Wearable computing device that adjusts current supplied to light source of sensor based on amount of ambient light detected by another sensor

Abstract

A wearable computing device includes a body, a first sensor disposed at an upper portion of the body, and a second sensor disposed at a lower portion of the body. The first sensor detects ambient light in the surroundings of the wearable computing device, and the second sensor includes one or more light sources that emit light toward a body part of a user when the wearable computing device is worn by the user, and one or more detectors that emit light toward the body part of the user when the wearable computing device is worn by the user. And the one or more detectors receive reflections of the light emitted toward the body part to generate a signal indicative of a biological characteristic of the user. The wearable computing device also includes one or more processors that adjust the current supplied to the one or more light sources based on the amount of ambient light detected by the first sensor.

Description

Technical Field

[0001] The present disclosure relates generally to wearable computing devices and more particularly to wearable computing devices that adjust the current supplied to a light source of a sensor based on the amount of ambient light detected by another sensor. Background Art

[0002] Some wearable computing devices (e.g., watches and fitness devices) may collect data about activities performed by a user or about the user's physiological state. Such data may include data representing the environment around the user or the user's interaction with the environment. For example, the data may include motion data about the user's movement and / or physiological data obtained by measuring various physiological characteristics of the user (such as heart rate, sweat level, etc.).

[0003] Some wearable computing devices perform continuous heart rate monitoring, where the LED of the PPG sensor is active (i.e., emitting light) at all times during the day, but at the expense of power consumption from the battery of the wearable computing device. To mitigate the amount of power consumption, the brightness of the LED of the PPG sensor can be dimmed. Some wearable computing devices do not perform continuous heart rate monitoring, but instead perform sampling at predetermined time periods (e.g., every 10-15 minutes), or only perform continuous heart rate monitoring when the user is exercising (e.g., during a fitness session), and drive the LED of the PPG sensor at a very high level during the time when the heart rate is monitored. That is, the LED can be driven at a very high level because the LED is not driven continuously throughout the day, but is only used periodically or during a fitness session. Therefore, there is a trade-off between PPG sensor accuracy (e.g., driving the LED at a high level) and power consumption, as well as a trade-off between PPG sensor availability / usage (continuous availability vs. periodic use) and power consumption. Summary of the invention

[0004] Aspects and advantages of embodiments of the present disclosure will be set forth in part in the description which follows, or may be learned from the description, or may be learned through practice of example embodiments.

[0005] In an example embodiment, a wearable computing device is provided. The wearable computing device includes a body, a first sensor disposed at an upper portion of the body, and a second sensor disposed at a lower portion of the body. The first sensor is configured to detect ambient light in an environment surrounding the wearable computing device, and the second sensor includes one or more light sources and one or more detectors. The one or more light sources are configured to emit light toward a body part of the user when the wearable computing device is worn by the user, and the one or more detectors are configured to receive reflections of the light emitted toward the body part to generate a signal indicating a biometric feature of the user. The wearable computing device also includes one or more processors configured to adjust a current supplied to the one or more light sources based on an amount of ambient light detected by the first sensor (i.e., depending on an amount of ambient light detected by the first sensor, in particular, depending on a signal generated by the first sensor corresponding to an amount of ambient light detected by the first sensor).

[0006] In an embodiment, the second sensor includes a photoplethysmography (PPG) sensor configured to monitor a user's heart rate when the wearable computing device is worn by the user. The one or more light sources include one or more light emitting diodes, and the one or more detectors include one or more photodiodes.

[0007] In an embodiment, the wearable computing device further comprises one or more inertial sensors disposed in the body. The one or more inertial sensors are configured to generate one or more motion signals based on movement of the wearable computing device. The one or more processors are configured to adjust the current supplied to the one or more light sources based on the one or more motion signals generated by the one or more inertial sensors.

[0008] In an embodiment, the one or more processors are configured to perform one or more functions of the wearable computing device based on the location determined by the one or more processors to apply the force to the display screen.

[0009] In an embodiment, the one or more processors are configured to increase current supplied to the one or more light sources in response to one or more motion signals generated by the one or more inertial sensors indicating that motion of the wearable computing device is of a predetermined type or greater than a threshold.

[0010] In an embodiment, the second sensor includes a photoplethysmography (PPG) sensor configured to monitor a heart rate of a user when the wearable computing device is worn by the user. The one or more light sources include a plurality of light emitting diodes, and the one or more detectors include a plurality of photodiodes. When the one or more motion signals generated by the one or more inertial sensors indicate that the lower portion of the body of the wearable computing device is spaced apart from a body part of the user, the one or more processors are configured to cut off current supplied to at least one of the plurality of light emitting diodes and increase current supplied to at least one other of the plurality of light emitting diodes.

[0011] In an embodiment, the one or more processors are configured to increase current supplied to the one or more light sources in response to the amount of ambient light detected by the first sensor being greater than a threshold value.

[0012] In an embodiment, the wearable computing device further comprises one or more inertial sensors disposed in a body of the wearable computing device. The one or more inertial sensors are configured to generate one or more motion signals based on movement of the wearable computing device, and the one or more processors are configured to increase the current supplied to the one or more light sources when the amount of ambient light detected by the first sensor is greater than a first threshold and the one or more motion signals generated by the one or more inertial sensors indicate that the motion of the wearable computing device is of a predetermined type or greater than a second threshold.

[0013] In an embodiment, the wearable computing device further comprises a display disposed at an upper portion of the body of the wearable computing device. The display is configured to display biometric information. The first sensor comprises an ambient light sensor disposed below the display, and according to another embodiment, the ambient light sensor has a first sampling rate for detecting ambient light in a first operating mode of the wearable computing device, and has a second sampling rate for detecting ambient light in a second operating mode of the wearable computing device (the sampling rate corresponds to a number of detections per unit time).

[0014] In an embodiment, the one or more detectors are configured to detect ambient light in a space between a lower portion of a body of the wearable computing device and a body part of a user. The one or more processors are configured to adjust a current supplied to the one or more light sources based on an amount of ambient light detected by the one or more detectors.

[0015] In an embodiment, the one or more processors are configured to determine a current value to which the current is to be adjusted using the amount of ambient light detected by the first sensor as input according to one or more of a lookup table, a linear scaling method, and a non-linear scaling method.

[0016] In an example embodiment, a computer-implemented method is provided. The computer-implemented method includes: detecting ambient light in an environment surrounding the wearable computing device by a first sensor disposed at an upper portion of a body of the wearable computing device, and adjusting, by one or more processors, a current supplied to one or more light sources of a second sensor based on an amount of ambient light detected by the first sensor. The second sensor is disposed at a lower portion of the body of the wearable computing device and includes one or more light sources and one or more detectors, the one or more light sources emitting light toward a body part of the user when the wearable computing device is worn by the user, the one or more detectors receiving reflections of the light emitted toward the body part to generate a signal indicative of a biometric characteristic of the user.

[0017] In an embodiment, the method further comprises: generating, by one or more inertial sensors disposed in a body of the wearable computing device, one or more motion signals based on movement of the wearable computing device. Adjusting, by the one or more processors, a current supplied to the one or more light sources of the second sensor is based on the one or more motion signals generated by the one or more inertial sensors.

[0018] In an embodiment, the method further comprises increasing current supplied to the one or more light sources in response to the one or more motion signals generated by the one or more inertial sensors indicating that the motion of the wearable computing device is of a predetermined type or greater than a threshold.

[0019] In an embodiment, the second sensor comprises a photoplethysmography (PPG) sensor configured to monitor a heart rate of a user when the wearable computing device is worn by the user, the one or more light sources comprise a plurality of light emitting diodes, and the one or more detectors comprise a plurality of photodiodes. In an embodiment, the method further comprises: in response to the one or more motion signals generated by the one or more inertial sensors indicating that the lower portion of the body of the wearable computing device is spaced apart from the body part of the user, cutting off current supplied to at least one of the plurality of light emitting diodes and increasing current supplied to at least one other of the plurality of light emitting diodes.

[0020] In an embodiment, the method further includes generating one or more motion signals by one or more inertial sensors disposed in a body of the wearable computing device based on movement of the wearable computing device, and increasing the current supplied to the one or more light sources to a peak value when the amount of ambient light detected by the first sensor is greater than a first threshold and the one or more motion signals generated by the one or more inertial sensors indicate that the movement of the wearable computing device is of a predetermined type or greater than a second threshold.

[0021] In an embodiment, the method further comprises: displaying the biometric information on a display of the wearable computing device. The first sensor comprises an ambient light sensor disposed below the display, and detecting the ambient light by the first sensor comprises: sampling the ambient light at a first sampling rate in a first operating mode of the wearable computing device, and sampling the ambient light at a second sampling rate in a second operating mode of the wearable computing device.

[0022] In an embodiment, the method further comprises: detecting, by one or more detectors, ambient light in a space between the lower portion of the body and the body part of the user. Adjusting, by one or more processors, the current supplied to the one or more light sources of the second sensor is also based on the amount of ambient light detected by the one or more detectors.

[0023] In an embodiment, the method further includes: determining, by one or more processors, a current value to which the current is to be adjusted based on one or more of a lookup table, a linear scaling method, and a non-linear scaling method, using one or more of the amount of ambient light detected by the first sensor, the amount of ambient light detected by one or more detectors, and one or more motion signals generated by one or more inertial sensors.

[0024] In an example embodiment, a non-transitory computer-readable medium is provided that stores instructions executable by one or more processors of a wearable computing device. The non-transitory computer-readable medium stores instructions that include instructions that cause the one or more processors to receive information indicating an amount of ambient light in an environment surrounding the wearable computing device detected by a first sensor disposed at an upper portion of a body of the wearable computing device, and instructions that cause the one or more processors to adjust a current supplied to one or more light sources of a second sensor based on the amount of ambient light detected by the first sensor. The second sensor is disposed at a lower portion of the body and includes one or more light sources that emit light toward a body part of the user when the wearable computing device is worn by the user, and one or more detectors that receive reflections of the light emitted toward the body part to generate a signal indicating a biometric feature of the user.

