System and method for determining a wearing state of a wearable computing device

Abstract

A computer-implemented method for determining a wear state of a wearable computing device may include obtaining first proximity data generated by a proximity sensor of the wearable computing device during a first time period. The method may also include activating an impedance sensor of the wearable computing device based at least in part on the first proximity data. Further, the method may include obtaining 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. Additionally, the method may include determining, based at least in part 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, a wrist contact state, or a wrist non-contact state.

Description

Technical Field

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

[0002] Recent technological advances, including those 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 are able to determine information related to the pulse or movement of the person wearing the device. For example, certain biometric monitoring devices include a variety of sensors for measuring multiple biological parameters that may be beneficial to the user of the device, such as heart rate sensors, multi-purpose electrical sensors compatible with electrocardiogram (ECG) and electrodermal activity (EDA) applications, infrared sensors, gyroscopes, altimeters, accelerometers, temperature sensors, ambient light sensors, Wi-Fi, GPS, vibration sensors, speakers, and microphones. Due to the capabilities of conventional devices, it may be difficult to determine when the device is being worn by the user and when the device fits the user properly, which may affect the accuracy of the biometric measurement.

[0003] Therefore, improved systems and methods for monitoring the wearing state of a wearable computing device would be welcomed in the art. Summary of the invention

[0004] Various aspects and advantages of embodiments of the present disclosure will be set forth in part in the following description, or may be learned from the description, or may be learned through practice of the embodiments.

[0005] In one aspect, a computer-implemented method for determining a wear state of a wearable computing device is provided. The method may include obtaining, 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 an impedance sensor of the wearable computing device based at least in part on the first proximity data, via the one or more processors. Further, the method may include obtaining, via the one or more processors, impedance data generated by the impedance sensor during a second time period occurring after the first time period. Moreover, the method may include obtaining, via the one or more processors, second proximity data generated by the proximity sensor during the second time period. In addition, the method may include determining, via the one or more processors, that the wear state of the wearable computing device corresponds to one of an off-wrist state, a wrist-in-contact state, or a wrist-out-contact state 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 obtain 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. Further, the one or more processors may be configured to obtain impedance data generated by the impedance sensor during a second time period occurring after the first time period. Moreover, the one or more processors may be configured to obtain second proximity data generated by the proximity sensor during the second time period. In addition, the one or more processors may be configured to determine that the wear state of the wearable computing device corresponds to one of an off-wrist state, a wrist contact state, or a wrist non-contact state based at least in part on the impedance data and the second proximity data.

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

[0008] In yet another aspect, a computer-implemented method for determining a wear state of a wearable computing device is provided. The method may include obtaining, 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, wherein a user is wearing the wearable computing device during the time period. The method may further include determining, via the one or more processors, that an electrode of the impedance sensor is not in contact with 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 a control action, via the one or more processors, in response to determining that an electrode of the impedance sensor is not in contact with 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 be better understood with reference to the following description and appended claims.The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate example embodiments of the present disclosure and, together with the description, serve to explain the relevant principles. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] A detailed discussion of the embodiments for those skilled in the art is set forth in this specification with reference to the accompanying drawings, in which:

[0011] Figure 1 A perspective view of a wearable computing device on a user's wrist is provided according to one embodiment of the present disclosure;

[0012] Figure 2 A front perspective view of a wearable computing device according to one embodiment of the present disclosure is provided;

[0013] Figure 3 Provided Figure 2 A rear perspective view of a wearable computing device;

[0014] Figure 4 Provided Figure 2 An exploded view of a display of a wearable computing device;

[0015] Figure 5 A schematic diagram of an example set of devices capable of communicating according to one embodiment of the present disclosure is provided;

[0016] Figure 6 illustrates various controller components of an example system that may be utilized in accordance with one embodiment of the present disclosure;

[0017] Figure 7 shows a state diagram illustrating different wear states of a wearable computing device according to the present disclosure;

[0018] Fig. 8A and Figure 8B A flowchart showing an embodiment of a method for detecting a wearing state of a wearable computing device according to the present disclosure is shown;

[0019] Fig. 9 A flowchart of another embodiment of a method for detecting a wearing state of a wearable computing device according to the present disclosure is shown;

[0020] Fig.10 A flowchart of an embodiment of a method for detecting a wearing state of a wearable computing device according to the present disclosure is shown;

