JP6442769B2 - Information processing apparatus, gesture detection method, and program - Google Patents

Description

  The disclosed technology relates to an information processing apparatus, a gesture detection method, and a program.

  As a technique for detecting a motion applied to an information processing apparatus using an angular velocity sensor, for example, there is a technique for preliminarily storing an angular velocity measured by the angular velocity sensor when a motion such as a drop is applied to the information processing apparatus. . In this technique, the stored angular velocity and the angular velocity measured by the angular velocity sensor are compared every predetermined time along the time axis, and if the difference between the compared angular velocities is less than a predetermined value, the motion is Determine that you have joined.

JP2007-304988A JP2012-230593

In related technology, in order to compare the angular velocities, for example, when the user is on a running vehicle, the external velocity such as shaking of the running vehicle is applied, and the measured value of the angular velocity is affected. May not be detected properly.
Motion detection using an angular velocity sensor can also be applied to a technique for detecting an action indicating a user's intention (hereinafter referred to as “gesture”), such as holding and shaking a housing of an information processing terminal. Even when a gesture is detected based on the angular velocity, the gesture may not be properly detected due to the influence of the external force described above.

  One aspect of the disclosed technique is to reduce the influence of an external force when detecting a gesture based on an angular velocity.

  In the disclosed technique, the angular velocity detection unit detects angular velocity signals around the three axes that are orthogonal to each other set in the housing, and the conversion unit detects the angular velocity around the three axes that is detected by the angular velocity detection unit. Each signal is converted to a spectrum in the frequency domain. Further, the acquisition unit performs a primary operation on the frequency indicating the maximum gain in the spectrum of the specific one-axis frequency domain having the largest value among the maximum values of the gain of the spectrum in the frequency domain of each axis converted by the conversion unit. Obtained as the main resonance frequency. In addition, the acquisition unit acquires, as a gain ratio, a ratio between a gain at the same frequency as the primary main resonance frequency of the spectrum in at least the biaxial frequency region and the maximum gain. The determination unit compares the primary main resonance frequency and the gain ratio of the reference gesture with the primary main resonance frequency and the gain ratio of the determination target gesture for each of the primary main resonance frequency and the gain ratio. It is determined whether or not the determination target gesture is a reference gesture. The primary main resonance frequency and the gain ratio of the reference gesture are acquired by the acquisition unit when the reference gesture is performed on the casing. Further, the primary main resonance frequency and the gain ratio of the determination target gesture are acquired by the acquisition unit when the determination target gesture is performed on the housing.

  As one aspect, the disclosed technology has an effect of reducing the influence of an external force when detecting a gesture based on an angular velocity.

It is a block diagram which shows an example of the principal part function of the smart device which concerns on embodiment. It is a conceptual diagram which shows an example of the gesture table which concerns on embodiment. It is a block diagram which shows an example of a structure of the electric system of the smart device which concerns on embodiment. It is a flowchart which shows an example of the gesture registration process which concerns on embodiment. It is a conceptual diagram which shows an example of the user interface screen of the gesture registration process which concerns on embodiment. It is a conceptual diagram which shows an example of the user interface screen of the gesture registration process which concerns on embodiment. It is a conceptual diagram which shows an example of the user interface screen of the gesture registration process which concerns on embodiment. It is a conceptual diagram which shows an example of the reference | standard gesture which concerns on embodiment. It is a conceptual diagram which shows an example of the reference | standard gesture which concerns on embodiment. It is a conceptual diagram which shows an example of the reference | standard gesture which concerns on embodiment. It is a conceptual diagram which shows an example of the reference | standard gesture which concerns on embodiment. It is a conceptual diagram which shows a smart device and the movement of a thumb when the reference | standard gesture which concerns on embodiment is performed. It is a conceptual diagram which shows a smart device and the movement of a thumb when the reference | standard gesture which concerns on embodiment is performed. It is a conceptual diagram which shows a smart device and the movement of a thumb when the reference | standard gesture which concerns on embodiment is performed. It is a conceptual diagram which shows the coordinate axis of the smart device which concerns on embodiment. It is a conceptual diagram which shows a smart device and the movement of a thumb when the reference | standard gesture which concerns on embodiment is performed. It is a conceptual diagram which shows a smart device and the movement of a thumb when the reference | standard gesture which concerns on embodiment is performed. It is a conceptual diagram which shows a smart device and the movement of a thumb when the reference | standard gesture which concerns on embodiment is performed. It is a conceptual diagram which shows a smart device and the movement of a thumb when the reference | standard gesture which concerns on embodiment is performed. It is a conceptual diagram which shows a smart device and the movement of a thumb when the reference | standard gesture which concerns on embodiment is performed. It is a conceptual diagram which shows a smart device and the movement of a thumb when the reference | standard gesture which concerns on embodiment is performed. It is a graph which shows an example of angular velocity detected when standard gesture concerning an embodiment is performed. It is a graph which shows an example of angular velocity detected when standard gesture concerning an embodiment is performed. It is a graph which shows an example of angular velocity detected when standard gesture concerning an embodiment is performed. It is a graph which shows an example of the result of having converted the angular velocity detected when the standard gesture concerning an embodiment is performed into the spectrum of a frequency domain. It is a graph which shows an example of the result of having converted the angular velocity detected when the standard gesture concerning an embodiment is performed into the spectrum of a frequency domain. It is a graph which shows an example of the result of having converted the angular velocity detected when the standard gesture concerning an embodiment is performed into the spectrum of a frequency domain. It is a flowchart which shows an example of the gesture detection process which concerns on embodiment. It is a graph which shows an example of angular velocity detected when standard gesture concerning an embodiment is performed. It is a graph which shows an example of angular velocity detected when standard gesture concerning an embodiment is performed. It is a graph which shows an example of angular velocity detected when standard gesture concerning an embodiment is performed. It is a graph which shows an example of the result of having converted the angular velocity detected when the standard gesture concerning an embodiment is performed into the spectrum of a frequency domain. It is a graph which shows an example of the result of having converted the angular velocity detected when the standard gesture concerning an embodiment is performed into the spectrum of a frequency domain. It is a graph which shows an example of the result of having converted the angular velocity detected when the standard gesture concerning an embodiment is performed into the spectrum of a frequency domain. It is a graph which shows an example of angular velocity detected when standard gesture concerning an embodiment is performed. It is a graph which shows an example of angular velocity detected when standard gesture concerning an embodiment is performed. It is a graph which shows an example of angular velocity detected when standard gesture concerning an embodiment is performed. It is a graph which shows an example of the result of having converted the angular velocity detected when the standard gesture concerning an embodiment is performed into the spectrum of a frequency domain. It is a graph which shows an example of the result of having converted the angular velocity detected when the standard gesture concerning an embodiment is performed into the spectrum of a frequency domain. It is a graph which shows an example of the result of having converted the angular velocity detected when the standard gesture concerning an embodiment is performed into the spectrum of a frequency domain. It is a graph which shows an example of angular velocity detected when standard gesture concerning an embodiment is performed. It is a graph which shows an example of angular velocity detected when standard gesture concerning an embodiment is performed. It is a graph which shows an example of angular velocity detected when standard gesture concerning an embodiment is performed. It is a graph which shows an example of the result of having converted the angular velocity detected when the standard gesture concerning an embodiment is performed into the spectrum of a frequency domain. It is a graph which shows an example of the result of having converted the angular velocity detected when the standard gesture concerning an embodiment is performed into the spectrum of a frequency domain. It is a graph which shows an example of the result of having converted the angular velocity detected when the standard gesture concerning an embodiment is performed into the spectrum of a frequency domain. It is a graph which shows an example of the result of having compared the gain ratio of the standard gesture concerning an embodiment. It is a conceptual diagram which shows an example of the model at the time of hold | maintaining a smart device with one hand. It is a conceptual diagram which shows an example of the balance model of angular momentum. It is a conceptual diagram which shows an example of the balance model of angular momentum.

