Walking assistance wearable device, control method and program

Abstract

To provide a wearable device for assisting smooth walking.SOLUTION: A walking assistance wearable device includes a sensor capable of measuring a state of a user, and output means capable of outputting visual content or auditory content to the user, where the output means outputs the visual content or auditory content in different output modes based on sensor data acquired by the sensor. The walking assistance wearable device can thus stimulate the brain with the visual content or auditory content and assist the user who has difficulty in walking to walk smoothly.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a walking assistance wearable device, a control method, and a program.

Background Art

[0002] Conventionally, a method of assisting a user's behavior, such as complementing information to a user wearing a wearable device by the wearable device that projects an extended reality space, has become widespread.

[0003] For example, Patent Document 1 discloses a mechanism for guiding a user to avoid a collision with an object in a physical space within an extended reality space projected by a device worn by the user.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] As described above, the technique of Patent Document 1 provides a mechanism for assisting user behavior in the real space. However, it is difficult to provide a method for improving such disorders for users with motor disorders such as walking disorders, such as the elderly and patients suffering from Parkinson's disease.

[0006] Therefore, the present invention provides a method for assisting smooth walking of a user.

Means for Solving the Problems

[0007] In one embodiment of the present invention, there is provided a walking assistance wearable device including output means capable of outputting visual content or auditory content to a user, wherein the output means adds display of the visual content at a predetermined speed and / or outputs the auditory content at a predetermined tempo.

Effects of the Invention

[0008] According to the present invention, by recognizing visual content or auditory content, it becomes easier for the user to walk.

Brief Description of the Drawings

[0009]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Modes for Carrying Out the Invention

[0010] Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that the examples described below do not unduly limit the content of the present invention described in the claims. Also, not all of the configurations described below are essential constituent elements of the present invention.

Examples

[0011] <Configuration> The wearable device according to this embodiment includes devices that can be worn on the user's body, such as smart glasses and head-mounted displays. Here, instead of the wearable device, or connected to the wearable device by short-range communication or the like, a smartphone, tablet, mobile terminal, or other information terminal can also be applied as the user terminal, and a general-purpose computer such as a workstation or personal computer can also be applied.

[0012] FIG. 1 is an explanatory diagram of a glasses-type wearable device according to this embodiment. As an example, the wearable device includes a projection device 1 and can project an image into the field of view of the wearing user. The projection device 1 projects the image content generated and controlled by the projector microcomputer 2. Further, the wearable device includes a camera 4 and can acquire image data. The wearable device of this embodiment includes various sensors. As an example, it can include an infrared sensor 5, a gyroscope 6, an acceleration sensor 7, a magnetometer 8, a GPS (Global Positioning System), etc., but it is not necessary to mount all of these sensors, and other sensors can also be mounted as needed. The sensor data acquired by each sensor is relayed by the sensor microcomputer 9. The wearable device includes a CPU 10 which is an operating system, expands various programs stored in the ROM into the RAM to execute the programs, and comprehensively controls each component to operate the wearable device. Further, as needed, a dimming lens 3, a rechargeable battery 11, a heat sink 12, a speaker 13 for outputting sound, a microphone 14 for collecting the user's speech, a frame size adjustment system 15, a wireless transceiver 16, etc., elements that can be mounted on the wearable device can be freely provided. Also, these elements do not necessarily have to be mounted on a single device and may be mounted on separate devices as appropriate. For example, a microphone and headphones can also be provided as separate independent devices from the glasses-type wearable device.

[0013] As the display (display unit), an optically transmissive type and a non-transmissive type display can be considered. In this embodiment, in order to provide an extended reality space, a transmissive display panel is exemplified. On the display panel, a left-eye image and a right-eye image are displayed, and by utilizing the parallax of both eyes, a stereoscopic image can be provided to the user. If the left-eye image and the right-eye image can be displayed, it is also possible to separately provide a left-eye display and a right-eye display, or it is also possible to provide an integrated display for both the left eye and the right eye.

