Human-powered vehicle information processing device, human-powered vehicle information processing method, human-powered vehicle information processing system, and computer program

Abstract

To provide a human power drive vehicle information processing device that can optimize a comfort degree of a human power drive vehicle for respective riders and allows riders to confirm the comfort degree, and to provide a human power drive vehicle information processing method, a human power drive vehicle information processing system, and a computer program.SOLUTION: A human power drive vehicle information processing device includes: an acquisition unit for acquiring state data of a human power drive vehicle in traveling; a learning unit for learning a comfort degree of traveling of the human power drive vehicle corresponding to the state data on the basis of motions of a rider in traveling and calculating an index value indicating the comfort degree corresponding to newly acquired state data from the learned result; and an output unit for outputting the index value.SELECTED DRAWING: Figure 10

Description

Technical Field

[0001] The present invention relates to a human-powered vehicle information processing apparatus, a human-powered vehicle information processing method, a human-powered vehicle information processing system, and a computer program.

Background Art

[0002] A system for automatically controlling devices mounted on a human-powered vehicle has been put into practical use (such as Patent Document 1). Automatic control in a human-powered vehicle is carried out based on the cadence, torque, etc. at the crank so that the rider can travel more comfortably.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The criteria for feeling comfortable during the running of a human-powered vehicle vary individually depending on the rider. For example, even if it is generally said that a cadence of around 60 rpm is comfortable, there are riders who feel comfortable at 50 rpm and riders who feel uncomfortable even at 61 rpm.

[0005] An object of the present invention is to provide a human-powered vehicle information processing apparatus, a human-powered vehicle information processing method, a human-powered vehicle information processing system, and a computer program that optimize the comfort level of a human-powered vehicle for each individual rider and that can be confirmed by the rider.

Means for Solving the Problems

[0006] A human-powered vehicle information processing device according to the first aspect of the present invention includes: an acquisition unit that acquires state data of a human-powered vehicle in motion; a learning unit that learns the degree of comfort of the human-powered vehicle's ride corresponding to the state data based on the rider's actions while riding, and calculates an index value indicating the degree of comfort corresponding to the newly acquired state data from the learning results; and an output unit that outputs the index value.

[0007] According to the human-powered vehicle information processing device described in the first aspect above, it is possible to output comfort level index values ​​optimized for each individual rider.

[0008] A human-powered vehicle information processing device according to the second aspect of the present invention displays an image of color, density, or brightness corresponding to the index value on a display unit, in the human-powered vehicle information processing device according to the first aspect described above.

[0009] According to the human-powered vehicle information processing device described in the second aspect above, the comfort level index value optimized for each rider can be displayed in a manner that the rider can intuitively understand.

[0010] A human-powered vehicle information processing device according to the third aspect of the present invention is the human-powered vehicle information processing device according to the second aspect, wherein the display unit is a display provided on the handlebars of the human-powered vehicle.

[0011] According to the human-powered vehicle information processing device described in the third aspect above, a comfort index value optimized for each individual rider can be displayed in a position where the rider can check it while riding.

[0012] A human-powered vehicle information processing device according to the fourth aspect of the present invention is the human-powered vehicle information processing device according to the second aspect, wherein the display unit is an information terminal device for the rider of the human-powered vehicle.

[0013] According to the human-powered vehicle information processing device described in the fourth aspect above, riders can check the comfort level index value, which is optimized for each individual rider, on the information terminal device owned by the rider.

[0014] A human-powered vehicle information processing device according to the fifth aspect of the present invention, in any one of the human-powered vehicle information processing devices according to the first to fourth aspects, the learning unit stores the correspondence between the state data and the index value, and updates the index value according to the operation of the rider.

[0015] According to the human-powered vehicle information processing device described in the fifth aspect above, the method for calculating the comfort index value is optimized for each rider based on the correspondence between the status data of the human-powered vehicle in motion and the rider's actions while riding.

[0016] A human-powered vehicle information processing device according to the sixth aspect of the present invention is a human-powered vehicle information processing device according to the fifth aspect, wherein the correspondence is a map of the index values ​​to the cadence and torque values ​​in the drive mechanism of the human-powered vehicle included in the state data.

[0017] According to the human-powered vehicle information processing device described in the sixth aspect above, a comfort index value optimized for each rider can be learned through a map of cadence and torque and the index value.

[0018] A human-powered vehicle information processing device according to the seventh aspect of the present invention displays the history of the state data during driving on the map on the display unit, in the human-powered vehicle information processing device according to the sixth aspect described above.

[0019] According to the human-powered vehicle information processing device described in the seventh aspect above, the rider can check the history of changes in the comfort index value, and the rider can feel that learning is progressing.

[0020] A human-powered vehicle information processing device according to the eighth aspect of the present invention is a human-powered vehicle information processing device according to any one of the first to fourth aspects, wherein the learning unit is a learning model that has been trained to output the index value when state data is input, and performs learning while driving.

[0021] According to the human-powered vehicle information processing device described in the eighth aspect above, each rider can learn comfort index values ​​using multiple variables, including cadence and torque.

[0022] According to the ninth aspect of the present invention, the human - powered vehicle information processing device, in any one of the human - powered vehicle information processing devices according to the first to eighth aspects, when an operation is performed on the device mounted on the human - powered vehicle from the rider via the operation device for the state data, the learning unit learns assuming that the comfort level has decreased.

[0023] According to the human - powered vehicle information processing device of the ninth aspect, the comfort level of the human - powered vehicle during travel can be learned based on whether there is an operation on the device via the individual operation devices of the rider. When there is an operation on the device, it is highly likely that the rider is trying to drive the human - powered vehicle more comfortably or more stably. When there is an operation on the device, the comfort level should be lower after the operation than before, so this can be used as a basis for learning.

[0024] According to the tenth aspect of the present invention, the human - powered vehicle information processing device, in the human - powered vehicle information processing device of the ninth aspect, the operation device is a shift operation device.

[0025] According to the human - powered vehicle information processing device of the tenth aspect, the comfort level of the human - powered vehicle during travel can be learned based on whether there is an operation on the individual shift operation devices of the rider.

[0026] According to the eleventh aspect of the present invention, the human - powered vehicle information processing device, in the human - powered vehicle information processing device of the ninth aspect, the operation device is an assist operation device.

[0027] According to the human - powered vehicle information processing device of the eleventh aspect, the comfort level of the human - powered vehicle during travel can be learned based on whether there is an operation on the individual assist operation devices of the rider.

[0028] According to the twelfth aspect of the present invention, the human - powered vehicle information processing device, in any one of the human - powered vehicle information processing devices according to the first to eighth aspects, the learning unit calculates and learns an index value indicating comfort level based on the biological information of the rider for the state data.

[0029] According to the human-powered vehicle information processing device described in the 12th aspect above, the comfort level of the human-powered vehicle while it is in motion can be learned from the biometric information of each rider.

[0030] A human-powered vehicle information processing method according to the 13th aspect of the present invention involves a computer mounted on a human-powered vehicle acquiring state data of the human-powered vehicle while it is in motion, learning the degree of comfort of the human-powered vehicle's ride corresponding to the state data based on the rider's actions while riding, calculating an index value indicating the degree of comfort corresponding to the newly acquired state data from the learning results, and outputting the index value.

