Information processing device, information processing system, program, and information processing method

Abstract

To provide an information processing device, an information processing system, a program, and an information processing method capable of accurately identifying a single movement state of at least one of the arms and legs.SOLUTION: An information processing device comprises a processing unit. The processing unit acquires first information concerning a first movement state of at least one of the arms and legs of a moving user, the first information having the movement state of the trunk of the user superimposed thereon, acquires second information concerning a second movement state of the trunk of the moving user, and generates third information concerning a third movement state of at least one of the arms and legs of the user on the basis of the first information and the second information.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing system, a program, and an information processing method. [Background technology]

[0002] Conventionally, a technique has been known in which an electronic device worn by a user is provided with a detection unit, such as an acceleration sensor and an angular velocity sensor, that detects the motion state of the device itself, and the user's motion state is analyzed based on the detection results of the detection unit when the user is exercising. For example, Patent Document 1 discloses a technique for analyzing arm swing form based on the detection results of the detection unit when the user runs with the electronic device worn on the arm. In addition, by wearing the electronic device on the leg, it is also possible to analyze leg swing form. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2013-143996 Summary of the Invention [Problem to be solved by the invention]

[0004] Analysis of arm and leg swing forms is usually performed to understand how the arms and legs are moving relative to the torso. However, because the movements of the arms and legs are linked to the movements of the torso, the movement state of the arms or legs determined by electronic devices attached to the arms or legs is superimposed with the movement state of the torso. Therefore, the above-mentioned conventional technology has a problem in that it is not possible to accurately determine the movement state of the arms and legs independently relative to the torso.

[0005] An object of the present invention is to provide an information processing device, an information processing system, a program, and an information processing method that can more accurately identify the independent motion state of at least one of an arm and a leg. [Means for solving the problem]

[0006] In order to solve the above problems, the information processing device according to the present invention comprises: a processing unit; The processing unit Involves movement The motion state of the user's torso while exercising is superimposed. In the first period acquire first information relating to a first motion state of at least one of the user's arms and legs; In each of a plurality of sections into which the movement route is divided after the movement is started in a second period prior to the first period, acquiring second information relating to a second motion state of the trunk of the user performing the exercise; Identifying to which of the plurality of sections a travel distance of the user after the travel has started in the first period belongs; The first information and , corresponding to the specified section and generating third information relating to a third motion state of at least one of the user's arm and leg based on the second information.

[0007] In order to solve the above problem, the information processing system according to the present invention comprises: a first device attached to at least one of a user's arm and leg, the first device having a first detection unit that detects the motion state of the device itself; a second device attached to the user's torso and having a second detection unit that detects the motion state of the device itself; a processing unit; Equipped with The processing unit generated based on the detection result of the first detection unit, Involves movement The motion state of the user's torso performing exercise is superimposed. In the first period acquiring first information related to a first motion state of at least one of the user's arm and leg; generated based on the detection result of the second detection unit, In each of a plurality of sections into which the movement route is divided after the movement is started in a second period prior to the first period, acquiring second information relating to a second motion state of the trunk of the user performing the exercise; Identifying to which of the plurality of sections a travel distance of the user after the travel has started in the first period belongs; The first information and , corresponding to the specified section and generating third information relating to a third motion state of at least one of the user's arm and leg based on the second information.

[0008] In order to solve the above problem, the program according to the present invention comprises: On the computer, Involves movement The motion state of the user's torso while exercising is superimposed. In the first period a function of acquiring first information relating to a first motion state of at least one of the user's arms and legs; In each of a plurality of sections into which the movement route is divided after the movement is started in a second period prior to the first period, a function of acquiring second information relating to a second motion state of the trunk of the user performing the exercise; Identifying to which of the plurality of sections a travel distance of the user after the travel has started in the first period belongs; The first information and , corresponding to the specified section a function of generating third information relating to a third motion state of at least one of the user's arm and leg based on the second information; Make this a reality.

[0009] In order to solve the above problem, an information processing method according to the present invention includes: An information processing method by a computer, comprising: Involves movement The motion state of the user's torso while exercising is superimposed. In the first period acquiring first information relating to a first motion state of at least one of the user's arms and legs; In each of a plurality of sections into which the movement route is divided after the movement is started in a second period prior to the first period, acquiring second information relating to a second motion state of the trunk of the user performing the exercise; Identifying to which of the plurality of sections a travel distance of the user after the travel has started in the first period belongs; The first information and , corresponding to the specified section generating third information related to a third motion state of at least one of the arm and the leg of the user based on the second information; Includes. [Effects of the Invention]

[0010] According to the present invention, the movement state of at least one of the arms and legs can be determined more accurately. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 illustrates an information processing system. [Figure 2] FIG. 2 is a block diagram showing the functional configuration of a first device. [Figure 3] FIG. 2 is a block diagram showing the functional configuration of a second device. [Figure 4] 1A and 1B are diagrams illustrating an example of the motion state of the trunk and arms while running. [Figure 5] FIG. 10 is a diagram showing an example of an index of an arm motion state. [Figure 6] FIG. 10 is a diagram showing an example of multiple sections in exercise involving movement. [Figure 7] 10 is a flowchart showing a control procedure for a trunk movement detection process. [Figure 8] 10 is a flowchart showing a control procedure for arm movement detection processing. [Figure 9] 10 is a flowchart showing a control procedure for arm movement detection processing. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0013] <Outline of the information processing system> 1 is a diagram showing an information processing system 1 of this embodiment. The information processing system 1 includes a first device 10 (information processing device) and a second device 20.

[0014] The first device 10 is worn on the arm A of a user U to acquire information related to the exercise state of the arm A. In FIG. 1, the first device 10 is worn on the wrist of the user U. The first device 10 in this embodiment is a wristwatch. However, the first device 10 is not limited to this, and may be any wearable device other than a wristwatch (for example, a healthcare device such as an activity monitor), or may be an electronic device that can be attached and fixed to the arm A, such as a smartphone. The position where the first device 10 is worn is not limited to the wrist, and it may be worn on any part of the body from which the exercise state is desired to be monitored, such as the elbow or upper arm.

[0015] The second device 20 is worn on the torso B of the user U to acquire information related to the motion state of the torso B. The torso B is the body part excluding the arms (parts from the shoulder joints) and legs (parts from the hip joints). In FIG. 1, the second device 20 is worn on the waist on the back side of the user U. The second device 20 is preferably worn near the trunk of the torso B, i.e., near a line passing through the spine. This is because the position change due to twisting of the torso is small near the trunk, and the overall position and motion of the torso B are easily captured. The second device 20 may be a wearable device dedicated to acquiring information related to the motion state of the torso B, or may be an electronic device that can be attached and fixed to the torso B, such as a smartphone.

[0016] The user U performs exercise while wearing the first device 10 and the second device 20. The type of exercise is not particularly limited, and may be exercise involving movement of the user U, such as walking or running. In this embodiment, a case in which the user U runs will be described as an example. In the following, the direction of movement of the user U while running will be referred to as the "forward direction," the direction opposite to the direction of movement will be referred to as the "backward direction," the direction of gravity (vertical downward direction) will be referred to as the "downward direction," the direction opposite to the direction of gravity (vertical upward direction) will be referred to as the "upward direction," the direction toward the user U's right hand and perpendicular to both the front-back direction and the up-down direction will be referred to as the "rightward direction," and the direction toward the user U's left hand and perpendicular to both the front-back direction and the up-down direction will be referred to as the "leftward direction." In addition, a coordinate system with the front-back direction, up-down direction, and left-right direction as its three axes will be referred to as the "global coordinate system."

[0017] The first device 10 and the second device 20 are communicatively connected via short-range wireless communication, and can transmit and receive data. An example of short-range wireless communication is Bluetooth (registered trademark), but this is not limited to this. Hereinafter, the communicative connection between the first device 10 and the second device 20 via short-range wireless communication will also be referred to as "pairing."

[0018] The first device 10 includes a first sensor unit 13 (first detection unit) (see FIG. 2) that detects the motion state of the device itself. The motion of the arm A wearing the first device 10 is linked to the motion of the torso B, and therefore the motion state of the arm A (first motion state) on which the motion state of the torso B is superimposed is reflected in the detection result of the first sensor unit 13. Based on the detection result of the first sensor unit 13, the first device 10 generates composite arm motion data 122 (see FIG. 2) as first information related to the first motion state of the arm A on which the motion state of the torso B is superimposed. Hereinafter, the first motion state of the arm A on which the motion state of the torso B is superimposed is also referred to as a "composite motion state."

