletic Training Systems is a biological performance testing and sports specific Access and measure performance tests to help athletes improve their performance in their sport. A unique athlete-based training program to achieve athletic gains You can customize your ram. Sports training unit can be customized based on age, height, Gender, ethnicity, sport, position in sport, body mass index, weight, oxygen level Captures biometric information such as bell, height, wingspan and finger height to measure athlete performance Athlete-specific and sport-specific training plans to improve performance Generate the model and scale. [Brief explanation of the drawings]

[0020] [Figure 1] FIG. 1 illustrates connectivity between a mobile device, a training unit, and an athlete data repository according to an example embodiment. [Figure 2] FIG. 10 illustrates a single athlete adaptation of a training unit according to one exemplary embodiment. [Figure 3] FIG. 1 illustrates a process for using a training unit, according to one example embodiment. [Figure 4a] 4 illustrates an arrangement of a jump ramp 400 as part of a V-station, according to one exemplary embodiment. [Figure 4b] 4b shows some components of the jump ramp 400 of FIG. 4a in more detail. [Figure 5] FIG. 10 illustrates a training unit when intended for multi-athlete use, according to one exemplary embodiment. [Figure 6] FIG. 1 illustrates an example database structure and data therein that can be used by computer software to predict the best training plan for an athlete, according to one example embodiment. [Figure 7] FIG. 1 illustrates a HAS station according to an exemplary embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0021] One exemplary embodiment of the adaptive athletic training system 10 includes at least one an athletic training unit 100 and an athlete data repository 105; Access this data repository and create sport- and athlete-specific training programs. a processing system 125 for generating a gram and at least one mobile device 110. The mobile device 110 includes the training unit 100 and computer software. A mobile app (application software) that communicates with the software 125 is installed. It has been installed.

[0022] Internet of Things (IoT) devices and sports training An example of the connectivity between the training unit and the athlete data repository is shown in Figure 1. The user can connect his / her mobile device 110 to a network using Bluetooth, Wi-Fi, training unit 100 via interface, cable, or other digital communication means. The data collected by the training unit for that user is Anything a user enters into their mobile app is sent to the user's mobile device. The mobile app also collects metadata about the user's activities. 125, which then stores the data in the athlete data repository. Although FIG. 1 shows some data paths as an example, the present invention , but is not so limited. Those skilled in the art will appreciate that the mobile device 1 shown in FIG. 10 and training unit 100 access the data repository via the Internet 115. It will be readily apparent that the present invention can be implemented in a variety of ways, including communicating directly with the network controller 105.

[0023] When the user wants to complete another workout, the user simply taps their mobile device. The mobile device then connects to the athlete data repository. Using computer software processing system 125 data from the current Create a training program that is personalized to the athlete's needs The mobile device may provide an athlete identifier and a training unit identifier. In some embodiments, the computer software 125 may include one or more at least one trained machine learning model or other suitable artificial intelligence technique; The computer software 125 includes adaptive algorithms. The data from the user can be used as a training set. Any data collected by the training unit during training will be used to to monitor the duration of the training session and measure the gains made by the user. Data from the Athlete Data Repository is collected and sent to a database for Progress, gains, and other metrics are provided via a web application over the Internet 115. However, the web application can be accessed through , it may not be interfaced with the training unit.

[0024] Training Unit 200 single unit used by a single athlete at a time. The shape of the single use unit is shown in Figure 2. The remaining stations may be similar in shape to the lead unit 500 (FIG. 5). This is the front of the training unit 200. 200 is a single station that combines V station and HAS station. The vertical component of the device (described in more detail in Figures 4a and 4b) The example includes a jump pad 205 and three V-nodes 210, 215, and 220. In an embodiment, each V-node is connected to a cable connected to a resistance source, which may be an electric motor. Each V-Node has a user It has a cable that connects to a spool inside the knit and a ring on the outside of the unit.

[0025] This unit includes horizontal, agility, and speed training. One HAS track 235 and one jig facing the athlete, used for the It also includes a camping platform 205. The HAS station is described in more detail with respect to FIG. The example HAS track 235 includes two HAS nodes 225 and 230. Nodes 225, 230 are used to adjust resistance in training drills The HAS node is connected by a cable that can reach the electric motor resistor. Ring 7 attached to the end of a cable that is connected to the athlete while performing the drill 20 (FIG. 7). HAS nodes 225, 230 move along HAS tracks 235. The HAS track 235 moves the node to provide a greater range of motion. It can also be rotated up to 90 degrees to level horizontally or vertically, thus further Provides a large range of motion.

[0026] A Single Athlete Device is a mobile device or other type of smart device. Alternatively, it may include a single cradle 240 that can accommodate IoT devices. This device is powered by a rechargeable battery to eliminate the need to always have the device plugged in. It can also include 245.

