Device and method

Abstract

In one embodiment of the present invention, a device is provided to assist in improving vehicle driving skills. The device comprises: a myoelectric information acquisition unit that acquires myoelectric information indicating the movement of muscles at a first point in time of a driver driving a vehicle; a behavior information acquisition unit that acquires behavior information indicating the behavior of the vehicle during a period of time including a second point in time prior to the first point in time; a prediction unit that uses a trained model to estimate myoelectric information of the driver at the first point in time from the behavior information acquired by the behavior information acquisition unit; an evaluation unit that evaluates the driving of the driver by comparing the myoelectric information of the driver acquired by the myoelectric information acquisition unit to the myoelectric information of the driver estimated by the prediction unit; and an output unit that outputs information indicating the evaluation of the driving of the driver. The trained model is trained using, as training data, myoelectric information indicating the movement of the muscles of skilled persons and behavior information indicating the behavior of vehicles driven by skilled persons.

Description

Device and Method

[0001] The present disclosure relates to a device and a method for assisting in the improvement of vehicle driving skills.

[0002] Patent Document 1 describes a system that measures the myoelectric potential of a user during the use of a bicycle-type exercise device, compares the measured myoelectric potential of the user with the myoelectric potential of a bicycle racer measured under the same conditions, and outputs the comparison result.

[0003] Japanese Unexamined Patent Application Publication No. 2018-110730

[0004] Generally, driving a vehicle is difficult for beginners and requires habituation. In particular, driving a motorcycle involves complex whole-body movements, so the difficulty level is high, and long-term training is required to improve motorcycle driving skills. This is the reason why many people hesitate to purchase a motorcycle. In the above-described system, it is possible to confirm the training effect of the user by comparing the measured myoelectric potential of the user with the myoelectric potential of a bicycle racer measured under the same conditions. However, this system does not contribute to the improvement of actual vehicle driving skills.

[0005] Therefore, an object of the present disclosure is to provide a device and a method that can assist in the improvement of vehicle driving skills.

[0006] In one aspect, a device is provided for assisting in the improvement of vehicle driving skills. This device includes a myoelectric information acquisition unit that acquires myoelectric information indicating the movement of the muscles of a driver driving a vehicle at a first time point, a behavior information acquisition unit that acquires behavior information indicating the behavior of the vehicle during a period including a second time point before the first time point, a prediction unit that predicts the myoelectric information at the first time point from the behavior information acquired by the behavior information acquisition unit using a trained prediction model, an evaluation unit that compares the myoelectric information of the driver acquired by the myoelectric information acquisition unit with the myoelectric information predicted by the prediction unit to evaluate the driving of the driver, and an output unit that outputs information indicating the evaluation of the driving of the driver. The prediction model is trained using, as teacher data, myoelectric information indicating the movement of the muscles of a skilled person and behavior information indicating the behavior of the vehicle driven by the skilled person.

[0007] According to this disclosure, it is possible to help improve driving skills for vehicles.

[0008] This is a block diagram showing the functional configuration of a support device according to one embodiment. This is a diagram showing an example of an electromyographic waveform. This is a diagram schematically showing the learning phase of a prediction model. This is a flowchart showing a support method according to one embodiment. This is a block diagram showing the functional configuration of a support device according to another embodiment. This is a block diagram showing the hardware configuration of a support device.

[0009] Embodiments of the present disclosure will be described below with reference to the drawings. In the following description, the same or equivalent elements will be denoted by the same reference numerals, and redundant descriptions will not be repeated.

[0010] Figure 1 is a block diagram showing the functional configuration of a support device according to one embodiment. The support device 1 shown in Figure 1 assists in improving driving skills for vehicles. A vehicle is a device used as a means of transportation, such as an automobile, motorcycle, bicycle, airplane, or ship. Vehicles include not only devices that operate using a power source such as an engine or motor, but also devices that operate using human power. The following description will explain an example of assisting in improving driving skills for a motorcycle.

[0011] As shown in Figure 1, the support device 1 includes an electromyography information acquisition unit 11, a behavior information acquisition unit 12, a prediction unit 13, an evaluation unit 14, and an output unit 15. In the example shown in Figure 1, the electromyography information acquisition unit 11, the behavior information acquisition unit 12, the prediction unit 13, the evaluation unit 14, and the output unit 15 are implemented by a single support device 1, but these functional elements may be distributed and arranged across multiple devices.