[0014] The camera 4 images the real space and is usually provided to be aligned with the line-of-sight direction of the user wearing the wearable device. The camera 4 can also function as a sensor (for example, a two-dimensional / three-dimensional camera, ultrasonic, depth, IR sensor, etc.) that captures the gestures of the user. The image of the real space captured by the camera 4 is synthesized with the image data as visual content such as 3D objects, and the image of the visual content is superimposed and displayed on the image of the real space. The CPU can include, for example, a GPU (Graphics Processing Unit) and can execute arithmetic processing related to image processing.

[0015] The GPS measures the current position information. The GPS can receive positioning radio waves from GPS satellites and measure the latitude and longitude information as the absolute position. Also, it can receive communication radio waves from a plurality of base stations for mobile phones and measure the current position information by the multi-base station positioning function. Further, the acceleration sensor 7 detects acceleration (change in velocity per unit time).

[0016] The acceleration sensor 7 can detect accelerations in three axial directions (x, y, z directions). For example, if the front-back direction is the x-axis, the left-right direction is the y-axis, and the up-down direction is the z-axis, with the forward direction being the positive x-axis direction, the left direction being the positive y-axis direction, and the downward direction being the z-axis direction, the acceleration sensor 7 detects the accelerations in each direction and also detects the rotation angle around the x-axis (roll angle), the rotation angle around the y-axis (pitch angle), and the rotation angle around the z-axis (yaw angle).

[0017] The wearable device according to this embodiment can be provided with a life information acquisition means capable of acquiring the user's life information. The life information may include, but is not limited to, pulse, heart rate, blood pressure, blood oxygen saturation, body temperature, information related to sleep (such as sleep time, when falling asleep and waking up, quality of sleep such as REM sleep and non-REM sleep), information related to activity level (such as energy consumption, amount and time of exercise with what kind of load, rest time, etc.), information related to diet (such as what, when, and how much was eaten, intake status of each nutrient, etc.), and the like, as long as it is information useful for estimating the user's condition. The life information acquisition means may be a sensor or the like capable of objectively measuring these life information, or may acquire these information by input from the user or other users who know the user's situation. In the case of a sensor, it may be provided in a glasses-type wearable device, or may be provided in a wearable device worn on other parts such as the arm or leg. Also, the input means by the user may be provided in the wearable device, or may be provided in another device such as a smartphone.

[0018] In addition, it is assumed that the user may have disabilities in hands and feet. In such a case, the wearable terminal can receive voice commands via a microphone built into the wearable device or other user terminals, and can also send instructions through visual content and auditory content based on the voice commands.

[0019] Figure 2 is a block diagram showing the configuration of the software module according to this embodiment. All of the software modules described below may be provided in a single wearable device, or some of them may be provided in other wearable devices, user terminals such as smartphones and personal computers, or server devices, and may jointly perform information processing.

[0020] The user data storage unit 31 stores data related to users such as the elderly and patients suffering from Parkinson's disease, including the basic information of the user (gender, age, nationality, place of origin, disease information, health information, hobbies, preferences, etc.) and environmental information obtained from wearable devices, etc. (image data related to the physical space imaged by a camera, sound collection data such as the user's voice and environmental sounds, various data obtained from sensors, etc.).

[0021] The content data storage unit 32 can include auditory content data such as music and voices that can be recognized by the user's hearing, and visual content data such as visible objects (including 2D, 3D, and videos, etc.). The auditory content data can also include music data that matches the user's hobbies and preferences, and music data specified by the user. The visual content data can include image data related to landmarks for walking assistance described later, image data related to objects such as desks and chairs, and image data related to characters, etc.

[0022] In addition, it can also be provided with a storage unit that stores a learning model that has been machine-learned using image data as input data in order to perform image analysis for identifying objects located in the physical space around the user based on the environmental information obtained from the wearable device.

