System and method of personalized exercise prescription

Abstract

This invention primarily discloses a personalized exercise prescription system, which includes an electronic device and a plurality of sensors, and has the following functions: (1) collecting the user's static physiological data; and (2) generating an exercise prescription / program based on the physiological data, the user's personal data, and an exercise goal input by the user. Furthermore, when the user performs at least one type of exercise according to the exercise prescription, the personalized exercise prescription system performs the following functions: (3) collecting the user's dynamic sensory data, including dynamic physiological data and somatosensory data; and (4) calculating an exercise intensity and evaluating exercise effectiveness based on the dynamic sensory data.

Description

Technical Field

[0001] This invention relates to the technical field of kinesthetic senses technology, and more particularly to an AI-based personalized exercise prescription system and method. Prior Technology

[0002] It is known that wearable devices such as smart bracelets and smartwatches are equipped with physiological measurement modules and / or motion sensing modules. The physiological measurement modules measure physiological data such as body temperature, heart rate, and blood pressure, while the motion sensing modules measure the user's movement data, including speed and distance traveled (steps). Furthermore, based on the physiological and / or movement data, the wearable device can also calculate the user's calorie consumption. In other words, during specific exercises (such as jogging), users can use wearable devices like smart bracelets and smartwatches to monitor their heart rate, body temperature, blood pressure, movement speed, distance traveled, and calorie consumption in real time.

[0003] Long-term users of any of the aforementioned wearable devices will know that existing wearable devices can only measure and display individual data such as heart rate, body temperature, blood pressure, movement speed, movement distance, and calorie consumption. They cannot use this measurement data to generate new or special functions, such as providing users with relevant analysis and / or suggestions on personalized sports programs.

[0004] In summary, existing wearable devices cannot provide personalized exercise program analysis and / or suggestions, thus exhibiting shortcomings in practical application that need improvement. In view of this, the inventors of this invention have diligently researched and developed a personalized exercise prescription system and method. Summary of the Invention

[0005] The main objective of this invention is to provide a personalized exercise prescription system, which has the following functions: (1) collecting the user's static physiological data; and (2) generating an exercise prescription / program based on the physiological data, the user's personal data, and an exercise goal input by the user. Furthermore, when the user performs at least one type of exercise according to the exercise prescription, the personalized exercise prescription system performs the following functions: (3) collecting the user's dynamic sensory data, including dynamic physiological data and somatosensory data; and (4) calculating an exercise intensity and evaluating exercise effectiveness based on the dynamic sensory data.

[0006] To achieve the above objectives, the present invention provides an embodiment of the personalized exercise prescription system, which includes: A first electronic device includes a first processor and a first memory, wherein the first memory stores at least one application and a pre-trained exercise prescription generation model; and A plurality of sensors are coupled to the first electronic device; After the application is executed, the first processor is configured to execute: The plurality of sensors are controlled to collect at least one static physiological signal from a user; Perform data processing on the static physiological signal to obtain at least one static physiological data; and The static physiological data and the user's personal data are input into the exercise prescription generation model, so that the exercise prescription generation model outputs an exercise prescription for the user; The static physiological data includes at least one selected from the group consisting of body temperature, pulse, heart rate, blood pressure, oxygen uptake (VO2), respiratory rate, and electromyographic intensity; The exercise prescription includes at least one exercise program selected from the group consisting of exercise programs for training muscle strength, exercise programs for training lung capacity, exercise programs for training cardiopulmonary function, exercise programs for burning calories, exercise programs for improving metabolism, and exercise programs for training grip strength.

[0007] In one embodiment, the user's personal data includes any one of the groups consisting of age, weight, height, BMI, waist circumference, thigh circumference, and arm circumference.

[0008] In one embodiment, the first electronic device is selected from the group consisting of fitness mirrors, mobile display devices, smartphones, tablets, desktop computers, all-in-one computers, smart TVs, and game consoles.

[0009] In one embodiment, the first memory is selected from any one of the group consisting of hard disk drive (HDD), solid state drive (SSD) and flash memory.

[0010] In one embodiment, the plurality of sensors includes at least one smartwatch, at least one smart bracelet, a smart garment with sensing electrodes, at least one arm sleeve with sensing electrodes, and at least one leg sleeve with sensing electrodes.