[0025] In an embodiment, the non-transitory computer-readable medium stores instructions including instructions to cause one or more processors to receive information indicating a type of motion of the wearable computing device and / or a magnitude of movement of the wearable computing device based on one or more motion signals generated by one or more inertial sensors disposed in a body of the wearable computing device. In an embodiment, the non-transitory computer-readable medium stores instructions including instructions to cause one or more processors to adjust a current supplied to one or more light sources based on the information indicating a type of motion of the wearable computing device and / or a magnitude of movement of the wearable computing device. The non-transitory computer-readable medium may store additional instructions for performing other aspects of the wearable computing device and computer-implemented methods as described herein.

[0026] These and other features, aspects and advantages of various embodiments of the present disclosure will be better understood with reference to the following description, drawings and appended claims.The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate example embodiments of the present disclosure and, together with the description, serve to explain the relevant principles. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] A detailed discussion of example embodiments for persons of ordinary skill in the art is set forth in this specification with reference to the accompanying drawings, in which:

[0028] Figure 1 An example wearable computing device according to one or more example embodiments of the present disclosure is depicted;

[0029] Figure 2 depicts an exploded view of an example wearable computing device according to one or more example embodiments of the present disclosure;

[0030] Figure 3 An example block diagram depicting a wearable computing device according to one or more example embodiments of the present disclosure;

[0031] Figure 4 An example block diagram depicting a wearable computing device according to one or more example embodiments of the present disclosure;

[0032] Figure 5A and Figure 5B depicts a bottom view of an example wearable computing device according to one or more example embodiments of the present disclosure;

[0033] Figure 6 An example wearable computing device spaced apart from a body part of a user is shown according to one or more example embodiments of the present disclosure; and

[0034] Figure 7A flowchart for adjusting a brightness value based on one or more inputs according to one or more example embodiments of the present disclosure is shown; and

[0035] Figure 8 A flowchart of an example non-limiting computer-implemented method is shown according to one or more example embodiments of the present disclosure. DETAILED DESCRIPTION

[0036] Reference will now be made to embodiments of the present disclosure, one or more examples of which are shown in the accompanying drawings. Each example is provided by way of explanation of the present disclosure and is not intended to limit the present disclosure. In fact, it will be apparent to those skilled in the art that various modifications and changes may be made to the present disclosure without departing from the scope or spirit of the present disclosure. For example, a feature shown or described as part of one embodiment may be used together with another embodiment to produce yet another embodiment. Therefore, the present disclosure is intended to cover modifications and changes within the scope of the appended claims and their equivalents.

[0037] The terms used herein are used to describe example embodiments and are not intended to limit and / or constrain the present disclosure. As used herein, the singular forms "a", "an", and "the" are also intended to include the plural forms unless the context clearly indicates otherwise. In the present disclosure, terms such as "including", "having", "comprising", etc. are used to specify features, numbers, steps, operations, elements, components, or combinations thereof, but do not exclude the presence or addition of one or more of the features, elements, steps, operations, elements, components, or combinations thereof.

[0038] It will be understood that although the terms first, second, third, etc. may be used herein to describe various elements, these elements should not be limited to these terms. Instead, these terms are used to distinguish one element from another element. For example, without departing from the scope of the present disclosure, a first element may be referred to as a second element, and a second element may be referred to as a first element.

[0039] The term "and / or" includes a combination of multiple related listed items or any of the multiple related listed items. For example, the scope of the expression or phrase "A and / or B" includes the item "A", the item "B" and the combination of the items "A and B".

[0040] In addition, the scope of the expression or phrase "at least one of A or B" is intended to include all of the following: (1) at least one of A; (2) at least one of B; and (3) at least one of A and at least one of B. Likewise, the scope of the expression or phrase "at least one of A, B, or C" is intended to include all of the following: (1) at least one of A; (2) at least one of B; (3) at least one of C; (4) at least one of A and at least one of B; (5) at least one of A and at least one of C; (6) at least one of B and at least one of C; and (7) at least one of A, at least one of B, and at least one of C.

[0041] Examples of the present disclosure relate to a wearable computing device that can be worn, for example, on a user's wrist. For example, the wearable computing device may include a watch or a fitness device. The wearable computing device includes a body having a first sensor (e.g., an ambient light sensor) disposed at an upper portion of the body. The first sensor is configured to detect ambient light in an environment surrounding the wearable computing device. The wearable computing device includes a second sensor (e.g., a PPG sensor) disposed at a lower portion of the body. The second sensor is configured to generate a signal indicating a biological attribute of the user (e.g., heart rate or pulse information). The second sensor includes one or more light sources (e.g., light emitting diodes (LEDs)) and one or more detectors (e.g., photodiodes), the one or more light sources being configured to emit light toward a body part of the user when the wearable computing device is worn by the user, and the one or more detectors being configured to receive reflections of the light emitted toward the body part to generate a signal indicating a biological attribute of the user. The wearable computing device includes one or more processors configured to adjust a current supplied to the one or more light sources based on the amount of ambient light detected by the first sensor. For example, based on the amount of ambient light detected by the first sensor, one or more processors may increase the current supplied to the one or more light sources (e.g., in a bright ambient environment) to improve the measurement accuracy of the second sensor, or may reduce the current supplied to the one or more light sources (e.g., in a dim ambient environment) to extend the battery life of the wearable computing device.

[0042] In an embodiment, the wearable computing device may further include one or more inertial sensors configured to generate one or more motion signals based on movement of the wearable computing device, wherein the one or more processors are configured to adjust the current supplied to the one or more light sources based on the one or more motion signals generated by the one or more inertial sensors. For example, the one or more inertial sensors may include an accelerometer and / or a gyroscope. The one or more processors may be configured to adjust the current supplied to the one or more light sources based on whether the one or more motion signals generated by the inertial sensors indicate that the movement of the wearable computing device is greater than a threshold level (i.e., a degree or rate of movement that is likely to expose the detector of the PPG sensor to ambient light, such as 2 m / s) and / or ... likely to affect the accuracy of the PPG sensor (e. For example, based on the amount of movement and / or type of movement indicated by one or more motion signals generated by the inertial sensor, the one or more processors may increase the current supplied to the one or more light sources (e.g., the amount of movement of the wearable computing device is greater than a threshold) to improve the measurement accuracy of the second sensor, or may reduce the current supplied to the one or more light sources (e.g., when the wearable computing device is stationary) to extend the battery life of the wearable computing device.

[0043] Photoplethysmography (PPG) is an optical technology for obtaining blood volume change data. A PPG sensor can optically measure biometric information, such as heart rate, using an emitter (light source) and a detector (e.g., a photodiode) at the surface of a user's body part to measure volume changes in blood circulation. A light source (e.g., one or more LEDs) emits light to a body part, and a photodetector measures light reflected from the body part, where the amount of reflected light can indicate biometric information about the user (e.g., blood flow, blood volume, etc.). The light source can emit infrared light, and the color of the LED can be red, green, or yellow. The PPG signal may include various components, such as: a DC component (or DC offset), which represents the constant absorption of light passing through tissue; an AC component generated by the heartbeat (heart activity) that affects blood volume, which depends on the systolic and diastolic periods; and an ambient light component, which represents the amount of ambient light received by the detector (e.g., a photodiode).

[0044] The quality of the PPG signal affects the accuracy of the measured heart rate. The quality of the PPG signal may depend on, among other things, the optical sensor design and parameters, such as the window and lens design and the spacing of the PPG detector (e.g., photodiode) and the light source (e.g., LED). The optical sensor design and parameters also include the brightness level of the light source (e.g., LED), the number of detectors (e.g., photodiodes) implemented, and the positions of the light source and detector. The optical sensor design and parameters also include multiple optical paths.

[0045] The PPG sensor of the wearable computing device may be susceptible to light leakage, due to which the detector of the PPG sensor is exposed to ambient light from the environment, thereby adversely affecting the accuracy of the PPG signal. For example, in a bright ambient environment, high motion activity may cause the wearable computing device to move or "swing" on a user's body part (e.g., their wrist), thereby causing a gap between the user's body part and the bottom of the wearable computing device where the PPG sensor is located. Light may leak into the gap and be exposed to the detector of the PPG sensor and / or interfere with the light emitted by the light source to the body part, thereby corrupting the PPG signal obtained by the detector.

[0046] According to one or more examples disclosed herein, the impairment of the PPG signal due to ambient light can be compensated, mitigated, or overcome by timely or selectively increasing the brightness of the light source (LED). For example, the brightness of the light source can be selectively increased based on the surrounding ambient light and / or user activity (e.g., the user's movement). For example, adjusting the brightness of the light source (e.g., by adjusting the current) can be performed only when the user is exercising (i.e., when the movement of the wearable computing device exceeds a threshold level), or can be performed at all time points during a day. For example, one or more processors can be configured to keep the brightness of the light source low when brighter light is not needed, thereby saving power. When brighter light is needed to increase the accuracy of the PPG signal, such as in a bright environment and / or during high motion activity, one or more processors can be configured to increase the current supply to the light source to increase the brightness of the light source, while consuming more power for a shorter amount of time (e.g., compared to a wearable computing device that continuously operates the light source at a high brightness level).

[0047] According to one or more examples of the present disclosure, operation of a wearable computing device includes: one or more of a first sensor (e.g., an ambient light sensor), a second sensor (e.g., a PPG sensor detector), and an inertial sensor (e.g., an accelerometer and / or a gyroscope) providing information (e.g., signals, data, etc.) as input to one or more processors, which determine whether a current supplied to a light source (e.g., an LED) of the PPG sensor should be adjusted.

