JP5867680B2 - Exercise instruction device, exercise analysis device, exercise instruction program, and recording medium - Google Patents

Description

  The present invention relates to an exercise instruction device, an exercise instruction program, and a recording medium on which the exercise instruction program is recorded.

  Guidance by good coaches and trainers is said to be a quick way to improve sports. For example, in golf, there is a service for taking a lesson directly from a teacher called a lesson pro, and a service for sending a video of one's own play for advice. However, it is difficult for general sports enthusiasts to spend money and time for coaches, so they often use commercially available training equipment to practice and improve themselves. Many of these practice devices simply capture and display the movements of the practitioner and the ball, but the motion analysis alone is insufficient to improve the exercise. Therefore, as a result of the analysis, an exercise training machine has been devised that teaches points to be improved. For example, in Patent Document 1, there is a golf improvement support device that notifies a point to be improved (the head is staggered, the movement of the line of sight of a spherical muscle is not smooth) from the movement of the golf swing arm and head by an image and sound. Proposed.

  In addition, devices have been devised that display the difference between ideal forms and movements taught by professional players and instructors. For example, in the exercise practice goal attainment system of Patent Document 2, an ideal movement considering the physical ability of the individual practitioner and the result of comparing one's movement are displayed. The practitioner can use this comparison result as a reference for improving exercise. In addition, for example, the body motion analysis apparatus of Patent Document 3 performs motion analysis on an image, compares the analysis result of the operator with the data of the expert, and gives advice that approaches the expert more. For example, as a result of analyzing the movement of the arm of the operator, if the shake is measured between 60 ° and 120 ° as the angle formed by the shoulder, elbow and wrist, the shake data of 80 ° to 100 ° is used in the expert data. If not, advice such as “Please keep the arm angle at 90 °” is provided.

  By using such a conventional exercise training machine, you can compare your ideal movement and your movement visually, so it is easier to understand what to do compared to a system that shows the movement trajectory etc. There is. However, these exercise training machines record the ideal movement (trajectory) by actual movement by professional players and instructors, and convert it into data suitable for comparison with the exerciser's movement in advance. Preparation is necessary. Guidance is difficult without this ideal motion data, but the task of creating ideal motion data is to have professional players and instructors actually move, as well as manual analysis by analysts who analyze the motion. In addition, there is a problem that the ideal motion data depends on the subjectivity of the analyst.

JP 2009-125507 A JP 2002-248187 A Japanese Patent Laid-Open No. 10-111940 JP-A-7-313648 JP-A-4-270372

  In order to solve this problem, Patent Document 4 and Patent Document 5 model basic human movements, and solve the dynamic equations of the motion model using the forces acting on the joints and the range of joint movements as constraints. In addition, a method has been devised to obtain a motion that can be used as a reference for training. According to these methods, it is possible to obtain training data mechanically and computationally without having an ideal motion data in advance without having a professional player or instructor actually move.

  However, in these methods, in order to obtain training data that fits the practitioner, the analyst accesses the database and gives feedback to obtain the result of solving the dynamic equation of the motion model for each practitioner. There is a problem that operation is required and it is difficult for the practitioner to use it easily.

  The present invention has been made in view of the above problems, and according to some aspects of the present invention, it provides guidance information suitable for individuality such as a user's body shape and movement habit. In addition, it is possible to provide an exercise instruction device, an exercise instruction program, and a recording medium that records the exercise instruction program that can be easily used by the user.

  (1) The present invention relates to a storage unit that stores a motion model including a human body model obtained by modeling at least a part of the structure of a human body and parameters of the human body model, a sensor worn by a user, and data from the sensor A data acquisition unit that acquires the parameter, a constraint condition generation unit that generates a constraint condition of the parameter according to the user based on the acquired data, a calculation using the motion model, and the parameter is the constraint A motion analysis unit that analyzes a suitable motion of the human body model in a range that satisfies a condition, and a guidance information generation unit that generates motion guidance information based on the analysis result of the motion analysis unit, It is an exercise guidance device.

  According to the present invention, a suitable motion (ideal motion) is analyzed using a motion model prepared in advance, and motion instruction information is generated based on the analysis result. Can be taken and the trouble of editing the information for guidance can be eliminated. Therefore, the user can easily use the exercise instruction apparatus of the present invention.

  In addition, according to the present invention, data is acquired from a sensor worn by the user, a constraint condition for the parameters of the human body model is generated for each user, and a suitable movement of the human body model within a range satisfying the constraint condition is analyzed. It is possible to provide guidance information suitable for individuality such as the user's body shape and movement habit.

  (2) In this exercise instruction apparatus, the instruction information generation unit calculates the movement of the human body model corresponding to the movement of the user based on the acquired data, and the movement of the human body model is determined as the suitable movement. The movement of the user may be evaluated by comparing with the above.

  In this way, since the user can see the difference between his / her movement and a suitable movement that suits him / her, the movement can be effectively improved.

  (3) In this exercise instruction device, the exercise analysis unit may calculate a theoretical maximum speed of a node provided in a part of the human body model within a range that satisfies the constraint condition.

  In many sports competitions, it is possible to improve the competitiveness by making movements that maximize the speed of joints, segments, end points, etc. farthest from the center of rotation. For example, pushing movements such as cannonball throwing and jumping crossing movements will maximize the extension speed of the upper and lower limbs, and movements such as pitching, throwing, disc throwing, and kicking will maximize the tangential speed of the wrist joints and legs. The movement is ideal. Therefore, by calculating the theoretical maximum speed of a specific node of the human body model, a suitable movement can be analyzed.

  (4) In this exercise guidance device, the guidance information generation unit calculates the speed of the node of the human body model based on the acquired data, and compares it with the theoretical maximum speed of the node, The user's movement may be evaluated.

  In this way, by comparing the speed of a specific node of the human body model calculated from the user's movement with the theoretical maximum speed of the specific node, the degree of difference between the user's movement and the preferred movement can be reduced. Can be evaluated.