  Hereinafter, an example of an embodiment of the disclosed technology will be described in detail with reference to the drawings. In the following description, a smart device will be described as an example of an information processing apparatus according to the disclosed technology, but the disclosed technology is not limited thereto. The disclosed technology can be applied to various information processing apparatuses such as an IC recorder, a game machine, a navigation device, a mobile phone, and a digital camera.

  As an example, the smart device 10 illustrated in FIG. 1 includes an angular velocity detection unit 12, a touch detection unit 14, a conversion unit 16, an acquisition unit 18, a determination unit 20, a storage unit 22, and a display unit 24. The angular velocity detection unit 12, the touch detection unit 14, the conversion unit 16, the acquisition unit 18, the determination unit 20, the storage unit 22, and the display unit 24 are connected to each other.

The angular velocity detection unit 12 detects the angular velocity of the smart device 10 with an angular velocity sensor and outputs an angular velocity signal. The touch detection unit 14 detects a touch on the touch panel display. The converter 16 converts the angular velocity signal output from the angular velocity detector 12 into a frequency domain spectrum. The storage unit 22 stores a gesture table 19 shown as an example in FIG. The gesture table 19 registers in advance a reference primary main resonance frequency and a reference gain ratio of a reference gesture described later in association with the reference gesture. The acquisition unit 18 acquires the primary main resonance frequency and the gain ratio. The determination unit 20 compares the acquired primary main resonance frequency and gain ratio with the reference primary main resonance frequency and reference gain ratio of the reference gesture, and performs an operation on the smart device 10 based on the comparison result. It is determined whether or not the received gesture is a reference gesture. The display unit 24 displays a user interface and the like for the user.
In the present embodiment, the gesture is an operation of applying motion to the smart device 10 such as, for example, an operation of shaking a finger of a hand holding the smart device 10 as described later (see, for example, FIGS. 5A to 5C). ). The user performed without touching the smart device 10 that can be detected by detecting the position and movement of the user's hand and fingers by analyzing information acquired by an infrared sensor or a camera in the gesture here It is not intended to include

  The primary resonance frequency is a frequency at which the gain is maximized in each spectrum in the frequency domain, and the primary main resonance frequency is a frequency at which the gain of the primary resonance frequency of a triaxial spectrum described later is maximized. The gain ratio is obtained by dividing each gain at a frequency corresponding to the primary main resonance frequency of the triaxial spectrum by the gain having the maximum value. The reference primary main resonance frequency and the reference gain ratio are the primary main resonance frequency and the gain ratio of the reference gesture, respectively.

  As shown in FIG. 3 as an example, the smart device 10 includes a CPU (Central Processing Unit) 60, a primary storage unit 62, a secondary storage unit 64, an external interface 70, a touch panel display 76, and a gyro which is an example of an angular velocity sensor. A sensor 78 is included. The touch panel display 76 includes a touch panel 72 and a display 74. The CPU 60, the primary storage unit 62, the secondary storage unit 64, the external interface 70, the touch panel display 76, and the gyro sensor 78 are connected to each other via a bus 80.

  The gyro sensor 78 measures an angular velocity around each of the three axes of the smart device 10 and outputs an angular velocity signal, and the touch panel 72 detects a touch on the touch panel 72. The display 74 displays a user interface and the like to the user. An external device is connected to the external interface 70 and controls transmission / reception of various information between the external device and the CPU 60.

  The primary storage unit 62 is a volatile memory such as a RAM (Random Access Memory), for example. The secondary storage unit 64 is a non-volatile memory such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive). The primary storage unit 62 and the secondary storage unit 64 function as the storage unit 22 of FIG.

  As an example, the secondary storage unit 64 stores a gesture registration program 66 including an angular velocity detection subprogram 66A, a touch detection subprogram 66B, a conversion subprogram 66C, an acquisition subprogram 66D, and a display subprogram 66F. In addition, the secondary storage unit 64 stores, as an example, a gesture detection program 67 including an angular velocity detection subprogram 67A, a conversion subprogram 67C, an acquisition subprogram 67D, and a determination subprogram 67E. The CPU 60 reads out the angular velocity detection subprogram 66A, the touch detection subprogram 66B, the conversion subprogram 66C, the acquisition subprogram 66D, and the display subprogram 66F from the secondary storage unit 64 and develops them in the primary storage unit 62. Further, the CPU 60 reads out the angular velocity detection subprogram 67A, the conversion subprogram 67C, the acquisition subprogram 67D, and the determination subprogram 67E from the secondary storage unit 64 and develops them in the primary storage unit 62.

  The CPU 60 operates as the angular velocity detection unit 12 shown in FIG. 1 by executing the angular velocity detection subprogram 66A. The CPU 60 operates as the touch detection unit 14 illustrated in FIG. 1 by executing the touch detection subprogram 66B. The CPU 60 operates as the conversion unit 16 illustrated in FIG. 1 by executing the conversion subprogram 66C. The CPU 60 operates as the acquisition unit 18 illustrated in FIG. 1 by executing the acquisition subprogram 66D. The CPU 60 operates as the display unit 24 shown in FIG. 1 by executing the display subprogram 66F. The CPU 60 operates as the angular velocity detection unit 12 shown in FIG. 1 by executing the angular velocity detection subprogram 67A. The CPU 60 operates as the conversion unit 16 illustrated in FIG. 1 by executing the conversion subprogram 67C. The CPU 60 operates as the acquisition unit 18 illustrated in FIG. 1 by executing the acquisition subprogram 67D. The CPU 60 operates as the determination unit 20 illustrated in FIG. 1 by executing the determination subprogram 67E. The secondary storage unit 64 stores data 68 for generating the gesture table 19. The gesture registration program 66 and the gesture detection program 67 are examples of programs according to the disclosed technology.

