WO2017130339A1 - Information processing method, information processing device, and program - Google Patents

Abstract

An information processing method according to disclosed technology is executed on a computer having a control unit and memory. The control unit executes: acceptance of information indicating prescribed training concerning the torso; identification of a threshold value with respect to the prescribed training on the basis of threshold information that is stored in the memory and in which a threshold value is associated with each training concerning the torso; acquisition of the value of a signal detected from a sensor mounted on a user to be evaluated; and evaluation of the posture of the user during the prescribed training on the basis of the result of comparing the signal value and the threshold value.

Description

Information processing method, information processing apparatus, and program

The present invention relates to an information processing method, an information processing apparatus, and a program.

2. Description of the Related Art Conventionally, a technique is known in which exercise information is determined using acceleration information from an acceleration sensor attached to a trunk and the amount of exercise of each exercise content is calculated (see, for example, Patent Document 1).

JP 2006-271893 A

However, since the conventional technique basically calculates the amount of exercise, it is not assumed to be applied to training that maintains a predetermined posture such as training on the trunk for a predetermined time. In addition, for training relating to the trunk, it is preferable to evaluate the stability of posture, but there is no conventional technique for evaluating the stability of posture in training relating to the trunk.

Therefore, the disclosed technique aims to be able to appropriately evaluate the user's posture in training for maintaining the predetermined posture for a predetermined time.

An information processing method according to an aspect of the disclosed technology is an information processing method executed by a computer having a control unit and a storage unit, wherein the control unit receives information indicating predetermined training related to a trunk, the storage Threshold information stored in the unit, wherein a threshold for the predetermined training is specified based on the threshold information associated with a training for each trunk, and detected from a sensor attached to a user to be evaluated Obtaining a signal value to be performed, and evaluating the posture of the user during the predetermined training based on a comparison result between the signal value and the threshold value.

According to the disclosed technique, it is possible to appropriately evaluate the posture of the user in training for maintaining the predetermined posture for a predetermined time.

It is a figure which shows an example of the information processing system in an Example. It is a schematic block diagram which shows the hardware constitutions of the information processing apparatus in an Example. It is a block diagram which shows an example of a structure of the processing apparatus in an Example. It is a block diagram which shows an example of a structure of the information processing apparatus in an Example. It is a block diagram which shows an example of the function of the acquisition part in an Example. It is a block diagram which shows an example of the function of the evaluation part in an Example. It is a block diagram which shows an example of a structure of the production | generation part in an Example. It is a figure which shows an example of threshold value information. It is a figure which shows an example of training information. It is a figure which shows an example of the objective information. It is a figure which shows an example of the evaluation result information with respect to a user's attitude | position. It is a figure which shows the specific example of the threshold value comparison in a predetermined signal. It is a figure which shows an example of a setting screen. It is a figure which shows an example of the purpose selection screen. It is a figure which shows an example of a training menu display screen. It is a figure which shows an example of the guidance screen of training. It is a figure which shows an example of a score display screen. It is a figure which shows an example of a training result screen. It is a figure which shows an example of an application selection screen. It is a figure which shows an example of the screen in which the predetermined site | part was notified from the other application. It is a figure which shows an example of a training menu display screen. It is a flowchart which shows an example of the whole process of the application which performs trunk training in an Example. It is a flowchart which shows an example of the production | generation process of the menu according to the objective. It is a flowchart which shows an example of the production | generation process of the menu according to a predetermined part. It is a flowchart which shows an example of a training process.

Hereinafter, embodiments of the present invention will be described with reference to the drawings. However, the embodiment described below is merely an example, and there is no intention to exclude various modifications and technical applications that are not explicitly described below. That is, the present invention can be implemented with various modifications without departing from the spirit of the present invention. In the following description of the drawings, the same or similar parts are denoted by the same or similar reference numerals. The drawings are schematic and do not necessarily match actual dimensions and ratios. In some cases, the dimensional relationships and ratios may be different between the drawings.

[Example]
In the embodiment, eyewear is taken as an example of an object on which an acceleration sensor and an angular velocity sensor are mounted. FIG. 1 is a diagram illustrating an example of an information processing system 1 in the embodiment. The information processing system 1 shown in FIG. 1 includes an external device 10 and eyewear 30, and the external device 10 and eyewear 30 are connected via a network so that data communication is possible.

The eyewear 30 mounts the processing device 20 on a temple portion, for example. The processing device 20 includes a three-axis acceleration sensor and a three-axis angular velocity sensor (may be a six-axis sensor). The eyewear 30 may have bioelectrodes 31, 33, and 35 at the pair of nose pads and the bridge portion, respectively. When biological electrodes are provided on the eyewear 30, electrooculogram signals acquired from these biological electrodes are transmitted to the processing device 20.

The installation position of the processing apparatus 20 is not necessarily a temple, but may be positioned in consideration of a balance when the eyewear 30 is attached.

The external device 10 is an information processing device having a communication function. For example, the external device 10 is a mobile communication terminal such as a mobile phone or a smartphone possessed by a user, a PC (Personal Computer), a tablet terminal, or the like. The external device 10 evaluates the posture during training related to the trunk (hereinafter also referred to as trunk training) based on the sensor signal received from the processing device 20. Hereinafter, the external device 10 will be described as the information processing device 10.

<Hardware Configuration of Information Processing Apparatus 10>
FIG. 2 is a schematic configuration diagram illustrating a hardware configuration of the information processing apparatus 10 according to the embodiment. A typical example of the information processing apparatus 10 is a mobile phone such as a smartphone. In addition, a network such as a mobile terminal that can be wirelessly or wired connected to a network, or an electronic device that includes a touch panel such as a tablet terminal. A general-purpose device capable of displaying a screen while processing data while communicating using the information processing apparatus 10 may correspond to the information processing apparatus 10 in the embodiment.

The information processing apparatus 10 according to the embodiment includes, for example, a rectangular thin housing (not shown), and a touch panel 102 is configured on one surface of the housing. In the information processing apparatus 10, each component is connected to the main control unit 150. The main control unit 150 is a processor, for example.

