SYSTEM AND METHOD FOR DETERMINING WEARABLE COMPUTING DEVICE WEARING CONDITIONS - Patent application

Abstract

A computer-implemented method for determining a wearing state of a wearable computing device may include acquiring first proximity data generated by a proximity sensor of the wearable computing device during a first time period. The method may further include activating an impedance sensor of the wearable computing device based at least in part on the first proximity data. Furthermore, the method may include acquiring impedance data generated by the impedance sensor and proximity data generated by the proximity sensor during a second time period occurring after the first time period. Furthermore, the method may include determining, at least in part based on the impedance data and the second proximity data, that the wearing state of the wearable computing device corresponds to one of an off-wrist state, an on-wrist state with contact, or an on-wrist state without contact.

Description

[Technical Field]

[0001] The present disclosure relates generally to wearable computing devices, and more particularly to systems and methods for determining the wearability of a wearable computing device. [Background technology]

[0002] Recent advances in technology, including technology available through consumer devices, have provided corresponding advances in health detection and monitoring. For example, wearable computing devices such as fitness trackers and smart watches can determine information about the pulse or movement of a person wearing the device. For example, certain biometric monitoring devices include various sensors for measuring multiple biological parameters that may be beneficial to the device user, such as a heart rate sensor, a multipurpose electrical sensor for electrocardiogram (ECG) and electrodermal activity (EDA) applications, an infrared sensor, a gyroscope, an altimeter, an accelerometer, a temperature sensor, an ambient light sensor, Wi-Fi, a GPS, a vibration sensor, a speaker, and a microphone, among others. Due to the functionality of conventional devices, it can be difficult to determine when a user is wearing the device and when the device is properly fitted to the user, which can affect the accuracy of the biometric measurements.

[0003] Therefore, improved systems and methods for monitoring the wearability of wearable computing devices would be well-received in the art. Summary of the Invention

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

[0005] In one aspect, a computer-implemented method for determining a wearing state of a wearable computing device is provided. The method may include acquiring, by one or more processors of the wearable computing device, first proximity data generated by a proximity sensor of the wearable computing device during a first time period. The method may further include activating, via the one or more processors, an impedance sensor of the wearable computing device based at least in part on the first proximity data. Furthermore, the method may include acquiring, via the one or more processors, impedance data generated by the impedance sensor during a second time period occurring after the first time period. Furthermore, the method may include acquiring, via the one or more processors, second proximity data generated by the proximity sensor during the second time period. Furthermore, the method may include determining, via the one or more processors, that the wearing state of the wearable computing device corresponds to one of an off-wrist state, an on-wrist state with contact, or an on-wrist state without contact based at least in part on the impedance data and the second proximity data.

[0006] In another aspect, a wearable computing device is provided. The wearable computing device may include a proximity sensor, an impedance sensor, and one or more processors. The one or more processors may be configured to acquire first proximity data generated by the proximity sensor during a first time period. The one or more processors may be further configured to activate the impedance sensor based at least in part on the first proximity data. Furthermore, the one or more processors may be configured to acquire impedance data generated by the impedance sensor during a second time period occurring after the first time period. Furthermore, the one or more processors may be configured to acquire second proximity data generated by the proximity sensor during the second time period. Furthermore, the one or more processors may be configured to determine, at least in part based on the impedance data and the second proximity data, that a wearing state of the wearable computing device corresponds to one of an off-wrist state, an on-wrist state with contact, or an on-wrist state without contact.

[0007] In another aspect, a computer-implemented method for determining a wearable computing device's wearable state is provided. The method may include, via one or more processors of the wearable computing device, acquiring impedance data generated by an impedance sensor of the wearable computing device during a time period. The method may further include, via the one or more processors, calculating an admittance value of the impedance sensor for the time period based at least in part on the impedance data. Furthermore, the method may include, via the one or more processors, determining, at least in part based on the calculated admittance value, that the wearable computing device's wearable state for the time period corresponds to an on-wrist state in which the wearable computing device is worn by a user or an off-wrist state in which the wearable computing device is not worn by a user. Furthermore, the method may include, via the one or more processors, performing a control operation based at least in part on the wearable state of the computing device.

[0008] In yet another aspect, a computer-implemented method for determining a wearable computing device's wearability is provided. The method may include acquiring, via one or more processors of the wearable computing device, impedance data generated by an impedance sensor of the wearable computing device during a time period when the wearable computing device is worn by a user during the time period. The method may further include determining, via the one or more processors, that electrodes of the impedance sensor have not contacted the user's skin for a threshold amount of the time period based at least in part on the impedance data. Additionally, the method may include executing, via the one or more processors, a control operation in response to determining that electrodes of the impedance sensor have not contacted the user's skin for the threshold amount of the time period.

[0009] These and other features, aspects, and advantages of various embodiments of the present disclosure will become better understood with reference to the following detailed description and the appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the present disclosure and, together with the detailed description, serve to explain associated principles.

[0010] Detailed descriptions of embodiments directed to those skilled in the art are set forth herein with reference to the accompanying drawings. [Brief explanation of the drawings]

[0011] [Figure 1] 1 provides a perspective view of a wearable computing device on a user's wrist, according to one embodiment of the present disclosure. [Figure 2] 1 provides a front perspective view of a wearable computing device according to one embodiment of the present disclosure. [Figure 3] 3 provides a rear perspective view of the wearable computing device of FIG. 2. [Figure 4] 3 provides an exploded view of the display of the wearable computing device of FIG. 2. [Figure 5] 1 provides a schematic diagram of an exemplary set of devices capable of communicating, according to one embodiment of the present disclosure. [Figure 6] 1 illustrates various controller components of an exemplary system that may be utilized, according to one embodiment of the present disclosure. [Figure 7] 1 shows a state diagram illustrating different wearing states of a wearable computing device according to the present disclosure. [Figure 8A] 1 shows a flowchart of an embodiment of a method for detecting the wearing state of a wearable computing device according to the present disclosure. [Figure 8B] 1 shows a flowchart of an embodiment of a method for detecting the wearing state of a wearable computing device according to the present disclosure. [Figure 9]10 shows a flowchart of another embodiment of a method for detecting the wearing state of a wearable computing device according to the present disclosure. [Figure 10] 10 shows a flowchart of yet another embodiment of a method for detecting the wearing state of a wearable computing device according to the present disclosure. [Figure 11] 10 shows a flowchart of a further embodiment of a method for detecting the wearing state of a wearable computing device according to the present disclosure. [Figure 12] 1 illustrates a first exemplary graphical display of a notification to a user of a wearable computing device according to the present disclosure. [Figure 13] 10 illustrates a second exemplary graphical display of a notification to a user of a wearable computing device according to the present disclosure. [Figure 14] 1 illustrates a graphical representation of an exemplary notification flow to a user of a wearable computing device according to the present disclosure. [Figure 15] 3 shows another rear perspective view of the wearable computing device of FIG. 2. [Figure 16] 3 shows a further rear perspective view of the wearable computing device of FIG. 2. [Figure 17] 3 illustrates an exploded view of the skin side of the wearable computing device of FIG. 2. [Figure 18] 3A-3C show various views of the skin side of the wearable computing device of FIG. 2, particularly when the coloring element is transparent, partially transparent, and opaque, respectively. DETAILED DESCRIPTION OF THE INVENTION

[0012] Reference will now be made in detail to the embodiments of the present invention, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation of the invention, and not limitation of the invention. Indeed, it will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the scope or spirit of the invention. For example, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that the present invention cover such modifications and variations as come within the scope of the appended claims and their equivalents.

[0013] In general, if a wearable computing device is worn improperly or is not sufficiently configured to distinguish between on-body and off-body states, the data generated by the wearable computing device may be inaccurate and / or misleading. For example, if a wearable computing device is worn loosely, there may be inconsistent skin contact or the area of ​​skin contact may vary, which may cause inaccurate data. Similarly, if a wearable computing device is worn too tightly, there may be reduced perfusion (fewer fluid pathways on the skin where the device is worn) or other undesirable effects, which may result in inaccurate data. If the wearable computing device is positioned improperly (e.g., straddling the styloid process), there may be insufficient contact with the skin, which may lead to inaccurate data. Furthermore, if one or more proximity sensors on the wearable computing device are configured to detect simple light reflection, the device may determine that it is in an on-wrist state when in proximity to any reflective surface, including inanimate objects, which may lead to inaccurate data or the unintended activation of one or more modes. For example, if a wearable computing device is in a bag and the proximity sensor detects a reflection from the bag or another object in the bag (e.g., keys), the device may sense an accidental fall and unnecessarily send a signal to assist the user if the bag is dropped, enable a payment app that may allow unintended payments, collect data inconsistent with the user's activity (e.g., sleep, exercise, etc.), and / or the like.

[0014] Accordingly, the present disclosure relates to a wearable computing device, particularly a wearable computing device having a body impedance sensor, and a computer-implemented method for determining the wear state of the wearable computing device, to accurately determine when the device is being worn and / or when the device is being worn correctly to ensure that measurements by the biometric sensor are accurate for determining biometric information, and to identify when the device is being worn in a particular activity mode, based at least in part on body impedance data from a body impedance sensor (hereinafter referred to as an "impedance sensor"). Generally, an impedance sensor measures the impedance or admittance (which is the reciprocal of impedance) of a user's skin using a multipath electrical sensor. These responses are observed as electrical changes that are sensitive to skin conductance, and are typically detected using a wet or dry electrode system, e.g., at least two electrodes, and skin conductance is calculated using the measured electrical impedance. For example, an impedance sensor may have a resistive component and a reactive component, which result in a two-dimensional output. The impedance sensor may include an electrodermal activity (EDA) sensor, which in one embodiment may be used to continuously collect data during movement.

