Wearable device, method, and non-transitory computer-readable storage medium for identifying input

Abstract

The wearable device comprises at least one motion sensor, an optical sensor, a display, and at least one processor, wherein the at least one processor is configured to: detect an event for deactivating identification of a touch input on the display; while the identification of the touch input is deactivated according to the event, identify, by using the optical sensor and the at least one motion sensor, a peripheral portion pressed by a user gesture from among peripheral portions of the wearable device; and on the basis of the identification of the peripheral portion pressed by the user gesture, change at least a portion of a screen displayed on the display.

Description

Wearable device, method, and non-transitory computer-readable storage medium for identifying input

[0001] The following descriptions relate to a wearable device, a method, and a non-transitory computer-readable storage medium for identifying input.

[0002] A wearable device can receive touch input through its display. The wearable device can change the screen displayed on the display based on the touch input. However, the identification of touch input on the display of the wearable device may be disabled or restricted in certain situations (e.g., underwater environments, wearing gloves).

[0003] The above information may be provided as background art to aid in understanding the present disclosure. No claim or determination is made as to whether any of the above is applicable as prior art related to the present disclosure.

[0004] A wearable device is provided. The wearable device may include at least one motion sensor. The wearable device may include an optical sensor. The wearable device may include a display. The wearable device may include a memory storing instructions and including one or more storage media. The wearable device may include at least one processor including a processing circuit. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to detect an event that deactivates identification of a touch input on the display. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify, among peripheral portions of the wearable device, a peripheral portion pressed by a user gesture using the optical sensor and the at least one motion sensor while the identification of the touch input is deactivated in response to the event. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to change at least a portion of a screen displayed on the display based on identifying the peripheral portion pressed by the user gesture.

[0005] A method is provided. The method can be executed in a wearable device including at least one motion sensor, an optical sensor, and a display. The method can include an operation of detecting an event that disables identification of a touch input on the display. The method can include an operation of identifying a peripheral portion pressed by a user gesture among peripheral portions of the wearable device using the optical sensor and the at least one motion sensor while the identification of the touch input is disabled in response to the event. The method can include an operation of changing at least a portion of a screen displayed on the display based on the identification of the peripheral portion pressed by the user gesture.

[0006] A non-transitory computer-readable storage medium is provided. The non-transitory computer-readable storage medium may store one or more programs. The one or more programs may include instructions that, when executed by a wearable device including at least one motion sensor, an optical sensor, and a display, cause the wearable device to detect an event that deactivates identification of a touch input on the display. The one or more programs may include instructions that, when executed by a wearable device including at least one motion sensor, an optical sensor, and a display, cause the wearable device to identify, using the optical sensor and the at least one motion sensor, a peripheral portion of the wearable device that is pressed by a user gesture while the identification of the touch input is deactivated in response to the event. The one or more programs may include instructions that, when executed by a wearable device including at least one motion sensor, an optical sensor, and a display, cause the wearable device to change at least a portion of a screen displayed on the display based on identifying the peripheral portion pressed by the user gesture.

[0007] Figures 1a to 1c illustrate examples of wearable devices.

[0008] FIG. 2a and FIG. 2b are drawings for explaining an example in which the posture of a wearable device changes according to a press input.

[0009] Figures 3a to 3c illustrate examples of charts showing measurement values ​​of an acceleration sensor and an optical sensor according to changes in the posture of a wearable device.

[0010] Figure 4 illustrates an example of a chart associated with an artificial intelligence model.

[0011] Figure 5 is a simplified block diagram of an exemplary wearable device.

[0012] Figure 6 is a flowchart illustrating an exemplary method for learning an artificial intelligence model.

[0013] FIG. 7 is a flowchart illustrating an exemplary method for identifying a peripheral portion of a wearable device pressed by a user gesture.

[0014] FIG. 8 is a drawing for explaining an example of identifying a peripheral portion of a wearable device pressed by a user gesture.

[0015] Figure 9 is a drawing for explaining an example of changing the screen of the display of a wearable device according to a press input.

[0016] Fig. 10 is a drawing for explaining an example of screen changes of a wearable device's display according to a press input.

[0017] Figure 11 is a drawing for explaining an example of setting activation of a press input.

[0018] Figure 12 is a drawing for explaining an example of controlling the operation of a wearable device according to a press input.

[0019] FIG. 13 is a flowchart illustrating an exemplary method for reducing the operating cycle of an optical sensor depending on a user status.

[0020] FIG. 14 is a block diagram of an electronic device within a network environment according to various embodiments.

[0021] The terms used in this disclosure are used only to describe specific embodiments and may not be intended to limit the scope of other embodiments. The singular expression may include plural expressions unless the context clearly indicates otherwise. Terms used herein, including technical or scientific terms, may have the same meaning as commonly understood by those of ordinary skill in the art described in this disclosure. Terms defined in general dictionaries among the terms used in this disclosure may be interpreted as having the same or similar meaning in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined in this disclosure. In some cases, even if a term is defined in this disclosure, it cannot be interpreted to exclude embodiments of the present disclosure.

[0022] The various embodiments of the present disclosure described below illustrate a hardware-based approach as an example. However, since the various embodiments of the present disclosure include techniques utilizing both hardware and software, the various embodiments of the present disclosure do not exclude a software-based approach.

[0023] In addition, in the present disclosure, expressions such as "more than" or "less than" may be used to determine whether a specific condition is satisfied or fulfilled, but this is merely a description for expressing an example and does not exclude descriptions such as "more than" or "less than." A condition described as "more than" may be replaced with "more than," a condition described as "less than" may be replaced with "less than," and a condition described as "more than and less than" may be replaced with "more than and less than." In addition, hereinafter, "A" to "B" mean at least one of elements from A (including A) to B (including B). hereinafter, "C" and / or "D" mean at least one of "C" or "D," that is, including {"C", "D", "C" and "D"}.

[0024] Figures 1a to 1c illustrate examples of wearable devices.

[0025] Referring to FIGS. 1A and 1B , a wearable device (100) may include a housing (102) including a first side (or front side (110A)), a second side (or back side) (110B), a side surface surrounding a space between the first side (110A) and the second side (110B), a key input device (103, 104) disposed on the side surface, and a fastening member (105, 106) connected to at least a portion of the housing (102) and configured to releasably fasten the wearable device (100) to at least a portion of a user's body (e.g., a wrist, an ankle). For example, the first side (110A) may be formed by a front plate (e.g., a glass plate including various coating layers, or a polymer plate) at least partially transparent. For example, the display (101) may be visually exposed through a significant portion of the front plate. For example, the shape of the display (101) may correspond to the shape of the front plate, and may have various shapes such as a circle, an oval, or a polygon. For example, the display (101) may be coupled with a touch sensor (including a touch detection circuit), a pressure sensor capable of measuring the intensity (pressure) of a touch (including a pressure detection circuit), and / or a fingerprint sensor (including a fingerprint detection circuit), and / or may be adjacent to the touch sensor, the pressure sensor, and / or the fingerprint sensor. For example, the key input device (103, 104) may include an electrode. For example, based on a combination of electrodes of a key input device (103, 104) and electrodes (107, 108) placed on a second side (110B) of a wearable device (100), an electrocardiogram, an electromyogram, an electroencephalogram, body impedance analysis, and electrodermal activity can be measured.

[0026] In one embodiment, the wearable device (100) may include an optical sensor (109). For example, the optical sensor (109) may be disposed on a second side (110B) of the wearable device (100) that faces (or is in contact with) the user's skin. For example, the optical sensor (109) may use a light-emitting element (e.g., a light emitting diode (LED)) and a light-receiving element (e.g., a photodiode (PD)) to emit light toward at least a part of the body of a user wearing the wearable device (100) and receive reflected light for the emitted light.

[0027] For example, referring to FIG. 1C, the optical sensor (109) may include a plurality of LEDs. For example, the optical sensor (109) may include LEDs (110), LEDs (111), LEDs (112), LEDs (113), and LEDs (114). In FIG. 1C, the optical sensor (109) is illustrated as including five LEDs, but the present disclosure is not limited thereto. For example, the optical sensor (109) may include four or fewer LEDs. In FIG. 1C, the LED (110) is illustrated as including one LED, but the present disclosure is not limited thereto. For example, the LED (110) may be composed of a plurality of LEDs. For example, the plurality of LEDs may be configured to emit light of different wavelengths. For example, the wavelengths of light emitted by LEDs can be comprised of various wavelengths including blue, red, yellow, infrared (IR), and / or ultraviolet (UV).

[0028] For example, the optical sensor (109) may include a plurality of PDs. For example, the optical sensor (109) may include PD (115), PD (116), PD (117), and PD (118). In FIG. 1C, the optical sensor (109) is illustrated as including four PDs, but the present disclosure is not limited thereto. For example, the optical sensor (109) may include three or fewer PDs.

[0029] Although FIG. 1c illustrates an LED emitting light and a PD receiving light, the present disclosure is not limited thereto. For example, the LED may be replaced with a vertical cavity surface emitting laser (VCSEL). For example, the PD may be replaced with an image sensor.

[0030] In one embodiment, the wearable device (100) may include at least one motion sensor. For example, the at least one motion sensor may be disposed on a printed circuit board disposed between the first surface (110A) and the second surface (110B). For example, the at least one motion sensor may include an acceleration sensor, a gyro sensor, a geomagnetic sensor, or a combination of at least two of the above sensors. For example, the acceleration sensor may identify (or measure, detect) acceleration of the wearable device (100) with respect to three directions of the x-axis, the y-axis, and the z-axis. For example, the gyro sensor may identify (or measure, detect) angular velocity of the wearable device (100) with respect to three directions of the x-axis, the y-axis, and the z-axis. For example, a geomagnetic sensor can identify (or measure, detect) magnetic force in three directions: the x-axis, the y-axis, and the z-axis.

[0031] In one embodiment, the wearable device (100) can obtain a touch input using a touch sensor on the display (101). For example, the wearable device (100) can control (or change) a screen displayed on the display (101) based on the touch input. For example, the wearable device (100) can change a screen displayed on the display to a previous screen based on a touch input (e.g., a swipe input in a first direction). For example, the wearable device (100) can change a screen displayed on the display to a next screen based on a touch input (e.g., a swipe input in a second direction opposite to the first direction). For example, the wearable device (100) can change a screen displayed on the display (101) to a screen for an application (e.g., a music application) corresponding to an object (e.g., an icon or an executable object) displayed on a portion of the display.

[0032] In one embodiment, the identification of a touch input on the display (101) of the wearable device (100) may be disabled. For example, a touch sensor of the display (101) may identify contact with water as a touch input. For example, a touch input caused by water may cause an operation of the wearable device (100) that is not intended by the user. For example, the wearable device (100) may disable the identification of a touch input on the display (101) to reduce the number of operations of the wearable device (100) that are not intended by the user.

[0033] In one embodiment, the identification of touch input on the display (101) of the wearable device (100) may be restricted. For example, a touch by a user's hand wearing a glove may cause contact points on the display (101), but the contact points may not be identified by the touch sensor. For example, the touch sensor may not have the ability to acquire touch data for the contact points.

[0034] Disabling (or limiting) the identification of touch input on the display (101) of the wearable device (100) may result in a degradation of the user experience of the wearable device (100). The wearable device (100) described below can identify a user input (e.g., a press input) that at least replaces a touch input on the display (101) of the wearable device (100). For example, the wearable device (100) can provide feedback on a user input that at least replaces a touch input on the display (101) through the display (101).

[0035] FIG. 2a and FIG. 2b are drawings for explaining an example in which the posture of a wearable device changes according to a press input.