[0012] Support device 1 is connected to server device 2 in a communication manner. Server device 2 includes a storage unit 21 and a prediction model 22. The storage unit 21 is a database accessible from each functional element of support device 1. The storage unit 21 stores learning electromyography information 24 and learning behavior information 25. The learning electromyography information 24 and learning behavior information 25 will be described later.

[0013] The prediction model 22 is an AI model having a machine learning-trained neural network. The prediction model 22 is machine learning-trained to take behavioral information indicating the behavior of a vehicle as input and output electromyography information of the driver. The prediction model 22 is trained by optimizing its parameters using known machine learning algorithms such as convolutional neural networks, recurrent neural networks, or LSTM (Long Short Term Memory). Details of the prediction model 22 will be described later. Note that the memory unit 21 and the prediction model 22 may be located outside the support device 1 for accessibility, as shown in Figure 1, or they may be located inside the support device 1.

[0014] The electromyography (EMG) information acquisition unit 11 acquires EMG information from the electromyograph 30 that shows the muscle movements of the driver 3 at a first point in time. The driver 3 is a person who wishes to improve their motorcycle 5 driving skills. The first point in time is a certain point in time while the driver 3 is driving the motorcycle 5. The electromyograph 30 measures EMG information that shows the muscle movements of the driver 3. For example, the electromyograph 30 has electrodes for detecting the electrical signals (electromyographic potentials) of the driver 3's muscles, and detects the electromyographic potentials generated in conjunction with the activity of the driver 3's muscles with these electrodes, and outputs an EMG waveform that shows the change in electromyographic potentials over time. In other words, the EMG information includes the EMG waveform.

[0015] The electromyograph 30 may be a wearable device attached to the driver 3. For example, clothing incorporating a special conductive functional material capable of acquiring biosignals may function as the electromyograph 30. As such a functional material capable of acquiring biosignals, Toray Industries, Inc.'s functional material hitoe (registered trademark) is known.

[0016] In one embodiment, the driver 3 is fitted with multiple electromyographs 30 that measure electromyographic information from multiple different body parts, and the electromyographic information acquisition unit 11 may acquire electromyographic information from multiple body parts of the driver 3 from these multiple electromyographs 30. For example, the electromyographic information acquisition unit 11 acquires electromyographic information from the muscles of the driver 3's legs and waist. Figure 2 shows examples of electromyographic waveforms of the driver 3's legs and waist acquired by the electromyographic information acquisition unit 11.

[0017] The behavior information acquisition unit 12 acquires behavior information indicating the behavior of the motorcycle 5 during a period including a second time point. The second time point is a time before the first time point when the driver 3 is driving the motorcycle 5. The period including the second time point is a period before the first time point. For example, the period including the second time point is a period from 0.01 seconds to 3 seconds before the first time point. The behavior information is information indicating the operation or state of the motorcycle 5, and for example, the behavior information includes information indicating the acceleration or steering angle of the motorcycle 5. That is, the behavior information is information indicating the change over time of the acceleration or steering angle of the motorcycle 5 during a period before the first time point. The behavior information may also include information indicating the motorcycle, position information, direction of travel, and engine speed, etc.

[0018] Behavioral information is acquired, for example, from an On-Board Diagnostics (OBD) system mounted on the motorcycle 5, which collects various data from the engine control unit (ECU) or various sensors. The behavioral information acquisition unit 12 acquires behavioral information from the OBD system for a period including a second point in time of the motorcycle 5, including acceleration and steering angle.

[0019] The prediction unit 13 uses the prediction model 22 to predict the electromyographic information of the driver 3 at the first time point based on the behavior information of the motorcycle 5 during the period including the second time point acquired by the behavior information acquisition unit 12. In the following description, the electromyographic information predicted by the prediction unit 13 will be referred to as "predicted electromyographic information". First, the learning phase of the prediction model 22 will be explained with reference to Figures 1 and 3.

[0020] As shown in Figure 1, the memory unit 21 stores learning electromyography information 24 and learning behavior information 25 as training data for training the prediction model 22. The learning electromyography information 24 is electromyography information showing the muscle movements of an expert at a first time point. The learning behavior information 25 acquires behavior information showing the behavior of the motorcycle 5 during a period including a second time point. An expert is someone with high motorcycle riding skills, such as a professional rider. That is, the learning electromyography information 24 includes electromyography waveforms showing the muscle movements of a professional rider immediately after the behavior of the motorcycle 5 shown by the learning behavior information 25.