[0027] The flow process using the training unit 200 (FIG. 2), 500 (FIG. 5) is shown in FIG. This process is shown in Figure 3. The user connects their mobile device to the training unit. It starts with connecting to the Knit 300. The system allows athletes to Check whether the program has been used before (305). In this case, computer software 125 is used for unit learning programs, AI programs, or using another type of computer program to conduct pre-training assessment drills 325 Users can upload their metadata and biometric data to the mobile app to create a Input via application (310a) or web application (310b) Athletes will be instructed to use the above units, along with a BMI scale and an oximeter. Use with related accessories such as for conditioning gains in addition to VASH gains Obtain data for the design of individualized training programs, including rest periods. The data and biometric data can be queried by the computer software 125. This metadata and biometric data is stored in the athlete data repository 315 as follows: , athlete identifier, gender, age, BMI, and the position the athlete plays. Sport, if applicable, area the athlete wants to improve, height, weight, finger height, maximum reach Including, but not limited to, points, wingspan, and sport-specific professional organization tests Second, new athletes should develop a baseline of their current athletic status. The biometric data entered must be combined with the base code (320). Train data to plan your training with personalized resistance levels and drills The training program is used to design a step-by-step program (325). The first application is then sent to the training units 200 and 500. Sleet uses the training unit 330 to Complete the training suggested by (335). If you are a first-time user, you can Data collected from the training can be used to suggest routines. The results of the training are collected by the training unit and used in the athlete data collection. It is stored in the repository 315 .

[0028] If the user has previously used athletic training system 10 The computer software queries the athlete data repository 315 for data. , specify the athlete's resistance level, drills, and training routine. The data used by the computer software is based on past workouts from the athlete. This may include crowd-sourced data from workouts by you or other athletes. Computer software125 generates a unique training program for the athlete The athlete then performs the drill (335) and the training unit 330 stores the data. Collect and record (340) the athlete data repository 315. If there are any improvements in performance, those improvements are also logged.

[0029] The computer software 125 generates a training program based on several factors. Resistors are trained on units with manually entered metadata. Sport-specific profiles for that athlete and all athletes in general and athlete-specific. Data is collected from current users and It is also crowd-sourced user data. The data collected from the athlete includes type 2a muscle fiber signals, resistance level, and cable These may include, but are not limited to, tension from the cable or acceleration of the cable. The program is tailored to the athlete's current and predicted future condition. Training with and without mats, resistance and non-resistance, and rest periods are included. Resistance in training plans can be used for concentric muscle movements. Simulating the opposing forces of muscle movement from eccentric to flexor Resistance can be measured by the athlete or by sport-based athletic test scores. Rest in a training plan can also be extracted between drills by It can be gradually changed based on the oxygen level in the sleet.

[0030] An example of a jump ramp 400 included as part of a V-station is shown in FIG. 4a. The V-station is a vertical training exercise that increases the height of the vertical jump. In the illustrated example, each jump ramp 400 includes three nodes. The number of nodes does not have to be limited to three. The three nodes in this example are the left hip node, The nodes are the right hip node 421, the left seam node 405, the right seam node 411, and the right hip node 421. Each node has a vertical Cables 403, 410, 420 and rings 4 are attached to the athlete performing the drill. Includes 02, 409, and 419. Each unit is attached to the training unit body. A swing-down flap arm (swing-down flap arm) having a mounting bracket 429 for securing the swing-down flap arm This exemplary embodiment may also include a flap arm. The hip nodes 405, 421 and seam node 411 connected to unit 426 are shown. Referring to FIG. 4b, the spool unit comprises a spool coupled to an electric motor 428. The spool 430 connects to an electric motor in the base of the training device. The HAS node has a cable connection port for connecting to the HAS node.

[0031] The electric motor 428 in the illustrated example may be configured to adaptively calibrate the resistance of the athlete. drills and the repetitions and effort or, if applicable, depending on the athlete in each drill. The device can be controlled or programmed to provide varying resistance levels. and collect data before, during, and after training drills. can be programmed to collect data and improve future training recommendations. An embedded system controls the electric motor 428. This is the training unit 1 This can be done via a mobile device 110 that obtains data from the training. Data from the unit 100 may be stored as a result in a data repository 105. can.

[0032] Another exemplary embodiment of the present invention is a training unit 500 intended for multi-athlete use. An embodiment is shown in Figure 5. This exemplary embodiment has eight stations, namely Although four V stations and four HAS stations are shown, the present invention is The present invention is not limited to these stations or to each of the four station types. The V stations in are V Station 1 505, V Station 3 515, V Station 5 525, and V-station 7 535. All of the games consist of a jump platform 400 as described with reference to FIG. Each jump platform has multiple nodes. FIG. 5 shows three nodes on each jump platform: right hip node 42 1, seam node 411, and left hip node 405. In this embodiment, Each node is a link that can be hooked onto the athlete while performing the drill. In this example, the cable runs from the left hip node to the Extends from the right hip node to the seam node. Inside the base is at least one electric motor. The motor is connected by cables between each node. The motor is controlled by computer software and a training unit. It can be automated to increase or decrease resistance based on recommendations provided by The V-Station is mounted on the main body of the training unit, facing the jump platform. It can also contain a set of rotation nodes that can rotate up to 90 degrees. It can also move along rails.

[0033] There is a HAS station between the V stations. Figure 5 shows HAS station 2 51 0, HAS Station 4 520, HAS Station 6 530, and HAS Station HAS Station 6 in this figure is the training station. It is located on the back of the unit and cannot be seen in Figure 5. The HAS station is Use the training unit to train the athlete's horizontality, agility, and speed at a minimum. Each HAS station is used to communicate with a network of two or more nodes. These nodes include rings and Each node or combination of nodes serves a specific purpose for training drills. These nodes can move up or down along the rails. The rails can rotate up to 90 degrees to allow for a greater range of motion Within the training unit, the HAS station provides a variable resistance for athletes to use. There may be an electric motor programmed to provide a resistance level. This station is described in more detail below with respect to FIG.