  Next, as an operation of the embodiment of the disclosed technique, a gesture registration process performed by the smart device 10 by the CPU 60 executing the gesture registration program 66 will be described with reference to FIG.

  The gesture registration process is started when the user indicates an intention to register a reference gesture in the smart device 10 when the user presses a button or the like displayed on the display 74 of the smart device 10, for example.

  In step 102, the CPU 60 displays the user interface screen 40A shown in FIG. Four reference gestures 46 to 49 are displayed on the user interface screen.

  As shown in FIG. 8, the x-axis, y-axis, and z-axis are orthogonal to each other. In order to perform the basic gestures 46 to 49 shown in FIG. 5A, the user first extends the interphalangeal joint and the metacarpophalangeal joint of the four fingers from the index finger to the little finger. The y-axis of the case 11 of the smart device 10 shown in FIG. 8 is substantially perpendicular to the four fingers, and one side of the smart device 10 is substantially in contact with the thumb metacarpophalangeal joint. The smart device 10 is placed on the palm so that the back is in contact with the palm. The smart device 10 is grasped by the four fingers from the index finger to the little finger and the thumb ball, and the interphalangeal joint and the metacarpophalangeal joint of the thumb are arranged so that the thumb is almost parallel to the front surface of the smart device 10. extend. Further, the thumb and the front surface of the smart device 10 are separated so that the thumb does not touch the front surface of the smart device 10. Further, the thumb is shaken around the carpal joint so that the thumb remains substantially parallel to the front surface of the smart device 10 and the tip of the thumb draws an arc trajectory. It is desirable that only the smart device 10 is in contact between the wrist of the hand holding the smart device 10 and the tip of each finger.

  In the gesture 46 in FIG. 5A, as shown in FIG. 6A, the thumb 151 indicated by a broken line existing at the first position near the lower side of the front surface of the smart device 10 passes through the position of the thumb indicated by the solid line, and on the right side. It moves so as to reach the position of the thumb 152 indicated by the alternate long and short dash line at the near second position. The gesture 46 is a gesture for shaking the thumb once. In the gesture 47 of FIG. 5A, as shown in FIG. 6B, the thumb 152 indicated by the alternate long and short dash line in the second position close to the right side of the front surface of the smart device 10 passes through the position of the thumb indicated by the solid line, It moves so that it may reach the position of the thumb 151 indicated by a broken line at the first position close to. The gesture 47 is a gesture for shaking the thumb once. As shown in FIG. 6C, the gesture 48 in FIG. 5A is a gesture that firstly performs a gesture 153 similar to the gesture illustrated in FIG. 6A, and then performs a gesture 154 similar to the gesture illustrated in FIG. 6B. Numbers 1 and 2 attached to the gesture 48 in FIG. 5A represent the order of operations. A gesture 49 illustrated in FIG. 5A is a gesture that firstly performs a gesture 154 similar to the gesture illustrated in FIG. 6B and then performs a gesture 153 similar to the gesture illustrated in FIG. 6A as illustrated in FIG. 6D. Numbers 1 and 2 attached to the gesture 49 in FIG. 5A represent the order of the operations.

  7A to 7C show movements of the smart device 10 and the user's thumb as viewed from the side of the smart device 10 when the user performs the reference gesture 47 shown in FIG. 5A. 7A to 7C, the x axis perpendicular to the right side surface and the left side surface of the housing 11 of the smart device 10 illustrated in FIG. 8 is illustrated.

  9A to 9C show the movement of the smart device 10 and the user's thumb when viewed from the top of the smart device 10 when the user performs the reference gesture 47 shown in FIG. 5A. 9A to 9C, the y axis perpendicular to the upper surface and the lower surface of the housing 11 of the smart device 10 illustrated in FIG. 8 is illustrated.

  FIGS. 10A to 10C show movements of the smart device 10 and the user's thumb as viewed from the front side of the smart device 10 when the user performs the reference gesture 47 shown in FIG. 5A. 10A to 10C, the z-axis and carpal metacarpal joint 155 perpendicular to the front surface and the back surface of the housing 11 of the smart device 10 illustrated in FIG. 8 are illustrated.

  As shown in FIGS. 10A and 10C, when the size of the smart device 10 is small compared to the length of the user's finger, the start position 152 ′ of the tip of the thumb that is the second position in the reference gesture 47 is It may be on the extended surface of the front surface instead of on the front surface. Further, the end position 151 ′ of the tip of the thumb that is the first position may be on the extended surface of the front surface instead of on the front surface. Similarly, the first position and the second position of the reference gestures 46, 48, and 49 may be on the extended surface of the front surface instead of on the front surface.

  If it is determined in step 104 that the user has selected any of the reference gestures by touching any of the reference gestures 46 to 49 of the user interface 40 </ b> A, the CPU 60 proceeds to step 106. In step 106, the CPU 60 displays the user interface screen 40B shown in FIG. Here, a case will be described in which the gesture 48 shown in FIG. 5A is selected and the user performs the reference gesture 48 in accordance with an instruction of the user interface 40B.

  In step 108, the CPU 60 detects the angular velocity signal ωx (t) around the x axis, the angular velocity signal ωy (t) around the y axis, and the angular velocity signal ωz (t around the z axis detected by the gyro sensor 78. ). A detection example of the angular velocity signal ωx (t) around the x axis is shown in FIG. 11A, a detection example of the angular velocity signal ωy (t) around the y axis is shown in FIG. 11B, and the angular velocity signal ωz (t) around the z axis is detected. An example is shown in FIG. 11C. The horizontal axis of FIGS. 11A to 11C is time, and the unit of time is second. The vertical axis in FIGS. 11A to 11C is the angular velocity, and the unit of the angular velocity is radians / second.

  In step 110, the CPU 60 converts the angular velocity signal ωx (t) around the x axis into a frequency domain spectrum Sx (f) and the angular velocity signal ωy (t) around the y axis into a frequency domain spectrum Sy (f). Further, the CPU 60 converts the angular velocity signal ωz (t) around the z axis into a spectrum Sz (f) in the frequency domain. For this conversion, FFT (Fast Fourier Transform) or DFT (Discrete Fourier Transform) can be used. A spectrum Sx (f) in the frequency domain converted from the angular velocity signal ωx (t) in FIG. 11A is shown in FIG. 12A, and a spectrum Sy (f) in the frequency domain converted from the angular velocity signal ωy (t) in FIG. Shown in 12B. FIG. 12C shows a spectrum Sz (f) in the frequency domain converted from the angular velocity signal ωz (t) in FIG. 11C. The vertical axis | shaft of FIG. 12A-FIG. 12C is a gain, and a unit is a decibel. The horizontal axis of FIGS. 12A to 12C is frequency, and the unit is hertz.