[0023] The sensor data acquisition unit 21 controls various sensors provided in the wearable device and acquires data.

[0024] The image data acquisition unit 22 receives image data related to the physical space around the user captured by the camera 4 mounted on the wearable device.

[0025] The content data control unit 23 can determine the output mode of the content and instruct the content output means such as a projection device and a speaker. The output mode can include the content of the output content and the way of its output (including the additional display speed, BPM, etc. described later).

[0026] The content data control unit 23 analyzes the received image data of the physical space and identifies the objects located in the physical space by image recognition. As the image recognition technology, the above machine learning model is applied, and based on the learning model that has been machine-learned based on content data such as the image group input in the past and the output object data, the image is recognized, and the object data stored in the content data storage unit 32 can be determined. For example, when there are a sofa, a desk, a bookshelf, etc. in the physical space, these can be identified as objects by image recognition.

[0027] Furthermore, as details of the analysis process, the content data control unit 23 outputs the corresponding object data as content such as image data related to the objects stored in the content data storage unit 32 based on the identified objects, and thereby executes the generation of visual content arranged in the physical space as an extended reality space. For example, target objects or obstacles such as a sofa, a desk, and a bookshelf that the user should pay attention to can be generated as visual content.

[0028] Also, as details of the analysis process, the content data control unit 23 can adjust the BPM (tempo) of the music data as the auditory content output to the user terminal. More specifically, the walking speed of the user is estimated based on the image of the camera 4 and the acceleration data detected by the acceleration sensor 7, and the process of adjusting the BPM of the music data according to the user's hobbies and preferences is executed. The content data control unit 23 can generate the music data with the adjusted BPM as the auditory content. The generated auditory content may be output from the speaker of the wearable device, or may be output using other devices such as earphones or smartphones as the output means.

[0029] By outputting music that matches the user's hobbies and preferences and has been adjusted for BPM, it is possible to guide the user to accelerate their walking, or by increasing the frequency of the beats, promote the production of dopamine in the user's brain and accelerate the user's walking speed. Also, as auditory content, it is possible to output voice data for giving the user instructions necessary for walking and exercise.

[0030] The content data control unit 23 causes an image content to be displayed in the user's field of view. As an example, a mark can be displayed on the floor surface in front of the user's feet. The content data control unit 23 may detect the user's feet based on the image data acquired by the camera 4. Also, when a foot region is detected within the user's field of view, it may be further possible to detect a floor surface region. The method for detecting the foot region and the floor surface region can be realized by using existing image analysis techniques. The mark is typically a line extending in a direction perpendicular to the user's direction of travel, and as shown in FIG. 3, it is preferable to display a plurality of lines at equal intervals, but it is not limited to this. By displaying such a mark, it is possible to help the user step out their foot more easily and walk smoothly. In particular, for example, it is expected to have an effect of improving the freezing gait symptom, which is one of the symptoms of walking disorder in Parkinson's disease patients, where it is difficult to take the first step when starting to walk.

[0031] The content data control unit 23 can control the display of image content so as to appropriately add and display marks. FIG. 3 shows an example of the additional display of image content. First, as shown in FIG. 3(a), marks (a plurality of lines in the example of FIG. 3) are displayed at equal intervals in front of the user's feet. Next, as shown in FIGS. 3(b) and (c), the additional display of image content can be performed so as to increase the number of marks. The additional display can be repeated, for example, as shown in FIG. 3(c), after adding the display up to a certain range, then returning only to the display in a narrower range closer to the front as shown in FIG. 3(a), such as from (a) to (b), from (b) to (c), and from (c) to (a). The additional display of content data can be performed, for example, after a certain period of time has elapsed. Whether the user is walking or standing still, the marks at the feet always change due to the additional display, so a stimulus is transmitted to the brain, dopamine production is promoted, and it becomes easier to walk.

