JP6689889B2 - Information processing method, information processing apparatus, and program - Google Patents

Description

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

  2. Description of the Related Art Conventionally, a technique is known in which the content of exercise is determined and the amount of exercise of each exercise is calculated using acceleration information from an acceleration sensor attached to the trunk (for example, see 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 for maintaining a predetermined posture for a predetermined time, such as training on the trunk. Further, it is preferable to evaluate posture stability in training regarding the trunk, but there is no conventional technique that evaluates posture stability in training regarding the trunk.

  Therefore, the disclosed technique has an object to appropriately evaluate the posture of a user in training for maintaining a predetermined posture for a predetermined time.

  An information processing method according to an aspect of the disclosed technique is an information processing method executed by a computer having a control unit and a storage unit, wherein the control unit receives information indicating a predetermined training regarding a core, the storage The threshold information stored in the unit, based on the threshold information associated with the threshold for each training on the core, to identify the threshold for the predetermined training, detected from the sensor worn by the user to be evaluated Acquiring a signal value to be performed, and evaluating a posture of the user at the time of the predetermined training based on a result of comparison between the signal value and the threshold value.

  According to the disclosed technology, it is possible to appropriately evaluate the posture of a user in training for maintaining a 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 showing an example of composition of an information processor 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 purpose information. It is a figure which shows an example of the evaluation result information with respect to a user's posture. 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 a 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 part was notified from the other application. It is a figure which shows an example of a training menu display screen. It is a flow chart which shows an example of the whole processing of the application which performs core training in an example. 9 is a flowchart illustrating an example of a menu generation process according to a purpose. 8 is a flowchart showing an example of a menu generation process according to a predetermined part. It is a flow chart which shows an example of training processing.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. However, the embodiments described below are merely examples, and are not intended to exclude various modifications and application of techniques not explicitly shown below. That is, the present invention can be implemented with various modifications without departing from the spirit of the present invention. Further, 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. The drawings may include portions having different dimensional relationships and ratios.

[Example]
In the embodiment, eyewear is given as an example of a target on which the acceleration sensor and the angular velocity sensor are mounted. FIG. 1 is a diagram illustrating an example of the information processing system 1 according to 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 the eyewear 30 are connected via a network so that data communication is possible.

  The eyewear 30 has the processing device 20 mounted 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). Further, the eyewear 30 may have the bioelectrodes 31, 33, and 35 on the pair of nose pads and the bridge portion, respectively. When the eyewear 30 is provided with bioelectrodes, the electro-oculography signals acquired from these bioelectrodes are transmitted to the processing device 20.

  The installation position of the processing device 20 does not necessarily have to be the temple, but may be positioned in consideration of the 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 and a smartphone owned by a user, a PC (Personal Computer), a tablet type terminal, or the like. The external device 10 evaluates the posture at the time of training on the core (hereinafter, also referred to as core training) based on the sensor signal received from the processing device 20. Hereinafter, the external device 10 will be described as an information processing device 10.

<Hardware Configuration of Information Processing Device 10>
FIG. 2 is a schematic configuration diagram illustrating a hardware configuration of the information processing device 10 according to the embodiment. A typical example of the information processing apparatus 10 is a mobile phone such as a smartphone, but in addition to this, a mobile terminal capable of wireless or wired connection to the network, or an electronic device equipped with a touch panel such as a tablet terminal, a network. A general-purpose device or the like that can display a screen while performing data processing while communicating using the device may correspond to the information processing device 10 in the embodiment.

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

  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, hard buttons 108, and hard keys 110. And the 6-axis sensor 111 is connected. Further, the touch panel 102, the camera 130, and the external interface 140 are further connected to the main control unit 150. The external interface 140 includes an audio output terminal 142.

  The touch panel 102 has both functions of a display device and an input device, and is configured by a display (display screen) 102A having a display function and a touch sensor 102B having an 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 touch operation, which is arranged on the upper surface of the display 102A, and a transparent operation surface laminated on the element. As a contact detection method of the touch sensor 102B, an arbitrary method among known methods such as an electrostatic capacitance type, a resistance film type (pressure sensitive type), and an electromagnetic induction type 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 a motion of a contact object (including a player's finger, stylus, etc., which will be described below as a representative example) that comes into contact with the operation surface. , Receives the operation input, and gives the information of the contact position 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, for example, coordinate values on the two axes of the touch panel 102 in the short side direction and the long side direction.