[0036] Referring to FIG. 2A, the wearable device (100) may include a plurality of peripheral portions. For example, the peripheral portions may include peripheral portion (201), peripheral portion (202), peripheral portion (203), peripheral portion (204), peripheral portion (205), peripheral portion (206), peripheral portion (207), and peripheral portion (208). Although FIG. 2A illustrates the wearable device (100) as including eight peripheral portions, the present disclosure is not limited thereto. For example, the peripheral portions of the wearable device (100) may include seven or fewer peripheral portions. For example, the peripheral portion of the wearable device may include at least a portion of a surface (e.g., a front plate) of the display (101) (e.g., a peripheral portion of the display (101)) and / or at least a portion of the housing (102) (e.g., a peripheral portion of the housing (102)). For example, a press input may be provided by pressing a peripheral portion of the wearable device (100) by a user gesture. For example, a press input to a peripheral portion of the wearable device (100) may change the posture of the wearable device (100). For example, a press input to a peripheral portion of the wearable device (100) may change an inclination associated with the x-axis, y-axis, or z-axis of the wearable device (100). For example, a press input to a peripheral portion of the wearable device (100) may change the amount of light received by photodiodes (PDs) of an optical sensor (109) disposed on a second face (110B) of the wearable device (100).

[0037] Referring to FIG. 2B, for example, a press input by a user gesture (211) may be provided to a peripheral part (204) of a wearable device (100) worn on a user's body (e.g., wrist) (209). For example, when a press input is provided to the peripheral part (204) of the wearable device (100), the posture of the wearable device (100) may be changed. For example, the wearable device (100) may be tilted with respect to a part of the user's body (209) so as to come into contact with the part of the user's body (209) in an area corresponding to the peripheral part (204) where the press input is provided. For example, the wearable device (100) may be tilted with respect to a part of the user's body (209) so as to be spaced apart from the part of the user's body (209) in an area corresponding to the peripheral part (208) opposite to the peripheral part (204). For example, when a press input is provided to the peripheral part (204) of the wearable device (100), the inclination associated with the x-axis, y-axis, or z-axis of the wearable device (100) may be changed. For example, when a press input is provided to the peripheral part (204) of the wearable device (100), the movement path of light emitted by the LED of the optical sensor (109) may be changed. For example, the movement path of light may be changed from the first movement path (212) to the second movement path (213). For example, when a press input is provided to the peripheral part (204) of the wearable device (100), since the movement path of light is changed, the amount of light received by the PDs of the optical sensor (109) disposed on the second face (110B) of the wearable device (100) may be changed.

[0038] As described above, a press input by a user gesture may cause a change in the posture of the wearable device (100). The change in the posture of the wearable device (100) may affect the data acquired by the optical sensor (109) and the motion sensor.

[0039] Figures 3a to 3c illustrate examples of charts showing measurement values ​​of an acceleration sensor and an optical sensor according to a change in the posture of a wearable device. Figure 3a illustrates measurement values ​​of sensors when a press input by a user gesture is provided to a peripheral portion (204) of a wearable device (100). Figure 3b illustrates measurement values ​​of sensors when a press input by a user gesture is provided to a peripheral portion (208) of the wearable device (100). Figure 3c illustrates measurement values ​​of sensors when the wrist of a user wearing the wearable device (100) rotates internally.

[0040] Referring to FIG. 3A, the horizontal axis of the chart (310) represents time, and the vertical axis of the chart (310) represents a value measured by an acceleration sensor of the wearable device (100). For example, a waveform (311) may represent acceleration of the wearable device (100) in the x-axis direction when a press input is provided to the peripheral part (204) of the wearable device (100). For example, a waveform (312) may represent acceleration of the wearable device (100) in the y-axis direction when a press input is provided to the peripheral part (204) of the wearable device (100). For example, a waveform (313) may represent acceleration of the wearable device (100) in the z-axis direction when a press input is provided to the peripheral part (204) of the wearable device (100).

[0041] Referring to FIG. 3A, the horizontal axis of the chart (320) represents time, and the vertical axis of the chart (320) represents a value measured by the optical sensor (109). For example, the waveform (321) may represent the amount of light measured by the PD (115) of the optical sensor (109) when a press input is provided to the peripheral part (204) of the wearable device (100). For example, the waveform (322) may represent the amount of light measured by the PD (116) of the optical sensor (109) when a press input is provided to the peripheral part (204) of the wearable device (100). For example, the waveform (323) may represent the amount of light measured by the PD (117) of the optical sensor (109) when a press input is provided to the peripheral part (204) of the wearable device (100). For example, the waveform (324) may represent the amount of light measured by the PD (118) of the optical sensor (109) when a press input is provided to the peripheral portion (204) of the wearable device (100).

[0042] Referring to FIG. 3B, the horizontal axis of the chart (330) represents time, and the vertical axis of the chart (330) represents a value measured by an acceleration sensor of the wearable device (100). For example, the waveform (331) may represent acceleration of the wearable device (100) in the x-axis direction when a press input is provided to the peripheral portion (208) of the wearable device (100). For example, the waveform (332) may represent acceleration of the wearable device (100) in the y-axis direction when a press input is provided to the peripheral portion (208) of the wearable device (100). For example, the waveform (333) may represent acceleration of the wearable device (100) in the z-axis direction when a press input is provided to the peripheral portion (208) of the wearable device (100).

[0043] Referring to FIG. 3B, the horizontal axis of the chart (340) represents time, and the vertical axis of the chart (340) represents a value measured by the optical sensor (109). For example, the waveform (341) may represent the amount of light measured by the PD (115) of the optical sensor (109) when a press input is provided to the peripheral portion (208) of the wearable device (100). For example, the waveform (342) may represent the amount of light measured by the PD (116) of the optical sensor (109) when a press input is provided to the peripheral portion (208) of the wearable device (100). For example, the waveform (343) may represent the amount of light measured by the PD (117) of the optical sensor (109) when a press input is provided to the peripheral portion (208) of the wearable device (100). For example, the waveform (344) may represent the amount of light measured by the PD (118) of the optical sensor (109) when a press input is provided to the peripheral portion (208) of the wearable device (100).

[0044] Referring to FIG. 3C, the horizontal axis of the chart (350) represents time, and the vertical axis of the chart (350) represents a value measured by the acceleration sensor of the wearable device (100). For example, the waveform (351) may represent the acceleration of the wearable device (100) in the x-axis direction when the wrist of the user wearing the wearable device (100) is internally rotated. For example, the waveform (352) may represent the acceleration of the wearable device (100) in the y-axis direction when the wrist of the user wearing the wearable device (100) is internally rotated. For example, the waveform (353) may represent the acceleration of the wearable device (100) in the z-axis direction when the wrist of the user wearing the wearable device (100) is internally rotated.

[0045] Referring to FIG. 3c, the horizontal axis of the chart (360) represents time, and the vertical axis of the chart (360) represents a value measured by the optical sensor (109). For example, the waveform (361) may represent the amount of light measured by the PD (115) of the optical sensor (109) when the wrist of the user wearing the wearable device (100) is internally rotated. For example, the waveform (362) may represent the amount of light measured by the PD (116) of the optical sensor (109) when the wrist of the user wearing the wearable device (100) is internally rotated. For example, the waveform (363) may represent the amount of light measured by the PD (117) of the optical sensor (109) when the wrist of the user wearing the wearable device (100) is internally rotated. For example, the waveform (364) may represent the amount of light measured by the PD (118) of the optical sensor (109) when the wrist of a user wearing the wearable device (100) is internally rotated.

[0046] Referring to FIGS. 3A and 3B , for example, a press input to a peripheral portion (204) of a wearable device (100) can be distinguished from a press input to a peripheral portion (208) that is different from the peripheral portion (204) based on data measured by the optical sensor (109) and the motion sensor. Referring to FIGS. 3A and 3C , for example, a user gesture of pressing a peripheral portion (204) of a wearable device (100) can be distinguished from a user gesture of rotating a wrist based on data measured by the optical sensor (109) and the motion sensor. Referring to FIGS. 3B and 3C , for example, a user gesture of pressing a peripheral portion (208) of a wearable device (100) can be distinguished from a user gesture of rotating a wrist based on data measured by the optical sensor (109) and the motion sensor.

[0047] As described above, the wearable device (100) can identify a press input on the wearable device (100) using the optical sensor and the motion sensor even if the touch input on the display (101) is disabled (or limited). However, the values ​​acquired by the optical sensor (109) and the motion sensor can change depending on the intensity of the force applied to the peripheral portion of the wearable device (100). Therefore, an exemplary artificial intelligence model for learning values ​​that change depending on the intensity of the force is described in FIG. 4.

[0048] Figure 4 illustrates an example of a chart associated with an artificial intelligence model. Figure 4 describes an artificial intelligence model (e.g., a k-nearest neighbor (KNN) model) for learning data measured by an optical sensor (109) and a motion sensor.

[0049] In the chart (400) illustrated in FIG. 4, the horizontal axis represents a first variable, and the vertical axis represents a second variable. For example, the first variable may be associated with a change in (or pattern of) a value measured by an optical sensor (109). For example, the second variable may be associated with a change in (or pattern of) a value measured by a motion sensor.

[0050] Referring to FIG. 4, in one embodiment, the plurality of data sets of the artificial intelligence model may include a first cluster and a second cluster. In one example, the first cluster may include data set (402), data set (403), data set (404), data set (405), and data set (406). For example, the data set of the first cluster may include data acquired by an optical sensor (109) and data acquired by a motion sensor when a press input is provided to a peripheral portion (204) of the wearable device (100). In one example, the second cluster may include data set (407), data set (408), data set (409), data set (410), and data set (411). For example, the data set of the second cluster may include data acquired by an optical sensor (109) and data acquired by a motion sensor when a press input is provided to a peripheral portion (208) of the wearable device (100).

[0051] In one embodiment, the wearable device (100) may obtain a data set (401). For example, the wearable device (100) may determine a Euclidean distance between the data set (401) and a plurality of data sets of an artificial intelligence model. For example, the wearable device (100) may identify a predetermined number (k) of data sets among the plurality of data sets based on the determined Euclidean distance. For example, the predetermined number (k) may represent the number of nearest neighbor data sets considered in the prediction of the artificial intelligence model.

[0052] In one example, when k is 3, the wearable device (100) may identify data set (402), data set (407), and data set (408) from among the plurality of data sets based on the Euclidean distance. For example, the wearable device (100) may determine the label of data set (401) based on the labels of the identified data sets. For example, the wearable device (100) may determine the label of data set (401) as the second cluster because the number of data sets (data set (407) and data set (408)) labeled as the second cluster is greater than the number of data sets (data set (402)) labeled as the first cluster among the identified data sets.

[0053] In one example, when k is 6, the wearable device (100) may identify data set (402), data set (403), data set (404), data set (405), data set (407), and data set (408) from among the plurality of data sets based on the Euclidean distance. For example, the wearable device (100) may determine the label of the data set (401) based on the labels of the identified data sets. For example, the wearable device (100) may determine the label of the data set (401) as the first cluster because the number of data sets (data set (402), data set (403), data set (404), and data set (405)) labeled as the first cluster is greater than the number of data sets (data set (407) and data set (408)) labeled as the second cluster among the identified data sets.

[0054] In one embodiment, the wearable device (100) can delete a data set from the plurality of data sets of the artificial intelligence model. For example, the wearable device (100) can identify the data set (411) having the longest Euclidean distance from the data set (401). For example, the wearable device (100) can delete the identified data set (411) from the plurality of data sets of the artificial intelligence model. For example, the wearable device (100) can maintain the capacity of the data set of the artificial intelligence model at a constant level by deleting the data set (411) from the plurality of data sets of the artificial intelligence model.