[0019] The second device 20 includes a second sensor unit 23 (second detection unit) (see FIG. 3) that detects the motion state of the device itself. The detection result of the second sensor unit 23 reflects the motion state (second motion state) of the trunk B of the user U. The second device 20 generates trunk motion data 123 (see FIG. 2) as second information related to the second motion state of the trunk B of the user U, and transmits the trunk motion data 123 to the first device 10.

[0020] The first device 10 generates arm motion data 124 (third information) relating to the independent motion state (third motion state) of the arm A relative to the user's torso B based on the difference between the combined arm motion data 122 and the torso motion data 123 received from the second device 20. Hereinafter, the third independent motion state of the arm A relative to the torso B will also be referred to as the "independent motion state." The arm motion data 124 makes it possible to grasp the motion state of the arm A itself, excluding the motion state of the torso B. The first device 10 displays information relating to the motion state of the arm A, for example, information relating to the determination result of the arm swing state, based on the arm motion data 124. A method for identifying the independent motion state of the arm A by the first device 10 will be described in detail later.

[0021] Note that the first device 10 may be capable of displaying various types of information related to the exercise state of the user U in addition to the exercise state of the arm A. For example, the first device 10 may be capable of displaying information such as the running distance, running pace, pitch (number of steps per minute), stride, magnitude of vertical movement, ground contact time per step, and trunk inclination.

[0022] <Configuration of the first device> FIG. 2 is a block diagram showing the functional configuration of first device 10. As shown in FIG. The first device 10 includes a CPU 11 (Central Processing Unit), a memory 12 (storage unit), a first sensor unit 13, a wireless communication unit 14, a satellite radio wave receiving and processing unit 15, a display unit 16, an operation receiving unit 17, a timing unit 18, and the like.

[0023] CPU 11 reads and executes program 121 stored in memory 12 and performs various arithmetic processing to control the operation of each part of first device 10. In this embodiment, CPU 11 corresponds to a "processing part." Note that the processing part may have two or more circuit elements, such as CPUs, that perform arithmetic processing.

[0024] The memory 12 is a non-transitory recording medium readable by the CPU 11 as a computer, provides the CPU 11 with a working memory space, and stores various data. The memory 12 includes, for example, a random access memory (RAM) and a non-volatile memory. The RAM is used for the CPU 11's arithmetic processing and also stores temporary data. The non-volatile memory is, for example, a flash memory, and stores various data in addition to the program 121. The program 121 is stored in the memory 12 in the form of computer-readable program code. The data stored in the memory 12 include combined arm motion data 122 (first information), trunk motion data 123 (second information), arm motion data 124 (third information), and trunk motion history data 125 (second information).

[0025] The combined arm motion data 122 is data generated based on the detection results of the first sensor unit 13, and is data representing a combined motion state of the arm A on which the motion state of the torso B of the user U is superimposed. The trunk motion data 123 is data received from the second device 20, and represents the motion state of the torso B of the user U. The arm motion data 124 is data generated based on the difference between the combined arm motion data 122 and the trunk motion data 123, and represents the independent motion state of the arm A relative to the torso B of the user. The trunk motion history data 125 is the trunk motion data 123 generated during a past exercise of the user U. The arm motion data 124 may be generated based on the difference between the combined arm motion data 122 and the trunk motion history data 125.

[0026] The first sensor unit 13 includes a three-axis acceleration sensor 131 , a three-axis gyro sensor 132 , and a three-axis geomagnetic sensor 133 .

[0027] The three-axis acceleration sensor 131 detects, at a predetermined sampling frequency, acceleration in each axial direction applied to the first device 10 in response to the movement of the user U, and outputs acceleration data as the detection result. The acceleration data output from the three-axis acceleration sensor 131 includes signal components for three mutually orthogonal axes (x-axis, y-axis, and z-axis). The orientations of the three axes are not particularly limited. For example, as shown in FIG. 1, two perpendicular axes in a plane parallel to the display surface of the first device 10 can be defined as the x-axis and y-axis, and a direction perpendicular to the x-axis and y-axis can be defined as the z-axis. Hereinafter, a coordinate system having the x-axis, y-axis, and z-axis as the three axes and having a fixed positional relationship with the first device 10 will be referred to as a "sensor coordinate system."

[0028] The triaxial gyro sensor 132 detects, at a predetermined sampling frequency, angular velocities around each axis applied to the first device 10 in response to the movement of the user U, and outputs angular velocity data as the detection result. The angular velocity data output from the triaxial gyro sensor 132 includes signal components for the x-axis, y-axis, and z-axis.

[0029] Triaxial geomagnetic sensor 133 detects the direction of the geomagnetism passing through first device 10 at a predetermined sampling frequency and outputs geomagnetic data as the detection result. The geomagnetic data output from triaxial geomagnetic sensor 133 includes signal components for the x-axis, y-axis, and z-axis.

[0030] The sampling frequencies of the three-axis acceleration sensor 131, the three-axis gyro sensor 132, and the three-axis geomagnetic sensor 133 are determined to have a resolution capable of identifying the arm swing motion, and are set within a range of 50 Hz to 200 Hz, for example. The detection timings of the three-axis acceleration sensor 131, the three-axis gyro sensor 132, and the three-axis geomagnetic sensor 133 are hereinafter referred to as "first detection timings." At each of the multiple first detection timings, a first timestamp indicating the detection time is recorded and stored in association with each measurement data by the first sensor unit 13.

[0031] The first sensor unit 13 also includes an amplifier (not shown) that amplifies the analog signals output from the three-axis acceleration sensor 131, the three-axis gyro sensor 132, and the three-axis geomagnetic sensor 133, and an AD converter (not shown) that converts the amplified analog signals into digital data and outputs it to the CPU 11.

[0032] The wireless communication unit 14 performs wireless communication, i.e., data transmission and reception using radio waves, with the second device 20. In this embodiment, the wireless communication unit 14 performs short-range wireless communication using Bluetooth with the second device 20 that is the pairing target.

[0033] The satellite radio wave reception processing unit 15 is a module that receives radio waves transmitted from positioning satellites of the Global Navigation Satellite System (GNSS) to acquire GPS data, and calculates the current position and date and time based on this GPS data. The global positioning satellite system used is not particularly limited, but may be, for example, the Global Positioning System (GPS), GLONASS, or a quasi-zenith satellite system using the quasi-zenith satellite "Michibiki." The satellite radio wave reception processing unit 15 calculates the current position and date and time under the control of the CPU 11, and outputs the results to the CPU 11.

[0034] The display unit 16 displays various information related to the exercise state, time, etc. under the control of the CPU 11. As the display unit 16, for example, a liquid crystal display device that displays using a dot matrix method can be used, but is not limited to this.

[0035] The operation reception unit 17 has a plurality of operation buttons, receives an input operation (e.g., a pressing operation) by the user on the operation buttons, and outputs the input signal to the CPU 11. The CPU 11 executes a process corresponding to the function of the operation button for which the input operation has been performed. The operation reception unit 17 may have a touch panel provided over the display screen of the display unit 16.

[0036] The timekeeping unit 18 includes an oscillation circuit, a frequency dividing circuit, a timekeeping circuit, etc. The frequency dividing circuit divides the clock signal generated by the oscillation circuit, and the timekeeping circuit counts the divided signal, thereby counting and holding the current date and time.

[0037] <Configuration of the second device> FIG. 3 is a block diagram showing the functional configuration of second device 20. As shown in FIG. The second device 20 includes a CPU 21, a memory 22 (storage unit), a second sensor unit 23, a wireless communication unit 24, a satellite radio wave reception processing unit 25, and the like.

[0038] The CPU 21 reads and executes a program 221 stored in the memory 22, and controls the operation of each part of the second device 20 by performing various arithmetic processing.

[0039] The memory 22 is a non-transitory recording medium readable by the CPU 21 as a computer, provides a working memory space for the CPU 21, and stores various data. The memory 22 includes, for example, a RAM and a non-volatile memory. The RAM is used for the calculation processing of the CPU 21 and also stores temporary data. The non-volatile memory is, for example, a flash memory, and stores various data in addition to the program 221. The program 221 is stored in the memory 22 in the form of a computer-readable program code.