[0015] In one or more embodiments, the impedance sensor is turned on or actively collects data only when the wearable computing device is likely to be in proximity to a user, thereby conserving battery life when data from the impedance sensor would not be accurate and / or useful. For example, the wearable computing device may further include a proximity sensor (e.g., a photoplethysmogram (PPG) sensor and / or a pressure sensor) configured to generate proximity data indicative of the device's proximity to the user. The proximity data may be compared to a proximity threshold to determine when the wearable computing device is likely to be worn by a user. If the wearable computing device is determined to be worn by a user based on a comparison of the proximity data to the proximity threshold, the impedance sensor may be turned on or activated to begin collecting data. To the extent that the wearable computing device is determined to be worn by a user based on the proximity data, the impedance data may be used to determine whether the device is being worn and / or properly worn.

[0016] In some embodiments, impedance data, with or without proximity data, may be used to determine whether the device is being worn and / or worn properly. For example, in one embodiment, impedance data is acquired over a period of time, and an admittance value of the impedance sensor over that period of time is calculated based at least in part on the impedance data. The wear state of the wearable computing device during that period of time may then be determined based at least in part on the admittance value. For example, the wear state may be determined to be an on-wrist state, corresponding to the user wearing the wearable computing device, if the admittance value is within the impedance range, or an off-wrist state, corresponding to the user not wearing the wearable computing device, if the admittance value is outside the impedance range.

[0017] In some embodiments, impedance data is acquired over a period of time, and the amount of time during which the electrodes of the impedance sensor are in contact with the user's skin is determined, at least in part, based on the impedance data. If the amount of time the electrodes of the impedance sensor are in contact with the user's skin is less than a threshold time but greater than zero, the wearing state may be determined to be an on-wrist, no-contact state, and a notification may be generated prompting the user to tighten a band securing the wearable computing device to the user. If the amount of time the electrodes of the impedance sensor are in contact with the user's skin is zero, the wearing state may be determined to be an off-wrist state. Alternatively, if the time the electrodes of the impedance sensor are in contact with the user's skin is greater than a threshold time, the wearing state may be determined to be an on-wrist, contact state.

[0018] In some examples, the shape of the impedance data can be monitored over time to determine contact between the user and the device. For example, after a user begins wearing the device, moisture (e.g., oil, sweat, etc.) slowly begins to accumulate on the device, causing the admittance value to slowly increase over time. In some examples, the shape of the increase appears logarithmic. Therefore, the shape of the impedance data can also indicate whether the user has begun wearing the device.

[0019] Additionally, in some examples, monitored impedance or admittance may be used to improve biometric measurements from other sensors. For example, measured changes (e.g., magnitude and / or angle) in contact impedance or admittance may be related to changes in applied pressure and used to improve estimated heart rate and / or the like. Similarly, a combination of proximity data and impedance data (e.g., from a PPG sensor) may be used to improve biometric readings from other sensors. For example, PPG data may be represented as reflectance plotted against wavelength, or as a decrease in the pulsatile component (AC or DC) of the impedance data, with a sudden shift in reflectance and / or a shift in the pulsatile component indicating a pressure change.

[0020] Furthermore, in some aspects, coloring elements across at least some of the sensors of a wearable computing device may be controlled based at least in part on the wearing state of the wearable computing device. For example, if it is determined that the wearable computing device is being worn, the coloring elements may be controlled to be transparent so that sensors covered by the coloring elements are not obstructed by the coloring elements. Similarly, if it is determined that the wearable computing device is off-list, the coloring elements may be controlled to be at least semi-transparent or completely opaque to at least partially hide the sensors covered by the coloring elements. The coloring elements may also be controlled to improve the sensing state of particular sensors. For example, if the ambient light is too high to accurately acquire PPG data, the coloring elements may be controlled to be slightly colored to reduce the ambient light.

[0021] The disclosed method thus allows for more accurate determination of the wearing state of a wearable computing device (which may include fitting the wearable computing device), thereby increasing the overall accuracy of biometric data acquired using the wearable computing device and allowing adjustments to be requested that may prevent certain activity modes from being enabled when the device is not actually being worn.

[0022] Referring now to the drawings, exemplary embodiments of the present disclosure will be discussed in further detail.

[0023] Referring now to the drawings, FIGS. 1-4 illustrate perspective views of a wearable computing device 100 according to the present disclosure. Specifically, as shown in FIG. 1 , the wearable computing device 100 may be worn on a user's forearm 102, similar to a wristwatch. Accordingly, as shown, the wearable computing device 100 may include a wristband 103 for securing the wearable computing device 100 to the user's forearm 102. However, it should be understood that the wearable computing device 100 may be worn by a user in any other suitable location, such as, for example, on the ankle. Further, as shown in FIGS. 1 , 2 , and 4 , the wearable computing device 100 has an outer cover 105 and a housing 104 that contains electronics associated with the wearable computing device 100. For example, in one embodiment, the outer cover 105 may be constructed of glass, polycarbonate, acrylic, or the like. Further, as shown in FIGS. 1 , 2 , and 4 , the wearable computing device 100 includes an electronic display screen 106 disposed within the housing 104 and viewable through the outer cover 105. Additionally, as shown, wearable computing device 100 may also include one or more buttons 108 that may be implemented to provide a mechanism for activating various sensors of wearable computing device 100 to collect specific health data of the user. Additionally, in one embodiment, electronic display 106 may cover an electronic package (not shown), which may also be housed within housing 104.

[0024] 3 , the housing 104 of the wearable computing device 100 further includes a dorsal wrist side 110 configured to rest against the dorsal wrist of a user when worn by the user, and a plurality of sensor electrodes 112 disposed on the dorsal wrist side 110 of the housing 104 to maintain skin contact with the user when worn on the user's wrist. Thus, in such embodiments, each of the sensor electrodes 112 may be configurable to measure the user's electrical impedance at least at the location of skin contact on the dorsal wrist. Thus, in one or more embodiments, one or more (or all) of the plurality of sensor electrodes 112 may be impedance sensor electrodes. In some embodiments, the wearable computing device 100 may also include at least one additional biosensor electrode in addition to the impedance sensor electrode. In such embodiments, the additional biosensor electrode 112 may further include one or more temperature sensors (such as an ambient temperature sensor or a skin temperature sensor), humidity sensors, light sensors, pressure sensors, microphones, optical sensors, or photoplethysmography (PPG) sensors.

[0025] The sensor electrodes 112 may include at least two electrodes spaced apart from each other to prevent excessive bridging of the sensor electrodes 112, such as by sweat. For example, in some embodiments, the wearable computing device 100 may further include an optical package 215 for measuring data related to one or more metrics of the human body, such as related to a person wearing the wearable computing device 100. In some cases, the sensor electrodes 112 may be generally disposed about the optical package 215. In one embodiment, as shown in FIG. 15 , the sensor electrodes 112 may be disposed about the optical package 215 such that the sensor electrodes 112 may be used to indicate partial seating of the wearable computing device 100. For example, sensor electrodes 112 may include a first sensor electrode 112(1), a second sensor electrode 112(2), a third sensor electrode 112(3), and a fourth sensor electrode 112(4), with each electrode 112(1)-112(4) associated with a given position around optical package 215 so that a partially unseated position may be more easily determined. For example, if first and second sensor electrodes 112(1), 112(2) have readings indicative of contact, but third and fourth sensor electrodes 112(3), 112(4) do not have readings indicative of contact, wearable computing device 100 may be determined to be partially unseated, with the upper portion (associated with first and second sensor electrodes 112(1), 112(2)) being seated and the lower portion (associated with third and fourth sensor electrodes 112(3), 112(4)) being unseated. Although only four sensor electrodes 112(1)-112(4) are shown, it should be understood that any number of electrodes may be used and that each electrode may be further segmented into multiple sensing regions.

[0026] Additionally, the sensor electrodes 112 described herein may be constructed of any suitable material. For example, in one embodiment, the sensor electrodes 112 described herein may be constructed of stainless steel, graphene, or any other material with suitable electrical conductivity and / or corrosion resistance, and may have an optional PVD coating, which may be titanium nitride, 1 micrometer thick. In such an embodiment, the PVD coating may provide the sensor electrode 112 with a desired color, prevent oxidation, and improve durability beyond what stainless steel already provides.

[0027] In additional embodiments, PVD and surface finishes can be used to increase / decrease moisture retention, which affects the impedance signal and user comfort. In certain embodiments, the sensor electrode 112 may be formed from a tin-nickel alloy (TiN) with a glossy or mirror-like finish. Furthermore, in one embodiment, the sensor electrode 112 may be constructed from a hydrophobic or transparent material. For example, as shown in FIG. 16 , the sensor electrode 112 is made from a transparent material (e.g., indium tin oxide, fluorotin oxide (FTO), etc.) and is disposed on the optical package 215. In some embodiments, it may be preferable for the sensor electrode 112 to be formed from FTO, at least because FTO can be particularly resilient to normal wear, scratches, etc. when the sensor electrode 112 is exposed to contact with the skin.