[0048] For example, the ambient light sensor may provide a raw signal to an ambient brightness determination unit (e.g., an analog-to-digital converter), which converts the raw signal into a value representing an amount of ambient light (e.g., a lux value). For example, the ambient brightness determination unit may be implemented as part of the ambient light sensor firmware, or may be separate from the ambient light sensor as an internal unit of the wearable computing device. The post-processed value representing the amount of ambient light (e.g., a value in lux, rather than the raw signal) may be transmitted to one or more processors for determining whether a current supplied to a light source (e.g., an LED) of the PPG sensor should be adjusted.

[0049] For example, an inertial sensor (e.g., an inertial measurement unit) may continuously measure motion data of the wearable computing device. The motion data may be transmitted to one or more processors. For example, the motion data may be transmitted to the one or more processors after passing through a coarse classifier model in order to understand or classify certain characteristics of the motion of the wearable computing device (e.g., where the motion is considered to be low, medium, high, periodic, non-periodic, etc.).

[0050] For example, one or more detectors of the PPG sensor may also sense ambient light when the bottom side of the wearable computing device is tilted or spaced apart from a body part of the user. The detectors of the PPG sensor may provide information related to ambient light conditions (e.g., via raw signals) to an ambient brightness determination unit (e.g., an analog-to-digital converter), which converts one or more raw signals from the detectors of the PPG sensor into a value representing an amount of ambient light (e.g., a lux value). For example, when the ambient brightness determination unit is implemented as part of the ambient light sensor firmware, the ambient brightness determination unit may receive one or more raw signals from one or more detectors of the PPG sensor and receive raw signals from the ambient light sensor, or may only receive one or more raw signals from one or more detectors of the PPG sensor. In another example, a separate ambient brightness determination unit may be provided to receive raw signals from the ambient light sensor and one or more raw signals from one or more detectors of the PPG sensor. A post-processed value (e.g., a value in lux, not a raw signal) representing the amount of ambient light sensed by one or more detectors of the PPG sensor may be transmitted to one or more processors for use in determining whether a current supplied to a light source (e.g., an LED) of the PPG sensor should be adjusted. For example, the one or more detectors may be configured to detect ambient light in a space between a lower portion of a body of the wearable computing device and a body part of a user, and the one or more processors may be configured to adjust the current supplied to the one or more light sources based on the amount of ambient light detected by the one or more detectors.

[0051] The wearable computing device may include one or more memories to store settings of the light source, including the current settings of the light source that serve as a reference point. For example, the PPG analog front end (AFE) may know the current LED brightness setting (e.g., the current level currently supplied to the LED) and provide the current LED brightness setting to one or more processors for determining whether the current of the light source (e.g., LED) supplied to the PPG sensor should be adjusted. The one or more processors may use one or more inputs from the ambient light sensor, the inertial sensor, and the detector and the current LED brightness setting to determine whether the current of the light source (e.g., LED) supplied to the PPG sensor should be adjusted. When the one or more processors determine that the brightness value required for the LED to generate an accurate PPG signal is met based on the current LED brightness setting, no adjustment may be made. Conversely, when the one or more processors determine that the brightness value required for the LED to generate an accurate PPG signal can be met by adjusting (e.g., reducing or increasing) the current LED brightness setting, the one or more processors may control the LED driver to adjust the amount of current supplied to the LED accordingly. After the updated brightness value is determined by the one or more processors, the one or more processors may feed the updated brightness value back to the AFE to adjust the brightness (i.e., current) to the updated brightness value. For example, one or more processors can control the LED driver to increase or decrease the current supplied to the LED.For example, the LED can be driven at various current levels based on one or more inputs from an ambient light sensor, an inertial sensor, and a detector.

[0052] The LED brightness setting value (i.e., the amount of current to be supplied to the LED) can be obtained or determined by one or more processors: (1) based on a lookup table derived from a laboratory evaluation of optimal parameters, where the ambient light value and motion data are the primary inputs; (2) based on a linear scaling (e.g., [motion + more ambient light] * scaling factor = new brightness value); and / or (3) based on a non-linear scaling developed by training a machine learning model that utilizes the ambient light value and motion data as inputs and outputs an optimal LED brightness setting value (i.e., the amount of current to be supplied to the LED) for a given ambient condition. For example, in the event that the ambient light sensor detects a brighter environment and / or in the event that the inertial sensor detects high movement of the wearable computing device, the one or more processors can control the LED driver to increase the amount of current supplied to the LED to increase the brightness of the LED.

[0053] For example, the LED may be driven at a low setting (e.g., about 50 mA), a medium setting (e.g., about 100 mA), a high setting (e.g., about 200 mA), and a very high setting (e.g., about 250 mA). The low setting may be appropriate when the user is stationary and the amount of ambient light detected is low; the medium setting may be appropriate when the user is stationary and the amount of ambient light detected is medium; the high setting may be appropriate when the user is moderately active and / or the amount of ambient light detected is high; and the very high setting may be appropriate when the user is very active and / or the amount of ambient light detected is very high. However, the present disclosure is not limited to these examples, and other conditions may result in different settings for the LED. Furthermore, the present disclosure is not limited to the example LED current values ​​and other values, for example, intermediate values ​​may be used, for example, as determined using a machine learning model.

[0054] The first sensor may include, for example, an ambient light sensor (ALS) disposed at an upper portion of a body of the wearable computing device and below a display that is also disposed at an upper portion of the body and configured to display biometric information. The ALS may output information indicating ambient light in the surrounding environment of the wearable computing device. One or more processors may receive information indicating ambient light in the surrounding environment of the wearable computing device and control a brightness level of the display based on the information received from the ALS. For example, the ambient light sensor may have a first sampling rate for detecting ambient light in a first operating mode (e.g., interactive mode) of the wearable computing device (e.g., detecting an amount of ambient light approximately every 200 ms), and a second sampling rate for detecting ambient light in a second operating mode (e.g., always-on display mode) of the wearable computing device (e.g., detecting an amount of ambient light approximately every 1 second).

[0055] The second sensor may include, for example, one or more multipath photoplethysmography (PPG) sensors that may be used to monitor the user's heart rate when the wearable computing device is worn by the user. The one or more PPG sensors may include one or more light sources or emitters (e.g., light emitting diodes (LEDs)) and one or more detectors (e.g., photodiodes). In an example configuration, the one or more PPG sensors may be disposed at a lower portion of the body of the wearable computing device to face a body part of the user when the wearable computing device is worn by the user. For example, the one or more PPG sensors may be in contact with a body part of the user when the wearable computing device is worn by the user, such that the accuracy of the one or more PPG signals output by the one or more PPG sensors is increased.

[0056] In an example configuration, one or more PPG sensors may include a single LED disposed in a central portion of the bottom of the wearable computing device, and a plurality of detectors (e.g., four detectors) disposed in a circumferential manner around the LED, wherein each of the four detectors is spaced apart from one another along the circumferential direction. In another example configuration, one or more PPG sensors may include a single LED and three detectors. The LED may be disposed on a first side of the bottom of the wearable computing device, opposite to a second detector disposed on a second side of the bottom of the wearable computing device. The first detector may be disposed on a third side of the bottom of the wearable computing device, opposite to a third detector disposed on a fourth side of the bottom of the wearable computing device. The LED and the second detector may be disposed between the first detector and the third detector in a first (x) direction, and the first detector and the third detector may be disposed between the LED and the second detector in a second (y) direction. As another example, one or more PPG sensors may include a plurality of LEDs and a plurality of detectors. The plurality of LEDs may be disposed in a central portion of the bottom of the wearable computing device, and the plurality of detectors may be disposed outside the LED.

[0057] According to one or more examples of the present disclosure, when the PPG sensor includes a plurality of LEDs and one or more motion signals generated by the one or more inertial sensors indicate that at least a portion of the bottom side of the wearable computing device is spaced apart from a body part of the user (e.g., spaced apart by a predetermined amount or a predetermined angle), the one or more processors may be configured to cut off current supplied to at least one of the LEDs (e.g., those LEDs located on a side of the wearable computing device that is most spaced apart from the body part of the user) and increase current supplied to one or more remaining LEDs (e.g., those LEDs located on a side of the wearable computing device that is in contact with or closest to the body part of the user). For example, the one or more processors may determine that at least a portion of the bottom side of the wearable computing device is not in contact with the body part of the user based on a tilt angle of the wearable computing device detected by the inertial sensors and / or based on movement of the wearable computing device.

[0058] Example aspects of the present disclosure also relate to a computer-implemented method of a wearable computing device. The method may include: detecting ambient light in an environment surrounding the wearable computing device by a first sensor disposed at an upper portion of a body of the wearable computing device, and adjusting, by one or more processors, a current supplied to one or more light sources of a second sensor based on the amount of ambient light detected by the first sensor. The second sensor may be disposed at a lower portion of the body and include one or more light sources and one or more detectors, the one or more light sources emitting light toward a body part of the user when the wearable computing device is worn by the user, and the one or more detectors receiving reflections of the light emitted toward the body part to generate a signal indicating a biological attribute of the user.

[0059] The method may also include generating one or more motion signals by one or more inertial sensors disposed in the body based on the movement of the wearable computing device, and adjusting, by one or more processors, the current supplied to one or more light sources of the second sensor based on the one or more motion signals generated by the one or more inertial sensors. The method may include increasing the current supplied to the one or more light sources in response to the one or more motion signals generated by the one or more inertial sensors indicating that the movement of the wearable computing device is of a predetermined type or greater than a threshold. For example, the method may include increasing the current supplied to the one or more light sources to a peak value when the amount of ambient light detected by the first sensor is greater than a first threshold and the one or more motion signals generated by the one or more inertial sensors indicate that the movement of the wearable computing device is of a predetermined type or greater than a second threshold. The peak value may correspond to a maximum current setting for the light source (e.g., an LED).