[0055] Figure 5 is a simplified block diagram of an exemplary wearable device.

[0056] Referring to FIG. 5, the wearable device (500) of FIG. 5 may correspond to the wearable device (100) of FIGS. 1A and 1B. The wearable device (500) may include a processor (510), a memory (520), a communication circuit (530), a display (540), an optical sensor (550), and at least one motion sensor (560). For example, the at least one motion sensor (560) may include an acceleration sensor, a geomagnetic sensor, a gyro sensor, or a combination of at least two of the above sensors. Although FIG. 5 illustrates the wearable device (500) as including an optical sensor (550) and at least one motion sensor (560), the present disclosure is not limited thereto. For example, the wearable device (500) may further include a gesture sensor, a pressure sensor, a magnetic sensor, a grip sensor, a proximity sensor, a color sensor, an infrared (IR) sensor, a biometric sensor, a temperature sensor, a humidity sensor, an illuminance sensor, or a combination of at least two of the above sensors.

[0057] In one embodiment, the processor (510) may be operatively or operably coupled with or connected with a memory (520), a communication circuit (530), a display (540), an optical sensor (550), and at least one motion sensor (560). For example, the processor (510) may control the memory (520), the communication circuit (530), the display (540), the optical sensor (550), and at least one motion sensor (560). For example, the processor (510) may be comprised of at least one processor. For example, the processor (510) may include at least one processor. For example, the at least one processor may include a processing circuit. For example, at least one processor may further include a neural processing unit (NPU) (e.g., including processing circuitry), a graphic processing unit (GPU) (e.g., including processing circuitry), a display processing unit (DPU) (e.g., including processing circuitry), and / or a sensor hub (or sensor interface) (e.g., including processing circuitry). For example, the processor (510) may include a hardware component for processing data based on instructions.

[0058] In one embodiment, the processor (510) may detect (or identify) an event that disables identification of a touch input on the display (540). For example, in response to detecting (or identifying) the event, the processor (510) may disable identification of a touch input on the display (540). For example, in response to detecting (or identifying) the event, the processor (510) may activate identification of a press input to peripheral portions of the wearable device (500). For example, the processor (510) may acquire a data set using an optical sensor (550) and at least one motion sensor (560). For example, the processor (510) may preprocess the acquired data set. For example, the processor (510) may identify a peripheral portion of the wearable device (500) corresponding to the preprocessed data set using an artificial intelligence model. For example, the processor (510) may change at least a portion of the screen displayed on the display (540) based on the identified peripheral portion.

[0059] In one embodiment, memory (520) may be used to store information or data. For example, memory (520) may be used to store data obtained from a user. For example, memory (520) may include non-volatile memory. For example, memory (520) may include volatile memory. For example, memory (520) may include a computer-readable storage medium, such as a magnetic or optical disk. For example, memory (520) may store data obtained based on operations performed by processor (510). For example, memory (520) may store data obtained by optical sensor (550), such as light intensity data. For example, memory (520) may store data obtained by at least one motion sensor (560). For example, memory (520) may store data obtained by acceleration sensor, such as acceleration data. For example, the memory (520) can store data (e.g., angular velocity data) acquired by a gyro sensor. For example, the memory (520) can store data (e.g., geomagnetic data) acquired by a geomagnetic sensor.

[0060] In one embodiment, the communication circuit (530) may be utilized for various radio access technologies (RATs). For example, the communication circuit (530) may be utilized to perform Bluetooth communication, wireless local area network (WLAN) communication (e.g., wireless fidelity (Wi-Fi)), and / or cellular communication. For example, the processor (510) may establish a connection with an external electronic device (e.g., electronic device (1401) of FIG. 14) via the communication circuit (530).

[0061] In one embodiment, the display (540) may be used to display various screens. For example, the display (540) may be used to output content, data, or signals through the screen. For example, the display (540) may display a screen processed by the processor (510). In one example, the display (540) may correspond to the display (101) of FIG. 1.

[0062] In one embodiment, the optical sensor (550) may include an integrated circuit (IC) (or sensor IC), a light emitting diode (LED), and a photodiode (PD). For example, the IC may control the operation of the LED and the PD. For example, the IC may control the LED to emit light of a specified wavelength based on instructions obtained from the processor (510). For example, the IC may reduce the frequency with which the LED emits light based on instructions obtained from the processor (510). For example, the IC may control the PD to receive light based on instructions obtained from the processor (510).

[0063] In one embodiment, at least one motion sensor (560) may include an acceleration sensor, a gyro sensor, a geomagnetic sensor, or a combination of two or more of the above sensors. For example, the acceleration sensor may obtain acceleration data for the x-axis direction of the wearable device (500), acceleration data for the y-axis direction of the wearable device (500), acceleration data for the z-axis direction of the wearable device (500), or a combination thereof, based on instructions obtained from the processor (510). For example, the gyro sensor may obtain angular velocity data for the x-axis direction of the wearable device (500), angular velocity data for the y-axis direction of the wearable device (500), angular velocity data for the z-axis direction of the wearable device (500), or a combination thereof, based on instructions obtained from the processor (510). For example, the geomagnetic sensor may obtain magnetic data for the x-axis direction of the wearable device (500), magnetic data for the y-axis direction of the wearable device (500), magnetic data for the z-axis direction of the wearable device (500), or a combination thereof, based on instructions obtained from the processor (510).

[0064] Figure 6 is a flowchart illustrating an exemplary method for training an artificial intelligence model. The method for training an artificial intelligence model illustrated in Figure 6 is described with reference to Figure 4.

[0065] In operation 601, the processor (510) may acquire a data set (401) using an optical sensor (550) and at least one motion sensor (560). For example, the data set (401) may include light quantity data acquired using the optical sensor (550) and motion data acquired using at least one motion sensor (560).

[0066] In one embodiment, the processor (510) may acquire light quantity data using the optical sensor (550). For example, the light quantity data may include first light quantity data acquired by PD (115), second light quantity data acquired by PD (116), third light quantity data acquired by PD (117), fourth light quantity data acquired by PD (118), fifth light quantity data representing an average of the light quantities acquired by the PDs, or a combination thereof.

[0067] In one embodiment, the processor (510) may obtain motion data using at least one motion sensor (560). For example, the motion data may include acceleration data of the wearable device (500), angular velocity data of the wearable device (500), magnetic data of the wearable device (500), or a combination thereof.

[0068] In one embodiment, the processor (510) may obtain acceleration data of the wearable device (500) using an acceleration sensor. For example, the acceleration data of the wearable device (500) may include acceleration data for the x-axis direction of the wearable device (500), acceleration data for the y-axis direction of the wearable device (500), acceleration data for the z-axis direction of the wearable device (500), or a combination thereof.

[0069] In one embodiment, the processor (510) may obtain angular velocity data of the wearable device (500) using a gyro sensor. For example, the angular velocity data of the wearable device (500) may include angular velocity data for the x-axis direction of the wearable device (500), angular velocity data for the y-axis direction of the wearable device (500), angular velocity data for the z-axis direction of the wearable device (500), or a combination thereof.

[0070] In one embodiment, the processor (510) may obtain magnetic data of the wearable device (500) using a geomagnetic sensor. For example, the magnetic data of the wearable device (500) may include magnetic data for the x-axis direction of the wearable device (500), magnetic data for the y-axis direction of the wearable device (500), magnetic data for the z-axis direction of the wearable device (500), or a combination thereof.

[0071] In operation 602, the processor (510) may identify a predetermined number (k) of data sets from among a plurality of data sets of the artificial intelligence model. For example, the processor (510) may determine a distance (e.g., Euclidean distance) between the data set (401) acquired in operation 601 and the plurality of data sets of the artificial intelligence model. For example, the processor (510) may identify a predetermined number (k) of data sets from among the plurality of data sets of the artificial intelligence model based on the determined distance.

[0072] For example, when k is 3, the processor (510) can identify data set (402), data set (407), and data set (408) among multiple data sets of the artificial intelligence model based on the Euclidean distance.

[0073] For example, when k is 6, the processor (510) can identify data set (402), data set (403), data set (404), data set (405), data set (407), and data set (408) among the multiple data sets of the artificial intelligence model based on the Euclidean distance.

[0074] In operation 603, the processor (510) may determine multiple data sets of the artificial intelligence model based on a pre-specified number (k) of data sets. For example, the processor (510) may identify labels of the pre-specified number of data sets. For example, the processor (510) may determine multiple data sets of the artificial intelligence model based on the identified labels.

[0075] For example, when k is 3, the processor (510) can identify labels of data set (402), data set (407), and data set (408). For example, the label of data set (402) can be identified by a press input for the peripheral portion (204). For example, the labels of data set (407) and data set (408) can be identified by a press input for the peripheral portion (203). For example, the processor (510) can label the data set (401) obtained in operation 601 by a press input for the peripheral portion (203) because the number of data sets labeled by a press input for the peripheral portion (203) is greater than the number of data sets labeled by a press input for the peripheral portion (204). For example, the processor (510) may determine a plurality of data sets of the artificial intelligence model based on labeling with a press input for the peripheral portion (203). For example, the plurality of data sets of the artificial intelligence model may include data set (401), data set (407), data set (408), data set (409), data set (410), and data set (411) labeled with a press input for the peripheral portion (203) and data set (402), data set (403), data set (404), data set (405), and data set (406) labeled with a press input for the peripheral portion (204).

[0076] For example, when k is 6, the processor (510) can identify labels of data set (402), data set (403), data set (404), data set (405), data set (407), and data set (408). For example, labels of data set (402), data set (403), data set (404), and data set (405) can be identified by a press input for the peripheral portion (204). For example, labels of data set (407) and data set (408) can be identified by a press input for the peripheral portion (203). For example, the processor (510) may label the data set (401) obtained in operation 601 as a press input for the peripheral portion (204) because the number of data sets labeled with a press input for the peripheral portion (204) is greater than the number of data sets labeled with a press input for the peripheral portion (203). For example, the processor (510) may determine a plurality of data sets of the artificial intelligence model based on labeling the data set (401) as a press input for the peripheral portion (204). For example, the plurality of data sets of the artificial intelligence model may include data set (401), data set (402), data set (403), data set (404), data set (405), and data set (406) labeled with a press input for the surrounding part (204), and data set (407), data set (408), data set (409), data set (410), and data set (411) labeled with a press input for the surrounding part (203).

[0077] FIG. 7 is a flowchart illustrating an exemplary method for identifying a peripheral portion of a wearable device pressed by a user gesture.

[0078] Referring to FIG. 7, at operation 701, the processor (510) may detect (or identify) an event that disables identification of a touch input on the display (540).

[0079] In one embodiment, the processor (510) may obtain (or identify) user input that triggers an event that disables identification of touch input on the display (540).

[0080] For example, the processor (510) may display a visual object on the display (540) for setting a waterproof mode (e.g., a water lock). For example, the waterproof mode may be a mode that disables identification of touch input on the display to reduce unintended operations of the wearable device (500) performed by the user due to a touch input identified by water. For example, the processor (510) may obtain a touch input for the visual object for setting the waterproof mode. For example, the processor (510) may detect (or identify) an event for disabling identification of touch input on the display (540) based on a touch input for the visual object for setting the waterproof mode.

[0081] For example, the processor (510) may display a visual object (e.g., the visual object (1111) of FIG. 11) on the display (540) for setting a press input mode. For example, the press input mode may be a mode in which identification of a touch input on the display (540) is disabled. For example, the press input mode may be a mode in which identification of a press input for peripheral parts of the wearable device (500) is enabled. For example, the processor (510) may obtain a touch input for the visual object for setting the press input mode. For example, the processor (510) may detect (or identify) an event for disabling identification of a touch input on the display (540) based on a touch input for the visual object for setting the press input mode.