[0028] In some embodiments, the wearable computing device 100 may further include one or more coloring elements that can selectively obscure one or more of the sensing components on the dorsal wrist side 110. For example, as shown in FIGS. 17-18C , the coloring element 107 may be sandwiched between a protective layer or cover 109 (e.g., glass, resin, plastic, etc.) and an optical package 215 on at least the dorsal wrist side 110 of the wearable device 100. The coloring element 107 may be a guest-host liquid crystal (GHLC) or electrochemical element that is controllable to vary between a range of transparency levels. For example, different levels of power or energy supplied to the coloring element 107 may vary the amount of transparency of the coloring element 107. For example, as shown in FIG. 18A , coloring element 107 may be transparent when supplied with a first amount of electricity so that optical package 215 is not obstructed and operates normally, partially transparent or diffused when supplied with a lesser amount of electricity so that optical package 215 is partially visible and may operate partially ( FIG. 18B ), or opaque when not supplied with electricity so that optical package 215 is fully hidden and does not operate ( FIG. 18C ). In the partially transparent state, light emitted by optical package 215 may be at least partially diffused so that the edges of sensors in optical package 215 are less visible and the overall appearance is more appealing. In some examples, it may be preferable for coloring element 107 to be fully transparent when wearable computing device 100 is detected as being worn, and partially transparent or opaque when not being worn so that optical package 215 is at least partially hidden so that data generated by optical package 215 is more accurate. However, in some cases, it may be desirable for the coloring element 107 to be slightly less than completely transparent when worn, such as when the ambient lighting is too bright, in order to improve the readings of certain sensors (e.g., the PPG sensor(s) 215).Thus, the level of tint between the transparent and opaque states may be determined at least in part based on the state in which the wearable computing device 100 is worn (e.g., determined based on pressure, proximity, etc.) and / or ambient lighting.

[0029] It should be appreciated that the coloring elements 107 may provide a global or pixelated coloring of the covered area. Pixelated coloring may particularly allow for improved sensor performance and placement, particularly for PPG sensors. It should further be appreciated that the coloring elements 107 may alternatively be configured to be transparent when not energized, and slightly transparent or opaque when energized.

[0030] 5, components of an exemplary system 200 of the wearable computing device 100 that may be utilized in accordance with various embodiments are shown. Specifically, as shown, the system 200 may also include at least one controller 202 communicatively coupled to the plurality of sensor electrodes 112. Additionally, in one embodiment, the controller(s) 202 may be a central processing unit (CPU) or a graphics processing unit (GPU) for executing instructions that may be stored in a memory device 204, such as flash memory or DRAM, among other options.

[0031] For example, in one embodiment, memory device 204 may include RAM, ROM, FLASH memory, or other non-transitory digital data storage and may include a control program including sequences of instructions that, when loaded from memory device 204 and executed using controller(s) 202, cause controller(s) 202 to perform the functions described herein. As will be apparent to one skilled in the art, computing system 200 can include many types of memory, data storage, or computer-readable media, such as data storage for program instructions for execution by a controller or any suitable processor. The same or separate storage can be used for images or data, removable memory can be made available for sharing information with other devices, and any number of communication approaches can be utilized for sharing with other devices.

[0032] Additionally, as shown, system 200 includes any suitable display 206, such as a touchscreen, organic light-emitting diode (OLED), or liquid crystal display (LCD), although the device may communicate information through other means, such as through audio speakers, a projector, or by casting the display or streaming data to another device, such as a mobile phone, where an application on the mobile phone displays the data.

[0033] System 200 may also include one or more wireless components 212 operable to communicate with one or more electronic devices within communication range of a particular wireless channel. The wireless channel may be any suitable channel used to allow devices to communicate wirelessly, such as a Bluetooth, cellular, NFC, Ultra-Wideband (UWB), or Wi-Fi channel. It should be understood that system 200 may have one or more conventional wired communication connections known in the art.

[0034] System 200 also includes one or more power components 208, such as a battery operable to be recharged via a conventional plug-in approach or via other approaches, such as capacitive charging via the proximity of a power mat or other such device. In further embodiments, system 200 also includes at least one additional input / output device 210 capable of receiving conventional input from a user. This conventional input may include, for example, push buttons, a touchpad, a touchscreen, a wheel, a joystick, a keyboard, a mouse, a keypad, or any other such device or element, by which a user can input commands into system 200. In other embodiments, input / output device(s) 210 may connect via a wireless infrared or Bluetooth or other link in some embodiments. In some embodiments, system 200 may also include a microphone or other audio capture element that accepts voice or other audio commands. For example, in certain embodiments, system 200 may not include any buttons at all, but may be controlled solely via a combination of visual and voice commands, such that a user can control wearable computing device 100 without having to come into contact with the device. In certain embodiments, the input / output element 210 may also include one or more sensor electrodes 112, optical sensors, barometric pressure sensors (e.g., altimeters, etc.), etc., as described herein.

[0035] 5, system 200 may include driver 214 and at least some combination of one or more emitters 216 and one or more detectors 218 (referred to herein as optical package 215) for measuring data of one or more metrics of a human body, such as a person wearing wearable computing device 100. In such an embodiment, as described above with reference to FIG. 3, for example, optical package 215 may be disposed within housing 104 and at least partially exposed through dorsal wrist side 110 of housing 104. Thus, as shown and further described herein, sensor electrodes 112 may be disposed around optical package 215 on dorsal wrist side 110 of housing 104. In alternative embodiments, various components of optical package 215 may be disposed around sensor electrodes 112 and / or in other suitable configurations, such as adjacent to, interspersed with, surrounded by, or above optical package 215. For example, in certain embodiments in which the sensor electrode 112 is transparent, the sensor electrode 112 may be disposed over the optical package 215 .

[0036] In some embodiments, system 200 may include at least one imaging element, such as one or more cameras, capable of capturing images of the surrounding environment and imaging a user, people, or objects in the device's vicinity. The imaging element may include any suitable technology, such as a CCD image capture element having sufficient resolution, focusing range, and viewing area to capture images of a user as the user operates the device. Additional image capture elements may also include depth sensors. Methods for capturing images using camera elements with computing devices are well known in the art and will not be described in detail herein. It should be understood that image capture can be performed using a single image, multiple images, periodic imaging, continuous image capture, image streaming, etc. Additionally, system 200 may include the ability to start and / or stop image capture, for example, upon receiving a command from a user, an application, or another device.

[0037] The emitter 216 and detector 218 of FIG. 5 may also be used to obtain optical PPG measurements, in one example. Some PPG techniques rely on detecting light at a single spatial location, adding signals obtained from two or more spatial locations, or an algorithmic combination thereof. Both of these approaches result in a single spatial measurement from which a heart rate (HR) estimate (or other physiological metric) can be determined. In some embodiments, the PPG device uses a single light source (i.e., a single optical path) coupled to a single detector. Alternatively, the PPG device may use multiple light sources coupled to a single detector or multiple detectors (i.e., two or more optical paths). In other embodiments, the PPG device uses multiple detectors coupled to a single light source or multiple light sources (i.e., two or more optical paths). In some cases, the light source(s) may be configured to emit one or more of green, red, infrared (IR) light, and any other suitable wavelengths in the spectrum (e.g., long IR for metabolic monitoring). For example, the PPG device may use a single light source and two or more photodetectors, each configured to detect a specific wavelength or range of wavelengths. In some cases, each detector is configured to detect a different wavelength or wavelength range from the others. In other cases, two or more detectors are configured to detect the same wavelength or wavelength range. In still other cases, one or more detectors are configured to detect a particular wavelength or wavelength range from one or more other detectors. In embodiments using multiple optical paths, the PPG device may determine an average of signals resulting from the multiple optical paths before determining an HR estimate or other physiological metric.

[0038] Additionally, in one embodiment, emitter 216 and detector 218 may be coupled directly or indirectly to controller 202 using driver circuits that enable controller 202 to drive emitter 216 and obtain signals from detector 218. Host computer 222 may communicate with wireless network component 212 via one or more networks 220, which may include one or more local area networks, wide area networks, UWB, and / or internetworks using either terrestrial or satellite links. In some embodiments, host computer 222 executes control and / or application programs configured to perform some of the functions described herein.

[0039] In some embodiments, system 200 may also include one or more coloring elements 107 for selectively hiding one or more components on wearable computing device 100 and may be configured to selectively power such coloring element(s) 107 (e.g., via power component(s) 208) to change the coloring level of coloring element(s) 107.

[0040] Referring to FIG. 6 , a schematic diagram of an environment 300 in which aspects of various embodiments can be implemented is shown. Specifically, as shown, a user may have several different devices that can communicate using at least one wireless communication protocol. For example, as shown, a user may have a smartwatch 302 or a fitness tracker (such as wearable computing device 100), and the user would like to be able to communicate with a smartphone 304 and a tablet computer 306. The ability to communicate with multiple devices allows the user to use applications installed on either the smartphone 304 or the tablet computer 306 to obtain information from the smartwatch 302, such as data captured using sensors on the smartwatch 302. The user may also want the smartwatch 302 to be able to communicate with a service provider 308, or other such entity, that can obtain and process data from the smartwatch and provide functionality not otherwise available in applications installed on the smartwatch or individual devices. Additionally, as shown, the smartwatch 302 may be able to communicate with the service provider 308 via at least one network 210, such as the Internet or a cellular network, or may communicate via a wireless connection, such as Bluetooth, to one of its respective devices, which in turn communicates via at least one network. In various embodiments, there may be some other type or reason for communication.