[0060] The second sensor may be a PPG sensor having a plurality of light emitting diodes (LEDs). The method may also include: in response to one or more motion signals generated by the one or more inertial sensors indicating that at least one of the LEDs is spaced apart from a body part of the user, cutting off current supplied to at least one of the LEDs, and increasing current supplied to one or more remaining LEDs of the plurality of LEDs that are in contact with the body part of the user.

[0061] The method may also include: displaying the biometric information on a display of the wearable computing device, wherein the first sensor is an ambient light sensor disposed below the display. The method may include: detecting ambient light by the first sensor sampling the ambient light at a first sampling rate in a first operating mode of the wearable computing device and at a second sampling rate in a second operating mode of the wearable computing device. The first operating mode may be an interactive mode, and the second operating mode may be an always-on display mode. The first sampling rate (e.g., detecting the amount of ambient light approximately every 200 ms) may be more frequent than the second sampling rate (e.g., detecting the amount of ambient light approximately every 1 second).

[0062] The method may also include: detecting, by one or more detectors, ambient light in a space between the lower portion of the body and a body part of the user, and adjusting, by one or more processors, a current supplied to one or more light sources of the second sensor based on the amount of ambient light detected by the one or more detectors.

[0063] The method may also include: determining, by one or more processors, a current value to which the current is to be adjusted based on one or more of a lookup table, a linear scaling method, and a non-linear scaling method, using as input one or more of an amount of ambient light detected by the first sensor, an amount of ambient light detected by one or more detectors, and one or more motion signals generated by one or more inertial sensors based on movement of the wearable computing device.

[0064] The method may also include any of the other operations of the wearable computing device as described herein.

[0065] Example aspects of the present disclosure also relate to a non-transitory computer-readable medium storing instructions executable by one or more processors of a wearable computing device. For example, the instructions may include instructions for causing the one or more processors to receive information indicating an amount of ambient light in the surrounding environment of the wearable computing device detected by a first sensor disposed at an upper portion of a body of the wearable computing device. The instructions may also include instructions for causing the one or more processors to adjust a current supplied to one or more light sources of a second sensor based on the amount of ambient light detected by the first sensor. The second sensor may be disposed at a lower portion of the body and include one or more light sources and one or more detectors, the one or more light sources emitting light toward a body part of the user when the wearable computing device is worn by the user, and the one or more detectors receiving reflections of the light emitted toward the body part to generate a signal indicating a biological attribute of the user.

[0066] The instructions may also include instructions for causing the one or more processors to receive information indicating a type of motion of the wearable computing device and / or a magnitude of movement of the wearable computing device based on one or more motion signals generated by one or more inertial sensors disposed in a body of the wearable computing device. The instructions for causing the one or more processors to adjust the current supplied to the one or more light sources may also be based on the information indicating a type of motion of the wearable computing device and / or a magnitude of movement of the wearable computing device.

[0067] The non-transitory computer-readable medium may also store instructions, including instructions to cause one or more processors to perform any of the operations of the wearable computing device and methods as described herein.

[0068] Example aspects of the present disclosure provide several technical effects, benefits, and / or improvements in computing technology and wearable computing device technology. For example, according to one or more examples of the present disclosure, the battery life of the wearable computing device can be extended by adjusting the current supplied to the light source based on the ambient light sensed by the wearable computing device and / or the movement of the wearable computing device. For example, when the amount of ambient light sensed is low (e.g., less than a threshold level), the power consumed by the wearable computing device can be saved by operating the light source at a lower power level. For example, when the amount of movement sensed is low or the type of movement of the wearable computing device is unlikely to degrade the accuracy of the PPG sensor (e.g., unlikely to degrade by more than a threshold amount), the power consumed by the wearable computing device can be saved by operating the light source at a lower power level.

[0069] For example, according to one or more examples of the present disclosure, the measurement accuracy of the PPG sensor of the wearable computing device may be increased (or maintained or prevented from being reduced) by adjusting the current supplied to the light source based on the ambient light sensed by the wearable computing device and / or the movement of the wearable computing device. For example, when the amount of ambient light sensed is high (e.g., greater than a threshold level), the measurement accuracy of the PPG sensor of the wearable computing device may be increased (or maintained or prevented from being reduced) by operating the light source at a higher power level. For example, when the amount of movement sensed is high or the type of movement of the wearable computing device is likely to degrade the accuracy of the PPG sensor (e.g., degraded by more than a threshold amount), the measurement accuracy of the PPG sensor of the wearable computing device may be increased (or maintained or prevented from being reduced) by operating the light source at a higher power level.

[0070] In addition, according to one or more examples of the present disclosure, the wearable computing device utilizes sensors that may be pre-existing in the wearable computing device. For example, the pre-existing sensors may include ambient light sensors used for other purposes (such as detecting ambient light to change the brightness level of the display screen according to environmental conditions), but these ambient light sensors may also be used to detect the amount of ambient light present that may affect the accuracy of the PPG sensor. Therefore, there is no need to add additional sensors to the wearable computing device to adjust the current level of the light source (e.g., LED) of the PPG sensor. For example, the pre-existing sensors may include inertial sensors (e.g., accelerometers and / or gyroscopes) used for other purposes (such as detecting the movement of the wearable computing device to count the number of steps of the user or measure other activities of the user), but these inertial sensors may also be used to determine that the accuracy of the PPG sensor may be degraded due to the movement of the wearable computing device. Therefore, there is no need to add additional sensors to the wearable computing device to adjust the current level of the light source (e.g., LED) of the PPG sensor. As another example, the pre-existing sensors may also include detectors of the PPG sensor, which are used to receive reflections of light emitted toward a body part of the user, but these detectors may also be used to detect the amount of ambient light present that may affect the accuracy of the PPG sensor. Therefore, there is no need to add an additional sensor to the wearable computing device to adjust the current level of the light source (eg, LED) of the PPG sensor based on the amount of ambient light detected.

[0071] Now referring to the accompanying drawings, Figures 1 to 6 An example of a wearable computing device 100 is shown according to various examples of the present disclosure. Figure 1 An example wearable computing device 100 is shown that may be worn, for example, on a body part 102 (e.g., an arm, a wrist, etc.) of a user. Figure 1 , the wearable computing device 100 includes a main body 110 . Figure 2 An exploded view of the wearable computing device 100 is shown, wherein the body 110 includes an upper portion 110a and a lower portion 110b, and the body 110 defines a cavity 112 in which one or more electronic components are disposed (e.g., disposed on one or more printed circuit boards). Figure 1In the example of , the wearable computing device 100 includes a printed circuit board disposed in the cavity 112. In addition, one or more electronic components are disposed on the printed circuit board. The wearable computing device 100 may also include a battery disposed in the cavity 112 defined by the body 110. The wearable computing device 100 also includes various sensors 170 disposed in the cavity 112 defined by the body 110. For example, the sensor 170 may include a multi-path photoplethysmography (PPG) sensor 172 disposed at the lower side 110b of the body 110, which may be used to monitor the heart rate of the user. The PPG sensor 172 includes one or more emitters (e.g., light emitting diodes (LEDs)) and a plurality of detectors (e.g., photodiodes). Light emitted from the one or more emitters is transmitted in a direction toward a body part of the user (e.g., a portion of the user's wrist) that is in contact with the lower side 110b of the body 110. The light then interacts with the user's blood vessels, where the light is modified to a degree affected by the current blood volume in the blood vessels. The modified light is directed back to the PPG detector by reflection and / or refraction. The PPG detector generates data (e.g., one or more signals) reflecting the current blood volume of the user's blood vessels that receive light emitted from the one or more emitters. For example, the sensor 170 may also include an inertial sensor 174 (which may include an accelerometer 174a and / or a gyroscope 174b), as well as other sensors (such as a magnetometer). The accelerometer 174a may be used to capture motion information about the wearable computing device 100. The gyroscope 174b may also be used in addition or alternatively to capture motion information about the wearable computing device 100. Other sensors (such as a magnetometer, a GPS sensor, etc.) may also be included in the wearable computing device 100.

[0072] exist Figure 1 , the wearable computing device 100 includes a first band 130 and a second band 132. As shown, the first band 130 is coupled to the body 110 at a first position on the body. Conversely, the second band 132 is coupled to the body at a second position on the body 110. In addition, the first band 130 and the second band 132 can be coupled to each other to fix the body 110 to the user's body part 102.

[0073] In some examples, the first strap 130 may include a buckle or a clasp (not shown). Additionally, the second strap 132 may include a plurality of holes (not shown) spaced apart from one another along the length of the second strap 132. In such implementations, the prongs of the buckle associated with the first strap 130 may extend through one of the plurality of openings defined by the second strap 132 to couple the first strap 130 to the second strap 132. It should be understood that any suitable type of fastener may be used to couple the first strap 130 to the second strap 132. For example, in an embodiment, the first strap 130 and the second strap 132 may include magnets. In such implementations, the first strap 130 and the second strap 132 may be magnetically coupled to one another to secure the body 110 to a body part 102 (e.g., an arm) of a user.

[0074] exist Figure 1 1, the wearable computing device 100 may include a cover 140 positioned on the body 110 such that the cover 140 is positioned on top of the display 182. In this way, the cover 140 may protect the display 182 from scratches. In an embodiment, the wearable computing device 100 may include a seal (not shown) positioned between the body 110 and the cover 140. For example, a first surface of the seal may contact the body 110, and a second surface of the seal may contact the cover 140. In this way, the seal between the body 110 and the cover 140 may prevent liquid (e.g., water) from entering the cavity 112 defined by the body 110.