  In addition, since the difference between the user's movement and the suitable movement can be evaluated without calculating the path difference, the amount of calculation can be greatly reduced, and the real-time property can be improved.

  (5) In this exercise instruction apparatus, the node may be a node provided at a terminal portion of the human body model.

  (6) In this exercise instruction apparatus, the storage unit stores a plurality of the exercise models, and the constraint condition generation unit selects an exercise model according to the type of exercise performed by the user from the plurality of exercise models. Then, the constraint condition of the parameter of the selected motion model may be calculated.

  In this way, the user can receive guidance for multiple types of exercise.

  (7) In this exercise instruction apparatus, there are a plurality of sensors, and the storage unit stores sensor selection information for selecting a sensor from which data is to be acquired from the plurality of sensors, and the data acquisition unit is Data may be acquired from a sensor selected by the sensor selection information corresponding to the type of exercise performed by the user.

  Since the type of sensor required for analysis and the place where it should be worn differ depending on the type of exercise, data is acquired from the necessary sensors according to the type of exercise in this way (data is not acquired from unnecessary sensors). As a result, the load of data communication can be reduced. Further, if only necessary sensors are activated according to the type of exercise (unnecessary sensors are not activated), power consumption can be reduced.

  (8) In this exercise instruction apparatus, there are a plurality of sensors, and the storage unit stores sensor selection information for selecting a sensor from which data is to be acquired from the plurality of sensors, and generates the instruction information The unit may present information about a sensor selected by the sensor selection information corresponding to a type of exercise performed by the user to the user.

  In this way, the user only has to install the necessary sensors, so the burden on the user can be reduced.

  (9) The present invention includes a storage unit that stores a motion model including a human body model obtained by modeling at least a part of the structure of a human body and parameters of the human body model, and the computer is attached to a user. A data acquisition unit that acquires data from the sensor, a constraint condition generation unit that generates a constraint condition of the parameter according to the user based on the acquired data, and a calculation using the motion model, As a motion analysis unit that analyzes a suitable motion of the human body model within a range where the parameters satisfy the constraint conditions, and a guidance information generation unit that generates motion guidance information based on the analysis result of the motion analysis unit A functioning exercise program.

  (10) The present invention is a computer-readable recording medium on which the exercise instruction program is recorded.

The figure which shows the structural example of the exercise | movement instruction apparatus of this embodiment. The figure which shows the example of arrangement | positioning of the component of an exercise | movement instruction apparatus. The figure for demonstrating the example of mounting | wearing of a sensor module. The figure which shows the structural example of basic data. The figure for demonstrating the exercise | movement model of an arm. The figure which shows an example of the data obtained from a motion sensor. The figure for demonstrating the exercise | movement model of a leg | foot. The figure which shows an example of the flowchart of the operation | movement which a user performs. The figure which shows an example of the flowchart of the whole process of a process part (CPU). The figure which shows an example of the flowchart of a process of acquisition of sensor data and calculation of constraint conditions. The figure for demonstrating the example of calculation of a constraint condition. The figure which shows an example of the flowchart of a process of acquisition of sensor data and evaluation of a user's movement. The figure which shows the flowchart of the operation | movement which the user in the modification 1 performs. The figure which shows the flowchart of the whole process of the process part (CPU) in the modification 1. The figure which shows the example of a display of the mounting instruction | indication of the sensor module in the modification 1. FIG. The figure which shows the flowchart of a process of acquisition of the sensor data in a modification 2, and evaluation of a user's motion.

  DESCRIPTION OF EMBODIMENTS Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the drawings. The embodiments described below do not unduly limit the contents of the present invention described in the claims. Also, not all of the configurations described below are essential constituent requirements of the present invention.

1. Configuration of Exercise Guidance Device FIG. 1 is a diagram illustrating a configuration example of an exercise guidance device according to the present embodiment. Moreover, FIG. 2 is a figure which shows the example of arrangement | positioning of the component of an exercise | movement instruction apparatus. In this embodiment, an explanation will be given by taking an exercise instruction apparatus for instructing golf as an example. However, the present invention is not limited to an exercise instruction apparatus for instructing various sports such as tennis or baseball, or a sport such as a rehabilitation exercise. The present invention can also be applied to an exercise guidance device that provides exercise guidance.

  The exercise instruction apparatus 1 according to the present embodiment includes a plurality of sensor modules 10, a controller 20, a head mounted display (HMD) 30, speakers 40, and a recording medium 50. However, the exercise instruction apparatus 1 of the present embodiment may have a configuration in which some of these configurations (elements) are omitted or a new configuration (element) is added.

  The sensor module 10 is necessary for calculating values of each parameter of the motion model described later, such as an acceleration sensor (3 axes), a gyro sensor (3 axes), an infrared sensor, a magnetic direction sensor (3 axes), and a pressure sensor. At least one sensor for acquiring accurate data is provided. For example, information such as speed, position, and degree of impact can be obtained from an acceleration sensor, a gyro sensor, and a pressure sensor. Further, information on the distance between two points can be obtained from two infrared sensors. Further, position information can be obtained from the magnetic direction sensor.

  As shown in FIG. 2, the sensor module 10 is mounted on, for example, the head, shoulders, waist, elbows, wrists, knees, ankles, and heels around the user's joints. For example, as shown in FIG. 2, the sensor module 10 may be affixed on the user's clothes, or as shown in FIG. 3, the sensor module 10 is attached to the band 14 and the band 14 is attached to each part of the user. You may make it wear. A part of the sensor modules 10 may be attached to the exercise equipment.

  The sensor module 10 may include only the minimum necessary sensors that can acquire the information to be acquired because the information to be acquired is not necessarily the same depending on the mounting location.

  The controller 20 wirelessly transmits control data for starting or stopping to the sensor module 10, and data from each activated sensor module 10 is wirelessly transmitted to the controller 20. For example, as shown in FIG. 2, the controller 20 may be attached to the player's waist or the like, or may be located away from the player.