[0032] Also, as shown in FIG. 4, the additional display of content data can also be performed in accordance with the user's forward movement. When the user moves forward by walking from the state of FIG. 4(a), the content data control unit 23 senses the user's forward movement from the information of the image data of the camera 4 and the sensor data such as the acceleration sensor 7, and can perform an additional display so as to always display marks in a certain range in front of the user's feet as shown in FIGS. 4(b) and (c). In the examples of FIGS. 3 and 4, for example, the content data control unit 23 can detect the actual walking speed of the user from the sensor data such as the acceleration sensor 7, and based on the walking speed, determine and output the additional display speed of the content data. The additional display speed may be the same as the actual walking speed, or may be a predetermined degree faster or slower than the actual walking speed. As a result, the user can always see marks appearing one after another at their feet, which transmits a stimulus to the brain and makes it easier to walk.

[0033] In this embodiment, the walking intention determination unit 24 can be further provided. The walking intention determination unit 24 determines the user's intention regarding walking, that is, the user's wishes such as "want to walk fast", "want to walk slowly", "want to stop", and "want to start walking". Then, the content data control unit 23 can control the mode of additional display according to the user's intention regarding walking. For example, when the user's intention regarding walking is "want to walk fast", the content data control unit 23 can increase the tracking speed of the landmark to be displayed in the user's field of view compared to the current speed. For example, by performing additional display of the landmark at a speed that is a certain degree faster than the user's actual walking speed, it becomes easier for the user to walk fast as desired. On the other hand, when the user's intention regarding walking is "want to walk slowly", the tracking speed of the landmark can be made slower than the current speed. Also, when the user's intention regarding walking is "want to stop", the additional display of the content may be stopped.

[0034] The walking intention determination unit 24 can determine the user's intention regarding walking based on the sensor data acquired by various sensors. Specifically, the inclination of specific body parts such as the whole body or the head is measured by a gyroscope, an accelerometer, a magnetometer, etc., and when the degree of slouching is large or when there is a forward lean, it can be determined that the user "wants to walk fast". Also, the intention regarding walking may be determined based on the living information such as the pulse, blood pressure, brain waves, and the amount of neurotransmitters acquired by the living information acquisition means. That is, for example, as an example, when the pulse and blood pressure are high or when the release amount of neurotransmitters such as dopamine is large, and when it can be determined from information such as brain waves that the user is excited, it is determined that the user "wants to walk fast", and conversely, when the pulse and blood pressure are low, when the release amount of neurotransmitters such as dopamine is small, and when it can be determined from information such as brain waves that the user is relaxed, it can be determined that the user "wants to walk slowly", but the method for determining the user's intention regarding walking is not limited to this.

[0035] In addition, the intention determination unit 24 can also determine the user's walking intention through machine learning. That is, it may store a learning model that has been machine-learned using sensor data such as body inclination and life information as input data, and estimate the walking intention from these input data. The estimation method by machine learning can be realized by appropriately using existing technologies.

[0036] Furthermore, it may be determined based on information regarding the walking intention input by the user. For example, the user can directly or indirectly input the user's intentions such as "I'm in good condition today, so I want to walk fast", "I'm in a hurry, so I want to walk fast", "I don't want to fall, so I want to walk slowly" into the device. Also, others other than the user may input this information. For example, a nurse, a helper, a physical therapist, or other persons who assist the user's walking, or the user's family members, etc. can also input it.

[0037] In this way, by changing the output mode of the content data according to the user's walking intention, even if the user's actual walking state at that time is contrary to the user's intention, it is possible to provide appropriate walking assistance.

[0038] Also, in this embodiment, a condition prediction unit 25 can be further provided. The condition prediction unit 25 predicts the condition, that is, states such as "in good condition", "in bad condition", "tired·prone to fatigue", "feeling light", etc. based on the user's life information. Then, the content data control unit 23 can determine the output mode of the content based on the predicted condition. That is, for example, when in a good condition such as "in good condition" or "feeling light", the additional display speed can be increased, and when in a bad condition such as "in bad condition" or "tired·prone to fatigue", the additional display speed can be decreased, etc.

