An adaptive system based on heart rate detection and its application method in electrically assisted bicycles

Abstract

This invention discloses an adaptive system based on heart rate detection and its application method in electric-assist bicycles, belonging to the field of electric-assist bicycle technology. It includes acquiring real-time data from the rider via a fitness tracker control component and obtaining user-preset settings; acquiring the rider's pedaling torque via a mid-mounted motor control component; comparing the real-time data with the corresponding set values ​​via the mid-mounted motor control component; calculating factor parameters based on the comparison results; and calculating the final motor output torque based on pedaling torque, speed, road conditions, slope, electric-assist bicycle weight, and factor parameters, and controlling the motor to output corresponding assistance. This invention focuses on rider health, intelligently adjusting assistance output through heart rate factors to prevent overexertion, utilizing vehicle sensors for high-precision calculation of mileage and calories, and constructing wireless collaborative control between the fitness tracker and the motor to achieve safe and personalized riding without significantly increasing costs.

Description

Technical Field

[0001] This invention relates to the field of electric-assisted bicycle technology, specifically to an adaptive system based on heart rate detection and its application method in electric-assisted bicycles. Background Technology

[0002] Traditional e-bikes output different levels of following torque based on the rider's pedaling torque and the different assist levels. For example, they follow the formula Tmot = K × Tp, where Tmot is the following torque output by the motor, K is a fixed coefficient corresponding to different assist levels, and Tp is the rider's pedaling torque. As can be seen from the formula, it's difficult to consider factors such as riders of different weights and different riding terrains. It cannot provide personalized, adaptive adjustments based on the rider's real-time physiological state, potentially leading to unsafe exercise intensity or a poor riding experience.

[0003] In addition, traditional single-unit fitness trackers generally estimate calorie consumption and cycling distance using accelerometers and parameters such as height, weight, and stride length, but this calculation method is difficult to be accurate.

[0004] To address the aforementioned issues, there is an urgent need for an adaptive system based on heart rate detection and its application in electric-assisted bicycles, which can solve the problems existing in traditional methods. Summary of the Invention

[0005] The purpose of this invention is to provide an adaptive system based on heart rate detection and its application method in electric-assisted bicycles. With rider health as the core, the system intelligently adjusts the power assist output through heart rate factors to prevent overexertion; it utilizes vehicle sensors to calculate mileage and calories with high precision; and it constructs a wireless collaborative control system between the wristband and the motor to achieve safe and personalized riding without significantly increasing costs.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: An adaptive system based on heart rate detection includes: a fitness tracker control component and a mid-mounted motor control component, wherein the fitness tracker is communicatively connected to the mid-mounted motor control component; The fitness tracker control component includes: a fitness tracker microprocessor, a heart rate and blood oxygen acquisition module, a body temperature acquisition module, a human-computer interaction module, a barometer module, and a wireless transmission module. The fitness tracker microprocessor connects to a mobile app and the fitness tracker microprocessor, heart rate and blood oxygen acquisition module, body temperature acquisition module, human-computer interaction module, and barometer module. The fitness tracker microprocessor connects to the mid-mounted motor control component via the wireless transmission module. The heart rate and blood oxygen acquisition module is used to collect heart rate and blood oxygen data in real time. The body temperature acquisition module is used to collect body temperature in real time. The barometer module is used to collect air pressure in real time. The human-computer interaction module is used to display and input relevant values. The mid-drive motor control component includes a mid-drive motor microcontroller, a pedaling information acquisition module, a vehicle speed sensor module, a voltage sensor module, and a position sensor module. The mid-drive motor microcontroller is connected to the pedaling information acquisition module, the vehicle speed sensor module, the voltage sensor module, and the position sensor module. The mid-drive motor microcontroller is connected to the fitness tracker microprocessor via a wireless transmission module. The pedaling information acquisition module is used to collect the cyclist's pedaling information, the vehicle speed sensor module is used to collect vehicle speed information, the voltage sensor is used to collect the voltage information of the mid-drive motor, and the position sensor module is used to collect position information.