[0040] Second sensor unit 23 includes a triaxial acceleration sensor 231, a triaxial gyro sensor 232, a triaxial geomagnetic sensor 233, an amplifier (not shown), and an AD converter (not shown). The configurations and operations of triaxial acceleration sensor 231, triaxial gyro sensor 232, and triaxial geomagnetic sensor 233 are similar to the configurations and operations of triaxial acceleration sensor 131, triaxial gyro sensor 132, and triaxial geomagnetic sensor 133 of first device 10, respectively, and therefore will not be described again. The sensor coordinate systems of triaxial acceleration sensor 231, triaxial gyro sensor 232, and triaxial geomagnetic sensor 233 of second device 20 are separate and independent from the sensor coordinate systems of triaxial acceleration sensor 131, triaxial gyro sensor 132, and triaxial geomagnetic sensor 133 of first device 10. The sampling frequencies of the triaxial acceleration sensor 231, the triaxial gyro sensor 232, and the triaxial geomagnetic sensor 233 of the second device 20 may be different from the sampling frequencies of the triaxial acceleration sensor 131, the triaxial gyro sensor 132, and the triaxial geomagnetic sensor 133 of the first device 10. However, by making the sampling frequencies the same, it is possible to align the time resolutions for analyzing the motion states of the arm A and the torso B. Furthermore, it is preferable that the detection timings of the triaxial acceleration sensor 231, the triaxial gyro sensor 232, and the triaxial geomagnetic sensor 233 of the second device 20 (hereinafter referred to as "second detection timings") are synchronized with the first detection timings of the triaxial acceleration sensor 131, the triaxial gyro sensor 132, and the triaxial geomagnetic sensor 133 of the first device 10. At each of the plurality of second detection timings, a second timestamp indicating the detection time is recorded and stored in association with each measurement data by the second sensor unit 23.

[0041] The wireless communication unit 24 performs wireless communication, i.e., data transmission and reception using radio waves, with the first device 10. In this embodiment, the wireless communication unit 24 performs short-range wireless communication using Bluetooth with the first device 10 that is the pairing target.

[0042] Satellite radio wave reception processing unit 25 is a module that receives radio waves transmitted from positioning satellites of the Global Positioning Satellite System to acquire GPS data, and calculates the current position and date and time based on this GPS data. Satellite radio wave reception processing unit 25 calculates the current position and date and time under the control of CPU 21, and outputs the results to CPU 21. Note that since the satellite radio wave reception processing unit 15 of first device 10 and the satellite radio wave reception processing unit 25 of second device 20 have overlapping functions, either one may be omitted.

[0043] <Operation of information processing system> Next, the operation of the information processing system 1 will be described, focusing on the operation related to identifying the movement state of the arm.

[0044] As described above, the motion state of arm A identified by first device 10 is not a single motion state based on trunk B (i.e., on a coordinate axis fixed to trunk B), but a composite motion state in which the motion state of trunk B is superimposed. This is because the movement of arm A is linked to the movement of trunk B, and the position, speed, and acceleration of arm A are affected by the position, speed, and acceleration of trunk B. For example, when user U is running, trunk B is in a motion state corresponding to the movement caused by running, and its position in the up-down direction changes periodically and it sways left and right.

[0045] FIG. 4 is a diagram schematically illustrating an example of the motion state of a trunk B and an arm A during running. The top part of FIG. 4 is a schematic diagram showing the movement of the user U's body at each time point in a motion cycle T while running. The middle part of FIG. 4 is a graph showing the fluctuation in the vertical height of the torso B (here, the waist to which the second device 20 is attached) as one aspect of the motion state of the torso B while running. Here, the fluctuation in the height of the torso B is schematically represented by a sine curve. As can be seen from the schematic diagram in the top part and the graph in the middle part, after the right leg touches the ground, the knee bends, causing the waist position to drop to its lowest point at time t1. Then, the right leg kicks off and takes off, and at time t2, the waist height rises to its highest point while floating in the air. Next, the left leg touches the ground, the knee bends, and at time t3, the waist position drops to its lowest point again. Next, the left leg kicks off and takes off, and at time t4, the waist height rises to its highest point while floating in the air. Next, the right leg touches the ground, the knee bends, and at time t5, the waist position drops to its lowest point. The period from time t1 to time t5 corresponds to a motion cycle T during running. The movements of this motion cycle T are repeated while running. As shown in the middle of Figure 4, the height of the hips in the vertical direction changes in synchronization with the movement of the legs, moving back and forth between the lowest point and the highest point twice during one motion cycle T.

[0046] In this way, trunk B is in a state of motion while running, and the motion state of this trunk B is superimposed on the combined motion state of arm A. The solid line L1 in the lower part of FIG. 4 is a graph showing fluctuations in the vertical height as one aspect of the combined motion state of arm A (here, the wrist on which first device 10 is attached) while running. The dashed line L2 in the lower part of FIG. 4 is a graph showing fluctuations in the vertical height as one aspect of the independent motion state of arm A relative to trunk B. As shown by dashed line L2, the height of arm A in the independent motion state increases from time t1 to time t2 in response to the movement of swinging arm A forward, decreases from time t2 to time t4 in response to the movement of pulling arm A backward, forms a small peak that is slightly higher when arm A is fully pulled around time t4, and then increases again around time t5 in response to the movement of bringing arm A back forward. Even if the fluctuation in height of arm A in an independent motion state is as shown by dashed line L2, the fluctuation in height of torso B in a moving state is superimposed, so that the fluctuation in height of arm A in a combined motion state forms a waveform different from that of dashed line L2, as shown by solid line L1. The motion state of arm A identified by first device 10 attached to arm A is the combined motion state shown by solid line L1, and the independent motion state of arm A shown by dashed line L2 cannot be identified and analyzed based solely on the detection result of first sensor unit 13 of first device 10.

[0047] One possible method for extracting the individual motion state of arm A from the combined motion state of arm A is to identify the average torso motion state of many runners while running and subtract it from the combined motion state of arm A. However, since such an average torso motion state is not necessarily similar to the motion state of torso B of user U, it is difficult to accurately identify the individual motion state of arm A.

[0048] Therefore, in the information processing system 1 of this embodiment, the second device 20 identifies the motion state of the torso B of the user U in real time, and identifies the independent motion state of the arm A based on the difference between the combined motion state of the arm A identified by the first device 10 and the motion state of the torso B. Specifically, the second device 20 generates trunk motion data 123 relating to the motion state of the torso B of the user U based on the detection result by the second sensor unit 23, and transmits the generated trunk motion data to the first device 10. The first device 10 also generates combined arm motion data 122 relating to the combined motion state of the arm A of the user U based on the detection result by the first sensor unit 13. The first device 10 then generates arm motion data 124 relating to the independent motion state of the arm A based on the difference between the combined arm motion data 122 and the trunk motion data 123. This method allows the motion state of the torso B of the user U to be subtracted from the combined motion state of the arm A, thereby enabling the independent motion state of the arm A to be identified with high accuracy.

[0049] For example, if the trunk motion data 123 includes data representing variations in waist height as shown in the middle of Fig. 4 and the combined arm motion data 122 includes data representing variations in wrist height in a combined motion state as shown by the solid line L1 in the bottom of Fig. 4, then by subtracting the waist height at each time point in the trunk motion data 123 from the wrist height at each time point in the combined arm motion data 122, arm motion data 124 representing variations in wrist height in an independent motion state as shown by the dashed line L2 in the bottom of Fig. 4 can be obtained. If the combined arm motion data 122 and trunk motion data 123 include time-series data, the combined arm motion data 122 is associated with the first timestamp representing each time point, and the trunk motion data 123 is associated with the second timestamp representing each time point. Then, the arm motion data 124 is generated by calculating the difference between the data at corresponding times in the combined arm motion data 122 and trunk motion data 123 with reference to the first timestamp and the second timestamp.

[0050] Although Fig. 4 illustrates an example of a change in height, the information included in the composite arm motion data 122 and the arm motion data 124 is not particularly limited as long as it represents the motion state of arm A. For example, the information may be information relating to the front-to-back swing width W1 of arm A shown in Fig. 5 or the trajectory of the front-to-back arm swing, or information relating to the up-to-down swing width W2 or the trajectory of the up-to-down arm swing (corresponding to the solid line L1 and dashed line L2 in the lower part of Fig. 4). The information may also include information relating to the left-to-right swing width or the trajectory of the left-to-right arm swing. Furthermore, the information is not limited to information relating to the position of arm A (first device 10), but may also be information relating to the speed or acceleration of arm A, etc.