[0039] The condition prediction unit 25 can predict the condition based on neurotransmitters. Typical neurotransmitters include, for example, dopamine, serotonin, melatonin, oxytocin, etc. These neurotransmitters are directly related to the user's condition and may affect the walking speed. For example, in patients with Parkinson's disease, when the secretion amount of dopamine in the brain is low, symptoms such as tremors and difficulty in movement become more severe. However, it is known that the secretion amount of dopamine increases after a good night's sleep, after rest, after a nap, or after light exercise. It is also known that dopamine is more likely to be produced by ingesting specific nutrients. Therefore, the condition prediction unit 25 estimates the secretion amount of dopamine based on information such as the user's sleep, rest, activity level, exercise amount, and nutrient intake obtained by the life information acquisition means, and predicts the condition.

[0040] A specific example of condition prediction will be described below. As the life information acquisition means, the acceleration data obtained from the acceleration sensor is used to estimate the user's sleep time and the quality of sleep (depth of sleep, number of interruptions, snoring, etc.). In addition to or instead of the acceleration sensor, known means such as a heart rate monitor or a microphone may be used to determine the sleep time and the quality of sleep. Based on the estimated sleep time and the quality of sleep, a comprehensive evaluation regarding sleep can be performed. For example, it can be determined as A when the sleep time is longer than a certain time and the quality of sleep is also good, as B when the sleep time is longer than a certain time but the quality of sleep is not good, and when the sleep time is less than a certain time but the quality of sleep is good, and as C when the sleep time is less than a certain time and the quality of sleep is not good. The condition prediction unit 25 can determine that "feeling good" when the comprehensive evaluation regarding the previous day's sleep is A, and "feeling bad" when it is C.

[0041] As another example of condition prediction, as a life information acquisition means, acceleration data obtained from an acceleration sensor is used to estimate the degree of user fatigue. For example, the activity amount is estimated by analyzing the acceleration data during a period retroactively from the current time by a certain period. If the activity amount is equal to or greater than a reference value, it is determined that the user is "tired", and if the activity amount is less than the reference value, it is determined that the user is "not tired". As the period for estimating the degree of fatigue, data within 2 to 3 hours from the current time can be used when evaluating short-term fatigue, and data within 1 day to 1 week can be used for evaluation when evaluating long-term fatigue.

[0042] Further, the condition prediction unit 25 may perform prediction by combining data related to sleep and data related to the degree of fatigue. For example, the reference value for determining whether the user is tired from the activity amount can be changed according to the evaluation of the previous day's sleep. Specifically, if the evaluation of the previous day's sleep is good, even if the activity amount in the recent few hours is more than usual, it is determined that the user is "not tired". On the contrary, if the evaluation of sleep is not good, even if the activity amount is less than usual, it can be determined that the user is "tired".

[0043] The condition prediction unit 25 can also predict the user's condition by machine learning. That is, a learning model learned by machine learning with the user's sleep, rest, activity amount, exercise amount, nutrient intake amount, etc. as input data can be stored, and the condition can be predicted from these input data. The estimation method by machine learning can be realized by appropriately using existing technologies.

[0044] Since the content data control unit 23 can change the additional display speed of the content data according to the user's state predicted by the condition prediction unit 25, it is possible to perform appropriate walking assistance regardless of the user's awareness.

[0045] The image content output by the content data control unit 23 only needs to be a landmark that can assist the user's walking, and is not limited to the linear form shown in FIGS. 3 and 4. Also, as the mode of additional display of the image content, it is not limited to the mode in which the number of landmarks increases as shown in FIGS. 3 and 4, and changes such as increasing the display area of the landmark, darkening the color or brightness may also be acceptable.