[0007] Furthermore, the human-computer interaction module includes an LCD display module, a button setting module, and a buzzer, which are connected to the fitness tracker microprocessor.

[0008] Furthermore, the fitness tracker control component also includes a clock module.

[0009] Furthermore, the pedaling information acquisition module includes a torque sensor module and a pedal frequency sensor module, which are connected to the mid-mounted motor microcontroller and are used to acquire the pedaling torque and pedaling frequency of the cyclist.

[0010] This invention also provides a method for applying a heart rate detection-based adaptive system to an electric-assisted bicycle, which is applied to the aforementioned heart rate detection-based adaptive system, including: Step 1: Obtain the cyclist's real-time data through the fitness tracker control components and obtain the user's preset settings; Step 2: Collect the rider's pedaling torque through the mid-mounted motor control component; Step 3: The mid-drive motor control component of the adaptive system compares the rider's real-time collected values ​​with the corresponding set values. If the real-time collected value is less than the corresponding first set value, a conventional fuzzy algorithm is triggered, and the heart rate factor parameter is set to 1. If the real-time collected value is greater than or equal to the corresponding first set value and less than or equal to the corresponding second set value, a first fuzzy algorithm is executed. A fuzzy set is constructed based on the error between the first set value and the real-time collected value, and it is divided into multiple classes. A fuzzy rule base is constructed based on the interrelationships between fuzzy variables. Fuzzy inference is performed based on the input of the fuzzy set and the fuzzy rule base to calculate the output of the fuzzy value. Then, using a defuzzification method, an accurate output is calculated based on the output of the fuzzy value, which is used as the first heart rate factor parameter. If the real-time collected value is greater than the corresponding second set value, a second fuzzy algorithm is executed. The execution process of the second fuzzy algorithm is the same as that of the first fuzzy algorithm. Step 4: Based on pedaling torque, vehicle speed, road gradient, weight of the electric bicycle, and other factors, calculate the final motor output torque and control the motor to output the corresponding assistance.

[0011] Furthermore, in step 3, the real-time collected values ​​include the cyclist's heart rate and blood oxygen levels, and the set values ​​include heart rate set values ​​and blood oxygen set values.

[0012] Furthermore, in step 3, the mid-drive motor control component of the adaptive system compares the rider's real-time collected values ​​with the corresponding set values, specifically: The rider's heart rate is compared with a set heart rate value using a mid-mounted motor control component. The set heart rate value includes a first heart rate set value and a second heart rate set value. If the cyclist's heart rate is less than the corresponding first heart rate setting, the conventional fuzzy algorithm is triggered, and the heart rate factor parameter is set to 1. If the cyclist's heart rate is greater than or equal to the corresponding first heart rate setting and less than or equal to the corresponding second heart rate setting, the first fuzzy algorithm is executed. A fuzzy set is constructed based on the error between the first heart rate setting and the cyclist's heart rate, and it is divided into multiple classes. A fuzzy rule base is constructed based on the interrelationships between fuzzy variables. Fuzzy inference is performed based on the input of the fuzzy set and the fuzzy rule base to calculate the output of the fuzzy value. Then, using a defuzzification method, the accurate output is calculated based on the output of the fuzzy value and used as the first heart rate factor parameter. If the cyclist's heart rate is greater than the corresponding second heart rate setting, the second fuzzy algorithm is executed. The execution process of the second fuzzy algorithm is the same as that of the first fuzzy algorithm. The calculation process for the factor parameters of a cyclist's blood oxygen level is the same as that for the factor parameters of a cyclist's heart rate.

[0013] Furthermore, in step 4, based on the pedaling torque, vehicle speed, road gradient, weight of the electric bicycle, and other factor parameters, the final motor output torque is calculated, and the motor is controlled to output corresponding assistance, specifically as follows: The initial torque is calculated using a pre-assist torque adjuster based on pedaling torque, vehicle speed, road gradient, and the weight of the electric-assist bicycle. The final output torque is calculated by the assist torque regulator based on the initial torque and heart rate factor or blood oxygen factor parameters.