  The controller 20 according to the present embodiment includes a processing unit (CPU) 22, an output unit 24, and a storage unit 26, acquires data from the sensor module 10, and generates and displays information for exercise guidance using the acquired data. I do.

  In particular, in the present embodiment, the processing unit (CPU) 22 includes a data acquisition unit 222, a constraint condition generation unit 224, an exercise analysis unit 226, and a guidance information generation unit 228, which will be described below, and analyzes the movement of the user. , Guidance data (guidance information) is generated. However, the processing unit (CPU) 22 of the present embodiment may have a configuration in which some of these configurations (elements) are omitted or a new configuration (element) is added.

  The recording medium 50 is a computer-readable recording medium, and stores an exercise instruction program for causing the computer to function as each of the above-described units. The processing unit 20 of the present embodiment functions as a data acquisition unit 222, a constraint condition generation unit 224, an exercise analysis unit 226, and an instruction information generation unit 228 by executing an exercise instruction program stored in the recording medium 50. To do. Alternatively, a communication unit is added to the controller 20, an exercise instruction program is received from the server via a wired or wireless communication network via the communication unit, and the received exercise instruction program is stored in the storage unit 26 or the recording medium 50. The exercise instruction program may be executed. However, at least a part of the data acquisition unit 222, the constraint condition generation unit 224, the exercise analysis unit 226, and the instruction information generation unit 228 may be realized by hardware (a dedicated circuit).

  The recording medium 50 can be realized by, for example, an optical disk (CD, DVD), a magneto-optical disk (MO), a magnetic disk, a hard disk, a magnetic tape, or a memory (ROM, flash memory, etc.).

  The data acquisition unit 222 performs a process of continuously acquiring data from each sensor included in each sensor module 10 at a predetermined time interval.

  Based on the data acquired by the data acquisition unit 222, the constraint condition generation unit 224 performs a process of generating a constraint condition of a human body model parameter, which will be described later, according to the user. Specifically, the constraint condition generation unit 224 is a range in which each part of the user can actually move based on the data acquired by the data acquisition unit 222 according to the type of competition and the type of practice selected by the user (movable range). And processing for generating constraints such as maximum speed, torque, and trajectory habit. These various constraints are stored in the personal exercise data 262 in the storage unit 26 in association with the user identification information.

  The motion analysis unit 226 performs calculation using a motion model, which will be described later, and performs a process of analyzing a suitable motion of the human body model in a range where the parameters of the human body model satisfy the constraint conditions generated by the constraint condition generation unit 224. Do.

  For example, the motion analysis unit 226 may calculate the theoretical maximum speed of the node provided in the part of the human body model within a range that satisfies the constraint conditions. Specifically, the motion analysis unit 226 is provided at the end of the human body model according to the type of competition and the type of practice selected by the user based on the constraint conditions generated by the constraint condition generation unit 224. A speed at which the node moves most efficiently (maximum end speed) is calculated, and a process for calculating an operation condition such as a parameter value for achieving the maximum end speed is performed. “Speed” here is a broad concept, and any speed such as an extension speed, a tangential speed, an acceleration, and an angular speed is selected according to the type of competition and the type of practice selected.

  In the present embodiment, the motion analysis unit 226 obtains information on the motion model corresponding to the type of competition and the type of practice selected by the user and the calculation formula of the end point speed from the basic data 264 stored in the storage unit 26. Read out and calculate the maximum end point speed by applying constraints to this formula.

  Note that the motion analysis unit 226 according to the present embodiment analyzes a suitable motion using the constraint conditions stored in the personal motion data 262 after the constraint conditions are stored in the personal motion data 262.

  The guidance information generation unit 228 performs processing for generating exercise guidance information based on the analysis result of the exercise analysis unit 226. Specifically, the guidance information generation unit 228 uses the original data included in the basic data 264 to perform a suitable movement (ideal movement suitable for the user's body shape, movement habit, and the like based on the analysis result of the movement analysis unit 226. A process of generating image information for guidance representing a form) is performed. The guidance information generation unit 228 may further generate voice information for guidance for instructing an ideal form.

  The instruction image information generated by the instruction information generation unit 228 is output to the HMD 30 via the output unit 24 and displayed. Also, the guidance voice information generated by the guidance information generation unit 228 is output to the speaker 40 via the output unit 24 and is converted into voice by the speaker 40. In addition to the HMD 30 and the speaker 40, the instruction information may be output to a wristwatch-type device or a display or speaker of a mobile phone, or a device such as a monitor or a speaker such as a personal computer. You may make it output.

  Note that the guidance information generation unit 228 may generate guidance information other than guidance information based on images and sounds. For example, the guidance information generation unit 228 may generate vibration information and vibrate a vibration device (not shown) via the output unit 24.

  The user can approach an ideal form suitable for the user's body shape and personality by practicing with reference to the guidance images and sounds provided from the HMD 30 and the speaker 40. Specifically, the data acquisition unit 222 acquires data of movements practiced by the user from each sensor module 10. Then, the guidance information generation unit 228 calculates the movement of the human body model corresponding to the movement of the user based on the data acquired by the data acquisition unit 222, and the motion analysis unit 226 analyzes the movement of the human body model. The user's movement is evaluated by comparing with the movement.

  In the present embodiment, the guidance information generation unit 228 calculates the speed of a specific node of the human body model based on the user's practice data acquired by the data acquisition unit 222, and the specific node in a range satisfying the constraint condition The user's movement may be evaluated by comparing with the theoretical maximum speed. Specifically, the motion analysis unit 226 calculates the speed (end point speed) of a node provided at the end of the human body model from the data acquired by the data acquisition unit 222, and the maximum end point calculated by the motion analysis unit 226. It is determined whether or not a predetermined rate of speed has been achieved. If not achieved, a difference between the user's form and the ideal form may be displayed.