The main control unit 150 includes a mobile communication antenna 112, a mobile communication unit 114, a wireless LAN communication antenna 116, a wireless LAN communication unit 118, a storage unit 120, a speaker 104, a microphone 106, a hard button 108, and a hard key 110. And the 6-axis sensor 111 is connected. The main control unit 150 is further connected with a touch panel 102, a camera 130, and an external interface 140. The external interface 140 includes an audio output terminal 142.

The touch panel 102 has functions of both a display device and an input device, and includes a display (display screen) 102A that bears the display function and a touch sensor 102B that bears the input function. The display 102A is configured by a general display device such as a liquid crystal display or an organic EL (Electro Luminescence) display. The touch sensor 102B includes an element for detecting a contact operation disposed on the upper surface of the display 102A and a transparent operation surface stacked on the element. As a contact detection method of the touch sensor 102B, an arbitrary method among known methods such as a capacitance method, a resistance film method (pressure-sensitive method), and an electromagnetic induction method can be adopted.

The touch panel 102 as a display device displays an image of an application (hereinafter also referred to as an application) generated by the execution of the program 122 by the main control unit 150. The touch panel 102 as an input device detects an action of a contact object (including a player's finger, stylus, and the like. Hereinafter, a case of a “finger” will be described as a representative example). The operation input is received, and information on the contact position is given to the main control unit 150. The movement of the finger is detected as coordinate information indicating the position or area of the contact point, and the coordinate information is represented as coordinate values on two axes of the short side direction and the long side direction of the touch panel 102, for example.

The information processing apparatus 10 is connected to the network N through the mobile communication antenna 112 and the wireless LAN communication antenna 116, and can perform data communication with the processing apparatus 20. The storage unit 120 records the program 122, and the storage unit 120 may be separate from the external device 10, and may be a recording medium such as an SD card or a CD-RAM.

<Configuration of Processing Device 20>
FIG. 3 is a block diagram illustrating an example of the configuration of the processing device 20 according to the embodiment. As illustrated in FIG. 3, the processing device 20 includes a processing unit 202, a transmission unit 204, a 6-axis sensor 206, and a power supply unit 208. In addition, each of the bioelectrodes 31, 33, and 35 is connected to the processing unit 202 using an electric wire via an amplification unit, for example. In addition, each part of the processing apparatus 20 may be distributed in a pair of temples instead of being provided in one temple.

The 6-axis sensor 206 is a 3-axis acceleration sensor and a 3-axis angular velocity sensor. Each of these sensors may be provided separately. The 6-axis sensor 206 outputs the detected sensor signal to the processing unit 202.

The processing unit 202 is, for example, a processor, processes the sensor signal obtained from the 6-axis sensor 206 as necessary, and outputs the processed signal to the transmission unit 204. For example, the processing unit 202 generates the following six signals using the sensor signals from the six-axis sensor 206, and acquires the signal value of each signal.
First signal: signal indicating acceleration in the X axis direction Second signal: signal indicating acceleration in the Y axis direction Third signal: signal indicating acceleration in the Z axis direction Fourth signal: pitch angle 5th signal: signal indicating roll angle, 6th signal: signal indicating yaw angle

For example, the first signal is a signal indicating acceleration in the longitudinal direction of the head, the second signal is a signal indicating acceleration in the lateral direction of the head, and the third signal is a signal indicating acceleration in the vertical direction of the head. It is. The fourth signal (pitch angle) indicates, for example, the vertical tilt of the head, the fifth signal (roll angle) indicates, for example, movement around the head's traveling axis, and the sixth signal (yaw angle) indicates the head. The left and right inclinations of are shown. The fourth to sixth signals may be calculated using a known technique. Further, the processing unit 202 may only amplify the sensor signal obtained from the 6-axis sensor 206.

The transmission unit 204 transmits each signal value of the sixth signal to the information processing apparatus 10 from the first signal processed by the processing unit 202. For example, the transmission unit 204 transmits the sensor signal or each data to the information processing apparatus 10 by wireless communication such as Bluetooth (registered trademark) and wireless LAN, or wired communication. The power supply unit 208 supplies power to the processing unit 202, the transmission unit 204, the 6-axis sensor 206, and the like.

<Configuration of Information Processing Apparatus 10>
Next, the configuration of the information processing apparatus 10 will be described. FIG. 4 is a diagram illustrating an example of the configuration of the information processing apparatus 10 according to the embodiment. The information processing apparatus 10 includes a storage unit 302, a communication unit 304, and a control unit 306.

The storage unit 302 can be realized by, for example, the storage unit 120 shown in FIG. The storage unit 302 relates to data related to the core training application in the embodiment, for example, data received from the processing device 20, training information related to training, threshold information used for determining posture of the user during training, and the purpose of training. Purpose information, result information of user posture determination, screen information displayed on the screen, and the like are stored.

The communication unit 304 can be realized by the mobile communication unit 114, the wireless LAN communication unit 118, and the like, for example. The communication unit 304 receives data from the processing device 20, for example. The communication unit 304 may transmit data processed in the information processing apparatus 10 to a server (not shown). That is, the communication unit 304 functions as a transmission unit and a reception unit.

The control unit 306 can be realized by the main control unit 150, for example. The control unit 306 executes a trunk training application. The trunk training application in the embodiment has functions of performing guidance for trunk training, evaluating the posture of a user during training, and automatically generating a training menu. In order to realize this function, the control unit 306 includes a reception unit 312, a specification unit 314, an acquisition unit 316, an evaluation unit 318, a generation unit 320, and a display control unit 322. Hereinafter, the functions of the control unit 306 will be described separately for an attitude determination function and a menu generation function.

≪Attitude judgment function≫
The reception unit 312 receives information (hereinafter, also referred to as a training ID) indicating predetermined training related to the trunk based on a user operation. For example, when the user's pressing on a plurality of trainings displayed on the display screen is detected, the receiving unit 312 receives a training ID corresponding to the pressing. In addition, the reception unit 312 may receive a training ID from another application in the internal or external device.

The identifying unit 314 identifies threshold values for the predetermined training accepted by the accepting unit 312 based on threshold information stored in the storage unit 302 and associated with threshold values for each training related to the trunk. For example, the specifying unit 314 specifies a threshold for a predetermined training indicated by the training ID received by the receiving unit 312 with reference to the threshold information. The number of specified thresholds is not particularly limited as long as one or a plurality of thresholds corresponding to the content of training is set. The threshold information is associated with a threshold of a signal to be used for each training. The threshold information will be described later with reference to FIG.