[0014] In summary, the present invention has at least one of the following beneficial technical effects: 1. With the rider's health as the starting point, it realizes closed-loop intelligent control of assist torque: using the heart rate factor as the core input for following torque calculation, when the rider's heart rate exceeds the set safety value, the system can automatically adjust the assist output, effectively control the exercise intensity, and ensure riding safety.

[0015] 2. Significantly improves the accuracy of exercise data calculation: By cleverly utilizing the original torque sensor, cadence sensor, and speed sensor of the mid-drive motor system, the calculation of cycling distance and calories burned is greatly improved compared to traditional wristbands that rely solely on accelerometers, without increasing the system cost.

[0016] 3. An efficient body-vehicle collaborative interaction architecture was constructed: Through wireless communication between the fitness tracker and the central motor control component, real-time interaction of physiological data such as heart rate and vehicle control commands was realized, laying the foundation for intelligent power assist control.

[0017] 4. Provides flexible parameter settings and safety warning mechanisms: Users can set and store the heart rate protection limit locally through mobile APP, fitness tracker or mid-drive motor. When the heart rate continues to exceed the limit and cannot be adjusted by algorithm, the system can trigger an audible and visual alarm to remind the rider. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the system structure of the present invention; Figure 2 This is a schematic diagram of the method flow of the present invention; Figure 3 A schematic diagram of the heart rate factor parameter calculation process; Figure 4 This is a schematic diagram of the final output torque calculation process; Figure 5 This is a schematic diagram of the calorie calculation process. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0020] like Figure 1 As shown, this invention provides an adaptive system based on heart rate detection, including: a fitness tracker control component and a mid-mounted motor control component, wherein the fitness tracker is communicatively connected to the mid-mounted motor control component; the fitness tracker control component and the mid-mounted motor control component will be described in detail below: I. Fitness Tracker Control Components The fitness tracker control component is mounted on the fitness tracker body and specifically includes: a fitness tracker microprocessor, a heart rate and blood oxygen acquisition module, a body temperature acquisition module, a human-computer interaction module, a barometer module, and a wireless transmission module. The fitness tracker microprocessor connects to a mobile app and the microprocessor, heart rate and blood oxygen acquisition module, body temperature acquisition module, human-computer interaction module, and barometer module. The fitness tracker microprocessor connects to the central motor control component via the wireless transmission module. The heart rate and blood oxygen acquisition module collects heart rate and blood oxygen data in real time. The body temperature acquisition module collects body temperature data in real time. The barometer module collects air pressure data in real time. The human-computer interaction module displays and allows input of relevant values. The human-computer interaction module includes an LCD display module, a button setting module, and a buzzer, all of which are connected to the fitness tracker microprocessor. The fitness tracker control component also includes a clock module.

[0021] II. Mid-Motor Control Components The mid-drive motor control component is mounted on and connected to the mid-drive motor. Specifically, it includes a mid-drive motor microcontroller, a pedaling information acquisition module, a vehicle speed sensor module, a voltage sensor module, and a position sensor module. The mid-drive motor microcontroller is connected to these modules and is wirelessly connected to the fitness tracker microprocessor. The pedaling information acquisition module collects the cyclist's pedaling information, the vehicle speed sensor module collects vehicle speed information, the voltage sensor collects the voltage information of the mid-drive motor, and the position sensor module collects position information. The pedaling information acquisition module includes a torque sensor module and a cadence sensor module, which are connected to the mid-drive motor microcontroller and are used to collect the cyclist's pedaling torque and pedaling frequency.

[0022] Depending on specific needs, the fitness tracker control component and the mid-mounted motor control component can be connected to other modules according to specific functional requirements.

[0023] In addition, it should be noted that the wireless transmission module is not limited to Bluetooth, WIFI, etc., and can be configured according to specific needs.