  FIG. 4 is a diagram illustrating a configuration example of the basic data 264. In this embodiment, the basic data 264 includes definition data necessary to generate competition menu information, practice menu information, and guidance information, and each practice data for each competition type in the competition menu. Menus are associated with each other, and individual definition data is associated with each practice menu. For example, there are practice menus such as driver shots, approach shots, and bunker shots in association with golf (type of competition), and definition data is associated with each practice menu. The definition data includes an exercise model, an end point velocity calculation formula, sensor selection information, instruction data, and the like.

  The motion model is a human body model in which at least a part of the human body is simply modeled by a line connecting the nodes, the coordinate system and parameters of this human body model, and the allowable range of each parameter value (a realistic range) And the upper limit value (the upper limit value that can be taken practically) of the end point speed is defined.

  The calculation formula of the end point speed is an expression for calculating the speed of the end node (end point node) farthest from the origin (rotation center) of the human body model using the parameters of the human body model as variables.

  The sensor selection information is selection information such as the type of sensor module 10 (the type of sensor included) and the mounting location necessary for acquiring data relating to user movement.

  The instruction original data is original data for the instruction information generation unit 228 to generate an image of the movement and instruction voice in order to indicate a movement to be performed by the user to acquire the constraint condition. The original data may be image frame data or time series data of parameter values of a human body model. When the former data is used, the HMD 30 displays a realistic moving image (frame image), and when the latter data is used, a human skeleton or an animated image (CG image) of the human body is displayed. Is done.

  The guidance source data is also used as source data for the guidance information generation unit 228 to generate guidance image information and sound information such as an ideal form according to the constraint conditions. For example, if the instruction source data is image frame data, the instruction information generation unit 228 processes the image frame data according to the constraint condition to generate an ideal form moving image and displays it on the HMD 30. In addition, when the instruction source data is time series data of the parameter values of the human body model, the instruction information generation unit 228 changes the time series data according to the constraint condition or represents the human body model representing the ideal form. An animation image of the model fleshed out is generated and displayed on the HMD 30.

2. Exercise Model FIG. 5 is a diagram for explaining an arm exercise model. As shown in FIG. 5, the arm motion model includes, for example, a node N ₀ corresponding to the shoulder, a node N ₁ corresponding to the elbow, a node N ₂ corresponding to the wrist, and a straight line S connecting the node N ₀ and the node N _{1. 1} , a human body model composed of a straight line S ₂ connecting the node N ₁ and the node N ₂ , a coordinate system of this human body model (xyz coordinate system with the node N ₀ as an origin) and parameters (θ ₁ , θ ₂ , L ₁ , L _2), in which the upper limit value, such as formulas node rate of N ₂ (end point velocity) V is defined tolerances and node N ₂ velocity V values of each parameter (corresponding to the end point velocity). Here, θ ₁ is an angle formed by the straight line S ₁ and the straight line S ₂ , θ ₂ is an angle formed by one specific axis (for example, the x axis) and the straight line S ₁ , and L ₁ is the length of the straight line S ₁ . And L ₂ is the length of the straight line S ₂ .

Such an arm motion model is used to provide an ideal form of arm. On the other hand, in order to acquire the actual movement of the arm, as shown in FIG. 6, the sensor module 10 is attached to the shoulder, elbow, and wrist of the user's arm using, for example, a band 14 as shown in FIG. . The coordinate system of the sensor module to be mounted on the shoulder (the coordinate system of the 3-axis acceleration sensor or 3-axis gyro sensor) is adjusted to the coordinate system of the motion model. At this time, the angle formed by the central axis of the upper arm and the central axis of the lower arm corresponds to θ _1, and the angle formed by one specific axis (for example, the x axis) of the sensor module mounted on the shoulder and the central axis of the upper arm is θ _2. Corresponding to The length of the upper arm (the distance of the shoulder and elbow) corresponds to L _1, the length of the lower arm (Distance elbow and wrist) corresponds to L _2.

θ ₁ and θ ₂ can be calculated from the integration results of the gyro sensor, and the positions of the nodes N ₁ and N ₂ (relative positions with respect to the node N ₀ ) can be calculated from the integration results of the acceleration sensor and the magnetic direction sensor. It is. Further, the speed of node N ₂ (corresponding to end point speed V) can be calculated from the integration result of the acceleration sensor worn on the wrist. Further, L ₁ and L ₂ can be measured using an infrared sensor or can be calculated from the calculation results of each node. Alternatively, assuming that L ₁ and L ₂ are unchanged, L ₁ and L ₂ may be measured and input to the basic data 264 when the sensor module 10 is mounted.

In addition, for example, a foot motion model as shown in FIG. 7 can be considered. The foot motion model includes a node N ₀ (origin) corresponding to the base of the foot, a node N ₁ corresponding to the knee, a node (end node) N ₂ corresponding to the ankle, and a straight line connecting the node N ₀ and the node N _1. S ₁ , a human body model composed of a straight line S ₂ connecting the node N ₁ and the node N ₂ , a coordinate system of this human body model (xyz coordinate system with the node N ₀ as an origin) and parameters (θ ₁ , θ ₂ , L ₁ , L _2), in which the upper limit value of the acceptable range and the node N ₂ velocity V values of each parameter (corresponding to the end point velocity), such as the node N ₂ velocity (end speed) V of the formula is defined . Here, θ ₁ is an angle formed by the straight line S ₁ and the straight line S ₂ , θ ₂ is an angle formed by one specific axis (for example, the x axis) and the straight line S ₁ , and L ₁ is the length of the straight line S ₁ . And L ₂ is the length of the straight line S ₂ .

  As an actual motion model such as a golf driver shot, for example, an arm motion model shown in FIG. 5, a foot motion model shown in FIG. 7, and other motion models combining necessary motion models are defined. Is done.

3. Process of Exercise Guidance Device FIG. 8 is a diagram showing an example of a flowchart of an operation performed by a user who uses the exercise guidance device 1, and FIG. 9 is a diagram showing an example of a flowchart of the overall processing of the processing unit (CPU) 22. It is.