The obtaining unit 316 obtains a signal value detected from a sensor attached to a user who is an evaluation target and performs predetermined training indicated by the training ID. For example, the acquisition unit 316 directly acquires a signal value of a signal used for posture determination from a sensor attached to the user (for example, the 6-axis sensor 206), or is temporarily stored in the storage unit 302. The signal value obtained indirectly.

The evaluation unit 318 evaluates the posture of the user during a predetermined training based on the comparison result between the signal value acquired by the acquisition unit 316 and the threshold value specified by the specifying unit 314. For example, the evaluation unit 318 evaluates the posture stability based on whether or not the absolute value of the signal value is less than a threshold value. More specifically, the evaluation unit 318 determines that the posture is stable if the signal value is within a predetermined range indicated by the threshold value, and if the signal value is not within the predetermined range, the posture is Judge that it is not stable. Thereby, the evaluation unit 318 can evaluate the stability of the posture.

The display control unit 322 controls the evaluation result of the evaluation unit 318 to be displayed on the display screen. Thereby, evaluation with respect to the posture of the user during trunk training can be fed back to the user.

Further, the identifying unit 314 may use threshold information in which one or a plurality of signals to be compared and a threshold value of this signal are associated with each other for posture determination for posture determination. For example, the specifying unit 314 specifies a signal to be compared and a threshold value for the predetermined training received by the receiving unit 312 based on the threshold information.

In this case, the acquiring unit 316 acquires the signal value of the comparison target signal specified by the specifying unit 314 from the sensor attached to the user. For example, the acquisition unit 316 may acquire all signals (first to sixth signals) from the sensor and acquire only the specified signal from the signals. The acquisition unit 316 may notify the sensor of the specified signal and acquire only the specified signal from the sensor. Thereby, when various training is performed, posture determination can be performed using a signal suitable for each training. Furthermore, since processing using a signal not related to the training is not performed, it is possible to contribute to power saving.

Further, the specifying unit 314 may use threshold information in which a peak threshold related to a signal from the sensor is further associated with each posture for posture determination. For example, the specifying unit 314 further specifies the peak threshold value of the comparison target signal for the predetermined training based on the threshold information.

In this case, the acquisition unit 316 includes calculating a difference value between peaks of signals to be compared. FIG. 5 is a block diagram illustrating an example of the function of the acquisition unit 316. The acquisition unit 316 illustrated in FIG. 5 includes a difference value calculation unit 3162. The difference value calculation unit 3162 calculates a difference value between peaks of the posture signal repeated every predetermined time. For example, when the exercise of maintaining the same posture for a predetermined time is repeated a plurality of times, the difference value calculation unit 3162 obtains a peak value within this predetermined time, and the difference between this peak value and the peak value within the next predetermined time The values are calculated one after another.

Next, each function of the evaluation unit 318 will be described. FIG. 6 is a block diagram illustrating an example of the function of the evaluation unit 318. The evaluation unit 318 illustrated in FIG. 6 includes a first determination unit 3181, a first evaluation unit 3183, a second evaluation unit 3185, a score calculation unit 3187, and a second determination unit 3189.

As described above, the first determination unit 3181 determines whether or not the signal value of the signal acquired during the predetermined training is included in the predetermined range represented by the threshold value. In the embodiment, for example, since training with relatively small movement is assumed, a signal value exceeding a predetermined range indicates that the movement is large.

In addition, when the signal to be compared is different for each training, the first determination unit 3181 performs the comparison determination using the signal value of the signal corresponding to the training and the threshold value of the signal. The first determination unit 3181 outputs the comparison result to the first evaluation unit 3183.

Based on the comparison result acquired from the first determination unit 3181, the first evaluation unit 3183 evaluates that the posture is stable if the absolute value of the signal value is less than the threshold value, and the absolute value of the signal value sets the threshold value. If it exceeds, evaluate that the posture is unstable.

The second evaluation unit 3185 evaluates the posture stability based on whether or not the absolute value of the difference value calculated by the difference value calculation unit 3162 is less than the peak threshold value stored in the storage unit 302. May be. For example, the second evaluation unit 3185 determines that the postures at the time of repetition are the same if the absolute value of the difference value between the peaks of the signals related to the postures repeated every predetermined time is less than the threshold, and the postures are stabilized. It is considered. Further, if the absolute value of the difference value between the peaks is equal to or greater than the threshold value, the second evaluation unit 3185 determines that the posture at the time of repetition is different, and regards the posture as unstable. Thereby, it is possible to appropriately evaluate the stability of the posture with respect to repetition of the predetermined posture executed in the trunk training. The peak threshold value may be set for each signal.

The score calculation unit 3187 may calculate a score indicating the stability of the posture according to the number of times that the absolute value of the signal value or the absolute value of the difference value is equal to or greater than the threshold value. For example, the score calculation unit 3187 may calculate the score by subtracting the number of times that the absolute value is equal to or greater than the threshold value from the full score of 100 points. Thereby, it is possible to provide the user with an evaluation index that can be easily grasped with respect to the user's posture.

The second determination unit 3189 uses the signal value from the sensor to determine whether the user is performing a predetermined training. For example, in the accepted training, the second determination unit 3189 has a signal value close to 0 and a small fluctuation or a signal value around the predetermined value even though it is during a predetermined time for maintaining a predetermined posture. If it does not change, it is determined that training is not in progress.

In this case, when calculating the score, the score calculation unit 3187 may reflect the determination result by the second determination unit 3189 as to whether or not the user is performing the predetermined training in the score. For example, when it is determined that training is not being performed based on the sensor signal, the score calculation unit 3187 decreases the score from the current value by a predetermined value. Note that the score calculation unit 3187 may increase the value of the predetermined value as the time determined to be not during training is longer. By reflecting the determination result during such training in the score, it is possible to give the user an opportunity to continue the repeated training, for example.

Also, each evaluation result by the first evaluation unit 3183 and the second evaluation unit 3185, and the score calculated by the score calculation unit 3187 are displayed on the display screen by the display control unit 322. Thereby, a more detailed evaluation result can be notified to the user.

