Intelligent exercise intensity evaluation system and evaluation method thereof

Abstract

This invention provides an intelligent exercise intensity assessment system and method. The intelligent exercise intensity assessment system includes: an exercise testing machine, a physiological information sensor, a signal transmitter connected to the physiological information sensor, a central control unit connected to the signal transmitter, and a cloud database connected to the central control unit. The physiological information sensor detects the exerciser's physiological information before and after operating the exercise testing machine. The signal transmitter transmits the physiological information to the central control unit, which then transmits it to the cloud database. The cloud database analyzes the physiological information and obtains its corresponding wattage value. Based on the wattage value, the cloud database obtains the resistance coefficient of different fitness equipment. Therefore, the intelligent exercise intensity assessment system of this invention can track and record the exerciser's data and calculate a suitable exercise prescription based on the exerciser's physiological information.

Description

Technical Field

[0001] This invention relates to sports training systems, and in particular to an intelligent sports intensity assessment system and its assessment method. Background Technology

[0002] Most fitness equipment allows users to monitor their physiological information during training or to train according to user-defined exercise modes. However, whether the training process meets individual needs depends solely on personal perception; it cannot adjust the intensity based on the user's physiological information. Therefore, traditional exercise training methods often fail to achieve optimal results. Summary of the Invention

[0003] The main objective of this invention is to provide an intelligent exercise intensity assessment system that can obtain exercise prescriptions suitable for exercisers.

[0004] To achieve the aforementioned main objectives, the intelligent exercise intensity assessment system of the present invention includes an exercise testing machine, a physiological information sensor, a signal transmitter, a central control unit, and a cloud database. The exercise testing machine is used for exercise testing by the user; the physiological information sensor is worn by the user to sense physiological information before and after operating the exercise testing machine; the signal transmitter is electrically connected to the physiological information sensor to receive the physiological information sensed by the sensor; the central control unit is electrically connected to the signal transmitter to collect the physiological information received by the signal transmitter; the cloud database is electrically connected to the central control unit to record and analyze the physiological information, obtaining the corresponding wattage value from the physiological information and, based on the wattage value, obtaining the resistance coefficient corresponding to different fitness equipment.

[0005] As can be seen from the above, the intelligent exercise intensity assessment system of the present invention can track and record the exercise status of the exerciser, and calculate an exercise prescription suitable for the exerciser after analyzing the data and combining it with the exerciser's physiological information, thereby enabling the exerciser's exercise training to achieve the best results.

[0006] Preferably, the central control unit is used to provide the athlete with basic information, and the central control unit obtains the athlete's maximum heart rate from the basic information.

[0007] Preferably, before the athlete operates the exercise testing machine, the physiological information sensor obtains the athlete's resting heart rate, the cloud database calculates the reserve heart rate based on the resting heart rate and the maximum heart rate, and uses the reserve heart rate to obtain the athlete's heart rate values ​​at different training intensities.

[0008] Preferably, the maximum heart rate minus the resting heart rate gives the heart rate reserve. Preferably, when the training intensity is warm-up, the heart rate value is RHR + (HRR × 20%), when the training intensity is low, the heart rate value is RHR + (HRR × 40%), when the training intensity is medium, the heart rate value is RHR + (HRR × 60%), and when the training intensity is high, the heart rate value is RHR + (HRR × 85%).

[0009] Preferably, when the athlete is operating the exercise testing machine, the cloud database uses the Rating of Perceived Exertion Scale (RPE scale) to obtain the athlete's physical state at different heart rates.

[0010] Preferably, after the athlete operates the exercise testing machine, the cloud database obtains the athlete's maximum oxygen uptake, and uses the maximum oxygen uptake and the heart rate reserve to obtain the athlete's metabolic equivalent of tasks (METs).

[0011] Preferably, the metabolic equivalent is calculated by the following formula: MET = VO2 max × MHRR ÷ 3.5 ÷ 60, where MET is the metabolic equivalent, VO2 max is the maximum oxygen uptake, and MHRR is the maximum value of the heart rate reserve under different training intensities.

[0012] Preferably, the exercise testing machine is an exercise bike.