[0011] In one embodiment, the first electronic device further includes a first display, the first memory further stores a pre-trained motion performance evaluation model, and the first processor is configured to execute, when executing the application: Control the first display to show a user interface; After the user interface is operated to input at least one exercise goal, the static physiological data, the user's personal data, and the exercise goal are input into the exercise prescription generation model, so that the exercise prescription generation model outputs the corresponding exercise prescription. The user interface provides a menu of exercise courses corresponding to the exercise prescription and / or at least one recommended exercise course. When any exercise course or recommended exercise course in the exercise course menu is selected, the first display is controlled to show / play the course content of that exercise course or recommended exercise course; The plurality of sensors are controlled to collect at least one dynamic physiological signal and at least one sensory signal from the user, and to monitor a period of time of movement of the user; Perform data processing on the dynamic physiological signal to obtain at least one dynamic physiological data, and perform data processing on the somatosensory signal to obtain somatosensory data; At least one data processing step is performed on the somatosensory data, the dynamic physiological data, and the exercise time to calculate an exercise intensity; The exercise course and / or the recommended exercise course, the exercise intensity, the dynamic physiological data, and the exercise time are input into the exercise effectiveness evaluation model, so that the exercise effectiveness evaluation model outputs an exercise effectiveness evaluation result for the user; and The system controls the first display to show the user's motion statistics. The motion data includes at least one selected from a group consisting of steps, movement speed, and movement distance; The dynamic physiological data includes at least one selected from the group consisting of body temperature, pulse, heart rate, blood pressure, oxygen uptake (VO2), respiratory rate, and electromyographic intensity.

[0012] In an optional embodiment, the aforementioned personalized exercise prescription system of the present invention further includes the first electronic device connected to the second electronic device, such that the first electronic device can transmit the exercise statistics data to the second electronic device.

[0013] In one embodiment, the second electronic device is selected from any one of the group consisting of smartphones, tablets, desktop computers, all-in-one computers, and smartwatches.

[0014] In one embodiment, the first processor, while executing the application, is also configured to perform: The user interface receives at least one suggestion for adjusting course content from the user; and The proposed adjustments to the course content are transmitted to a server, which then transmits them to at least one electronic device, held by a sports coach.

[0015] Furthermore, the present invention also provides a personalized exercise prescription generation method, which is executed by a first electronic device coupled to a plurality of sensors, wherein the first electronic device has a first display and stores a pre-trained exercise prescription generation model, and the personalized exercise prescription generation method includes: The plurality of sensors are controlled to collect at least one static physiological signal from a user; Perform data processing on the static physiological signal to obtain at least one static physiological data; and The static physiological data and the user's personal data are input into the exercise prescription generation model, so that the exercise prescription generation model outputs an exercise prescription for the user; The static physiological data includes at least one selected from the group consisting of body temperature, pulse, heart rate, blood pressure, oxygen uptake (VO2), respiratory rate, and electromyographic intensity; The exercise prescription includes at least one exercise program selected from the group consisting of exercise programs for training muscle strength, exercise programs for training lung capacity, exercise programs for training cardiopulmonary function, exercise programs for burning calories, exercise programs for improving metabolism, and exercise programs for training grip strength.

[0016] In one embodiment, the user's personal data includes any one of the groups consisting of age, weight, height, BMI, waist circumference, thigh circumference, and arm circumference.

[0017] In one embodiment, the first electronic device is selected from the group consisting of fitness mirrors, mobile display devices, smartphones, tablets, desktop computers, all-in-one computers, smart TVs, and game consoles.

[0018] In one embodiment, the first memory is selected from any one of the group consisting of hard disk drive (HDD), solid state drive (SSD) and flash memory.

[0019] In one embodiment, the plurality of sensors includes at least one smartwatch, at least one smart bracelet, a smart garment with sensing electrodes, at least one arm sleeve with sensing electrodes, and at least one leg sleeve with sensing electrodes.