[0021] As shown in Figure 3, during the learning phase, the prediction model 22 receives learning behavior information 25 from the ProRider and outputs predicted electromyography (EMG) information corresponding to the learning behavior information 25. The prediction model 22 compares the EMG waveform of the predicted EMG information with the actual EMG waveform of the ProRider included in the learning EMG information 24, and updates the parameters of the neural network of the prediction model 22 using backpropagation or the like to minimize the error between the EMG waveform of the predicted EMG information and the EMG waveform of the ProRider. By repeating the above learning process using a large amount of training data, the EMG waveform of the predicted EMG information output from the prediction model 22 is made to closely resemble the EMG waveform of the ProRider.

[0022] Next, the operational phase of the prediction model 22 will be described. The prediction unit 13 inputs the behavior information of the motorcycle 5 during the period including the second time point, which has been acquired by the behavior information acquisition unit 12, into the trained prediction model 22. Once the behavior information of the motorcycle 5 during the period including the second time point is input, the prediction model 22 outputs predicted electromyography information predicted from the behavior information of the motorcycle 5. The predicted electromyography information output from the prediction model 22 includes information indicating muscle movements corresponding to the driving operations of the motorcycle 5 that are predicted to be performed by a professional rider immediately after the behavior of the motorcycle 5 during the period including the input second time point.

[0023] The evaluation unit 14 compares the electromyography (EMG) information of driver 3 acquired by the EMG information acquisition unit 11 with the predicted EMG information predicted by the prediction unit 13 to evaluate driver 3's driving. For example, the evaluation unit 14 calculates the mean square error between the EMG waveform of driver 3's EMG information acquired by the EMG information acquisition unit 11 and the EMG waveform of the predicted EMG information, and determines whether the mean square error is greater than or equal to a standard value. If the mean square error is less than the standard value, it can be said that driver 3's driving operations at the first time point in relation to the behavior of the motorcycle 5 during the period including the second time point are substantially the same as the driving operations of a professional rider at the first time point, and therefore the evaluation unit 14 evaluates driver 3's driving operations as appropriate. On the other hand, if the mean square error is greater than or equal to a standard value, it can be said that driver 3's driving operations at the first time point in relation to the behavior of the motorcycle 5 during the period including the second time point are different from the driving operations of a professional rider at the first time point, and therefore the evaluation unit 14 evaluates driver 3's driving operations as inappropriate.

[0024] The output unit 15 outputs information indicating an evaluation of the driver's (driver's) driving. For example, as information indicating an evaluation of the driving, the output unit 15 outputs a signal to stimulate the driver's (driver's) muscles. For example, if the evaluation unit 14 evaluates the driver's (driver's) driving operation as inappropriate, the output unit 15 outputs a signal to the vibrator 32 attached to the driver 3, causing vibrations to the driver's body. The driver 3 can recognize in real time that their driving operation was inappropriate through the vibrations of the vibrator 32.

[0025] In one embodiment, the evaluation unit 14 may compare the electromyography information of the driver 3 with the predicted electromyography information to determine whether the driver 3 should have moved their muscles. For example, if the electromyographic potential of the driver 3 at the first time point is lower than the electromyographic potential of the predicted electromyographic information, the evaluation unit 14 determines that the driver 3 should have moved their muscles. Conversely, if the electromyographic potential of the driver 3 at the first time point is higher than the electromyographic potential of the predicted electromyographic information, the evaluation unit 14 determines that the driver 3 should not have moved their muscles.

[0026] The output unit 15 may output different signals depending on whether it determines that the muscles should have been moved or not. For example, when the evaluation unit 14 determines that the muscles should have been moved, the output unit 15 outputs a first signal to the vibrator 32 attached to the driver 3, causing the vibrator 32 to vibrate in a first vibration pattern. Conversely, when the evaluation unit 14 determines that the muscles should not have been moved, the output unit 15 outputs a second signal to the vibrator 32 attached to the driver 3, causing the vibrator 32 to vibrate in a second vibration pattern different from the first vibration pattern. In this way, by vibrating the vibrator 32 in different vibration patterns, the driver 3 operating the motorcycle 5 can learn how to use their muscles and improve their motorcycle riding skills.