[0041] In addition to being able to communicate, users may also want devices to be able to communicate in some way or in a specific manner. For example, users may want communications between devices to be secure, especially if the data may include personal health data or other such communications. Device or application providers may also need to protect this information, at least in some circumstances. Users may want devices to be able to communicate with each other simultaneously, rather than sequentially. This may be particularly true when pairing may be required, as users may prefer that each device be paired at most once so that manual pairing is not necessary. Users may also want communications to be as standards-based as possible, not only so that little manual intervention on the part of the user is required, but also so that devices can communicate with as many other types of devices as possible, which is often not the case with various proprietary formats. Thus, users may want to be able to walk around a room with one device and have such device automatically communicate with another target device with little or no effort on the part of the user. In various conventional approaches, devices utilize communication technologies such as Wi-Fi to communicate with other devices using wireless local area networks (WLANs). Smaller or lower volume devices, such as many Internet of Things (IoT) devices, instead utilize communication technologies such as Bluetooth®, and particularly Bluetooth Low Energy (BLE), which has very low power consumption.

[0042] 6 allows data to be captured, processed, and displayed in a number of different ways. For example, data may be captured using sensors on the smartwatch 302, but due to limited resources on the smartwatch 302, the data may be transferred to the smartphone 304 or service provider 308 (or cloud resources) for processing, and the results of that processing may then be presented to the user on another such device associated with the user, such as the smartwatch 302, smartphone 304, and / or tablet computer 306. In at least some embodiments, the user may also be able to use an interface on any of these devices to provide input, such as health data, which can then be taken into account when making their decisions.

[0043] 7, a state diagram illustrating different wearing states of a wearable computing device according to the present disclosure is provided. In one embodiment, for example, the wearable computing device may be any suitable wearable computing device, such as wearable computing device 100 described herein with reference to FIGS. 1-6. Accordingly, the wearing state diagrams are generally described herein with reference to wearable computing device 100 of FIGS. 1-6. However, it should be understood that the disclosed wearing states may be implemented using any other suitable wearable computing device having any other suitable configuration.

[0044] 7, the wearable computing device 100 has multiple states depending on the biosensor electrodes 112 of the device 100, particularly the proximity and impedance sensors. For example, in one embodiment, the wearable computing device 100 has four wearing states, including an off-wrist impedance sensor off state 402, an off-wrist impedance sensor active state 404, an on-wrist with contact state 406, and an on-wrist without contact state 408.

[0045] For purposes of explanation, the proximity sensor will be alternately described with reference to a PPG sensor, but it should be understood that the proximity sensor may be any other suitable proximity sensor or combination of suitable proximity sensors, such as other suitable optical proximity sensor(s) including, for example, an infrared sensor. Similarly, in some embodiments, the impedance sensor may be an EDA sensor. However, in other embodiments, the impedance sensor may be any other suitable impedance sensor or combination of impedance sensors, including, but not limited to, an ultrasonic sensor and / or an inductive sensor.

[0046] In some examples, the wearable computing device 100 defaults to or starts in the off-wrist impedance sensor off state 402. In the off-wrist impedance sensor off state 402, the impedance sensor (e.g., EDA sensor) is off and the proximity sensor (e.g., PPG sensor) is on. Data generated by the PPG sensor in the off-wrist impedance sensor off state 402 indicates the amount of reflected light detected. This indicates the proximity of the PPG sensor, and therefore the wearable computing device 100, to an object (e.g., a user). The PPG data is compared to a PPG threshold (e.g., indicating proximity) to determine whether the wearable computing device 100 is likely being worn by a user. If the comparison of the PPG data to the PPG threshold indicates that the wearable computing device 100 is not in proximity to an object (e.g., if the PPG data is less than the PPG threshold or the proximity determined from the PPG data is greater than the threshold proximity), it is determined that the wearable computing device 100 is not being worn by a user, and the impedance sensor remains in the off state. In some examples, when device 100 is in off-wrist impedance sensor off state 402, certain functions of wearable computing device 100, such as workout mode, sleep mode, payment mode, etc., may be disabled or unable to be enabled. Additionally or alternatively, as noted above, when device 100 is in off-wrist impedance sensor off state 402, it may be desirable for coloring element 107 to be fully opaque or translucent.

[0047] If the comparison of the PPG data with the PPG threshold otherwise indicates that the wearable computing device 100 is near the object (e.g., the PPG data exceeds the PPG threshold or the proximity determined from the PPG data is less than the proximity threshold), it is determined that the wearable computing device 100 may be being worn by a user, the impedance sensor is turned on, and the device 100 transitions to one of the other states 404, 406, 408.

[0048] Specifically, in one embodiment, when the impedance sensor is first turned on, device 100 first transitions to off-list impedance sensor active state 404. In off-list impedance sensor active state 404, both the PPG sensor and the impedance sensor are on. Data (e.g., admittance values) generated by the impedance sensor in off-list impedance sensor active state 404 is compared to an impedance range (e.g., a range of admittance values ​​associated with skin contact of the EDA sensor) to determine whether the impedance sensor is in contact with a user and, therefore, whether device 100 is actually being worn by a user. If the data generated by the impedance sensor in off-list impedance sensor active state 404 is within the impedance range, it is determined or confirmed that device 100 is being worn by a user, and device 100 transitions to on-list state 406 in contact. Otherwise, if the data generated by the impedance sensor in off-list impedance sensor active state 404 is not within the impedance range, it is determined or confirmed that device 100 is not being worn, and device 100 remains in off-list impedance sensor active state 404. In some embodiments, the shape of the impedance data can additionally or alternatively be monitored over time to determine whether the impedance sensor is in contact with the user. For example, after a user begins wearing the device, moisture (e.g., oil, sweat, etc.) slowly begins to accumulate on the device, causing the admittance value to slowly increase over time. In some examples, the shape of the increase resembles a logarithmic function. Therefore, the shape of the impedance data generated by the impedance sensor in the off-wrist impedance sensor active state 404 can be compared to an expected shape to determine whether the user has begun wearing the device 100.

[0049] In some examples, when device 100 is in off-wrist impedance sensor active state 404, certain functions of wearable computing device 100, such as workout mode, sleep mode, payment mode, etc., may still be disabled or may not be allowed to be enabled. If the proximity detected by the PPG sensor in off-wrist impedance sensor active state 404 indicates that wearable computing device 100 is no longer in proximity to an object (e.g., if the PPG data is below a PPG threshold or the proximity determined from the PPG data is greater than a proximity threshold), it is determined that device 100 may no longer be worn by a user, and device 100 is returned to off-wrist impedance sensor off state 402, with the impedance sensor turned off or disabled to conserve battery, prevent erroneous measurements, prevent unnecessary LED flashes, etc. Additionally or alternatively, as described above, it may be desirable for coloring element 107 to be fully opaque or translucent when device 100 is in off-wrist impedance sensor active state 404.

[0050] When device 100 is in contact-on-wrist state 406, both the PPG sensor and the impedance sensor remain on. Data generated by the impedance sensor in contact-on-wrist state 406 continues to be compared to an impedance range (e.g., skin contact range) to determine whether the impedance sensor is still in contact with the user and, therefore, whether device 100 is still being worn by the user. If the data generated by the impedance sensor in contact-on-wrist state 406 remains within the impedance range, it is determined that the impedance sensor is still in contact with the user and that device 100 is still being worn by the user, and device 100 remains in contact-on-wrist state 406. When device 100 is in contact-on-wrist state 406, one or more control operations may be performed, such as adjusting the proximity sensor (e.g., adjusting the LED light intensity of the PPG sensor, adjusting the receiver sensitivity, etc., based on characteristics of the user's skin that may have changed since last wear due to activity, sun exposure, hygiene, etc.). Similarly, in some examples, when device 100 is in contact on-wrist state 406, certain features of wearable computing device 100, such as workout mode, sleep mode, payment mode, etc., may be enabled or permitted to be enabled. Additionally, in some examples, when device 100 is in contact on-wrist state 406, monitored impedance or admittance may be used to improve biometric readings and / or other insights based at least in part on data from other sensors. For example, measured changes (e.g., magnitude and / or angle) in contact impedance or admittance may be related to changes in applied pressure and used to improve estimated heart rate and / or the like. Similarly, a combination of proximity data and impedance data (e.g., from a PPG sensor) may be used to improve biometric readings from other sensors.For example, PPG data may be represented as reflectance plotted against wavelength, or a decrease in the pulsatile component (AC or DC) of impedance data, where a sudden shift in reflectance and / or a shift in the pulsatile component may indicate a pressure change. In some examples, a machine learning model may collect data when device 100 is in contact on-wrist state 406 and improve biometric readings and / or other insights (e.g., battery life) based at least in part on data from other sensors. Additionally or alternatively, as described above, it may be desirable for coloring element 107 to be fully transparent or translucent when device 100 is in contact on-wrist state 406.