  First, the user wears the sensor module 10, the controller 20, the HMD 30, and the speaker 40, and activates the exercise instruction apparatus 1 (S10 in FIG. 8).

  The processing unit (CPU) 22 displays the competition menu on the HMD 30 (S30 in FIG. 9) and waits until the user selects the type of competition (N in S32 in FIG. 9). Information of this competition menu is included in the basic data 264, and the processing unit (CPU) 22 refers to the basic data 264 and displays it on the HMD 30. In the example of FIG. 4, “tennis”, “golf”, “baseball”, “table tennis”,... Are displayed as the competition menu.

  When the user selects the type of competition according to the display on the HMD 30 (S12 in FIG. 8, Y in S32 in FIG. 9), the processing unit (CPU) 22 displays an exercise menu associated with the type of competition selected by the user. Displayed on the HMD 30 (S34 in FIG. 9) and waits until the user selects the type of practice (N in S36 in FIG. 9). The information on the practice menu is information on the practice menu that is included in the basic data 264 and associated with the competition selected by the user, and is displayed on the HMD 30 by the processing unit (CPU) 22 with reference to the basic data 264. In the example of FIG. 4, when “golf” is selected by the user in S <b> 12 of FIG. 8, “driver shot”, “approach shot”, “bunker shot”,.

  When the user selects the type of practice according to the display on the HMD 30 (S14 in FIG. 8, Y in S36 in FIG. 9), the processing unit (CPU) 22 performs processing for obtaining sensor data and calculating constraint conditions ( S38 of FIG. 9). FIG. 10 is a diagram showing an example of a flowchart of the process of S38. First, the processing unit (CPU) 22 displays a basic motion image corresponding to the type of exercise selected (type of competition and practice) (S110 in FIG. 10). The basic motion image is created by the processing unit (CPU) 22 with reference to the basic data 264 and using the instruction source data included in the definition data associated with the type of exercise selected by the user. Displayed on the HMD 30. In the example of FIG. 4, when “golf” is selected in S12 of FIG. 8 and “driver shot” is selected in S14, a golf driver shot image (frame image or CG image) is displayed. By displaying the basic motion image on the HMD 30, the user can move without shifting his / her eyes.

  The processing unit (CPU) 22 starts displaying the basic motion image and activates the sensor module 10 (S112 in FIG. 10). In this embodiment, in S10 of FIG. 8, the user wears the sensor module 10 on each part of the body centering on the joint, and the processing unit (CPU) 22 refers to the basic data 264 and exercises selected by the user. The plurality of sensor modules 10 are selected according to the sensor selection information included in the definition data associated with the type of the selected one, and the selected plurality of sensor modules 10 are activated.

  Next, the processing unit (CPU) 22 starts acquisition of sensor data from the sensor module 10 activated in S112 of FIG. 10 (S114 of FIG. 10). For example, when the user presses a start switch (not shown) provided in the controller 20 or the like, the processing unit (CPU) 22 may start acquisition of sensor data. A message such as “Please start exercise” may be displayed or output as a voice, and acquisition of sensor data may be started.

  Then, the user performs the instructed movement in accordance with the displayed basic movement image (S16 in FIG. 8). In the example of FIG. 4, when “golf” is selected in S <b> 12 of FIG. 8 and “driver shot” is selected in S <b> 14, a golf driver shot image (frame image or CG image) is displayed. Let's actually make a driver shot move.

  The processing unit (CPU) 22 continues to acquire the sensor data while the user is exercising (N in S116 in FIG. 10), and when the acquisition of the sensor data is finished (Y in S116 in FIG. 10), in S112 in FIG. The activated sensor module 10 is stopped (S118 in FIG. 10). For example, when the user presses an end switch (not shown) provided in the controller 20 or the like, the processing unit (CPU) 22 may end the acquisition of sensor data, or the processing unit (CPU) 22 It may be determined whether or not the user's exercise has ended from the change in the acquired sensor data. If it is determined that the exercise has ended, the acquisition of the sensor data may be ended.

Next, the processing unit (CPU) 22 calculates the value of each parameter of the motion model and the end point speed based on the acquired sensor data (S120 in FIG. 10). Specifically, the processing unit (CPU) 22 refers to the basic data 264, and each parameter of the human body model included in the exercise model of the definition data associated with the type of exercise selected in S12 and S14 of FIG. And the end point speed are calculated from the acquired sensor data. In the example of the arm motion model shown in FIG. 5 and the foot motion model shown in FIG. 7, the parameters θ ₁ , θ ₂ , L ₁ , L ₂ , and end point velocity V are calculated.

Next, the processing unit (CPU) 22 determines whether or not the acquisition of valid data has succeeded (S122 in FIG. 10). If the processing has failed (N in S122 in FIG. 10), for example, “Please ask again. A message such as “Masu” is displayed or output by voice, and the processing of S110 to S120 in FIG. 10 is performed again. Specifically, the processing unit (CPU) 22 determines the appropriate data if each parameter value or end point speed calculated in S120 of FIG. 10 exceeds the allowable range or the upper limit value defined in the motion model. It is determined that acquisition of the data has failed, and otherwise, it is determined that acquisition of valid data has been successful. In the example of the arm motion model shown in FIG. 5 and the foot motion model shown in FIG. 7, whether the parameters θ ₁ , θ ₂ , L ₁ , L ₂ exceed the defined allowable range, the end point speed It is determined whether V exceeds a defined upper limit value.