[0082] For example, the processor (510) may set a press input mode based on at least one sensor (e.g., the sensor module (1476) of FIG. 14). For example, the processor (510) may set a press input mode based on at least one sensor (e.g., the sensor module (1476) of FIG. 14) detecting water.

[0083] For example, the processor (510) can identify an area of ​​visual objects displayed on the screen of the display (540). For example, when the execution of an application (e.g., waterproof mode, press input mode) is identified, the processor (510) can identify an area of ​​visual objects displayed on the screen of the display (540). For example, when the screen of the display (540) is switched while the application is being executed, the processor (510) can identify an area of ​​visual objects displayed on the screen of the display (540). For example, the processor (510) can identify whether a visual object of the application overlaps an area of ​​visual objects displayed on the screen of the display (540). For example, when a visual object of the application overlaps an area of ​​visual objects displayed on the screen of the display (540), the processor (510) can display the visual object of the application translucently. In another example, the processor (510) may display visual objects of the application in a different area than the area of ​​visual objects displayed on the screen of the display (540).

[0084] For example, the processor (510) may display a visual object (e.g., a user interface (UI) displayed on the screen (930) of FIG. 9) on the display (540) for executing an application that disables identification of a touch input. For example, the application may be an application for measuring a record of exercise (e.g., outdoor cycling, swimming) of a user wearing the wearable device (500). For example, the processor (510) may obtain a touch input to the visual object. For example, the processor (510) may detect (or identify) an event that disables identification of a touch input on the display (540) based on a touch input to the visual object.

[0085] In one embodiment, the processor (510) may detect (or identify) the execution of an application that disables the identification of touch input using at least one motion sensor (560). For example, the application may be an application for measuring the record of exercise (e.g., outdoor cycling, swimming) of a user wearing the wearable device (500). For example, the processor (510) may detect (or identify) an event based on detecting (or identifying) the execution of the application.

[0086] In one embodiment, the processor (510) may disable identification of touch input on the display (540) based on detecting an event. For example, the processor (510) may enable identification of press input on peripheral parts of the wearable device (500) based on detecting an event. For example, the processor (510) may control the operation of the optical sensor (550) and at least one motion sensor (560) based on detecting an event.

[0087] In operation 702, the processor (510) may acquire a data set using an optical sensor (550) and at least one motion sensor (560). For example, the data set may include light quantity data acquired using the optical sensor (550) and motion data acquired using at least one motion sensor (560).

[0088] In one embodiment, the processor (510) may acquire light quantity data using the optical sensor (550). For example, the light quantity data may include first light quantity data acquired by PD (115), second light quantity data acquired by PD (116), third light quantity data acquired by PD (117), fourth light quantity data acquired by PD (118), fifth light quantity data representing an average of the light quantities acquired by the PDs, or a combination thereof.

[0089] In one embodiment, the processor (510) may obtain motion data using at least one motion sensor (560). For example, the motion data may include acceleration data of the wearable device (500), angular velocity data of the wearable device (500), magnetic data of the wearable device (500), or a combination thereof.

[0090] In one embodiment, the processor (510) may obtain acceleration data of the wearable device (500) using an acceleration sensor. For example, the acceleration data of the wearable device (500) may include acceleration data for the x-axis direction of the wearable device (500), acceleration data for the y-axis direction of the wearable device (500), acceleration data for the z-axis direction of the wearable device (500), or a combination thereof.

[0091] In one embodiment, the processor (510) may obtain angular velocity data of the wearable device (500) using a gyro sensor. For example, the angular velocity data of the wearable device (500) may include angular velocity data for the x-axis direction of the wearable device (500), angular velocity data for the y-axis direction of the wearable device (500), angular velocity data for the z-axis direction of the wearable device (500), or a combination thereof.

[0092] In one embodiment, the processor (510) may obtain magnetic data of the wearable device (500) using a geomagnetic sensor. For example, the magnetic data of the wearable device (500) may include magnetic data for the x-axis direction of the wearable device (500), magnetic data for the y-axis direction of the wearable device (500), magnetic data for the z-axis direction of the wearable device (500), or a combination thereof.

[0093] In one embodiment, the processor (510) may preprocess a data set acquired using an optical sensor (550) and at least one motion sensor (560). For example, the preprocessing may include normalization and standardization of the acquired data set.

[0094] In operation 703, the processor (510) can identify a peripheral portion pressed by a user gesture among peripheral portions of the wearable device (500).

[0095] In one embodiment, an artificial intelligence model (e.g., a k-nearest neighbor (KNN) model) may be used to identify a peripheral portion pressed by a user gesture. For example, the plurality of data sets of the artificial intelligence model (or trained model) may include a data set labeled with a press input for the peripheral portion (201), a data set labeled with a press input for the peripheral portion (202), a data set labeled with a press input for the peripheral portion (203), a data set labeled with a press input for the peripheral portion (204), a data set labeled with a press input for the peripheral portion (205), a data set labeled with a press input for the peripheral portion (206), a data set labeled with a press input for the peripheral portion (207), a data set labeled with a press input for the peripheral portion (208), or a combination thereof. In a non-limiting example, the plurality of data sets of the artificial intelligence model may further include data sets labeled with movements of the body of a user wearing the wearable device (500) (e.g., internal rotation of the wrist, external rotation of the wrist).

[0096] In one embodiment, the processor (510) may determine a label of the data set acquired in operation 702 based on the plurality of data sets of the artificial intelligence model. For example, the processor (510) may determine a distance (e.g., a Euclidean distance) between the data set acquired in operation 702 and the plurality of data sets of the artificial intelligence model. For example, the processor (510) may identify a predetermined number (k) of data sets among the plurality of data sets of the artificial intelligence model based on the determined distance. For example, the processor (510) may identify a peripheral portion of the wearable device (500) corresponding to the data set acquired using the optical sensor (550) and at least one motion sensor (560) based on the labels of the predetermined number (k) of data sets.

[0097] In one embodiment, the predetermined number (k=5) of data sets may include data sets (e.g., three) labeled with a press input of the peripheral portion (204) and data sets (e.g., two) labeled with a press input of the peripheral portion (203). For example, the processor (510) may identify that the peripheral portion (204) is pressed by the user gesture because, among the identified data sets, the number of data sets labeled with a press input of the peripheral portion (204) exceeds the number of data sets labeled with a press input of the peripheral portion (203). In another example, the predetermined number (k=5) of data sets may include data sets (e.g., two) labeled with a press input of the peripheral portion (204) and data sets (e.g., three) labeled with a press input of the peripheral portion (203). For example, the processor (510) can identify that the peripheral portion (203) is pressed by a user gesture because, among the identified data sets, the number of data sets labeled with a press input of the peripheral portion (203) exceeds the number of data sets labeled with a press input of the peripheral portion (204).

[0098] In operation 704, the processor (510) may change at least a portion of the screen displayed on the display (540).

[0099] In one embodiment, the processor (510) may change the first screen displayed on the display (540) to a second screen prior to the first screen. For example, the processor (510) may display the first screen (e.g., screen (910) of FIG. 9) through the display (540). For example, while the first screen is displayed through the display (540), a portion of the wearable device (500) (e.g., portion (902) of FIG. 9) may be pressed by a user gesture. For example, the processor (510) may identify a peripheral portion (206) of the wearable device (500) pressed by the user gesture using an optical sensor (550) and at least one motion sensor (560). For example, the processor (510) may change a first screen (e.g., screen (910) of FIG. 9) displayed on the display (540) to a second screen (e.g., screen (920) of FIG. 9) prior to the first screen based on identifying the peripheral portion (206).

[0100] In one embodiment, the processor (510) may change the first screen displayed on the display (540) to a third screen following the first screen. For example, the processor (510) may display the first screen (e.g., screen (910) of FIG. 9) through the display (540). For example, while the first screen is displayed through the display (540), a portion of the wearable device (500) (e.g., portion (903) of FIG. 9) may be pressed by a user gesture. For example, the processor (510) may identify a peripheral portion (202) of the wearable device (500) pressed by the user gesture using an optical sensor (550) and at least one motion sensor (560). For example, the processor (510) may change a first screen (e.g., screen (910) of FIG. 9) displayed on the display (540) to a third screen (e.g., screen (930) of FIG. 9) following the first screen based on identifying the peripheral portion (202).

[0101] In one embodiment, the processor (510) can change at least a portion of a screen displayed on the display (540) based on a press input to a plurality of peripheral portions during a time interval.

[0102] For example, the processor (510) may display a first screen (e.g., screen (910) of FIG. 9) through the display (540). For example, a first portion (e.g., portion (902) of FIG. 9) and a second portion (e.g., portion (903) of FIG. 9) of the wearable device (500) may be sequentially pressed by a user gesture during a time interval. For example, the processor (510) may identify a peripheral portion (206) pressed by the user gesture during the time interval. For example, the processor (510) may identify a peripheral portion (202) pressed by the user gesture after identifying the peripheral portion (206) during the time interval. For example, the processor (510) may change a first screen (e.g., screen (910) of FIG. 9) displayed on the display (540) to a second screen (e.g., screen (920) of FIG. 9) prior to the first screen based on identifying the peripheral portion (206) and the peripheral portion (202).

[0103] For example, the processor (510) may display a first screen (e.g., screen (910) of FIG. 9) through the display (540). For example, a second portion (e.g., portion (903) of FIG. 9) and a first portion (e.g., portion (902) of FIG. 9) of the wearable device (500) may be sequentially pressed by a user gesture during a time interval. For example, the processor (510) may identify a peripheral portion (202) pressed by the user gesture during the time interval. For example, the processor (510) may identify a peripheral portion (206) pressed by the user gesture after identifying the peripheral portion (202) during the time interval. For example, the processor (510) may change the first screen (e.g., screen (910) of FIG. 9) displayed on the display (540) to a third screen (e.g., screen (930) of FIG. 9) following the first screen based on identifying the peripheral portion (202) and the peripheral portion (206).

[0104] In one embodiment, the processor (510) may change the screen displayed on the display (540) to a screen for an application. For example, the processor (510) may display a visual object for the application (e.g., visual object (1212) of FIG. 12) on the display (540) while the application is running. For example, the visual object may be displayed on a portion of the display (540) corresponding to the peripheral portion (204) of the wearable device (500). For example, a user may provide a press input to a portion of the wearable device (e.g., portion (1213) of FIG. 12) to control an operation associated with the application. For example, the processor (510) may use the optical sensor (550) and at least one motion sensor (560) to identify the peripheral portion (204) pressed by the user gesture. For example, the processor (510) may change the screen displayed on the display (540) to a screen for an application (e.g., screen (1220) of FIG. 12) based on identifying the peripheral portion (204). For example, the screen for the application may include a user interface (UI) associated with the application (e.g., UI (1221), UI (1223), and UI (1225) of FIG. 12).

[0105] In one embodiment, the processor (510) may control a component of the wearable device (500) so that, when a press input is provided to a peripheral portion corresponding to the UI, an operation of the wearable device (500) corresponding to the UI is performed. For example, the processor (510) may display a screen for an application (e.g., screen (1220) of FIG. 12) through the display (540) while the application is running. For example, the processor (510) may display a UI for an operation of the wearable device (500) associated with the application (e.g., UI (1223) of FIG. 12). For example, while the screen for the application is displayed through the display (540), a portion of the wearable device (500) (e.g., portion (1224) of FIG. 12) may be pressed by a user gesture. For example, the processor (510) may identify a peripheral portion (204) of the wearable device (500) pressed by a user gesture using an optical sensor (550) and at least one motion sensor (560). For example, the processor (510) may control a component (e.g., an audio module, a communication circuit (530)) of the wearable device (500) so that an operation (e.g., pausing music playback) of the wearable device (500) associated with a UI (e.g., UI (1223) of FIG. 12) corresponding to the peripheral portion (204) is performed based on the identification of the peripheral portion (204).