[0046] So far, an example of outputting image content as content data has been described. However, in this embodiment, audio content can also be output. The audio content only needs to be content that can be perceived by the ear, such as mechanical sounds, voices of people or animals, songs, music, etc. The audio content can be output from the speaker of the wearable device, but is not limited thereto, and may be output from a device separate from the wearable device (for example, the user's smartphone).

[0047] The content data control unit 23 can select the audio content to be output based on the user data acquired in advance. The user data may be, for example, age, gender, nationality, ethnicity, place of origin, personality, favorite music genre, etc. The content data control unit 23 can select and output appropriate audio content from the content data storage unit 32 based on the user data.

[0048] The content data control unit 23 can change the BPM (tempo) of the audio content to be output according to the user's walking speed. For example, the actual walking speed of the user can be detected from sensor data such as the acceleration sensor 7 and the image data of the camera 4, and based on the walking speed, the BPM of the audio content data can be determined and output. Thereby, the user can listen to the audio content at a BPM that matches his / her walking speed, so that appropriate stimulation is transmitted to the brain and it becomes easier to walk.

[0049] In addition, the content data control unit 23 can also determine the BPM of the auditory content based on the determination result by the aforementioned orientation determination unit 24. That is, when the user's orientation regarding walking is "want to walk fast", the BPM can be increased. On the other hand, when the user's orientation regarding walking is "want to walk slowly", the BPM can be decreased. Also, when the user's orientation regarding walking is "want to stop", it may be possible to stop the output of the auditory content.

[0050] In addition, the content data control unit 23 can also determine the BPM of the auditory content based on the prediction result by the aforementioned condition prediction unit 25. That is, when in a good condition such as "feeling good" or "feeling light in body", the BPM can be increased, and when in a bad condition such as "feeling bad" or "tired·prone to fatigue", the BPM can be decreased and so on.

[0051] In this way, by outputting the auditory content, it is possible to stimulate the user's brain and assist the user's walking. As described above, through the actions of sound and image (video), the user's brain is stimulated to help generate dopamine in the brain, thereby restoring the connection of commands from the brain to the motor system and unfreezing the movement, so that the user's walking ability can be restored.

[0052] Figure 5 is a flowchart for explaining the content control method in this embodiment. This process can be executed by the CPU control unit of the wearable device, but it can also be executed by the control unit of a server terminal (not shown), or it can also be realized by their joint execution. Hereinafter, the case where the CPU of the wearable device executes will be described as an example.

[0053] First, the sensor data acquisition unit 21 acquires sensor data obtained by various sensors (S101). Also, the image data acquisition unit 22 acquires image data obtained by the camera 4 of the wearable device. The content data control unit 23 calculates the current walking speed of the user based on this sensor data or image data (S102).

[0054] Next, the intention determination unit 24 determines the user's intention regarding walking based on sensor data, input from the user, etc. (S103). Then, when the user's intention regarding walking is "want to walk fast" (S104 = Yes), the content data control unit 23 changes the control information of the content (S105). That is, when outputting image content, it changes to increase the additional display speed, and when outputting auditory content, it increases the BPM. Also, when the user's intention regarding walking is "want to walk slowly" (S104 = Yes), it can change to decrease the additional display speed of the image content and decrease the BPM of the auditory content.

[0055] On the other hand, when the user's intention regarding walking is "want to maintain the current walking speed" (S104 = No), since there is no need to change the output mode of the content, it returns to S101.

[0056] According to the output mode of the content data determined by the content data control unit 23, the content output means of the wearable device outputs the content (S106). That is, in the case of image content, a predetermined image content is displayed on the display of the wearable device, and in the case of auditory content, the auditory content is emitted from the speaker.

[0057] The acquisition of sensor information (S101) can be performed at any time while the user is wearing the wearable device. Therefore, by repeating the steps of S101 to S106, the output mode of the content can be adjusted even when the user's state changes.