On the other hand, when the acquisition of the appropriate data is successful (Y in S122 in FIG. 10), the processing unit (CPU) 22 displays a message such as “Thank you” or outputs the voice, and calculates the constraint condition. And stored in the personal exercise data 262 (S124 in FIG. 10). For example, the processing unit (CPU) 22 calculates a range (movable range) that each parameter can take as one of the constraint conditions unique to the user, from the maximum value and the minimum value of each parameter value calculated in S120 of FIG. can do. In the example of the arm motion model shown in FIG. 5 and the foot motion model shown in FIG. 7, for example, when the calculated values of the parameters θ ₁ and θ ₂ change with time as shown in FIG. The solid line is the calculated value of θ ₁ , and the broken line is the calculated value of θ ₂ ), and the processing unit (CPU) 22 determines whether the maximum value of θ ₁ from the start to the end or a value a slightly larger than this and the minimum value of θ ₁ or the slightly smaller value b than this determined in a predetermined rule, the movable range of the theta ₁ is calculated with a _{<θ 1} <b. Similarly, the processing unit (CPU) 22 determines a maximum value of θ ₂ from the start to the end or a value c slightly larger than this and a minimum value d of θ ₂ or a value d slightly smaller than this by a predetermined rule. , Θ ₂ is calculated as c <θ ₂ <d.

Next, the processing unit (CPU) 22 applies a constraint condition to the end point velocity calculation formula of the motion model, and calculates the theoretical maximum end point velocity (S40 in FIG. 9). Specifically, the processing unit (CPU) 22 refers to the basic data 264, and each variable of the end point speed calculation formula included in the definition data associated with the type of exercise selected in S12 and S14 in FIG. The constraint condition calculated in S124 of FIG. 10 is applied to the variable corresponding to each parameter, and the theoretical maximum value of the end point speed (maximum end point speed) is calculated. In the example the arm of a motion model and 7 foot motion model shown illustrated in FIG. 5, the maximum value of the node rate of N ₂ (end point velocity) V is calculated as the maximum end point theoretical speed.

  Next, the processing unit (CPU) 22 analyzes an ideal form from which a theoretical maximum end point speed can be obtained, and generates guidance information (S42 in FIG. 9). Specifically, the processing unit (CPU) 22 analyzes the ideal form from the operating conditions for achieving the theoretical maximum end point speed calculated in S40 of FIG. 9, refers to the basic data 264, Using instructional source data included in the definition data associated with the type of exercise selected in S12 and S14, instructional image information representing the ideal form and instructional audio for explaining the ideal form Generate information. In the example of FIG. 4, when “golf” is selected in S12 of FIG. 8 and “driver shot” is selected in S14, an ideal form image (frame image or CG image) of a golf driver shot or this ideal A sound is generated that explains the form.

  Next, the processing unit (CPU) 22 performs sensor data acquisition and user movement evaluation processing (S44 in FIG. 9). FIG. 12 is a diagram showing an example of a flowchart of the process of S44. First, the processing unit (CPU) 22 outputs guidance information according to the type of exercise selected in S12 and S14 of FIG. 10 (S210 of FIG. 12). Specifically, the processing unit (CPU) 22 outputs the guidance image information and the guidance voice information generated in S <b> 42 of FIG. 9 from the HMD 30 and the speaker 40.

  The processing unit (CPU) 22 starts outputting the guidance information and activates the sensor module 10 (S212 in FIG. 12).

  Next, the processing unit (CPU) 22 starts acquiring sensor data from the sensor module 10 activated in S212 of FIG. 12 (S214 of FIG. 12).

  Then, the user performs the instructed movement once again with reference to the guidance information (the image and sound of the ideal form) (S18 in FIG. 8).

  The processing unit (CPU) 22 continues to acquire the sensor data while the user is exercising (N in S216 in FIG. 12), and when the acquisition of the sensor data is finished (Y in S216 in FIG. 12), in S212 in FIG. The activated sensor module 10 is stopped (S218 in FIG. 12).

  Note that the processing of S212 to S218 in FIG. 12 may be the same as the processing of S112 to S118 in FIG.

  Next, the processing unit (CPU) 22 calculates the value of each parameter of the motion model and the end point speed based on the acquired sensor data (S220 in FIG. 12).

  Next, the processing unit (CPU) 22 determines whether or not the acquisition of valid data has succeeded (S222 in FIG. 12). If the processing has failed (N in S222 in FIG. 12), for example, “Please ask again. A message such as “mass” is displayed or output by voice, and the processing of S210 to S220 in FIG. 12 is performed again.

  On the other hand, when the acquisition of the appropriate data is successful (Y in S222 in FIG. 12), the processing unit (CPU) 22 displays or voices a message such as “Thank you”, and the maximum value of the end point speed. Is compared with the theoretical maximum end point speed to evaluate the user's movement (S224 in FIG. 12). Specifically, the processing unit (CPU) 22 compares the end point speed calculated in S220 of FIG. 12 with the theoretical maximum end point speed calculated in S40 of FIG. 9, and the ratio of the former to the latter (the former is the latter). Calculate what percentage. Then, the processing unit (CPU) 22 evaluates that the user's movement is appropriate if the ratio is equal to or greater than a preset threshold, and if the ratio is less than the threshold, the user's movement is inappropriate. (There is room for improvement.)

  If the user's movement is inappropriate as a result of the evaluation (N in S20 in FIG. 8, N in S46 in FIG. 9), the processing unit (CPU) 22 displays a message such as “Please ask again”, for example. Alternatively, a voice output is performed, and the processes of S40 to S44 in FIG. 9 are performed again, and the user performs the instructed movement again with reference to the guidance information (S18 in FIG. 8).

  On the other hand, if the user's movement is appropriate (Y in S20 in FIG. 8, Y in S46 in FIG. 9) as a result of the evaluation, for example, the processing unit (CPU) 22 displays a message such as “It is an appropriate movement”. Display or audio output, and the process ends.

  As described above, according to the exercise instruction device of the present embodiment, calculation based on the exercise theory is performed using an exercise model prepared in advance to analyze a suitable movement (ideal movement), and guidance information Therefore, it is possible to eliminate the trouble of acquiring data from an instructor or the like and editing instruction information. Therefore, the user can easily use the exercise instruction apparatus of the present embodiment.

  In addition, according to the exercise instruction apparatus of the present embodiment, data is acquired from the sensor module 10 attached to the user, the constraint condition is generated for each user, and a suitable movement in the range satisfying the constraint condition is analyzed. It is possible to provide guidance information that suits the individuality of the user's body shape, movement habit, and the like. The user can effectively improve the movement because the difference between his / her movement and a suitable movement suitable for him / her can be found with reference to the guidance information.