[0031] According to the human-powered vehicle information processing method described in the 13th aspect above, it is possible to output an optimized comfort index value for each rider.

[0032] A human-powered vehicle information processing system according to the fourteenth aspect of the present invention includes a display device and a computer mounted on a human-powered vehicle, the computer having an acquisition unit that acquires state data of a human-powered vehicle in motion, a learning unit that learns the degree of comfort of the human-powered vehicle's ride corresponding to the state data based on the rider's actions while riding, and calculates an index value indicating the degree of comfort corresponding to the newly acquired state data from the learning results, and an output unit that outputs the index value.

[0033] According to the human-powered vehicle information processing system described in the 14th aspect above, it is possible to output comfort level index values ​​optimized for each individual rider.

[0034] A computer program according to the 15th aspect of the present invention causes a computer mounted on a human-powered vehicle to perform the following processes: acquire state data of the human-powered vehicle while it is in motion; learn the degree of comfort of the human-powered vehicle's ride corresponding to the state data based on the rider's actions while riding; calculate an index value indicating the degree of comfort corresponding to the newly acquired state data from the learning results; and output the index value.

[0035] According to the computer program described in Aspect 15 above, it is possible to output a comfort index value optimized for each individual rider. [Effects of the Invention]

[0036] According to this disclosure, it becomes possible to optimize the comfort index value, which represents the level of comfort perceived by a rider in a human-powered vehicle, for each individual rider based on the vehicle's condition data. Furthermore, the rider can monitor the process by which the comfort index value, which they perceive as comfortable, adapts to their individual needs. [Brief explanation of the drawing]

[0037] [Figure 1] This is a side view of a human-powered vehicle to which the control device in the first embodiment is applied. [Figure 2] This is a block diagram illustrating the configuration of the control device. [Figure 3] This flowchart shows an example of the procedure for learning comfort levels using a control device. [Figure 4] This flowchart shows an example of the procedure for outputting comfort level data using a control device. [Figure 5] An example of the display on the output unit in the first embodiment is shown. [Figure 6] An example of a map of index values ​​is shown. [Figure 7] This flowchart shows an example of a comfort level learning process procedure by the control device of the second embodiment. [Figure 8] This diagram shows the update of indicator values ​​on the map. [Figure 9] This figure shows an example of the map's learning results. [Figure 10] Here is another example of the map's learning results. [Figure 11] This flowchart shows an example of the output processing procedure for comfort level by the control device of the second embodiment. [Figure 12] An example of the display on the output unit in the second embodiment is shown. [Figure 13] This is a block diagram showing the configuration of the information terminal device 7 in the third embodiment. [Figure 14]This flowchart shows an example of a comfort level learning process procedure in a control system for a human-powered vehicle according to the third embodiment. [Figure 15] This flowchart shows an example of the display procedure in the second embodiment. [Figure 16] This shows an example of how indicator values ​​are displayed on the display unit of an information terminal device. [Figure 17] This shows the history of status data displayed on the display unit of the information terminal device. [Figure 18] Here is another example of the history of status data displayed on the display unit of an information terminal device. [Figure 19] This is a block diagram illustrating the configuration of the control device in the fourth embodiment. [Figure 20] This is an overview diagram of the learning model. [Figure 21] This is a flowchart showing an example of the learning process procedure. [Figure 22] This flowchart shows an example of the output processing procedure for comfort level in the fourth embodiment. [Figure 23] This is a block diagram showing the configuration of the control device in a modified example of the fourth embodiment. [Figure 24] This is a block diagram showing the configuration of an information terminal device. [Modes for carrying out the invention]

[0038] The following descriptions of each embodiment are illustrative of possible forms of the human-powered vehicle information processing device according to the present invention and are not intended to limit its form. The human-powered vehicle information processing device according to the present invention may take forms different from each embodiment, such as variations of each embodiment and forms that combine at least two mutually non-contradictory variations.

[0039] In the following descriptions of each embodiment, terms indicating directions such as front, rear, forward, backward, left, right, side, up, and down are used with reference to the direction in which the rider is seated on the saddle of the human-powered vehicle.

[0040] (First Embodiment) Figure 1 is a side view of a human-powered vehicle 1 to which the control device 100 in the first embodiment is applied. The human-powered vehicle 1 is a vehicle that uses human power at least partially for propulsion. Vehicles that use only an internal combustion engine or an electric motor as their power source are excluded from the human-powered vehicle 1 of this embodiment. The human-powered vehicle 1 is a bicycle, including, for example, a mountain bike, road bike, cross bike, city bike, electric assist bike (e-bike), etc.

[0041] The human-powered vehicle 1 comprises a vehicle body 11, handlebars 12, front wheels 13, rear wheels 14, and a saddle 15. The human-powered vehicle 1 also comprises a drive mechanism 20, devices 30 (31-32), an operating device 33, a battery 40, and sensors 50 (51-56).

[0042] The control unit 110 of the control device 100 controls devices including the transmission 31 and assist device 32 that are mounted on the human-powered vehicle 1. In one example, the control device 100 is mounted on the battery 40, cycle computer, drive unit, etc., of the human-powered vehicle 1.

[0043] The control device 100 is connected to the device 30, the operating device 33, and the battery 40. The connection configuration and details of the control device 100 will be described later.

[0044] The vehicle body 11 comprises a frame 11A and a front fork 11B. The front wheel 13 is rotatably supported by the front fork 11B. The rear wheel 14 is rotatably supported by the frame 11A. The handlebars 12 are supported by the frame 11A to allow the direction of travel of the front wheel 13 to be changed.

[0045] The drive mechanism 20 transmits human power to the rear wheel 14. The drive mechanism 20 includes a crank 21, a first sprocket assembly 22, a second sprocket assembly 23, a chain 24, and a pair of pedals 25.

[0046] The crank 21 includes a crank axle 21A, a right crank 21B, and a left crank 21C. The crank axle 21A is rotatably supported on the frame 11A. The right crank 21B and the left crank 21C are each connected to the crank axle 21A. One of a pair of pedals 25 is rotatably supported on the right crank 21B. The other of the pair of pedals 25 is rotatably supported on the left crank 21C.

[0047] The first sprocket assembly 22 is rotatably connected to the crankshaft 21A. The first sprocket assembly 22 includes one or more sprockets 22A. In one example, the first sprocket assembly 22 includes multiple sprockets 22A with different outer diameters.

[0048] The second sprocket assembly 23 is rotatably supported on the rear hub of the rear wheel 14. The second sprocket assembly 23 includes one or more sprockets 23A. In one example, the second sprocket assembly 23 includes multiple sprockets 23A with different outer diameters.

[0049] The chain 24 is wrapped around one of the sprockets 22A of the first sprocket assembly 22 and one of the sprockets 23A of the second sprocket assembly 23. When the crank 21 rotates forward due to the human-powered force applied to the pedal 25, the sprocket 23A rotates forward with the crank 21, and the rotation of the sprocket 23A is transmitted to the second sprocket assembly 23 via the chain 24, and this rotation rotates the rear wheel 14. A belt or shaft may be used instead of the chain 24.

[0050] The human-powered vehicle 1 is powered by electricity supplied from a battery 40 and includes a device 30 whose operation is controlled by a control device 100. The device 30 includes a transmission 31 and an assist device 32. The transmission 31 and the assist device 32 are basically operated by control of the control device 100 in accordance with the operation of the operating device 33.