[0034] The body of the training unit must have at least one display that displays training drills or other information. The unit may also include a display console 545. The number of training stations can be adjusted to accommodate the number of training stations equipped with mobile devices or other IoT devices. This example embodiment also has three cradles 550-560. However, a training unit with eight stations is shown with this device. Hold at least eight mobile devices for each of the eight athletes using them simultaneously. The device may have two cradles. The device may be plugged into an electrical outlet. It can also include a rechargeable battery so that plugging in may not always be required. .

[0035] Figure 7 can be used to improve horizontality, agility, speed, or another training area 7 shows an exemplary embodiment of a HAS station 700. 700 can be used for HAS stations 510, 520, 530, and 540. The HAS station illustrated in this example embodiment is comprised of two nodes 705 and 711. 0. The two nodes are electrical motors that vary the resistance level during training drills. Each node is connected by a HAS cable 725 that can extend to the Rings 715 and 720 are attached to the end of the cable to be used by the athlete performing the training drill. The cable has various tensions depending on the level of resistance provided by the electric motor. You can unwind and rewind in the level. The nodes are HAS rails. and moves along rail 730 to provide increased mobility to athletes performing drills. The rail can swivel up to 90 degrees (735°) to provide a wide range of motion for the athlete. The figure also shows the rail after a 90 degree turn. and two nodes are logically shown.

[0036] Machine learning algorithms to design the best training plans and predict athlete gains An exemplary database structure that can be used by the algorithm, and the data therein, The data is shown in Figure 6. The athlete data repository 105 collects data from different sources. It may contain several tables or collections of different data collected. The data in the data repository is used by athletes in conjunction with their training units. Used by computer software to predict the best training plan for The database structure can be used in cloud, SQL Server, relational The data contained in the database may include a database structure. Data can be made anonymous and available only to the user to whom it belongs. Cut.

[0037] This example shows four data collection bodies, but other data sources may be used for future training planning. These data sources do not need to be kept separate. The first data source shown in the figure is the Loud source data 605. This may be generated manually by a person or by other training units. This can include training results collected by the training unit. This may also include data collected by the team from other athletes. Training results from athletes fall into this category. This data is similar to By comparing the results of other athletes with the same physique, gender, age, sport, etc. It can be used to improve training plans for current athletes. The algorithm looks at the data collected from every athlete to determine the best gains. Increase the accuracy and effectiveness of planning for athletes with similar profiles to the athlete selected It can be developed like this.

[0038] The next source of data is current athlete metadata and biometric data 610. , gender, age, BMI, the sport the athlete plays, and the area the athlete wants to improve. , height, weight, finger height, highest reach, wingspan, and sport-specific professional associations This data may include, but is not limited to, crowd-sourced data. Use this in conjunction with your data to compare your data with other athletes who have similar metadata to the current user. Training results can be compared.

[0039] The third source of data in this example is the current athlete's baseline testing 615. Baseline tests may be performed, for example, on the initial vertical jump, broad jump, or training unit. This may include other athletic metrics that may be collected by the It is used as a benchmark to measure any gains or to suggest areas for improvement.

[0040] The final data source in this example is the current athlete's training results 620. These results measure improvements in athlete performance and provide further insight into further improvements. The athlete's baseline test is collected to suggest areas of focus for the athlete. Can be combined with training results.

[0041] As will be explained in more detail below, the four data sources in this exemplary embodiment and other The data source is the future training of athletes using the Training Unit 100. Computer software, such as AI or machine learning algorithms, to forecast plans1 25. These four sources of information are part of the Athlete Data Report. Data can be collected and stored in the data repository, but each athlete can only access their own data. It is not possible to

[0042] The machine learning / artificial intelligence component of the adaptive athletic training system is known to those skilled in the art. This can be done in many ways, as described in the following machine learning example: Although provided as an illustrative example, the invention is not limited to this particular example.

[0043] In the examples herein, multiple machine learning models (ML models) are used to generate adaptive athletic performance data. The training system is developed for different uses. This is based on the specific needs of each athlete. To meet your individual needs / goals and achieve maximum athletic gains, Provide each athlete with the exact specifications for the routines. These routines are resistance levels and accessories, each optimized and tailored for a given individual athlete. and individualized.

[0044] The combined goal of the machine learning model is to improve performance for each athlete and each sport. Predict the optimal drill routine and resistance level for each drill to maximize performance gains. Each focuses on a different aspect of the personal training program. Multiple machine learning models can be used.

[0045] In some embodiments, the ML model engages in adaptive training programs. Developed to create an individualized training routine for each athlete. training session over the duration of the training period selected by the athlete Drill routines are designed to be applicable to each individual athlete's sport. should be individualized to improve athlete performance in the four VASH areas Routine customization is based on, firstly, the areas prioritized by the user, and secondly, areas important to the user's sport, and finally, weaknesses identified by the user evaluation session. Areas of weakness should be improved by isolating each leg in the same way. and more generally, VASH areas where users received lower scores than expected. The routine for each training session will include: Resisted sets with concentric and eccentric resistance levels non-resistance sets during the time the athlete is disconnected and during rest periods An adaptive training program consists of a sequence of drills and Coil and recoil based on training performance and heart rate ) resistance and rest time are calibrated.