  In addition, according to the exercise instruction apparatus of the present embodiment, the user's movement is evaluated based on the ratio of the end point speed calculated from the user's movement to the theoretical maximum end point speed. As a result, the amount of calculation can be greatly reduced, and real-time performance can be improved.

  Moreover, according to the exercise instruction apparatus of the present embodiment, since only the necessary sensor module 10 is activated and data is acquired, the load of data communication between the sensor module 10 and the controller 20 can be reduced and consumption can be reduced. Electric power can be reduced.

4). The present invention is not limited to this embodiment, and various modifications can be made within the scope of the present invention.

[Modification 1]
For example, the mounting location of the sensor module 10 and the type of sensor that each sensor module 10 should have may be changed according to the type of exercise performed by the user. In this case, the sensor selection information included in the definition data of the basic data 264 may include correspondence information between the mounting location of the sensor module 10 and the type of sensor that each sensor module 10 should have. Then, according to the type of exercise selected by the user, an instruction on the mounting location of the sensor module 10 and the type of sensor that each sensor module 10 should have may be displayed on the HMD 30 or the like.

  FIG. 13 is a diagram illustrating a flowchart of operations performed by the user in the present modification, and FIG. 14 is a diagram illustrating a flowchart of overall processing of the processing unit (CPU) 22. In FIG. 13 and FIG. 14, the same processes as those in FIG. 8 and FIG.

  First, the user wears the controller 20, the HMD 30, and the speaker 40, and activates the exercise instruction apparatus 1 (S11 in FIG. 13).

  Next, similarly to the processing of S30 to S34 of FIG. 9, the processing unit (CPU) 22 displays the type of sensor that each sensor module 10 should have after performing the processing of S30 to S34 of FIG. FIG. 14 S37). Specifically, the processing unit (CPU) 22 refers to the basic data 324 and installs the sensor module 10 according to the sensor selection information included in the definition data associated with the type of exercise selected by the user. Information on the type of sensor that each sensor module 10 should have is displayed on the HMD 30. For example, as shown in FIG. 15, a sensor module 10 including a three-axis gyro sensor is attached to each of “elbow” and “shoulder”, and a three-axis gyro sensor and a three-axis acceleration sensor are included on “wrist”. An instruction or the like for mounting the sensor module 10 is displayed.

  The user installs the sensor module 10 according to the display on the HMD 30 (S15 in FIG. 13), performs the operations in S16 to S20 in FIG. 13 in the same manner as the operations in S16 to S20 in FIG. Similarly to the processing of S38 to S46 of FIG. 9, the processing of S38 to S46 of FIG. 14 is performed and the processing is terminated.

  In this way, since the user only has to install the necessary sensor module 10, the burden on the user can be reduced.

[Modification 2]
For example, every time sensor data is acquired to evaluate a user's movement, a new constraint condition is calculated from the acquired sensor data, and part of the existing constraint condition included in the personal exercise data 262 as necessary. Or you may make it update all.

  The flowchart of the operation performed by the user and the flowchart of the overall processing of the processing unit (CPU) 22 in the present modification are the same as those in FIGS. 8 and 9, respectively. FIG. 16 is a diagram showing a flowchart of the sensor data acquisition and user movement evaluation process (the process of S44 of FIG. 9) in the present modification. In FIG. 16, the same processes as those in FIG. 12 are denoted by the same reference numerals, and the description thereof is omitted.

  The processing unit (CPU) 22 performs the processing of S210 to S224 of FIG. 16 in the same manner as the processing of S210 to S224 of FIG. 12, and then calculates the constraint condition and is included in the personal exercise data as necessary. A part or all of the existing constraints are updated (S226 in FIG. 16). For example, the processing unit (CPU) 22 may overwrite and update all of the existing constraint conditions, compare each newly calculated constraint condition with the existing constraint condition, and update the new constraint condition. Only existing constraint conditions that are appropriate (for example, constraint conditions are too loose or too stringent) may be updated to new constraint conditions.

  Then, the processing unit (CPU) 22 analyzes the ideal form again using the updated constraint conditions, creates guidance image information and guidance voice information, and outputs them from the HMD 30 and the speaker 40 (FIG. 9 S40, S42).

  In this way, even if the user gets used to the same movement repeatedly, and the movable range of each part of the user, the maximum speed and torque that can be produced, the habit of the trajectory, etc. change, the latest Since the ideal form can be finely corrected while sequentially updating to the constraint conditions, more appropriate guidance information can be provided.

[Other variations]
For example, in FIG. 10, the processing unit (CPU) 22 calculates the trajectory of each part of the user's body from the acquired sensor data, and the trajectory obtained by the calculation is the exercise selected by the user in S14 of FIG. Compared with the reference trajectory prepared for each type of the data, if the trajectory obtained by calculation deviates significantly from the reference trajectory, it is determined that the acquisition of reasonable data has failed. Otherwise, it may be determined that the acquisition of valid data has succeeded.

  Further, for example, in this embodiment, the user's movement is evaluated by the ratio of the end point speed to the theoretical maximum end point speed. However, the movement trajectory of the human body model calculated from the user's movement is used as the movement theory. It may be compared with an ideal movement trajectory obtained by performing calculation based on this, and the user's movement may be evaluated based on the difference.

  In addition, the exercise | movement guidance apparatus of this embodiment is applicable also to exercises other than sports, such as golf. For example, the present invention can be applied to rehabilitation exercises such as walking training and raising and lowering limbs. By pointing out the differences between actual body movements and rehabilitation programs in HMD, you can work on rehabilitation positively while checking your achievements and achievements. In addition, what is necessary is just to mount | wear the sensor module 10 at the point of the movement at the time of rehabilitation, such as a waist | hip | lumbar, a shoulder, and an elbow.