[0051] The gear shifter 31 changes the ratio of the rotational speed of the rear wheel 14 to the rotational speed of the crank 21, that is, the gear ratio of the human-powered vehicle 1. The gear ratio is expressed by the ratio of the output rotational speed output by the gear shifter 31 to the input rotational speed input to the gear shifter 31. The gear ratio can be expressed by the formula: "Gear Ratio = Output Rotational Speed ​​ / Input Rotational Speed". In the first example, the gear shifter 31 is an external derailleur (rear derailleur) that changes the connection between the second sprocket assembly 23 and the chain 24. In the second example, the gear shifter 31 is an external derailleur (front derailleur) that changes the connection between the first sprocket assembly 22 and the chain 24. In the third example, it is an internal gear shifter provided in the hub of the rear wheel 14. The gear shifter 31 may also be a continuously variable transmission.

[0052] The assist device 32 is a device that assists the human-powered driving force of the human-powered vehicle 1. The assist device 32 includes, for example, a motor. In one example, the assist device 32 is interposed between the crankshaft 21A and the frame 11A and transmits torque to the first sprocket assembly 22 to assist the human-powered driving force of the human-powered vehicle 1. More specifically, the assist device 32 is located inside a drive unit (not shown) provided near the crankshaft 21A. The drive unit has a case, and the assist device 32 is located inside the case. The assist device 32 may also drive a chain 24 that transmits driving force to the rear wheel 14 of the human-powered vehicle 1.

[0053] The operating device 33 is provided on the handlebars 12. The operating device 33 includes, for example, an operating section 33A operated by the rider. An example of the operating section 33A is one or more buttons. Another example of the operating section 33A is a brake lever. The operating section 33A can be operated by tilting the brake bars, which are provided on the left and right handlebars, to the left or right. An information terminal device 7 held by the rider may be used as the operating section 33A. An operating button is displayed on the display panel included in the information terminal device 7, and when the information terminal device 7 detects that an operating button has been operated, it notifies the control device 100.

[0054] The operating device 33 includes a gear shift operating device 33B. The gear shift operating device 33B consists of multiple buttons included in the operating unit 33A. The gear shift operating device 33B is a device attached to the brake bar. Each time the rider tilts the brake bar relative to the gear shift operating device 33B or presses a button provided on the brake bar, manual operation of the gear shift device 31 is possible, such as increasing or decreasing the gear ratio.

[0055] The operating device 33 includes an assist operating device 33C. The assist operating device 33C is, for example, a button included in the operating unit 33A. By pressing the assist operating device 33C, the assist mode can be set to one of several levels (high / medium / low). The operating device 33 may also include a notification unit for notifying the operating status.

[0056] The operating device 33 includes an output unit 33D. The output unit 33D includes a display device capable of displaying characters or images, such as a liquid crystal panel, organic EL, or LED. The output unit 33D outputs the index value calculated by the control device 100 (described later) as characters or images. The buttons on the operating unit 33A may be buttons displayed on the output unit 33D.

[0057] The operating device 33 is connected to the control device 100 via communication so that it can transmit signals to the control device 100 in response to the operation of the operating unit 33A, the gear shift operating device 33B, and the assist operating device 33C. The operating device 33 may also be connected to the gear shift 31 and the assist device 32 via communication so that it can transmit signals to the gear shift 31 or the assist device 32 in response to the operation of the operating unit 33A, the gear shift operating device 33B, and the assist operating device 33C. In the first example, the operating device 33 communicates with the control device 100 via a communication line or a wire capable of PLC (Power Line Communication). The operating device 33 may also communicate with the gear shift 31, the assist device 32, and the control device 100 via a communication line or a wire capable of PLC. In the second example, the operating device 33 communicates with the control device 100 via wireless communication. The operating device 33 may also communicate with the gear shift 31, the assist device 32, and the control device 100 via wireless communication.

[0058] The battery 40 includes a battery body 41 and a battery holder 42. The battery body 41 is a storage battery containing one or more battery cells. The battery holder 42 is fixed to the frame 11A of the human-powered vehicle 1. The battery body 41 is detachable from the battery holder 42. The battery 40 is electrically connected to the device 30, the operating device 33 and the control device 100 and supplies power as needed. Preferably, the battery 40 includes a control unit for communicating with the control device 100. Preferably, the control unit includes a processor using a CPU.

[0059] The human-powered vehicle 1 is equipped with sensors 50 at various locations to detect the rider's condition and the riding environment. The sensors 50 include a speed sensor 51, an acceleration sensor 52, a torque sensor 53, a cadence sensor 54, a gyro sensor 55, and a seating sensor 56.

[0060] The speed sensor 51 is installed, for example, on the front wheel 13 and transmits a signal corresponding to the number of rotations per unit time of the front wheel 13 to the control device 100. Based on the output of the speed sensor 51, the control device 100 can calculate the vehicle speed and distance traveled of the human-powered vehicle 1.

[0061] The acceleration sensor 52 is fixed to the frame 11A, for example. The acceleration sensor 52 is a sensor that outputs vibrations of the human-powered vehicle 1 in three axes (front-back direction, left-right direction, and up-down direction) with respect to the frame 11A, and is provided to detect the movement and vibration of the human-powered vehicle 1. The acceleration sensor 52 transmits signals corresponding to the magnitude of the movement and vibration to the control device 100.

[0062] The torque sensor 53 is provided, for example, to measure the torque applied to the right crank 21B and the left crank 21C, respectively. The torque sensor 53 transmits a signal corresponding to the torque measured in at least one of the right crank 21B and the left crank 21C to the control device 100.

[0063] The cadence sensor 54 is configured, for example, to measure the cadence of either the right crank 21B or the left crank 21C. The cadence sensor 54 transmits a signal corresponding to the measured cadence to the control device 100.

[0064] The gyro sensor 55 is fixed to, for example, the frame 11A. The gyro sensor 55 is provided to detect the yaw, roll, and pitch rotations of the human-powered vehicle 1. The gyro sensor 55 transmits signals corresponding to the amount of rotation of each of the three axes to the control device 100.

[0065] The seating sensor 56 is installed on the inner surface of the saddle 15 to measure whether or not a rider is seated on the saddle 15. The seating sensor 56 uses, for example, a piezoelectric sensor to transmit a signal to the control device 100 that corresponds to the weight applied to the saddle 15.

[0066] Figure 2 is a block diagram illustrating the configuration of the control device 100. The control device 100 comprises a control unit 110 and a storage unit 112.

[0067] The control unit 110 is a processor using a CPU. The control unit 110 uses built-in memory such as ROM (Read Only Memory) and RAM (Random Access Memory). According to the control program P1, the control unit 110 controls the operation of the controlled object mounted on the human-powered vehicle 1, the power supply to the controlled object, and communication with the controlled object based on the determined control data.

[0068] The storage unit 112 includes, for example, non-volatile memory such as flash memory. The storage unit 112 stores the control program P1. The control program P1 may be a copy of the control program P2 stored in the non-temporary storage medium 200, which the control unit 110 reads and stores in the storage unit 112.