  In step 112, the CPU 60 obtains the gain Gcx at the primary resonance frequency fcx of the x-axis spectrum, the gain Gcy at the primary resonance frequency fcy of the y-axis spectrum, and the gain Gcz at the primary resonance frequency fcz of the z-axis spectrum. Get each. The primary resonance frequency is a frequency when the gain shows the maximum value in each spectrum. The gain having the largest value among the gains of the primary resonance frequency of the triaxial spectrum is defined as the main gain Gcm. Further, the frequency corresponding to the main gain Gcm is set as the primary main resonance frequency fcm.

12A and 12B, the gain Gcx indicating the maximum value at the primary resonance frequency of the x-axis spectrum is 0.044, and the gain Gcy indicating the maximum value at the primary resonance frequency of the y-axis spectrum is 0.078. It is. In the example of FIG. 12C, it is difficult to determine the primary resonance frequency because there is no peak indicating a remarkable maximum value in the z-axis spectrum. Therefore, in the example shown in FIGS. 12A to 12C, as shown in Table 1, the value of the gain Gcy of the primary resonance frequency fcx of the y-axis spectrum is the largest, so that the main gain Gcm is obtained. Further, 0.71 Hz, which is the primary resonance frequency fcy of the y-axis spectrum, becomes the primary main resonance frequency fcm.

  In step 114, the CPU 60 normalizes the gain Gcmx at a frequency corresponding to the primary main resonance frequency of the x-axis spectrum and the gain Gcmy at a frequency corresponding to the primary main resonance frequency of the y-axis spectrum. Specifically, each of the gains Gcmx and Gcmy is divided by the main gain Gcm. Further, the CPU 60 divides the gain Gcmz by the main gain Gcm in order to normalize the gain Gcmz at a frequency corresponding to the primary main resonance frequency of the z-axis spectrum. By these divisions, gain ratios Rgx, Rgy, Rgz are obtained. That is, Rgx = Gcmx / Gcm, Rgy = Gcmy / Gcm, and Rgz = Gcmz / Gcm are calculated.

  As shown in Table 1, gains Gcmx, Gcmy, and Gcmz at frequencies corresponding to the primary main resonance frequencies of the x-axis, y-axis, and z-axis spectra are 0.042, 0.078, and 0.006, respectively. It is. When these gains are divided by 0.078, which is the main gain Gcm, the gain ratios Rgx, Rgy, Rgz of the x-axis, y-axis, and z-axis spectra are 0.53, 1, 0.07.

  In step 116, the CPU 60 uses the primary primary resonance frequency fcm and the normalized triaxial spectrum gain ratios Rgx, Rgy, Rgz as the reference primary main data to the data 68 for generating the gesture table of the secondary storage device 64. Save as resonance frequency and reference gain ratio. Next, the user interface 40C shown in FIG. 5C is displayed on the touch panel display 76, and the gesture registration process is terminated.

Table 2 shows the primary main resonance frequency acquired in step 112 and the gain ratio acquired in step 114 when the reference gesture 48 shown in FIG. 5A is performed five times. In the x-axis frequency domain spectrum, the variation in gain ratio at the primary main resonance frequency is 0.021, in the z-axis frequency domain spectrum, the variation in gain ratio at the primary main resonance frequency is 0.015, The variation of the primary main resonance frequency is 0.025. Therefore, the reproducibility of the gain ratio and the primary main resonance frequency by the gesture 48 shown in FIG. 5A is high.

  Next, gesture detection processing performed by the smart device 10 by the CPU 60 executing the gesture detection program 67 will be described with reference to FIG.

  When the smart device 10 is turned on, the CPU 60 determines whether or not a touch is detected by the touch panel display 76 in step 202. If a touch is not detected in step 202, the CPU 60 captures each angular velocity signal around each of the x-axis, y-axis, and z-axis of the smart device 10 detected by the gyro sensor 78 in step 204. Steps 204 to 210 are the same processes as steps 108 to 114 in FIG. 4 and will not be described in detail.

  In step 206, the CPU 60 converts each detected angular velocity into a frequency domain spectrum. In step 208, the CPU 60 determines each of the primary resonance frequencies from each of the converted spectrum in the frequency domain, and acquires the gain having the maximum value among the gains in each of the primary resonance frequencies as the main gain. Next, each gain at the primary main resonance frequency, which is a frequency corresponding to the main gain, is acquired. In step 210, the CPU 60 divides each gain by the main gain to normalize each gain.

  In step 212, the CPU 60 determines each of the reference primary main resonance frequency and the reference gain ratio stored in the data 68 for generating the gesture table, and each of the primary main resonance frequency and the gain ratio acquired in step 210. , Compare. Specifically, the comparison is made between the primary resonance frequencies and each of the gain ratios. In step 212, the CPU 60 determines that all differences between the reference primary main resonance frequency and the reference gain ratio and each of the primary main resonance frequency and the gain ratio acquired in step 210 are within a predetermined range. It is determined whether or not. For example, when each of the primary main resonance frequency and the gain ratio is included in a range of ± 10% of each of the reference primary main resonance frequency and the reference gain ratio, it is determined that the difference is within a predetermined range. Is possible. If the determination in step 212 is affirmed, in step 214, the CPU 60 executes a predetermined process associated with the reference gesture in advance, and the gesture detection process ends. The gesture detection process is repeatedly executed at predetermined time intervals until the smart device 10 is turned off. The predetermined time may be 1/100 second, for example.

  The reference gesture 46 shown in FIG. 5A can be associated with, for example, a process of down-scrolling or page-turning backward such as a browser, an electronic book, and a PDF file. Further, in order to facilitate the operation of the smart device 10, the reference gesture 46 can be associated with a process of reducing the operation screen on the side of the hand holding the smart device 10.

  The reference gesture 47 shown in FIG. 5A can be associated with, for example, an up-scrolling process such as a browser, an electronic book, and a PDF file, or a forward page feed process. Alternatively, the reference gesture 47 may be associated with a process that is set to be performed when a swipe is performed from the upper part to the lower part of the screen.

  The reference gestures 48 and 49 shown in FIG. 5A can be associated with, for example, a process for starting a predetermined application. The predetermined app may be a frequently used app. The reference gestures 48 and 49 can be associated with processes for starting different applications. Further, the reference gestures 48 and 49 can be associated with double speed scrolling or double speed page turning processing of a browser, an electronic book, a PDF file, or the like. Each of the reference gestures 48 and 49 can be assigned a scrolling or page turning process in different directions. In addition, the said process matched with a reference | standard gesture is an example, and the technique of an indication is not limited to matching a reference | standard gesture and the said process.