[0020] In an optional embodiment, the aforementioned personalized exercise prescription generation method of the present invention further includes: Control the first display to show a user interface; After the user interface is operated to input at least one exercise goal, the static physiological data, the user's personal data, and the exercise goal are input into the exercise prescription generation model, so that the exercise prescription generation model outputs the corresponding exercise prescription. The user interface provides a menu of exercise courses corresponding to the exercise prescription and / or at least one recommended exercise course. When any exercise course or recommended exercise course in the exercise course menu is selected, the first display is controlled to show / play the course content of that exercise course or recommended exercise course; The plurality of sensors are controlled to collect at least one dynamic physiological signal and at least one sensory signal from the user, and to monitor a period of time of movement of the user; Perform data processing on the dynamic physiological signal to obtain at least one dynamic physiological data, and perform data processing on the somatosensory signal to obtain somatosensory data; At least one data processing step is performed on the somatosensory data, the dynamic physiological data, and the exercise time to calculate an exercise intensity; The exercise course and / or the recommended exercise course, the exercise intensity, the dynamic physiological data, and the exercise time are input into the exercise effectiveness evaluation model, so that the exercise effectiveness evaluation model outputs an exercise effectiveness evaluation result for the user; and The system controls the first display to show the user's motion statistics. The motion data includes at least one selected from a group consisting of steps, movement speed, and movement distance; The dynamic physiological data includes at least one selected from the group consisting of body temperature, pulse, heart rate, blood pressure, oxygen uptake (VO2), respiratory rate, and electromyographic intensity.

[0021] In an alternative embodiment, the first electronic device is informationally connected to a second electronic device, such that the second electronic device can receive the motion statistics data from the first electronic device.

[0022] In one embodiment, the second electronic device is selected from any one of the group consisting of smartphones, tablets, desktop computers, all-in-one computers, and smartwatches.

[0023] In yet another alternative embodiment, the first electronic device is further configured to perform: The user interface receives at least one suggestion for adjusting course content from the user; and The proposed adjustments to the course content are transmitted to a server, which then transmits them to at least one electronic device, held by a sports coach. Simple Explanation of the Diagram

[0024] Figure 1 is a schematic diagram of a personalized exercise prescription system according to the present invention; Figure 2 is a front view of the first electronic device shown in Figure 1; and Figures 3 and 4 are schematic diagrams illustrating one application example of the smart clothing of this invention. Implementation

[0025] To enable your review committee to further understand the structure, features, purpose, and advantages of this invention, detailed descriptions of the preferred embodiments are attached below with drawings.

[0026] Figure 1 is a schematic diagram of a personalized exercise prescription system according to the present invention. As shown in Figure 1, the personalized exercise prescription system 1 of the present invention mainly includes a first electronic device 11 and a plurality of sensors coupled to the first electronic device 11. The plurality of sensors include, but are not limited to, a smartwatch 12, a smart bracelet 13, a smart garment 14 equipped with sensing electrodes, an arm sleeve 15 equipped with sensing electrodes, and a leg sleeve 16 equipped with sensing electrodes. It should be understood that the smartwatch 12 has the following functions: physiological value measurement and somatosensory data measurement. The physiological values ​​include body temperature, pulse, heart rate, blood pressure, oxygen uptake (VO2), and respiratory rate, and the somatosensory data includes steps, movement speed, and movement distance. It is also known that the smart bracelet 13 can also perform physiological value measurement and somatosensory data measurement; however, the smart bracelet 13 can measure fewer physiological value items. Therefore, in a preferred embodiment, the smart bracelet 13 can be used to collect the user's somatosensory data, and the smartwatch 12 can be used to collect the user's physiological data.

[0027] Furthermore, smart garments with embedded electrodes are already available on the market, and these smart garments can measure electrocardiogram (ECG) signals. On the other hand, it should be known that in the field of sports science applications, electrodes can be embedded in smart garments 14, arm sleeves 15, or leg sleeves 16 to measure electromyographic (EMG) signals to monitor the degree of muscle fatigue.

[0028] Furthermore, since the user will wear the smart clothing 14 and exercise during actual use and application, or more precisely, perform various different actions, intensities, or degrees of stretching, the following structure is provided to ensure that the smart clothing 14 can continuously collect the signals required by the system of the present invention during the process. More precisely, the shoulder strap device 10 can be placed on the outside of the smart clothing 14.