[0027] Furthermore, if the electromyography information acquisition unit 11 acquires electromyography information from multiple body parts, the evaluation unit 14 may compare the electromyography information of the driver 3 with the predicted electromyography information for each of the multiple body parts to evaluate the driver 3's driving operation. For example, the electromyography information acquisition unit 11 acquires electromyography information (first electromyography information) showing the movement of the muscles in the driver 3's legs (first body part) and electromyography information (second electromyography information) showing the movement of the muscles in the driver 3's waist (second body part). The evaluation unit 14 then compares the electromyography information of the driver 3's legs and waist with the predicted electromyography information of the legs and waist predicted by the prediction unit 13 to determine whether or not the muscles in the legs and waist should be moved.

[0028] The output unit 15 outputs information indicating the evaluation of the driver's 3 driving for each of several body parts. For example, if it is determined that the leg muscles should have been moved and the waist muscles should not have been moved, the output unit 15 outputs a first signal to the vibrator 32 attached to the driver's 3 legs, stimulating the driver's 3 legs with a first vibration pattern, and outputs a second signal to the vibrator 32 attached to the driver's 3 waist, stimulating the driver's 3 waist with a second vibration pattern. In this way, by stimulating each part of the driver's body when an inappropriate driving operation is evaluated, the driver 3 can learn how to use the muscles in each part of their body, thereby efficiently improving their motorcycle driving skills.

[0029] Next, with reference to Figure 4, a support method according to one embodiment will be described. The support method assists in improving vehicle driving skills. Figure 4 is a flowchart of the support method according to one embodiment.

[0030] As shown in Figure 4, in the support method according to one embodiment, first the electromyography information acquisition unit 11 acquires electromyography information of the driver 3 at a first time point from the electromyograph 30 (step ST1). At this time, the electromyography information acquisition unit 11 may acquire electromyography information from multiple different parts of the driver 3 from multiple electromyographs 30.

[0031] Next, the behavior information acquisition unit 12 acquires behavior information of the motorcycle 5 for a period including a second time point prior to the first time point (step ST2). For example, the behavior information acquisition unit 12 acquires information as behavior information that shows the change over time of the motorcycle 5's acceleration or steering angle during the period immediately preceding the first time point.

[0032] Next, the prediction unit 13 estimates the electromyographic information of the driver 3 at the first time point from the behavior information of the motorcycle 5 during the period including the second time point (step ST3). That is, the prediction unit 13 inputs the behavior information of the motorcycle 5 acquired in step ST2 into the prediction model 22 and obtains the predicted electromyographic information predicted from the behavior information of the motorcycle 5 from the prediction model 22.

[0033] Next, the evaluation unit 14 compares the electromyography (EMG) information of driver 3 acquired in step ST1 with the predicted EMG information predicted in step ST3 to evaluate driver 3's driving (step ST4). For example, the evaluation unit 14 calculates the mean square error between the EMG waveform of driver 3's EMG information and the EMG waveform of the predicted EMG information. If the mean square error is less than a reference value, the evaluation unit 14 evaluates that driver 3's driving operation at the first time point in relation to the behavior of the motorcycle 5 during the period including the second time point is appropriate. On the other hand, if the mean square error is greater than or equal to the reference value, the evaluation unit 14 evaluates that driver 3's driving operation at the first time point is inappropriate.

[0034] Next, the evaluation unit 14 determines whether the driver's driving operation is deemed appropriate (step ST5). If the driver's driving operation is deemed appropriate, the output unit 15 terminates the support method according to one embodiment without outputting a signal.

[0035] On the other hand, if the driver's operation is evaluated as inappropriate, the evaluation unit 14 determines whether the driver should have moved their muscles (step ST6). If the evaluation unit 14 determines that the driver should have moved their muscles, the output unit 15 outputs a first signal to the vibrator 32 attached to the driver, causing the vibrator 32 to vibrate in a first vibration pattern to stimulate the driver's muscles (step ST7). On the other hand, if the evaluation unit 14 determines that the driver should not have moved their muscles, the output unit 15 outputs a second signal to the vibrator 32 attached to the driver, causing the vibrator 32 to vibrate in a second vibration pattern to stimulate the driver's muscles (step ST8).