  14A to 14C show angular velocity signals around the x-axis, y-axis, and z-axis acquired by the reference gesture 46 shown in FIG. 5A. FIGS. 15A to 15C show FIGS. The result of converting the angular velocity signal of 14C into a spectrum in the frequency domain is shown. FIGS. 16A to 16C show angular velocity signals around the x-axis, y-axis, and z-axis obtained by the reference gesture 47 shown in FIG. 5A. FIGS. 17A to 17C show FIGS. The result of converting the angular velocity signal of 16C into a spectrum in the frequency domain is shown. FIGS. 18A to 18C show angular velocity signals around the x-axis, y-axis, and z-axis acquired by the reference gesture 49 shown in FIG. 5A. FIGS. 19A to 19C show FIGS. The result of converting the angular velocity signal of 18C into a spectrum in the frequency domain is shown.

  FIG. 20 shows the gain ratio 94 of the x-axis spectrum of the reference gestures 46 to 49 acquired from FIGS. 15A to 15C, 17A to 17C, 12A to 12C, and 19A to 19C, and the x-axis spectrum. Gain ratio + 10% 93. 20 shows the gain ratio of the x-axis spectrum of −10% 95 acquired from FIGS. 17A to 17C, FIGS. 12A to 12C, and FIGS. 19A to 19C. FIG. 20 shows a y-axis spectrum gain ratio 92, a z-axis spectrum gain ratio 97, a z-axis spectrum gain ratio + 10% 96, and a z-axis spectrum gain ratio−10% 98.

  As shown in FIGS. 15A to 15C, FIGS. 17A to 17C, FIGS. 12A to 12C, and FIGS. 19A to 19C, the gain at the primary resonance frequency of the y-axis spectrum is a triaxial spectrum in all the reference gestures. Is the largest of the gains of the primary resonance frequency. Therefore, the gain ratios of the y-axis spectrum are all 1. The gain ratio ± 10% of the x-axis spectrum of the reference gesture 46 and the gain ratio ± 10% of the reference gesture 47 and the reference gesture 48 do not overlap. The range of ± 10% of the gain ratio of the x-axis spectrum of the reference gesture 46 and the reference gesture 49 overlaps, but the range of ± 10% of the gain ratio of the z-axis spectrum of the reference gesture 46 and the reference gesture 49 is There is no duplication.

  The gain ratios ± 10% of the x-axis spectra of the reference gesture 47 and the reference gesture 49 do not overlap. The gain ratios ± 10% of the x-axis spectrum of the reference gesture 47 and the reference gesture 49 overlap, but the gain ratios ± 10% of the z-axis spectrum do not overlap. The gain ratios ± 10% of the x-axis spectra of the reference gesture 48 and the reference gesture 49 do not overlap. Therefore, the reference gestures 46 to 49 do not overlap each other in the spectrum gain ratio ± 10% of all the axes, and the reference gestures 46 to 49 can be discriminated by the disclosed technique.

  Note that the reference gestures 46 to 49 shown in FIG. 5A are described as gestures in which the smart device 10 is placed on the palm of the hand so that the y-axis of the casing 11 of the smart device 10 shown in FIG. However, the disclosed technique is not limited to this. For example, the x axis of the housing 11 of the smart device 10 shown in FIG.

  In addition, although the reference gestures 46 to 49 illustrated in FIG. 5A have been described as gestures that are gripped by four fingers from the index finger to the little finger, the disclosed technique is not limited thereto. For example, it may be a gesture for holding the smart device 10 with any one of four fingers. In this case, it is desirable that the remaining fingers not used for gripping be fixed at a certain position with respect to the smart device 10.

  Note that the reference gestures 46 to 49 illustrated in FIG. 5A have been described as gestures in which the thumb is substantially parallel to the front surface of the smart device 10, but the disclosed technique is not limited thereto. For example, a gesture in which the thumb is substantially parallel to the back surface of the smart device 10 may be used. In this case, the metacarpophalangeal joints of the four fingers from the index finger to the little finger are in contact with the front surface of the smart device 10.

  In addition, although the reference gestures 46 to 49 illustrated in FIG. 5A have been described as gestures for extending the interphalangeal joint and the metacarpophalangeal joint of the thumb, the disclosed technique is not limited thereto. For example, the interphalangeal joint and the metacarpophalangeal joint of the thumb may be lightly bent to such an extent that the thumb can easily move.

  In addition, although the reference gestures 46 to 49 illustrated in FIG. 5A have been described as gestures in which the smart device 10 is gripped with four fingers and the thumb is shaken, the disclosed technique is not limited thereto. For example, the gesture may be such that the smart device 10 is grasped with four fingers other than the index finger including the thumb and the index finger is shaken. Alternatively, the smart device 10 may be grasped with three fingers other than the index finger and the middle finger including the thumb and shaken with the index finger and the middle finger in close contact with each other.

  In addition, although the example which detects the angular velocity around each axis | shaft of x-axis, y-axis, and z-axis was demonstrated in step 108 of FIG. 4, the technique of an indication is not limited to this. For example, the angular velocity of two axes out of the three axes may be detected. However, it is desirable to include the y-axis in the two axes.

  Note that although the case where the normalized gain ratio is stored in the gesture table has been described in step 116 of FIG. 4, the disclosed technique is not limited to this. For example, the gain of the triaxial spectrum corresponding to the reference primary main resonance frequency may be stored. In this case, when comparing in step 212 of FIG. 13, a normalized gain ratio may be calculated from the stored gain.

  Instead of registering the primary main resonance frequency and gain ratio by performing gesture registration processing, standard values of the primary main resonance frequency and gain ratio are set and registered in advance, and the registered primary The gesture may be detected using the main resonance frequency and the gain ratio. Further, the primary main resonance frequency and gain ratio registered by the gesture registration process are stored in a server (not shown), and when the smart device is changed, the stored primary main resonance frequency and gain are stored. The ratio may be registered with a new smart device. In these cases, the gesture registration process may be performed only when the gesture is not properly detected.

  Step 212 in FIG. 13 describes an example in which the reference gain ratio of the x-axis, y-axis, and z-axis spectra is compared with the gain ratio of the x-axis, y-axis, and z-axis spectra acquired in step 210. However, the disclosed technique is not limited to this. For example, the reference gain ratio of the biaxial spectrum among the three axes may be compared with the gain ratio of the corresponding biaxial spectrum acquired in step 210. However, it is desirable that the two axes include the axis having the largest gain at the primary resonance frequency and the second largest axis.

  In the above description, the mode in which the gesture registration program 66 and the gesture detection program 67 are stored (installed) in the secondary storage unit 64 in advance has been described. However, the gesture registration program 66 and the gesture detection program 67 can be provided in a form recorded on a non-temporary recording medium such as a CD-ROM or a DVD-ROM.