≪Menu generation function≫
Returning to FIG. 4, the menu generation function will be described. First, the specifying unit 314 specifies one or more trainings for a predetermined attribute based on the training information stored in the storage unit 302 and associated with one or more attributes for each training. . For example, when the receiving unit 312 receives a predetermined attribute, the specifying unit 314 specifies one or a plurality of trainings for the predetermined attribute. For example, in the training information, for each training, the type, rest time, required time, predetermined part, purpose, and the like are associated as attributes. The training information will be described later with reference to FIG.

The generating unit 320 generates a training menu by combining a predetermined number of trainings from one or a plurality of trainings specified by the specifying unit 314. For example, when extracting the training, the generation unit 320 uses the time required for the training so that it is shorter than the predetermined time as a whole, or only the same type of training is not extracted using the type of training. Or Thereby, since the generation unit 320 generates a menu by appropriately combining a plurality of trainings, the training can be continued without getting tired of the user.

Next, a more detailed function of the generation unit 320 will be described. FIG. 7 is a diagram illustrating an example of functions of the generation unit 320. The generation unit 320 illustrated in FIG. 7 includes a first generation unit 3202 and a second generation unit 3204. The first generation unit 3202 generates a menu from the received predetermined purpose, and the second generation unit 3204 generates a menu from the received predetermined part.

First, the case where a menu is generated for a predetermined purpose will be described. Here, the attributes include the purpose and type of training. The accepting unit 312 accepts information indicating a predetermined purpose related to training (hereinafter also referred to as purpose-specific ID). The predetermined purpose is, for example, “to train the basic trunk”, “to make the walking posture clean”, “easy to lose weight”, or the like. The storage unit 302 stores purpose information in which purpose-specific IDs and purpose names are associated (for example, see FIG. 10).

In this case, the specifying unit 314 specifies a plurality of trainings for the predetermined purpose indicated by the received purpose-specific ID based on the training information associated with the attributes including the purpose and type regarding the training. The types of training include, for example, “stretch”, “strengthening”, “balance interlock”, and the like.

The first generation unit 3202 extracts each type of training from the plurality of trainings specified by the specifying unit 314 so that the ratio of each type becomes a predetermined rate, and performs each type of training extracted. Combine to generate a training menu.

For example, the first generation unit 3202 extracts training so that the ratio of “stretch”, “strengthening”, and “balance interlock” is 3: 2: 1 as the type of training. More specifically, the first generation unit 3202 extracts three “stretch” randomly, two “strengthen” randomly, and one “balance interlock” randomly from the identified training. These are combined to generate a menu.

This makes it possible to automatically generate a training menu according to the purpose of the user who wants to train the trunk. In addition, since a training menu is automatically generated at random, even if the same purpose is selected, the same menu is not used every time, and the training can be continued without causing the user to feel bored.

Next, a case where a menu is generated from a predetermined part will be described. Here, the attribute includes a predetermined part. The accepting unit 312 accepts information indicating a predetermined part related to the user's body (hereinafter also referred to as a part ID). For example, the reception unit 312 receives a part ID from another application or receives a part ID based on a user operation. The predetermined part is, for example, the back, the abdomen, the ankle, or the like. The other application is, for example, an application that supports running or walking. When this other application can detect a site that is a weak point of running or walking, the reception unit 312 receives a site ID from this other application. Other applications may be installed in an external device or installed in the same device.

The specifying unit 314 specifies a plurality of trainings for a predetermined part indicated by the part ID based on training information associated with an attribute including one or a plurality of parts related to the user's body. For example, the identifying unit 314 refers to the training information stored in the storage unit 302 and identifies the training associated with a predetermined part (for example, “back”).

The second generation unit 3204 generates a training menu by combining a predetermined number of trainings from among the plurality of trainings specified by the specifying unit 314. For example, the second generation unit 3204 generates a menu by randomly extracting a predetermined number from a plurality of trainings associated with “back”. Thereby, a training menu can be automatically generated for a predetermined part that the user wants to train or a predetermined part notified from another application. Further, the first generation unit 3202 and the second generation unit 3204 may extract the training in consideration of the time required for each training and other attributes.

In addition, each menu generated by the first generation unit 3202 and the second generation unit 3204 is displayed on the display screen by the display control unit 322. Thereby, the user can grasp | ascertain the content of the menu produced | generated automatically.

<Data example>
Next, examples of various data used for the trunk training application in the embodiment will be described. FIG. 8 is a diagram illustrating an example of threshold information. In the example illustrated in FIG. 8, for each training identification information (for example, training ID), a training name, parameters for specifying a comparison target (judgeFactor and judgePeak), and a threshold value of the parameter are associated.

JudgeFactor is represented by 6 digits of 0 or 1, and corresponds to the first signal (thr_accX), second signal (thr_accY),. Further, “0” indicates that it is not a comparison target, and “1” indicates that it is a comparison target. For example, when the judgeFactor is “000110”, it indicates that the fourth signal and the fifth signal are used as comparison targets. The judgeFactor is used by the first evaluation unit 3183. Further, the numerical values set in each signal row shown in FIG. 8 indicate threshold values. For example, the threshold for the first signal (thr_accX) with ID “4” is “2”.

JudgePeak is represented by 6 digits of 0 or 1, and corresponds to the peak of the first signal, the peak of the second signal,..., The peak of the sixth signal (thr_yaw_pk) in order from the left. Further, “0” indicates that it is not a comparison target, and “1” indicates that it is a comparison target. For example, when “judge Peak” is “010010”, it indicates that the peak of the second signal and the peak of the fifth signal are used as comparison targets. The judge Peak is used by the second evaluator 3185. Moreover, the numerical value provided in the peak row shown in FIG. 8 indicates the threshold value. For example, the threshold for the peak of the sixth signal with ID “3” is “2”.

For example, for the training of number 1, the training name “stretching the iliopsoas muscle”, the judgefactor “000110”, and the judgepeak “000000” are associated. According to this example, the threshold value of the fourth signal (thr_pitch) is “1”, and the threshold value between peaks is not applied.