  The information processing device 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 device 20. The storage unit 120 records the program 122, and the storage unit 120 may be a separate body from the external device 10, and may be, for example, a recording medium such as an SD card or a CD-RAM.

<Structure 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 biological electrodes 31, 33, and 35 is connected to the processing unit 202 using an electric wire, for example, via an amplification unit. Each part of the processing device 20 may be provided 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. Also, 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 sensor signal to the transmission unit 204. For example, the processing unit 202 uses the sensor signals from the 6-axis sensor 206 to generate the following six signals, 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 Signal indicating: -Fifth signal: Signal indicating roll angle-Sixth 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. Is. Further, the fourth signal (pitch angle) indicates, for example, the inclination of the head up and down, the fifth signal (roll angle) indicates, for example, the movement of the head around the traveling axis, and the sixth signal (yaw angle) indicates the head. Shows the left and right inclination of. 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 first signal to the sixth signal processed by the processing unit 202 to the information processing device 10. For example, the transmission unit 204 transmits the sensor signal or each data to the information processing device 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 device 10>
Next, the configuration of the information processing device 10 will be described. FIG. 4 is a diagram illustrating an example of the configuration of the information processing device 10 according to the embodiment. The information processing device 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 a core training application in the embodiment, for example, data received from the processing device 20, training information related to training, threshold information used for posture determination of a user during training, and purpose of training. The target information, the result information of the posture determination of the user, the screen information displayed on the screen, and the like are stored.

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

  The control unit 306 can be realized by, for example, the main control unit 150 or the like. The control unit 306 executes a core training application. The core training application in the embodiment has a function of performing core training guidance, evaluating the posture of the user during training, and automatically generating a training menu. 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 function of the control unit 306 will be described separately for the posture determination function and the menu generation function.

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

  The identifying unit 314 identifies the threshold value for the predetermined training accepted by the accepting unit 312 based on the threshold value information stored in the storage unit 302 and associated with the threshold value for each training regarding the trunk. For example, the identifying unit 314 identifies the threshold for the 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 more thresholds are set according to the content of the training. The threshold value information is associated with a threshold value of a signal used for each training. The threshold information will be described later with reference to FIG.

  The acquisition unit 316 is an evaluation target, and acquires a signal value detected from a sensor attached to a user who performs a 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 worn by the user (for example, the 6-axis sensor 206) via a network, or is temporarily stored in the storage unit 302. Indirectly obtain the signal value that was saved.

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

  The display control unit 322 controls to display the evaluation result of the evaluation unit 318 on the display screen. Thereby, the evaluation of the posture of the user during the core 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 threshold values of the signals are associated with each other for the training for the posture determination. For example, the identifying unit 314 identifies the signal to be compared and the threshold for the predetermined training received by the receiving unit 312 based on the threshold information.

  In this case, the acquisition unit 316 acquires the signal value of the comparison target signal identified by the identification unit 314 from the sensor worn by 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. Further, the acquisition unit 316 may notify the sensor of the specified signal and acquire only the specified signal from the sensor. Accordingly, when various types of training are performed, the posture determination can be performed using a signal suitable for each training. Further, since processing using a signal that is not related to the training is not performed, it is possible to contribute to power saving.

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

  In this case, the acquisition unit 316 includes calculating the difference value between the peaks of the signals to be compared. FIG. 5 is a block diagram showing 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 the difference value between the peaks of the posture signals that are repeated at every predetermined time. For example, when the exercise for 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 value is calculated one after another.

  Next, each function of the evaluation unit 318 will be described. FIG. 6 is a block diagram showing 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 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, training in which the motion is relatively small is assumed, so a signal value exceeding the predetermined range indicates that the motion is large.

  Further, when the signals to be compared differ 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.

