Method for controlling auxiliary power by fatigue prediction model

Abstract

This invention relates to a method for controlling assisted power through a fatigue prediction model, comprising the following steps: Step 1: A user begins riding; Step 2: Collecting riding data from the user for a predetermined riding time; Step 3: Generating a fatigue prediction model based on the riding data; Step 4: Calculating a fatigue prediction threshold using the fatigue prediction model; Step 5: Measuring whether the user's exercise intensity data exceeds the fatigue prediction threshold; Step 6: If the exercise intensity data exceeds the fatigue prediction threshold, analyzing a deviation value between the exercise intensity data and the fatigue prediction threshold; and Step 7: Calculating an assisted power value from the exercise intensity data and the deviation value. Therefore, this invention can generate a personalized fatigue prediction model to achieve the benefits of preventing fatigue and improving riding safety.

Description

[Technical Field]

[0001] This invention relates to a method for controlling auxiliary power through a fatigue prediction model, and more particularly to a method for establishing a user-personalized fatigue prediction model by collecting riding data of users riding electric-assisted bicycles. This method can monitor the user's exercise intensity in real time and automatically control the required auxiliary power to achieve the benefits of preventing fatigue and improving riding safety. [Previous Technology]

[0002] Note: Cycling is not only a daily mode of transportation for Taiwanese people. Through the promotion and popularization of public bicycles and the rise of health awareness among Taiwanese people, cycling has become one of their leisure and entertainment activities. Furthermore, bicycles equipped with electric motors can reduce the physical burden on users. Older people and those without exercise habits can make up for their lack of physical strength through electric assistance systems, lowering the barrier to cycling. Therefore, electric-assisted bicycles can further enhance people's willingness to ride bicycles.

[0003] Because electric-assisted bicycles have the function of assistive power, they can also provide stable training or recovery conditions for competitive cyclists, and can also provide good assistance to people with rehabilitation needs. It can be seen that electric-assisted bicycles have many advantages over traditional bicycles, which has led to a continuous increase in the demand for electric-assisted bicycles in recent years, making them a new riding trend. Republic of China Patent No. I448402, "Bicycle Gear Control System Adjusted According to Rider's Physiological State", provides a system that can detect the rider's physiological state. By detecting information such as heart rate, riding time, riding distance or average speed, it obtains the corresponding calorie consumption, and then determines the gear condition to be used based on this calorie consumption. The shifting conditions are determined according to the rider's condition, making the riding state more comfortable.

[0004] Furthermore, Republic of China Patent No. I767208, "Electric Bicycle Assist Control Method and Assist Control System," provides a technical means with multiple fitness levels and multiple power ranges. The user first inputs their fitness level, and based on the input fitness level and the user's age, the corresponding power range and target heart rate range are obtained. The pedaling power and heart rate information during riding are measured to determine if the heart rate falls within the target heart rate range, and the corresponding level of motor assistance is output. However, in the past, electric bicycle assistance systems typically only provided auxiliary power and heart rate detection functions. The definition of the user's individual fitness level was only predefined by the manufacturer within a specific range, and could not make more precise distinctions based on individual differences. Therefore, how to provide an auxiliary power technology that conforms to an individual's fatigue level, and can adjust the individual fatigue prediction model in real time based on the user's current riding feedback information, thereby improving the accuracy of bicycle auxiliary power and achieving the effect of preventing fatigue, is the direction the inventor has been considering. [Summary of the Invention]

[0005] Today, the inventor, in view of the fact that the existing electric-assisted bicycles still have many shortcomings in actual use, has made improvements with a tireless spirit and with the help of his rich professional knowledge and many years of practical experience, and has created this invention.

[0006] The main objective of this invention is to provide a method for controlling auxiliary power through a fatigue prediction model. This method can collect data when a user is riding an electric-assisted bicycle, establish a personalized fatigue prediction model based on the riding data, and continuously update the fatigue prediction model as the riding data accumulates over a fixed period of time. When the exercise intensity is higher than the fatigue prediction threshold, the required auxiliary power is intervened, which accurately meets the user's individual physical needs and reduces fatigue, allowing the user to ride for a longer period of time.