[0013] On the other hand, the present invention further provides an evaluation method for the aforementioned intelligent exercise intensity assessment system, comprising the following steps: a) sensing the physiological information of the exerciser before and after operating the exercise testing machine using the physiological information sensor; b) receiving the physiological information using the signal transmitter and transmitting the physiological information back to the central control host; c) transmitting the physiological information to the cloud database for analysis using the central control host, so that the cloud database obtains the corresponding wattage value, and obtains the resistance coefficient corresponding to different fitness equipment based on the wattage value; and d) allocating the exerciser to one of the fitness equipment using the cloud database, and controlling the fitness equipment used by the exerciser to provide the resistance coefficient of step c).

[0014] Detailed descriptions of the construction, features, assembly, and usage of the intelligent exercise intensity assessment system and method provided by this invention will be provided in the subsequent detailed descriptions of the embodiments. However, those skilled in the art will understand that these detailed descriptions and the specific embodiments listed for implementing this invention are for illustrative purposes only and are not intended to limit the scope of this patent application. Attached Figure Description

[0015] Figure 1 A block diagram of the intelligent exercise intensity assessment system of the present invention is shown schematically.

[0016] Figure 2 The schematic diagram illustrates the system interface of the intelligent assessment system provided by the intelligent sports training method of the present invention.

[0017] Figure 3 The flowchart illustrates the assessment method provided by the intelligent exercise intensity assessment system of the present invention.

[0018] Figure 4 The diagram illustrates a graph of wattage versus drag coefficient provided by the intelligent motion intensity assessment system of the present invention.

[0019] 10: Intelligent exercise intensity assessment system;

[0020] 12: Athletes;

[0021] 14: Mobile phone;

[0022] 20: Motion testing machine;

[0023] 30: Physiological information sensor;

[0024] 40: Signal transmitter;

[0025] 42: Screen;

[0026] 50: Central control unit;

[0027] 60: Cloud-based database;

[0028] B: Blue;

[0029] G: Green;

[0030] Y: Yellow;

[0031] O: Orange;

[0032] R: Red;

[0033] S1-S6: Steps;

[0034] L1-L3: Curve. Detailed Implementation

[0035] The applicant hereby clarifies that throughout this specification, including the embodiments described below and the claims in the patent application, all directional terms are based on the directions shown in the accompanying drawings. Secondly, in the embodiments and drawings described below, the same element reference numerals represent the same or similar elements or their structural features.

[0036] like Figure 1 As shown, the intelligent exercise intensity assessment system 10 of the present invention includes an exercise testing machine 20, a physiological information sensor 30, a signal transmitter 40, a central control host 50, and a cloud database 60.

[0037] The exercise testing machine 20 is used for exercise testing by the exerciser 12. In this embodiment, the exercise testing machine 20 is an exercise bike to obtain the predicted maximum oxygen uptake.

[0038] A physiological information sensor 30 is worn by the exerciser 12 to sense the exerciser's physiological information before and after operating the exercise testing machine 20. Before operating the exercise testing machine 20, the physiological information sensor 30 obtains the exerciser's resting heart rate (RHR). For example, as shown in Tables 1 and 2, which contain basic information and data for two different exercisers, Table 1 shows that the resting heart rate obtained by the exerciser 12 after remaining at rest for a period of time (approximately 3 minutes) was 74 bpm, and Table 2 shows that the resting heart rate obtained by the exerciser 12 after remaining at rest for a period of time (approximately 3 minutes) was 62 bpm.

[0039] In this embodiment, the signal transmitter 40 is a TV stick (this is just an example and does not limit the type of signal transmitter 40). The signal transmitter 40 connects to the physiological information sensor 30 via Bluetooth to receive the physiological information sensed by the physiological information sensor 30 and displays the aforementioned physiological information on the screen 42 (e.g., ...). Figure 2 (As shown).