Also, the training name “Standing Rotation”, judgeFactor “111000”, and judge Peak “000001” are associated with the training of No. 3. According to this example, the threshold value of the first signal (thr_accX) is “1”, the threshold value of the second signal (thr_accY) is “1”, and the threshold value of the third signal (thr_accZ) is “1”. The threshold value “2” between the peaks of the sixth signal (thr_yaw_pk).

According to the threshold information shown in FIG. 8, the specifying unit 314 can easily specify a signal to be compared and a threshold for training by referring to the judgeFactor and the judgePeak and the threshold.

FIG. 9 is a diagram showing an example of training information. In the example shown in FIG. 9, for each training identification information (for example, training ID), the training name, type (effect), lottery exception flag, rest time, number of times, required time, predetermined part (for example, back, trunk side surface) ID for each purpose.

The type indicates the type or effect of training. Types include, for example, stretching, strengthening, and balance interlocking. The lottery exception flag indicates the number of training that is not executed simultaneously. For example, “Agura Rotation” of training ID “5” is not included in the training menu at the same time as “Standing Rotation” of training ID “3”. These are the same types of convolution training among stretching training.

The rest time indicates the rest time of the posture that the user takes during training. For example, when the stationary time is “3”, this indicates that the posture is maintained for 3 seconds. The number of times indicates the number of times a predetermined posture is repeated in one training. For example, when the number of times is “3”, this indicates that the posture is repeated three times. The required time indicates the time required for one training.

The predetermined part indicates a human part effective for the training. In the example illustrated in FIG. 9, the part name is used as the part ID, but a number or the like may be used. One training may be effective for a plurality of parts. “◯” is set for a portion having an effect, and “−” is set for a portion having no effect.

The purpose-specific ID is information for identifying the purpose of the training. As will be described later with reference to FIG. 10, purpose information associated with the purpose of training is stored in the storage unit 302 for each purpose ID. For example, the purpose-specific ID “1” is “train the basic trunk”, and the purpose-specific ID “2” is “clean walking posture”.

In the example illustrated in FIG. 9, for the training ID “4”, the training name “standing position side bending”, the type “stretch”, the lottery exception flag “6”, the rest time “3”, the number of times “3”, and the required time “170”. ”, Back“ ◯ ”, trunk side“ ◯ ”, abdomen“-”,...

According to the training information shown in FIG. 9, the specifying unit 314 can specify the predetermined training using the purpose-specific ID and can specify the predetermined training using the predetermined part. .

FIG. 10 is a diagram showing an example of the purpose information. In the example illustrated in FIG. 10, a training purpose name is associated with each purpose ID. For example, the purpose ID “1” is associated with the purpose name “Train the basic trunk”.

FIG. 11 is a diagram showing an example of evaluation result information for the user's posture. In the example illustrated in FIG. 11, a score evaluated in each training, a signal value of each signal, and a threshold value of a peak value are associated with a predetermined user.

For example, the user “AAA” has scored “78” for the training “Lunge”. As shown in FIG. 11, for each training, a score indicating an evaluation regarding the posture is associated.

<Specific example of threshold comparison>
FIG. 12 is a diagram illustrating a specific example of threshold comparison for a predetermined signal. In the example shown in FIG. 12, the fluctuation of one signal S1 among the first to sixth signals is shown. In the example shown in FIG. 12, it is assumed that training is started in the following order.
T1: 1st set (right) guidance start T2: 1st set (right) ready posture start T3: 1st set (right) posture rest start T4: 1st set (right) posture rest end T5: 1 Set posture (right) posture end and first set (left) guidance start T6: first set (left) ready posture start T7: first set (left) posture static start T8: first set (left) Posture stationary end T9: First set (left) posture end and second set (right) guidance start T10: Second set (right) ready posture start T11: Second set (right) posture stationary start・ ・

At this time, the first determination unit 3181 determines whether or not the signal S1 is included in a predetermined range between the corresponding threshold value Th1 and threshold value Th2. The threshold value Th1 is a threshold value set in the reference value + storage unit 302, and the threshold value Th2 is a threshold value set in the reference value−storage unit 302. The reference value is, for example, an average value of signal values within the moving time window. The first determination unit 3181 determines that the values V1, V2, V3, and V4 are not included in the predetermined range in the section T3-T4. In this case, the first evaluation unit 3183 evaluates that the posture with respect to these values is not stable. In addition, the threshold value Th1 and the threshold value Th2 may be set as the threshold value stored in the storage unit 320 for each training period.

Also, the score calculation unit 3187 subtracts the number of times that the value of the signal S1 has exceeded a predetermined range from a predetermined value (for example, 100) set in advance. In this case, at the end of T4, the score calculation unit 3187 calculates the score “96 (= 100−4)”. The determination of whether or not the value of the signal S1 falls within the predetermined range is not limited to the posture stationary time, and may be applied to, for example, the posture preparation time (T2-T3, etc.).

Further, the difference value calculation unit 3162 calculates a difference value between the peak value P1 near T3 and the peak value P2 near T11 with respect to posture stability in repeated postures. The first determination unit 3181 determines whether or not the difference value between peaks (P1−P2) is less than a threshold value. If the difference value between the peaks is less than the threshold, the second evaluation unit 3185 evaluates that the repeated posture is the same and is stable, and if the difference value between the peaks is equal to or greater than the threshold, the repeated posture Is different and is evaluated as not stable. When the second evaluation unit 3185 evaluates that the posture is not stable, the score calculation unit 3187 may subtract a preset value from the current score.

<Screen example>
Next, a screen example of the trunk training application in the embodiment will be described. FIG. 13 is a diagram illustrating an example of the setting screen. The screen shown in FIG. 13 is a screen that is displayed, for example, in an initial stage after the application is started. The user can select either the “Train training by purpose” button B10 or the “Training from another application” button B12. This is a screen to be selected.

For example, when the button B10 is pressed by the user, the reception unit 312 notifies the display control unit 322 of information indicating this button press, and the display control unit 322 displays a screen that allows the user to select a purpose. Control (for example, see FIG. 14).

In addition, when the button B12 is pressed by the user, the display control unit 322 notifies the display control unit 322 of information indicating this button press, and the display control unit 322 displays a screen that allows the user to select another application. (See, for example, FIG. 19).