[0051] Similarly, the information included in the trunk motion data 123 is not particularly limited as long as it represents the motion state of trunk B. For example, it may be information relating to the trajectory or range of change of the vertical position of trunk B (corresponding to the middle part of FIG. 4), or information relating to the trajectory or range of change of the horizontal position. Furthermore, it is not limited to information relating to the position of trunk B (second device 20), and may include information relating to the speed or acceleration of trunk B.

[0052] In preparation for various reasons where the first device 10 is unable to receive real-time trunk motion data 123 from the second device 20, the first device 10 may store the received trunk motion data 123 in the memory 12 as trunk motion history data 125. Examples of cases where the real-time trunk motion data 123 cannot be received include a case where the pairing between the first device 10 and the second device 20 is canceled or detection by the second sensor unit 23 is disabled due to a malfunction of the second device 20 for some reason (e.g., a short circuit) or a dead battery of the second device 20 (the battery output voltage is below a threshold due to discharge), or a case where the user does not (or cannot) wear the second device 20. By storing the trunk motion data 123 in the memory 12 as the trunk motion history data 125, even when the real-time trunk motion data 123 cannot be received from the second device 20, the arm motion data 124 can be generated by using the trunk motion history data 125 instead of the trunk motion data 123. That is, the arm motion data 124 may be generated based on the difference between the combined arm motion data 122 relating to the motion state of the user U's arm A in a certain first period and the trunk motion history data 125 relating to the motion state of the user U's trunk B in a second period prior to the first period. Although the trunk motion history data 125 does not reflect the real-time motion state of the trunk B, it represents the motion state of the user U's own trunk B, and therefore the isolated motion state of the arm A can be identified with high accuracy compared to subtracting the average value of many runners.

[0053] Furthermore, during the second period (e.g., a recent run), trunk movement data 123 relating to the movement state of trunk B may be acquired for each of a plurality of sections divided into which a movement route is divided after the start of movement, and stored as trunk movement history data 125. For example, as shown in FIG. 6, if a 15-kilometer run is performed during the second period, the movement route may be divided into sections S1 to S3, each 5 kilometers long, and trunk movement history data 125 may be stored for each of sections S1 to S3. The movement distance of user U may be calculated from the results of positioning performed by satellite radio wave reception processing unit 15 of first device 10 or satellite radio wave reception processing unit 25 of second device 20. The length of the section is not limited to 5 kilometers and can be determined appropriately depending on the purpose of analysis, etc. Furthermore, the sections may be divided according to the time elapsed since the start of movement, instead of the movement distance from the start of movement.

[0054] When storing trunk motion history data 125 for each section, the travel distance of user U after starting movement during a first period (e.g., during the current run) is identified to which of a plurality of sections it belongs, and arm motion data 124 is generated based on the difference between the combined arm motion data 122 and the trunk motion history data 125 corresponding to the identified section. In the example of FIG. 6, the travel distance up to time ta during the first period is 8 km, which belongs to section S2. Therefore, at time ta, arm motion data 124 is generated based on the difference between the combined arm motion data 122 and the trunk motion history data 125 corresponding to section S2. With this method, even if the running form (motion state of trunk B) changes due to fatigue or the like depending on the running distance, the motion state of trunk B reflecting the change can be subtracted from the combined motion state of arm A. This makes it possible to more accurately identify the individual motion state of arm A.

[0055] Furthermore, if the travel distance of the user U in the first time period is longer than the entirety of the multiple sections for which trunk motion history data 125 is stored, the arm motion data 124 may be generated based on the difference between the combined arm motion data 122 and the trunk motion history data 125 corresponding to the last section of the multiple sections. In the example of Fig. 6, the travel distance up to time tb in the first time period is 18 km, which is longer than the entirety (15 km) of the sections for which trunk motion history data 125 is stored. Therefore, the arm motion data 124 can be generated using the trunk motion history data 125 of section S3, which is the last section.

[0056] Instead of or in addition to the sections, route information relating to the state of the route along which the user U ran may be recorded, and trunk movement history data 125 may be stored for each section of the route with different route information. Examples of the route state represented by the route information include the gradient of the route (uphill, downhill), the curvature of the route (straight, curved), the surface condition of the route (asphalt, soil, degree of unevenness, wetness, icy), wind direction, and wind speed. These route conditions (external factors) may also change the movement state of trunk B and the relationship between the movements of arm A and trunk B. Therefore, by identifying the route state during the first period and generating arm movement data 124 using trunk movement history data 125 stored in association with the identified state, the independent movement state of arm A can be more accurately identified.

[0057] Next, the trunk movement detection process executed in the second device 20 and the arm movement detection process executed in the first device 10 to identify the independent movement state of the arm A as described above will be described.

[0058] FIG. 7 is a flowchart showing a control procedure by the CPU 21 for the trunk movement detection process. The trunk movement detection process is started, for example, when a predetermined process start signal is received from the paired first device 10.

[0059] When the trunk movement detection process is started, the CPU 21 of the second device 20 acquires measurement data (detection results) of the second sensor unit 23 (step S101). Here, the CPU 21 acquires measurement data detected by the three-axis acceleration sensor 231, the three-axis gyro sensor 232, and the three-axis geomagnetic sensor 233 at a predetermined sampling frequency and at a second detection timing synchronized with the first detection timing by the first sensor unit 13. The CPU 21 also stores the acquired measurement data in the memory 22 in association with a second timestamp indicating the second detection timing of the measurement data.

[0060] The CPU 21 determines whether or not measurement data for the motion cycle T has been accumulated (step S102). The method of determination in step S102 is not particularly limited, and for example, a method of determination based on peaks included in the detection result of the triaxial acceleration sensor 231 can be used. Since peaks corresponding to the contact of the legs with the ground appear in the acceleration detected by the triaxial acceleration sensor 231, when two peaks corresponding to the contact of the legs with the ground for two steps are detected, it can be determined that measurement data for the motion cycle T has been accumulated. In other words, the measurement data from the peak corresponding to the contact with the ground to the peak two peaks after the peak can be identified as measurement data for the motion cycle T. When it is determined that measurement data for the motion cycle T has not been accumulated ("NO" in step S102), the CPU 21 returns the process to step S101.

[0061] If it is determined that measurement data for the movement period T has been accumulated ("YES" in step S102), the CPU 21 identifies the direction of gravity and the direction of travel from the measurement data of the second sensor unit 23, and converts the measurement data into data on the up-down direction, the left-right direction, and the front-back direction (step S103). Here, the CPU 21 identifies the direction of gravity (downward direction) based on the direction of gravity detected by the triaxial acceleration sensor 231 and / or the direction of geomagnetism detected by the triaxial geomagnetic sensor 233. The CPU 21 also identifies the direction of travel (forward direction) of the user U based on the identified direction of gravity and the magnitude of acceleration of each axis of the triaxial acceleration sensor 231. The CPU 21 also converts (separates) the measurement data in the sensor coordinate system of the second sensor unit 23 into measurement data in a global coordinate system whose coordinate axes are the up-down direction, the left-right direction, and the front-back direction.

[0062] The CPU 21 generates trunk motion data 123 relating to the motion states of the trunk B in the up-down, left-right, and front-back directions based on the measurement data of the motion cycle T of the second sensor unit 23 (step S104). For example, the CPU 21 calculates data relating to predetermined indices representing the motion states of the trunk B in the up-down, left-right, and front-back directions. Here, the indices representing the motion states can be appropriately determined depending on the purpose of analyzing the motion states, etc. Examples of the indices include the up-down movement trajectory of the trunk B shown in the middle of FIG. 4, the range of change in the up-down position, the trajectory or range of change in the left-right position, the transition of the rotation angle around the trunk, and the transition of the tilt angle of the trunk. Furthermore, the indices are not limited to those represented by the position or angle of the trunk B, but may also be those represented by the velocity or acceleration of the trunk B. The position data of the trunk B can be obtained by integrating the acceleration measurement data twice, and the velocity data can be obtained by integrating the acceleration measurement data once. The rotation angle around each axis can be obtained by integrating the angular velocity. If the measurement data of the second sensor unit 23 includes a twisting (rotational) motion around the trunk (rotation axis) of the trunk B, the trunk motion data 123 may be generated based on data from which the twisting motion has been subtracted in advance. This provides trunk motion data 123 that represents the motion state of a part (e.g., the trunk) that represents the entire trunk B. Data at each point in time in the trunk motion data 123 is stored in association with a second time stamp that represents the point in time.