[0069] The memory unit 112 stores learning data for comfort level. The learning data for comfort level is data that shows the correspondence between state data and comfort level index values. The state data includes one of the following: the running speed of the human-powered vehicle 1, acceleration, torque in the drive mechanism 20, cadence, and the tilt of the vehicle body 11 of the human-powered vehicle 1. The state data may also be whether or not the rider is seated, or the rider's biometric information. The learning data for comfort level is trained to output a comfort level index value when state data is obtained. In the first example, the comfort level index value is a numerical value on a scale of 6, "0, 1, 2, 3, 4, 5", with a higher number indicating greater comfort. In the second example, the comfort level index value is a numerical value on a scale of 10. The number of scales is not limited to "6" or "10". In the third example, the comfort level index value is the letters "S, A, B, C, D, E", with "S, A, B, C, D, E" indicating the degree of comfort in that order. The comfort level index value is not limited to these representations.

[0070] The control unit 110 communicates with the controlled object. In this case, the control unit 110 itself may have a communication unit (not shown) for the controlled object, or the control unit 110 may be connected to a communication unit for the controlled object provided inside the control device 100. It is preferable that the control unit 110 has a connection unit for communicating with the controlled object or the communication unit.

[0071] The control unit 110 preferably communicates with the controlled object by at least one of PLC and CAN communication. The communication between the control unit 110 and the controlled object is not limited to wired communication, but may also be wireless communication such as ANT®, ANT+®, Bluetooth®, WiFi®, ZigBee®, etc.

[0072] The control unit 110 is connected to the sensor 50 via a signal line. The control unit 110 acquires status data related to the movement of the human-powered vehicle 1 from the signal output by the sensor 50 via the signal line.

[0073] The control unit 110 can communicate with the LiDAR information terminal device 7 via a wireless communication device 60 having an antenna. The wireless communication device 60 may be built into the control unit 100. The wireless communication device 60 is a device that enables communication via the so-called Internet. The wireless communication device 60 may be a wireless communication device such as ANT(registered trademark), ANT+(registered trademark), Bluetooth(registered trademark), WiFi(registered trademark), ZigBee(registered trademark), or LTE (Long Term Evolution). The wireless communication device 60 may comply with communication networks such as 3G, 4G, 5G, LTE (Long Term Evolution), WAN (Wide Area Network), LAN (Local Area Network), Internet line, dedicated line, or satellite line. The control unit 110 can output data to the information terminal device 7.

[0074] The control content of the control device 100 configured in this way will now be explained. The control unit 110 of the control device 100 controls the device 30 based on input information acquired from the sensor 50. In addition to controlling the device 30, the control unit 110 of the first embodiment functions as a "human-powered vehicle information processing device" that acquires state data of the human-powered vehicle 1 using the control program P1, learns the degree of comfort of riding corresponding to the state data based on the rider's actions while riding, calculates an index value indicating the degree of comfort corresponding to the newly acquired state data from the learning results, and outputs the index value.

[0075] In the first embodiment, the control device 100 acquires cadence and torque in the drive mechanism 20 as state data for the human-powered vehicle 1. The control device 100 learns the comfort level corresponding to the cadence and torque based on the rider's actions operating the control device 33 while riding. In the first embodiment, the control device 100 learns the comfort level based on whether or not the gear shift control device 33B for the gear shift device 31, which affects cadence and torque, has been operated.

[0076] In the first embodiment, the control unit 110 stores in the memory unit 112 the correspondence between state data, such as cadence and torque, and index values, which are expressed as six numerical values ​​from "0" to "5," as learning data for comfort level. Initially, all index values ​​associated with cadence and torque may be set to "5." For example, data such as (T=t1, C=c1, index value=5), (T=t2, C=c2, index value=4), ... are stored in the memory unit 112 as learning data. The control unit 110 updates the index values ​​according to whether or not the same cadence and torque are being operated.

[0077] Figure 3 is a flowchart showing an example of the comfort level learning process procedure by the control device 100. The control unit 110 of the control device 100 repeatedly executes the following processing procedure based on the control program P1.

[0078] The control unit 110 acquires torque from the torque sensor 53 (step S101) and cadence from the cadence sensor 54 (step S103). The processing in steps S101 and S103 corresponds to the "acquisition unit".

[0079] The control unit 110 waits for a predetermined time from the timing of acquiring state data in steps S101 and S103 (step S105), and determines whether or not an operation was performed on the gear shift operating device 33B within the predetermined period (step S107). The predetermined period is, for example, 1 second, 2 seconds, etc., the time required from a certain state until the rider actually performs the operation when they feel the need to operate it.

[0080] If it is determined that the gear shifting device 33B has been operated within a predetermined period (S107: YES), the control unit 110 updates the comfort index value stored in association with the torque and cadence acquired in steps S101 and S103 to decrease the comfort level (step S109). The control unit 110 then completes one learning process.

[0081] If it is determined that no operation was performed on the gear shifting device 33B within a predetermined period (S107: NO), the control unit 110 updates the comfort level index value stored in association with the torque and cadence acquired in steps S101 and S103 to maintain or increase the comfort level (step S111). The control unit 110 then completes one learning process.

[0082] In the first embodiment, it was determined in step S107 above whether or not an operation was performed on the gear shift operating device 33B. The control unit 110 may also determine in step S107 whether or not an operation was performed on the assist operating device 33C for the assist device 32, and if an operation was performed, it may learn that the level of comfort has decreased.

[0083] The control unit 110 performs the following processes in parallel with the processes shown in the flowchart of Figure 3. Figure 4 is a flowchart of an example of the comfort level output processing procedure by the control device 100. Based on the control program P1, the control unit 110 continuously performs the following processing procedures while the human-powered vehicle 1 is in motion.

[0084] The control unit 110 acquires torque from the torque sensor 53 (step S201) and cadence from the cadence sensor 54 (step S203).

[0085] The control unit 110 calculates comfort index values ​​corresponding to the torque and cadence acquired in steps S201 and S203 from the learning data stored in the memory unit 112 (step S205).

[0086] In step S205, if the torque and cadence combination acquired in steps S201 and S203 exists in the learning data of the memory unit 112, the control unit 110 may read the comfort level stored in association with that combination. If the torque and cadence combination acquired in steps S201 and S203 does not exist in the learning data of the memory unit 112, the control unit 110 calculates it from nearby torque and cadence combinations. For example, if the torque and cadence combination acquired in steps S201 and S203 is (T=42, C=62), and the memory unit 112 has (T=40, C=60, index value=4) and (T=44, C=64, index value=5), the control unit 110 can calculate the index value as 5 (= rounded to the nearest whole number ((4+5) / 2)). The comfort level index value may be output rounded to an integer between "0" and "5", or as a decimal number within the range of "0" to "5".

[0087] The control unit 110 outputs the index value calculated in step S205 to the output unit 33D (step S207), and terminates the process.

[0088] Figure 5 shows an example of the display on the output unit 33D in the first embodiment. Figure 5 shows the output unit 33D, which is a display provided on the handlebar 12. As shown in Figure 5, the display shows the index value in numbers. The rider can check the visualized index value as shown in Figure 5. While riding the human-powered vehicle 1 and operating the control device 33, the rider can confirm that their perceived comfort level and the comfort index value displayed on the output unit 33D gradually match.