[0040] The central control unit 50 connects to the signal transmitter 40 via Wi-Fi or Bluetooth to collect physiological information received by the signal transmitter 40. Additionally, the central control unit 50 is used to establish basic data for the exerciser 12, enabling the central control unit 50 to obtain the exerciser 12's maximum heart rate (MHR) from this data. In this embodiment, the central control unit 50 estimates the exerciser 12's maximum heart rate using the formula (220 - age) based on the exerciser 12's basic data. For example, as shown in Table 1, if the exerciser's age is 48, the estimated maximum heart rate is 220 - 48 = 172 bpm. Similarly, in Table 2, if the exerciser's age is 31, the estimated maximum heart rate is 220 - 31 = 189 bpm.

[0041] The cloud database 60 connects to the central control unit 50 via Wi-Fi or Bluetooth. It stores basic data established by the exerciser through the central control unit 50, and records and analyzes physiological information collected by the central control unit 50. The cloud database 60 uses this physiological information to obtain the corresponding wattage value and, based on the wattage value, obtains the resistance coefficient corresponding to different fitness equipment.

[0042] Furthermore, the cloud database 60 calculates the heart rate reserve (HRR) using resting heart rate (RHR) and maximum heart rate (MHR), and uses the heart rate reserve to obtain the heart rate values ​​of the exerciser 12 at different training intensities. In this embodiment, the cloud database 60 subtracts the resting heart rate (RHR) from the maximum heart rate (MHR) to obtain the heart rate reserve (HRR). In addition, the training intensity is divided into five levels (ranges): warm-up, low, medium, high, and dangerous. When the training intensity is warm-up, the heart rate value is RHR + (HRR × 20%); when the training intensity is low, the heart rate value is RHR + (HRR × 40%); when the training intensity is medium, the heart rate value is RHR + (HRR × 60%); and when the training intensity is high, the heart rate value is RHR + (HRR × 85%).

[0043] For example, as shown in Table 1, when the training intensity is warm-up, the heart rate is 74 + (172 - 74) × 20% = 93.6 bpm, which is rounded to 94 bpm. In Table 2, when the training intensity is moderate, the heart rate is 62 + (189 - 62) × 60% = 138.2 bpm, which is rounded to 138 bpm. The calculation method for other heart rate values ​​is similar and will not be repeated here.

[0044] Table 1

[0045]

[0046] Table 2

[0047]

[0048] The above describes the architecture of the intelligent exercise intensity assessment system 10 of the present invention. The assessment method of the intelligent exercise intensity assessment system 10 of the present invention will be described below. Figure 3 As shown, the evaluation method includes steps S1-S6:

[0049] First, before the first training session, the athlete 12 needs to establish basic data on the central control unit 50. The central control unit 50 will then transmit the aforementioned basic data to the cloud database 60 for storage. For the next training session, the athlete can simply scan the QR code with their mobile phone 14 to retrieve the previously established basic data from the cloud database 60, without needing to establish it again. After the initial establishment of basic data is completed, the cloud database 60 can estimate the athlete 12's maximum heart rate based on the athlete 12's basic data. Then, the athlete 12 wears the physiological information sensor 30 and remains still for a period of time (approximately 3 minutes). The physiological information sensor 30 obtains the athlete 12's resting heart rate. Next, the athlete 12 can start operating the exercise testing machine 20 to conduct exercise tests to obtain maximum oxygen uptake. The athlete 12's metabolic equivalent of tasks (METs) is obtained using maximum oxygen uptake and heart rate reserve. The energy consumption of the athlete 12 under different training intensities is obtained through the metabolic equivalents.

[0050] After the exerciser 12 operates the exercise testing machine 20 for a period of time, the system will monitor the exerciser 12's heart rate in real time and use the Rating of Perceived Exertion Scale (RPE scale) to store the exerciser 12's subjective feelings at different heart rates in the cloud database 60. For example, if the exerciser 12's heart rate has not yet reached the danger zone, but the exerciser 12 already feels uncomfortable or unwell, this information will be stored in the cloud database 60 to monitor the exerciser 12's exercise status and provide appropriate training intensity.