[0024] like Figure 2 As shown, the present invention also provides a method for applying a heart rate detection-based adaptive system to an electric-assisted bicycle, which is applied to the aforementioned heart rate detection-based adaptive system, including: Step 1: Obtain the cyclist's real-time data through the fitness tracker control components and obtain the user's preset settings; Step 2: Collect the rider's pedaling torque through the mid-mounted motor control component; Step 3: The real-time data collected by the rider is compared with the corresponding set value through the mid-mounted motor control component, and the factor parameters are calculated based on the comparison results; Step 4: Based on pedaling torque, vehicle speed, road gradient, weight of the electric bicycle, and other factors, calculate the final motor output torque and control the motor to output the corresponding assistance.

[0025] Steps 1 and 2 specifically involve: Data transmission from the fitness tracker control component to the mid-mount motor control component is defined as downlink data transmission, and data transmission from the mid-mount motor control component to the fitness tracker control component is defined as uplink data transmission. The data downlink is mainly provided through the button setting module and various acquisition modules. These include real-time heart rate and blood oxygen values ​​of the cyclist collected by the heart rate and blood oxygen acquisition module, as well as cycling altitude, electric bicycle assist gear, cycling mileage reminder, cycling calorie reminder, and cycling time time reminder collected by other modules. In addition, the button setting module is used to set the first heart rate setting value, the second heart rate setting value, the first blood oxygen setting value, and the second blood oxygen setting value. Data uploads mainly include actual cycling mileage, actual pedaling torque, and actual calories burned.

[0026] In step 3, the real-time data collected by the rider is compared with the corresponding set values ​​using the mid-mounted motor control component. Based on the comparison results, factor parameters are calculated, specifically: The real-time collected values ​​include the cyclist's heart rate and blood oxygen levels, and the set values ​​include heart rate set values ​​and blood oxygen set values. like Figure 3 As shown, this invention is described using heart rate as an example: The rider's heart rate is compared with a heart rate setting value by a mid-mounted motor control component, the heart rate setting value including a first heart rate setting value and a second heart rate setting value; If the cyclist's heart rate is lower than the first heart rate setting, the normal algorithm is triggered, and the heart rate factor parameter is set to 1. If the cyclist's heart rate is greater than or equal to the first heart rate setting value and less than or equal to the second heart rate setting value, the first fuzzy algorithm is triggered to obtain the first heart rate factor parameter; if the cyclist's heart rate is greater than the second heart rate setting value, the second fuzzy algorithm is triggered to obtain the second heart rate factor parameter. The first and second fuzzy algorithms are implemented on the same principle, with the main difference being the definition of the fuzzy set and the formulation of the fuzzy rules. Therefore, this invention only introduces the first fuzzy algorithm.

[0027] The fuzzy set is defined based on the error between the initial heart rate setting and the cyclist's actual heart rate. If the initial heart rate setting is greater than the cyclist's actual heart rate, the heart rate factor is 1, and no fuzzy rules or defuzzification calculations are performed.

[0028] If the initial heart rate setting is less than the cyclist's actual heart rate, it will be divided into multiple categories based on the magnitude of the error, such as five categories: large, relatively large, medium, relatively small, and small.

[0029] A fuzzy rule base typically represents the relationships between fuzzy variables, such as the magnitude of heart rate deviation. Fuzzy inference calculates fuzzy values ​​based on the fuzzy set input and fuzzy rules. Then, based on the fuzzy output derived from fuzzy inference, defuzzification methods are used to calculate the accurate output, i.e., the magnitude of the heart rate parameter.

[0030] The calculation process for the factor parameters of a cyclist's blood oxygen level is the same as that for the factor parameters of a cyclist's heart rate, and will not be elaborated on here.