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

  The second evaluation unit 3185 evaluates the stability of the posture 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 stored in the storage unit 302. May be. For example, if the absolute value of the difference value between the peaks of the signals related to the posture repeated every predetermined time is less than the threshold, the second evaluation unit 3185 determines that the postures at the time of repetition are the same, and the posture is stable. Are considered to be 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. This makes it possible to appropriately evaluate the stability of the posture with respect to the repetition of the predetermined posture executed in the core training. The peak threshold 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 more than the threshold value. For example, the score calculation unit 3187 may calculate the score by subtracting the number of times the absolute value is equal to or more than the threshold value from the maximum score of 100 points. Accordingly, it is possible to provide the user with an evaluation index that is easy to grasp with respect to the user's posture.

  The second determination unit 3189 uses the signal value from the sensor to determine whether or not the user is undergoing predetermined training. For example, in the received training, the second determination unit 3189 has a signal value around 0 and a small variation, or a signal value around a predetermined value, even during a predetermined time period for maintaining a predetermined posture. If there is no transition, 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, which indicates whether or not the user is performing predetermined training, in the score. For example, the score calculation unit 3187 lowers the score from the current value by a predetermined value when it is determined that the training is not in progress based on the signal from the sensor. The score calculation unit 3187 may increase the value of the predetermined value as the time when it is determined that the training is not being performed is longer. By reflecting the determination result during such training on the score, it is possible to give the user an opportunity to continuously perform repeated training, for example.

  Further, the evaluation results 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. Accordingly, it is possible to notify the user of a more detailed evaluation result.

≪Menu generation function≫
Returning to FIG. 4, the menu generation function will be described. First, the identifying unit 314 identifies one or a plurality of trainings for a predetermined attribute based on the training information stored in the storage unit 302 and the training information associated with one or a plurality of 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, the training information is associated with attributes such as type, stationary time, required time, predetermined part, and purpose for each training. The training information will be described later with reference to FIG.

  The generation unit 320 generates a training menu by combining a predetermined number of trainings out of 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 as to be shorter than the predetermined time as a whole, or uses the type of training so that only the same kind of training is not extracted. Or Accordingly, the generation unit 320 generates a menu by appropriately combining a plurality of trainings, and thus the training can be continued without the user getting tired.

  Next, more detailed functions 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 the menu from the received predetermined portion.

First, a case where a menu is generated for a predetermined purpose will be described. Here, the attributes include the purpose and type of training. Accepting unit 312, information indicating a predetermined object TRAINING (hereinafter, also referred to as object-specific ID.) Accepts. The predetermined purpose is, for example, “training the basic trunk”, “cleaning the walking posture”, “easy to lose weight”, and the like. It should be noted that the storage unit 302 stores purpose information in which the purpose-specific ID and the purpose name are associated with each other (see, for example, FIG. 10).

  In this case, the identifying unit 314 identifies a plurality of trainings for a predetermined purpose indicated by the received purpose-specific ID, based on the training information associated with the attributes including the purpose and type of training. The types of training are, for example, “stretch”, “strengthen”, “balance-linked” and the like.

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

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

  Thereby, the training menu can be automatically generated according to the purpose of the user who wants to train the core. Further, since a training menu is automatically generated at random, even if the same purpose is selected, the same menu is not obtained every time, and the training can be continued without making the user tired.

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

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

  The menus generated by the first generation unit 3202 and the second generation unit 3204 are displayed on the display screen by the display control unit 322. Thereby, the user can grasp the contents of the automatically generated menu.

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

  The judgefactor is represented by 6 digits of 0 or 1, and corresponds to the first signal (thr_accX), the second signal (thr_accY), ..., The sixth signal in order from the left. Further, “0” indicates that the object is not a comparison object, and “1” indicates that the object is a comparison object. For example, when the jitterFactor 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 value set in each signal line shown in FIG. 8 indicates the threshold value. For example, the threshold for the first signal (thr_accX) with ID “4” is “2”.