[0021] Fig.11 A flowchart showing an embodiment of a further method for detecting a wearing state of a wearable computing device according to the present disclosure is shown;

[0022] Fig.12 showing a graphical representation of a first example notification to a user of a wearable computing device according to the present disclosure;

[0023] Fig.13 showing a graphical representation of a second example notification to a user of a wearable computing device according to the present disclosure;

[0024] Fig.14 shows a graphical representation of an example notification flow to a user of a wearable computing device according to the present disclosure;

[0025] Fig.15 Shows Figure 2 Another rear perspective view of the wearable computing device;

[0026] Fig.16 Shows Figure 2 A further rear perspective view of the wearable computing device;

[0027] Fig.17 Shows Figure 2 an exploded view of a skin side of a wearable computing device; and

[0028] Figures 18A to 18C Shows Figure 2 Various views of the skin side of a wearable computing device of FIG. 1 , which specifically shows the skin side of the wearable computing device when a tinting element is transparent, partially transparent, and opaque, respectively. DETAILED DESCRIPTION

[0029] Reference will now be made in detail to embodiments of the present invention, one or more examples of which are shown in the accompanying drawings. Each example is provided by way of explanation of the present invention rather than by way of limitation. In fact, it will be apparent to those skilled in the art that various modifications and changes may be made in the present invention without departing from the scope or spirit of the present invention. For example, a feature shown or described as part of one embodiment may be used together with another embodiment to produce yet another embodiment. Therefore, the present invention is intended to encompass such modifications and changes within the scope of the appended claims and their equivalents.

[0030] In general, when the wearable computing device is not worn properly or the wearable computing device is not adequately configured to distinguish between on-body conditions and off-body conditions, the data generated by the wearable computing device may be inaccurate and / or misleading. For example, when the wearable computing device is worn loose, inconsistent skin contact or shifted skin contact areas may occur, which may cause inaccurate data. Similarly, when the wearable computing device is worn too tightly, reduced perfusion (reduced fluid passage at the skin of the wearer) or other adverse effects may occur, which may cause inaccurate data. When the wearable computing device is improperly positioned (e.g., positioned across a bony prominence), insufficient contact with the skin may occur, resulting in inaccurate data. In addition, if one or more proximity sensors of the wearable computing device are configured to detect simple light reflections, the device may determine an on-wrist condition when approaching any reflective surface (including inanimate objects), which may cause inaccurate data or accidentally activate one or more modes. For example, if the wearable computing device is inside a bag and a proximity sensor detects a reflection from the bag or another object inside the bag (e.g., keys), the device may inadvertently sense the fall if the bag is dropped and unnecessarily signal to seek help for the user; a payment app may be enabled, which may allow accidental payments; data that does not match user activity (e.g., sleep, exercise, etc.) may be collected; and / or other.

[0031] Thus, the present disclosure relates to a wearable computing device (specifically, a wearable computing device with a body impedance sensor), and a computer-implemented method for determining a wear state of a wearable computing device to perform the following actions: accurately determine when the device is being worn and / or when the device is worn correctly based at least in part on body impedance data from a body impedance sensor (hereinafter referred to as an "impedance sensor") to ensure that measurements made by a biometric sensor are accurate for determining biometric information and to confirm when the device is being worn for certain activity modes. In general, impedance sensors use multi-path electrical sensors to measure the impedance or admittance (which is the inverse of impedance) of a user's skin. These responses are observed as sensitive electrical changes in skin conductance and are typically detected using a wet or dry electrode system, such as by using at least two electrodes, where skin conductance is calculated using the measured electrical impedance. For example, an impedance sensor can have a resistive component and a reactive component that produce a two-dimensional output. Impedance sensors can include electrodermal activity (EDA) sensors, which in one embodiment can be used to continuously collect data while operating.

[0032] 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 close to the user, which saves battery life when the data from the impedance sensor will be inaccurate and / or will not be valuable. For example, the wearable computing device may further have a proximity sensor (e.g., a photoplethysmography (PPG) sensor and / or a pressure sensor) that is configured to generate proximity data indicating the proximity of the device 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 the user. When it is determined based on the comparison of the proximity data to the proximity threshold that the wearable computing device is being worn by the user, the impedance sensor may be turned on or activated to begin collecting data. As long as it is determined based on the proximity data that the wearable computing device is being worn by the user, the impedance data may be used to determine whether the device is being worn and / or whether it is being worn appropriately.