FIG. 14 is a diagram showing an example of the purpose selection screen. The screen shown in FIG. 14 is a screen on which buttons for selecting one purpose from one or a plurality of purposes are displayed. For example, a “basic trunk training” button B20 indicating training for training the basic trunk and a “want to clean walking posture” button B22 indicating training for cleaning the walking posture are displayed.

In the example shown in FIG. 14, it is assumed that the user presses the “Train the basic trunk” button B20. The receiving unit 312 detects the pressing of the button B20 and notifies the specifying unit 314 of the purpose-specific ID “1” for training the basic trunk. The specifying unit 314 acquires the purpose-specific ID “1”, refers to the training information illustrated in FIG. 9, and specifies a plurality of trainings corresponding to the purpose-specific ID “1”.

The generation unit 320 extracts a predetermined number of trainings by random lottery using random numbers from the training specified by the specifying unit 314, for example, and generates a training menu.

FIG. 15 is a diagram showing an example of a training menu display screen. In the example illustrated in FIG. 15, four trainings of “stretch” TR10, “position” TR12, “lift” TR14, and “up” TR16 are extracted by the generation unit 320. The screen shown in FIG. 15 displays a required time of 10 minutes when all four trainings are performed.

Further, in the screen shown in FIG. 15, each training can be selected in a button format. When the user presses a predetermined training display area, the training is selected. Press the “Start” button to start the training. For example, it is assumed that the accepting unit 312 accepts that the user has pressed the “up” TR16 button, and then accepts that the “start training” button has been pressed.

At this time, the display control unit 322 displays training guidance corresponding to the “up” TR16 on the display screen, and the specifying unit 314 displays the signal to be compared and the threshold corresponding to the “up” TR16 in FIG. It specifies with reference to the threshold value information to show.

FIG. 16 is a diagram showing an example of a training guidance screen for the “up” TR 16. On the screen shown in FIG. 16, guidance for “stretching the iliopsoas muscle” which is a training corresponding to “up” is displayed. In addition, the screen shown in FIG. 16 displays “30 seconds” as the required time and the number of sets “3set”.

After the screen shown in FIG. 16, the display control unit 322 controls to display the training screen according to a preset procedure. For example, the procedure is (1) Guidance start (preparation posture), (2) Posture transition count, (3) Posture stop, and (4) Count back to the preparatory posture. The procedures (2) to (4) are repeated for the number of sets.

The evaluation unit 318 evaluates the posture of the user as described above by using the signal sensed by the 6-axis sensor 206 mounted on the eyewear 30 during (2) to (4). The evaluation unit 318 may calculate a score as an evaluation index for posture.

FIG. 17 is a diagram showing an example of the score display screen. In the example illustrated in FIG. 17, the display control unit 322 performs control so that “76” calculated by the score calculation unit 3187 is displayed on the screen as the score SC10 with respect to the posture of the user with respect to the “intestinal psoas muscle stretch” training. To do.

Next, when all the trainings shown in FIG. 15 are performed, a training result screen is displayed. FIG. 18 is a diagram illustrating an example of a training result screen. In the screen shown in FIG. 18, a character is displayed, and a score is displayed for each part of the character. As the score of each part, an average score of scores of all the trainings performed by the user associated with the part is displayed. For example, when the user performs trainings A to C and the training regarding the abdomen is trainings A and B, the score displayed on the abdomen is the average score of the training A and B scores. For example, the average score is calculated by the score calculation unit 3187. As shown in FIG. 18, since the score of each part is displayed on one screen, the user can grasp which part is not properly trained.

FIG. 19 is a diagram illustrating an example of an application selection screen. The screen shown in FIG. 19 is a screen on which buttons for selecting one application from one or a plurality of applications are displayed. For example, a “RUN” button B30 indicating another application and a “WALK” button 32 are displayed.

In the example shown in FIG. 19, it is assumed that the user presses the “RUN” button B30. The accepting unit 312 detects the pressing of the button B30, and acquires a region ID indicating the region “thigh sole” from the application corresponding to “RUN” using a URL scheme or the like.

FIG. 20 is a diagram illustrating an example of a screen in which a predetermined part is notified from another application. The screen shown in FIG. 20 indicates that “back of thigh” is notified from the running application. In the screen shown in FIG. 20, the part notified from the running application is shown using the character, and a “training” button B40 for starting training is displayed.

Here, it is assumed that the user presses this button B40. The accepting unit 312 notifies the specifying unit 314 of a site ID indicating the site “thigh back side”. The specifying unit 314 acquires the part ID, refers to the training information illustrated in FIG. 9, and specifies a plurality of trainings corresponding to the part “thigh reverse side” indicated by the part ID.

The generation unit 320 extracts a predetermined number of trainings by random lottery using random numbers from the training specified by the specifying unit 314, for example, and generates a training menu.

FIG. 21 is a diagram showing an example of a training menu display screen. In the example shown in FIG. 21, the “Lung Stretch” TR20, “Hamstring Stretch” TR22, “Intestinal Lumbar Muscle Stretch” TR24, “Plank Position (with Knee)” TR26, and “Squat on the Thigh Back” TR28 are generated by the generation unit 320. , “Training from one leg to raising one hand” TR30 is extracted. The screen shown in FIG. 21 displays a required time of 11 minutes when all six trainings are performed.

In the screen shown in FIG. 21, each training can be selected in a button format. When the user presses a predetermined training display area, the training is selected. Press the “Start” button to start the training.

Further, the generation unit 320 may generate a training menu so that each type of training has a predetermined ratio. For example, when the ratio of stretch: strengthening: balance interlock is 3: 2: 1, the generation unit 320 changes the type “stretch”, “range stretch” TR20, “hamstring stretch” TR22, and “intestinal lumbar muscle stretch”. "TR24" is extracted, two types of "plank position (with knee)" TR26 and "thigh squat squat" TR28 are extracted from the type "strengthened", and from the type "balance interlock" One of "Third hand up from" TR30 is extracted, and a menu is automatically generated.

When training is selected and executed, a predetermined procedure is displayed on the screen as described above, and the user performs training according to the procedure. At this time, the evaluation unit 318 evaluates the posture of the user during training.