  The judgegePeak is represented by 6 digits of 0 or 1, and corresponds to the peak of the first signal, the peak of the second signal, ..., And the peak of the sixth signal (thr_yaw_pk) in order from the left. Further, “0” indicates that the object is not a comparison object, and “1” indicates that the object is a comparison object. For example, when the peakPeak is “010010”, it indicates that the peak of the second signal and the peak of the fifth signal are used as comparison targets. judgegePeak is used by the second evaluation unit 3185. Further, the numerical value provided in the row of peaks 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, with respect to the training of number 1, the training names “Iliopsoas muscle stretch”, judgeFactor “000110”, and 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.

  Further, with respect to the training of number 3, the training names “standing standing rotation”, judgeFactor “111000”, and judgePeak “000001” are associated. 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 (thr_yaw_pk) of the sixth signal.

  According to the threshold information illustrated in FIG. 8, the identifying unit 314 can easily identify a signal to be compared and a threshold for training by referring to the judgefactor and the judgepeak and the threshold thereof.

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

  The type indicates the type or effect of training. The types include, for example, stretching, strengthening, and balance interlocking. The lottery exception flag indicates a training number that is not executed at the same time. For example, “Agura swirl” with the training ID “5” is not included in the training menu at the same time as “Standing swirl” with the training ID “3”. This is because these are the same types of convolutive training in stretching training.

  The stationary time indicates the stationary time of the posture taken by the user during training. For example, when the stationary time is “3”, it 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”, it indicates that the posture is repeated three sets. The required time indicates the time required for one training.

  The predetermined part indicates a human part effective for the training. In the example shown in FIG. 9, the site name is used as the site ID, but a number or the like may be used. In addition, one training may have an effect on a plurality of regions. "O" is set to the effective part, and "-" is set to the ineffective part.

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

  In the example shown in FIG. 9, for the training ID “4”, the training name “standing posture lateral bending”, the type “stretching”, the lottery exception flag “6”, the stationary time “3”, the number of times “3”, the required time “170”. , Back “◯”, trunk side “◯”, abdomen “−”, ..., Purpose-specific IDs “1, 2” are associated.

  According to the training information shown in FIG. 9, the identifying unit 314 can identify the predetermined training by using the purpose-specific ID, and can also identify the predetermined training by using the predetermined site. .

  FIG. 10 is a diagram showing an example of the purpose information. In the example shown in FIG. 10, a training purpose name is associated with each purpose-based ID. For example, the purpose-specific ID “1” is associated with the purpose name “train the basic core”.

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

  For example, the user “AAA” has acquired the score “78” for the training “Lanji”. As shown in FIG. 11, a score indicating a posture evaluation is associated with each training.

<Specific example of threshold comparison>
FIG. 12 is a diagram showing a specific example of threshold comparison for a predetermined signal. In the example shown in FIG. 12, the fluctuation of one signal S1 of 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: Start guidance for 1st set (right) T2: Start preparation for 1st set (right) T3: Start posture for 1st set (right) T4: End posture for 1st set (right) T5: 1 Posture of the 1st set (right) ends, guidance of the 1st set (left) starts T6: Preparation posture of the 1st set (left) T7: Posture rest of the 1st set (left) T8: 1st set (left) Posture stop of T9: End of posture of 1st set (left), start of 2nd set (right) T10: Start of preparatory posture of 2nd set (right) T11: Start of posture stop of 2nd set (right)・ ・

  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 Th1 and threshold Th2. The threshold value Th1 is a reference value + a threshold value set in the storage unit 302, and the threshold value Th2 is a reference value−a threshold value set in the 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 within 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. As the threshold value stored in the storage unit 320, the threshold value Th1 and the threshold value Th2 may be set for each training period.

  Further, the score calculation unit 3187 subtracts the number of times the value of the signal S1 exceeds the predetermined range from a predetermined value (for example, 100) set in advance. In this case, the score calculation unit 3187 calculates the score “96 (= 100−4)” at the end of T4. Further, the determination as to whether or not the value of the signal S1 falls within the predetermined range may be applied not only to the posture rest time but also to the posture preparation time (T2-T3 etc.), for example.