[0075] It should be understood that the cover 140 can be optically transparent so that a user can view information displayed on the display 182. For example, in an embodiment, the cover 140 can include a glass material. However, it should be understood that the cover 140 can include any suitable optically transparent material.

[0076] exist Figure 1 , the wearable computing device 100 includes an ambient light sensor (ALS) 176 positioned at an upper portion of the body 110 and below the display 182 and the cover 140. The ALS 176 is configured to detect ambient light in the surrounding environment of the wearable computing device 100. For example, the ALS 176 may detect ambient light passing through the cover 140, which may be transparent. For example, the ALS 176 may be disposed at a peripheral area of ​​the body 110. For example, the ALS 176 may have a first sampling rate (e.g., detecting an amount of ambient light approximately every 200 ms) for detecting ambient light when the wearable computing device 100 operates in a first operating mode (e.g., an interactive mode), and a second sampling rate (e.g., detecting an amount of ambient light approximately every 1 second) for detecting ambient light when the wearable computing device 100 operates in a second operating mode (e.g., an always-on display mode).

[0077] Included in Figure 3 The one or more processors 150 in the illustrated wearable computing device 100 receive information indicating ambient light in the surrounding environment of the wearable computing device 100, and control the brightness level of the display 182 based on the information received from the ALS 176. For example, the output of the ALS 176 may be used to both control the brightness level of the display 182 and control the amount of current supplied to the light source of the PPG sensor 172. Thus, the use (e.g., duty cycle) of the ALS 176 is not increased for the purpose of adjusting the current supplied to the light source of the PPG sensor 172, because the one or more processors 150 may use the same output from the ALS 176 to control the brightness of the display 182 and to adjust the current supplied to the light source of the PPG sensor 172.

[0078] Figure 3 1 shows an example block diagram of a wearable computing device 100 according to one or more example embodiments of the present disclosure. Figure 3 In the embodiment of the present invention, the wearable computing device 100 includes one or more processors 150 , one or more memory devices 160 , one or more sensors 170 , and a user interface 180 .

[0079] For example, the one or more processors 150 may be any suitable processing device that may be included in the wearable computing device 100. For example, such processors 150 may include one or more of a processor, a processor core, a controller and an arithmetic logic unit, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an image processor, a microcomputer, a field programmable array, a programmable logic unit, an application specific integrated circuit (ASIC), a microprocessor, a microcontroller, etc. and combinations thereof, including any other device capable of responding to and executing instructions in a defined manner. The one or more processors 150 may be a single processor or a plurality of processors operably connected (e.g., in parallel).

[0080] The memory 160 may include one or more non-transitory computer-readable storage media, such as read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM) and flash memory, USB drive, volatile memory device (such as random access memory (RAM)), hard disk, floppy disk, Blu-ray disk or optical media (such as CD ROM disk and DVD), and combinations thereof. However, examples of the memory 160 are not limited to the above description, and the memory 160 may be implemented by other various devices and structures as will be understood by those skilled in the art.

[0081] For example, the memory 160 may store instructions that, when executed, cause the one or more processors 150 to adjust the current supplied to one or more light sources of the PPG sensor 172 based on one or more of the amount of ambient light sensed by the ALS 176, the amount of ambient light sensed by one or more detectors of the PPG sensor 172, and the magnitude and / or type of motion of the wearable computing device 100 sensed by the inertial sensor 174, as described according to examples of the present disclosure.

[0082] The memory 160 may also include data 162 and instructions 164 that may be retrieved, manipulated, created, or stored by the one or more processors 150. In some example embodiments, such data may be accessed and used as input to adjust current supplied to one or more light sources of the PPG sensor 172 based on one or more of an amount of ambient light sensed by the ALS 176, an amount of ambient light sensed by one or more detectors of the PPG sensor 172, and an amplitude and / or type of motion of the wearable computing device 100 sensed by the inertial sensor 174, as described according to examples of the present disclosure.

[0083] exist Figure 3 In the embodiment of the present invention, the wearable computing device 100 includes a user interface 180, which is configured to receive input from the user (e.g., via touch input such as a thumb, finger, or an input device such as a stylus or pen). The wearable computing device 100 can perform functions in response to receiving input from the user (e.g., checking health information about the user (such as blood pressure), making and / or receiving phone calls, sending and / or receiving text messages, obtaining the current time, setting a timer, stopwatch function, controlling an external device (such as a household appliance), etc.).

[0084] exist Figure 3 In the embodiment, the user interface 180 includes a display 182 that displays information that can be viewed by the user (e.g., time, date, biometric information, notifications, etc.). For example, the display 182 can be a non-touch-sensitive display or a touch-sensitive display. For example, the display 182 can include a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, an active matrix organic light emitting diode (AMOLED), a flexible display, a 3D display, a plasma display panel (PDP), a cathode ray tube (CRT) display, etc. However, the present disclosure is not limited to these example displays, and other types of displays may be included. The display 182 may have a square or rectangular shape, or may be an annular shape (e.g., an ellipse, a circle, etc.). However, the shape of the display 182 is not limited thereto.

[0085] The user interface 180 may additionally or alternatively include one or more buttons 184 for receiving input from a user by the user applying force to the buttons 184. For example, the buttons 184 may include Figure 1 The buttons 184 are shown on one or more peripheral sides of the wearable computing device 100. The buttons 184 may include mechanical components and / or circuits to implement functions of the wearable computing device 100 (e.g., setting the time, changing the settings and / or views of the display 182, selecting options displayed on the display 182).

[0086] Figure 4 4 shows an example block diagram of a wearable computing device 400 according to one or more example embodiments of the present disclosure. Figure 4 , the wearable computing device 400 includes a controller 470 including one or more processors 450 and one or more memory devices 460. The one or more processors 450 and the one or more memory devices 460 may correspond to the one or more processors 150 and the one or more memory devices 160 described previously. The wearable computing device 400 includes a PPG sensor 472, an inertial sensor 474 (optional), and an ambient light sensor 476. The PPG sensor 472, the inertial sensor 474, and the ambient light sensor 476 may correspond to the PPG sensor 172, the inertial sensor 174, and the ambient light sensor 176 described previously. Figure 4 Other components not shown in FIG. 4 (eg, user interface 180 , display 182 , and button 184 ) may be included in wearable computing device 400 , but are not shown for ease of explanation.

[0087] The ambient light sensor 476 may provide a raw signal to an ambient brightness determination unit 480 (e.g., an analog-to-digital converter), which is configured to convert the raw signal into a value representing the ambient light intensity (e.g., a lux value). For example, the ambient brightness determination unit 480 may be integrated with the ambient light sensor 476 and implemented as part of the ambient light sensor firmware, or may be provided separately from the ambient light sensor 476 as a separate internal unit of the wearable computing device 400. A post-processed value representing the ambient light amount value (e.g., a value in lux, rather than the raw signal) may be transmitted from the ambient brightness determination unit 480 to the one or more processors 450 and / or the memory 460 for use in determining whether the current supplied to the light source (e.g., LED) 472b of the PPG sensor 472 should be adjusted.

[0088] The inertial sensor 474 (e.g., an inertial measurement unit) may measure motion data of the wearable computing device 400. The motion data may be transmitted to the one or more processors 450 and / or the memory 460 for use in determining whether the current supplied to the light source (e.g., LED) 472b of the PPG sensor 472 should be adjusted. According to an example of the present disclosure, the motion data may first pass through a motion data classifier 490 (e.g., a coarse classifier model) in order to understand or classify certain characteristics of the motion of the wearable computing device 400 (e.g., to determine and / or classify the motion as low, medium, high, periodic, non-periodic, etc.).

[0089] The one or more detectors 472a of the PPG sensor 472 may also sense ambient light when the bottom side of the wearable computing device 400 is tilted or separated from the body part of the user. For example, the one or more detectors 472a may be configured to detect ambient light introduced into the one or more detectors 472a in the space between the lower portion of the body of the wearable computing device 400 and the body part of the user, such as Figure 6 The one or more processors 450 may be configured to adjust the current supplied to the one or more light sources (eg, LEDs) 472b via the LED driver 440 based on the amount of ambient light detected by the one or more detectors 472a.

[0090] The detector 472a of the PPG sensor 472 may provide ambient light information related to ambient light conditions (e.g., via raw signals) to an analog-to-digital converter (ADC) 420, which converts one or more raw signals from the detector 472a of the PPG sensor 472 into a value (e.g., a lux value) representing an amount of ambient light. Post-processed values ​​representing the amount of ambient light (e.g., values ​​in lux, rather than raw signals) may be transmitted from the ADC 420 directly to one or more processors 450 and / or memory 460 for use in determining whether a current supplied to a light source (e.g., LED) 472b of the PPG sensor 472 should be adjusted or indirectly adjusted (e.g., via an analog front end (AFE) interface 410).

[0091] According to another example, the detector 472a of the PPG sensor 472 may provide ambient light information (e.g., via raw signals) to the ambient brightness determination unit 480, which converts one or more raw signals from the detector 472a of the PPG sensor 472 into a value (e.g., a lux value) representing an amount of ambient light. The value may be transmitted from the ambient brightness determination unit 480 to the one or more processors 450 and / or the memory 460 for use in determining whether the current supplied to the light source (e.g., LED) 472b of the PPG sensor 472 should be adjusted. Thus, the ambient brightness determination unit 480 may receive one or more raw signals from the one or more detectors 472a of the PPG sensor 472 and one or more raw signals from the ambient light sensor 476.

[0092] According to another example, an additional ambient brightness determination unit (not shown) may be provided to receive one or more raw signals from one or more detectors 472a of the PPG sensor 472. A post-processed value (e.g., a value in lux, instead of the raw signal) representing the ambient light amount value sensed by the one or more detectors 472a of the PPG sensor 472 may be transmitted from the additional ambient brightness determination unit to the one or more processors 450 and / or the memory 460 for use in determining whether the current supplied to the light source (e.g., LED) 472b of the PPG sensor 472 should be adjusted.