[0063] CPU 21 determines whether pairing with first device 10 is ongoing (i.e., whether communication is ongoing) (step S105). If it is determined that pairing is ongoing ("YES" in step S105), CPU 21 transmits trunk movement data 123 (including the second timestamp) generated in step S104 to first device 10 (step S106).

[0064] When step S106 is completed, or when it is determined in step S105 that pairing with the first device 10 is not continuing (communication connection is disconnected) (“NO” in step S105), the CPU 21 determines whether an instruction to end detection of the motion state of the torso B has been given (step S107). The instruction may be an operation by the user U instructing to end detection or an operation instructing to turn off the power. Alternatively, when the end of the user U's motion is detected based on the detection result by the second sensor unit 23, the instruction to end detection may be considered to have been given. When it is determined that an instruction to end detection of the motion state of the torso B has not been given (“NO” in step S107), the CPU 21 returns the process to step S101. When it is determined that an instruction to end detection of the motion state of the torso B has been given (“YES” in step S107), the CPU 21 ends the torso motion detection process.

[0065] 8 and 9 are flowcharts showing the control procedure of the arm movement detection process by the CPU 11. The arm movement detection process is started, for example, when the user U performs an operation to instruct the start of movement measurement.

[0066] When the arm movement detection process is started, CPU 11 of first device 10 acquires the current position calculated by satellite wave reception processing unit 15, and identifies the section (for example, one of sections S1 to S3 in FIG. 6) to which the movement distance from the movement start position to the current position belongs (step S201). Alternatively, the current position may be identified by receiving the current position acquired by satellite wave reception processing unit 25 of second device 20.

[0067] CPU 11 acquires measurement data (detection results) of first sensor unit 13 (step S202). Here, CPU 11 acquires measurement data detected by triaxial acceleration sensor 131, triaxial gyro sensor 132, and triaxial geomagnetic sensor 133 at the same sampling frequency as second sensor unit 23 of second device 20 and at a first detection timing synchronized with the second detection timing by second sensor unit 23. CPU 11 also stores the acquired measurement data in memory 12 in association with a first timestamp indicating the first detection timing of the measurement data.

[0068] The CPU 11 determines whether or not measurement data for the motion cycle T has been accumulated (step S203). The determination in step S203 can be performed in the same manner as the determination in step S102 of the trunk motion detection process. Here, too, the measurement data from the acceleration peak corresponding to the ground contact to the peak two peaks after that can be identified as measurement data for the motion cycle T. If it is determined that measurement data for the motion cycle T has not been accumulated ("NO" in step S203), the CPU 11 returns the process to step S202.

[0069] If it is determined that measurement data for the movement cycle T has been accumulated ("YES" in step S203), the CPU 11 identifies the direction of gravity and the direction of travel from the measurement data of the first sensor unit 13, and converts the measurement data into data for the up-down direction, left-right direction, and front-back direction (step S204). Here, the CPU 11 identifies the direction of gravity and the direction of travel using a method similar to that used in step S103 of the trunk movement detection process, and converts the data into measurement data in the global coordinate system.

[0070] Based on the measurement data of the motion cycle T of the first sensor unit 13, the CPU 11 generates composite arm motion data 122 relating to the composite motion state of the arm A, in which the motion state of the trunk B is superimposed, for each of the up-down, left-right, and front-back directions of the arm A (step S205). For example, the CPU 11 calculates data relating to a predetermined index representing the composite motion state of the arm A in the up-down, left-right, and front-back directions. Here, the index representing the composite motion state can be appropriately determined depending on the purpose of analyzing the motion state, etc. Examples of the index include the up-down movement trajectory of the arm A shown by the solid line L1 in the lower part of FIG. 4, the range of change in the up-down position, the trajectory or range of change in the left-right position, the trajectory or range of change in the front-back position, and the transition of the rotation angle around the shoulder joint. Furthermore, the index is not limited to an index represented by the position or angle of the arm A, but may be an index represented by the speed or acceleration of the arm A, etc. The position data of arm A can be obtained by integrating the acceleration measurement data twice, and the velocity data can be obtained by integrating the acceleration measurement data once. The rotation angle around each axis can be obtained by integrating the angular velocity. The data at each point in time of the combined arm motion data 122 is stored in association with a first timestamp representing that point in time.

[0071] The CPU 11 receives from the second device 20 the trunk motion data 123 (including the second time stamp) corresponding to the motion cycle T of the combined arm motion data 122 generated in step S205 (step S206).

[0072] The CPU 11 determines whether the reception of the trunk motion data 123 was successful (step S207), and if it is determined that the reception was successful ("YES" in step S207), it determines whether it is time to register the trunk motion history data 125 corresponding to the section (step S208). The timing is determined so that the trunk motion history data 125 is registered at least once in each section. For example, if the section identified in step S101 is different from the section identified in the previous step S101 (i.e., if the current section has changed), it may be determined that it is time to register the trunk motion history data 125.

[0073] If it is determined that it is time to register trunk motion history data 125 corresponding to the section ("YES" in step S208), CPU 11 registers (stores) trunk motion data 123 received in step S206 in memory 12 as trunk motion history data 125 in association with the current section (step S209). Note that if trunk motion history data 125 corresponding to the current section is already stored in memory 12, the acquired trunk motion data 123 may overwrite the existing trunk motion history data 125, or the existing trunk motion history data 125 may be retained without overwriting. Alternatively, new trunk motion history data 125 may be generated based on the existing trunk motion history data 125 stored in association with the current section and the trunk motion data 123 acquired in step S206. The newly generated trunk motion history data 125 may be, for example, data representing a representative value (e.g., an average value) of the values ​​of the existing trunk motion history data 125 and the values ​​of the acquired trunk motion data 123.

[0074] When step S209 is completed, or when it is determined in step S208 that it is not time to register the trunk motion history data 125 corresponding to the section ("NO" in step S208), the CPU 11 generates arm motion data 124 relating to the independent motion state based on the difference between the combined arm motion data 122 generated in step S205 and the trunk motion data 123 acquired in step S206 for each of the up-down direction, left-right direction, and front-back direction (step S210). Here, the CPU 11 refers to the first timestamp of the combined arm motion data 122 and the second timestamp of the trunk motion data 123, calculates the difference between the data at corresponding times in the combined arm motion data 122 and the trunk motion data 123, and generates the arm motion data 124. For example, if the trunk motion data 123 includes data representing variations in waist height as shown in the middle of Fig. 4 and the combined arm motion data 122 includes data representing variations in wrist height in a combined motion state as shown by the solid line L1 in the bottom of Fig. 4, arm motion data 124 is generated including data obtained by subtracting the waist height at each time point in the trunk motion data 123 from the wrist height at each time point in the combined arm motion data 122 (data representing variations in wrist height in an independent motion state as shown by the dashed line L2). If the combined arm motion data 122 includes data related to multiple different indices representing the combined motion state of arm A and the trunk motion data 123 includes data related to multiple different indices representing the motion state of trunk B, data representing the independent motion state of arm A may be generated for each indices based on the difference between the combined arm motion data 122 and trunk motion data 123, and arm motion data 124 including the data may be generated. Here, examples of multiple different indicators that represent the combined movement state of arm A include the vertical movement trajectory or range of change of arm A, the horizontal position movement trajectory or range of change, the front-to-back position movement trajectory or range of change, the change in the rotation angle around the shoulder joint, the change in the speed of arm A, and the change in the acceleration of arm A.In addition, examples of multiple different indices that represent the movement state of torso B include the trajectory or range of change in the vertical position of torso B, the trajectory or range of change in the left-right position, the change in the rotation angle around the trunk, the change in the inclination angle of the trunk from the vertical direction, the change in the speed of torso B, and the change in the acceleration of torso B.

[0075] If it is determined in step S207 that reception of trunk motion data 123 has failed ("NO" in step S207), CPU 11 determines whether pairing with second device 20 is ongoing (i.e., whether communication is ongoing) (step S211). If it is determined that pairing is ongoing ("YES" in step S211), CPU 11 returns the process to step S206 and attempts to receive trunk motion data 123 again.