[0007] To achieve the above-mentioned objectives, the present invention proposes a method for controlling auxiliary power through a fatigue prediction model, comprising the following steps: Step 1: A user begins riding; Step 2: Collect riding data of the user for a predetermined riding time; Step 3: Generate a fatigue prediction model based on the riding data; Step 4: Calculate a fatigue prediction threshold using the fatigue prediction model; Step 5: Measure whether the user's exercise intensity data exceeds the fatigue prediction threshold; Step 6: If the exercise intensity data exceeds the fatigue prediction threshold, analyze a deviation value based on the exercise intensity data and the fatigue prediction threshold; and Step 7: Calculate an auxiliary power value from the exercise intensity data and the deviation value.

[0008] In one embodiment of the present invention, the riding data system includes a plurality of heart rate values, a plurality of power values, a plurality of blood lactate values, a plurality of oxygen uptake values, or a plurality of ventilation values.

[0009] In one embodiment of the present invention, each power value corresponding to a plurality of heart rate values ​​has a maximum heart rate value and a minimum heart rate value, and an average heart rate value is obtained from the maximum heart rate value and the minimum heart rate value.

[0010] In one embodiment of the present invention, the fatigue prediction model is a slope model calculated from a plurality of maximum heart rate values ​​and a plurality of average heart rate values.

[0011] In one embodiment of the present invention, the fatigue prediction threshold is taken as 10 to 90% of the range between the maximum heart rate value and the average heart rate value.

[0012] In one embodiment of the present invention, the fatigue prediction model is adjusted in real time based on riding data.

[0013] In one embodiment of the present invention, the exercise intensity data are an exercise heart rate and an exercise power.

[0014] In one embodiment of the present invention, the deviation value is calculated to obtain a target heart rate and a target power, and then the auxiliary power value is calculated through the target heart rate, target power and exercise intensity data.

[0015] In one embodiment of the present invention, when the exercise intensity data in step six exceeds the fatigue prediction threshold by 1 to 10 seconds, an auxiliary power value is calculated to assist the user in riding.

[0016] In one embodiment of the present invention, a step eight is further included: after the auxiliary power value is applied for 10 to 15 seconds, the user's exercise intensity data is measured again to see if it exceeds the fatigue prediction threshold.

Implementation Method

[0018] In order to explain the technical features of the present invention in detail, the following preferred embodiment is described in conjunction with Figures 1 to 3.

[0019] Please refer to Figures 1 to 3. The present invention provides a method for controlling auxiliary power through a fatigue prediction model, the steps of which include: Step 1: A user starts riding; Step 2: Collect riding data of the user riding for a predetermined time. This riding data contains a plurality of heart rate values ​​1 and a plurality of power values ​​2. Each power value 2 has a maximum heart rate value 11 and a minimum heart rate value 12 for each of the plurality of heart rate values ​​1. An average heart rate value 13 is obtained from the maximum heart rate value 11 and the minimum heart rate value 12. The riding data for the predetermined time refers to the time after the user rides, when any of the plurality of heart rate values ​​1 and the plurality of power values ​​2 changes.

[0020] Step 3: Generate a fatigue prediction model 3 based on the riding data. The fatigue prediction model 3 is a slope model, which is calculated by statistically analyzing a plurality of maximum heart rate values ​​11 and a plurality of average heart rate values ​​13. The fatigue prediction model 3 will be continuously and dynamically adjusted based on the riding data. Step 4: Calculate a fatigue prediction threshold 4 through the fatigue prediction model 3. The fatigue prediction threshold 4 is taken as 10%-90% of the range between the maximum heart rate value 11 and the average heart rate value 13.

[0021] Step 5: Measure whether the exercise intensity data of one of the users exceeds the fatigue prediction threshold 4, wherein the exercise intensity data is an exercise heart rate 5 and an exercise power 6; Step 6: If the exercise intensity data exceeds the fatigue prediction threshold 4 for about 1 to 10 seconds, then analyze the exercise intensity data and the fatigue prediction threshold 4 to obtain a deviation value; and Step 7: Calculate an auxiliary power value 7 from the exercise intensity data and the deviation value to assist the user in riding, wherein the deviation value will calculate a target heart rate 8 and a target power 9, and then calculate the auxiliary power value 7 from the target heart rate 8, the target power 9 and the exercise intensity data;

[0022] Further includes step eight, after assisting the user's exercise intensity data for 10 to 15 seconds at the assist power value 7, measuring again whether the user's exercise intensity data still exceeds the fatigue prediction threshold 4. If it still exceeds, return to step six and analyze the deviation value again.

[0023] Furthermore, the following specific embodiments further demonstrate the scope of practical application of the present invention, but are not intended to limit the scope of the present invention in any way.