[0093] The ADC 420 receives the PPG signal from the detector 472a and converts the raw analog PPG signal into a digital signal. The analog front end (AFE) interface 410 is connected to the controller 470 and the PPG sensor 472. The AFE interface 410 receives the digital signal from the ADC and transmits the digital signal to one or more processors 150 and / or the memory 460 of the controller 470 for measuring the biometrics (e.g., heart rate) of the user. The AFE interface 410 may also be configured to communicate with the one or more processors 450 to control the LED driver 440. For example, the AFE interface 410 may relay information (e.g., a signal) from the one or more processors 450 to the LED driver 440 and / or the timing controller 430 to drive current to one or more LEDs 472b of the PPG sensor 472. The information may include a current value to be applied by the LED driver 440 and / or a timing of when to apply the current.

[0094] The PPG sensor 472 includes a timing controller 430 for controlling timing aspects such as when light is emitted by one or more LEDs 472b, when light is received by one or more detectors 472a, and when the ADC performs conversion of a raw signal to a digital signal so that a PPG signal can be generated.

[0095] The PPG sensor 472 may also include various other components (eg, filters for filtering various components of the PPG signal, such as noise) that are not shown for ease of description. Figure 4 The ADC 420, the timing controller 430, and the LED driver 440 are shown by dashed lines as being part of the PPG sensor 472. However, one or more of the ADC 420, the timing controller 430, and the LED driver 440 may be provided separately, for example, as part of the AFE interface 410.

[0096] The memory 460 may be configured to store settings of the LED driver 440 and / or one or more LEDs 472b, including current settings of the one or more LEDs 472b that serve as reference points. In addition, the AFE interface 410 may also include a memory that stores settings of the LED driver 440 and / or one or more LEDs 472b, including current settings of the one or more LEDs 472b that serve as reference points. For example, the AFE interface 410 may know the current LED brightness setting (e.g., the current level currently supplied to the one or more LEDs 472b) and provide the current LED brightness setting to the one or more processors 450 for determining whether the current supplied to the one or more LEDs 472b should be adjusted. The one or more processors 450 may use one or more of the inputs from the ambient light sensor 476, the inertial sensor 474, and the detector 472a and the current LED brightness setting to determine whether the current supplied to the one or more LEDs 472b of the PPG sensor 472 should be adjusted. When the one or more processors 450 determine that the brightness value required to generate an accurate PPG signal for one or more LEDs 472b is met based on the current LED brightness setting, no adjustment may be made. Conversely, when the one or more processors 450 determine that the brightness value required to generate an accurate PPG signal for one or more LEDs 472b can be met by adjusting (e.g., reducing or increasing) the current LED brightness setting, the one or more processors 450 may control the LED driver 440 (e.g., via the AFE interface 410 and / or the timing controller 430) to adjust the amount of current supplied to the one or more LEDs 472b accordingly. After the updated brightness value is determined by the one or more processors 450, the one or more processors 450 may feed the updated brightness value back to the AFE interface 410 to adjust the brightness (i.e., current) to the updated brightness value. For example, the one or more processors 450 may control the LED driver 440 (e.g., via the AFE interface 410 and / or the timing controller 430) to increase or decrease the current supplied to the one or more LEDs 472b. For example, one or more LEDs 472b may be driven at various current levels based on one or more of inputs from ambient light sensor 476, inertial sensor 474, and detector 472a.

[0097] The LED brightness setting value (i.e., the amount of current to be supplied to the one or more LEDs 472b) can be obtained or determined by the one or more processors: (1) based on a lookup table derived from a laboratory evaluation of optimal parameters, where the ambient light value and the motion data are the primary inputs; (2) based on a linear scaling (e.g., [motion + more ambient light] * scaling factor = new brightness value), where the scaling factor can be a value used to ensure that the new brightness value does not exceed the capabilities of the LED; and / or (3) based on a non-linear scaling developed by training a machine learning model that utilizes the ambient light value and the motion data as inputs and outputs an optimal LED brightness setting value (i.e., the amount of current to be supplied to the one or more LEDs 472b) for a given environmental condition. For example, in the event that the ambient light sensor 476 detects a brighter environment and / or in the event that the inertial sensor 474 detects high movement of the wearable computing device 400, the one or more processors 450 can control the LED driver 440 to increase the amount of current supplied to the one or more LEDs 472b to increase the brightness of the one or more LEDs 472b.

[0098] For example, one or more LEDs 472b may be driven at a low setting (e.g., about 50 mA), a medium setting (e.g., about 100 mA), a high setting (e.g., about 200 mA), and a very high setting (e.g., about 250 mA). The low setting may be appropriate when the motion data indicates that the user is stationary and the amount of ambient light detected is low (e.g., a lux value of 5,000 to 10,000 in outdoor conditions); the medium setting may be appropriate when the motion data indicates that the user is stationary and the amount of ambient light detected is medium (e.g., a lux value of 10,001 to 30,000 in outdoor conditions); the high setting may be appropriate when the motion data indicates that the user is moderately active and / or the amount of ambient light detected is high (e.g., a lux value of 30,001 to 65,000); and the very high setting may be appropriate when the motion data indicates that the user is very active and / or the amount of ambient light detected is very high (e.g., a lux value of over 65,000). However, the present disclosure is not limited to these examples, and other conditions may cause different settings for one or more LEDs 472b. In addition, the present disclosure is not limited to the example LED current values ​​and other values, for example, intermediate values ​​may be used, for example, as determined using a machine learning model. For example, the ambient lux value does not need to be classified as low, medium, but the actual determined lux value can be provided as an input to the machine learning model, and the brightness value (i.e., current value) can be obtained from the model.

[0099] The PPG sensor 472 can optically measure biometric information, such as heart rate, using one or more light sources and one or more photodetectors at the surface of a user's body part to measure volume changes in blood circulation. The light source (e.g., one or more LEDs) of the PPG sensor 472 emits light to the user's body part, and the photodetectors of the PPG sensors 172, 472 measure the light reflected from the body part, where the amount of reflected light can indicate biometric information about the user (e.g., blood flow, blood volume, etc.). The light source (e.g., one or more LEDs) can emit infrared light, and the color of the LED can be red, green, or yellow. The PPG signal may include various components, such as: a DC component (or DC offset), which represents the constant absorption of light passing through tissue; an AC component generated by the heartbeat (heart activity) that affects blood volume, which depends on the systolic and diastolic periods; and an ambient light component, which represents the amount of ambient light received by the detector (e.g., a photodiode).

[0100] refer to Figure 5A , shows a bottom view of an example wearable computing device according to one or more example embodiments of the present disclosure. The wearable computing device may include multiple PPG sensors, for example to help reject motion artifacts. Each PPG sensor may correspond to a combination of one or more light sources and one or more detectors. For example, the wearable computing device may include two or more PPG sensors. In addition, more than one light source (e.g., LED) may be included, such that different detectors may be combined with different LEDs and / or each detector may be combined with one or more LEDs to output a corresponding PPG signal. For example, multiple detectors may be arranged in a circular or elliptical arrangement, wherein the multiple detectors may be spaced apart from each other at regular or irregular intervals. In Figure 5A , a light source (e.g., LED) 172e is disposed in a central portion of the PPG sensor 172. The detector 172a is spaced apart from the detectors 172b and 172d along the circumferential direction C, and is spaced apart from the light source (e.g., LED) 172e in the radial direction R. Likewise, each of the detectors 172b, 172c, and 172d is spaced apart from adjacent detectors in the circumferential direction C, and is spaced apart from the light source (e.g., LED) 172e in the radial direction R. However, the configuration of the detectors and the light source may be different. Figure 5A The configuration shown in FIG. 1 is different, and the present disclosure is not limited to Figure 5A .

[0101] refer to Figure 5B , shows a bottom view of another example wearable computing device according to one or more example embodiments of the present disclosure. For example, in Figure 5B, detector 172a is spaced apart from detector 172c in the horizontal or "X" direction, and is spaced apart from detector 172b in both the horizontal (X) direction and the vertical or "Y" direction. Detector 172b is spaced apart from both detectors 172a and 172c in the horizontal (X) direction and the vertical (Y) direction. Detector 172c is spaced apart from detector 172a in the horizontal (X) direction, and is spaced apart from detector 172b in both the horizontal (X) direction and the vertical (Y) direction. Light source (e.g., LED) 172e is spaced apart from detector 172b in the vertical (Y) direction, and is spaced apart from both detectors 172a and 172c in each of the horizontal (X) direction and the vertical (Y) direction. However, the configuration of the detectors and light sources may be different. Figure 5B The configuration shown in FIG. 1 is different, and the present disclosure is not limited to Figure 5B .

[0102] When the bottom side of the wearable computing device (and, by extension, the LED) is spaced apart from the body part by too great a distance, the amount of ambient light introduced between the bottom side of the wearable computing device and the user's body part may interfere with the light emitted by the LED and / or the light received by the detector, degrading the accuracy of the PPG signal, or the distance from the LED to the user's body part may be too far, such that the PPG signal may be inaccurate because the emitted light may not be able to sufficiently penetrate the body part. Therefore, according to an example of the present disclosure, in such a case, the current to the LED may be cut off, for example, to save energy.