[0036] As described above, the support device 1 and support method evaluate the driver's driving by comparing the electromyography information of the driver 3 acquired by the electromyography information acquisition unit 11 with the predicted electromyography information estimated by the prediction unit 13, outputting a signal to stimulate the driver's muscles, and presenting the driving evaluation to the driver 3. Since the predicted electromyography information acquired by the prediction unit 13 reflects the body movements of a professional rider, the driver 3 can learn how to use their muscles based on the output from the support device 1. Therefore, this support device 1 and support method can contribute to improving the driver's motorcycle 5 driving skills.

[0037] Furthermore, by comparing the electromyographic information of driver 3 with predicted electromyographic information for multiple body parts to evaluate driver 3's driving operations, and by stimulating each body part, driver 3 can learn how to use the muscles in each part of their body, thereby efficiently improving their motorcycle 5 driving skills. In addition, by applying different vibration patterns of stimulation to the muscles depending on whether the muscles should have been moved or not, motorcycle 5 driving skills can be improved even more efficiently.

[0038] Furthermore, the support device 1 is not limited to the configuration shown in Figure 1. As shown in Figure 5, at least a portion of the prediction model 22, the learning electromyography information 24, and the learning behavior information 25 may be stored in the support device 1. In addition, each component of the support device 1 may be implemented in a user terminal (for example, a mobile phone, smartphone, tablet, etc.).

[0039] The block diagram shown in Figure 1 represents functional units. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one device that is physically or logically coupled, or it may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wired or wireless connections). A functional block may also be realized by combining software with the one or more devices described above.

[0040] Functions include, but are not limited to, judgment, decision, determination, calculation, calculation, processing, derivation, investigation, exploration, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, assumption, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating (mapping), and assigning. For example, a functional block (configuration part) that enables transmission is called a transmitting unit or transmitter. In all cases, as mentioned above, the method of implementation is not particularly limited.

[0041] For example, the support device 1 in one embodiment may function as a computer. FIG. 6 is a diagram showing an example of the hardware configuration of the support device 1 according to the present embodiment. Physically, the support device 1 may be configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, and the like.

[0042] In the following description, the term "device" can be read as a circuit, a device, a unit, or the like. The hardware configuration of the support device 1 may be configured to include one or more of the devices shown in FIG. 6, or may be configured without including some of the devices.

[0043] Each function in the support device 1 is realized by causing the processor 1001 to perform calculations by loading a predetermined software (program) onto hardware such as the processor 1001 and the memory 1002, and controlling communication by the communication device 1004 and reading and / or writing data in the memory 1002 and the storage 1003.

[0044] The processor 1001 controls the entire computer by operating an operating system, for example. The processor 1001 may be configured as a central processing unit (CPU: Central Processing Unit) including an interface with peripheral devices, a control device, an arithmetic device, a register, and the like. For example, each component shown in FIG. 1 may be realized by the processor 1001.

[0045] Furthermore, the processor 1001 reads programs (program code), software modules, and data from the storage 1003 and / or communication device 1004 into the memory 1002, and executes various processes accordingly. The program used is one that causes the computer to execute at least a part of the operations described in the above embodiment. For example, each component of the support device 1 may be stored in the memory 1002 and implemented by a control program that runs on the processor 1001. Although the above-described processes have been explained as being executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented on one or more chips. The program may also be transmitted from a network via a telecommunications line.

[0046] The memory 1002 is a computer-readable recording medium and may consist of at least one of the following: ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), RAM (Random Access Memory), etc. The memory 1002 may also be called a register, cache, main memory, etc. The memory 1002 can store executable programs (program code), software modules, etc., for carrying out an information processing method according to one embodiment of the present invention.

[0047] Storage 1003 is a computer-readable recording medium and may be composed of at least one of, for example, optical discs such as CD-ROM (Compact Disc ROM), hard disk drives, flexible disks, magneto-optical disks (e.g., compact discs, digital versatile discs, Blu-ray (registered trademark) discs), smart cards, flash memories (e.g., cards, sticks, key drives), floppy (registered trademark) disks, magnetic strips, etc. Storage 1003 may also be referred to as an auxiliary storage device. The above-mentioned storage medium may be, for example, a database including memory 1002 and / or storage 1003, a server, or other appropriate media.