[0024] Please continue to refer to Figure 1. The present invention provides a method for controlling assisted power through a fatigue prediction model. First, the user rides an electric assisted bicycle. While the user is riding, riding data is collected. The collected riding data includes multiple heart rate values ​​1 and multiple power values ​​2. Refer to Figure 3. The X-axis represents power and the Y-axis represents heart rate. Each power value 2 corresponds to heart rate data consisting of a minimum heart rate value 12 to a maximum heart rate value 11. The average heart rate value 13 is taken between the minimum heart rate value 12 and the maximum heart rate value 11. Following this logic, different power values ​​2 will correspond to a series of heart rate data. Although the present invention uses heart rate value 1 and power value 2 as an example, it can also detect and refer to the user's blood lactate value, oxygen uptake value, and ventilation value as riding data.

[0025] The linear relationship between heart rate value 1 and power value 2 can be known from the user's riding data. Based on the accumulation of riding data, multiple maximum heart rate values ​​11 and multiple average heart rate values ​​13 will be collected. Therefore, a slope model can be further generated based on multiple maximum heart rate values ​​11 and multiple average heart rate values ​​13, which is the fatigue prediction model 3. Considering that the user will have a warm-up process when riding the electric-assisted bicycle, the electric-assisted bicycle will continuously collect riding data and continuously update heart rate value 1 and power value 2. This means that the minimum heart rate value 12, maximum heart rate value 11 and average heart rate value 13 corresponding to different power values ​​2 will be continuously updated. In this way, as the user's riding time accumulates, the fatigue prediction model 3 will be continuously corrected to make the fatigue prediction model 3 more consistent with the user's physical condition.

[0026] In this embodiment, the fatigue prediction model 3 uses 80% of the overall range from the maximum heart rate value 11 to the average heart rate value 13 as the fatigue prediction threshold 4. For example, when the power value 2 of the riding data detected by the electric-assisted bicycle is 60 W, the maximum heart rate value 11 is 120 bpm, the minimum heart rate value 12 is 90 bpm, and the average heart rate value 13 is 105 bpm. The fatigue prediction threshold 4 is calculated to be 117 bpm. Therefore, different power values ​​2 in the fatigue prediction model 3 will have corresponding fatigue prediction thresholds 4. In other preferred embodiments, the fatigue prediction threshold 4 can also be adjusted to 50%, 60%, 70%, or 90% as needed, or be in the range of 10% to 90%.

[0027] After riding for a period of time, the fatigue prediction model 3 and the fatigue prediction threshold 4 will gradually become more stable and accurate. The electric-assisted bicycle will also measure the user's exercise intensity data again. It will determine whether the exercise heart rate 5 and exercise power 6 exceed the fatigue prediction threshold 4. The exercise power 6 will correspond to a range of maximum heart rate value 11 and average heart rate value 13 in the fatigue prediction model 3. The electric-assisted bicycle will determine whether the exercise heart rate 5 is higher than the fatigue prediction threshold 4 in this range. If it is not higher than the fatigue prediction threshold 4 for about 3 seconds or is not higher than the fatigue prediction threshold 4, it will be determined that it does not exceed the fatigue prediction threshold 4. No assistance power will be given, and the measurement of exercise intensity data will continue to be maintained to see if it exceeds the fatigue prediction threshold 4. In other preferred embodiments, the time when the exercise heart rate 5 is higher than the fatigue prediction threshold 4 can also be adjusted to between 1 and 10 seconds as needed.

[0028] If the exercise heart rate 5 is continuously higher than the fatigue prediction threshold 4 for about 3 seconds, it is determined that the fatigue prediction threshold 4 has been exceeded. The deviation value of the exercise intensity data that is higher than the fatigue prediction threshold 4 is analyzed. Based on this deviation value, the target heart rate 8 and the target power 9 are obtained. The target heart rate 8 is usually taken as the average heart rate value 13 corresponding to the exercise power 6 in the fatigue prediction model 3. Then, the average heart rate value 13 is mapped to a fatigue prediction threshold 4 along the X-axis. The target power 9 is obtained based on this fatigue prediction threshold 4. In this way, the auxiliary power value 7 can be calculated from the difference between the target power 9 and the exercise power 6, so that the electric motor of the electric-assisted bicycle can intervene in time and output a suitable auxiliary power value 7, so that the user can achieve the target power 9 and the target heart rate 8, reducing the occurrence of excessive fatigue of the user.