[0103] refer to Figure 6 , shows a side view of an example wearable computing device 600 according to one or more example embodiments of the present disclosure. For example, in Figure 6In the embodiment, the wearable computing device 600 is displaced or spaced apart from the user's body part 102. The wearable computing device 600 includes a plurality of detectors (e.g., detectors 172a, 172b, 172c, and 172d) and a plurality of light sources (e.g., LEDs 172e and 172f). For example, when the wearable computing device 600 is spaced apart from the user's body part 102 by an amount exceeding a threshold amount (e.g., the angle of the spaced apart exceeds an angle α), the LEDs spaced apart from the body part 102 by a distance exceeding a distance d2 may be turned off (i.e., the current supplied to the LEDs may be cut off), while the LEDs spaced apart from the body part 102 by a distance less than the distance d2 may remain turned on (i.e., the current supplied to the LEDs may be maintained), and in an embodiment, the current supplied to the LEDs may be increased (e.g., increased to a peak or maximum level). As another example, when the wearable computing device 600 is spaced apart from the user's body part 102 by an amount exceeding a threshold amount (e.g., the angle of the spaced apart exceeds the angle α), the first LED spaced apart from the body part 102 by a distance greater than the distance the second LED is spaced apart from the body part may be turned off (i.e., the current supplied to the LED may be cut off), while the second LED may remain on (i.e., the current supplied to the second LED may be maintained), and in an embodiment, the current supplied to the second LED may be increased (e.g., increased to a peak or maximum level). For example, the inertial sensor 174, 474 may include one or more of an accelerometer, a gyroscope, and a magnetometer to detect or sense motion data (e.g., tilt angle, movement rate, etc.) of the wearable computing device 100, 400. The motion data (such as the tilt angle) detected by the inertial sensor 174, 474 may be used to determine whether the wearable computing device 600 is spaced apart from the user's body part 102 by an amount exceeding the threshold amount.

[0104] For example, refer to Figure 6 When the wearable computing device 600 is spaced apart from the user's body part 102 by an amount exceeding a threshold amount (e.g., the angle of the spaced apart exceeds the angle α), and the LED 172f is spaced apart from the body part 102 by a distance exceeding the distance d2, the power to the LED 172f may be turned off (i.e., the current supplied to the LED 172f may be cut off). However, the LED 172e spaced apart from the body part 102 by a distance d1 less than the distance d2 may remain on (i.e., the current supplied to the LED 172e may be maintained), and in embodiments, the current supplied to the LED 172e may be increased (e.g., increased to a peak or maximum level).

[0105] As another example, when the wearable computing device 600 is spaced apart from the user's body part 102 by an amount exceeding a threshold amount (e.g., the angle of separation exceeds angle α), the LED farthest from the body part 102 may be turned off, while one or more remaining LEDs may remain on. Figure 6 In one embodiment, LED 172f is spaced apart from body part 102 by a distance d2 that is greater than a distance d1 that separates LED 172e from body part 102. Thus, one or more processors may be configured to turn off LED 172f (i.e., current supplied to LED 172f may be cut off), while LED 172e may remain on (i.e., current supplied to LED 172e may be maintained), and in embodiments, current supplied to LED 172e may be increased (e.g., increased to a peak or maximum level).

[0106] According to one or more examples of the present disclosure, one or more inertial sensors (e.g., Figure 3 and Figure 4 The one or more motion signals generated by the inertial sensors 174, 474 in the wearable computing device 100, 400 may indicate that at least a portion of the bottom side of the wearable computing device 100, 400 is spaced apart (e.g., spaced apart by a predetermined distance or a predetermined angle) from the user's body part 102. The one or more processors 150, 450 may be configured to cut off the current supplied to at least one of the LEDs (e.g., one or more LEDs on a side of the wearable computing device 100, 400 that is spaced apart from the user's body part 102) and increase the current supplied to one or more remaining LEDs (e.g., one or more LEDs on a side of the wearable computing device 100, 400 that is in contact with or closest to the user's body part 102). For example, the one or more processors 150, 450 may determine that at least a portion of the bottom side of the wearable computing device 100, 400 is not in contact with the user's body part 102 based on the tilt angle of the wearable computing device 100, 400 detected by the inertial sensor 174, 474 and / or based on the movement of the wearable computing device 100, 400. In one or more examples, the inertial sensor 174, 474 may include one or more of an accelerometer, a gyroscope, and a magnetometer to detect or sense motion data (e.g., tilt angle, movement rate, etc.) of the wearable computing device 100, 400.

[0107] When the bottom side of the wearable computing device (and, by extension, the LEDs) is spaced apart from the body part, in some cases, the accuracy of the PPG signal can be maintained or made sufficient for measurement purposes by increasing the power (current) to one or more LEDs. That is, the distance from the LED to the body part of the user cannot be too far so that the PPG signal can be accurate or acceptable because the emitted light can sufficiently penetrate the body part. In an embodiment of the present disclosure, the wearable computing device may include a plurality of light sources (e.g., LEDs), wherein at least one of the light sources is turned off and at least one of the light sources is turned on. If the one or more motion signals generated by the one or more inertial sensors 174, 474 indicate that at least a portion of the bottom side of the wearable computing device 100, 400 is spaced apart from the body part 102 of the user (e.g., the distance of the spacing is less than a predetermined distance or the angle of the spacing is less than a predetermined angle), the one or more processors 150, 450 may be configured to turn on the power supplied to at least one of the light sources that was turned off. In addition, the current to the one or more light sources that were previously turned on may be increased (e.g., increased to a peak or maximum level).

[0108] refer to Figure 7 , showing a flowchart for adjusting a brightness value based on one or more inputs according to one or more example embodiments of the present disclosure. Figure 7 The flowchart of FIG. 71 is another illustration explaining the operation of a wearable computing device, which may include any of the wearable computing devices described herein. As previously explained and as shown at operation 710, one or more ambient light sensors of the wearable computing device may detect an amount of ambient light in the surrounding environment of the wearable computing device. The one or more ambient light sensors may be disposed below a display, in an upper portion of a body of the wearable computing device, and detect ambient light through a transparent display panel (cover).

[0109] At operation 720, one or more inertial sensors (e.g., accelerometers, magnetometers, gyroscopes, etc.) of the wearable computing device may detect movement of the wearable computing device (e.g., tilt angle, speed, angular velocity, acceleration, angular acceleration, orientation information, etc. of the wearable computing device) and generate motion data.

[0110] At operation 730, one or more PPG detectors of the PPG sensor may generate raw data indicating ambient light conditions in the surrounding environment of the wearable computing device. For example, one or more processors may control an LED of the PPG sensor to be turned off (e.g., via one or more of a timing controller, an AFE interface, and an LED driver) to obtain a measurement of light received by the PPG detector when the LED is turned off, thereby more easily assessing the ambient light conditions.

[0111] At operation 740 , the ALS raw data may be converted into an ambient brightness value (eg, a lux value).

[0112] At operation 750, the motion data may be classified, for example, according to the magnitude of the motion and the type of motion. For example, the motion may be classified according to the rate of acceleration. The motion data may also be used to describe or infer the state of the user wearing the wearable computing device (e.g., stationary, walking, running, climbing stairs, swimming, etc.). For example, the classifier model may classify the motion as low, medium, high, periodic, non-periodic, etc.

[0113] At operation 760, the PPG raw data may be converted into an ambient brightness value (eg, a lux value).

[0114] At operation 770, one or more processors receive as inputs one or more of the ambient brightness value obtained via the ALS, and optionally also the ambient brightness value obtained via the PPG detector and the motion information obtained from the inertial sensor. The one or more processors may generate one or more LED brightness values ​​based on the inputs, and adjust the current of one or more LEDs of the PPG sensor via one or more of the AFE interface, the LED driver, and the timing controller.

[0115] For example, one or more processors can determine whether to adjust the current of one or more LEDs as needed or according to a predetermined schedule (e.g., every few seconds).For example, one or more processors can determine whether to adjust the current of one or more LEDs while determining whether to adjust the brightness of the display.

[0116] At operation 780, the LED driver may adjust the current supplied to the one or more LEDs of the PPG sensor according to the control of the one or more processors. The real-time value of the current applied to the one or more LEDs may be reported by the AFE interface to the one or more processors as a reference point.

[0117] Examples of the present disclosure also relate to computer-implemented methods of wearable computing devices. Figure 8FIG. 8 is a flowchart of an example non-limiting computer-implemented method 800 according to one or more example embodiments of the present disclosure. The features of the method 800 may be implemented in any of the wearable computing devices described herein (including the wearable computing devices described above). Figure 1 The wearable computing device 100 discussed above with reference to Figure 4 The wearable computing device 400 discussed above and referenced above Figure 6 The wearable computing device 600 discussed is implemented.

[0118] At (810), the method 800 includes: detecting, by a first sensor (e.g., the ALS 176) disposed at an upper portion of a body of the wearable computing device (e.g., the upper portion 110a of the body 110 of the wearable computing device 100), ambient light in a surrounding environment of the wearable computing device. At (820), the method 800 includes: determining, by one or more processors of the wearable computing device, a magnitude of motion of the wearable computing device and / or a type of motion of the wearable computing device based on motion data generated by one or more inertial sensor devices disposed in the body of the wearable computing device (e.g., the inertial sensor 174 of the wearable computing device 100). At (830), the method 800 includes: adjusting, by the one or more processors, a current supplied to one or more light sources of a second sensor (e.g., one or more LEDs of the PPG sensor 172 of the wearable computing device 100) based on one or more of the detected ambient light, the determined magnitude of motion, and the determined type of motion. The second sensor (e.g., the PPG sensor 172) may be disposed at a lower portion of a body of the wearable computing device (e.g., the lower portion 110b of the body 110 of the wearable computing device 100), and include one or more light sources and one or more detectors, the one or more light sources emitting light toward a body part of the user when the wearable computing device is worn by the user, the one or more detectors receiving reflections of the light emitted toward the body part to generate a signal indicating a biometric characteristic of the user.