[0106] FIG. 8 is a diagram for explaining an example of identifying a peripheral portion of a wearable device pressed by a user gesture. FIG. 8 explains an example of identifying a peripheral portion pressed by a user gesture using two artificial intelligence models. For example, the two artificial intelligence models may include a first artificial intelligence model and a second artificial intelligence model. For example, the first artificial intelligence model may be an artificial intelligence model for identifying a press input for the peripheral portion (202), the peripheral portion (204), the peripheral portion (206), and the peripheral portion (208) of the wearable device (500). For example, the second artificial intelligence model may be an artificial intelligence model for identifying the peripheral portion (201), the peripheral portion (203), the peripheral portion (205), and the peripheral portion (207) of the wearable device (500).

[0107] In operation 801, the processor (510) may identify a first likelihood value by which a first peripheral portion is identified among peripheral portions of the wearable device (500). For example, the processor (510) may identify a first likelihood value by which the first peripheral portion is identified based on a predefined number (k) of the first artificial intelligence model and the number of data sets labeled as the first peripheral portion.

[0108] For example, the processor (510) may acquire a data set using an optical sensor (550) and at least one motion sensor (560). For example, the processor (510) may determine a Euclidean distance between a data set and a plurality of data sets of the first artificial intelligence model. For example, the processor (510) may identify a predetermined number (e.g., 10) of data sets from among the plurality of data sets based on the determined Euclidean distance. For example, the processor (510) may identify a number (e.g., 9) of data sets labeled as a first peripheral portion (e.g., peripheral portion (204)) from among the identified data sets. For example, the processor (510) may identify a first likelihood value (e.g., 0.9) based on the predetermined number (e.g., 10) of the first artificial intelligence model and the number (e.g., 9) of data sets labeled as the first peripheral portion.

[0109] In operation 802, the processor (510) may identify a second likelihood value by which a second peripheral portion is identified among peripheral portions of the wearable device (500). For example, the processor (510) may identify a second likelihood value by which the second peripheral portion is identified based on a predefined number (k) of the second artificial intelligence model and the number of data sets labeled as the second peripheral portion.

[0110] For example, the processor (510) may acquire a data set using an optical sensor (550) and at least one motion sensor (560). For example, the processor (510) may determine a Euclidean distance between a data set and a plurality of data sets of a second artificial intelligence model. For example, the processor (510) may identify a predetermined number (e.g., 10) of data sets from among the plurality of data sets based on the determined Euclidean distance. For example, the processor (510) may identify a number (e.g., 8) of data sets labeled as a second peripheral portion (e.g., peripheral portion (203)) from among the identified data sets. For example, the processor (510) may identify a second likelihood value (e.g., 0.8) based on the predetermined number (e.g., 10) of the first artificial intelligence model and the number (e.g., 8) of data sets labeled as the second peripheral portion.

[0111] In operation 803, the processor (510) may identify a peripheral portion of the wearable device (500) pressed by the user gesture based on the first likelihood value and the second likelihood value. For example, the processor (510) may identify a first peripheral portion (e.g., peripheral portion (204)) pressed by the user gesture based on the first likelihood value (e.g., 0.9) and the second likelihood value (e.g., 0.8). For example, the processor (510) may change at least a portion of a screen displayed on the display (540) of the wearable device (500) based on identifying the first peripheral portion. For example, the processor (510) may control a component (e.g., an audio module, a communication circuit (530)) of the wearable device (500) to perform an operation of the wearable device (500) based on identifying the first peripheral portion.

[0112] Figure 9 is a drawing for explaining an example of changing the screen of the display of a wearable device according to a press input.

[0113] Referring to FIG. 9, in one example, the processor (510) may display a screen (910) through a display (540).

[0114] In one embodiment, the screen (910) may include a visual object (901). For example, the visual object (901) may indicate the execution of a waterproof mode (e.g., water lock) of the wearable device (500). For example, the waterproof mode may be a mode in which identification of touch input on the display is disabled. For example, the processor (510) may activate identification of press inputs to peripheral parts of the wearable device (500) when the waterproof mode is executed.

[0115] In one embodiment, the processor (510) may change the screen (910) displayed on the display (540) to the screen (920) prior to the screen (910). For example, while the screen (910) is displayed on the display (540), a portion (902) of the wearable device (500) may be pressed by a user gesture. For example, the processor (510) may identify a peripheral portion (206) of the wearable device (500) pressed by the user gesture using an optical sensor (550) and at least one motion sensor (560). For example, the processor (510) may change the screen (910) displayed on the display (540) to the screen (920) prior to the screen (910) based on identifying the peripheral portion (206) pressed by the user gesture.

[0116] In one embodiment, the processor (510) may change the screen displayed on the display (540) to a screen (930) following the screen (910). For example, while the screen (910) is displayed on the display (540), a portion (903) of the wearable device (500) may be pressed by a user gesture. For example, the processor (510) may identify a peripheral portion (202) of the wearable device (500) pressed by the user gesture using an optical sensor (550) and at least one motion sensor (560). For example, the processor (510) may change the screen (910) displayed on the display (540) to a screen (930) following the screen (910) based on identifying the peripheral portion (202) pressed by the user gesture.

[0117] In one embodiment, the processor (510) may identify an area of ​​visual objects displayed on the screen (930) of the display (540) when the screen (910) displayed on the display (540) is switched to the screen (930). For example, the processor (510) may identify an area where visual objects (931), visual objects (932), visual objects (933), and visual objects (934) are displayed on the screen (930). For example, the processor (510) may identify whether an area of ​​a visual object (901) in a waterproof mode overlaps with an area of ​​visual objects displayed on the screen (930) of the display (540). For example, the processor (510) may identify that an area of ​​a visual object (901) in a waterproof mode overlaps with an area of ​​a visual object (932). For example, the processor (510) may display the visual object (901) translucently when the area of ​​the visual object (901) and the area of ​​the visual object (932) overlap. In another example, the processor (510) may display the visual object (901) in a different area (935) that is distinct from the area of ​​the visual objects displayed on the screen of the display (540).

[0118] In one embodiment, the processor (510) may disable the waterproof mode of the wearable device (500). For example, while a visual object (901) is displayed through the display (540), a portion of the wearable device (500) corresponding to the location of the visual object (901) may be pressed by a user gesture. For example, the processor (510) may identify a peripheral portion (201) of the wearable device (500) that is pressed by the user gesture using the optical sensor (550) and at least one sensor. For example, the processor (510) may disable the waterproof mode of the wearable device (500) based on identifying the peripheral portion (201). For example, the processor (510) may disable the press input mode of the wearable device (500) based on identifying the peripheral portion (201). For example, the processor (510) may activate identification of a touch input on the display (540) based on identifying the peripheral portion (201).

[0119] Fig. 10 is a drawing for explaining an example of screen changes of a wearable device's display according to a press input.

[0120] Referring to FIG. 10, in one example, the processor (510) may display a visual object (901) on the screen (1010) via the display (540). For example, the visual object (901) may indicate the execution of a waterproof mode (e.g., water lock) of the wearable device (500). For example, the waterproof mode may be a mode in which identification of touch input on the display (540) is disabled. For example, when the waterproof mode is executed, the processor (510) may activate identification of press inputs to peripheral parts of the wearable device (500).

[0121] In one embodiment, the processor (510) may display a screen (1010) via the display (540). For example, when a message is received for a user wearing the wearable device (500), the processor (510) may display a visual object (1001) via the display (540). For example, the visual object (1001) may indicate the presence of a message that has not been confirmed by the user wearing the wearable device (500).

[0122] In one embodiment, the processor (510) may change the screen (1010) displayed on the display (540) to a screen (1020) displaying summary information of the message. For example, while the screen (1010) is displayed on the display (540), a portion (1002) of the wearable device (500) may be pressed by a user gesture. For example, the processor (510) may identify a peripheral portion (206) of the wearable device (500) pressed by the user gesture using an optical sensor (550) and at least one motion sensor (560). For example, the processor (510) may change the screen (1010) displayed on the display (540) to a screen (1020) displaying summary information of the message based on identifying the peripheral portion (206) pressed by the user gesture. For example, the summary information of the message may display the contents of the most recently received message on the wearable device (500).

[0123] In one embodiment, the processor (510) may remove a visual object (1001) displayed on the display (540). For example, while the screen (1020) is displayed through the display (540), a portion (1003) of the wearable device (500) may be pressed by a user gesture. For example, the processor (510) may identify a peripheral portion (208) of the wearable device (500) pressed by the user gesture using the optical sensor (550) and at least one motion sensor (560). For example, the processor (510) may control the display (540) so that the visual object (1001) is not displayed on the screen (1010) based on identifying the peripheral portion (208) pressed by the user gesture. For example, the processor (510) can change the screen (1020) displayed on the display (540) to the screen (920) based on identifying the peripheral portion (208) pressed by the user gesture.

[0124] In one embodiment, the processor (510) may change the screen (1020) displayed on the display (540) to a screen (1030) displaying detailed information of a message. For example, while the screen (1020) is displayed on the display (540), a portion (1004) of the wearable device (500) may be pressed by a user gesture. For example, the processor (510) may identify a peripheral portion (204) of the wearable device (500) pressed by the user gesture using an optical sensor (550) and at least one motion sensor (560). For example, the processor (510) may change the screen (1020) displayed on the display (540) to a screen (1030) displaying detailed information of a message based on identifying the peripheral portion (204) pressed by the user gesture.

[0125] In one embodiment, the processor (510) may control the display (540) to display a message prior to a message displayed on the screen (1030) of the display (540). For example, while the screen (1030) is displayed on the display (540), a portion (1005) of the wearable device (500) may be pressed by a user gesture. For example, the processor (510) may identify a peripheral portion (208) of the wearable device (500) pressed by the user gesture using the optical sensor (550) and at least one motion sensor (560). For example, the processor (510) may control the display (540) to display a message prior to a message displayed on the screen (1030) of the display (540) based on identifying the peripheral portion (208) pressed by the user gesture.

[0126] In one embodiment, the processor (510) may control the display (540) to display the most recent message on the screen (1030) of the display (540). For example, a portion (1007) of the wearable device (500) may be pressed by a user gesture. For example, the processor (510) may identify a peripheral portion (204) of the wearable device (500) pressed by the user gesture using the optical sensor (550) and at least one motion sensor (560). For example, the processor (510) may control the display (540) to display the most recent message on the screen of the display (540) based on identifying the peripheral portion (204) pressed by the user gesture.

[0127] In one embodiment, the processor (510) may control the audio module of the wearable device (500) to generate a message. For example, while the screen (1030) is displayed through the display (540), a portion (1006) of the wearable device (500) may be pressed by a user gesture. For example, the processor (510) may identify a peripheral portion (202) of the wearable device (500) pressed by the user gesture using the optical sensor (550) and at least one motion sensor (560). For example, the processor (510) may control the audio module of the wearable device (500) to generate a message based on identifying the peripheral portion (202) pressed by the user gesture. For example, the processor (510) may control the communication circuit (530) to transmit the message generated based on the audio module.