[0046] In some embodiments, the ML model is based on the athlete's biometric data and evaluation results. Each drill is individually developed to have a specific resistance level that is optimized for the specific application. Athletes who score lower than expected based on their biometric data should be evaluated for vertical drills. The system experiences a lower initial resistance in the After collecting the training data, the system will compare the athlete's performance the last time they performed the drill. You know what the performance was like, so you can easily gauge the resistance level of a particular drill. can be optimized.

[0047] In some embodiments, the ML model is configured to estimate the athlete's activity according to the biodata and the assessment results. Some athletes may have a height restriction that requires them to wear a specific accessory. Some people are not tall enough to use lyometric boxes or hurdles. Some people may not have enough strength to lift a heavier medicine ball. Therefore, training routines are individualized for athletes with applicable drills. First, the model will include the athlete's test results and athlete's biodata. Based on the data, we predict which attachment the athlete should use. Similar to the resistance prediction, the model adjust accessory regulations as they have more training data on athletes and optimize how the athlete performs with the accessory. This allows for better modeling of how the

[0048] In one example, five ML models are used. The models described herein are , can be combined and further specialized into more models. More or fewer ML models can be used, and the user can As you start using your training unit, more training data will become available. ML models can be added. Each of the modules can be used individually or in combination with each other. Cut.

[0049] The more data the adaptive athletic training system 10 collects, the more Learning becomes more and more powerful.

[0050] The first ML model is the Predict Combine Scores model. This model is based on user metadata and biometric data ("profile data"). The output of this model is a combination of predicted and actual scores. can be used to identify areas of weakness for the user by comparing

[0051] The score prediction combination model uses age, sex, height, weight, BMI, sports, and position. Enter user profile data, including the application and test results predicted. The input of the model includes the user's biometric information. The output of the model is the current user's biometric information during the input test. Optionally, the type of evaluation (e.g., the type of can include as inputs: evaluate, appraise, verify The output of this model can have several useful applications. First, it can be used to test the user's By predicting test results and comparing them to actual results, the system analyzes data in four areas: Areas of weakness in (VASH) can be identified. These areas of weakness are These can then be input into a second model that recommends drills. Second, the four areas (VA The identification of weak points in the SH was input to a third model that predicted the optimum resistance level for the drill. Can be used as a force.

[0052] The second ML model is the Drill Routine Prediction model. This model combines the user's target field with the first score prediction model. Weak areas identified from user evaluation scores in the system and maximized user gains in the system Predict the drills you will need.

[0053] The drill routine prediction model inputs the user's evaluation results and uses Model 1, "Score Prediction Model." Use the "Align" function to input predicted evaluation results. The actual results are below the predicted results in each of the four measurement areas ( These percentages are converted into average percentages (at or above the average) selected by the user. This will be used along with the chosen goals and the importance of each area to the athlete's sport. You can predict the drills of the session.

[0054] The output of the Drill Routine Prediction Model is a user-selected training routine for the next training session. Currently, these drills are listed in the User Training The learning plan is stored in a table, but in future machine learning, it will be stored on the user's mobile device. The mobile device is capable of interfacing with the training unit. and provides drill instructions to the machine and user.

[0055] The third ML model is Initial Drill Resistance Prediction. The model is based on the following scenario: an athlete has drill results stored in the athlete data repository; If not, the system will notify you of the resistance level at which the athlete struggled or was successful. Therefore, we do not have data on the optimal initial resistance level. A learning model is provided.

[0056] This initial drill resistance prediction model is based on age, gender, height, weight, BMI, finger height, and sports Enter your profile data including drills, positions, and predicted resistance. In addition, since the drill is linked to the measurement field (VASH), the initial drill resistance prediction model Dell uses the "score prediction combination" output of the first model to calculate the user's score for a given field. The percentage of under- or over-predicted values ​​can then be determined. The initial drill resistance prediction model is only a percentage below or above the The resistance can be increased or decreased appropriately and provided as an output, e.g., initial drill resistance Predictive ML models determine maximum jump (verticality drills) based on user profile data However, for example, On average, users scored 20% below the predicted result on the verticality test. In that case, the initial drill resistance prediction ML model increased the predicted resistance by 15 pounds to 2. It can be lowered by 0% to 5.4 kg (12 lbs).

[0057] The output of the initial drill resistance prediction model is not necessarily used as input to other models. However, the results of training a user based on the output of this model can be used as input to a new model. Can be used as a force.

[0058] The fourth ML model is the Predict Recursive Resistance model. After Sleet performs the first drill, the system monitors how the user responds to the resistance level. The system uses this information to improve future Resistance levels can be better optimized.

[0059] For example, after an athlete completes a drill for the first time, the system detects that the resistance was too strong. First, the system has more information about whether the The system can track the time between repetitions. If the repetitions are evenly spaced, If the user has a long break between repetitions, it can be predicted that the resistance was too low. If you take a bite, the system can infer that the resistance is too high. If applicable, sensors are used to track the applied muscle force. Both data sets can be accurately analyzed using LOESS curves, giving a more accurate view of fatigue. The LOESS predictions of the last iteration are used as inputs to the fourth machine learning model. Can be used as a force.