[0029] In one embodiment, please refer to Figures 3-4. The smart garment 14 includes a shoulder strap device 10, which includes a shoulder strap 20 and a chest strap 40. The shoulder strap 20 includes a shoulder strap body 21, a hook means 26, and an anti-slip strap 25.

[0030] In addition, the shoulder strap 20 includes: a back panel cover that is located on the back of the wearer when worn; and two extensions that are integral with the back panel cover, split to both sides and extend along the length direction, with their ends located in front of the upper body. Hook means 26 are provided on each extension for supporting carried items.

[0031] The chest strap 40 is fixed to the back panel covering of the shoulder strap 20 and extends along the length direction. Both ends have interlocking fasteners and are interlocked with the support of the anti-slip strap, so that the strap body fits snugly against the upper body.

[0032] The upper body wearable multi-purpose shoulder strap device 10 of this embodiment includes a shoulder strap 20 worn on the user's shoulders and a chest strap 40 whose central part is fixed to the shoulder strap 20 and extends along the length direction.

[0033] The shoulder strap 20 includes a shoulder strap body 21, a hook means 26, and an anti-slip strap 25.

[0034] When worn, the shoulder strap body 21 includes a back panel cover 21a located above the user's back, and two extensions 21b integral with the back panel cover 21a, extending to both sides and along the length direction, with their ends located in front of the upper body. The angle between the two extensions 21b or the size of the extensions can vary depending on the situation. Furthermore, the extensions 21b can be straight or curved as needed.

[0035] The inner side of the shoulder strap body 21, i.e., the side facing the body, is provided with a cushioning section 20a. The cushioning section 20a is filled with elastic materials such as sponge, so that even if the shoulder strap device 10 is subjected to high load, it will not cause physical burden to the wearer. The cushioning section 20a can be wrapped with mesh fabric.

[0036] Hook means 26 are installed on the two side extensions 21b for supporting carried items.

[0037] The support stop 26a is a common accessory used in products such as backpacks, used to pass through the handle part 26d. The handle part 26d is a strip with a fixed width. After passing through the support stop 26a, the support stop fixes or restricts its movement along the length direction.

[0038] After the grip portion 26d passes through the support stop 26a, it can move along its length by operating the support stop 26a. In other words, by flipping up one end of the support stop 26a, the friction between the grip portion 26d and the support stop 26a is reduced, thereby adjusting the length position of the grip portion 26d. If the support stop 26a is not flipped up, the friction between the grip portion 26d and the support stop 26a will prevent the grip portion 26d from moving along its length.

[0039] Pull the handle 26d in the direction of arrow a in the diagram.

[0040] The anti-slip band 25 is a strip-shaped anti-slip band located at the ends of the two side extensions 21b, with both ends sewn and fixed to the extensions 21b, and the central portion separated from the extensions 21b. When the central portion of the anti-slip band 25 is separated from the extensions 21b, a channel is formed between the extensions 21b and the anti-slip band 25. This channel is for the passage of the chest strap 40, which will be described later.

[0041] The chest strap 40 serves to keep the shoulder strap body 21 close to the wearer's upper body and has a fastening part.

[0042] The fastening part includes support buckles 42a and elastic buckles 42b fixed to both ends of the wearing strap 41. The elastic buckle 42b is a buckle that can be elastically deformed and can be detachably connected to the support buckle 42a.

[0043] The two ends of the wearing strap 41 of the chest strap 40 are fastened together with the support of the anti-slip strap 25. In this state, pulling the chest strap 40 in the direction of arrow z will tighten it, making it fit more closely to the wearer's upper body.

[0044] In addition, the length of the anti-slip straps 25 at both ends of the shoulder strap 20 can be made to be more than 15 centimeters. For example, the upper and lower positions of the shoulder strap 20 on the chest strap 40 can be adjusted according to the wearer's chest circumference so that the shoulder strap 20 fits snugly against the shoulder.

[0045] As shown in Figure 1, the first electronic device 11 includes a first processor 11P, a first memory 11M, and a first display 11D. It should be understood that with the end of the pandemic, users are gradually becoming accustomed to using fitness mirrors, mobile display devices, and / or smart TVs to perform home aerobic exercise and online yoga. In other words, the aforementioned three types of devices can be used as the first electronic device 11. However, for users with limited budgets, they can still use smartphones, tablets, desktop computers, or all-in-one computers that can install applications as the first electronic device 11. Furthermore, game consoles such as PS5, XBOX, and SWITCH that can install applications can also be used as the first electronic device 11.