[0089] (Second Embodiment) In the control device 100 of the second embodiment, the storage unit 112 stores a map of index values ​​for the cadence and torque values ​​of the drive mechanism 20 of the human-powered vehicle 1 included in the state data, as a correspondence between state data and index values ​​of comfort level. The configuration of the control device 100 in the second embodiment is the same as that of the first embodiment, except for the processing described later. Therefore, for the configuration of the control device 100 of the second embodiment that is common with the first embodiment, the same reference numerals are used and detailed explanations are omitted.

[0090] Figure 6 shows an example of an index value map 114. In the map 114 of Figure 6, the horizontal axis shows cadence and the vertical axis shows torque. The right side of the map represents a higher cadence, and the upper side represents a higher torque. The map 114 shown in Figure 6 is grid-like. The map 114 divides the ranges of cadence and torque, and the index value can be identified depending on which range the cadence and torque fall into. In the map 114 of Figure 6, the height of the index value is numerically shown in six levels from "0" to "5" corresponding to the combination of cadence and torque. Figure 6 shows the initial training data before learning. As shown in Figure 6, initially, the comfort index value is set to "5" whether both cadence and torque are small or both are large.

[0091] Figure 7 is a flowchart showing an example of the comfort level learning process procedure by the control device 100 of the second embodiment. The control unit 110 of the control device 100 repeatedly executes the following processing procedure based on the control program P1. Of the processing procedure shown in the flowchart of Figure 7, the same step numbers are used for the procedure that is common to the processing procedure shown in the flowchart of Figure 3 of the first embodiment, and detailed explanations are omitted.

[0092] In the second embodiment, if the control unit 110 of the control device 100 determines that an operation has been performed on the gear shifting device 33B (S107: YES), it updates the index values ​​stored in the map 114, which are associated with the range in which the acquired torque and cadence are located, to decrease the comfort level (step S121).

[0093] The control unit 110 updates the index values ​​in other ranges (masses) around the range corresponding to the acquired torque and cadence on the map 114 to decrease the comfort level (step S123), and then terminates the process.

[0094] In step S123, the control unit 110 updates the comfort index values ​​for cadence and torque corresponding to the range (mass) in which the comfort level has been reduced, either in the range where both are small or in the range where both are large. The comfort level in the range adjacent to the range in which the comfort level was reduced in step S121 may be reduced in a gradual manner so that it becomes smaller than the index value after reduction in step S121.

[0095] In updating the index value in step S123, it may be reduced depending on the operation performed on the gear shifting device 33. For example, if the operation is to change to OW (Outward) to increase the gear ratio, the rider will feel that the pedal 25 is lighter. Therefore, the control unit 110 reduces the index value so as to decrease the comfort level in the torque range smaller than the relevant torque range and in the cadence range larger than the relevant cadence range.

[0096] If the operation involves changing to IW (Inward) to reduce the gear ratio, the rider will feel that pedal 25 is heavy. Therefore, the control unit 110 may lower the index value to reduce the comfort level in the torque range greater than the relevant torque range and in the cadence range less than the relevant cadence range.

[0097] If the operation involves changing the gear ratio by two or more steps to increase the gear ratio, the decrease in comfort level may be increased. Similarly, if the operation involves changing the gear ratio by two or more steps to decrease the gear ratio, the decrease in comfort level may be increased.

[0098] If the control unit 110 of the control device 100 of the second embodiment determines that no operation was performed on the gear shifting device 33B (S107: NO), it updates the index values ​​stored in the map 114, which are associated with the range in which the acquired torque and cadence are located, to maintain or increase the comfort level (step S125).

[0099] The control unit 110 updates the index values ​​in other ranges (masses) around the range corresponding to the acquired torque and cadence on the map 114 to maintain or increase the comfort level (step S127), and then terminates the process.

[0100] In step S127, if the comfort index value of a range adjacent to the range (mass) where the comfort level has been increased deviates from the index value updated in step S125, the control unit 110 updates the value so that it becomes continuous.

[0101] Figure 8 shows the update of the index value on map 114. Map 114 shown in Figure 8 is an example of when the gear shifting device 33B is operated at the timing when state data, indicated by a thick frame, is acquired, where the cadence is small and the torque is large, compared to map 114 shown in Figure 6. The control unit 110 updates the comfort index value in the range where the acquired cadence and torque correspond from "5" to "4" (S121). The control unit 110 then updates the index values ​​in the range where the cadence is even smaller and the torque is larger, gradually from the corresponding range to "3", "2", and "1" (S123).

[0102] Figure 9 shows an example of the learning results of map 114. The map 114 shown in Figure 9 shows the distribution of index values ​​after learning has been completed by the update shown in Figure 8. With the updated map 114, the control unit 110 can identify the degree to which the rider feels comfortable in that state, if it can identify the combination of cadence and torque.

[0103] Figure 10 shows another example of the learning results of map 114. Map 114 shown in Figure 10 is a contour map generated from the grid map shown in Figure 9. The control unit 110 curves the boundaries of grids with the same index value in the grid map and draws smooth contour lines to create a contour map. The boundaries can be determined by a regression equation. In map 114 shown in Figure 10, the higher the numerical value of the comfort index, the higher the density shown.

[0104] Figure 11 is a flowchart showing an example of the comfort level output processing procedure by the control device 100 of the second embodiment. Of the processing procedures shown in the flowchart of Figure 11, those that are common with the processing procedures shown in the flowchart of Figure 4 of the first embodiment are given the same step numbers and detailed explanations are omitted.

[0105] The control unit 110 reads comfort index values ​​corresponding to the torque and cadence acquired in steps S201 and S203 from the map 114 (step S215).

[0106] The control unit 110 outputs the read index value to the output unit 33D (step S217), and terminates the process.

[0107] Figure 12 shows an example of display on the output unit 33D in the second embodiment. Figure 12 shows the output unit 33D, which is a display provided on the handlebar 12. In the second embodiment, the display of the output unit 33D shows the index value as a number and color density. The display of the output unit 33D may also display an image of color and brightness corresponding to the index value. In the human-powered vehicle 1 of the second embodiment, the rider can intuitively confirm the visualized index value as shown in Figure 12. While riding the human-powered vehicle 1 and operating the control device 33, the rider can confirm that the perceived comfort level and the comfort index value displayed on the output unit 33D gradually match.

[0108] (Third embodiment) In the control device 100 of the third embodiment, an index value is calculated using the biological information of the LiDAR via the LiDAR's information terminal device 7. The biological information may include respiratory rate and blood flow rate.

[0109] Figure 13 is a block diagram showing the configuration of the information terminal device 7 in the third embodiment. The information terminal device 7 comprises a control unit 70, a storage unit 72, a display unit 74, a communication unit 76, and a biosensor 78. The information terminal device 7 is, for example, at least one of a smartphone, a tablet terminal, and a cycle computer. The information terminal device 7 is not limited to a smartphone or a tablet terminal as long as it comprises a control unit, a display unit (operation unit), and a communication unit and is a device that cooperates with the human-powered vehicle 1. The information terminal device 7 may also be a personal computer or a wearable device.

[0110] The control unit 70 is a processor using a CPU. The control unit 70 uses built-in memory such as ROM and RAM. The control unit 70 controls communication with the control device 100 of the human-powered vehicle 1 according to the application program P7 described later.