  Consider using a rotational angular momentum balance equation to detect a reference gesture of the disclosed technique. When the reference gesture of the disclosed technology is modeled, a model of an inertial support system in which the smart device 10 ′ is spring-supported by one arm 82 as shown in FIG. 21 is obtained. In the inertial support system, since the acting force between the movement of the thumb 81 and the smart device 10 ′ can be considered as an internal force, the momentum at the beginning of the movement is equal to the momentum at the end of the movement.

It is assumed that the inertia moment about the xt axis that is the x axis of the smart device 10 ′ shown in FIG. 22A is Itx, and the inertia moment about the yt axis that is the y axis of the smart device 10 ′ is Ity. Further, the moment of inertia around the zt axis, which is the z axis of the smart device 10 ′ shown in FIG. 22B, is assumed to be Itz. Further, the inertia moment around the xo axis that is the x axis of the thumb 81 that moves around the thumb carpal joint shown in FIG. 22A is Iox, and the inertia moment around the yo axis that is the y axis of the thumb 81 is shown. Let Ioy be the moment of inertia around the zo axis, which is the z axis of the thumb 81. Further, it is assumed that the angular velocity around the xt axis of the smart device 10 ′ is Ktx, the angular velocity around the yt axis is Kty, and the angular velocity around the zt axis is Ktz. Further, it is assumed that the angular velocity of the thumb 81 around the xo axis is Kox, the angular velocity around the yo axis is Koy, and the angular velocity around the xo axis is Koz. The angular momentum of the smart device 10 ′ around the xt axis is equal to the angular momentum of the thumb 81 around the xo axis, and the angular momentum of the smart device 10 ′ around the yt axis is equal to the angular momentum of the thumb 81 around the yo axis. Further, the angular momentum around the zt axis of the smart device 10 ′ is equal to the angular momentum around the zo axis of the thumb 81. Therefore, the balance equations (1) to (3) of the rotational angular momentum hold.
Itx × Ktx = Iox × Kox (1)
Ity × Kty = Ioy × Koy (2)
Itz × Ktz = Ioz × Koz (3)

By dividing each of the equations (1) to (3) by the moment of inertia about each axis of the smart device 10 ′, the angular velocity about each axis of the smart device 10 ′ shown in the equations (4) to (6) Ask for.
Ktx = (Iox × Kox) / Itx (4)
Kty = (Ioy × Koy) / Ity (5)
Ktz = (Ioz × Koz) / Itz (6)

  The inertia moments Itx, Ity, Itz of the smart device 10 ′ are determined by the shape of the smart device 10 ′, the inertia moments Iox, Ioy, Ioz of the thumb 81 are determined by the mass of the thumb 81, and the magnitude of the internal force is It is determined by the route of the finger 81. The length and mass of the thumb, the path of the thumb, that is, the unconscious habit of how to move the thumb depends on the user. Therefore, it is difficult to calculate the inertia moment of the smart device 10 ′, the inertia moment of the thumb 81, and the magnitude of the internal force by measuring the length and mass of the thumb and the path of the thumb.

On the other hand, the length and mass of the user's thumb 81 and the path of the thumb are not easily changed. In the disclosed technology, the inertial moment, angular velocity, and inertial moment of the smart device included in the right side of the equations (4) to (6) are not obtained by calculation or measurement. Instead, the angular velocity of the smart device included in the left side of Expressions (4) to (6) is measured using a gyro sensor. By registering representative values obtained by converting angular velocities into spectrum in the frequency domain, it is possible to detect user-specific features and smart device-specific features and detect reference gestures in the smart device where the measurement is performed. It is said.
In other words, the angular velocity measured by the gyro sensor reflects the reaction force generated in the smart device as the user's finger (for example, the thumb) moves. That is, in the disclosed technology, the gyro sensor of the smart device can be used as a reaction force detection unit.

  In the disclosed technology, the angular velocity signal is converted into a spectrum in the frequency domain and the gain ratios are compared with each other. Therefore, when the user is on a traveling vehicle, it is affected by the shaking of the traveling vehicle. Instead, it is possible to detect the reference gesture. For example, the vibration of an automobile engine is around 10 Hz, but the primary main resonance frequency of the reference gesture is around 1 Hz. Therefore, the representative frequency frequency spectrum indicating the characteristics of the reference gesture has a frequency due to the vibration of the automobile engine. This is because there is no effect.

  Also, when comparing angular velocity signals, which are information along the time axis, if the timing of comparison is shifted or noise is included in some information, it is difficult to appropriately detect the reference gesture. . However, in the disclosed technique, the primary main resonance frequency, which is a representative value of information converted into a frequency domain spectrum, and a gain ratio are used for comparison. Therefore, it is possible to detect the reference gesture without being affected by a difference in timing of comparison or noise (for example, noise caused by external force) included in some information.

  In the disclosed technique, by comparing the gain ratios, not the gains, the gain fluctuation due to the difference in the user's power that may be different each time the reference gesture is performed is absorbed, and the reference gesture is appropriately performed. It is possible to detect.

  In the disclosed technology, for example, gestures such as the reference gestures 46 to 49 can be detected. Therefore, an operation method that does not cause a conflict with existing gestures such as touch and swipe increases, so that the smart device can be detected. It is possible to widen the range of operations. Since the reference gestures 46 to 49 can be performed without visually recognizing the position of the touch panel display in the housing and the content displayed on the touch panel display, it is possible to widen the range of scenes that can be used.

  Since the reference gesture detection of the disclosed technique does not use touch detection, it can also be used in an information processing apparatus that does not have a touch panel. In this case, when registering a gesture, for example, a hard key can be used.