[0051] In this embodiment, the metabolic equivalent is calculated by the following formula: METs=VO2 max×MHRR÷3.5÷60, where METs is the metabolic equivalent, VO2 max is the maximum oxygen uptake, and MHRR is the maximum value of the heart rate reserve under different training intensities. For example, please refer to Table 1. If an athlete's maximum oxygen uptake is 41.2 ml / kg / min, then after exercising for 60 seconds at low intensity, METs = (41.2 × 0.39 ÷ 3.5 ÷ 60) × 60 (seconds) = 4.59; after exercising for 60 seconds at medium intensity, METs = (41.2 × 0.59 ÷ 3.5 ÷ 60) × 60 (seconds) = 6.95; and after exercising for 60 seconds at high intensity, METs = (41.2 × 0.85 ÷ 3.5 ÷ 60) × 60 (seconds) = 10.01. Therefore, the total METs obtained after completing the three different training intensities is 4.59 + 6.95 + 10.01 = 21.55.

[0052] On the other hand, after the athlete 12 has been operating the exercise testing machine 20 for a period of time, the cloud database 60 will obtain the predicted wattage values ​​for different training intensities such as warm-up, low, medium, and high (as shown in Tables 1 and 2). Figure 4 As shown, Figure 4 The horizontal axis represents the resistance coefficient of the fitness equipment. Figure 4 The vertical axis represents the wattage of the fitness equipment, compared with the predicted wattage obtained from the cloud database 60. Figure 4 The different curves shown indicate the resistance coefficient of the fitness equipment (using an elliptical machine as an example here) at different speeds. In this embodiment, curve L1 corresponds to a speed of 50 rpm, curve L2 corresponds to a speed of 60 rpm, and curve L3 corresponds to a speed of 70 rpm. For example, as shown in Table 1, when the predicted wattage is 49, compared to... Figure 4 The drag coefficient can be obtained from curve L2, which is between 3 and 4, and the larger drag coefficient is taken as the main factor. In addition, in Table 2, when the predicted watt value is 95, refer to... Figure 4 The drag coefficient can be obtained from the curve L2, which is between 8 and 9, and the larger drag coefficient is taken as the main one.

[0053] After obtaining the resistance coefficient, the cloud database 60 will assign the exerciser 12 to an upright bicycle, elliptical machine, rowing machine, or any exercise equipment whose speed, resistance coefficient, or wattage can be controlled, and control the fitness equipment used by the exerciser 12 to provide the corresponding resistance coefficient so that the exerciser 12 can carry out relevant training. During training, the physiological information of the athlete 12 is recorded and uploaded to the cloud database 60. The cloud database 60 evaluates the athlete 12's exercise prescription based on the received data to determine if adjustments are needed (e.g., increasing or decreasing the resistance coefficient or allocating it to other fitness equipment). For example, when the cloud database 60 detects a change in the predicted wattage value obtained under the same resistance coefficient, such as when the predicted wattage value drops from 97 corresponding to curve L2 to 78 corresponding to curve L1 with a resistance coefficient of 9, it indicates a decrease in the athlete's physical fitness, causing a decrease in the rotation speed of the fitness equipment. Alternatively, when the predicted wattage value rises from 97 corresponding to curve L2 to 117 corresponding to curve L3 with a resistance coefficient of 9, it indicates an improvement in the athlete's physical fitness, causing an increase in the rotation speed of the fitness equipment. In either case, the cloud database 60 will adjust the resistance coefficient in real time based on the physiological information to optimize the athlete's training effect. Alternatively, the cloud database 60 will adjust the resistance coefficient for the next use based on the physiological information. In addition, athletes 12 can also intuitively see their own physical condition through screen 42, such as Figure 2As shown, screen 42 uses different colored blocks to distinguish different training intensities. For example, blue (B) represents warm-up, green (G) represents low, yellow (Y) represents medium, orange (O) represents high, and red (R) represents danger. In this way, the exerciser can more intuitively understand their training status through the identification of colored blocks. When the exerciser 12 wants to actively lose weight, the system will lengthen the time of the original orange (O) (high intensity) block or increase the resistance of the orange block to increase the intensity.