[0033] In some embodiments, the impedance data may be used with or without proximity data to determine whether the device is being worn and / or whether it is being properly worn. For example, in one embodiment, impedance data is acquired during a time period, and an admittance value of the impedance sensor during the time period is calculated based at least in part on the impedance data. Thereafter, a wear state of the wearable computing device during the time period may be determined based at least in part on the admittance value. For example, when the admittance value is within the impedance range, the wear state may be determined to be an on-wrist state, corresponding to the wearable computing device being worn by the user, or when the admittance value is outside the impedance range, the wear state may be determined to be an off-wrist state, corresponding to the wearable computing device not being worn by the user.

[0034] In some embodiments, impedance data is acquired over a time period, and an amount of time that an electrode of the impedance sensor is in contact with the user's skin during the time period is determined based at least in part on the impedance data. If the amount of time that the electrode of the impedance sensor is in contact with the user's skin is less than a threshold amount of time but greater than zero, the wear state can be determined to be a wrist-free state, and a notification can be generated that prompts the user to tighten the band that secures the wearable computing device to the user. If the amount of time that the electrode of the impedance sensor is in contact with the user's skin is zero, the wear state can be determined to be an off-wrist state. Otherwise, if the amount of time that the electrode of the impedance sensor is in contact with the user's skin is greater than a threshold amount of time, the wear state can be determined to be a wrist-contact state.

[0035] In some cases, the shape of the impedance data can be monitored over time to determine contact between the user and the device. For example, after the user starts wearing the device, moisture (e.g., grease, sweat, etc.) slowly begins to accumulate on the device, which causes the admittance value to also slowly increase over time. In some cases, the increase looks like a logarithmic function in shape. Therefore, the shape of the impedance data can also indicate whether the user has started wearing the device.

[0036] Further, in some cases, the monitored impedance or admittance can be used to improve biometric readings from other sensors. For example, changes in measured contact impedance or admittance (e.g., changes in amplitude and / or angle) are correlated to changes in applied pressure, which can be used to improve estimated heart rate and / or other. Similarly, proximity data (e.g., from a PPG sensor) can be used in combination with impedance data to improve biometric readings from other sensors. For example, PPG data can be presented as a decrease in the pulsatile component (AC or DC) of reflectivity or impedance data plotted against wavelength, where a sudden change in reflectivity and / or a change in the pulsatile component would indicate a pressure change.

[0037] In addition, in some aspects, dimming elements on at least some sensors of the wearable computing device can be controlled based at least in part on the wear state of the wearable computing device. For example, if it is determined that the wearable computing device is worn, the dimming element can be controlled to be transparent so that the sensor covered by the dimming element is not obstructed by the dimming element. Similarly, if it is determined that the wearable computing device is off-wrist, the dimming element can be controlled to be at least semi-transparent or completely opaque to at least partially hide the sensor covered by the dimming element. The dimming element can also be controlled to improve the sensing conditions of certain sensors. For example, if the ambient light is too high to accurately obtain PPG data, the dimming element can be controlled to be slightly dimmed to reduce the ambient light.

[0038] Thus, the disclosed methods allow for a more accurate determination of the wear state of a wearable computing device (which may include the fit of the wearable computing device), thereby allowing for adjustments to be requested that may improve the overall accuracy of biometric data acquired using the wearable computing device and prevent certain activity modes from being activated when the device is not actually being worn.

[0039] Referring now to the accompanying drawings, example embodiments of the present disclosure will be discussed in greater detail.

[0040] Now referring to the accompanying drawings, Figures 1 to 4 1 shows a perspective view of a wearable computing device 100 according to the present disclosure. Figure 1As shown, the wearable computing device 100 can be worn on the user's forearm 102 like a wristwatch. Therefore, as shown, the wearable computing device 100 can include a wristband 103 for fixing the wearable computing device 100 to the user's forearm 102. However, it should be understood that the wearable computing device 100 can be worn by the user in any other suitable position, such as, for example, on an ankle. In addition, as shown in FIG. Figure 1 , Figure 2 and Figure 4 As shown, the wearable computing device 100 has a housing 105 and a case 104 containing electronic components associated with the wearable computing device 100. For example, in an embodiment, the housing 105 can be made of glass, polycarbonate, acrylic, or a similar material. Figure 1 , Figure 2 and Figure 4 As shown, the wearable computing device 100 includes an electronic display screen 106 disposed within the housing 104 and viewable through the housing 105. Moreover, as shown, the wearable computing device 100 may also include one or more buttons 108, which may be implemented to provide a mechanism for activating various sensors of the wearable computing device 100 to collect certain health data of the user. Moreover, in an embodiment, the electronic display 106 may cover an electronic device package (not shown), which may also be housed within the housing 104.