Regarding the above embodiment, the following additional notes are disclosed.
(Appendix 1)
An angular velocity detection unit that detects angular velocity signals around three mutually orthogonal axes set in the housing;
A conversion unit that converts each of the angular velocity signals around the three axes detected by the angular velocity detection unit into a spectrum in a frequency domain;
The frequency indicating the maximum gain in the spectrum of the specific one-axis frequency domain having the largest value among the maximum values of the spectrum gain of the frequency domain of each axis converted by the conversion unit is acquired as the primary main resonance frequency. And an acquisition unit that acquires, as a gain ratio, a ratio between the gain and the maximum gain at the same frequency as the primary main resonance frequency of the spectrum in the frequency domain of at least two axes;
When a reference gesture is performed on the casing, the primary main resonance frequency and the gain ratio acquired by the acquisition unit, and when a determination target gesture is performed on the casing, Whether or not the determination target gesture is the reference gesture by comparing the primary main resonance frequency and the gain ratio acquired by the acquisition unit for each of the primary main resonance frequency and the gain ratio. A determination unit for determining whether or not
An information processing apparatus including:
(Appendix 2)
The at least two axes used for comparison by the determination unit are an axis having the largest gain value in each axis and the second largest axis among the three axes,
The information processing apparatus according to attachment 1.
(Appendix 3)
A storage unit for storing the primary main resonance frequency acquired by the acquisition unit and the gain ratio of the spectrum in the frequency domain of at least two axes when the reference gesture is performed;
The determination unit stores, in the storage unit, the gain ratio of the primary main resonance frequency acquired by the acquisition unit and the spectrum of the frequency domain of at least two axes when the determination target gesture is performed. The determination target gesture is determined by comparing the primary main resonance frequency and the gain ratio of the spectrum in the frequency domain of at least two axes.
The information processing apparatus according to appendix 1 or 2.
(Appendix 4)
The reference gesture is a gesture for grasping the housing with one hand and moving the thumb with the middle metacarpal joint of the thumb of the one hand.
The information processing apparatus according to any one of appendices 1 to 3.
(Appendix 5)
The reference gesture extends the interphalangeal joint and the metacarpophalangeal joint of the thumb, grips the housing using at least one of the index finger, the middle finger, the ring finger, and the little finger, and the carpal metacarpal joint Is a gesture in which the tip of the thumb moves on one surface of the housing to move the thumb apart from the surface and draw an arc. ,
The information processing apparatus according to appendix 4.
(Appendix 6)
The reference gesture includes a first gesture in which a tip of the thumb moves in a circular arc from a first position on the surface or an extended surface of the surface to a second position on the surface or an extended surface of the surface, A second gesture in which the tip of the thumb moves from the second position to the first position in an arc, and after the tip of the thumb moves from the first position to the second position in an arc, the second gesture A third gesture that draws an arc from the position and moves to the first position, and a tip of the thumb moves from the second position to the first position by drawing an arc and then draws an arc from the first position. Including at least one of a fourth gesture moving to a second position,
The information processing apparatus according to appendix 5.
(Appendix 7)
The angular velocity signal detected by the angular velocity detection unit includes a signal indicating a reaction force generated in the casing with the movement of the thumb.
The information processing apparatus according to any one of appendices 4 to 6.
(Appendix 8)
The information processing apparatus according to any one of appendices 1 to 7, wherein when the determination unit determines that the reference gesture has been performed, the process corresponding to the reference gesture associated in advance is executed.
(Appendix 9)
Processor
Detecting angular velocity signals around three mutually orthogonal axes set in the housing,
Each of the detected angular velocity signals around the three axes is converted into a frequency domain spectrum,
Obtaining a frequency indicating the maximum gain in the spectrum of the specific one-axis frequency domain having the largest value among the maximum values of the gain of the spectrum of the frequency domain of each axis as the primary main resonance frequency, and at least A ratio of a gain at the same frequency as the primary main resonance frequency of a spectrum in a biaxial frequency region and the maximum gain is obtained as a gain ratio;
The primary main resonance frequency and the gain ratio of the spectrum in the frequency domain of at least two axes acquired when the reference gesture is performed on the casing and the determination target gesture are performed on the casing. Whether the determination target gesture is the reference gesture by comparing the primary main resonance frequency and the gain ratio acquired at the time of each of the primary main resonance frequency and the gain ratio. Determine
A gesture detection method including the above.
(Appendix 10)
The at least two axes used for comparison are an axis having the largest gain value in each axis and the second largest axis among the three axes.
The gesture detection method according to appendix 9.
(Appendix 11)
The processor is
Storing the primary main resonance frequency acquired when the reference gesture is performed and the gain ratio of the spectrum in the frequency domain of at least two axes in a storage unit;
The primary main resonance frequency acquired when the determination target gesture is performed and the gain ratio of the spectrum in the frequency domain of at least two axes, the primary main resonance frequency stored in the storage unit, and The determination target gesture is determined by comparing the gain ratio of the spectrum in the frequency domain of at least two axes.
The gesture detection method according to appendix 9 or 10.
(Appendix 12)
The reference gesture is a gesture for grasping the housing with one hand and moving the thumb with the middle metacarpal joint of the thumb of the one hand.
The gesture detection method according to any one of appendices 9 to 11.
(Appendix 13)
The reference gesture extends the interphalangeal joint and the metacarpophalangeal joint of the thumb, grips the housing using at least one of the index finger, the middle finger, the ring finger, and the little finger, and the carpal metacarpal joint Is a gesture in which the tip of the thumb moves on one surface of the housing to move the thumb apart from the surface and draw an arc. ,
The gesture detection method according to attachment 12.
(Appendix 14)
The reference gesture includes a first gesture in which a tip of the thumb moves in a circular arc from a first position on the surface or an extended surface of the surface to a second position on the surface or an extended surface of the surface, A second gesture in which the tip of the thumb moves from the second position to the first position in an arc, and after the tip of the thumb moves from the first position to the second position in an arc, the second gesture A third gesture that draws an arc from the position and moves to the first position, and a tip of the thumb moves from the second position to the first position by drawing an arc and then draws an arc from the first position. Including at least one of a fourth gesture moving to a second position,
The gesture detection method according to attachment 13.
(Appendix 15)
The detected angular velocity signal includes a signal indicating a reaction force generated by the movement of the thumb on the housing.
The information processing apparatus according to any one of appendices 12 to 14.
(Appendix 16)
The processor is
When it is determined that the reference gesture has been performed, processing corresponding to the reference gesture associated in advance is executed.
The gesture detection method according to any one of appendices 9 to 15.
(Appendix 17)
Detecting angular velocity signals around three mutually orthogonal axes set in the housing,
Each of the detected angular velocity signals around the three axes is converted into a frequency domain spectrum,
Obtaining a frequency indicating the maximum gain in the spectrum of the specific one-axis frequency domain having the largest value among the maximum values of the gain of the spectrum of the frequency domain of each axis as the primary main resonance frequency, and at least A ratio of a gain at the same frequency as the primary main resonance frequency of a spectrum in a biaxial frequency region and the maximum gain is obtained as a gain ratio;
The primary main resonance frequency and the gain ratio of the spectrum in the frequency domain of at least two axes acquired when the reference gesture is performed on the casing and the determination target gesture are performed on the casing. The primary main resonance frequency and the gain ratio acquired at the time are compared with each of the primary main resonance frequency and the gain ratio to determine whether or not the determination target gesture is the reference gesture. judge,
The program for making a processor perform the gesture detection process containing this.
(Appendix 18)
The at least two axes used for comparison are an axis having the largest gain value in each axis and the second largest axis among the three axes.
The program according to appendix 17.
(Appendix 19)
Storing the primary main resonance frequency acquired when the reference gesture is performed and the gain ratio of the spectrum in the frequency domain of at least two axes in a storage unit;
The primary main resonance frequency acquired when the determination target gesture is performed and the gain ratio of the spectrum in the frequency domain of at least two axes, the primary main resonance frequency stored in the storage unit, and The determination target gesture is determined by comparing the gain ratio of the spectrum in the frequency domain of at least two axes.
The program according to appendix 17 or 18.
(Appendix 20)
The reference gesture is a gesture for grasping the housing with one hand and moving the thumb with the middle metacarpal joint of the thumb of the one hand.
The program according to any one of appendices 17 to 19.
(Appendix 21)
The reference gesture extends the interphalangeal joint and the metacarpophalangeal joint of the thumb, grips the housing using at least one of the index finger, the middle finger, the ring finger, and the little finger, and the carpal metacarpal joint Is a gesture in which the tip of the thumb moves on one surface of the housing to move the thumb apart from the surface and draw an arc. ,
The program according to appendix 20.
(Appendix 22)
The reference gesture includes a first gesture in which a tip of the thumb moves in a circular arc from a first position on the surface or an extended surface of the surface to a second position on the surface or an extended surface of the surface, A second gesture in which the tip of the thumb moves from the second position to the first position in an arc, and after the tip of the thumb moves from the first position to the second position in an arc, the second gesture A third gesture that draws an arc from the position and moves to the first position, and a tip of the thumb moves from the second position to the first position by drawing an arc and then draws an arc from the first position. Including at least one of a fourth gesture moving to a second position,
The program according to appendix 21.
(Appendix 23)
The detected angular velocity signal includes a signal indicating a reaction force generated by the movement of the thumb on the housing.
The program according to any one of appendices 20 to 22.
(Appendix 24)
When it is determined that the reference gesture has been performed, processing corresponding to the reference gesture associated in advance is executed.
The program according to any one of appendices 18 to 23.