[0058] Figure 6 is another example of a flowchart for explaining the content control method in this embodiment.

[0059] First, the sensor data acquisition unit 21 acquires sensor data obtained by various sensors (S201). Also, the image data acquisition unit 22 acquires image data obtained by the camera 4 of the wearable device. The content data control unit 23 calculates the current walking speed of the user based on this sensor data or image data (S202).

[0060] Also, the life information acquisition means acquires life information such as pulse, heart rate, blood pressure, blood oxygen saturation, body temperature, information related to sleep (sleep time, when falling asleep / waking up, quality of sleep such as REM sleep / non-REM sleep, etc.), information related to activity level (how much exercise with what load and for what time, rest time, etc.), and information related to diet (what, when, and how much eaten, intake status of each nutrient, etc.) from sensors or the user (S203).

[0061] The condition prediction unit 25 predicts the condition of the user based on the life information (S204). The content data control unit 23 determines the output mode of the content based on the predicted condition and generates control information (S205). For example, and according to the output mode of the content data determined by the content data control unit 23, the content output means of the wearable device outputs the content (S206).

[0062] According to the present invention, by outputting image content and auditory content in a manner suitable for the user's state, it is possible to assist the user to walk smoothly. The users targeted by the present invention are typically the elderly or patients with diseases accompanied by movement disorders such as Parkinson's disease, but it is effective for users who have some difficulty in walking.

Embodiment

[0063] This embodiment can measure and provide feedback on the walking assistance effect by content output, and other aspects are the same as those in Embodiment 1.

[0064] In this embodiment, as a software module, in addition to those described in Embodiment 1, a content evaluation unit 26 can be further provided. The content evaluation unit 26 evaluates the effect of the content based on the user's walking state when visual content and / or auditory content is output.

[0065] The content evaluation unit 26 can evaluate the effect of the content based on the walking speed. For example, the walking speed of the user is detected by sensor data such as the acceleration sensor 7 when a certain content is output. If the user is walking faster than a certain reference value, it can be evaluated that the content is effective. If the user is walking slower than the certain reference value, has stopped, or cannot start walking, it can be evaluated that the effect of the content is low. The reference value can be set uniformly or for each user. Also, instead of comparing with the reference value, when the walking speed at time t2 after content output is faster than the walking speed at time t1 before content output, it can be evaluated that the content is effective, and when it is slower, it can be evaluated that the content has no effect.

[0066] The content evaluation unit 26 can also evaluate the effect of the content based on life information. For example, the release amount of neurotransmitters such as dopamine is measured. If the release amount is more than a certain reference value, it can be evaluated that the content is effective. If it is less than the certain reference value, it can be evaluated that the effect is low. The reference value can be set uniformly or for each user. Also, instead of comparing with the reference value, when the release amount at time t2 after content output is more than the release amount at time t1 before content output, it can be evaluated that the content is effective, and when it is less, it can be evaluated that the content has no effect. Not limited to the release amount of neurotransmitters, the effect can be evaluated by other life information such as pulse, heart rate, blood pressure, and body temperature.

[0067] The content evaluation unit 26 can also evaluate the walking state based on inputs from the user or other users. After the content is output, it is possible to receive inputs of information regarding the evaluation of the content from the user or other users.

[0068] The content control unit 23 can change the output mode of the content based on the evaluation result of the content evaluation unit 26. When the evaluation of the content is low, the content being output can be changed. For example, in the case of auditory content, it may be changed to and output as something with a different sound type, melody, tempo, rhythm, etc., or the BPM may be changed. In the case of visual content, it may be changed to and output as something with a different type, color, shape, size of the content to be displayed, or the additional display speed may be changed. Also, whether to output auditory content or visual content, or the combination mode thereof, may be changed. For example, when the content evaluation is low when only visual content is being output, it can be switched to output auditory content, or both visual content and auditory content can be output simultaneously. The evaluation process by the content evaluation unit 26 and the process of changing the content can be performed at any time, and for example, it may be executed at regular intervals. Also, when the evaluation result by the content evaluation unit 26 is good, the content control unit 23 can continue to output the same content without changing the output mode of the content.