[0060] The output of this model is not just to more accurately optimize resistance levels for a particular drill; Used to provide resistance levels to devices and mobile apps.

[0061] The fifth ML model is the Predict Accessory Configuration model. The system preferably requires a drill (i.e., a 3-hurdle shuttle) It is possible to predict the optimal attachment for athletes performing a full (three hurdle shuffle) Some athletes just don't have the right body type for some accessories. However, some tests have high enough scores that accessories can be used. For example, an athlete who is 1.2 m (4 ft) tall may not be 0. Using a 6m (2ft) plyometric box is not recommended. The athlete is simply not tall enough. Athletes with a landing point of less than 0.6m (2 feet) can perform a 0.6m (2 feet) plyometric It is also undesirable to use trick boxes.

[0062] This model includes age, sex, height, weight, wingspan, finger height, hand and palm size. Size, foot size, waist and seam length, BMI, sports, position, and accessories Enter the user's profile data including the relevant drills for which predictions about the product will be made. The model outputs a reasonable accessory configuration for the current athlete and drill. The force is the applicable drill attachment configuration. For example, the output is "6 lbs medicine ball" It can be written as "ru".

[0063] In some embodiments, drills based on the athlete's biometric data and test results; These constraints include constraints on resistance and attachments. The machine learning model can then be When outputting a result, the system outputs it so that the result remains within the constrained range of values. For example, the ML model can be adjusted to The system predicts a resistance of 40 pounds for a 2-year-old athlete. However, the system predicts a resistance of 40 pounds for a 12-year-old athlete. For athletes performing "ski jumps," limit the resistance to 5 to 30 pounds. The system can be configured to reduce the resistance from 40 lbs to 30 lbs. Other restrictions may be implemented for other exercises, athlete categories, etc. can.

[0064] In some embodiments, a recurrent neural network is used to generate a machine learning model. This type of model is suitable when the input is a mixture of numerical and categorical variables. Therefore, it is well suited for adaptive athletic training systems. Other algorithms, such as decision tree regression, may also be suitable. There are also many robust supervised regression models that can be used.

[0065] The variables input to predict a user's score on a given test are: Age, gender, ethnicity, height, weight, finger height, wingspan, sports, sports position Sports The position is the athlete's position indicated at the start of their training program. This can be implemented as a single variable and and position cannot be implemented as two separate variables. Football wide receivers score differently than American football linemen In addition, including only positions is not sufficient. So, there can be a difference between the center in basketball and the center in hockey. Instead, this variable is entered as a unique identifier for the sport and position. For example, id1 = American football wide receiver, id2 = American football id1=baseball lineman, id2=basketball center, id3=hockey center A drill is a drill or test where the system predicts the athlete's outcome. The output of the model is the predicted result, i.e., the input test given the user's biometric information. This is the result of a strike.

[0066] Here is one example of how to create an ML model that corresponds to the score prediction combination ML model: This and other techniques are used to train various ML models. It is possible.

[0067] To train a machine learning model, training data is typically generated from the machine learning algorithm. related data is compiled into a single table that is input to the algorithm. These tables can be stored in different normalized database tables. Related data can be extracted and unrelated data discarded to combine them.

[0068] Once the training dataset is compiled, the relevant biometric data is used to analyze each test and result. This is the athlete's GUID (Globally Unique Identifier). (used to identify users across all tables) and use this to By filtering the dataset to extract only the athlete's evaluation results, Then, height, weight, finger height, wingspan, oxygen level, or body fat All the athlete's evaluation results, including stored biodata such as fat percentage, are extracted. do.

[0069] The assessment test and results will be compared with the biodata closest to the date the current assessment test was taken. For example, if an athlete completes the "long jump" on February 20, 2019, If an athlete has not measured their height on the date, the system will You can check other dates that were measured. In addition, the system can check the time between two measurement dates. Biometric data can be interpolated. Various existing algorithms can be used to crunch the data. can be leaned (also known as imputation) (This is true.)

[0070] You can choose different algorithms for different variables based on the variable's data type and function. In addition, for missing values ​​that may be dependent on other variables, a chained equation approach can be used. Multivariate Imputation using Chained Equations (MICE) or Probabilistic cleaners such as Principal Component Analysis (PCA) A probabilistic cleaning algorithm can be used. For example, the finger height is , it is reasonable to assume that the combination of height and wingspan is roughly correlated; Therefore, the system will use the current height and wingspan values, if they exist. From this, missing finger heights can be inferred probabilistically.

[0071] The score prediction combination model requires a supervised machine learning algorithm. Machine learning is what happens when inputs are mapped to an output space, and the model is trained with labeled inputs and outputs, and generates the desired Specify the output to the model.

[0072] Additionally, this requires a regression model rather than a classification model. Classification is a method where the range of outputs is It is discrete and finite, and the only possible outputs are the labeled outputs for the model. On the other hand, regression can have a continuous output space. For example, Two athletes with scores of 2 and 3 seconds respectively for the 47-foot sprint If there is a third user, the model can predict that the third user will have a score of 4 seconds. It is entirely possible. The difference is that classification deals with categorical outputs, while regression deals with numerical The key is to handle the output.