  Further, the difference value calculation unit 3162 calculates the difference value between the peak value P1 near T3 and the peak value P2 near T11 regarding the stability of the posture in the repeated postures. The 1st determination part 3181 determines whether the difference value (P1-P2) between peaks is less than a threshold value. If the difference value between peaks is less than the threshold value, the second evaluation unit 3185 evaluates that the repeated postures are similar and stable, and if the difference value between the peaks is greater than or equal to the threshold value, the repeated posture is determined. But different and 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, an example of a screen of an application for core training in the embodiment will be described. FIG. 13 is a diagram showing an example of the setting screen. The screen shown in FIG. 13 is, for example, a screen that is displayed in the initial stage after the application is started, and the user selects either the “Train training by purpose” button B10 or the “Training from another application” button B12. This is a screen for selection.

  For example, when the button B10 is pressed by the user, the reception unit 312 notifies the display control unit 322 of information indicating that the button has been pressed, and the display control unit 322 causes the user to display a screen for selecting the purpose. Control (see, for example, FIG. 14).

  Further, when the user presses the button B12, the display control unit 322 notifies the display control unit 322 of information indicating that the button has been pressed, and the display control unit 322 displays a screen that allows the user to select another application. Control (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 “train basic trunk” button B20 indicating training for training the basic trunk and a “want to improve 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 has pressed the “train basic core” button B20. The reception unit 312 detects the pressing of the button B20 and notifies the identification unit 314 of the purpose-specific ID “1” for training the basic trunk. The identifying 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 generates a training menu by extracting a predetermined number of trainings from the training specified by the specifying unit 314 by random lottery using random numbers, for example.

  FIG. 15 is a diagram showing an example of a training menu display screen. In the example shown in FIG. 15, the generating unit 320 extracts four trainings of “stretch” TR10, “position” TR12, “lift” TR14, and “up” TR16. Further, the screen shown in FIG. 15 displays that the time required for performing all four trainings is 10 minutes.

  Further, in the screen shown in FIG. 15, each workout can be selected in the button format, and when the user presses the display area of the workout specified by the user, the workout is brought into the selected state. Press the "Start" button to start the workout. For example, it is assumed that the accepting unit 312 accepts that the user has pressed the “up” TR16 button, and then has accepted that the “start training” button has been pressed.

  At this time, the display control unit 322 displays the training guidance corresponding to “up” TR16 on the display screen, and the identifying unit 314 displays the comparison target signal corresponding to “up” TR16 and the threshold value thereof in FIG. It identifies by referring to the threshold information shown.

  FIG. 16 is a diagram showing an example of a training guidance screen for the “up” TR16. On the screen shown in FIG. 16, guidance for “Iliopsoas stretch”, which is training corresponding to “up”, is displayed. Further, the screen shown in FIG. 16 displays the required time “30 seconds” and the number of sets “3 set”.

  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 stationary, and (4) count returning to the preparation posture. The steps (2) to (4) are repeated for the number of sets.

  During (2) to (4), the evaluation unit 318 evaluates the user's posture using the signal sensed by the 6-axis sensor 206 mounted on the eyewear 30 as described above. The evaluation unit 318 may also calculate a score as a posture evaluation index.

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

  Next, when all the training shown in FIG. 15 has been performed, a training result screen is displayed. FIG. 18 is a diagram showing an example of the training result screen. On 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, the average score of all the trainings performed by the user, which is associated with the part, is displayed. For example, when the user performs training A to C and the training related to the abdomen is training A and B, the score displayed on the abdomen is the average score of the training A and B. For example, the average score is calculated by the score calculator 3187. As shown in FIG. 18, since the score of each part is displayed on one screen, the user can grasp which part the training is not properly performed.

  FIG. 19 is a diagram showing an example of the application selection screen. The screen shown in FIG. 19 is a screen on which each button for selecting one application from one or a plurality of applications is 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 has pressed the “RUN” button B30. The reception unit 312 detects the pressing of the button B30, and acquires the site ID indicating the site “back of the thigh” from the application corresponding to “RUN” using a URL scheme or the like.