[0051] Otherwise, if the data generated by the impedance sensor in contact on-wrist state 406 is no longer within the impedance range, it is determined that the impedance sensor is no longer in contact with the user and that device 100 may no longer be worn by the user, and device 100 transitions to no-contact on-wrist state 408. If the proximity detected by the PPG sensor in contact on-wrist state 406 indicates that wearable computing device 100 is no longer in proximity to the object (e.g., if the PPG data is below a PPG threshold or the proximity determined from the PPG data is greater than a proximity threshold), it is determined that device 100 may no longer be worn by the user, and device 100 is returned to off-wrist impedance sensor off state 402, and the impedance sensor is turned off.

[0052] When the device transitions to the no-contact-on-wrist state 408, both the PPG sensor and the impedance sensor remain on. The data generated by the impedance sensor in the no-contact-on-wrist state 408 continues to be compared to an impedance range (e.g., skin contact range) to determine whether the impedance sensor is in contact with the user and, therefore, whether the device 100 is still being worn by the user. If the data generated by the impedance sensor in the no-contact-on-wrist state 408 returns within the impedance range, the impedance sensor is again in contact with the user and, therefore, the device 100 is again confirmed as being worn by the user, and the device 100 returns to the no-contact-on-wrist state 406. Otherwise, if the data generated by the impedance sensor in the no-contact-on-wrist state 408 remains outside the impedance range, the device 100 remains in the no-contact-on-wrist state 408 because the impedance sensor is not yet in contact with the user but may still be worn by the user. In some examples, as described below, control operations may be performed when the device 100 is in the no-contact-on-wrist state 408. For example, in one example, device 100 may prompt the user to adjust device 100 (e.g., tighten the wristband 103 of the wearable device), and optionally, if a particular mode (e.g., exercise, sleep, etc.) is active or is enabled or intended to be enabled from a previous on-wrist-with-contact state 406, device 100 may prompt the user to perform an action to check fit after adjusting device 100 (e.g., move the wrist and ensure device 100 maintains contact during the action). In one embodiment, other data generated by device 100 while in on-wrist-without-contact state 408 may be adjusted to account for the possibility that device 100 is out of sync with the user, for example, because it is too loose.If the proximity detected by the PPG sensor in the no-contact on-wrist state 408 indicates that the wearable computing device 100 is no longer in proximity to the object (e.g., if the PPG data is below a PPG threshold or the proximity determined from the PPG data is greater than a proximity threshold), it is determined that the device 100 may no longer be worn by the user, and the device 100 is returned to the off-wrist impedance sensor off state 402, and the impedance sensor is turned off.

[0053] Thus, in the illustrated embodiment, it should be appreciated that device 100 is first confirmed as being worn by a user (i.e., in on-wrist state 406 in contact) only if both the PPG data exceeds the PPG threshold and the impedance data is within the impedance range (e.g., above a minimum threshold admittance value and below a maximum threshold admittance value), and continues to be confirmed until the PPG data falls below the PPG threshold. It should also be appreciated that using a combination of impedance and PPG data as described prevents device 100 from unilaterally predicting that device 100 is in an on-wrist state based on the proximity of the PPG sensor to a reflective surface. Similarly, it should be appreciated that in some embodiments, using a combination of impedance and PPG data can prevent erroneous on-wrist determinations. For example, if the PPG data indicates proximity is not close enough for the user to be wearing the device, but the impedance data simultaneously indicates contact with the user's skin, device 100 may remain in on-wrist state 406, 408. Additionally or alternatively, as described above, it may be desirable for the coloring element 107 to be fully transparent or semi-transparent when the device 100 is in the non-contact wrist state 408.

[0054] Furthermore, although device 100 is shown only to be in non-contacting on-list state 408 after being in contacting on-list state 406, it should be understood that in other embodiments, device 100 may not initially be in contacting on-list state 406 but may be in non-contacting on-list state 408. For example, in some embodiments, device 100 may not initially be in contacting on-list state 406 but may be in non-contacting on-list state 408 if the admittance value is non-zero or otherwise outside the impedance range. Furthermore, it should be understood that in some embodiments, device 100 may have any other suitable number or combination of states. For example, in one embodiment, device 100 has only three states, such as off-list impedance sensor off state 402, contacting on-list state 406, and non-contacting on-list state 408. Here, after determining that device 100 is within a proximity threshold based on the PPG data and after turning on the impedance sensor, a contact-on-wrist state 406 is determined when the impedance data is within the impedance range, or a no-contact-on-wrist state 408 is determined when the impedance data is outside the impedance range. It should further be appreciated that a combination of PPG data and impedance data may be used to determine the user's skin tone, which may then be used to improve the determination of the wearing condition. For example, different skin tones may be associated with different predicted reflectances detected by PPG data for a particular level of contact detected by the impedance sensor (or other pressure-indicating sensor). Thus, once the user's skin tone is determined, interpretations of the PPG data and other data may be calibrated for such skin tone.

[0055] 8A and 8B, a flowchart of one embodiment of a method 450 for detecting a wearable computing device's wearable state is provided. In one embodiment, the wearable computing device may be any suitable wearable computing device, such as, for example, the wearable computing device 100 described herein with reference to FIGS. 1-6. Accordingly, the method 450 is generally described herein with reference to the wearable computing device 100 of FIGS. 1-6 and the wearable states 402, 404, 406, and 408 described in FIG. 7. However, it should be understood that the disclosed method 450 may be implemented in any other suitable wearable computing device having any other suitable configuration and with any other suitable wearable state. Additionally, while FIGS. 8A and 8B depict steps occurring in a particular order for purposes of illustration and explanation, the methods described herein are not limited to any particular order or arrangement. Using the disclosure provided herein, one skilled in the art will understand that various steps of the methods disclosed herein may be omitted, rearranged, combined, added, and / or adapted in various ways without departing from the scope of the present disclosure.

[0056] 8A , in step (452) of method 450, it is assumed that wearable computing device 100 is off-wrist or otherwise not being worn by a user. Thus, in step (454) of method 450, the proximity sensor (e.g., PPG sensor) is on and the impedance sensor (e.g., EDA sensor) is off (or becomes off if not already off). This corresponds to off-wrist impedance sensor off state 402 described above with reference to FIG. 7 . Then, in step (456) of method 450, data collected by the PPG sensor is compared to a PPG threshold to determine whether the proximity range of wearable computing device 100 is within a certain proximity range of an object (e.g., a user) associated with wearable computing device 100 that may be worn by the user. If the PPG data generated by the PPG sensor is less than the PPG threshold (the proximity determined from the PPG data is greater than or farther than the proximity threshold), it is determined that the wearable computing device 100 is not near the object, the wearable computing device 100 is not likely to be worn by a user, and an off-wrist impedance sensor off state is confirmed in step (458) of method 450, and the impedance sensor remains in the off state. However, if the PPG data generated by the PPG sensor is greater than the PPG threshold (the proximity determined from the PPG data is less than the proximity threshold), it is determined that the wearable computing device 100 is likely to be worn by a user, and the impedance sensor is turned on in step (460) of method 450. Once the impedance sensor is powered on, the device 100 transitions to the off-wrist impedance sensor active state 404 in step (462) of method 450.

[0057] In the off-wrist impedance sensor active state 404, in step (464) of method 450 of FIG. 8B , the data collected by the PPG sensor is again compared to the PPG threshold to confirm that the wearable computing device 100 is still within a proximity range associated with a wearable computing device 100 that may be worn by a user. If the PPG data generated by the PPG sensor is less than the PPG threshold (the proximity determined from the PPG data is greater than the proximity threshold), it is determined that the wearable computing device 100 is no longer near the object, and the wearable computing device 100 is no longer likely to be worn by a user, so the device 100 returns to the off-wrist impedance sensor off state in step (458) of method 450, and the impedance sensor returns to the off state in step (454) ( FIG. 8A ). Otherwise, if the PPG data generated by the PPG sensor is greater than the PPG threshold (the proximity determined from the PPG data is less than the proximity threshold), it is determined that the wearable computing device 100 is still near the object and that the wearable computing device 100 may still be worn by the user. To the extent that the wearable computing device 100 is still likely to be worn by the user according to the PPG data in step (464), the impedance data may be evaluated in step (466) of method 450.

[0058] For example, in step (466), the impedance data (e.g., admittance value) is compared to an impedance range (e.g., an admittance value associated with skin contact of the impedance sensor) to determine whether the impedance sensor is in contact with the user's skin. If the data generated by the impedance sensor in the off-wrist impedance sensor active state 404 is not within the impedance range, it is determined or confirmed that the device 100 is not being worn, the device 100 remains in the off-wrist impedance sensor active state 404, and the method returns to step (462) ( FIG. 8A ). Otherwise, if the data generated by the impedance sensor is determined to be within the impedance-related range, it is determined or confirmed that the device 100 is being worn by a user, and the device 100 transitions to the on-wrist state 406 in contact in step (468) of method 450. Thus, the impedance data may be used to avoid erroneous on-wrist determinations from PPG data when the PPG sensor is detecting reflectivity from a reflective surface other than skin.