[0073] When training an ML model to split a dataset into two, The dataset to train on and the model to test and score (validate) It is common to create a second dataset, a test set, and a second dataset. In addition to providing a way to test whether a particular It also prevents "overfitting" of the model, where successive data points are learned as rules by the model. do.

[0074] For example, a dataset may consist of 80% of rows for the training set and 20% for the test set. 0% rows and 70% rows. If the dataset is large, a 70-30 split is recommended. is sometimes a better division. The rows can be split by choosing the top 80% of the rows, or Alternatively, the selection can be random.

[0075] When training a machine learning algorithm, the inputs are usually all weighted equally. For example, without scaling, a weight of 150 would be 1 It has a bigger impact than an age of 2 because 150 is a much larger number. To solve this, each variable is scaled from 0 to 1. Various methods for scaling features, including scaling or binning features There is a law.

[0076] Each neural network is constructed by adjusting the number of hidden layers and hidden nodes as parameters in the algorithm. The number of hidden layers and hidden nodes is chosen as the "correct" answer. For example, the first neural network is implemented with 12 hidden nodes. The second neural network can be implemented with 15 hidden nodes. They can function equally well. There are other neural network parameters that can be used. Different regression algorithms can be used. When used, the type of parameter may change. For example, In regression, there is no such thing as a hidden node.

[0077] The initial configuration, adjusted according to a small training dataset, has the following parameters: The parameters include: hidden nodes - 100; learning rate - 0.005; learning iteration Regression - 100; Initial training weight - 0.15; and Normalizer - Min / Max normalization. For the reasons stated above, this example is provided for the completeness of the disclosure and is not intended to be limiting. These parameters are not intended to limit the scope of the present invention. can be modified taking into account the

[0078] Depending on the type of model used, the model may be adjusted after it has been scored. For example, it may be necessary to use a model such as random forest regression. Once the model is adjusted, a process called "cross-validation" is completed, and the current statistical analysis Predict parameters based on how they perform on an independent dataset This process also reduces overfitting. is not applicable to some regression algorithms.

[0079] Once the model is trained, it is scored using a test dataset. The data from the test set (which is In this example, inputs (age, gender, ethnicity, test, etc.) are generated and output test results are calculated by the model. Then you can evaluate how the model performed. A common way to judge the validity of a rule is to use the coefficient of determination, also known as R2, or the root mean square error. (RMSE: Root Mean Squared Error), or other statistics. These scores are If it falls short of the assumed case, remodel it using different inputs or algorithm parameters. It may be necessary to retrain the user.

[0080] Once the model is trained, it will predict future outcomes based on biometric data collected from the athlete. Application programming interface to predict athlete test results It is possible to build and use an API (Application Programming Interface). The results of this model are stored along with other user information for further machine learning or predictive analysis. It can be used as an input to the model.

[0081] The training unit captures sport-specific performance tests and The test is based on the test of standing maximum reach, running maximum reach, Horizontal jump on both legs, horizontal jump on right leg and horizontal jump on left leg, side step jump on right leg and side step jump on left leg Right leg step jump, right leg step back jump and left leg step back jump, 4.55 meters (5 yards) right shuffle and left shuffle, 5 yards forward shuffle Includes sprints and backward sprints, 40-yard dash, and 75-foot sprints For example, in basketball, athletes can perform a side-step jump shot. The training unit will measure the athlete's best sidestep jump performance. This can include a test to measure the pre-training span of the training. The training unit simulates the exact movements required for athletes. The training unit measures the athlete's side step span. Require a training routine that includes a full-motion and a controlled component. After the training period, the athlete's entire custom-designed training program After completion, the athlete is retested to verify the gains.

[0082] The training unit consists of at least four exercises including vertical, agility, speed, and horizontal. Designed to improve athletic performance in three specific areas: The unit consists of a standing vertical jump, a right leg take-off jump, a left leg take-off jump, a parallel jump, and a Improve verticality using drills such as the long jump and the two-foot takeoff. Use drills such as the right leg horizontal jump, left leg horizontal jump, and two-leg horizontal jump, also known as the left leg horizontal jump. The unit also allows for improved levelness by using the left and right lateral thrusters. Provides measurable improvements in agility through drills such as lateral sprints. Afterwards, the unit can sprint forward or backward. print) to improve speed. Athletes will perform drills to improve their skills. The cable attached to the unit provides the athlete with the specific resistance needed to improve their performance. Perform these drills by hooking yourself onto the cable.