[0031] like Figure 4 As shown, in step 4, based on pedaling torque, vehicle speed, road gradient, weight of the electric bicycle, and other factor parameters, the final motor output torque is calculated, and the motor is controlled to output corresponding assistance. Specifically: The initial torque is calculated using a pre-assist torque adjuster based on pedaling torque, vehicle speed, road gradient, and the weight of the electric-assist bicycle. The final output torque is calculated by the assist torque regulator based on the initial torque and heart rate factor or blood oxygen factor parameters.

[0032] This invention also includes a calculation process for calories and cycling distance, specifically as follows: Figure 5 As shown, traditional single-unit wristbands typically calculate calories using accelerometers, height, weight, and stride length. This method is difficult to calculate accurately. However, the calorie calculation method and cycling distance used in this invention are more scientific. It cleverly utilizes the torque sensor, cadence sensor, and speed sensor already present in the mid-drive motor system, greatly improving calculation accuracy without increasing the overall system cost.

[0033] The torque sensor cleverly utilizes the characteristics of a one-way bearing, mounting it on the axle of the mid-drive motor system. Therefore, the torque sensor only outputs torque when the rider is pedaling forward; there is no torque output when pedaling backward. The torque sensor can detect both the rider's pedaling torque and the frequency of pedaling the axle in real time. Cycling distance is calculated using the speed sensor of the mid-drive motor system, offering significantly higher accuracy and real-time performance than a standalone fitness tracker. The mid-drive motor control component wirelessly transmits the calculated calories and mileage parameters to the fitness tracker control component, which allows users to switch between different interfaces to view exercise time, calories, and distance. For more detailed data, cyclists can view it on a mobile app.

[0034] Embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0035] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0036] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0037] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0038] Contents not described in detail in this specification are prior art known to those skilled in the art. It is hereby indicated that the above description is intended to help those skilled in the art understand this invention, but does not limit the scope of protection of this invention. Any equivalent substitutions, modifications, improvements, or simplifications of the above descriptions that do not depart from the essential content of this invention fall within the scope of protection of this invention.

Claims

1. A method for applying an adaptive system based on heart rate detection in an electric-assisted bicycle, characterized in that, include: Step 1: Obtain the cyclist's real-time data through the adaptive system's fitness tracker control component, and also obtain the user's preset settings; Step 2: Collect the rider's pedaling torque through the mid-mounted motor control component of the adaptive system; Step 3: The mid-drive motor control component of the adaptive system compares the rider's real-time collected values ​​with the corresponding set values. If the real-time collected value is less than the corresponding first set value, a conventional fuzzy algorithm is triggered, and the heart rate factor parameter is set to 1. If the real-time collected value is greater than or equal to the corresponding first set value and less than or equal to the corresponding second set value, a first fuzzy algorithm is executed. A fuzzy set is constructed based on the error between the first set value and the real-time collected value, and it is divided into multiple classes. A fuzzy rule base is constructed based on the interrelationships between fuzzy variables. Fuzzy inference is performed based on the input of the fuzzy set and the fuzzy rule base to calculate the output of the fuzzy value. Then, using a defuzzification method, an accurate output is calculated based on the output of the fuzzy value, which is used as the first heart rate factor parameter. If the real-time collected value is greater than the corresponding second set value, a second fuzzy algorithm is executed. The execution process of the second fuzzy algorithm is the same as that of the first fuzzy algorithm. Step 4: Based on pedaling torque, vehicle speed, road gradient, weight of the electric bicycle, and other factors, calculate the final motor output torque and control the motor to output the corresponding assistance.

2. The method for applying an adaptive system based on heart rate detection in an electric-assisted bicycle according to claim 1, characterized in that, In step 3, the real-time collected values ​​include the cyclist's heart rate and blood oxygen levels, and the set values ​​include heart rate set values ​​and blood oxygen set values.