  The present invention includes configurations that are substantially the same as the configurations described in the embodiments (for example, configurations that have the same functions, methods, and results, or configurations that have the same objects and effects). In addition, the invention includes a configuration in which a non-essential part of the configuration described in the embodiment is replaced. In addition, the present invention includes a configuration that exhibits the same operational effects as the configuration described in the embodiment or a configuration that can achieve the same object. Further, the invention includes a configuration in which a known technique is added to the configuration described in the embodiment.

1 exercise instruction device, 10 sensor module, 14 bands, 20 controller, 22 processing unit (CPU), 24 output unit, 26 storage unit, 30 head mounted display (HMD), 40 speaker, 50 recording medium, 222 data acquisition unit, 224 constraint condition generation unit, 226 exercise analysis unit, 228 guidance information generation unit, 262 personal exercise data, 264 basic data

Claims (13)

A storage unit for storing a motion model including a human body model that models at least a part of the structure of the human body and parameters of the human body model;
Based on data acquired from a sensor attached to a user, a constraint condition generation unit that generates a constraint condition of the parameter according to the user;
Using the motion model, seen including a motion analysis unit, the said parameter analyzing the suitable movement of the human body model in the constraint conditions are satisfied,
There are multiple sensors,
The storage unit
Storing sensor selection information for selecting a sensor from which data is to be acquired from the plurality of sensors;
Obtaining data from a sensor selected by the sensor selection information according to the type of exercise performed by the user;
Exercise guidance device.   A storage unit for storing a motion model including a human body model that models at least a part of the structure of the human body and parameters of the human body model;
  Based on data acquired from a sensor attached to a user, a constraint condition generation unit that generates a constraint condition of the parameter according to the user;
  Using the motion model, and analyzing a suitable motion of the human body model in a range where the parameters satisfy the constraint conditions,
  There are multiple sensors,
  The storage unit
  Storing sensor selection information for selecting a sensor from which data is to be acquired from the plurality of sensors;
  Presenting to the user the information of the sensor selected by the sensor selection information according to the type of exercise performed by the user;
Exercise guidance device. In claim 1 or 2 ,
An exercise instruction apparatus including an instruction information generation unit for generating exercise instruction information based on the analysis result of the exercise analysis unit. In any one of Claims 1 thru | or 3 ,
An exercise instruction device that calculates the movement of the human body model corresponding to the movement of the user based on the acquired data, and evaluates the movement of the user by comparing the movement of the human body model with the suitable movement. In any one of Claims 1 thru | or 4 ,
The motion analysis unit
An exercise instruction apparatus that calculates a virtual maximum speed of a node provided in a part of the human body model within a range that satisfies the constraint condition. In claim 5 ,
An exercise guidance device that evaluates the movement of the user by calculating the speed of the node of the human body model based on the acquired data and comparing it with the virtual maximum speed. In claim 5 or 6 ,
The node is
An exercise instruction device, which is a node provided at a terminal portion of the human body model. In any one of Claims 1 thru | or 7 ,
The storage unit
Storing a plurality of the motion models;
The constraint condition generation unit
An exercise instruction device that selects an exercise model according to the type of exercise performed by the user from the plurality of exercise models, and calculates a constraint condition of the parameters of the selected exercise model. A storage unit for storing a motion model including a human body model that models at least a part of the structure of the human body and parameters of the human body model;
A sensor attached to the user,
A data acquisition unit for acquiring data from the sensor;
Based on the acquired data, a constraint condition generation unit that generates a constraint condition of the parameter according to the user;
The performed calculations using the motion model, see contains and a motion analysis unit for the parameter to analyze the suitable movement of the human body model in the constraint conditions are satisfied,
There are multiple sensors,
The storage unit
Storing sensor selection information for selecting a sensor from which data is to be acquired from the plurality of sensors;
Obtaining data from a sensor selected by the sensor selection information according to the type of exercise performed by the user;
Motion analysis device.   A storage unit for storing a motion model including a human body model that models at least a part of the structure of the human body and parameters of the human body model;
  A sensor attached to the user,
  A data acquisition unit for acquiring data from the sensor;
  Based on the acquired data, a constraint condition generation unit that generates a constraint condition of the parameter according to the user;
  A motion analysis unit that performs a calculation using the motion model and analyzes a suitable motion of the human body model in a range where the parameters satisfy the constraint conditions,
  There are multiple sensors,
  The storage unit
  Storing sensor selection information for selecting a sensor from which data is to be acquired from the plurality of sensors;
  Presenting to the user the information of the sensor selected by the sensor selection information according to the type of exercise performed by the user;
Exercise guidance device. A procedure for reading a motion model including a human body model that models at least a part of the structure of the human body and parameters of the human body model;
The procedure to get data from the sensor attached to the user,
A procedure for generating a constraint condition of the parameter according to the user based on the acquired data;
Performing a calculation using the motion model, and analyzing a suitable motion of the human body model within a range where the parameters satisfy the constraint conditions;
To the computer ,
There are multiple sensors,
In the reading procedure,
Read sensor selection information for selecting a sensor from which data is to be acquired from the plurality of sensors,
Causing the computer to execute a procedure of acquiring data from a sensor selected by the sensor selection information according to a type of exercise performed by the user;
Exercise guidance program.   A procedure for reading a motion model including a human body model that models at least a part of the structure of the human body and parameters of the human body model;
  The procedure to get data from the sensor attached to the user,
  A procedure for generating a constraint condition of the parameter according to the user based on the acquired data;
  Performing a calculation using the motion model, and analyzing a suitable motion of the human body model within a range where the parameters satisfy the constraint conditions;
To the computer,
  There are multiple sensors,
  In the reading procedure,
  Read sensor selection information for selecting a sensor from which data is to be acquired from the plurality of sensors,
  Causing the computer to execute a procedure of presenting information on a sensor selected by the sensor selection information corresponding to a type of exercise performed by the user to the user.
Exercise guidance program. A computer-readable recording medium on which the exercise instruction program according to claim 11 or 12 is recorded.

Images