[0111] The storage unit 72 includes, for example, non-volatile memory such as flash memory. The storage unit 72 stores the application program P7. The application program P7 may be one that the control unit 70 reads from the application program P7 stored in the non-temporary storage medium 200 and copies to the storage unit 72, or it may be one that has been downloaded through a public network.

[0112] The display unit 74 is a display device such as a liquid crystal panel or an organic EL display. The display unit 74 displays information output from the control unit 70. In the third embodiment, the display unit 74 displays a screen including the rider's comfort level of the human-powered vehicle 1 based on the application program P7.

[0113] The communication unit 76 has an antenna and can communicate wirelessly with the control device 100. The communication unit 76 is a device that corresponds to a wireless communication device 60 that conforms to a protocol capable of communicating with the control device 100.

[0114] The biosensor 78 is, for example, an optical heart rate sensor. The biosensor 80 is attached to the rider's wrist and transmits a signal corresponding to the heart rate to the control unit 70.

[0115] In the third embodiment, a human-powered vehicle information processing system, including an information terminal device 7 and a control device 100, calculates and learns an index value indicating comfort level based on the rider's biometric information from the state data of the human-powered vehicle 1 while it is in motion. In the third embodiment, the display unit that displays an image of color, density, or brightness according to the index value is the display unit 74 of the rider's information terminal device 7 of the human-powered vehicle 1.

[0116] Figure 14 is a flowchart showing an example of the comfort level learning process procedure in the control system for a human-powered vehicle according to the third embodiment. The control unit 110 of the control device 100 and the control unit 70 of the information terminal device 7 repeatedly execute the following processing procedure based on the control program P1 and the application program P7, respectively.

[0117] The control unit 70 of the information terminal device 7 acquires the heart rate based on the signal from the biosensor 78 (step S301), and transmits the acquired heart rate data from the communication unit 76 to the control device 100 (step S303).

[0118] In step S301, the control unit 70 may predict and derive the number of heartbeats per unit time (e.g., 1 minute) from the instantaneous interval between heartbeats, and transmit the calculated data of the number of heartbeats per unit time in step S303. The control unit 70 may also transmit the interval between heartbeats itself as the heart rate data.

[0119] The control unit 110 of the control device 100 acquires torque from the torque sensor 53 (step S131) ​​and cadence from the cadence sensor 54 (step S133).

[0120] The control unit 110 receives heart rate data via the wireless communication device 60 (step S135) and calculates an index value indicating the level of comfort based on the heart rate data (step S137).

[0121] In step S137, the control unit 110 calculates the level of comfort based on the heart rate, in one example. The control unit 110 calculates a lower level of comfort if the heart rate is high, and conversely, if the heart rate is below a predetermined low level. In another example, the control unit 110 calculates the level of comfort based on the magnitude of the change in the interval of the heart rate in the most recent predetermined first period (e.g., 2-3 minutes). If the heart rate rises suddenly, the control unit 110 calculates a lower level of comfort, considering it to be a high load on the body. In yet another example, the heart rate control unit 110 calculates a higher level of comfort if the variability in the interval of the heart rate in the most recent predetermined period (e.g., 2 minutes) is small. If there is a large variability, such as when the heart rate suddenly rises and then returns to normal, the control unit 110 may calculate a lower level of comfort, considering it to be an increase in stress.

[0122] The control unit 110 stores the index value calculated in step S137 in the storage unit 112 in association with the acquired torque and cadence (step S139), and completes one learning process.

[0123] As described above, the comfort level is calculated based on biometric information, the correspondence between torque and cadence and the comfort level is learned, and stored in the memory unit 112 as learning data.

[0124] Figure 15 is a flowchart showing an example of the display procedure in the second embodiment. The control device 100 and the information terminal device 7, based on the control program P1 and the application program P7, respectively, continuously execute the following processing procedure while the human-powered vehicle 1 is in motion.

[0125] The control unit 110 of the control device 100 acquires torque from the torque sensor 53 (step S231) and cadence from the cadence sensor 54 (step S233).

[0126] The control unit 110 calculates comfort index values ​​corresponding to the torque and cadence acquired in steps S231 and S233 from the learning data stored in the memory unit 112 (step S235).

[0127] The control unit 110 transmits data including the calculated index values ​​and status data to the information terminal device 7 via the wireless communication device 60 (step S237). In step S237, the control unit 110 transmits cadence and torque as status data. The control unit 110 may also transmit data on driving speed and acceleration to the information terminal device 7 as status data.

[0128] The control unit 70 of the information terminal device 7 receives data including index values ​​and status data via the communication unit 76 (step S331), and stores the index value data and status data in chronological order (step S333). The control unit 70 displays the status data on the display unit 74 (step S335), and based on the index value data, displays an image of the color, density, or brightness corresponding to the index value on the display unit 74 (step S337), and then terminates the display process. In step S335, the control unit 70 may calculate the mileage, etc., from the status data received together with the index value data, and then display it on the display unit 74.

[0129] Figure 16 shows an example of the display of index values ​​on the display unit 74 of the information terminal device 7. Figure 16 shows an example of the display on the information terminal device 7 (smartphone) fixed to the handlebar 12. Figure 16 shows a screen 740 that shows the status of the human-powered vehicle 1 while it is in motion, based on the application program P7 displayed on the display unit 74. The screen 740 displays text 742 of the status data and an image 744 with a density corresponding to the magnitude of the comfort index value.

[0130] As shown in Figure 16, the rider can intuitively confirm the visualized index values. While driving the human-powered vehicle 1 and operating the control device 33, the rider can confirm that their perceived comfort level and the comfort index value displayed on the display unit 74 of the information terminal device 7 gradually match.

[0131] As shown in the flowchart of Figure 15, the information terminal device 7 stores status data each time it is received (S333). Therefore, the control unit 70 can display the history of status data during driving on the display unit 74 as the driving history of the human-powered vehicle 1. Figure 17 shows the history of status data displayed on the display unit 74 of the information terminal device 7. In the example of Figure 17, the history of status data acquired during driving is shown on a map of index values, connected by lines. The screen shown in Figure 17 allows the rider to visually review the changes in cadence, torque, and comfort level as a review of the driving of the human-powered vehicle 1.

[0132] Figure 18 shows another example of the history of status data displayed on the display unit 74 of the information terminal device 7. In the example in Figure 18, the horizontal axis shows the distance traveled, and the vertical axis shows the index value and the history of status data. The control unit 70 may also overlay the history of the travel speed, or it may overlay the elevation. The screen shown in Figure 18 allows the rider to visually review the changes in cadence, torque, and comfort level as a review of the ride of the human-powered vehicle 1.

[0133] (Fourth Embodiment) In the first to third embodiments, the control device 100 stored the correspondence between state data and comfort index values, and the map 114, in the storage unit 112. In the fourth embodiment, the control device 100, as a learning unit, uses a learning model that has been trained to output index values ​​when state data is input.

[0134] Figure 19 is a block diagram illustrating the configuration of the control device 100 in the fourth embodiment. In the fourth embodiment, the control device 100 stores the learning model M1 in the storage unit 112. Except for the fact that the learning model M1 is stored and the processing procedure using the learning model M1, the configuration of the control device 100 in the fourth embodiment is the same as in the first embodiment. Therefore, for the configuration of the control device 100 in the fourth embodiment that is common with the first embodiment, the same reference numerals are used and detailed explanations are omitted.