[0059] It should be understood that in one embodiment, steps (464) and (466) may alternatively be performed in reverse order. Thus, if, at the same time, the impedance data is within the impedance-related range and the PPG data indicates that the proximity is equal to or greater than the proximity threshold (the device 100 is not sufficiently close to the user), the impedance data may override the proximity determined from the PPG data such that the proximity may still be determined to be less than the proximity threshold (the device 100 is sufficiently close to the user), and one of the on-list states 406, 408 is determined instead of the off-list impedance sensor off state 402 for the time instance. Otherwise, if the impedance data is outside the impedance range and the PPG data indicates that the proximity is equal to or greater than the proximity threshold (the device 100 is not sufficiently close to the user), the impedance data confirms that the proximity determined from the PPG data is equal to or greater than the proximity threshold for that time instance, and step (458) returns to the off-list impedance sensor off state 402. Thus, the impedance data can be used to avoid erroneous off-list determinations from PPG data when PPG sensor readings are affected by moisture (e.g., sweat, rain, etc.).

[0060] Returning to the illustrated method 450, in step (470), the data collected by the PPG sensor is again compared to the PPG threshold to confirm that the wearable computing device 100 is still within a proximity range associated with a wearable computing device 100 that may be worn by a user. If the PPG data generated by the PPG sensor is less than the PPG threshold (the proximity determined from the PPG data is greater than the proximity threshold), it is determined that the wearable computing device 100 is no longer near the object, and the wearable computing device 100 is no longer likely to be worn by a user, so the device 100 returns to the off-list impedance sensor off state in step (458) of method 450, and the impedance sensor goes to the off state in step (454) ( FIG. 8A ). Otherwise, if the PPG data generated by the PPG sensor is greater than the PPG threshold (the proximity determined from the PPG data is less than the proximity threshold), it is determined that the wearable computing device 100 is still near the object and that the wearable computing device 100 may still be worn by a user. As long as the wearable computing device 100 may still be worn by the user according to the PPG data in step (464), the impedance data may be evaluated in step (472) of method 450.

[0061] If the data generated by the impedance sensor is still determined to be within the impedance-related range, then device 100 is determined or confirmed to be worn by a user, device 100 remains in contact on-wrist state 406, and the method returns to step (468). Otherwise, if the data generated by the impedance sensor in off-wrist impedance sensor active state 404 is no longer within the impedance range, device 100 transitions to non-contact on-wrist state 408 (FIG. 7) in step (474) of method 450.

[0062] After determining the out-of-touch on-wrist state 408 in step (474), method 450 provides a notification in step (476) prompting the user to adjust the wearable device (e.g., tighten the wristband 103 of the wearable device). For example, as shown in FIG. 12 , an example of a notification is shown in which the user interface (e.g., display 206) provides a visual notification to the user indicating that the wearable device 100 needs to be adjusted (e.g., “Band is too loose”). It should be understood that the notification may display any other appropriate message (e.g., “Check the fit of the band,” “The band may be too loose or too tight,” etc.). Furthermore, it should be understood that the notification can be provided in any other appropriate manner or combination of manners, for example, by haptic feedback, audio feedback, etc. In some embodiments, the notification is provided only upon the occurrence of a certain threshold event. For example, the notification may be provided if the out-of-touch on-wrist state 408 is determined over a certain time period or for a certain number of instances in a time period. In one embodiment, the aggressiveness of the notification may be set based at least in part on the activity mode. For example, if a particular activity mode is initiated, the device may prompt a notification if the no-contact on-list state 408 is determined for a shorter period of time or in fewer instances than if the particular activity mode had not been initiated.

[0063] Further, in some examples, the notification may request confirmation that the notification was received. For example, as shown in FIG. 13 , an example of a notification is shown in which a user interface (e.g., display 206) provides a text-based notification requesting confirmation that the notification was received by interacting with the user interface (e.g., by pressing “OK”). In one example, as shown in FIG. 14 , the notification may alternatively or additionally include an image-based notification requesting adjustment and / or confirmation. Further, in some embodiments, the notification may request confirmation that the adjustment was performed. For example, as shown in FIG. 14 , the notification may prompt the user to perform an action or a predetermined gesture after adjusting device 100 (e.g., “After adjustment, shake and rotate your wrist for 3 seconds to check the fit”). It should be understood that any other suitable action, such as tapping device 100, may be requested.

[0064] If the notification includes a prompt confirming that the adjustment was performed, then in step (478) of method 450, it is again determined whether confirmation indicating that the adjustment (e.g., tightening) was performed has been received. For example, motion data indicative of the user performing a predefined gesture may be received from one or more motion sensors (e.g., gyroscope, altimeter, accelerometer, etc.) of device 100, and the method may return to step (470) to confirm whether the tightening is sufficient. Otherwise, if confirmation has not been received, the method returns to step (476) and continues to wait.

[0065] After returning to step (470) after adjusting device 100 to redetermine the wearing state of the wearable computing device, if an on-wrist state without contact is again determined in step (474), in some examples, the next notification in step (476) may indicate to the user that device 100 is not yet properly fitted (e.g., "Please try again. Band is still loose," as shown in FIG. 14). Otherwise, after returning to step (464) after adjusting device 100, if an on-wrist state is determined in step (468), a further notification may be provided to the user indicating that adjustment of device 100 was successful (e.g., "Great. The band is properly adjusted and provides the best possible detection," as shown in FIG. 14).

[0066] Referring now to FIG. 9 , a flowchart of one embodiment of a method 500 for detecting a wearable computing device's wearable state is provided. In one embodiment, the wearable computing device may be any suitable wearable computing device, such as, for example, the wearable computing device 100 described herein with reference to FIGS. 1-6 . Accordingly, the method 500 is generally described herein with reference to the wearable computing device 100 of FIGS. 1-6 and the wearable states 402, 404, 406, and 408 described in FIG. 7 . However, it should be understood that the disclosed method 500 may be implemented in any other suitable wearable computing device having any other suitable configuration and with any other suitable wearable state. Additionally, while FIG. 9 depicts steps occurring in a particular order for purposes of illustration and explanation, the methods described herein are not limited to any particular order or arrangement. Using the disclosure provided herein, one skilled in the art will understand that various steps of the methods disclosed herein may be omitted, rearranged, combined, added, and / or adapted in various ways without departing from the scope of the present disclosure.

[0067] Method 500 is similar to method 450 described above in FIGS. 8A and 8B , except that device 100 can enter non-contact on-list state 408 without first entering contact on-list state 406. For example, method 500 includes the same steps (452) through (460). However, after step (460), method 500 does not include step (462). Instead, after step (460), method 500 proceeds directly to step (464′), which is the same as step (464) of method 450, and then to step (466′). Similar to step (466) of method 450, in step (466) of method 500, the impedance data (e.g., an admittance value included in the impedance data) is compared to an impedance range (e.g., an admittance value associated with skin contact of the EDA sensor) to determine whether the impedance sensor is in contact with the user's skin. If the data generated by the impedance sensor is determined to be within the impedance range, then the device 100 is determined or confirmed to be worn by a user, and the device 100 transitions to the on-wrist state 406 in contact at step (468). However, unlike step (466) of method 450, if the data generated by the impedance sensor in the off-wrist impedance sensor active state 404 is not within the impedance range at step (466') of method 500, then the device 100 is determined or confirmed to be not being worn, and the device 100 transitions to the on-wrist state 404 in contact at step (474'). Thereafter, steps (476') and (478') of method 500 are the same as steps (476) and (478) of method 450, except that after step (478') of method 500 confirms that an adjustment (e.g., tightening) has been performed, the method returns to step 464'.

[0068] Referring now to FIG. 10 , a flowchart of one embodiment of a method 600 for detecting a wearable computing device's wearable state is provided. In one embodiment, the wearable computing device may be any suitable wearable computing device, such as, for example, the wearable computing device 100 described herein with reference to FIGS. 1-6 . Accordingly, the method 600 is generally described herein with reference to the wearable computing device 100 of FIGS. 1-6 and, optionally, the wearable states 402, 404, 406, and 408 described in FIG. 7 . However, it should be understood that the disclosed method 600 may be implemented in any other suitable wearable computing device having any other suitable configuration and with any other suitable wearable state. Additionally, while FIG. 10 depicts steps occurring in a particular order for purposes of illustration and explanation, the methods described herein are not limited to any particular order or arrangement. Using the disclosure provided herein, one skilled in the art will understand that various steps of the methods disclosed herein may be omitted, rearranged, combined, added, and / or adapted in various ways without departing from the scope of the present disclosure.

[0069] At step (602), method 600 may include acquiring impedance data for a period of time via an impedance sensor of the wearable computing device. For example, impedance data generated over a period of time by an impedance sensor (e.g., an EDA sensor) of wearable computing device 100 may be acquired. The period of time may be any suitable period, such as, for example, about one minute.

[0070] Further, at step (604), method 600 may include calculating an admittance value of the impedance sensor for the time period based at least in part on the impedance data. Generally, admittance is the inverse of impedance. Thus, in one example, the inverse of the impedance data provides the admittance value. However, in other embodiments, the impedance data may already be configured to include an admittance value. The admittance value of the impedance sensor for the time period may be, for example, an average of the admittance values ​​determined from the impedance data.