[0128] In one embodiment, the processor (510) may change the screen (1030) displayed on the display (540) to the screen (1020) prior to the screen. For example, while the screen (1030) is displayed on the display (540), a portion (1008) of the wearable device (500) may be pressed by a user gesture. For example, the processor (510) may identify a peripheral portion (206) of the wearable device (500) pressed by the user gesture using an optical sensor (550) and at least one motion sensor (560). For example, the processor (510) may change the screen (1030) displayed on the display (540) to the screen (1020) prior to the screen based on identifying the peripheral portion (206) pressed by the user gesture.

[0129] Figure 11 is a drawing for explaining an example of setting activation of a press input.

[0130] Referring to FIG. 11, the processor (510) may display a screen (1110) through the display (540) to activate identification of a press input for peripheral parts of the wearable device (500). For example, the processor (510) may display a visual object (1111) associated with a setting of the press input and guide information (1112) associated with the press input through the display (540). For example, the processor (510) may obtain a touch input for the visual object (1111) through a touch sensor of the display (540). For example, the processor (510) may deactivate identification of the touch input on the display (540) based on obtaining a touch input for the visual object (1111). For example, the processor (510) may activate identification of a press input to peripheral parts of the wearable device (500) based on acquiring a touch input to a visual object (1111).

[0131] Referring to FIG. 11, the processor (510) may display a screen (1120) through the display (540) to activate identification of a press input for peripheral parts of the wearable device (500). For example, the processor (510) may display visual objects for movement associated with the setting of the press input through the display (540).

[0132] In one embodiment, the processor (510) may acquire a touch input for a visual object (1121) through a touch sensor of the display (540). For example, the processor (510) may set a press input for an application for a movement (e.g., rafting) associated with the visual object (1121) based on acquiring the touch input for the visual object (1121). For example, the processor (510) may identify (or detect) the execution of the application for the movement (e.g., rafting) using at least one motion sensor (560). For example, the processor (510) may disable the identification of the touch input on the display (540) based on identifying the execution of the application for the movement (e.g., rafting). For example, the processor (510) may activate identification of a press input to peripheral parts of the wearable device (500) based on identifying execution of an application for exercise (e.g., rafting).

[0133] In one embodiment, the processor (510) may acquire a touch input to the visual object (1122) via a touch sensor of the display (540). For example, the processor (510) may set a press input for an application for an exercise (e.g., hiking) associated with the visual object (1122) based on acquiring the touch input to the visual object (1122). For example, the processor (510) may identify (or detect) the execution of the application for the exercise (e.g., hiking) using at least one motion sensor (560). For example, the processor (510) may disable the identification of the touch input on the display (540) based on identifying the execution of the application for the exercise (e.g., hiking). For example, the processor (510) may activate identification of a press input to peripheral parts of the wearable device (500) based on identifying execution of an application for exercise (e.g., hiking).

[0134] In one embodiment, the processor (510) may acquire a touch input for the visual object (1123) through the touch sensor of the display (540). For example, the processor (510) may set a press input for an application for exercise (e.g., outdoor cycling) associated with the visual object (1123) based on acquiring the touch input for the visual object (1123). For example, the processor (510) may identify (or detect) the execution of the application for exercise (e.g., outdoor cycling) using at least one motion sensor (560). For example, the processor (510) may disable the identification of the touch input on the display (540) based on identifying the execution of the application for exercise (e.g., outdoor cycling). For example, the processor (510) may activate identification of a press input to peripheral parts of the wearable device (500) based on identifying the execution of an application for exercise (e.g., outdoor cycling).

[0135] Fig. 12 is a diagram illustrating an example of controlling the operation of a wearable device according to a press input. Fig. 12 illustrates an exemplary method of controlling the operation of a wearable device according to a press input while an exercise application (e.g., hiking, outdoor cycling) for which a press input is set is running.

[0136] Referring to FIG. 12, in one example, the processor (510) may display a screen (1210) via the display (540). For example, the processor (510) may execute an application for measuring a user's exercise record using at least one motion sensor (560). For example, the processor (510) may execute an application for measuring a user's exercise record based on a user input. For example, the processor (510) may display information about the user's exercise record via the screen (1210) on the display (540) based on executing the application for measuring the user's exercise record.

[0137] In one embodiment, the processor (510) may identify the execution of the press input mode. For example, the processor (510) may identify the area of ​​visual objects displayed on the screen (1210) of the display (540) based on identifying the execution of the press input mode. For example, the processor (510) may identify whether the area of ​​the visual object (1211) of the press input mode overlaps with the area of ​​the visual objects displayed on the screen (1210) of the display (540). For example, the processor (510) may identify that the area of ​​the visual object (1211) overlaps with the area of ​​another visual object displayed on the screen of the display (540). For example, if the area of ​​the visual object (1211) overlaps with the area of ​​another visual object, the processor (510) may display the visual object (1211) as translucent. In another example, the processor (510) may display a visual object (1211) in a different area (1214) distinct from the area of ​​visual objects displayed on the screen of the display (540).

[0138] In one embodiment, the processor (510) may display a visual object (1211) through the display (540). The visual object (1211) may indicate the execution of a press input mode of the wearable device (500). For example, the press input mode may be a mode in which identification of touch input on the display (540) is disabled. For example, the press input mode may be a mode in which identification of press input for peripheral parts of the wearable device (500) is enabled.

[0139] In one embodiment, the processor (510) may display a visual object (1212) via the display (540). The visual object (1211) may indicate the execution of an application (e.g., a music application). For example, the visual object (1211) may be displayed on a portion of the display (540) corresponding to the peripheral portion (204) of the wearable device (500).

[0140] In one embodiment, the processor (510) may change the screen (1210) displayed on the display (540) to a screen (1220) for an application. For example, while a visual object (1212) is displayed on the display (540), a portion of the wearable device (500) corresponding to the location of the visual object (1212) may be pressed by a user gesture. For example, the processor (510) may identify a peripheral portion (204) of the wearable device (500) pressed by the user gesture using an optical sensor (550) and at least one motion sensor (560). For example, the processor (510) may change the screen (1210) displayed on the display (540) to a screen (1220) for an application based on identifying the peripheral portion (204). For example, a screen (1220) for an application may include a user interface (UI) associated with the application (e.g., UI (1221), UI (1223), and UI (1225)).

[0141] In one embodiment, the processor (510) may control the operation of the wearable device (500) based on a press input.

[0142] For example, the processor (510) may sense that a portion (1224) of the wearable device (500) is pressed by a user gesture while a screen (1220) for an application is displayed. The processor (510) may identify a peripheral portion (204) of the wearable device (500) pressed by the user gesture using an optical sensor (550) and at least one motion sensor (560). Based on identifying the peripheral portion (204), the processor (510) may control a component (e.g., an audio module, a communication circuit (530)) of the wearable device (500) to pause music played by the application.

[0143] For example, the processor (510) may sense that a portion (1222) of the wearable device (500) is pressed by a user gesture while a screen (1220) for an application is displayed. The processor (510) may identify a peripheral portion (205) of the wearable device (500) pressed by the user gesture using an optical sensor (550) and at least one motion sensor (560). Based on identifying the peripheral portion (205), the processor (510) may control a component (e.g., an audio module, a communication circuit (530)) of the wearable device (500) to play music listed before music played by the application.

[0144] For example, the processor (510) may sense that a portion (1226) of the wearable device (500) is pressed by a user gesture while a screen (1220) for an application is displayed. The processor (510) may identify a peripheral portion (203) of the wearable device (500) pressed by the user gesture using an optical sensor (550) and at least one motion sensor (560). Based on identifying the peripheral portion (203), the processor (510) may control a component (e.g., an audio module, a communication circuit (530)) of the wearable device (500) to play music listed after the music played by the application.

[0145] FIG. 13 is a flowchart illustrating an exemplary method for reducing the operating cycle of an optical sensor depending on a user's status. FIG. 13 describes an exemplary method for controlling the operating cycle of an optical sensor (550) while an application for measuring the exercise records of a user wearing a wearable device (500) is running.

[0146] Referring to FIG. 13, in operation 1301, the processor (510) may identify a state of a user wearing the wearable device (500). For example, the processor (510) may identify a state of a user wearing the wearable device (500) using at least one motion sensor (560). For example, the state of the user may include a movement state or a stationary state. For example, the processor (510) may identify the user state as a movement state if the amount of change in values ​​measured by at least one motion sensor (560) exceeds a threshold. For example, the processor (510) may identify the user state as a stationary state if the amount of change in values ​​measured by at least one motion sensor (560) is less than a threshold.

[0147] In operation 1302, the processor (510) may reduce the operating cycle of the optical sensor (550) if the state of the user wearing the wearable device (500) is determined to be stationary. For example, the processor (510) may control the optical sensor (550) to reduce the cycle at which light emitting diodes (LEDs) emit light if the state of the user wearing the wearable device (500) is determined to be stationary. By reducing the operating cycle of the optical sensor (550), the power consumption of the wearable device (500) may be reduced. For example, the operating cycle may be referred to as a duty cycle. For example, the duty cycle may represent the ratio of time that the optical sensor operates in an ON state during a certain period.

[0148] The wearable device performing the above-described operations can operate in conjunction with the electronic device (1401) in FIG. 14 below.

[0149] FIG. 14 is a block diagram of an electronic device within a network environment according to various embodiments.

[0150] Referring to FIG. 14, in a network environment (1400), an electronic device (1401) may communicate with an electronic device (1402) via a first network (1498) (e.g., a short-range wireless communication network), or may communicate with at least one of an electronic device (1404) or a server (1408) via a second network (1499) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (1401) may communicate with the electronic device (1404) via the server (1408). According to one embodiment, the electronic device (1401) may include a processor (1420), a memory (1430), an input module (1450), an audio output module (1455), a display module (1460), an audio module (1470), a sensor module (1476), an interface (1477), a connection terminal (1478), a haptic module (1479), a camera module (1480), a power management module (1488), a battery (1489), a communication module (1490), a subscriber identification module (1496), or an antenna module (1497). In some embodiments, the electronic device (1401) may omit at least one of these components (e.g., the connection terminal (1478)), or may have one or more other components added. In some embodiments, some of these components (e.g., sensor module (1476), camera module (1480), or antenna module (1497)) may be integrated into a single component (e.g., display module (1460)).

[0151] The processor (1420) may, for example, execute software (e.g., a program (1440)) to control at least one other component (e.g., a hardware or software component) of the electronic device (1401) connected to the processor (1420) and perform various data processing or operations. According to one embodiment, as at least a part of the data processing or operations, the processor (1420) may store commands or data received from other components (e.g., a sensor module (1476) or a communication module (1490)) in a volatile memory (1432), process the commands or data stored in the volatile memory (1432), and store result data in a non-volatile memory (1434). According to one embodiment, the processor (1420) may include a main processor (1421) (e.g., a central processing unit or an application processor) or a secondary processor (1423) (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor) that can operate independently or together with the main processor (1421). For example, when the electronic device (1401) includes the main processor (1421) and the secondary processor (1423), the secondary processor (1423) may be configured to use less power than the main processor (1421) or to be specialized for a given function. The secondary processor (1423) may be implemented separately from the main processor (1421) or as a part thereof.