[0041] Specific reference Figure 3 , the housing 104 of the wearable computing device 100 further includes a dorsal wrist side surface 110 and a plurality of sensor electrodes 112, the dorsal wrist side surface being configured to rest against the back of the user's wrist when the wearable computing device is being worn by the user, and the plurality of sensor electrodes being positioned on the dorsal wrist side surface 110 of the housing 104, thereby maintaining skin contact with the user when the wearable computing device is being worn on the wrist of the user. Therefore, in such an embodiment, each of the sensor electrodes 112 may be configured to at least measure the electrical impedance of the location of the user's skin contact on the back of the wrist. Therefore, 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, in addition to the impedance sensor electrode, the wearable computing device 100 may further include at least one additional biometric sensor electrode. In such an embodiment, the additional biometric sensor electrode 112 may include one or more temperature sensors (such as an ambient temperature sensor or a skin temperature sensor), a humidity sensor, a light sensor, a pressure sensor, a microphone, an optical sensor, or a photoplethysmography (PPG) sensor.

[0042] The sensor electrodes 112 may include at least two electrodes spaced apart from each other, such that the sensor electrodes 112 are less susceptible to being bridged by sweat and / or other factors. For example, in some embodiments, the wearable computing device 100 may further include an optical device package 215 for measuring data of one or more indicators of a human body (such as a person wearing the wearable computing device 100). In some cases, the sensor electrodes 112 may be generally arranged around the optical device package 215. In one embodiment, as shown in FIG. Fig.15 As shown, the sensor electrodes 112 can be arranged around the optical device package 215 so that the sensor electrodes 112 can be used to indicate that a portion of the wearable computing device 100 is in place. For example, the sensor electrodes 112 can 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), wherein each electrode 112(1) to 112(4) is associated with a given position around the optical device package 215, making it easier to determine that the portion is not in place. For example, if first sensor electrode 112(1) and second sensor electrode 112(2) have readings indicating contact, but third sensor electrode 112(3) and fourth sensor electrode 112(4) do not have readings indicating contact, it can be determined that wearable computing device 100 is partially not in place, where the upper portion (associated with first sensor electrode 112(1) and second sensor electrode 112(2)) is in place, and the lower portion (associated with third sensor electrode 112(3) and fourth sensor electrode 112(4)) is not in place. Although only four sensor electrodes 112(1)-112(4) are shown, it should be understood that any number of electrodes may be used, and further, each electrode may be further divided into multiple sensing regions.

[0043] Further, the sensor electrodes 112 described herein may be constructed of any suitable material. For example, in embodiments, the sensor electrodes 112 described herein may be constructed of stainless steel, graphene, or any other material having suitable conductivity and / or corrosion resistance, and may have an optional PVD coating, which may be 1 micron thick titanium nitride. In such embodiments, the PVD coating may provide the sensor electrodes 112 with a desired color, prevent oxidation beyond that provided by stainless steel, and also increase durability.

[0044] In additional embodiments, PVD and surface treatments may be used to increase / decrease moisture retention, which may affect the impedance signal and user comfort. In certain embodiments, the sensor electrodes 112 may be formed of a tin and nickel (TiN) alloy with a shiny or mirrored surface treatment. Also, in embodiments, the sensor electrodes 112 may be formed of a hydrophobic material or a transparent material. For example, Fig.16As shown, the sensor electrodes 112 are made of a transparent material (e.g., made of indium tin oxide, fluoride tin oxide (FTO), and / or others) and are arranged on top of the optical device package 215. In some embodiments, the sensor electrodes 112 may preferably be formed of FTO, at least because, since the sensor electrodes 112 will be exposed to contact with the skin, FTO may be particularly resistant to general wear, scratching, and / or other.