[0048] Communication device 1004 is hardware (a transmission / reception device) for performing communication between computers via a wired and / or wireless network and is also referred to as, for example, a network device, a network controller, a network card, a communication module, etc.

[0049] Input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that receives external input. Output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that performs output to the outside. Note that input device 1005 and output device 1006 may have an integrated configuration (e.g., a touch panel).

[0050] Also, each device such as processor 1001 and memory 1002 is connected by a bus 1007 for communicating information. Bus 1007 may be composed of a single bus or may be composed of different buses between devices.

[0051] Furthermore, the support device 1 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), and an FPGA (Field Programmable Gate Array), and some or all of each functional block may be realized by such hardware. For example, the processor 1001 may be implemented using at least one of these hardware components.

[0052] The notification of information is not limited to the embodiments described herein and may be carried out by other means. For example, the notification of information may be carried out by physical layer signaling (e.g., DCI (Downlink Control Information), UCI (Uplink Control Information)), upper layer signaling (e.g., RRC (Radio Resource Control) signaling, MAC (Medium Access Control) signaling, broadcast information (MIB (Master Information Block), SIB (System Information Block))), other signals, or combinations thereof. RRC signaling may also be called RRC messages, and may be, for example, RRC Connection Setup messages, RRC Connection Reconfiguration messages, etc.

[0053] Each aspect / embodiment described in this disclosure may be applied to at least one of the following systems: LTE (Long Term Evolution), LTE-A (LTE-Advanced), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), FRA (Future Radio Access), NR (new Radio), W-CDMA®, GSM®, CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi®), IEEE 802.16 (WiMAX®), IEEE 802.20, UWB (Ultra-WideBand), Bluetooth®, and other appropriate systems, as well as next-generation systems extended based thereon. Furthermore, multiple systems may be applied in combination (for example, a combination of at least one of LTE and LTE-A with 5G).

[0054] The processing procedures, sequences, flowcharts, etc., of each aspect / embodiment described in this disclosure may be reordered, provided they do not contradict each other. For example, the methods described in this disclosure present various step elements using exemplary order and are not limited to the specific order presented.

[0055] The specific operations described in this disclosure as being performed by a base station may, in some cases, be performed by its upper node. In a network consisting of one or more network nodes having a base station, it is clear that various operations performed for communication with a terminal can be performed by the base station and at least one other network node (for example, an MME or S-GW, but not limited to these). Although the above example illustrates the case where there is one other network node besides the base station, it may also be a combination of multiple other network nodes (for example, an MME and an S-GW).

[0056] Information can be output from a higher layer (or lower layer) to a lower layer (or higher layer). Input and output may also occur via multiple network nodes.

[0057] Input and output information may be stored in a specific location (e.g., memory) or managed in a management table. Input and output information may be overwritten, updated, or appended to. Output information may be deleted. Input information may be sent to other devices.

[0058] The determination may be made by a value represented by one bit (0 or 1), by a boolean value (true or false), or by a numerical comparison (for example, a comparison with a predetermined value).

[0059] Each aspect / embodiment described in this disclosure may be used individually, in combination, or switched between as needed during implementation. Furthermore, notification of specific information (e.g., notification that "X is") is not limited to explicit notification, but may also be implicit (e.g., by not providing such notification).

[0060] Although the present disclosure has been described in detail above, it will be clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the intent and scope of the present disclosure as defined by the claims. Therefore, the descriptions in the present disclosure are illustrative and not intended to be restrictive in any way.

[0061] Software should be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, and so on, whether they are called software, firmware, middleware, microcode, hardware description languages, or by any other name.

[0062] Furthermore, software, instructions, etc., may be transmitted and received via a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies such as coaxial cable, fiber optic cable, twisted pair, and digital subscriber lines (DSL) and / or wireless technologies such as infrared, radio, and microwave, these wired and / or wireless technologies are included in the definition of a transmission medium.

[0063] The information, signals, etc. described in this disclosure may be represented using any of the various different techniques. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0064] In addition, terms described in this disclosure and / or terms necessary for understanding this specification may be replaced with terms having the same or similar meaning.

[0065] The terms “system” and “network” as used in this disclosure are interchangeable.