3. The method for applying an adaptive system based on heart rate detection in an electric-assisted bicycle according to claim 2, characterized in that, In step 3, the mid-drive motor control component of the adaptive system compares the rider's real-time collected values ​​with the corresponding set values, specifically: The rider's heart rate is compared with a set heart rate value using a mid-mounted motor control component. The set heart rate value includes a first heart rate set value and a second heart rate set value. If the cyclist's heart rate is less than the corresponding first heart rate setting, the regular fuzzy algorithm is triggered, and the heart rate factor parameter is set to 1. If the cyclist's heart rate is greater than or equal to the corresponding first heart rate setting and less than or equal to the corresponding second heart rate setting, the first fuzzy algorithm is executed. A fuzzy set is constructed based on the error between the first heart rate setting and the cyclist's heart rate, and it is divided into multiple classes. A fuzzy rule base is constructed based on the interrelationships between the fuzzy variables. The fuzzy value is calculated by fuzzy inference based on the input of the fuzzy set and the fuzzy rule base. Then, the accurate output is calculated based on the fuzzy value using the defuzzification method. This output is used as the first heart rate factor parameter. If the cyclist's heart rate value is greater than the corresponding second heart rate setting value, the second fuzzy algorithm is executed. The execution process of the second fuzzy algorithm is the same as that of the first fuzzy algorithm. The calculation process for the factor parameters of a cyclist's blood oxygen level is the same as that for the factor parameters of a cyclist's heart rate.

4. The method for applying an adaptive system based on heart rate detection in an electric-assisted bicycle according to claim 3, characterized in that, In step 4, based on pedaling torque, vehicle speed, road gradient, weight of the e-bike, and other parameters, the final motor output torque is calculated, and the motor is controlled to output corresponding assistance. Specifically: The initial torque is calculated using a pre-assist torque adjuster based on pedaling torque, vehicle speed, road gradient, and the weight of the electric-assist bicycle. The final output torque is calculated by the assist torque regulator based on the initial torque and heart rate factor or blood oxygen factor parameters.

5. An adaptive system based on heart rate detection, used to implement the application method of the adaptive system based on heart rate detection according to any one of claims 1-4 in an electric-assisted bicycle, characterized in that, include: A fitness tracker control component and a mid-mounted motor control component, wherein the fitness tracker is communicatively connected to the mid-mounted motor control component; The fitness tracker control component includes: a fitness tracker microprocessor, a heart rate and blood oxygen acquisition module, a body temperature acquisition module, a human-computer interaction module, a barometer module, and a wireless transmission module. The fitness tracker microprocessor connects to a mobile app and the fitness tracker microprocessor, heart rate and blood oxygen acquisition module, body temperature acquisition module, human-computer interaction module, and barometer module. The fitness tracker microprocessor connects to the mid-mounted motor control component via the wireless transmission module. The heart rate and blood oxygen acquisition module is used to collect heart rate and blood oxygen data in real time. The body temperature acquisition module is used to collect body temperature in real time. The barometer module is used to collect air pressure in real time. The human-computer interaction module is used to display and input relevant values. The mid-drive motor control component includes a mid-drive motor microcontroller, a pedaling information acquisition module, a vehicle speed sensor module, a voltage sensor module, and a position sensor module. The mid-drive motor microcontroller is connected to the pedaling information acquisition module, the vehicle speed sensor module, the voltage sensor module, and the position sensor module. The mid-drive motor microcontroller is connected to the fitness tracker microprocessor via a wireless transmission module. The pedaling information acquisition module is used to collect the cyclist's pedaling information, the vehicle speed sensor module is used to collect vehicle speed information, the voltage sensor is used to collect the voltage information of the mid-drive motor, and the position sensor module is used to collect position information.

6. An adaptive system based on heart rate detection according to claim 5, characterized in that, The human-computer interaction module includes an LCD display module, a button setting module, and a buzzer, which are connected to the fitness tracker microprocessor.

7. An adaptive system based on heart rate detection according to claim 5, characterized in that, The fitness tracker control components also include a clock module.

8. An adaptive system based on heart rate detection according to claim 5, characterized in that, The pedaling information acquisition module includes a torque sensor module and a pedal frequency sensor module, which are connected to the mid-mounted motor microcontroller and are used to collect the pedaling torque and pedaling frequency of the cyclist.

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