[0152] The auxiliary processor (1423) may control at least a portion of functions or states associated with at least one component (e.g., the display module (1460), the sensor module (1476), or the communication module (1490)) of the electronic device (1401), for example, on behalf of the main processor (1421) while the main processor (1421) is in an inactive (e.g., sleep) state, or together with the main processor (1421) while the main processor (1421) is in an active (e.g., application execution) state. In one embodiment, the auxiliary processor (1423) (e.g., an image signal processor or a communication processor) may be implemented as a part of another functionally related component (e.g., a camera module (1480) or a communication module (1490)). In one embodiment, the auxiliary processor (1423) (e.g., a neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. The artificial intelligence models may be generated through machine learning. This learning can be performed, for example, on the electronic device (1401) where the artificial intelligence model is executed, or can be performed through a separate server (e.g., server (1408)). The learning algorithm can include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model can include multiple artificial neural network layers.The artificial neural network may be one of a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above networks, but is not limited to the examples described above. In addition to, or alternatively to, a hardware structure, an artificial intelligence model may include a software structure.

[0153] The memory (1430) can store various data used by at least one component (e.g., the processor (1420) or the sensor module (1476)) of the electronic device (1401). The data can include, for example, software (e.g., the program (1440)) and input data or output data for commands related thereto. The memory (1430) can include volatile memory (1432) or non-volatile memory (1434).

[0154] The program (1440) may be stored as software in memory (1430) and may include, for example, an operating system (1442), middleware (1444), or an application (1446).

[0155] The input module (1450) can receive commands or data to be used in a component of the electronic device (1401) (e.g., a processor (1420)) from an external source (e.g., a user) of the electronic device (1401). The input module (1450) can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0156] The audio output module (1455) can output audio signals to the outside of the electronic device (1401). The audio output module (1455) can include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. In one embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0157] The display module (1460) can visually provide information to an external party (e.g., a user) of the electronic device (1401). The display module (1460) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling the device. In one embodiment, the display module (1460) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of a force generated by the touch.

[0158] The audio module (1470) can convert sound into an electrical signal, or vice versa. According to one embodiment, the audio module (1470) can acquire sound through the input module (1450), output sound through the sound output module (1455), or an external electronic device (e.g., electronic device (1402)) (e.g., speaker or headphone) directly or wirelessly connected to the electronic device (1401).

[0159] The sensor module (1476) can detect the operating status (e.g., power or temperature) of the electronic device (1401) or the external environmental status (e.g., user status) and generate an electrical signal or data value corresponding to the detected status. According to one embodiment, the sensor module (1476) can include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0160] The interface (1477) may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device (1401) with an external electronic device (e.g., the electronic device (1402)). In one embodiment, the interface (1477) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0161] The connection terminal (1478) may include a connector through which the electronic device (1401) may be physically connected to an external electronic device (e.g., the electronic device (1402)). According to one embodiment, the connection terminal (1478) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0162] The haptic module (1479) can convert electrical signals into mechanical stimuli (e.g., vibration or movement) or electrical stimuli that a user can perceive through tactile or kinesthetic sensations. In one embodiment, the haptic module (1479) can include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0163] The camera module (1480) can capture still images and videos. According to one embodiment, the camera module (1480) may include one or more lenses, image sensors, image signal processors, or flashes.

[0164] The power management module (1488) can manage power supplied to the electronic device (1401). According to one embodiment, the power management module (1488) can be implemented, for example, as at least a part of a power management integrated circuit (PMIC).

[0165] A battery (1489) may power at least one component of the electronic device (1401). In one embodiment, the battery (1489) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0166] The communication module (1490) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (1401) and an external electronic device (e.g., electronic device (1402), electronic device (1404), or server (1408)), and the performance of communication through the established communication channel. The communication module (1490) may operate independently from the processor (1420) (e.g., application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (1490) may include a wireless communication module (1492) (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module (1494) (e.g., a local area network (LAN) communication module, or a power line communication module). Among these communication modules, a corresponding communication module can communicate with an external electronic device (1404) via a first network (1498) (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (1499) (e.g., a long-range communication network such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a local area network or a wide area network)). These various types of communication modules can be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (1492) can verify or authenticate the electronic device (1401) within a communication network such as the first network (1498) or the second network (1499) by using subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identification module (1496).

[0167] The wireless communication module (1492) can support 5G networks and next-generation communication technologies following the 4G network, such as NR access technology (new radio access technology). NR access technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimizing terminal power and connecting multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency communications (URLLC (ultra-reliable and low-latency communications)). The wireless communication module (1492) can support, for example, a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate. The wireless communication module (1492) may support various technologies for securing performance in high-frequency bands, such as beamforming, massive multiple-input and multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module (1492) may support various requirements specified in the electronic device (1401), an external electronic device (e.g., the electronic device (1404)), or a network system (e.g., the second network (1499)). According to one embodiment, the wireless communication module (1492) may support a peak data rate (e.g., 20 Gbps or more) for eMBB realization, a loss coverage (e.g., 164 dB or less) for mMTC realization, or a U-plane latency (e.g., 0.5 ms or less for downlink (DL) and uplink (UL), or 1 ms or less for round trip) for URLLC realization.

[0168] The antenna module (1497) can transmit or receive signals or power to or from an external device (e.g., an external electronic device). In one embodiment, the antenna module (1497) may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). In one embodiment, the antenna module (1497) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as the first network (1498) or the second network (1499), may be selected from the plurality of antennas by, for example, the communication module (1490). A signal or power may be transmitted or received between the communication module (1490) and the external electronic device via the at least one selected antenna. In some embodiments, in addition to the radiator, another component (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module (1497).

[0169] According to various embodiments, the antenna module (1497) may form a mmWave antenna module. According to one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent a first side (e.g., a bottom side) of the printed circuit board and capable of supporting a designated high frequency band (e.g., a mmWave band), and a plurality of antennas (e.g., an array antenna) disposed on or adjacent a second side (e.g., a top side or a side side) of the printed circuit board and capable of transmitting or receiving signals in the designated high frequency band.

[0170] At least some of the above components can be interconnected and exchange signals (e.g., commands or data) with each other via a communication method between peripheral devices (e.g., a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0171] According to one embodiment, commands or data may be transmitted or received between the electronic device (1401) and an external electronic device (1404) via a server (1408) connected to a second network (1499). Each of the external electronic devices (1402 or 1404) may be the same or a different type of device as the electronic device (1401). According to one embodiment, all or part of the operations executed in the electronic device (1401) may be executed in one or more of the external electronic devices (1402, 1404, or 1408). For example, when the electronic device (1401) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (1401) may, instead of or in addition to executing the function or service itself, request one or more external electronic devices to perform the function or at least a part of the service. One or more external electronic devices that receive the request may execute at least a portion of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device (1401). The electronic device (1401) may process the result as is or additionally and provide it as at least a portion of a response to the request. For this purpose, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device (1401) may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device (1404) may include an Internet of Things (IoT) device. The server (1408) may be an intelligent server utilizing machine learning and / or a neural network.In one embodiment, an external electronic device (1404) or server (1408) may be included in the second network (1499). The electronic device (1401) may be applied to intelligent services (e.g., smart homes, smart cities, smart cars, or healthcare) based on 5G communication technology and IoT-related technology.

[0172] A wearable device according to the present disclosure can control a screen displayed on a display of the wearable device in response to a press input in a situation where touch input on the display is disabled (or limited). A wearable device according to the present disclosure can control a screen displayed on the display of the wearable device in response to a press input without using a physical button (e.g., a key input device (103, 104) of FIG. 1). A wearable device according to the present disclosure can intuitively control a screen displayed on the display of the wearable device.

[0173] The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects that are not mentioned can be clearly understood by a person having ordinary skill in the art to which the present disclosure belongs from the above description.

[0174] As described above, the wearable device (500) may include at least one motion sensor (560). The wearable device may include an optical sensor (550). The wearable device may include a display (540). The wearable device may include a memory (520) that stores instructions and includes one or more storage media. The wearable device may include at least one processor (510) that includes a processing circuit. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to detect an event that disables identification of a touch input on the display. The instructions, when individually or collectively executed by the at least one processor, may identify, by using the optical sensor and the at least one motion sensor, a peripheral portion pressed by a user gesture among peripheral portions of the wearable device while the identification of the touch input is disabled according to the event. The instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to change at least a portion of a screen displayed on the display based on identifying the peripheral portion pressed by the user gesture.

[0175] For example, identification of a press input on said peripheral parts of said wearable device may be activated by said event that deactivates said identification of said touch input on said display.

[0176] For example, the instructions, when individually or collectively executed by the at least one processor to detect the event, may cause the wearable device to identify, through the display, a user input that triggers the event, identify execution of a first application that disables the identification of the touch input, or identify execution of a second application for measuring an amount of movement of a user wearing the wearable device.

[0177] For example, the instructions, when individually or collectively executed by the at least one processor to change at least a portion of the screen, may cause the wearable device to change at least a portion of the screen by changing a first screen displayed on the display to a second screen subsequent to the first screen based on identifying, using the optical sensor and the at least one motion sensor, a first peripheral portion of the wearable device among the peripheral portions of the wearable device, and to change at least a portion of the screen displayed on the display by changing the first screen to a third screen preceding the first screen based on identifying, using the optical sensor and the at least one motion sensor, a second peripheral portion of the wearable device located opposite the first peripheral portion and pressed by a user gesture among the peripheral portions of the wearable device.

[0178] For example, the instructions, when individually or collectively executed by the at least one processor to change at least a portion of the screen, may cause the wearable device to change the at least portion of the screen by changing a first screen displayed on the display to a second screen subsequent to the first screen based on identifying a second peripheral portion after a first peripheral portion has been identified during a time interval using the optical sensor and the at least one motion sensor, and to change the at least portion of the screen by changing the first screen displayed on the display to a third screen prior to the first screen based on identifying the first peripheral portion after the second peripheral portion has been identified during a time interval using the optical sensor and the at least one motion sensor.

[0179] For example, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify, using the optical sensor and the at least one motion sensor, a third peripheral portion pressed by a user gesture while a first object is displayed on a portion of a third peripheral portion between the first peripheral portion and the second peripheral portion of the display, and to activate the identification of the touch input based on identifying the third peripheral portion of the wearable device.

[0180] For example, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify a first likelihood value by which the first peripheral portion is identified among the peripheral portions through a first artificial intelligence model using first sensing data acquired through the optical sensor and second sensing data acquired through the at least one motion sensor, identify a second likelihood value by which the third peripheral portion adjacent to the first peripheral portion is identified through a second artificial intelligence model using the first sensing data and the second sensing data, identify the first peripheral portion pressed by a user gesture if the first likelihood value exceeds the second likelihood value, and identify the third peripheral portion pressed by the user gesture if the first likelihood value is less than the second likelihood value.

[0181] For example, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to change at least a portion of the screen displayed on the display by, while a second object is displayed on a portion of the peripheral portion of the display, identifying the peripheral portion pressed by the user gesture using the optical sensor and the at least one motion sensor, identifying an application corresponding to the second object based on the identifying the peripheral portion of the wearable device, and displaying a user interface (UI) of the application through the display.

[0182] For example, the instructions, when individually or collectively executed by the at least one processor, may cause the wearable device to identify a state of a user wearing the wearable device using the at least one motion sensor, and to reduce an operating cycle of the optical sensor when the state of the user is identified as the stationary state among a motion state and a stationary state.

[0183] For example, the at least one motion sensor may include an acceleration sensor, a gyro sensor, and a geomagnetic sensor.

[0184] A method performed by a wearable device including at least one motion sensor, an optical sensor, and a display as described above may include detecting an event that deactivates identification of a touch input on the display, using the optical sensor and the at least one motion sensor while the identification of the touch input is deactivated in response to the event, identifying a peripheral portion pressed by a user gesture among peripheral portions of the wearable device, and changing at least a portion of a screen displayed on the display based on the identification of the peripheral portion pressed by the user gesture.

[0185] For example, a press input to said peripheral parts of said wearable device may be activated by said event that deactivates said identification of said touch input on said display.