[0066] Furthermore, the information, parameters, etc., described in this disclosure may be expressed as absolute values, relative values ​​from a given value, or by corresponding other information. For example, wireless resources may be indicated by an index.

[0067] The names used for the parameters described above are not restrictive in any way. Furthermore, the formulas and other expressions using these parameters may differ from those expressly disclosed in this disclosure. Various channels (e.g., PUCCH, PDCCH, etc.) and information elements can be identified by any suitable name, and therefore, the various names assigned to these various channels and information elements are not restrictive in any way.

[0068] As used in this disclosure, the terms “determining” and “determining” may encompass a wide variety of actions. “Determining” may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, or inquiring (e.g., searching in a table, database, or other data structure), or ascertaining. “Determining” may also include receiving (e.g., receiving information), transmitting (e.g., sending information), inputting, outputting, or accessing (e.g., accessing data in memory). Furthermore, "judgment" and "decision" can include considering something as having been "judged" or "decided" after resolving, selecting, choosing, establishing, comparing, etc. In other words, "judgment" and "decision" can include considering something as having been "judged" or "decided" after some action. Also, "judgment (decision)" can be reinterpreted as "assuming," "expecting," or "considering."

[0069] As used in this disclosure, the phrase "based on" does not mean "based solely on" unless otherwise specified. In other words, the phrase "based on" means both "based solely on" and "based on at least."

[0070] Where the terms “first,” “second,” etc., are used in this disclosure, no reference to those elements shall generally limit the quantity or order of those elements. These terms may be used herein as a convenient way to distinguish between two or more elements. Accordingly, references to the first and second elements shall not imply that only two elements may be employed therein, or that the first element must precede the second element in any way.

[0071] In the configuration of each of the above devices, "means" may be replaced with "part," "circuit," "device," etc.

[0072] To the extent that “include,” “including,” and their variations are used herein or in the claims, these terms are intended to be inclusive, as is the term “comprising.” Furthermore, as used herein or in the claims, the term “or” is not intended to be exclusive OR.

[0073] In this disclosure, if articles are added through translation, such as a, an, and the in English, this disclosure may include the fact that the noun following these articles is plural.

[0074] In this disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "combine" may be interpreted similarly to "different."

[0075] The support device 1 and support method of this disclosure may have the following configurations.

[0076] [1] A device for supporting the improvement of driving skills of a vehicle, comprising: an electromyography information acquisition unit that acquires electromyography information indicating muscle movements of a driver operating the vehicle at a first time point in time; a behavior information acquisition unit that acquires behavior information indicating the behavior of the vehicle during a period including a second time point prior to the first time point; a prediction unit that uses a trained prediction model to predict the electromyography information at the first time point from the behavior information acquired by the behavior information acquisition unit; an evaluation unit that compares the electromyography information of the driver acquired by the electromyography information acquisition unit with the electromyography information predicted by the prediction unit to evaluate the driver's driving; and an output unit that outputs information indicating the evaluation of the driver's driving, wherein the prediction model is trained using electromyography information indicating muscle movements of an expert and behavior information indicating the behavior of a vehicle operated by the expert as training data.

[0077] [2] The apparatus according to claim 1, wherein the electromyography information includes an electromyography waveform showing changes in electromyographic potential over time, the evaluation unit determines whether the mean square error between the electromyography waveform of the driver acquired by the electromyography information acquisition unit and the electromyography waveform predicted by the prediction unit is greater than or equal to a reference value, and the output unit outputs information indicating an evaluation of the operation to the driver when the mean square error is greater than or equal to a reference value.

[0078] [3] The apparatus according to claim 1 or 2, wherein the output unit outputs a signal for stimulating the driver's muscles as information indicating the evaluation of the operation.

[0079] [4] The apparatus according to any one of claims [1] to [3], wherein the electromyography information includes first electromyography information indicating the movement of muscles in a first part of the driver and second electromyography information indicating the movement of muscles in a second part of the driver, the evaluation unit compares the electromyography information of the driver acquired by the electromyography information acquisition unit with the electromyography information predicted by the prediction unit to determine whether or not the muscles in the first part and the muscles in the second part should have been moved, the output unit outputs a first signal for stimulating the muscles in the first part and the muscles in the second part that were determined to have been moved, and outputs a second signal for stimulating the muscles in the first part and the muscles in the second part that were determined not to have been moved, with a stimulus different from the stimulus of the first signal.