[0076] If it is determined that pairing with the second device 20 is not continuing (communication connection is disconnected) (“NO” in step S211), the CPU 11 determines whether trunk motion history data 125 corresponding to the current section is registered in the memory 12 (step S212). If it is determined that trunk motion history data 125 corresponding to the current section is registered in the memory 12 (“YES” in step S212), the CPU 11 acquires the trunk motion history data 125 corresponding to the current section from the memory 12 (step S213). On the other hand, if it is determined that trunk motion history data 125 corresponding to the current section is not registered in the memory 12 (“NO” in step S212), the CPU 11 acquires the trunk motion history data 125 corresponding to the final section (section S3 in the example of FIG. 6) from the memory 12 (step S214). If step S209 has not been executed in the past and no trunk movement history data 125 is stored in memory 12, CPU 11 acquires predetermined trunk movement data (torso movement data of a predetermined value) from memory 12 as trunk movement history data 125.

[0077] After step S213 or step S214 is completed, the CPU 11 generates arm motion data 124 relating to an individual motion state based on the difference between the combined arm motion data 122 generated in step S205 and the trunk motion history data 125 acquired in step S213 or step S214 for each of the up-down direction, left-right direction, and front-back direction (step S215). Here, the first timestamp of the combined arm motion data 122 and the second timestamp of the trunk motion history data 125 indicate different times. However, for example, the first timestamp and the second timestamp may be converted into timestamps indicating the elapsed time from the start of the motion cycle T and then compared. The processing of step S215 is the same as the processing of step S210, except that the trunk motion history data 125 is used instead of the trunk motion data 123.

[0078] When the processing of step S210 or step S215 is completed, the CPU 11 generates arm swing determination data based on the arm motion data 124 and performs a determination regarding the arm swing state (step S216). The data items included in the arm swing determination data are set appropriately depending on the item to be determined. For example, when determining whether or not the swing width W2 of the arm A in the up-down direction shown in FIG. 5 is equal to or greater than a predetermined reference value, the arm swing determination data indicating the swing width W2 of the arm A is generated based on the arm motion data 124. Then, it is determined whether or not the swing width W2 indicated by the arm swing determination data is equal to or greater than the reference value. The arm swing determination data generated here is generated based on the arm motion data 124 related to the independent motion state of the arm A, so that an accurate determination can be performed on the swing width W2 of the arm A without the motion state of the trunk B being superimposed. The object of the arm swing state determination is not limited to the swing width W2 of arm A in the up-down direction shown in FIG. 5 , but may also be the trajectory of the arm swing in the up-down direction, the swing width W1 or the trajectory of the arm swing in the front-back direction, the swing width or the trajectory of the arm swing in the left-right direction, the speed of arm A, and the acceleration of arm A. The arm swing state determination is not limited to determining whether or not a determination criterion is satisfied. For example, it may be determining which of a plurality of evaluation levels the swing widths W1, W2, etc. of arm A correspond to, or it may be indicating the numerical values ​​themselves representing the swing widths W1, W2, etc. of arm A. The type of arm swing form may also be determined based on the trajectory of the arm swing in each direction. Examples of types of arm swing form include a linear type arm swing form in which arm A moves back and forth relatively straight, and a rotational type arm swing form in which arm A rotates during the arm swing motion.

[0079] CPU 11 causes display unit 16 to display information including the judgment result of the arm swing state (step S217). Here, in addition to the judgment result itself, various additional information, such as advice information showing how to improve the arm swing form, may be displayed. In addition to (or instead of) displaying information on display unit 16, the judgment result may be notified by other methods. For example, first device 10 may be provided with a vibration unit, and the judgment result may be notified by the vibration pattern or vibration magnitude of the vibration unit. Furthermore, first device 10 may be provided with an alarm sound output unit, and the judgment result may be notified by the alarm sound pattern or the like. Furthermore, first device 10 may be provided with a light-emitting unit, and the judgment result may be notified by the light-emitting pattern of the light-emitting unit or the like.

[0080] The CPU 11 determines whether an instruction to end detection of the motion state of the arm A has been given (step S218). The instruction may be an operation by the user U to instruct to end detection or an operation to instruct to turn off the power. Alternatively, when the end of the motion of the user U is detected based on the detection result by the first sensor unit 13, the instruction to end detection may be considered to have been given. If the CPU 11 determines that an instruction to end detection of the motion state of the arm A has not been given ("NO" in step S218), the process returns to step S201, and if the CPU 11 determines that an instruction to end detection of the motion state of the arm A has been given ("YES" in step S218), the arm motion detection process ends.

[0081] The trunk motion detection process shown in Fig. 7 and the arm motion detection process shown in Fig. 8 and Fig. 9 may be executed in each of a plurality of consecutive motion cycles T, or may be executed once for a predetermined number of motion cycles T (two or more). The arm swing state may be determined based on a representative value (e.g., an average value) of the plurality of arm motion data 124 generated in the plurality of arm motion detection processes. After information including the determination result of the arm swing state is displayed on display unit 16 in step S217 of Fig. 9, the trunk motion detection process and arm motion detection process may not be executed during this period because user U's arm swing becomes disrupted in order to check display unit 16 of first device 10 worn on the arm.

[0082] 9 of the arm motion detection process do not necessarily have to be performed during the user's exercise and may be performed after the user finishes exercising. Furthermore, in the case where the second device 20 acquires measurement data using the second sensor unit 23 and generates trunk motion data 123 but is not paired with the first device 10 and cannot transmit the trunk motion data 123 to the first device 10 ("NO" in step S105 of FIG. 7), the trunk motion data 123 may be transmitted to the first device 10 when pairing is subsequently established. In this case, steps S210, S216, and S217 of FIG. 9 may be performed when the smartphone 10 receives the transmitted trunk motion data 123. Also, steps S210 and S215 in FIG. 9 may be performed during exercise, and the processes related to determining the arm swing state in steps S216 and S217 may be performed after exercise (for example, for post-exercise analysis).

[0083] <Modification> The first device 10 may be attached to the leg (for example, the ankle or thigh) of the user U. When the first device 10 is attached to the leg, the first device 10 acquires combined leg motion data (first information) relating to a combined motion state (first state) of the leg on which the motion state of the trunk B is superimposed. In this case, too, by performing processing similar to that in the above embodiment, it is possible to generate leg motion data (third information) relating to an independent motion state of the leg based on the trunk B, based on the difference between the combined leg motion data and the trunk motion data 123. Furthermore, by attaching the first device 10 to each of the arm and leg, it is also possible to identify the independent motion state of the arm and the independent motion state of the leg.

[0084] <Effects> As described above, the first device 10 as an information processing device according to this embodiment includes a CPU 11 as a processing unit. The CPU 11 acquires composite arm motion data 122 (first information) relating to a composite motion state (first motion state) of at least one of the arms and legs of the user U, on which a motion state of the trunk of the user U performing an exercise is superimposed, acquires trunk motion data 123 (second information) relating to a motion state (second motion state) of the trunk of the user U performing an exercise, and generates arm motion data 124 (third information) relating to an independent motion state (third motion state) of at least one of the arms and legs of the user U based on the composite arm motion data 122 and the trunk motion data 123 (or trunk motion history data 125). This allows for more accurate determination of the individual motion state of at least one of the arms and legs compared to conventional techniques that determine the combined motion state of the arms or legs using only an electronic device worn on the arm. Furthermore, since the motion state of the user U's own torso can be subtracted from the combined motion state of the arms, the individual motion state of the arms can be determined with higher accuracy compared to methods that subtract the motion state of another person's torso. Therefore, for example, the arm swing form and leg swing form during running can be analyzed more accurately, leading to more effective form improvements, etc.

[0085] Furthermore, the combined arm motion data 122 may be data relating to a combined motion state of at least one of the arms and legs of the user U during a first period, and the trunk motion data 123 may be data relating to a motion state of the trunk of the user U during the first period. This allows the real-time motion state of the trunk when the combined arm motion data 122 is acquired to be subtracted from the combined motion state of the arm. This allows the individual motion state of the arm to be identified more accurately.

[0086] Furthermore, the composite arm motion data 122 is generated based on the detection results of the composite motion state at each of the plurality of first detection times, and the trunk motion data 123 is generated based on the detection results of the trunk motion state at each of the plurality of second detection times synchronized with the plurality of first detection times. This makes it possible to subtract the trunk motion state at each time point during the motion from the composite motion state of the arm, thereby more accurately identifying the individual motion state of the arm.