[0054] In summary, the intelligent exercise intensity assessment system 10 of the present invention can track and record the exercise status of the exerciser 12 every time, and calculate a suitable exercise prescription for the exerciser 12 after analyzing the relevant data and combining it with the physiological information of the exerciser 12, thereby enabling the exercise training of the exerciser 12 to achieve the optimal effect.

[0055] Those skilled in the art will understand that the features described in the various embodiments and / or claims of the present invention can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, the features described in the various embodiments and / or claims of the present invention can be combined or combined in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or combinations fall within the scope of the present invention.

[0056] The embodiments of the present invention have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of the invention. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of the invention is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of the invention, and all such substitutions and modifications should fall within the scope of the invention.

Claims

1. An intelligent exercise intensity assessment system, comprising: A motion testing machine is used to allow athletes to conduct motion tests. A physiological information sensor, worn by the athlete, is used to sense the athlete's physiological information before and after operating the exercise testing machine; A signal transmitter, electrically connected to the physiological information sensor, is used to receive the physiological information sensed by the physiological information sensor; The central control unit is electrically connected to the signal transmitter and is used to collect the physiological information received by the signal transmitter. as well as The cloud database is electrically connected to the central control unit and is used to record and analyze the physiological information. The cloud database obtains the corresponding predicted watt value through the physiological information and obtains the resistance coefficient corresponding to different fitness equipment based on the predicted watt value. The central control unit is used to establish basic information for the athlete, and the central control unit obtains the athlete's maximum heart rate from the basic information. In this process, before the athlete operates the exercise testing machine, the physiological information sensor obtains the athlete's resting heart rate, the cloud database calculates the reserve heart rate based on the resting heart rate and the maximum heart rate, and uses the reserve heart rate to obtain the athlete's heart rate values ​​under different training intensities. When the athlete operates the exercise testing machine, the cloud database uses a self-reported exercise scale to obtain the athlete's physical state at different heart rates. After the athlete operates the exercise testing machine, the cloud database obtains the athlete's maximum oxygen uptake and uses the maximum oxygen uptake and heart rate reserve to obtain the athlete's metabolic equivalent. The metabolic equivalent is calculated using the following formula: MET = VO2max × MHRR ÷ 3.5 ÷ 60 MET is the metabolic equivalent, VO2max is the maximum oxygen uptake, and MHRR is the maximum heart rate reserve under different training intensities.

2. The intelligent exercise intensity assessment system according to claim 1, wherein, The cloud database obtains the resistance coefficient of different fitness equipment at different speeds based on the predicted watt value. When the predicted watt value obtained by the cloud database at the same resistance coefficient changes, the cloud database will adjust the resistance coefficient.

3. The intelligent exercise intensity assessment system according to claim 2, wherein, The cloud-based database adjusts the resistance coefficient in real time based on this physiological information.

4. The intelligent exercise intensity assessment system according to claim 2, wherein, The cloud-based database adjusts the resistance coefficient for the next use based on this physiological information.

5. The intelligent exercise intensity assessment system according to claim 1, wherein, MHR - RHR = HRR, where MHR is the maximum heart rate, RHR is the resting heart rate, and HRR is the heart rate reserve. When the training intensity is warm-up, the heart rate value is RHR + (HRR × 20%). When the training intensity is low, the heart rate value is RHR + (HRR × 40%). When the training intensity is medium, the heart rate value is RHR + (HRR × 60%). When the training intensity is high, the heart rate value is RHR + (HRR × 85%).

6. The intelligent exercise intensity assessment system according to claim 1, wherein, The exercise testing machine is an exercise bike.

7. An evaluation method for an intelligent exercise intensity evaluation system according to any one of claims 1 to 6, comprising the following steps: a) The physiological information sensor is used to sense the physiological information of the athlete before and after operating the exercise testing machine; b) Receive the physiological information using the signal transmitter and transmit the physiological information back to the central control unit; c) The central control unit transmits the physiological information to the cloud database for analysis, enabling the cloud database to obtain the corresponding predicted wattage value, and based on the predicted wattage value, obtains the resistance coefficient corresponding to different fitness equipment; and d) Assign the exerciser to the fitness equipment using the cloud database, and control the fitness equipment used by the exerciser to provide the resistance coefficient of step c).

Images