[0080] [5] The apparatus according to any one of items [1] to [4], wherein the vehicle is a motorcycle.

[0081] [6] The apparatus according to any one of [1] to [5], wherein the behavior information includes information indicating the acceleration or steering angle of the vehicle.

[0082] [7] A method for supporting the improvement of driving skills of a vehicle, comprising: acquiring electromyography information indicating muscle movements of a driver operating the vehicle at a first time point; acquiring behavioral information indicating the behavior of the vehicle during a period including a second time point prior to the first time point; predicting the electromyography information at the first time point from the behavioral information acquired in the step of acquiring the behavioral information using a trained predictive model; evaluating the driver's driving by comparing the electromyography information of the driver acquired in the step of acquiring the electromyography information with the electromyography information predicted in the step of predicting the electromyography information; and outputting information indicating the evaluation of the driver's driving, wherein the predictive model is trained using electromyography information indicating muscle movements of an expert and behavioral information indicating the behavior of a vehicle operated by the expert as training data.

[0083] 1...Support device, 3...Driver, 5...Motorcycle, 11...Electrical muscle information acquisition unit, 12...Behavioral information acquisition unit, 13...Prediction unit, 14...Evaluation unit, 15...Output unit, 22...Prediction model.

Claims

1. A device for supporting the improvement of vehicle driving skills, comprising: an electromyography (EMG) information acquisition unit that acquires EMG information indicating muscle movements of a driver operating the vehicle at a first time point; a behavior information acquisition unit that acquires behavior information indicating the behavior of the vehicle during a period including a second time point prior to the first time point; a prediction unit that uses a trained prediction model to predict the EMG information at the first time point from the behavior information acquired by the behavior information acquisition unit; an evaluation unit that compares the EMG information of the driver acquired by the EMG information acquisition unit with the EMG information predicted by the prediction unit to evaluate the driver's driving; and an output unit that outputs information indicating the evaluation of the driver's driving, wherein the prediction model is trained using EMG information indicating muscle movements of an expert and behavior information indicating the behavior of a vehicle operated by the expert as training data.

2. The apparatus according to claim 1, wherein the electromyography information includes an electromyography waveform showing a change in electromyographic potential over time, the evaluation unit determines whether the mean square error between the electromyography waveform of the driver acquired by the electromyography information acquisition unit and the electromyography waveform predicted by the prediction unit is greater than or equal to a reference value, and the output unit outputs information indicating an evaluation of the operation to the driver when the mean square error is greater than or equal to a reference value.

3. The apparatus according to claim 1, wherein the output unit outputs a signal for stimulating the driver's muscles as information indicating an evaluation of the operation.

4. The apparatus according to claim 1, wherein the electromyography information includes first electromyography information indicating the movement of muscles in a first part of the driver and second electromyography information indicating the movement of muscles in a second part of the driver, the evaluation unit compares the electromyography information of the driver acquired by the electromyography information acquisition unit with the electromyography information predicted by the prediction unit to determine whether or not the muscles in the first part and the muscles in the second part should have been moved, and the output unit outputs a first signal to stimulate the muscles in the first part and the muscles in the second part that were determined to have been moved, and outputs a second signal to stimulate the muscles in the first part and the muscles in the second part that were determined not to have been moved, with a stimulus different from the stimulus of the first signal.

5. The apparatus according to claim 1, wherein the vehicle is a motorcycle.

6. The apparatus according to claim 1, wherein the behavior information includes information indicating the acceleration or steering angle of the vehicle.

7. A method for supporting the improvement of vehicle driving skills, comprising: acquiring electromyography information indicating muscle movements of a driver operating the vehicle at a first time point; acquiring behavioral information indicating the behavior of the vehicle during a period including a second time point prior to the first time point; predicting the electromyography information at the first time point from the behavioral information acquired in the step of acquiring the behavioral information using a trained predictive model; evaluating the driver's driving by comparing the electromyography information of the driver acquired in the step of acquiring the electromyography information with the electromyography information predicted in the step of predicting the electromyography information; and outputting information indicating the evaluation of the driver's driving, wherein the predictive model is trained using electromyography information indicating muscle movements of an expert and behavioral information indicating the behavior of a vehicle operated by the expert as training data.

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