[0087] The combined arm motion data 122 may be data relating to the combined motion state of at least one of the user U's arms and legs during a first period, and the trunk motion history data 125 may be data relating to the motion state of the user U's trunk during a second period prior to the first period. This allows the user U's own trunk motion state to be subtracted from the combined motion state of the arm, thereby enabling more accurate determination of the arm's independent motion state compared to conventional techniques that determine the combined motion state of the arm or leg using only an electronic device worn on the arm. Furthermore, because past trunk motion history data 125 is used, there is no need to determine the trunk motion state in real time to generate trunk motion data 123 when acquiring the combined arm motion data 122. Therefore, the arm's independent motion state can be determined even when only the first device 10 is worn. Furthermore, the arm's independent motion state can be determined even when the communication connection between the first device 10 and the second device 20 is disconnected and trunk motion data 123 cannot be acquired from the second device 20.

[0088] Furthermore, the exercise involves movement of the user U, and the CPU 11 acquires trunk motion history data 125 relating to the trunk motion state in each of a plurality of sections divided into sections of the movement path after the movement has started in the second period, identifies to which of the plurality of sections the movement distance of the user U after the movement has started in the first period belongs, and generates arm motion data 124 based on the combined arm motion data 122 and the trunk motion history data 125 corresponding to the identified section. As a result, even if the running form (torso motion state) changes due to fatigue or the like depending on the running distance, the trunk motion state reflecting the change can be subtracted from the combined motion state of the arm. This makes it possible to more accurately identify the individual motion state of the arm.

[0089] Furthermore, when the movement distance of the user U in the first period is longer than the total of the multiple sections, the CPU 11 generates the arm movement data 124 based on the combined arm movement data 122 and the trunk movement history data 125 corresponding to the last section of the multiple sections. This makes it possible to generate the arm movement data 124 using the trunk movement history data 125 of a movement state closest to the trunk movement state for the current movement distance. Therefore, even when there is no section equivalent to the current movement distance, it is possible to identify the individual movement state of the arm as accurately as possible.

[0090] Furthermore, at least one of the arms and legs of the user U performing an exercise performs a periodic movement, and the combined arm movement data 122 is information relating to a combined movement state in one movement cycle T (one cycle) of the periodic movements, and the trunk movement data 123 is information relating to the movement state of the trunk B in the movement cycle T. This makes it possible to identify the independent movement state of the arm in the movement cycle T.

[0091] Furthermore, first device 10 is worn on the arm or leg of user U and includes first sensor unit 13 as a detection unit that detects the motion state of the device itself, and CPU 11 generates combined arm motion data 122 based on the detection result of first sensor unit 13. As a result, first device 10 worn on the arm can generate arm motion data 124 and identify the independent motion state of the arm.

[0092] The information processing system 1 according to this embodiment includes a first device 10 attached to at least one of a user U's arm and leg and having a first sensor unit 13 for detecting the motion state of the device itself, a second device 20 attached to the user U's torso and having a second sensor unit 23 for detecting the motion state of the device itself, and a CPU 11 as a processing unit. The CPU 11 acquires combined arm motion data 122 relating to a combined motion state of at least one of the user U's arms and legs, generated based on the detection results of the first sensor unit 13 and superimposed with the motion state of the user U's torso while exercising, acquires trunk motion data 123 relating to the motion state of the user U's torso while exercising, generated based on the detection results of the second sensor unit 23, and generates arm motion data 124 relating to an independent motion state of at least one of the user U's arms and legs based on the combined arm motion data 122 and the trunk motion data 123. This allows for more accurate identification of an independent motion state of at least one of the arms and legs, compared to conventional techniques that identify a combined motion state of an arm or leg using only an electronic device attached to the arm.

[0093] Furthermore, the program 121 according to this embodiment causes the CPU 11 as a computer to realize the following functions: acquire composite arm motion data 122 relating to a composite motion state of at least one of the arms and legs of the user U, on which the motion state of the torso of the user U performing an exercise is superimposed; acquire trunk motion data 123 relating to the motion state of the torso of the user U performing an exercise; and generate arm motion data 124 relating to an independent motion state of at least one of the arms and legs of the user U based on the composite arm motion data 122 and the trunk motion data 123. This makes it possible to more accurately identify the independent motion state of at least one of the arms and legs, compared to conventional techniques that identify the composite motion state of an arm or leg using only an electronic device worn on the arm.

[0094] Furthermore, the information processing method by the CPU 11 as a computer according to this embodiment includes the steps of acquiring composite arm motion data 122 relating to a composite motion state of at least one of the arms and legs of the user U, on which the motion state of the torso of the user U performing an exercise is superimposed, acquiring the composite arm motion data 122 relating to the motion state of the torso of the user U performing an exercise, and generating arm motion data 124 relating to an independent motion state of at least one of the arms and legs of the user U based on the composite arm motion data 122 and the torso motion data 123. This makes it possible to more accurately identify the independent motion state of at least one of the arms and legs compared to conventional techniques that identify the composite motion state of the arms or legs using only an electronic device worn on the arms.

[0095] <Other> The above-described embodiments are merely examples of the information processing device, information processing system, program, and information processing method according to the present invention, and the present invention is not limited to these. For example, in the above embodiment, the arm motion data 124 serving as the third information is generated based on the difference between the combined arm motion data 122 and the trunk motion data 123 (or the trunk motion history data 125). However, the third information may be generated by a method that does not use the difference between the combined arm motion data 122 and the trunk motion data 123 (or the trunk motion history data 125). For example, the combined arm motion data 122 and the trunk motion data 123 (or the trunk motion history data 125) may be input to a classifier generated by supervised machine learning, and the classifier may output third information relating to the independent motion state of the arm. In this case, the third information may be, for example, a determination result of the arm swing state (evaluation data relating to the evaluation level of the arm swing). That is, the classifier generated by machine learning may be used to directly obtain the determination result of the arm swing state as the third information from the combined arm motion data 122 and the trunk motion data 123 (or the trunk motion history data 125) without calculating the difference therebetween (without passing through the arm motion data 124). The classifier is not particularly limited, but may be, for example, a neural network or a support vector machine.

[0096] Furthermore, the first device 10 may have multiple processors (e.g., multiple CPUs), and the multiple processes executed by the CPU 11 of the first device 10 in the above embodiment may be executed by the multiple processors. In this case, the multiple processors correspond to "processing units." Furthermore, the second device 20 may have multiple processors (e.g., multiple CPUs), and the multiple processes executed by the CPU 21 of the second device 20 in the above embodiment may be executed by the multiple processors. In these cases, the multiple processors may be involved in a common process, or the multiple processors may independently execute different processes in parallel.

[0097] Alternatively, an external device (e.g., a smartphone or a PC (Personal Computer)) separate from the first device 10 and the second device 20 may acquire the combined arm motion data 122 from the first device 10, acquire the trunk motion data 123 from the second device 20, and generate arm motion data 124 based on the acquired combined arm motion data 122 and trunk motion data 123. In this case, the external device corresponds to an "information processing device." Such an external device allows the motion state of the user U to be analyzed retrospectively based on the arm motion data 124.

[0098] Alternatively, the second device 20 may receive the combined arm motion data 122 from the first device 10, and the CPU 21 of the second device 20 may generate the arm motion data 124 based on the combined arm motion data 122 and the trunk motion data 123. In this case, the second device 20 corresponds to the "information processing device."

[0099] Furthermore, first sensor unit 13 is not limited to the configuration exemplified in the above embodiment, as long as it is capable of detecting the motion state of first device 10. Furthermore, second sensor unit 23 is not limited to the configuration exemplified in the above embodiment, as long as it is capable of detecting the motion state of second device 20. For example, if a triaxial geomagnetic sensor 133 (233) is provided, this may be used as a magnetic gyro instead of a triaxial angular velocity sensor to detect the magnitude of angular velocity around each axis.

[0100] In addition, in the above embodiment, an example has been described in which the combined arm motion data 122, torso motion data 123, and arm motion data 124 are generated based on measurement data for a motion cycle T, but this is not limited to this, and the combined arm motion data 122, torso motion data 123, and arm motion data 124 may be generated based on measurement data corresponding to any period.