[0045] In some embodiments, the wearable computing device 100 may further include one or more dimming elements that can selectively hide one or more of the sensing components on the dorsal side of the wrist 110. Figures 17 to 18C As shown, the dimming element 107 can be sandwiched between a protective layer or cover 109 (e.g., glass, resin, plastic, etc.) and at least the optical device package 215 on the back wrist side 110 of the wearable computing device 100. The dimming element 107 can be a guest-host liquid crystal (GHLC) or an electrochemical element that can be controlled to change between a range of transparencies. For example, different levels of power or energy supplied to the dimming element 107 can change the amount of transparency of the dimming element 107. For example, as Fig.18A As shown, when supplied with a first power, the dimming element 107 may be transparent so that the optical device package 215 is not obstructed and can operate normally; when supplied with a lower power, the dimming element may be partially transparent or diffuse ( Fig.18B ), so that the optics package 215 is partially visible and possibly partially operational; or the dimming element may be opaque ( Fig.18C ), such that the optics package 215 is completely hidden and inoperable. In the partially transparent state, light emitted by the optics package 215 can be at least partially diffused, such that the edges of the sensors of the optics package 215 are less visible and the overall appearance is more attractive. In some cases, when it is detected that the wearable computing device 100 is being worn, it may be preferable for the dimming element 107 to be completely transparent so that the data generated by the optics package 215 is more accurate, and when it is detected that the wearable computing device 100 is not being worn, it may be preferable for the dimming element 107 to be partially transparent or opaque to at least partially hide the optics package 215. However, in some cases, such as when the ambient lighting is too bright, it may be desirable for the dimming element 107 to be slightly less than completely transparent when worn to improve the readings of certain sensors (e.g., the PPG sensor 215). Therefore, the dimming level between the transparent state and the opaque state can be determined at least in part based on the wear state of the wearable computing device 100 (e.g., determined based on pressure, proximity, and / or other determinations) and / or the ambient lighting.

[0046] It should be understood that the dimming element 107 can provide overall or pixelated dimming of the covered area. Pixelated dimming may be particularly capable of improving the performance and layout of sensors, especially for PPG sensors. In addition, it should also be understood that the dimming element 107 can alternatively be configured to be transparent when not powered and slightly transparent or opaque when powered.

[0047] Reference now Figure 5 , illustrates components of an example system 200 of a wearable computing device 100 that may be utilized in accordance with various embodiments. Specifically, as shown, the system 200 may also include at least one controller 202 communicatively coupled to the plurality of sensor electrodes 112. Moreover, in embodiments, the controller 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 such options).

[0048] For example, in embodiments, the 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 the memory device 204 and executed using the controller 202, cause the controller 202 to perform the functions described herein. As will be apparent to one of ordinary skill in the art, the system 200 may include many types of memory, data storage, or computer-readable media, such as data storage for program instructions executed by the controller or any suitable processor. The same or separate storage may be used for images or data, removable memory may be used to share information with other devices, and any number of communication methods may be used to share with other devices.

[0049] Additionally, as shown, system 200 includes any suitable display 206, such as a touch screen, organic light emitting diode (OLED), or liquid crystal display (LCD), but the device may communicate information via other means, such as through an audio speaker, a projector, or by projecting a display or streaming data to another device such as a mobile phone, where an application on the mobile phone displays the data.

[0050] The system 200 may also include one or more wireless components 212 operable to communicate with one or more electronic devices within the communication range of a particular wireless channel. The wireless channel may be any suitable channel for enabling devices to communicate wirelessly, such as a Bluetooth, cellular, NFC, ultra-wideband (UWB), or Wi-Fi channel. It should be understood that the system 200 may have one or more conventional wired communication connections as known in the art.

[0051] The system 200 also includes one or more power supply components 208, such as batteries that can be operated to recharge by conventional plug-in mode or by other modes such as capacitive charging (by approaching a power pad or other such devices). In further embodiments, the system 200 may also include at least one additional I / O device 210 capable of receiving conventional input from a user. The conventional input may include, for example, a button, a touch pad, a touch screen, a roller, a joystick, a keyboard, a mouse, a keypad, or any other such device or element that allows a user to input a command to the system 200. In another embodiment, the I / O device 210 may also be connected by wireless infrared or Bluetooth or other links in some embodiments. In some embodiments, the system 200 may also include a microphone or other audio capture element that accepts voice or other audio commands. For example, in a particular embodiment, the system 200 may not include any buttons at all, but may be controlled only by a combination of visual commands and audio commands, so that the user can control the wearable computing device 100 without contacting it. In some embodiments, the I / O element 210 may also include one or more of the sensor electrodes 112, optical sensors, air pressure sensors (e.g., altimeters, etc.) described herein, and others.