[0071] Further, at step (606), method 600 may include determining, at least in part based on the calculated admittance value, that the wear state of the wearable computing device for the time period corresponds to an on-wrist state in which the wearable computing device is worn by a user or an off-wrist state in which the wearable computing device is not worn by a user. For example, the admittance value may be compared to an impedance range to determine the wear state of wearable computing device 100. If the admittance value is within the impedance range (e.g., greater than or equal to a minimum threshold admittance value and less than or equal to a maximum threshold admittance value), the wear state corresponds to an on-wrist state in which wearable computing device 100 is worn by a user. Otherwise, if the admittance value is outside the impedance range, the wear state corresponds to an off-wrist state in which wearable computing device 100 is not worn by a user. In some cases, a 2D region in the complex admittance space (e.g., based on phasor notation of admittance, where the complex part of the admittance (representing reactance) is plotted against the real part of the admittance (representing resistance)) may additionally or alternatively be defined to correspond to human skin contact. When the admittance (e.g., reactance and resistance) from the impedance data falls within the 2D region, the worn state corresponds to an on-wrist state in which the wearable computing device 100 is being worn by a user. Otherwise, if the admittance from the impedance data is outside the 2D region, the worn state corresponds to an off-wrist state in which the wearable computing device 100 is not being worn by a user. Using such a 2D admittance region can prevent materials with similar overall magnitude of admittance (combined reactance and resistance) from being erroneously detected as skin. This is because at least one of the reactance or resistance of the admittance may differ from that of skin.In some examples, the total time that the admittance value is within or outside the impedance range is used to determine the wear state. For example, if the total time that the admittance value is within the impedance range for a given period of time is greater than a threshold period, the device 100 can be determined to be in an on-list state. Conversely, if the total time that the admittance value is within the impedance range for a given period of time is less than the threshold period, the device 100 can be determined to be in an off-list state.

[0072] Further, at step (608), method 600 may include performing a control action based, at least in part, on the wearing state of the computing device. For example, the control action may include generating a notification indicating the wearing state of the wearable computing device for a period of time. In some examples, the control action may include generating a notification prompting the user to adjust the fit of the wearable computing device 100 (e.g., tightening the band 103, rotating the band 103, etc.), and / or adjusting the date from a sensor if the wearing state corresponds to an off-wrist state intermittently, and / or disabling one or more features of the wearable computing device 100 (e.g., a payment mode) if the wearing state corresponds to an off-wrist state for a sustained period of time. In one example, if the wearing state corresponds to an on-wrist state for a sustained period of time, the control action may include enabling or allowing a particular function of the wearable computing device 100, such as a workout mode, a sleep mode, or a payment mode.

[0073] Referring now to FIG. 11 , a flowchart of one embodiment of a method 700 for detecting a wearable computing device's wearable state is provided. In one embodiment, the wearable computing device may be any suitable wearable computing device, such as, for example, the wearable computing device 100 described herein with reference to FIGS. 1-6 . Accordingly, the method 700 is generally described herein with reference to the wearable computing device 100 of FIGS. 1-6 and, optionally, the wearable states 402, 404, 406, and 408 described in FIG. 7 . However, it should be understood that the disclosed method 700 may be implemented in any other suitable wearable computing device having any other suitable configuration and with any other suitable wearable state. Additionally, while FIG. 11 depicts steps occurring in a particular order for purposes of illustration and explanation, the methods described herein are not limited to any particular order or arrangement. Using the disclosure provided herein, one skilled in the art will understand that various steps of the methods disclosed herein may be omitted, rearranged, combined, added, and / or adapted in various ways without departing from the scope of the present disclosure.

[0074] At step (702), method 700 may include acquiring impedance data via an impedance sensor (e.g., an EDA sensor) of the wearable computing device during a time period while the user is wearing the wearable computing device. For example, impedance data generated over a time period by the impedance sensor of wearable computing device 100 may be acquired. Generally, the impedance data includes multiple measurements indicative of which electrodes of the impedance sensor are in contact with the user's skin during that time period.

[0075] Further, in step (704), method 700 may include determining, at least in part based on the impedance data, that the electrodes of the impedance sensor have not been in contact with the user's skin for a threshold amount of the time period. For example, the impedance data may only have data points when the impedance electrodes are in contact with the skin. Thus, the total number of measurements taken during the time period may be compared to a minimum threshold number of measurements associated with the threshold amount of the time period (e.g., a minimum of 100 measurements associated with contact between the electrodes and the skin for at least 50% of one minute (30 seconds) should be taken within one minute). If the total number of measurements taken during the time period is less than the threshold number of measurements but greater than zero, it is determined that the electrodes of the impedance sensor have not been in contact with the user's skin for the threshold amount of the time period, and a no-contact on-list state 408 is determined. If the total number of measurements taken during that time period is less than the threshold number of measurements, in particular if the total number of measurements taken during that time period is zero, it is determined that the electrodes of the impedance sensor are not in contact with the user's skin at all during that time period, and an off-list state is determined. Otherwise, if the total number of measurements taken during that time period is greater than the threshold number of measurements, it is determined that the electrodes of the impedance sensor are in contact with the user's skin for a threshold amount during that time period, and an on-list state 406 is determined.

[0076] Additionally, at step (706), method 700 may include executing a control action in response to determining that electrodes of the impedance sensor have not contacted the user's skin for a threshold amount of the time period. For example, if the total number of measurements taken during the time period is less than the threshold number of measurements, wearable computing device 100 may determine that device 100 is improperly fitted to the user and then execute a control action. For example, as described above, device 100 may generate a notification prompting the user to tighten a band securing the wearable computing device to the user, and the user may be prompted by the notification to adjust wearable computing device 100 (e.g., tighten band 103, rotate band 103, etc.). Similarly, as described above, device 100 may adjust collected data to account for device 100 being improperly fitted.

[0077] It should be understood that methods 600, 700 may be performed without determining that the proximity of device 100 is below a proximity threshold based on PPG data from a PPG sensor. However, in some examples, methods 600, 700 may be performed only if the proximity of device 100 is determined to be below a proximity threshold based on PPG data from a PPG sensor, as described with reference to steps (452)-(460) of methods 450, 500.

[0078] It should further be appreciated that methods 450, 500, 600, and 700 can be executed upon the initiation of a particular activity mode of wearable computing device 100. For example, certain applications (e.g., workout tracking, sleep tracking, meditation, etc.) may require more accurate data to generate reports or may have more subjective value to the user than data during other activities. When wearable computing device 100 is not properly fitted (e.g., loose), the data generated by biometric sensors of wearable computing device 100 may be insufficient (e.g., sporadic or inaccurate) to generate reports. Thus, when one of these applications on wearable computing device 100 is likely to be started (based on user habits, preprogrammed start times, biometric readings, etc.) or explicitly started by the device, methods 450, 500, 600, and 700 may be used to ensure that wearable computing device 100 is on the user and / or that device 100 is properly fitted to the user. Furthermore, prompting the user to correct the fit of the wearable computing device 100 can avoid a situation in which an application (e.g., sleep tracking) is unable to generate a report (e.g., sleep score, snoring report, etc.) due to insufficient biometric data caused, at least in part, by the wearable computing device 100 being worn improperly (e.g., loosely) on the user's wrist. Data generated by the wearable computing device 100 may also be edited or highlighted to indicate the reliability of the data depending on the wearing state during the activity mode. For example, if the wearing state is an on-wrist state 408 with no contact, a lower confidence value for the data may be suggested and / or no data may be shown, and therefore the data is of lower quality and / or less reliable. On the other hand, if the wearing state is an on-wrist state 406 with contact, a higher confidence value for the data may be suggested.Additionally, certain applications, such as payment applications, may require a higher level of certainty that the wearable computing device 100 is being worn by a user in order to be valid. Thus, it may be beneficial to further require an authentication action by the user using the wearable computing device 100 for payment (e.g., with a PIN entry, a motion pattern, etc.) when the wear state is one of the on-list states 406, 408, and to de-authenticate the wearable computing device 100 for payment when the wear state is off-list states 402, 404, etc.

[0079] The technology described herein refers to servers, databases, software applications, and other computer-based systems, as well as actions performed on and information sent to and from such systems. The inherent flexibility of computer-based systems allows for a wide variety of possible configurations, combinations, and divisions of tasks and functionality among components. For example, the processes described herein may be implemented using a single device or component, or multiple devices or components working in combination. Databases and applications may be implemented on a single system or distributed across multiple systems. Distributed components may operate sequentially or in parallel.

[0080] Again, while the above method for determining the wearing state of a wearable computing device has been discussed in the context of using a PPG sensor, it should be understood that the method is not limited to determining the wearing state based on PPG data in combination with impedance data. It should be understood that the impedance sensor can be used with any suitable proximity sensor to determine the wrist state. For example, in some embodiments, the wearable computing device may include one or more capacitive sensors, ultrasonic sensors, inductive sensors, etc. configured to determine the proximity of the wearable computing device to a user. In such embodiments, the wearing state of the wearable computing device may be determined based on impedance data obtained from the impedance sensor and capacitance data obtained from one or more capacitive sensors, ultrasonic sensors, inductive sensors, etc.