[0069] Also, the content evaluation unit 26 can generate evaluation information associating the content of the output content with the evaluation result, and store it in the storage unit. The content of the content to be stored as evaluation information can include information specifying visual content, information specifying auditory content, information regarding output modes such as additional display speed and BPM, date and time, information specifying the user, the user's living information at that time, etc. The said evaluation information may be stored in the storage unit of the wearable device, or may be transmitted and stored in a server device (not shown) or other user terminals, etc.

[0070] Based on the evaluation information, the content control unit 23 can determine the content to be output. By referring to the evaluation information stored in a wearable device, a server device, or the like, it is possible to select and output content with good evaluation. For example, when selecting content based on the past evaluation information of the user, it is possible to select content suitable for this user from the next time onwards. Also, when the evaluation information of other users is also stored in a server device or the like, it is possible to select content based on the evaluation information of other users. In that case, by extracting the evaluation information of other users having characteristics similar to the target user (such as gender, age, nationality, underlying disease, symptoms, etc.) and selecting the content with good evaluation results, it is possible to select content that is likely to be suitable for the target user.

[0071] The above-described embodiments are merely examples for facilitating the understanding of the present invention and are not for limiting the interpretation of the present invention. It goes without saying that the present invention can be changed and improved without departing from its gist, and equivalents thereof are included in the present invention.

Explanation of Reference Numerals

[0072] 1 Projection device 2 Microcomputer for projector 4 Camera 5 Infrared sensor 6 Gyroscope 7 Acceleration sensor

Claims

1. A walking assistance wearable device comprising an output means capable of outputting visual content and / or auditory content to a user, A walking assistance wearable device, characterized in that the output means adds display of the visual content at a predetermined speed and / or outputs the auditory content at a predetermined tempo.

2. Further comprising a sensor capable of measuring data related to the user's condition, The walking assistance wearable device according to claim 1 , wherein the output means determines an output mode of the visual content and / or the auditory content based on sensor data acquired by the sensor.

3. The walking assistance wearable device according to claim 2 , wherein the output means determines a speed at which to add display of the visual content based on a walking speed of the user.

4. The walking assistance wearable device according to claim 2 , wherein the output means determines a tempo of the auditory content based on a walking speed of the user.

5. The walking assistance wearable device according to claim 1 , wherein the output means determines an output mode of the visual content and / or the auditory content based on the user's walking inclination.

6. The walking assistance wearable device according to claim 5 , wherein the user's walking inclination is determined based on the user's posture.

7. The walking assistance wearable device according to claim 1 , wherein the output means determines an output mode of the visual content and / or the auditory content based on a condition of the user.

8. The walking assistance wearable device of claim 7 , wherein the condition relates to an amount of dopamine present in the user.

9. The walking assistance wearable device according to claim 7 , wherein the condition is predicted based on at least one of information on the user's sleep, diet, and activity level.

10. A walking assistance wearable device comprising an output means capable of outputting visual content and / or auditory content to a user, a content evaluation unit that evaluates an effect of the output visual content and / or audio content on walking assistance and generates evaluation information; A walking assistance wearable device, characterized in that the output means determines an output mode of the visual content and / or the auditory content based on the evaluation information.

11. The walking assistance wearable device according to claim 10 , wherein the evaluation information includes evaluation information of other users.

12. A method for controlling a walking assistance wearable device capable of outputting visual and / or auditory content to a user, comprising: A method of control comprising the steps of: adding the presentation of the visual content at a predetermined rate and / or outputting the auditory content at a predetermined tempo.

13. A program for causing the wearable device to execute the control method according to claim 12.

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