[0083] Between the V stations are HAS stations, each of which includes a rail with two or more nodes. These nodes include rings and cables to which athletes can be attached during training. These nodes can move up or down along the rail. Each node or combination of nodes serves a specific purpose for a training drill. The main body of the unit also includes at least one display console. The main body of the unit also has several cradles corresponding to the number of training stations for mounting mobile devices or other IoT devices. In order to maintain the disclosure of the present application as originally filed, the contents of claims 1 to 20 as originally filed are added below. (Claim 1) an athlete data repository containing athlete biometric and performance data; an adaptive algorithm that accesses the athlete data repository and is trained on biometric and performance data of the type stored in the athlete data repository to generate a plurality of athlete-specific training programs for use with a number of training units, each athlete-specific training program adapted to an individual athlete's biometric and performance data; a training unit including an adjustable athletic resistance device and a controller, the controller configured to receive an athlete-specific training program from the plurality of athlete-specific training programs, adjust a resistance level of the adjustable resistance device in response to the received athlete-specific training program, and record performance data of an athlete's use of the training unit in accordance with the received athlete-specific training program; An athletic training system comprising: (Claim 2) 10. The athletic training system of claim 1, wherein the training unit further comprises an interface for at least one mobile device of a user, the mobile device receiving the athlete-specific training program from the adaptive algorithm and communicating the athlete-specific training program to the training unit. (Claim 3) 3. The athletic training system of claim 2, wherein the mobile device receives the performance data recorded by the controller and communicates the performance data to the adaptive algorithm. (Claim 4) 10. The athletic training system of claim 1, wherein the adaptive algorithm communicates the performance data to the athlete data repository. (Claim 5) 10. The athletic training system of claim 1, wherein the adaptive algorithm comprises a plurality of adaptive algorithms. (Claim 6) 10. The athletic training system of claim 1, wherein the adaptive algorithm comprises a plurality of machine learning models. (Claim 7) 7. The athletic training system of claim 6, wherein the plurality of machine learning models includes an evaluation predictive model that receives as input biometric data for a given athlete and performance data for other athletes and generates an evaluation training program. (Claim 8) 10. The athletic training system of claim 7, wherein the plurality of machine learning models further comprises a drill routine model that receives as input performance data recorded by the controller during use of a previously generated evaluation training program. (Claim 9) 10. The athletic training system of claim 8, wherein the drill routine model also receives as input the performance data recorded by the controller during use of the athlete-specific training program generated by the drill routine model. (Claim 10) 10. The athletic training system of claim 1, wherein the training unit further comprises a vertical jump station. (Claim 11) 10. The athletic training system of claim 1, wherein the training unit further comprises a horizontal agility speed station. (Claim 12) 10. The athletic training system of claim 1, wherein the training unit further comprises a vertical jump station and a horizontal agility speed station. (Claim 13) 10. The athletic training system of claim 1, wherein the training unit further comprises a plurality of vertical jump stations and a plurality of horizontal agility speed stations. (Claim 14) 10. The athletic training system of claim 1, wherein the training unit further comprises a motor responsive to the controller to adjust a resistance level of the adjustable equipment. (Claim 15) 10. The athletic training system of claim 1, wherein the adaptive algorithm accesses biometric and performance data for a given athlete to generate an athlete-specific training program for that athlete. (Claim 16) 1. A computer-implemented method for generating an athlete-specific training program adapted to an individual athlete, comprising: storing biometric and performance data for the individual athlete and additional athletes with comparable biometric and performance data in an athlete data repository; the athlete accessing a training unit including electronically adjustable athletic resistance equipment and a controller, the controller configured to receive an athlete-specific training program from a plurality of athlete-specific training programs, adjust resistance levels of the adjustable resistance equipment in accordance with the athlete-specific training program, and record performance data of the athlete's use of the training unit in accordance with the athlete-specific training program; providing an identifier for the individual athlete and an identifier for the training unit being accessed to an adaptive algorithm, the adaptive algorithm being trained on biometric and performance data of the type stored in the athlete data repository to generate a plurality of athlete-specific training programs for use with the training unit; the adaptive algorithm accessing the athlete data repository and generating at least one athlete-specific training program adapted to the individual athlete's biometric and performance data; providing the athlete-specific training program to the training unit; The method comprising: (Claim 17) 17. The method for generating an athlete-specific training program of claim 16, wherein the training unit further comprises an interface for at least one mobile device of a user, and wherein accessing the training unit comprises the individual athlete coupling a mobile device to the training unit, the method further comprising the mobile device receiving the athlete-specific training program from the adaptive algorithm and communicating the athlete-specific training program to the training unit. (Claim 18) 20. The method of generating an athlete-specific training program of claim 17, further comprising the step of the mobile device receiving the performance data recorded by the controller and communicating the performance data to the adaptive algorithm. (Claim 19) 17. The method of generating an athlete-specific training program of claim 16, wherein the adaptive algorithm comprises a plurality of machine learning models. (Claim 20) 17. The method of generating an athlete-specific training program of claim 16, wherein the adaptive algorithm accesses biometric and performance data for the individual athlete to generate an athlete-specific training program for that athlete.

Claims

1. an athlete data repository containing athlete biometric and performance data; a processing system; Training unit and 1. An athletic training system comprising: The processing system includes: executing one or more trained artificial intelligence adaptive algorithms to generate a plurality of athlete-specific training programs for use with a training unit, wherein the one or more trained artificial intelligence adaptive algorithms have access to the athlete data repository, the one or more trained artificial intelligence adaptive algorithms are trained on the biometric data and the performance data of the type stored in the athlete data repository, and each athlete-specific training program is adapted to the biometric data and the performance data of an individual athlete; receiving user profile data and baseline data; providing the profile data and the baseline data to the one or more trained artificial intelligence adaptive algorithms to generate an athlete-specific training program for the user; It is structured as follows: The training unit comprises: a horizontal agility speed station including a cable, a ring at the end of the cable that is attached to the athlete, and an electric motor connected to the cable that adjusts the resistance level of the cable; Controller and It contains The controller receiving the athlete-specific training program for the user from the processing system; adjusting a resistance level applied to the cable by the electric motor in response to the received athlete-specific training program; Collecting and recording performance data of the user's use of the training unit in accordance with the received athlete-specific training program. An athletic training system that is configured as follows.