[0029] Additionally, referring to Figure 2, after the electric-assisted bicycle provides the assistive power value 7 for approximately 10 seconds, the electric-assisted bicycle will re-measure whether the user's exercise intensity data exceeds the fatigue prediction threshold 4. If it still exceeds the threshold, it will again analyze the deviation value of the exercise intensity data above the fatigue prediction threshold 4 and continue to provide the assistive power value 7 to intervene in the user's riding condition. In other preferred embodiments, the duration for which the electric-assisted bicycle provides the assistive power value 7 can also be adjusted to between 10 and 15 seconds as needed.

[0030] As can be seen from the above description of the embodiments, compared with the prior art, the present invention has the following advantages:

[0031] 1. The method of controlling auxiliary power through fatigue prediction model of the present invention collects relevant riding data only after the user starts riding and updates the fatigue prediction model based on the accumulation of riding time. It is not a specific value or data table set in advance by the manufacturer. Therefore, the present invention can adjust the model more in response to changes in the user's physical condition.

[0032] 2. The method of controlling auxiliary power through fatigue prediction model of the present invention determines the output auxiliary power value based on the latest fatigue prediction model. When auxiliary power intervention is required, the auxiliary power value can be automatically controlled. Therefore, the present invention can output a suitable auxiliary power value more accurately.

[0033] In summary, the method of controlling auxiliary power through fatigue prediction model of the present invention can indeed achieve the expected use effect through the above-disclosed embodiments; however, the above-disclosed drawings and descriptions are only preferred embodiments of the present invention, and the methods and constituent elements disclosed in the above-disclosed embodiments are only illustrative examples and are not intended to limit the scope of the present invention. Substitution or variation of other equivalent elements should also be covered by the patent application scope of the present invention. [Simplified Explanation of the Diagram]

[0017] Figure 1 is a flowchart (I) of a preferred embodiment of the present invention; Figure 2 is a flowchart (II) of a preferred embodiment of the present invention; and Figure 3 is a schematic diagram of a fatigue prediction model of a preferred embodiment of the present invention.

Claims

1. A method for controlling assisted power through a fatigue prediction model, comprising the following steps: Step 1: A user begins riding an electric-assisted bicycle; Step 2: The electric-assisted bicycle collects riding data from the user for a predetermined riding time; Step 3: The electric-assisted bicycle generates a fatigue prediction model based on the riding data; Step 4: The electric-assisted bicycle calculates a fatigue prediction threshold using the fatigue prediction model; Step 5: The electric-assisted bicycle measures whether one of the user's exercise intensity data exceeds the fatigue prediction threshold; Step 6: If the exercise intensity data exceeds the fatigue prediction threshold, the electric-assisted bicycle analyzes a deviation value between the exercise intensity data and the fatigue prediction threshold; and Step 7: The electric-assisted bicycle calculates an assisted power value based on the exercise intensity data and a target power to assist the user in riding; wherein, The riding data system includes a plurality of heart rate values ​​and a plurality of power values; each of the plurality of heart rate values ​​corresponds to a maximum heart rate value and a minimum heart rate value, and an average heart rate value is obtained from the maximum heart rate value and the minimum heart rate value. The fatigue prediction model is a slope model calculated from a plurality of the maximum heart rate values ​​and a plurality of the average heart rate values; the exercise intensity data is an exercise heart rate and an exercise power; the deviation value is calculated by determining a target heart rate, then obtaining the target power through the target heart rate, and calculating the assist power value from the difference between the target power and the exercise power of the exercise intensity data, and in step seven, the assist power value is used to enable the user to achieve the target power and target heart rate.

2. The method for controlling assistive power through a fatigue prediction model as described in claim 1, wherein the fatigue prediction threshold is taken as 10-90% of the range between the maximum heart rate value and the average heart rate value.

3. The method for controlling auxiliary power through a fatigue prediction model as described in claim 1, wherein the fatigue prediction model is adjusted in real time based on the riding data.

4. The method for controlling auxiliary power through a fatigue prediction model as described in claim 1, wherein when the exercise intensity data in step six exceeds the fatigue prediction threshold for 1 to 10 seconds, the auxiliary power value is calculated to assist the user in riding.

5. The method for controlling assistive power through a fatigue prediction model as described in claim 1, further comprising step eight, after the assistive power value is applied for 10 to 15 seconds, measuring again whether the user's exercise intensity data exceeds the fatigue prediction threshold.

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