[0101] In the above description, an example has been disclosed in which memory 12 is used as a computer-readable medium for the program according to the present invention, but the present invention is not limited to this example. Other computer-readable media may be used, such as information recording media including HDDs, SSDs, flash memories, and CD-ROMs. Furthermore, a carrier wave may also be used as a medium for providing data for the program according to the present invention via a communication line.

[0102] Furthermore, it goes without saying that the detailed configurations and operations of the components of the first device 10 and the second device 20 in the above embodiment can be modified as appropriate without departing from the spirit of the present invention.

[0103] Although the embodiments of the present invention have been described, the scope of the present invention is not limited to the above-described embodiments, but includes the scope of the invention described in the claims and its equivalents. The inventions described in the claims originally attached to this application are as follows. The claim numbers described in the appendix are the same as those of the claims originally attached to this application. [Note] <Claim 1> a processing unit; The processing unit acquiring first information relating to a first motion state of at least one of an arm and a leg of a user, on which a motion state of a trunk of the user performing an exercise is superimposed; acquiring second information relating to a second motion state of the trunk of the user performing the exercise; generating third information related to a third motion state of at least one of the arm and the leg of the user based on the first information and the second information; Information processing device. <Claim 2> the first information is information regarding the first motion state of at least one of the arm and the leg of the user during a first period; The second information is information related to the second motion state of the trunk of the user during the first period. The information processing device according to claim 1. <Claim 3> the first information is generated based on detection results of the first motion state at each of a plurality of first detection timings, the second information is generated based on detection results of the second motion state at each of a plurality of second detection timings synchronized with the plurality of first detection timings; The information processing device according to claim 2. <Claim 4> the first information is information regarding the first motion state of at least one of the arm and the leg of the user during a first period; The second information is information related to the second motion state of the trunk of the user in a second period before the first period. The information processing device according to claim 1. <Claim 5> the exercise involves movement of the user; The processing unit acquiring the second information relating to the second motion state in each of a plurality of sections obtained by dividing the movement route after the movement has started during the second period; identifying to which of the plurality of sections a travel distance of the user after the movement has started in the first period belongs, and generating the third information based on the first information and the second information corresponding to the identified section. The information processing device according to claim 4. <Claim 6> The processing unit If the moving distance of the user in the first period is longer than the entirety of the plurality of sections, the third information is generated based on the first information and the second information corresponding to the last section of the plurality of sections. The information processing device according to claim 5. <Claim 7> the at least one of the arms and legs of the user performing the exercise undergoes a periodic motion; the first information is information relating to the first motion state in one cycle of the periodic motion, the second information is information relating to the second motion state in the one cycle. 7. The information processing device according to claim 1. <Claim 8> a detection unit attached to an arm or a leg of the user to detect an exercise state of the device itself; the processing unit generates the first information based on the detection result of the detection unit. 8. The information processing device according to claim 1. <Claim 9> a first device attached to at least one of a user's arm and leg, the first device having a first detection unit that detects the motion state of the device itself; a second device attached to the user's torso and having a second detection unit that detects the motion state of the device itself; a processing unit; Equipped with The processing unit acquire first information related to the first motion state of at least one of the arms and legs of the user, the first information being generated based on the detection result of the first detection unit and having a motion state of the trunk of the user performing exercise superimposed thereon; acquiring second information related to a second motion state of the trunk of the user performing the exercise, the second information being generated based on the detection result of the second detection unit; generating third information related to a third motion state of at least one of the arm and the leg of the user based on the first information and the second information; Information processing system. <Claim 10> On the computer, a function of acquiring first information relating to a first motion state of at least one of the user's arms and legs, on which a motion state of the user's trunk is superimposed; a function of acquiring second information relating to a second motion state of the trunk of the user performing the exercise; a function of generating third information relating to a third motion state of at least one of the user's arm and leg based on the first information and the second information; A program that makes this happen. <Claim 11> An information processing method by a computer, comprising: acquiring first information relating to a first motion state of at least one of an arm and a leg of a user, the first information being superimposed with a motion state of a trunk of the user performing exercise; acquiring second information relating to a second motion state of the trunk of the user performing the exercise; generating third information related to a third motion state of at least one of the arm and the leg of the user based on the first information and the second information; An information processing method, including: [Explanation of symbols]

[0104] 1. Information Processing Systems 10. First device (information processing device) 11 CPU (processing unit) 12 Memory 121 Programs 122 Combined arm movement data (first information) 123 Torso movement data (second information) 124 Arm movement data (third information) 125 Torso movement history data (second information) 13 First sensor unit (first detection unit, detection unit) 131 3-axis acceleration sensor 132 3-axis gyro sensor 133 3-axis geomagnetic sensor 14. Radio Communication Section 15 Satellite radio wave receiving processing unit 16 Display section 17 Operation reception section 18 Timing section 20 Second device 21 CPU 22 Memory 221 Program 23 Second sensor unit (second detection unit) 231 3-axis acceleration sensor 232 3-axis gyro sensor 233 3-axis geomagnetic sensor 24 Radio Communication Department 25 Satellite radio wave receiving processing unit A Arm B Torso S1~S3 section T motion period U User

Claims

1. a processing unit; The processing unit acquiring first information relating to a first motion state of at least one of an arm and a leg of a user during a first period, the first information having a motion state of a trunk of the user performing a moving exercise superimposed thereon; acquire second information relating to a second motion state of the trunk of the user performing the exercise in each of a plurality of sections obtained by dividing the movement path after the movement has started in a second period prior to the first period; identifying to which of the plurality of sections a distance traveled by the user after the movement has started in the first period belongs, and generating third information relating to a third motion state of at least one of an arm and a leg of the user based on the first information and the second information corresponding to the identified section. Information processing device.

2. The processing unit If the travel distance of the user in the first period is longer than the entirety of the plurality of sections, the third information is generated based on the first information and the second information corresponding to the last section of the plurality of sections. The information processing device according to claim 1 .

3. the at least one of the arms and legs of the user performing the exercise undergoes a periodic motion; the first information is information related to the first motion state in one period of the periodic motion, the second information is information related to the second motion state in the one cycle.

3. The information processing device according to claim 1 or 2.

4. a detection unit attached to an arm or a leg of the user to detect an exercise state of the device itself; the processing unit generates the first information based on a detection result of the detection unit. The information processing device according to any one of claims 1 to 3.

5. a first device attached to at least one of a user's arm and a user's leg, the first device having a first detection unit that detects an exercise state of the device; a second device attached to the user's torso and having a second detection unit that detects the motion state of the device itself; a processing unit, The processing unit acquire first information related to a first motion state of at least one of an arm and a leg of the user during a first period, the first information being generated based on a detection result of the first detection unit and having a motion state of a trunk of the user performing a motion involving movement superimposed thereon; acquire second information related to a second motion state of the trunk of the user performing the exercise in each of a plurality of sections that divide a movement path after the movement has started in a second period that is prior to the first period, the second information being generated based on a detection result of the second detection unit; identifying to which of the plurality of sections a distance traveled by the user after the movement has started in the first period belongs, and generating third information relating to a third motion state of at least one of an arm and a leg of the user based on the first information and the second information corresponding to the identified section. Information processing system.

6. On the computer, a function of acquiring first information relating to a first motion state of at least one of the arms and legs of a user during a first period, the first information being superimposed with a motion state of the user's trunk while the user is performing an exercise involving movement; a function of acquiring second information relating to a second motion state of the trunk of the user performing the exercise in each of a plurality of sections obtained by dividing a movement path after the movement has started in a second period prior to the first period; a function of identifying to which of the plurality of sections a movement distance of the user after the movement has started in the first period belongs, and generating third information relating to a third motion state of at least one of the arms and legs of the user based on the first information and the second information corresponding to the identified section; A program that makes this happen.

7. An information processing method by a computer, comprising: acquiring first information relating to a first motion state of at least one of an arm and a leg of a user during a first period, the first information having a motion state of a trunk of the user performing a movement-related exercise superimposed thereon; acquiring second information related to a second motion state of the trunk of the user performing the exercise in each of a plurality of sections obtained by dividing a movement path after the movement has started in a second period that is earlier than the first period; identifying to which of the plurality of sections a movement distance of the user after the movement has started in the first period belongs, and generating third information related to a third motion state of at least one of an arm and a leg of the user based on the first information and the second information corresponding to the identified section; An information processing method, including:

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