[0046] According to the invention, the first memory 11M of the first electronic device 11 stores at least one application as well as a pre-trained (pre-trained) exercise prescription generation model and a pre-trained exercise effectiveness evaluation model. In feasible embodiments, the first memory may be, but is not limited to, a hard disk drive (HDD), solid state drive (SSD), or flash memory (flash memory).

[0047] Thus, after the user operates the first electronic device 11 to run (enable) the application, the personalized exercise prescribing system 1 of the invention displays a user operating interface through the first display 11D . It is difficult to understand that upon first use, the user must enter his user's personal data, of which at least one of that user's personal data age, weight, height, BMI value, waist, leg circumference, and arm circumference.

[0048] and, at first use, the personalized exercise prescribing system1 performs the following functions: Collect at least one static physiological signal from a user through the complex number of sensors; Perform a data processing on the static physiological signal to obtain at least one static physiological data; and The static physiological data and the user's personal data are entered into the exercise prescription generation model such that the exercise prescription generating model outputs an exercise prescription for the user.

[0049] The static physiological data includes at least one of body temperature, pulse, heart rate, blood pressure, oxygen uptake (VO2), respiratory rate, and electromyography (EMG). On the other hand, the exercise prescription includes at least one of exercise programs for training muscle strength, training lung capacity, training cardiopulmonary function, burning calories, improving metabolism, and training grip strength. Simply put, upon first use, the personalized exercise prescription system 1 of this invention recommends an exercise program for the user based on data such as age and BMI, as well as physiological data such as heart rate, respiratory rate, and EMG. For example, when the sensor data shows that the user's heart rate or respiratory rate is too high, the system will recommend exercise programs for training lung capacity and / or for training cardiopulmonary function. For example, since the smart garment 14 is used to measure the strength of the pectoral or abdominal muscles, the arm sleeve 15 is used to measure the strength of the arm muscles, and the leg sleeve 16 is used to measure the strength of the leg muscles, the system can process electromyographic signals from different parts of the body to determine the strength level of each part. Thus, when the data shows that the user's specific muscle group is weak, the system will recommend exercise programs to strengthen that muscle group.

[0050] Of course, most users have their own exercise goals, such as: fat loss, improving metabolism, increasing lung capacity, or improving leg or arm muscle strength. Therefore, correspondingly, the personalized exercise prescription system 1 of this invention performs the following functions: The first display 11D is controlled to display a user interface 11U (as shown in Figure 2); After the user interface 11U is operated to input at least one exercise goal, the static physiological data, the user's personal data, and the exercise goal are input into the exercise prescription generation model, so that the exercise prescription generation model outputs a corresponding personalized exercise prescription. The user interface 11U provides a menu of exercise courses corresponding to the exercise prescription and / or at least one recommended exercise course. When any exercise course or recommended exercise course in the exercise course menu is selected, the first display 11D is controlled to display / play the course content of the exercise course or recommended exercise course; The plurality of sensors (smartwatch 12, smart bracelet 13, smart clothing 14, arm sleeve 15, leg sleeve 16) are controlled to collect at least one dynamic physiological signal and at least one sensory signal from the user, and to monitor the user's exercise time; Perform data processing on the dynamic physiological signal to obtain at least one dynamic physiological data, and perform data processing on the somatosensory signal to obtain somatosensory data; At least one data processing step is performed on the somatosensory data, the dynamic physiological data, and the exercise time to calculate an exercise intensity; The exercise course and / or the recommended exercise course, the exercise intensity, the dynamic physiological data, and the exercise time are input into the exercise effectiveness evaluation model, so that the exercise effectiveness evaluation model outputs an exercise effectiveness evaluation result for the user; and The first display 11D is controlled to display the user interface 11U to show the user's motion statistics (as shown in Figure 2).