[0081] While the present subject matter has been described in detail with reference to various specific exemplary embodiments thereof, each example is provided by way of explanation and not as a limitation of the present disclosure. Those skilled in the art, once they arrive at the foregoing understanding, will be able to readily create modifications, variations, and equivalents to such embodiments. Accordingly, the disclosure of the subject matter does not exclude the inclusion of such modifications, variations, and / or additions to the subject matter as would be readily apparent to one skilled in the art. For example, features illustrated or described as part of one embodiment may be used with other embodiments to yield still other embodiments. Accordingly, the present disclosure is intended to cover such modifications, variations, and equivalents.

Claims

1. 1. A computer-implemented method for determining a wearable computing device's wearability, comprising: acquiring, by one or more processors of the wearable computing device, first proximity data generated by a proximity sensor of the wearable computing device during a first time period; activating, via the one or more processors, an impedance sensor of the wearable computing device based at least in part on the first proximity data; acquiring, via the one or more processors, impedance data generated by the impedance sensor during a second time period occurring after the first time period; obtaining, via the one or more processors, second proximity data generated by the proximity sensor during the second time period; and determining, via the one or more processors, that the wearing state of the wearable computing device corresponds to one of an off-wrist state, an on-wrist state with contact, or an on-wrist state without contact based at least in part on the impedance data and the second proximity data.

2. activating the impedance sensor based on the first proximity data includes: determining, via the one or more processors, whether the wearable computing device is in proximity to a user's skin based at least in part on the first proximity data; and and activating the impedance sensor in response to determining, via the one or more processors, that the wearable computing device is in proximity to the skin of the user based at least in part on the first proximity data.

3. Determining that the wear state of the wearable computing device corresponds to the contact-on-wrist state includes: determining, via the one or more processors, that the wearable computing device is still in proximity to the skin of the user based at least in part on the second proximity data; determining, via the one or more processors, that the impedance sensor is in contact with the skin of the user based at least in part on the impedance data; and and determining, via the one or more processors, that the wearing state of the wearable computing device corresponds to the on-wrist state in contact with the skin of the user in response to determining that the wearable computing device is still in proximity to the skin of the user and that the impedance sensor is in contact with the skin of the user.

4. 4. The computer-implemented method of claim 3, wherein determining that the impedance sensor is in contact with the skin of the user includes determining, via the one or more processors, that an admittance value included in the impedance data is within a range of admittance values ​​included in the impedance data.

5. Determining that the wear state of the wearable computing device corresponds to the out-of-touch, on-wrist state includes: determining, via the one or more processors, that the wearable computing device is still in proximity to the skin of the user based at least in part on the second proximity data; determining, via the one or more processors, based at least in part on the impedance data, that the impedance sensor is not in contact with the skin of the user; and determining, via the one or more processors, that the wearing state of the wearable computing device corresponds to the no-contact, on-wrist state in response to determining that the wearable computing device is still in proximity to the skin of the user and that the impedance sensor is not in contact with the skin of the user.

6. 6. The computer-implemented method of claim 5, wherein determining that the impedance sensor is not in contact with the skin of the user includes determining, via the one or more processors, that an admittance value included in the impedance data is outside a range of admittance values ​​that indicates the impedance sensor is in contact with the skin of the user.

7. 6. The computer-implemented method of claim 5, further comprising, after determining that the wearing state of the wearable computing device corresponds to the out-of-touch on-wrist state, generating, via the one or more processors, a notification prompting the user to tighten a band of the wearable computing device.

8. The computer-implemented method of claim 7 , wherein a notification prompts the user to perform a predefined gesture to confirm tightness of the band.

9. acquiring, via the one or more processors, motion data generated by one or more motion sensors of the wearable computing device, the motion data indicating the user performing the predefined gesture; The method comprises: acquiring, via the one or more processors, second impedance data generated by the impedance sensor in response to acquiring the motion data indicative of the user performing the predefined gesture; acquiring, via the one or more processors, third proximity data generated by the proximity sensor in response to acquiring the motion data indicative of the user performing the predefined gesture; 10. The computer-implemented method of claim 8, further comprising: via the one or more processors, redetermining the wearing state of the wearable computing device based at least in part on the second impedance data and the third proximity data.

10. After determining that the wear state of the wearable computing device corresponds to the out-of-touch, on-wrist state, adjusting data generated by the wearable computing device acquired during the non-contact, on-wrist state; or and adjusting a confidence value of the data generated by the wearable computing device acquired during the out-of-contact, on-wrist state.

11. Determining that the wear state of the wearable computing device corresponds to the off-wrist state includes: determining, via the one or more processors, that the wearable computing device is no longer in proximity to the skin of the user based at least in part on the second proximity data; and determining, via the one or more processors, based at least in part on the impedance data, that the impedance sensor is not in contact with the skin of the user; and determining, via the one or more processors, that the wearing state of the wearable computing device corresponds to an off-wrist state in response to determining that the wearable computing device is not still in proximity to the skin of the user and that the impedance sensor is not contacting the skin of the user.

12. 12. The computer-implemented method of claim 11, further comprising: disabling the impedance sensor via the one or more processors in response to determining that the wearable computing device's wearing state corresponds to the off-wrist state and is no longer in proximity to the skin of the user.

13. 1. A wearable computing device, comprising: A proximity sensor; an impedance sensor; one or more processors, wherein the one or more processors: acquiring first proximity data generated by the proximity sensor during a first time period; activating the impedance sensor based at least in part on the first proximity data; acquiring impedance data generated by the impedance sensor during a second time period occurring after the first time period; acquiring second proximity data generated by the proximity sensor during the second time period; and determining, based at least in part on the impedance data and the second proximity data, that a wearing state of the wearable computing device corresponds to one of an off-wrist state, an on-wrist state with contact, or an on-wrist state without contact.

14. The wearable computing device of claim 13 , wherein the proximity sensor includes one or more ultrasonic sensors, inductive sensors, or both.

15. The wearable computing device of claim 13 , wherein the proximity sensor includes an optical sensor.

16. Cover and a coloring element between the cover and at least the proximity sensor; 14. The wearable computing device of claim 13, wherein the one or more processors are further configured to control the coloring element to change between a transparent state and an opaque state based at least in part on the wearing state.

17. the impedance sensor includes at least a first electrode and a second electrode spaced apart from each other; The wearable computing device of claim 13 , wherein the impedance data is indicative of contact at the first electrode and contact at the second electrode.

18. The wearable computing device of claim 17 , wherein the proximity sensor is between the first electrode and the second electrode.

19. 20. The wearable computing device of claim 17, wherein the first electrode and the second electrode are made from a transparent material, and the first electrode and the second electrode are disposed over the proximity sensor.

20. 1. A computer-implemented method for determining a wearable computing device's wearability, comprising: acquiring, via one or more processors of the wearable computing device, impedance data generated by an impedance sensor of the wearable computing device during a period of time; calculating, via the one or more processors, an admittance value of the impedance sensor for that time period based at least in part on the impedance data; and determining, via the one or more processors, that the wearing state of the wearable computing device during the time period corresponds to an on-wrist state in which the wearable computing device is worn by a user or an off-wrist state in which the wearable computing device is not worn by the user, based at least in part on the calculated admittance value; and via the one or more processors, performing a control action based at least in part on the wearing state of the wearable computing device.

21. determining that the wearing state of the wearable computing device during the time period corresponds to the on-wrist state; determining, via the one or more processors, that the admittance value of the impedance sensor calculated for the time period is within a range of values ​​indicative of electrodes of the impedance sensor contacting skin; and determining, via the one or more processors, that the wearing state of the wearable computing device corresponds to the on-wrist state in response to determining that the calculated admittance value is within the range of values.

22. Determining that the wearable state of the wearable computing device during the time period corresponds to the off-list state includes: determining, via the one or more processors, that the admittance value of the impedance sensor calculated for the time period is not within a range of values ​​indicative of electrodes of the impedance sensor contacting skin; and determining, via the one or more processors, that the wear state of the wearable computing device corresponds to the off-wrist state.

23. 21. The computer-implemented method of claim 20, wherein performing the control operation includes generating, via the one or more processors, a notification indicative of the wearable state of the wearable computing device for the time period.

24. 24. The computer-implemented method of claim 23, wherein generating the notification includes controlling, via the one or more processors, a display screen of the wearable computing device to display a visual notification indicating the wearability state of the wearable computing device determined for the time period.

25. 21. The computer-implemented method of claim 20, wherein the time period is approximately one minute.

26. 1. A computer-implemented method for determining a wearable computing device's wearability, comprising: acquiring, via one or more processors of the wearable computing device, impedance data generated by an impedance sensor of the wearable computing device during a period of time that the user is wearing the wearable computing device; determining, via the one or more processors, based at least in part on the impedance data, that electrodes of the impedance sensor have not been in contact with the user's skin for the threshold amount of time period; and executing, via the one or more processors, a control action in response to determining that the electrodes of the impedance sensor have not been in contact with the skin of the user for the threshold amount of the time period.

27. the impedance data includes a plurality of measurements indicative of the electrodes contacting the skin of the user during the time period; Determining that the electrodes of the impedance sensor are not in contact with the skin for the threshold amount of the time period includes: determining, by the one or more processors, that a total number of the plurality of measurements taken during the time period is less than a threshold number of measurements; 27. The computer-implemented method of claim 26, comprising: in response to determining that the total number of the measurements taken during the time period is less than the threshold number of measurements, determining, via the one or more processors, that the electrodes have not been in contact with the skin of the user for the threshold amount of the time period.

Images