[0052] Still reference Figure 5 , the system 200 may also include a driver 214 and at least some combination of one or more emitters 216 and one or more detectors 218 (referred to herein as an optical device package 215) for measuring data of one or more indicators of a human body (such as a person wearing the wearable computing device 100). In such an embodiment, as described above with reference to Figure 3 As described, for example, the optics package 215 can be disposed within the housing 104 and at least partially exposed through the dorsal wrist side 110 of the housing 104. Thus, as shown and as further described herein, the sensor electrodes 112 can be positioned around the optics package 215 on the wrist side 110 of the housing 104. In alternative embodiments, the various components of the optics package 215 can be positioned around the sensor electrodes 112 and / or in another other suitable configuration, such as adjacent to the optics package 215, interlaced with the optics package, surrounded by the optics package, or on top of the optics package. In some embodiments, for example, where the sensor electrodes 112 are transparent, the sensor electrodes 112 can be disposed on top of the optics package 215.

[0053] 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 capable of imaging users, people or objects near the device. The imaging element may include any appropriate technology, such as a CCD image capture element, which has sufficient resolution, focal range and viewable area to capture images of the user when the user is operating the device. Further 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 discussed in detail herein. It should be understood that image capture may be performed using a single image, multiple images, periodic imaging, continuous image capture, image streaming, etc. Further, system 200 may include the ability to start and / or stop image capture, such as when receiving a command from a user, application or other device.

[0054] Figure 5 The emitter 216 and detector 218 of can also be used to obtain optical PPG measurements in one example. Some PPG technologies 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 produce a single spatial measurement from which a heart rate (HR) estimate (or other physiological indicator) can be determined. In some embodiments, the PPG device employs a single light source (i.e., a single optical path) coupled to a single detector. Alternatively, the PPG device may employ multiple light sources (i.e., two or more optical paths) coupled to a single detector or multiple detectors. In other embodiments, the PPG device employs multiple detectors (i.e., two or more optical paths) coupled to a single light source or multiple light sources. In some cases, the light source may be configured to emit one or more of green light, red light, infrared (IR) light, and any other suitable wavelength in the spectrum (such as long IR for metabolic monitoring). For example, the PPG device may employ a single light source and two or more light detectors, each of which is configured to detect a specific wavelength or wavelength range. In some cases, each detector is configured to detect a wavelength or wavelength range that is different from each other. In other cases, two or more detectors are configured to detect the same wavelength or wavelength range. In yet another case, one or more detectors are configured to detect a specific wavelength or wavelength range that is different from one or more other detectors. In embodiments employing multiple optical paths, the PPG device can determine an average of the signals generated from the multiple optical paths before determining an HR estimate or other physiological indicator.

[0055] Moreover, in embodiments, the transmitter 216 and the detector 218 may be coupled to the controller 202 directly or indirectly using a drive circuit system, by which the controller 202 may drive the transmitter 216 and obtain a signal from the detector 218. The host computer 222 may communicate with the wireless networking component 212 via one or more networks 220, which may include one or more local area networks, wide area networks, UWB, and / or the Internet using any terrestrial or satellite links. In some embodiments, the host computer 222 executes a control program and / or application configured to perform some of the functions described herein.

[0056] In some embodiments, the system 200 may also include one or more dimming elements 107 that are used to selectively hide one or more components on the wearable computing device 100 and are configured to selectively power such dimming elements 107 (e.g., via a power supply component 208) to change the dimming level of the dimming elements 107.

[0057] Reference now Figure 6 , a schematic diagram of an environment 300 in which aspects of various embodiments may be implemented is shown. Specifically, as shown, a user may have multiple different devices that are capable of communicating using at least one wireless communication protocol. For example, as shown, a user may have a smart watch 302 or a fitness tracker (such as wearable computing device 100), and the user wishes that the smart watch or the fitness tracker be able to communicate with a smart phone 304 and a tablet computer 306. The ability to communicate with multiple devices may enable the user to obtain information from the smart watch 302 using an application installed on the smart phone 304 or tablet computer 306, for example, using data captured by sensors on the smart watch 302. The user may also wish that the smart watch 302 be able to communicate with a service provider 308 or other such entity that is able to obtain and process data from the smart watch and provide functionality that may not otherwise be available on the smart watch or on applications installed on separate 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 with one of the devices via a wireless connection, such as Bluetooth®, which may then communicate via the at least one network. In various embodiments, there may be a variety of other communication types or reasons for communication.