[0051] The somatosensory data includes at least one of steps, movement speed, and distance traveled. Conversely, the dynamic physiological data includes at least one of body temperature, pulse, heart rate, blood pressure, oxygen uptake (VO2), respiratory rate, and electromyography (EMG). For example, heart rate is a reference standard for exercise intensity. According to the Health Promotion Administration's exercise guidelines, an effective exercise intensity is achieved when the heart rate reaches at least 60% of the maximum heart rate. Furthermore, the formula for calculating the maximum heart rate for adults is 220 - 60 = 160 beats per minute. In other words, when a user's heart rate during exercise reaches or exceeds 160 beats per minute, the exercise effectiveness assessment result will indicate that the user has achieved an effective exercise intensity. On the other hand, the formula for calculating maximum oxygen uptake is VO₂Max = MHR x SVmax x AVO₂ difference, where MHR is the maximum heart rate, SVmax is the maximum stroke volume (i.e., the maximum blood volume pumped with each heartbeat), and AVO₂ difference is the difference in oxygen content between arteries and veins (the difference in maximum oxygen content between arteries and veins). Therefore, VO₂Max is the result of the coordinated operation of the respiratory, circulatory, and muscular systems in delivering and utilizing inhaled oxygen. Thus, the threshold for VO₂Max can also be used to assess whether a user has achieved an effective exercise intensity.

[0052] It is added that if the user uses a fitness mirror, mobile display device or smart television as the first electronic device 11 , after completing one or more exercise sessions, the user may operate the first electronic device 11 to transmit the relevant exercise statistics to his / her second electronic device 17 . In feasible embodiments, the second electronic device 17 may be, but is not limited to, a smartphone, tablet, desktop computer, all-in-one computer, or smartwatch. Of course, if the user is directly using a smartphone, tablet, desktop, or all-in-one computer as the first electronic device 11 , there is no need to do so.

[0053] It should be understood that the various exercise courses contained in the exercise prescription are designed by an exercise instructor (fitness instructor), however the designed exercise course does not necessarily satisfy the needs of all users. Accordingly, correspondingly, the personalized exercise prescribing system 1 of the invention performs the following functions: Receive suggestions for at least one course content adjustment entered by the user through the user operating interface 11U;and Send that course content adjustment advice to a server through which to at least one electronic device;

[0054] In addition, in order to realize the AI ​​dynamic adjustment and automatic recommendation system based on collected data, it is necessary to integrate data collection and processing, AI model training and dynamic analysis and recommendation technology to form a complete set of motion management processes. First, the system collects the user's multimodal data from the sensor, including static physiological data (such as age, BMI, resting heart rate, respiratory rate, etc.), dynamic physiological data during exercise (such as heart rate change, oxygen uptake), somatosensory data (such as steps, speed, posture and electromyographic signals), and exercise intensity and effectiveness data (such as exercise time, course completion, calorie consumption). After cleaning and feature extraction, these data, such as removal of outliers, normalization of data range as well as calculation of key features (such as fatigue index or muscle load fluctuation), form a high-quality dataset suitable for training AI models.

[0055] The system's core AI model consists of two parts. The first part is the exercise intensity adjustment model, which aims to predict the appropriate exercise intensity based on the user's static data, exercise performance, and dynamic data. This model can employ deep learning or other regression models (such as random forest regression or XGBoost), and its output includes suggested training load, repetitions, or duration. The second part is the muscle group recommendation model, which aims to identify muscle groups that need focused training and recommend corresponding exercise programs. This model uses a classification algorithm to combine electromyographic signal data from the chest, arms, and legs with exercise performance data to generate a priority order for the next exercise session.

[0056] During model training, the dataset needs to be divided into training, validation, and test sets to ensure the model's generalization ability. Training is performed using deep learning frameworks (such as TensorFlow or PyTorch), and appropriate evaluation metrics (such as mean squared error, accuracy, and F1 score) are used to measure model performance. If the results are unsatisfactory, the model can be optimized through data augmentation techniques, adjusting the model structure, or increasing the amount of data.

[0057] During operation, the system monitors the user's exercise process in real time, collecting dynamic data and estimating the instantaneous exercise intensity. After each workout, the system analyzes the collected exercise data to calculate the recommended intensity and target muscle groups for the next workout, presenting the results on the user interface and providing personalized course suggestions. Effectiveness evaluation and feedback mechanisms are also crucial components of the system. By collecting user satisfaction data and exercise results, the accuracy and practicality of the model are further improved.