10 Smart Device 16 Conversion Unit 18 Acquisition Unit 20 Determination Unit 22 Storage Unit 60 CPU
62 Primary storage unit 64 Secondary storage unit

Claims (9)

An angular velocity detection unit that detects angular velocity signals around three mutually orthogonal axes set in the housing;
A conversion unit that converts each of the angular velocity signals around the three axes detected by the angular velocity detection unit into a spectrum in a frequency domain;
The frequency indicating the maximum gain in the spectrum of the specific one-axis frequency domain having the largest value among the maximum values of the spectrum gain of the frequency domain of each axis converted by the conversion unit is acquired as the primary main resonance frequency. And an acquisition unit that acquires, as a gain ratio, a ratio between the gain and the maximum gain at the same frequency as the primary main resonance frequency of the spectrum in the frequency domain of at least two axes;
When a reference gesture is performed on the casing, the primary main resonance frequency and the gain ratio acquired by the acquisition unit, and when a determination target gesture is performed on the casing, Whether or not the determination target gesture is the reference gesture by comparing the primary main resonance frequency and the gain ratio acquired by the acquisition unit for each of the primary main resonance frequency and the gain ratio. A determination unit for determining whether or not
An information processing apparatus including: The at least two axes used for comparison by the determination unit are an axis having the largest gain value in each axis and the second largest axis among the three axes,
The information processing apparatus according to claim 1. A storage unit for storing the primary main resonance frequency acquired by the acquisition unit and the gain ratio of the spectrum in the frequency domain of at least two axes when the reference gesture is performed;
The determination unit stores, in the storage unit, the gain ratio of the primary main resonance frequency acquired by the acquisition unit and the spectrum of the frequency domain of at least two axes when the determination target gesture is performed. The determination target gesture is determined by comparing the primary main resonance frequency and the gain ratio of the spectrum in the frequency domain of at least two axes.
The information processing apparatus according to claim 1 or 2. The reference gesture is a gesture for grasping the housing with one hand and moving the thumb with the middle metacarpal joint of the thumb of the one hand.
The information processing apparatus according to any one of claims 1 to 3. The reference gesture extends the interphalangeal joint and the metacarpophalangeal joint of the thumb, grips the housing using at least one of the index finger, the middle finger, the ring finger, and the little finger, and the carpal metacarpal joint Is a gesture in which the tip of the thumb moves on one surface of the housing to move the thumb apart from the surface and draw an arc. ,
The information processing apparatus according to claim 4. The reference gesture includes a first gesture in which a tip of the thumb moves in a circular arc from a first position on the surface or an extended surface of the surface to a second position on the surface or an extended surface of the surface, A second gesture in which the tip of the thumb moves from the second position to the first position in an arc, and after the tip of the thumb moves from the first position to the second position in an arc, the second gesture A third gesture that draws an arc from the position and moves to the first position, and a tip of the thumb moves from the second position to the first position by drawing an arc and then draws an arc from the first position. Including at least one of a fourth gesture moving to a second position,
The information processing apparatus according to claim 5.   The information processing apparatus according to claim 1, wherein when the determination unit determines that the reference gesture has been performed, the information processing apparatus performs processing corresponding to the reference gesture associated in advance. . Processor
Detecting angular velocity signals around three mutually orthogonal axes set in the housing,
Each of the detected angular velocity signals around the three axes is converted into a frequency domain spectrum,
Obtaining a frequency indicating the maximum gain in the spectrum of the specific one-axis frequency domain having the largest value among the maximum values of the gain of the spectrum of the frequency domain of each axis as the primary main resonance frequency, and at least A ratio of a gain at the same frequency as the primary main resonance frequency of a spectrum in a biaxial frequency region and the maximum gain is obtained as a gain ratio;
The primary main resonance frequency and the gain ratio of the spectrum in the frequency domain of at least two axes acquired when the reference gesture is performed on the casing and the determination target gesture are performed on the casing. Whether the determination target gesture is the reference gesture by comparing the primary main resonance frequency and the gain ratio acquired at the time of each of the primary main resonance frequency and the gain ratio. Determine
A gesture detection method including the above. Detecting angular velocity signals around three mutually orthogonal axes set in the housing,
Each of the detected angular velocity signals around the three axes is converted into a frequency domain spectrum,
Obtaining a frequency indicating the maximum gain in the spectrum of the specific one-axis frequency domain having the largest value among the maximum values of the gain of the spectrum of the frequency domain of each axis as the primary main resonance frequency, and at least A ratio of a gain at the same frequency as the primary main resonance frequency of a spectrum in a biaxial frequency region and the maximum gain is obtained as a gain ratio;
The primary main resonance frequency and the gain ratio of the spectrum in the frequency domain of at least two axes acquired when the reference gesture is performed on the casing and the determination target gesture are performed on the casing. The primary main resonance frequency and the gain ratio acquired at the time are compared with each of the primary main resonance frequency and the gain ratio to determine whether or not the determination target gesture is the reference gesture. judge,
The program for making a processor perform the gesture detection process containing this.