<Operation>
Next, the operation of the information processing apparatus 10 in the embodiment will be described. FIG. 22 is a flowchart illustrating an example of overall processing of an application that executes trunk training in the embodiment. The flowchart shown in FIG. 22 is a process performed when this application is executed by the user wearing the eyewear 30, operating the information processing apparatus 10, and touching the application icon described above by the user. is there. The connection between the processing device 20 and the information processing device 10 may be performed in advance.

In step S102 shown in FIG. 22, the control unit 306 determines based on the user operation whether the purpose of training or another application has been selected. For example, the control unit 306 determines whether or not the user has touched the “Training by Purpose” button B10 or the “Training from another application” button B12 illustrated in FIG. If the purpose is selected, the process proceeds to step S104, and if another application is selected, the process proceeds to step S108.

In step S104, the display control unit 322 displays a plurality of purposes on the screen and allows the user to select one purpose. The accepting unit 312 accepts the selected purpose. For example, information indicating the purpose corresponding to the pressed button is detected by the user pressing either the “Train the basic trunk” button B20 or the “I want to clean my walking posture” button B22 shown in FIG. To get.

In step S <b> 106, when the generation unit 320 acquires information indicating the purpose from the reception unit 312, the generation unit 320 refers to the training information illustrated in FIG. 9 and combines a predetermined number of trainings from among the trainings associated with the purpose. Is generated. At this time, for example, a training menu screen shown in FIG. 15 is displayed. The menu generation process according to the purpose will be described later with reference to FIG.

In step S108, the display control unit 322 displays a plurality of other applications on the screen and causes the user to select one application. The accepting unit 312 accepts the selected application. For example, the user detects which of the “RUN” button B30 or the “WALK” button B32 shown in FIG. 19 is pressed, and acquires information indicating an application corresponding to the pressed button.

In step S110, when the generation unit 320 acquires information indicating an application from the reception unit 312, the generation unit 320 acquires information indicating a predetermined part from the application using a URL scheme or the like. At this time, as shown in FIG. 20, the acquired predetermined part may be notified to the user. Next, the generation unit 320 refers to the training information illustrated in FIG. 9 and generates a menu by combining a predetermined number of trainings from among the trainings associated with a predetermined part acquired from another application. At this time, for example, a training menu screen shown in FIG. 21 is displayed. The menu generation process corresponding to the predetermined part will be described later with reference to FIG.

In step S112, the control unit 306 determines whether training has started. For example, the control unit 306 determines whether or not a button for starting training displayed on the screen has been pressed by the user. If training is started (step S112—YES), the process proceeds to step S114. If training is not started (step S112—NO), the process returns to step S112.

In step S114, the control unit 306 starts a training process. The control unit 306 evaluates the posture during training using a signal sensed by the 6-axis sensor 206 mounted on the eyewear 30. This training process will be described later with reference to FIG.

In step S116, the display control unit 322 controls to display the training result on the screen. At this time, for example, a training result screen shown in FIG. 18 is displayed.

According to the above processing, when performing training processing on the trunk, appropriate evaluation is performed by automatically generating a training menu and evaluating the result of training using a signal from a sensor worn by the user. It can be performed.

FIG. 23 is a flowchart showing an example of a menu generation process according to the purpose. In step S202 illustrated in FIG. 23, the identifying unit 314 refers to the training information illustrated in FIG. 9 and identifies one or a plurality of trainings based on the accepted purpose.

In step S204, the first generation unit 3202 extracts the training so that the ratio of each type becomes a predetermined ratio based on the specified type of training. For example, when the types of training are A, B, and C, the training is randomly extracted so as to be 1: 1: 1. The predetermined ratio can be set as appropriate.

In step S206, the first generation unit 3202 generates a menu by combining the extracted training. For example, the first generation unit 3202 may determine the order of training according to a predetermined criterion such as the time required for training or the same training type not continuing.

According to the above processing, the user can automatically generate a training menu suitable for the purpose only by selecting the purpose of training the trunk. Even if the same purpose is selected repeatedly, the training is randomly extracted, so that the training can be continued without getting tired of the user instead of the same training menu every time.

FIG. 24 is a flowchart showing an example of menu generation processing corresponding to a predetermined part. In step S302 illustrated in FIG. 24, the reception unit 312 acquires information indicating a predetermined part from another application using a URL scheme or the like.

In step S314, the identifying unit 314 refers to the training information illustrated in FIG. 9 and identifies one or more trainings based on the received predetermined part. For example, the specifying unit 314 specifies the training associated with the predetermined part as having an effect.

In step S204, the second generation unit 3204 randomly extracts a predetermined number of trainings from the identified trainings, and generates a menu by combining the extracted trainings. For example, the second generation unit 3204 may determine the order of training according to a predetermined criterion such as the time required for training and the same training type not continuing.

According to the above processing, a training menu suitable for a predetermined part notified from another application can be automatically generated. Even if the same part is repeatedly notified, since the training is randomly extracted, the training can be continued without getting tired of the user instead of the same training menu every time. In addition to the notification from another application, the process shown in FIG. 24 may be executed by inputting a part that the user wants to train.

FIG. 25 is a flowchart showing an example of the training process. In step S402 illustrated in FIG. 25, the display control unit 322 performs control so as to display a screen that prompts the user to perform a posture before training (also referred to as a preparation posture). At this time, the evaluation unit 318 may also evaluate the preparation posture based on the signal from the sensor.

In step S404, the display control unit 322 controls the user to display a screen for maintaining a posture during training (also referred to as a stationary posture). At this time, the evaluation unit 318 evaluates the stationary posture based on the signal from the sensor.

In step S406, the score calculation unit 3187 scores a posture during training using a threshold value for the training, and calculates a score. In addition, by using a signal corresponding to the training, it is possible to omit processing using a signal having low relevance and contribute to power saving.

In step S408, the control unit 306 determines whether or not all sets have been completed. If all sets have been completed (step S408-YES), the process proceeds to step S410. If all sets have not been completed (step S408-NO), the process returns to S402.

In step S410, the score calculation unit 3187 calculates the average score (average score) by summing up the scores in each set and dividing by the number of sets. The calculated average score is displayed on the screen by the display control unit 322.

According to the above processing, it is possible to perform an appropriate evaluation with respect to training for maintaining a predetermined posture for a predetermined time.