[0058] To achieve the above functions, the choice of technical tools is also crucial. For example, Pandas and NumPy can be used for data processing, TensorFlow or PyTorch can be used to train AI models, and MongoDB or PostgreSQL can be used to store data. Furthermore, a user-friendly interface can be built using React.js or Vue.js, allowing users to easily set goals, view recommended courses, and analyze results, achieving intelligent exercise management throughout the entire process. Such a system is not only technically feasible but also ensures practicality and security, providing users with precise guidance and continuous improvement during their exercise.

[0059] In summary, the personalized exercise prescription system and method of the present invention have been fully and clearly described. However, it must be emphasized that the above-disclosed embodiments are preferred embodiments, and any partial changes or modifications that originate from the technical concept of the present invention and are easily deduced by those skilled in the art are not outside the scope of the patent rights of this invention.

[0060] In conclusion, this case demonstrates a significant difference from conventional technology in terms of purpose, means, and effectiveness. Furthermore, its invention is practical and meets the patent requirements for an invention. We earnestly request that your esteemed examiner carefully review the case and grant a patent as soon as possible to benefit society. This is our sincere prayer.

[0061] 1: Personalized Exercise Prescription System 10: Shoulder strap device 11: First Electronic Device 11P: First Processor 11M: First Memory 11D: First Display 11U: User Interface 12: Smartwatch 13: Smart Bracelet 14: Smart Clothing 15: Arm Gloves 16: Leg protectors 17: Second electronic device 20: Shoulder strap 20a: Buffer section 21: Shoulder strap body 21a: Panel Cover Section 21b: Extension 25: Anti-detachment tape 26: Linkage Methods 26a: Support stop 26d: Grip section 40: Chest strap 41: Wearing a belt 42a: Support fastener 42b: Flexible fastener

Claims

1. A personalized exercise prescription system, comprising: A first electronic device includes a first processor, a first display, and a first memory, wherein the first memory stores at least one application, a pre-trained exercise prescription generation model, and a pre-trained exercise performance evaluation model; and a plurality of sensors coupled to the first electronic device; wherein, after executing the application, the first processor is configured to: control the plurality of sensors to collect at least one static physiological signal from a user; perform data processing on the static physiological signal to obtain at least one static physiological data, input the static physiological data and the user's personal data into the exercise prescription generation model, causing the exercise prescription generation model to output an exercise prescription for the user; control the first display to display a user interface; after the user interface is operated to input at least one exercise goal, input the static physiological data, the user's personal data, and the exercise goal into the exercise prescription generation model, causing the exercise prescription generation model to output a corresponding personalized exercise prescription; and provide an exercise course menu and / or at least one recommended exercise course corresponding to the exercise prescription through the user interface; When any exercise course or recommended exercise course in the exercise course menu is selected, the system controls the first display to show / play the course content of the exercise course or recommended exercise course; controls the plurality of sensors to collect at least one dynamic physiological signal and at least one somatosensory signal from the user, and monitors the user's exercise time; performs data processing on the dynamic physiological signal to obtain at least one dynamic physiological data, and performs data processing on the somatosensory signal to obtain somatosensory data; performs at least one data processing on the somatosensory data, the dynamic physiological data, and the exercise time to calculate an exercise intensity; inputs the exercise course and / or the recommended exercise course, the exercise intensity, the dynamic physiological data, and the exercise time into the exercise effectiveness evaluation model, so that the exercise effectiveness evaluation model outputs an exercise effectiveness evaluation result for the user; and controls the first display to show the user's exercise statistics; wherein, the static physiological data includes at least one selected from the group consisting of body temperature, pulse, heart rate, blood pressure, oxygen uptake (VO2), respiratory rate, and electromyographic intensity. The exercise prescription includes at least one exercise program selected from the group consisting of exercise programs for training muscle strength, exercise programs for training lung capacity, exercise programs for training cardiopulmonary function, exercise programs for burning calories, exercise programs for improving metabolism, and exercise programs for training grip strength; the plurality of sensors include smartwatches, smart bracelets, smart clothing, arm sleeves, and / or leg sleeves; and the motion data includes at least one selected from the group consisting of steps, movement speed, and movement distance.The dynamic physiological data includes at least one selected from a group consisting of body temperature, pulse, heart rate, blood pressure, oxygen uptake (VO2), respiratory rate, and electromyographic intensity.