[0135] The learning model M1 stored in the memory unit 112 may be a copy of the learning model M2 stored in the non-temporary storage medium 200, which the control unit 110 reads and stores in the memory unit 112.

[0136] Figure 20 is an overview diagram of the learning model M1. The learning model M1 is a learning model that is trained using deep learning with a neural network. The learning model M1 may also be a model that is trained using a recurrent neural network. The learning model M1 is trained to output a comfort index value when it receives state data of a human-powered vehicle 1 in motion, acquired by sensor 50, as input.

[0137] The learning model M1 comprises an input layer M11 for inputting input information, an output layer M12 for outputting comfort index values, and an intermediate layer M13 containing a group of nodes consisting of one or more layers. The intermediate layer M13, connected to the output layer M12, is a coupling layer that aggregates a large number of nodes to the number of nodes in the output layer M12. The output layer M12 has one node. Each node in the intermediate layer M13 has a parameter that includes at least one of weight and bias in relation to the nodes of the preceding layer. The learning model M1 is learned using training data that includes state data (input) obtained from sensors 50 such as cadence and torque, as well as driving speed, acceleration, tilt of the vehicle body 11, and rider's seating position, while the human-powered vehicle 1 is running, and output labels (0: none, 1: yes) indicating whether or not the rider operated the gear shifting device 33B (or assist device 33C) after a predetermined time has passed since the state data was acquired. The learning model M1 is trained by backpropagating the error between the index value output from the output layer M12 and the label corresponding to the state data when state data (input information) is input to the input layer M11, and updating the parameters at the nodes of the hidden layer M13.

[0138] The learning model M1 may not only receive state data obtained from sensor 50, including cadence and torque, directly into the input layer M11 at each point in time, but may also receive the amount of change over the most recent few seconds (e.g., 2 seconds). The learning model M1 may also be trained by a recurrent neural network to output index values ​​while being influenced by previously input information.

[0139] Since the learning model M1 needs to be learned for each rider, it is stored in the memory unit 112 in a partially learned state before the control device 100 is shipped. After the human-powered vehicle 1 is shipped and purchased, the control unit 110 learns the learning model M1 as follows.

[0140] Figure 21 is a flowchart showing an example of a learning process procedure. The control unit 110 of the control device 100 performs the following processes based on the control program P1 while the human-powered vehicle 1 is in motion.

[0141] The control unit 110 acquires state data from the sensor 50, including torque from the torque sensor 53 and cadence from the cadence sensor 54 (step S141).

[0142] The control unit 110 waits for a predetermined time from the timing of acquiring the state data in step S141 (step S143), and determines whether or not an operation was performed on the gear shift operating device 33B within the predetermined period (step S145). The predetermined period is, for example, 1 second, 2 seconds, etc., the time required from a certain state until the rider actually performs the operation when they feel the need to operate the gear shift.

[0143] If it is determined that the gear shift control device 33B has been operated (S145: YES), the control unit 110 immediately determines (for example, within 2 seconds) whether the gear shift control device 33B has performed the opposite operation to the operation in step S145 (step S147).

[0144] If it is determined that the reverse operation was not performed (S147: NO), the control unit 110 confirms that the operation was performed (operation performed, label = 1) (step S149).

[0145] If it is determined in step S145 that the gear shift control device 33B was not operated (S145: NO), the control unit 110 confirms that no operation was performed (no operation, label = 0) (step S151) and proceeds to step S153.

[0146] The control unit 110 inputs the state data acquired in step S141 as input information to the input layer M11 of the learning model M1 (step S153). The control unit 110 acquires the comfort level index value output from the output layer M12 of the learning model M1 according to the processing in step S153 (step S155). The control unit 110 calculates the error between the output of the learning model M1 in step S155 and the confirmed content of whether or not an operation was performed using a predetermined error function (step S157).

[0147] The control unit 110 determines whether the learning conditions have been met (step S161). The second control unit 116 updates the parameters of the intermediate layer M13 based on the calculated error (step S159).

[0148] The control unit 110 terminates the learning process if it determines that the learning conditions have been met (S161: YES).

[0149] If it is determined that the learning conditions are not met (S161: NO), the process returns to step S141 and learning continues.

[0150] If the control unit 110 determines in step S147 that the reverse operation was performed (S147: YES), it proceeds to step S161. This is to avoid learning from incorrect operations.

[0151] Using the learned model M1 after learning is complete, the control device 100 calculates and outputs the comfort level. Figure 22 is a flowchart showing an example of the comfort level output processing procedure in the fourth embodiment. Based on the control program P1, the control unit 110 continuously executes the following processing procedure while the human-powered vehicle 1 is running.

[0152] The control unit 110 acquires state data from the sensor 50, including torque from the torque sensor 53 and cadence from the cadence sensor 54 (step S241).

[0153] The control unit 110 inputs the acquired state data to the trained learning model M1 (step S243) and obtains the comfort level index value output from the learning model M1 (step S245).

[0154] The control unit 110 outputs the acquired index value to the output unit 33D (step S247) and terminates the process.

[0155] Since the output example in the fourth embodiment is the same as in the first embodiment, illustration and detailed description are omitted.

[0156] (modified version) In the fourth embodiment, the control device 100 calculates a comfort index value using the learning model M1. Learning using the learning model M1 may be performed on the LIDA's information terminal device 7. Figure 23 is a block diagram showing the configuration of the control device 100 in a modified example of the fourth embodiment.

[0157] The configuration of the control device 100 in the modified version of the fourth embodiment is the same as that of the fourth embodiment, except that it does not store a learning model and performs processing described later. Therefore, for the configuration of the control device 100 in the modified version of the fourth embodiment that is common with the fourth embodiment, the same reference numerals are used and detailed descriptions are omitted.

[0158] Figure 24 is a block diagram showing the configuration of the information terminal device 7. The configuration of the information terminal device 7 is the same as that of the information terminal device 7 described in the third embodiment, except that it does not require a biosensor 78, stores a learning model M3, and performs processing using the learning model M3. Therefore, components common to the third embodiment are denoted by the same reference numerals and detailed explanations are omitted.

[0159] The modified information terminal device 7 of the fourth embodiment comprises a control unit 70, a storage unit 72, a display unit 74, and a communication unit 76. The storage unit 72 stores a learning model M3. The learning model M3 is trained for use by the Writer.

[0160] In a modified example, in the learning procedure of the learning model M1 in the fourth embodiment shown in Figure 21, steps S141 to S151 are performed by the control device 100, and steps S153 to S161 are performed by the information terminal device 7.

[0161] The control unit 110 of the control device 100 determines whether or not an operation has been performed (S149, S151), and transmits the status data acquired in step S141 and the data indicating whether or not an operation has been performed to the information terminal device 7.

[0162] When the control unit 70 of the information terminal device 7 receives state data and data indicating whether or not a confirmed operation has been performed, it learns a learning model M3 using the state data and the data indicating whether or not an operation has been performed (S153 to S157).

[0163] As explained in the modified example, the learning process calculations may be performed on the information terminal device 7. If the computing resources of the information terminal device 7 are more abundant than those of the control device 100, the computing load on the control device 100 can be reduced by performing the learning process and the calculations for the learning model M3 on the information terminal device 7.