[0186] For example, the operation of detecting the event of the method may include an operation of identifying a user input that triggers the event through the display, an operation of identifying execution of a first application that disables the identification of the touch input, or an operation of identifying execution of a second application for measuring the amount of exercise of a user wearing the wearable device.

[0187] For example, the operation of changing at least a portion of the screen of the method may include an operation of changing the first screen displayed on the display to a second screen following the first screen based on identifying a first peripheral portion pressed by a user gesture among peripheral portions of the wearable device using the optical sensor and the at least one motion sensor, thereby changing the at least a portion of the screen displayed on the display, and an operation of changing the first screen displayed on the display to a third screen preceding the first screen based on identifying a second peripheral portion positioned opposite the first peripheral portion pressed by a user gesture among peripheral portions of the wearable device using the optical sensor and the at least one motion sensor, thereby changing the at least a portion of the screen displayed on the display.

[0188] For example, the operation of changing at least a portion of the screen of the method may include an operation of changing the at least a portion of the screen displayed on the display by changing the first screen displayed on the display to a second screen subsequent to the first screen based on identifying a second peripheral portion pressed by the user gesture after the first peripheral portion pressed by the user gesture is identified during the time interval using the optical sensor and the at least one motion sensor, and an operation of changing the at least a portion of the screen displayed on the display by changing the first screen displayed on the display to a third screen prior to the first screen based on identifying the first peripheral portion pressed by the user gesture after the second peripheral portion pressed by the user gesture is identified during the time interval using the optical sensor and the at least one motion sensor.

[0189] For example, the method may include an operation of identifying a third peripheral portion pressed by a user gesture using the optical sensor and the at least one motion sensor while a first object is displayed on a portion of a third peripheral portion between the first peripheral portion and the second peripheral portion of the display, and an operation of activating the identification of the touch input based on the identification of the third peripheral portion of the wearable device.

[0190] For example, the method may include an operation of identifying, through a first artificial intelligence model, a first possibility value by which the first peripheral portion is identified among the peripheral portions using first sensing data acquired through the optical sensor and second sensing data acquired through the at least one motion sensor; an operation of identifying, through a second artificial intelligence model, a second possibility value by which the third peripheral portion adjacent to the first peripheral portion is identified among the peripheral portions using the first sensing data and the second sensing data; an operation of identifying, if the first possibility value exceeds the second possibility value, the first peripheral portion pressed by a user gesture; and an operation of identifying, if the first possibility value is less than the second possibility value, the third peripheral portion pressed by the user gesture.

[0191] For example, the method may include an operation of identifying a peripheral portion pressed by the user gesture using the optical sensor and the at least one motion sensor while a second object is displayed on a portion of the peripheral portion of the display, an operation of identifying an application corresponding to the second object based on the identification of the peripheral portion of the wearable device, and an operation of changing at least a portion of the screen displayed on the display by displaying a user interface (UI) of the application through the display.

[0192] For example, the method may include an operation of identifying a state of a user wearing the wearable device using the at least one motion sensor, and an operation of reducing an operating cycle of the optical sensor when the state of the user is identified as a stationary state among a movement state and a stationary state.

[0193] For example, the at least one motion sensor may include an acceleration sensor, a gyro sensor, and a geomagnetic sensor.

[0194] The methods according to the embodiments described in the claims or specification of the present disclosure may be implemented in the form of hardware, software, or a combination of hardware and software.

[0195] When implemented in software, a computer-readable storage medium storing one or more programs (software modules) may be provided. The one or more programs stored in the computer-readable storage medium are configured for execution by one or more processors in an electronic device. The one or more programs include instructions that cause the electronic device to execute methods according to embodiments described in the claims or specifications of the present disclosure. The one or more programs may be provided as included in a computer program product. The computer program product may be traded between sellers and buyers as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read only memory (CD-ROM)) or an application store (e.g., Play Store). ™ ) or directly between two user devices (e.g., smart phones), online distribution (e.g., downloading or uploading). In the case of online distribution, at least a portion of the computer program product may be at least temporarily stored or temporarily created in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0196] These programs (software modules, software) may be stored in random access memory, non-volatile memory including flash memory, read only memory (ROM), electrically erasable programmable read only memory (EEPROM), magnetic disc storage devices, compact disc-ROM (CD-ROM), digital versatile discs (DVDs) or other forms of optical storage devices, magnetic cassettes, or may be stored in memories formed by a combination of some or all of these. In addition, each configuration memory may include multiple copies.

[0197] Additionally, the program may be stored on an attachable storage device that is accessible via a communication network, such as the Internet, an intranet, a local area network (LAN), a wide area network (WAN), a storage area network (SAN), or a combination thereof. Such a storage device may be connected to a device implementing an embodiment of the present disclosure via an external port. Additionally, a separate storage device on the communication network may be connected to a device implementing an embodiment of the present disclosure.

[0198] In the specific embodiments of the present disclosure described above, components included in the disclosure are expressed singularly or plurally, depending on the specific embodiment presented. However, the singular or plural expressions are selected to suit the presented situation for convenience of explanation, and the present disclosure is not limited to singular or plural components. Components expressed in plural may be composed of singular elements, or components expressed in singular may be composed of plural elements.

[0199] According to embodiments, one or more of the components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., modules or programs) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0200] Meanwhile, although the detailed description of the present disclosure has described specific embodiments, it is obvious that various modifications are possible within the scope of the present disclosure.

Claims

1. In wearable devices, At least one motion sensor; optical sensor; display; A memory that stores instructions and includes one or more storage media; At least one processor comprising a processing circuit, The above instructions, when individually or collectively executed by the at least one processor, Detecting an event that disables identification of touch input on the above display, While the identification of the touch input is disabled according to the above event, using the optical sensor and the at least one motion sensor, among the peripheral parts of the wearable device, a peripheral part pressed by a user gesture is identified, and To change at least a portion of the screen displayed on the display based on identifying the peripheral portion pressed by the user gesture; causing the above wearable device, Wearable devices.

2. In paragraph 1, Identification of a press input to said peripheral parts of said wearable device is activated by said event that deactivates said identification of said touch input on said display. Wearable devices.

3. In the first paragraph, the instructions, when individually or collectively executed by the at least one processor to detect the event, Through the above display, identify the user input that triggers the above event, or Identify the execution of a first application that disables the identification of said touch input, or To identify the execution of a second application for measuring the amount of exercise of a user wearing the wearable device, causing the above wearable device, Wearable devices.

4. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor to change at least a portion of the screen, By using the optical sensor and the at least one motion sensor, based on identifying a first peripheral portion pressed by a user gesture among the peripheral portions of the wearable device, changing the first screen displayed on the display to a second screen following the first screen, thereby changing at least a part of the screen displayed on the display, and By using the optical sensor and the at least one motion sensor, based on identifying a second peripheral portion located opposite the first peripheral portion, which is pressed by a user gesture among the peripheral portions of the wearable device, changing at least a part of the screen displayed on the display by changing the first screen displayed on the display to a third screen prior to the first screen, causing the above wearable device, Wearable devices.

5. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor to change at least a portion of the screen, By using the optical sensor and the at least one motion sensor, changing the first screen displayed on the display to a second screen following the first screen based on identifying the second peripheral portion after the first peripheral portion is identified during a time interval, thereby changing at least a portion of the screen, and By using the optical sensor and the at least one motion sensor, based on identifying the first peripheral portion after the second peripheral portion is identified during the time period, changing the first screen displayed on the display to a third screen prior to the first screen, thereby changing at least a portion of the screen, causing the above wearable device, Wearable devices.

6. In the fourth paragraph, when the instructions are individually or collectively executed by the at least one processor, While the first object is displayed on a part of a third peripheral portion between the first peripheral portion and the second peripheral portion of the display, the third peripheral portion is identified by a user gesture using the optical sensor and the at least one motion sensor, and To activate the identification of the touch input based on identifying the third peripheral part of the wearable device, causing the above wearable device, Wearable devices.

7. In the sixth paragraph, when the instructions are individually or collectively executed by the at least one processor, Using the first sensing data acquired through the optical sensor and the second sensing data acquired through the at least one motion sensor, a first probability value is identified by which the first peripheral part is identified among the peripheral parts through the first artificial intelligence model, Using the first sensing data and the second sensing data, a second possibility value is identified by identifying the third peripheral part adjacent to the first peripheral part through a second artificial intelligence model, If the first probability value exceeds the second probability value, the first peripheral portion pressed by the user gesture is identified, and If the first possibility value is less than the second possibility value, identify the third peripheral portion pressed by the user gesture. causing the above wearable device, Wearable devices.

8. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor to change at least a portion of the screen, While a second object is displayed on a portion of the peripheral portion of the display, using the optical sensor and the at least one motion sensor, identifying the peripheral portion pressed by the user gesture, Based on identifying the peripheral portion of the wearable device, identifying an application corresponding to the second object, and By displaying the user interface (UI) of the application through the display, at least a part of the screen displayed on the display is changed. causing the above wearable device, Wearable devices.

9. In the first paragraph, when the instructions are individually or collectively executed by the at least one processor, Using at least one motion sensor, identifying the status of a user wearing the wearable device, When the user's state is identified as the stationary state among the movement state and the stationary state, the operating cycle of the optical sensor is reduced. causing the above wearable device, Wearable devices.

10. In paragraph 1, The at least one motion sensor comprises an acceleration sensor, a gyro sensor, and a geomagnetic sensor. Wearable devices.

11. A method performed by a wearable device including at least one motion sensor, an optical sensor, and a display, An action to detect an event that disables identification of a touch input on the display; While the identification of the touch input is disabled according to the event, an operation of identifying a peripheral portion pressed by a user gesture among peripheral portions of the wearable device using the optical sensor and the at least one motion sensor; and An action that includes changing at least a portion of a screen displayed on the display based on identifying the peripheral portion pressed by the user gesture. A method performed by a wearable device.

12. In paragraph 11, A press input to said peripheral parts of said wearable device is activated by said event that deactivates said identification of said touch input on said display. A method performed by a wearable device.

13. In the 11th paragraph, the operation of detecting the event is as follows: An action to identify a user input that triggers the event through the display; An action identifying the execution of a first application that disables the identification of the touch input; or Including an action of identifying the execution of a second application for measuring the amount of exercise of a user wearing the wearable device, A method performed by a wearable device.

14. In paragraph 11, the action of changing at least a part of the screen is: An operation of changing at least a part of the screen displayed on the display by changing the first screen displayed on the display to a second screen following the first screen based on identifying a first peripheral part pressed by a user gesture among peripheral parts of the wearable device using the optical sensor and the at least one motion sensor; and An operation of changing at least a part of the screen displayed on the display by changing the first screen displayed on the display to a third screen prior to the first screen based on identifying a second peripheral portion located opposite the first peripheral portion, which is pressed by a user gesture among the peripheral portions of the wearable device, using the optical sensor and the at least one motion sensor, A method performed by a wearable device.

15. In paragraph 11, the action of changing at least a part of the screen is: An operation of changing at least a portion of the screen displayed on the display by changing the first screen displayed on the display to a second screen following the first screen based on identifying a second peripheral portion pressed by the user gesture after the first peripheral portion pressed by the user gesture is identified during a time interval using the optical sensor and the at least one motion sensor; and An operation of changing at least a portion of the screen displayed on the display by changing the first screen displayed on the display to a third screen prior to the first screen based on identifying the first peripheral portion pressed by the user gesture after the second peripheral portion pressed by the user gesture during the time period is identified using the optical sensor and the at least one motion sensor. A method performed by a wearable device.

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