2. The personalized exercise prescription system as described in claim 1, the smart garment further includes a shoulder strap device, and the shoulder strap device includes a shoulder strap and a chest strap, the shoulder strap including a shoulder strap body, a hook means and an anti-slip strap.

3. The personalized exercise prescription system as described in claim 2, wherein, The first processor, when executing the application, is also configured to: receive at least one course content adjustment suggestion input by the user through the user interface; and transmit the course content adjustment suggestion to a server, thereby transmitting it to at least one electronic device; wherein the electronic device is held by a sports coach.

4. A method for generating a personalized exercise prescription, performed by a first electronic device coupled to a plurality of sensors, wherein, The first electronic device has a first display and stores a pre-trained exercise prescription generation model and a pre-trained exercise effectiveness evaluation model. The personalized exercise prescription generation method includes: controlling the plurality of sensors to collect at least one static physiological signal from a user; performing data processing on the static physiological signal to obtain at least one static physiological data; inputting the static physiological data and the user's personal data into the exercise prescription generation model, causing the exercise prescription generation model to output an exercise prescription for the user; controlling the first display to show a user interface; after the user interface is operated to input at least one exercise goal, inputting the static physiological data, the user's personal data, and the exercise goal into the exercise prescription generation model, causing the exercise prescription generation model to output a corresponding personalized exercise prescription; providing an exercise course menu and / or at least one recommended exercise course corresponding to the personalized exercise prescription through the user interface; when any exercise course in the exercise course menu or the recommended exercise course is selected, controlling the first display to display / play the course content of the exercise course or the recommended exercise course. The system controls a plurality of sensors to collect at least one dynamic physiological signal and at least one somatosensory signal from the user, and monitors the user's exercise time; performs data processing on the dynamic physiological signal to obtain at least one dynamic physiological data, and performs data processing on the somatosensory signal to obtain somatosensory data; performs at least one data processing on the somatosensory data, the dynamic physiological data, and the exercise time to calculate an exercise intensity; inputs the exercise course and / or the recommended exercise course, the exercise intensity, the dynamic physiological data, and the exercise time into the exercise effectiveness evaluation model, so that the exercise effectiveness evaluation model outputs an exercise effectiveness evaluation result for the user; and controls the first display to display a set of exercise statistics for the user; wherein the static physiological data includes at least one selected from the group consisting of body temperature, pulse, heart rate, blood pressure, oxygen uptake (VO2), respiratory rate, and electromyographic intensity. The exercise prescription includes at least one exercise program selected from the group consisting of exercise programs for training muscle strength, exercise programs for training lung capacity, exercise programs for training cardiopulmonary function, exercise programs for burning calories, exercise programs for improving metabolism, and exercise programs for training grip strength. The plurality of sensors include smartwatches, smart bracelets, smart clothing, arm sleeves, and / or leg sleeves. The dynamic physiological data includes at least one selected from the group consisting of body temperature, pulse, heart rate, blood pressure, oxygen uptake (VO2), respiratory rate, and electromyographic intensity.

5. The personalized exercise prescription generation method as described in claim 4, wherein, The first electronic device is selected from any one of the group consisting of fitness mirrors, mobile display devices, smartphones, tablets, desktop computers, all-in-one computers, smart TVs, and game consoles.

6. The method for generating a personalized exercise prescription as described in claim 4, wherein, The somatosensory data includes at least one selected from the group consisting of steps, movement speed, and distance traveled. The dynamic physiological data includes at least one selected from the group consisting of body temperature, pulse, heart rate, blood pressure, oxygen uptake (VO2), respiratory rate, and electromyographic intensity.

7. The method for generating a personalized exercise prescription as described in claim 4, wherein, The first electronic device is connected to a second electronic device, enabling the second electronic device to receive the motion statistics data from the first electronic device.

8. The personalized exercise prescription generation method as described in claim 6 further includes: The user interface receives at least one suggestion for adjusting course content. And transmit the course content adjustment suggestions to a server, thereby transmitting them to at least one electronic device held by a sports coach.

Images