[0164] The embodiments disclosed above are illustrative in all respects and not restrictive. The scope of the present invention is indicated by the claims, and all modifications within the meaning and scope equivalent to the claims are included. [Explanation of Symbols]

[0165] 1...Human-powered vehicle, 11...Vehicle body, 11A...Frame, 11B...Front fork, 11C...Light, 12...Handlebar, 13...Front wheel, 14...Rear wheel, 15...Saddle, 20...Drive mechanism, 21...Crank, 21A...Crank axle, 21B...Right crank, 21C...Left crank, 22...First sprocket assembly, 22A...Sprocket, 23...Second sprocket assembly, 23A...Sprocket, 24...Chain, 25...Pedal, 30...Device, 31...Gear shifter, 32...Assist device, 33...Operating device, 33A...Operating unit, 33B...Gear shifting device, 33C...Assist device, 3 3D…Output unit, 40…Battery, 41…Battery body, 42…Battery holder, 50…Sensor, 51…Speed ​​sensor, 52…Accelerometer, 53…Torque sensor, 54…Cadence sensor, 55…Gyro sensor, 56…Seat sensor, 60…Wireless communication device, 100…Control device, 110…Control unit, 112…Storage unit, P1…Control program, M1…Learning model, 200…Non-temporary storage medium, P2…Control program, M2…Learning model, 7…Information terminal device, 70…Control unit, 72…Storage unit, 74…Display unit, 76…Communication unit, 78…Biometric sensor, P7…Application program, M3…Learning model

Claims

1. An acquisition unit that acquires status data of a human-powered vehicle in motion, A learning unit learns the degree of comfort of the human-powered vehicle's ride corresponding to the aforementioned state data based on the rider's operation of the transmission via the gear shift control device of the human-powered vehicle while riding, and calculates an index value indicating the degree of comfort corresponding to the newly acquired state data from the learning results. It has an output unit that outputs the aforementioned index value, The learning unit learns that when the rider operates the transmission via the gear shift control device, the level of comfort decreases based on the state data. Human-powered vehicle information processing device.

2. An acquisition unit that acquires status data of a human-powered vehicle in motion, A learning unit learns the degree of comfort of the riding of the human-powered vehicle corresponding to the state data based on the rider's operation of the assist device via the assist control device of the human-powered vehicle while riding, and calculates an index value indicating the degree of comfort corresponding to the newly acquired state data from the learning results. It has an output unit that outputs the aforementioned index value, The learning unit learns that when an operation is performed from the rider to the assist device via the assist operation device, the level of comfort decreases based on the state data. Human-powered vehicle information processing device.

3. An image of color, density, or brightness corresponding to the aforementioned index value is displayed on the display unit. The human-powered vehicle information processing device according to claim 1 or 2.

4. The display unit is a display mounted on the handlebar of the human-powered vehicle. The human-powered vehicle information processing device according to claim 3.

5. The display unit is an information terminal device for the rider of the human-powered vehicle. The human-powered vehicle information processing device according to claim 3.

6. The learning unit stores the correspondence between the state data and the index value. The indicator value is updated according to the operation of the aforementioned rider. The human-powered vehicle information processing device according to any one of claims 1 to 5.

7. The aforementioned correspondence is a map of the index values ​​to the cadence and torque values ​​in the drive mechanism of the human-powered vehicle included in the state data. The human-powered vehicle information processing device according to claim 6.

8. The display unit will show the history of the status data while driving on the aforementioned map. The human-powered vehicle information processing device according to claim 7.

9. The learning unit is a learning model that has been trained to output the index value when state data is input, and performs learning while driving. The human-powered vehicle information processing device according to any one of claims 1 to 5.

10. The learning unit calculates and learns an index value indicating the degree of comfort based on the state data and the biological information of the rider. The human-powered vehicle information processing device according to any one of claims 1 to 9.

11. The computer installed in the human-powered vehicle Acquire status data of a human-powered vehicle while it is in motion. The degree of comfort of riding the human-powered vehicle corresponding to the aforementioned state data is learned based on the rider's operation of the transmission via the gear shift control device of the human-powered vehicle while riding. The index value indicating the level of comfort corresponding to the newly acquired state data is calculated from the learning results. Output the aforementioned index value, In the learning process described above, the computer learns that when the rider operates the transmission via the gear shift control device, the level of comfort decreases based on the state data. Information processing method for human-powered vehicles.

12. A computer mounted on a human-powered vehicle, Acquire status data of a human-powered vehicle while it is in motion. The degree of comfort of the ride of the human-powered vehicle corresponding to the state data is learned based on the rider's operation of the assist device via the assist control device of the human-powered vehicle while riding. The index value indicating the level of comfort corresponding to the newly acquired state data is calculated from the learning results. Output the aforementioned index value, In the learning process described above, the computer learns that when an operation is performed from the rider to the assist device via the assist operation device, the level of comfort decreases. Information processing method for human-powered vehicles.

13. Display device and Including a computer installed in a human-powered vehicle, The aforementioned computer, An acquisition unit that acquires status data of a human-powered vehicle in motion, A learning unit learns the degree of comfort of the human-powered vehicle's ride corresponding to the aforementioned state data based on the rider's operation of the transmission via the gear shift control device of the human-powered vehicle while riding, and calculates an index value indicating the degree of comfort corresponding to the newly acquired state data from the learning results. It has an output unit that outputs the aforementioned index value, The learning unit learns that when the rider operates the transmission via the gear shift control device, the level of comfort decreases based on the state data. Human-powered vehicle information processing system.

14. A display device, Including a computer installed in a human-powered vehicle, The aforementioned computer, An acquisition unit that acquires status data of a human-powered vehicle in motion, A learning unit learns the degree of comfort of the riding of the human-powered vehicle corresponding to the state data based on the rider's operation of the assist device via the assist control device of the human-powered vehicle while riding, and calculates an index value indicating the degree of comfort corresponding to the newly acquired state data from the learning results. It has an output unit that outputs the aforementioned index value, The learning unit learns that when an operation is performed from the rider to the assist device via the assist operation device, the level of comfort decreases based on the state data. Human-powered vehicle information processing system.

15. The computer installed in the human-powered vehicle, Acquire status data of a human-powered vehicle while it is in motion. The degree of comfort of riding the human-powered vehicle corresponding to the aforementioned state data is learned based on the rider's operation of the transmission via the gear shift control device of the human-powered vehicle while riding. The index value indicating the level of comfort corresponding to the newly acquired state data is calculated from the learning results. Output the aforementioned index value, In the learning process described above, the computer is instructed to learn that when the rider operates the transmission via the gear shift control device, the level of comfort decreases, based on the state data. A computer program that executes a process.

16. A computer mounted on a human-powered vehicle, Acquire status data of a human-powered vehicle while it is in motion. The degree of comfort of the ride of the human-powered vehicle corresponding to the state data is learned based on the rider's operation of the assist device via the assist control device of the human-powered vehicle while riding. The index value indicating the level of comfort corresponding to the newly acquired state data is calculated from the learning results. Output the aforementioned index value, In the learning process described above, the computer is instructed to learn that when an operation is performed from the rider to the assist device via the assist operation device, the level of comfort decreases, based on the state data. A computer program that executes a process.

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