  FIG. 20 is a diagram showing an example of a screen notified of a predetermined part 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 displayed using a character, and a “train” button B40 for starting training is displayed.

  Here, it is assumed that the user presses the button B40. The reception unit 312 notifies the identification unit 314 of the site ID indicating the site “back of thigh”. The identifying unit 314 acquires the site ID, refers to the training information illustrated in FIG. 9, and identifies a plurality of trainings corresponding to the site “back of the thigh” indicated by the site ID.

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

  FIG. 21 is a diagram showing an example of the training menu display screen. In the example shown in FIG. 21, the generation unit 320 causes the “lung stretch” TR20, the “hamstring stretch” TR22, the “iliopsoas muscle stretch” TR24, the “plank position (with knees)” TR26, and the “thigh rear squat” TR28. , Six trainings of "one leg standing to one hand raised" TR30 are extracted. Further, the screen shown in FIG. 21 shows that the time required for performing all six trainings is 11 minutes.

  Further, in the screen shown in FIG. 21, each workout can be selected in a button format, and when the user presses the display area of the workout specified by the user, the workout is brought into a selected state. Press the "Start" button to start the workout.

  Further, the generation unit 320 may generate the training menu so that each type of training has a predetermined ratio. For example, when the ratio of stretch: enhancement: balance interlocking is 3: 2: 1, the generation unit 320 selects the “stretch” type “range stretch” TR20, the “hamstring stretch” TR22, and the iliopsoas stretch. Extracting three of TR24, extracting two types of "Plank position (with knees)" TR26 and "Squat on the back of thigh" TR28 from the type "strengthening", and standing on one leg from the type "balance interlocking" One of "from one hand" TR30 is extracted and a menu is automatically generated.

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

<Operation>
Next, the operation of the information processing device 10 in the embodiment will be described. FIG. 22 is a flowchart showing an example of the overall processing of the application that executes the core training in the embodiment. The flowchart shown in FIG. 22 is a process performed when the user wears the eyewear 30, operates the information processing apparatus 10, and the user touches the icon of the application described above to execute the application. is there. The processing device 20 and the information processing device 10 may be connected in advance.

  In step S102 shown in FIG. 22, the control unit 306 determines whether the purpose of training or another application is selected based on the user operation. For example, the control unit 306 determines whether or not the user has touched the “purpose-specific core training” button B10 or the “training from another application” button B12 shown 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 reception unit 312 receives the selected purpose. For example, information indicating the purpose corresponding to the button that is detected by the user detecting which one of the "train basic core" button B20 and the "I want to improve the walking posture" button B22 shown in FIG. 14 is pressed. To get.

  In step S106, when the generation unit 320 acquires the 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 to obtain a menu. To generate. At this time, for example, the training menu screen shown in FIG. 15 is displayed. The menu generation processing 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 prompts the user to select one application. The reception unit 312 receives the selected application. For example, it is detected which of the “RUN” button B30 and the “WALK” button B32 shown in FIG. 19 is pressed by the user, and the information indicating the application corresponding to the pressed button is acquired.

  In step S110, when the generation unit 320 acquires the information indicating the application from the reception unit 312, the generation unit 320 acquires the information indicating the predetermined part from the application using the 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 the trainings associated with the predetermined site acquired from another application. At this time, for example, the training menu screen shown in FIG. 21 is displayed. It should be noted that the process of generating a menu according to a 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 the button for starting the training displayed on the screen has been pressed by the user. If the training is started (step S112-YES), the process proceeds to step S114, and if the training is not started (step S112-NO), the process returns to step S112.

  In step S114, the control unit 306 starts the training process. The control unit 306 evaluates the posture during training using the signals 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, the training result screen shown in FIG. 18 is displayed.

  According to the above processing, when executing the training processing regarding the core, the training menu is automatically generated, and the result of the training is evaluated by using the signal from the sensor worn by the user, thereby performing an appropriate evaluation. 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 based on the identified training type so that the ratio of each type becomes a predetermined ratio. For example, when the types of training are A, B, and C, the training is randomly extracted so as to have a ratio of 1: 1: 1. The predetermined ratio can be set appropriately.