Each of the processing steps included in the processing flow described with reference to FIGS. 22 to 24 can be executed in any order or in parallel within a range in which there is no contradiction in processing contents. Other steps may be added in between. Further, a step described as one step for convenience can be executed by being divided into a plurality of steps, while a step described as being divided into a plurality of steps for convenience can be grasped as one step.

As described above, according to the embodiment, it is possible to appropriately evaluate the posture of the user in the training for maintaining the predetermined posture for a predetermined time. In addition, according to the embodiment, by changing the signal value used for each training, it is possible to perform an evaluation suitable for the training, and further, it is possible to contribute to power saving by omitting unnecessary processing. it can. In addition, according to the embodiment, by automatically generating a training menu based on a predetermined purpose or part,

In the embodiment, the case where the eyewear 30 is glasses has been described. However, eyewear is not limited to this. The eyewear may be any device related to the eye, and may be a face wearing device or a head wearing device such as glasses, sunglasses, goggles and a head mounted display and their frames.

In the embodiment, it has been described that the eyewear 30 may be provided with a biological electrode. However, eye movement or blink may be detected based on an electrooculogram signal that can be obtained from the biological electrode. At this time, each data that can be acquired from the 6-axis sensor 206 may be stored in association with the line-of-sight movement and the blink. This makes it possible to analyze blinks and line-of-sight movement during exercise.

In the embodiment, the detection data from the six-axis sensor 206 mounted on the eyewear 30 has been described. However, the detection data from the six-axis sensor 111 mounted on the information processing apparatus 10 may be used. It is possible to execute the application described in the example. That is, the 6-axis sensor only needs to be mounted not only on the head but also on any position of the human body. The 6-axis sensor is preferably attached to the trunk.

In addition, in the embodiment, it is not necessary for the user to mount many sensors, and if there is at least one 6-axis sensor, it can be mounted. Therefore, the posture at the time of training regarding the trunk can be evaluated without making the user feel bothersome about wearing the sensor.

In the embodiment, the application described above has been described using an example in which the application is applied to trunk training. However, the present invention can also be applied to cases where effective sensor signals differ for each training. In addition to trunk training, the generation of training menus can also be applied to training that has a plurality of types and training that is effective for parts with different results.

As mentioned above, although this invention was demonstrated using the Example, the technical scope of this invention is not limited to the range as described in the said Example. It will be apparent to those skilled in the art that various modifications and improvements can be made to the above-described embodiments. It is apparent from the description of the scope of claims that embodiments with such changes or improvements can be included in the technical scope of the present invention.

DESCRIPTION OF SYMBOLS 10 Information processing apparatus 20 Processing apparatus 30 Eyewear 302 Storage part 304 Communication part 306 Control part 312 Reception part 314 Identification part 316 Acquisition part 318 Evaluation part 320 Generation part 322 Display control part

Claims (11)

1. An information processing method executed by a computer having a control unit and a storage unit,
   The controller is
   Accepting information indicating predetermined training on the trunk,
   Identifying threshold values for the predetermined training based on the threshold information stored in the storage unit and associated with a threshold value for each training related to the trunk,
   Obtaining a signal value detected from a sensor attached to the user to be evaluated;
   Evaluating the posture of the user during the predetermined training based on a comparison result between the signal value and the threshold;
   Information processing method to execute.
2. The specifying is
   For each training, based on the threshold information associated with one or more signals to be compared and the thresholds of the signals, identify the signals and thresholds to be compared for the predetermined training,
   The obtaining is
   The information processing method according to claim 1, wherein a signal value of the signal to be compared is acquired from the sensor.
3. The evaluation is
   The information processing method according to claim 2, wherein the posture stability is evaluated based on whether or not an absolute value of the signal value is less than the threshold value.
4. The specifying is
   For each of the trainings, based on the threshold information further associated with a peak threshold for the signal, further identifying a peak threshold of the signal to be compared for the predetermined training,
   The obtaining is
   Calculating a difference value between peaks of the signal to be compared;
   The evaluation is
   The information processing method according to claim 2, wherein the posture stability is evaluated further based on whether or not an absolute value of the difference value is less than the peak threshold value.
5. The evaluation is
   The information processing method according to claim 4, further comprising: calculating a score indicating stability of the posture according to the number of times that the absolute value of the signal value or the absolute value of the difference value is equal to or greater than a threshold value.
6. To calculate the score,
   The information processing method according to claim 5, wherein a determination result of whether or not the user is in the predetermined training is reflected in the score.
7. Identifying one or more trainings for a predetermined attribute based on the training information associated with one or more attributes for each training, the training information stored in the storage unit;
   Generating a training menu by combining a predetermined number of trainings from one or more identified trainings;
   The information processing method according to claim 1, wherein the control unit further executes.
8. The accepting is
   Accepts information indicating a predetermined purpose for the training,
   Identifying the training is
   Based on the training information associated with the purpose and type related to the training as the attribute, a plurality of trainings for the predetermined purpose are identified,
   Said generating is
   Extracting each type of training from a plurality of identified training so that the proportion of each type becomes a predetermined rate,
   Combining each extracted type of training to generate a training menu,
   The information processing method according to claim 7.
9. The accepting is
   Receiving information indicating a predetermined part of the user's body;
   Identifying the training is
   Based on the training information associated with one or more parts related to the user's body as the attribute, identify a plurality of training for the predetermined part,
   Said generating is
   The information processing method according to claim 7, wherein a menu for training is generated by combining a predetermined number of trainings from among the plurality of trainings specified.
10. A reception unit that receives information indicating a predetermined training on the trunk;
    A storage unit that stores threshold information associated with a threshold for each training related to the trunk;
    A specifying unit for specifying a threshold for the predetermined training based on the threshold information;
    An acquisition unit for acquiring a signal value detected from a sensor attached to a user to be evaluated;
    An evaluation unit that evaluates the posture of the user during the predetermined training based on a comparison result between the signal value and the threshold;
    An information processing apparatus comprising:
11. On the computer,
    Accepting information indicating predetermined training on the trunk,
    Identifying a threshold for the predetermined training based on threshold information associated with a threshold for each training on the trunk;
    Obtaining a signal value detected from a sensor attached to the user to be evaluated;
    Evaluating the posture of the user during the predetermined training based on a comparison result between the signal value and the threshold;
    A program that executes
    
    
    
    
    

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