[0119] Aspects of the above-described example embodiments may be recorded in a non-transitory computer-readable medium including program instructions for implementing various operations embodied by a computer. The medium may also include data files, data structures, etc., alone or in combination with program instructions. Examples of non-transitory computer-readable media include magnetic media, such as hard disks, floppy disks, and tapes; optical media, such as CD ROMs, Blu-ray discs, and DVDs; magneto-optical media, such as optical discs; and other hardware devices specifically configured to store and execute program instructions, such as semiconductor memories, read-only memories (ROMs), random access memories (RAMs), flash memories, USB memories, etc. Examples of program instructions include machine codes (such as those generated by a compiler) and files containing higher-level codes that can be executed by a computer using an interpreter. Program instructions may be executed by one or more processors. The described hardware devices may be configured to act as one or more software modules in order to perform the operations of the above-described embodiments, and vice versa. In addition, non-transitory computer-readable storage media may be distributed between computer systems connected via a network, and computer-readable codes or program instructions may be stored and executed in a decentralized manner. Furthermore, the non-transitory computer-readable storage medium may also be embodied in at least one application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

[0120] Each block of the flow chart may represent a unit, module, segment or portion of code, which includes one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions mentioned in the blocks may occur out of order. For example, two blocks shown in succession may actually be executed substantially concurrently (simultaneously), or the blocks may sometimes be executed in reverse order, depending on the functionality involved.

[0121] Although the present disclosure has been described with respect to various example embodiments, each example is provided by way of explanation, not limitation of the present disclosure. Those skilled in the art, after understanding the foregoing, may easily produce changes, modifications, and equivalents of such embodiments. Therefore, the present disclosure does not exclude such modifications, changes, and / or additions to the disclosed subject matter that would be readily understood by those of ordinary skill in the art. For example, a feature shown or described as part of one embodiment may be used together with another embodiment to produce yet another embodiment. Therefore, the present disclosure is intended to cover such changes, modifications, and equivalents.

Claims

1. A wearable computing device, comprising: main body; a first sensor disposed at an upper portion of the body, the first sensor configured to detect ambient light in an environment surrounding the wearable computing device; a second sensor disposed at a lower portion of the body, the second sensor comprising one or more light sources and one or more detectors, the one or more light sources being configured to emit light toward a body part of the user when the wearable computing device is worn by the user, and the one or more detectors being configured to receive reflections of the light emitted toward the body part to generate a signal indicative of a biometric characteristic of the user; as well as One or more processors configured to adjust current supplied to the one or more light sources based on the amount of ambient light detected by the first sensor.

2. The wearable computing device of claim 1, wherein the second sensor comprising a photoplethysmography (PPG) sensor configured to monitor a heart rate of the user when the wearable computing device is worn by the user, The one or more light sources include one or more light emitting diodes, and The one or more detectors include one or more photodiodes.

3. The wearable computing device of claim 1 , further comprising: one or more inertial sensors disposed in the body, the one or more inertial sensors configured to generate one or more motion signals based on movement of the wearable computing device, Wherein the one or more processors are configured to adjust the current supplied to the one or more light sources based on the one or more motion signals generated by the one or more inertial sensors.

4. The wearable computing device of claim 3, wherein The one or more processors are configured to increase the current supplied to the one or more light sources in response to the one or more motion signals generated by the one or more inertial sensors indicating that the motion of the wearable computing device is of a predetermined type or is greater than a threshold.

5. The wearable computing device of claim 3, wherein the second sensor comprising a photoplethysmography (PPG) sensor configured to monitor a heart rate of the user when the wearable computing device is worn by the user, The one or more light sources include a plurality of light emitting diodes, The one or more detectors include a plurality of photodiodes, and When the one or more motion signals generated by the one or more inertial sensors indicate that the lower portion of the body of the wearable computing device is spaced apart from the body part of the user, the one or more processors are configured to cut off current supplied to at least one of the plurality of light emitting diodes and increase current supplied to at least one other of the plurality of light emitting diodes.

6. The wearable computing device of claim 1 , wherein The one or more processors are configured to increase the current supplied to the one or more light sources in response to the amount of ambient light detected by the first sensor being greater than a threshold.

7. The wearable computing device of claim 1 , further comprising: one or more inertial sensors disposed in the body, the one or more inertial sensors configured to generate one or more motion signals based on movement of the wearable computing device, Wherein the one or more processors are configured to increase the current supplied to the one or more light sources when: the amount of ambient light detected by the first sensor is greater than a first threshold and the one or more motion signals generated by the one or more inertial sensors indicate that the motion of the wearable computing device is of a predetermined type or greater than a second threshold.

8. The wearable computing device of claim 1 , further comprising: a display disposed at the upper portion of the body of the wearable computing device, the display configured to display biometric information, in: The first sensor includes an ambient light sensor disposed below the display, and The ambient light sensor has a first sampling rate for detecting the ambient light in a first operating mode of the wearable computing device and a second sampling rate for detecting the ambient light in a second operating mode of the wearable computing device.

9. The wearable computing device of claim 1 , wherein The one or more detectors are configured to detect ambient light in a space between the lower portion of the body of the wearable computing device and the body part of the user, and The one or more processors are configured to adjust the current supplied to the one or more light sources based on an amount of ambient light detected by the one or more detectors.

10. The wearable computing device of claim 1, wherein The one or more processors are configured to determine a current value to which the current is to be adjusted based on one or more of a lookup table, a linear scaling method, and a non-linear scaling method using the amount of ambient light detected by the first sensor as an input.

11. A computer-implemented method comprising: detecting ambient light in a surrounding environment of the wearable computing device by a first sensor disposed at an upper portion of a body of the wearable computing device; as well as The one or more processors adjust the current supplied to one or more light sources of a second sensor based on the amount of ambient light detected by the first sensor, wherein the second sensor is disposed at a lower portion of the body and includes the one or more light sources and one or more detectors, the one or more light sources emitting light toward a body part of the user when the wearable computing device is worn by the user, and the one or more detectors receiving reflections of the light emitted toward the body part to generate a signal indicating a biometric characteristic of the user.

12. The computer-implemented method of claim 11 , further comprising: One or more motion signals are generated by one or more inertial sensors disposed in the body of the wearable computing device based on movement of the wearable computing device, wherein Adjusting, by the one or more processors, the current supplied to the one or more light sources of the second sensor is also based on the one or more motion signals generated by the one or more inertial sensors.

13. The computer-implemented method of claim 12, further comprising: In response to the one or more motion signals generated by the one or more inertial sensors indicating that the motion of the wearable computing device is of a predetermined type or greater than a threshold, the current supplied to the one or more light sources is increased.

14. The computer-implemented method of claim 12, wherein the second sensor comprising a photoplethysmography (PPG) sensor configured to monitor a heart rate of the user when the wearable computing device is worn by the user, The one or more light sources include a plurality of light emitting diodes, and The one or more detectors include a plurality of photodiodes, The method further comprises: In response to the one or more motion signals generated by the one or more inertial sensors indicating that the lower portion of the body of the wearable computing device is spaced apart from the body part of the user, current supplied to at least one of the plurality of light emitting diodes is cut off, and current supplied to at least one other of the plurality of light emitting diodes is increased.

15. The computer-implemented method of claim 11, further comprising: generating, by one or more inertial sensors disposed in the body, one or more motion signals based on movement of the wearable computing device; as well as When the amount of ambient light detected by the first sensor is greater than a first threshold and the one or more motion signals generated by the one or more inertial sensors indicate that the motion of the wearable computing device is of a predetermined type or greater than a second threshold, the current supplied to the one or more light sources is increased to a peak value.

16. The computer-implemented method of claim 11, further comprising: displaying biometric information on a display of the wearable computing device, in The first sensor is an ambient light sensor disposed below the display, and Detecting the ambient light by the ambient light sensor includes sampling the ambient light at a first sampling rate in a first operating mode of the wearable computing device and sampling the ambient light at a second sampling rate in a second operating mode of the wearable computing device.

17. The computer-implemented method of claim 12, further comprising: detecting, by the one or more detectors, ambient light in a space between the lower portion of the body of the wearable computing device and the body part of the user, and Adjusting, by the one or more processors, the current supplied to the one or more light sources of the second sensor is also based on an amount of ambient light detected by the one or more detectors.

18. The computer-implemented method of claim 17, further comprising: The one or more processors determine a current value to which the current is to be adjusted based on one or more of a lookup table, a linear scaling method, and a non-linear scaling method, using one or more of the amount of ambient light detected by the first sensor, the amount of ambient light detected by the one or more detectors, and one or more of the one or more motion signals generated by the one or more inertial sensors.

19. A non-transitory computer-readable medium storing instructions executable by one or more processors of a wearable computing device, the instructions comprising: instructions causing the one or more processors to receive information indicative of an amount of ambient light in an environment surrounding the wearable computing device detected by a first sensor disposed at an upper portion of a body of the wearable computing device; as well as Instructions for causing the one or more processors to adjust current supplied to one or more light sources of a second sensor based on the amount of ambient light detected by the first sensor, wherein the second sensor is disposed at a lower portion of the body and includes the one or more light sources and one or more detectors, the one or more light sources emitting light toward a body part of the user when the wearable computing device is worn by the user, and the one or more detectors receiving reflections of the light emitted toward the body part to generate a signal indicative of a biometric characteristic of the user.

20. The non-transitory computer readable medium of claim 19, the instructions further comprising: instructions that cause the one or more processors to receive information indicating a type of motion of the wearable computing device and / or a magnitude of movement of the wearable computing device based on one or more motion signals generated by one or more inertial sensors disposed in the body of the wearable computing device; and The instructions to cause the one or more processors to adjust the current supplied to the one or more light sources are further based on the information indicating the type of motion of the wearable computing device and / or the magnitude of the movement of the wearable computing device.

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