  In step S206, the first generation unit 3202 combines the extracted trainings to generate a menu. For example, the first generation unit 3202 may determine the order of training according to a required time for training or a predetermined criterion such that the same type of training does not continue.

  According to the above processing, the user can automatically generate a training menu suitable for the purpose only by selecting the purpose for training the core. Further, even if the same purpose is repeatedly selected, the training is randomly extracted, so that it is not the same training menu every time, and the training can be continued without the user getting tired.

  FIG. 24 is a flowchart showing an example of a menu generation process 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 a plurality of trainings based on the received predetermined part. For example, the identifying unit 314 identifies the training associated with the predetermined site as having an effect.

  In step S204, the second generation unit 3204 randomly extracts a predetermined number of trainings from the identified trainings, and combines the extracted trainings to generate a menu. For example, the second generation unit 3204 may determine the order of training according to a required time for training, or a predetermined criterion such that the same type of training does not continue.

  According to the above processing, it is possible to automatically generate a training menu suitable for a predetermined part notified from another application. Further, even if the same part is repeatedly notified, the training is randomly extracted, so that it is not the same training menu each time, and the training can be continued without the user getting tired. In addition to the notification from another application, the process shown in FIG. 24 may be executed by the user inputting a portion to be trained.

  FIG. 25 is a flowchart showing an example of the training process. In step S402 illustrated in FIG. 25, the display control unit 322 controls to display a screen that prompts the user to take 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 the posture during training (also referred to as a static posture). At this time, the evaluation unit 318 evaluates the static posture based on the signal from the sensor.

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

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

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

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

  22 to 24, the processing steps included in the processing flows described in FIGS. 22 to 24 can be arbitrarily changed in order or executed in parallel as long as the processing contents do not conflict with each other. Other steps may be added in between. Further, a step described as one step for convenience can be divided into a plurality of steps and executed, while a step described as 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 the predetermined time. Further, 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 useless processing. I can .

  In the embodiment, the case where the eyewear 30 is glasses has been described. However, the eyewear is not limited to this. The eyewear may be eye-related equipment, and may be face-wearing equipment or head-wearing equipment such as glasses, sunglasses, goggles and head mounted displays, and frames thereof.

  Although it has been described in the embodiment that the eyewear 30 may be provided with the bioelectrode, the eye movement or the blink may be detected based on the electro-oculography signal acquired from the bioelectrode. At this time, each data that can be acquired from the 6-axis sensor 206 may be stored in association with the eye movement or the blink. This makes it possible to analyze blinks and eye movements during exercise.

  In the embodiment, the detection data from the 6-axis sensor 206 mounted on the eyewear 30 is used, but the detection data from the 6-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 may be attached not only to the head but also to any position on the human body. The 6-axis sensor is preferably attached to the trunk.

  Further, in the embodiment, it is not necessary for the user to wear many sensors, and it is possible to implement it if there is at least one 6-axis sensor. Therefore, the posture at the time of training regarding the trunk can be evaluated without making the user feel uncomfortable with wearing the sensor.

  Further, in the embodiment, the example in which the above-described application is applied to the core training has been described, but it is also applicable to the case where the effective sensor signal is different for each training. Further, with respect to the generation of the training menu, in addition to the core training, the training having a plurality of types and the training that is effective for the parts having different results can be applied.

  Although the present invention has been described above using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications and improvements can be added to the above embodiment. It is apparent from the description of the scope of claims that the embodiments added with such changes or improvements can be included in the technical scope of the present invention.