2. The athletic training system of claim 1 , wherein the profile data includes the user's age, the user's gender, the user's height, the user's weight, the user's sport, and the user's sport position.

3. The athletic training system of claim 1 , wherein the baseline data includes the user's results from an assessment test.

4. 10. The athletic training system of claim 1, wherein the training unit includes a jump ramp for vertical training.

5. 10. The athletic training system of claim 1, wherein the training unit further includes an interface for at least one mobile device of the user, the mobile device receiving the athlete-specific training program from the one or more trained artificial intelligence adaptive algorithms and communicating the athlete-specific training program to the training unit.

6. the controller is configured to communicate the performance data to the mobile device; 6. The athletic training system of claim 5, wherein the processing system is configured to receive the performance data from the mobile device and provide the performance data to the one or more trained artificial intelligence adaptive algorithms.

7. The athletic training system of claim 1 , wherein the processing system is configured to communicate the performance data to the athlete data repository.

8. 10. The athletic training system of claim 1, wherein the one or more trained artificial intelligence adaptive algorithms comprise a plurality of trained artificial intelligence adaptive algorithms.

9. 10. The athletic training system of claim 1, wherein the one or more trained artificial intelligence adaptive algorithms include a plurality of machine learning models.

10. 10. The athletic training system of claim 9, wherein the plurality of machine learning models includes an evaluation predictive model that receives as input biometric data of a given athlete and performance data of other athletes and generates an evaluation training program.

11. 11. The athletic training system of claim 10, wherein the plurality of machine learning models further comprises a drill routine model that receives as input performance data recorded by the controller during use of a previously generated evaluation training program.

12. 12. The athletic training system of claim 11, wherein the drill routine model receives as input performance data recorded by the controller during use of an athlete-specific training program previously generated by the drill routine model.

13. 10. The athletic training system of claim 1, wherein the training unit further comprises a plurality of vertical jump stations and a plurality of horizontal agility speed stations.

14. 10. The athletic training system of claim 1, wherein the processing system, upon execution of the one or more trained artificial intelligence adaptive algorithms, is configured to provide the one or more trained artificial intelligence adaptive algorithms with access to biometric and performance data for a given athlete to generate an athlete-specific training program for that athlete.

15. 10. The athletic training system of claim 1, wherein the processing system is configured to execute the one or more trained artificial intelligence adaptive algorithms to determine the user's accessory configuration for the athlete-specific training program.

16. The horizontal agility speed station Rails and a node including a cable and a ring configured to move along the rail; 10. The athletic training system of claim 1, comprising:

17. 1. A computer-implemented method for generating an athlete-specific training program tailored to an individual athlete, the method comprising: receiving user profile data and baseline data; storing the user's profile data and baseline data in an athlete data repository; providing a training unit, the horizontal agility speed station including a cable, a ring at an end of the cable that is attached to an athlete, and an electric motor connected to the cable and that adjusts a resistance level of the cable; and a controller configured to receive an athlete-specific training program for a user, adjust the resistance level applied to the cable by the electric motor in accordance with the athlete-specific training program, and collect and record performance data of the user's use of the training unit in accordance with the athlete-specific training program; providing an identifier of the user and an identifier of the training unit to be accessed by one or more trained artificial intelligence adaptive algorithms trained to generate a plurality of athlete-specific training programs for use with the training unit, the one or more trained artificial intelligence adaptive algorithms being trained based on biometric and performance data of the type stored in the athlete data repository; generating an athlete-specific training program for the user using the one or more trained artificial intelligence adaptive algorithms, the profile data, the baseline data, and the athlete data repository; providing the athlete-specific training program to the training unit; A method comprising:

18. 20. The method of generating an athlete-specific training program of claim 17, wherein the profile data includes the user's age, the user's gender, the user's height, the user's weight, the user's sport, and the user's position in the sport.

19. 20. The method of generating an athlete-specific training program of claim 17, wherein the baseline data includes a user's results from an assessment test.

20. 20. The method of generating an athlete-specific training program of claim 17, wherein the training unit includes a jump ramp for vertical training.

21. the training unit further includes an interface for at least one mobile device of the user; coupling the mobile device to the training unit; receiving, using the mobile device, an athlete-specific training program from the one or more trained artificial intelligence adaptive algorithms; communicating the athlete-specific training program to the training unit using the mobile device; 20. The method of generating an athlete specific training program of claim 17, comprising:

22. receiving, using the mobile device, performance data recorded by the controller; using the mobile device to communicate the performance data to one or more trained artificial intelligence adaptive algorithms; 22. The method of generating an athlete-specific training program of claim 21, further comprising:

23. 20. The method of generating an athlete-specific training program of claim 17, wherein the one or more trained artificial intelligence adaptive algorithms include a plurality of machine learning models.

24. 20. The method of generating an athlete-specific training program of claim 17, comprising using the one or more trained artificial intelligence adaptive algorithms to access the user's profile data and the user's baseline data to generate an athlete-specific training program for the user.

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