10 information processing device 20 processing device 30 eyewear 302 storage unit 304 communication unit 306 control unit 312 reception unit 314 identification unit 316 acquisition unit 318 evaluation unit 320 generation unit 322 display control unit

Claims (11)

An information processing method executed by a computer having a control unit and a storage unit,
The control unit is
Accepting information indicating a predetermined training for maintaining a predetermined posture for a predetermined time ,
The threshold information stored in the storage unit, based on the threshold information associated with a threshold for each training to maintain a predetermined posture for a predetermined time , to specify a threshold for the predetermined training,
To acquire the signal value detected from the sensor attached to the user to be evaluated,
Determining whether the user to be evaluated is in the predetermined training based on the signal value,
Evaluating the posture of the user based on a result of comparison between the signal value and the threshold value and a result of determination as to whether or not the predetermined training is being performed;
Run
The evaluation is
With respect to the evaluation of the posture when it is determined that the predetermined training is not being performed, including performing an evaluation so as to be lower than the evaluation of the posture when it is determined that the predetermined training is being performed, Information processing method. The identifying is
For each of the training, based on the threshold information associated with one or more signals to be compared and the threshold of the signal, specify the signal and the threshold of the comparison target for the predetermined training,
Acquiring is
The information processing method according to claim 1, wherein a signal value of the signal to be compared is acquired from the sensor. The evaluation is
The information processing method according to claim 2, wherein the stability of the posture is evaluated based on whether the absolute value of the signal value is less than the threshold value. The identifying is
For each of the training, based on the threshold information further associated with the peak threshold for the signal, further specify the peak threshold of the signal to be compared to the predetermined training,
Acquiring is
Comprising calculating a difference value between peaks of the signals to be compared,
The evaluation is
The information processing method according to claim 2, wherein the stability of the posture is evaluated further based on whether or not the absolute value of the difference value is less than the peak threshold value. The evaluation is
The information processing method according to claim 4, further comprising calculating 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 more than a threshold value. Calculating the score is
The information processing method according to claim 5, wherein a result of the determination as to whether the user is in the predetermined training is reflected in the score. Specifying one or more trainings for a predetermined attribute based on the training information stored in the storage unit, wherein the training information is associated with one or more attributes for each training,
Generating a training menu by combining a predetermined number of trainings out of the identified one or more trainings;
The information processing method according to claim 1, wherein the control unit further executes. The acceptance is
Accepting information indicating the stated purpose of the training,
Identifying the training is
As the attribute, based on the training information associated with the purpose and type of the training, to identify a plurality of training for the predetermined purpose,
The generating is
Extracting each type of training so that the ratio of each type becomes a predetermined ratio from the identified plurality of trainings,
Combining each of the extracted types of training to generate a training menu,
The information processing method according to claim 7. The acceptance is
Accepting information indicating a predetermined part of the user's body,
Identifying the training is
As the attribute, based on the training information associated with one or more parts related to the body of the user, a plurality of training for the predetermined part is specified,
The generating is
The information processing method according to claim 7, wherein a training menu is generated by combining a predetermined number of trainings from the identified plurality of trainings. A reception unit that receives information indicating a predetermined training for maintaining a predetermined posture for a predetermined time ,
A storage unit that stores threshold information in which a threshold is associated with each training for maintaining a predetermined posture for a predetermined time ,
A specifying unit that specifies a threshold value for the predetermined training based on the threshold value information,
An acquisition unit that acquires a signal value detected from the sensor attached to the user to be evaluated,
Based on the signal value, a determination unit that determines whether the user to be evaluated is in the predetermined training,
Based on the comparison result of the signal value and the threshold value, and the determination result of the determination unit, an evaluation unit that evaluates the posture of the user,
Equipped with
The evaluation unit is
An information processing apparatus that performs evaluation so that the evaluation of the posture when it is determined that the predetermined training is not being performed is lower than the evaluation of the posture when it is determined that the predetermined training is being performed. . On the computer,
Accepting information indicating a predetermined training for maintaining a predetermined posture for a predetermined time ,
Specifying a threshold for the predetermined training based on threshold information associated with a threshold for each training for maintaining a predetermined posture for a predetermined time ,
To acquire the signal value detected from the sensor attached to the user to be evaluated,
Determining whether the user to be evaluated is in the predetermined training based on the signal value,
Evaluating the posture of the user based on a result of comparison between the signal value and the threshold value and a result of determination as to whether or not the predetermined training is being performed;
Run
The evaluation is
With respect to the evaluation of the posture when it is determined that the predetermined training is not being performed, including performing an evaluation so as to be lower than the evaluation of the posture when it is determined that the predetermined training is being performed, program.

Images