[0058] In addition to being able to communicate, users may also want devices to be able to communicate in a variety of ways or in certain aspects. For example, users may want communications between devices to be secure, especially when the data may include personal health data or other such communications. Devices or application providers may also need to protect this information in at least some cases. Users may want devices to be able to communicate with each other concurrently rather than sequentially. This is especially true in cases where pairing may be required, because users may prefer that each device be paired at most once, so that manual pairing is not required. Users may also want communications to be based on standards as much as possible, so that not only little manual intervention is required on the part of the user, but also that the device can communicate with as many other types of devices as possible, which is often not the case for various proprietary formats. Therefore, users may want to be able to walk around the room with a device and have such a device automatically communicate with another target device, with almost no effort on the part of the user. In various conventional ways, devices will use communication technologies such as Wi-Fi to communicate with other devices using wireless local area networks (WLANs). Smaller or lower-capacity devices (such as many Internet of Things (IoT) devices) instead use communication technologies such as Bluetooth®, and in particular low-power Bluetooth (BLE) with extremely low power consumption.

[0059] In a further embodiment, Figure 6 The environment 300 shown can capture, process, and display data in a variety of different ways. For example, data can be captured using sensors on a smartwatch 302, but due to limited resources on the smartwatch 302, the data can be transmitted to a smartphone 304 or a service provider 308 (or cloud resources) for processing, and the results of the processing can then be presented back to the user on the smartwatch 302, smartphone 304, and / or another such device associated with the user (such as a 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 considered when making the determination.

[0060] Reference now Figure 7 According to the present disclosure, a state diagram showing different wearing states of a wearable computing device is provided. In an embodiment, for example, the wearable computing device can be any suitable wearable computing device, such as the one described herein with reference to Figures 1 to 6 Therefore, in general, the present invention refers to the wearable computing device 100 described herein. Figures 1 to 6 However, it should be understood that the disclosed wear states may be implemented using any other suitable wearable computing device having any other suitable configuration.

[0061] like Figure 7 As shown, the wearable computing device 100 has multiple states, depending on the biometric sensor electrodes 112 of the device 100, particularly the proximity sensor and the impedance sensor. 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, a wrist contact state 406, and a wrist no contact state 408.

[0062] It should be understood that for ease of discussion, the proximity sensor will be described with reference to the PPG sensor alternately, however, the proximity sensor may be any other suitable proximity sensor or combination of suitable proximity sensors, such as, for example, any other suitable optical proximity sensor, including 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 ultrasonic sensors and / or inductive sensors.

[0063] In some cases, 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 an amount of reflected light detected, which 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 the user. When the comparison of the PPG data to the PPG threshold indicates that the wearable computing device 100 is not close to the object (e.g., 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 the user, and the impedance sensor remains in the off state. In some cases, when the device 100 is in the off-wrist impedance sensor off state 402, certain features of the wearable computing device 100 may be disabled or not allowed to be enabled, such as exercise mode, sleep mode, payment mode, etc. Additionally or alternatively, as described above, when the device 100 is in the off-wrist impedance sensor off state 402, it may be desirable for the dimming element 107 to be in a fully opaque or semi-transparent state.

[0064] In addition, when the comparison of the PPG data with the PPG threshold indicates that the wearable computing device 100 is close to the object (e.g., the PPG data is above the PPG threshold or the proximity determined based on the PPG data is less than the proximity threshold), it is determined that the wearable computing device 100 may be worn by the user, and the impedance sensor is turned on, and the device 100 is transitioned to one of the other states 404, 406, 408.

[0065] Specifically, in one embodiment, when the impedance sensor is first activated, the device 100 initially transitions to the off-wrist impedance sensor active state 404. In the off-wrist impedance sensor active state 404, both the PPG sensor and the impedance sensor are turned on. The data (e.g., admittance value) generated by the impedance sensor in the off-wrist impedance sensor active state 404 is compared with the impedance range (e.g., the admittance value range associated with skin contact of the EDA sensor) to confirm whether the impedance sensor is contacting the user, and thus confirm whether the device 100 is indeed being worn by the user. If the data generated by the impedance sensor in the off-wrist impedance sensor active state 404 is within the impedance range, the device 100 is determined or confirmed to be worn by the user, and the device 100 is transitioned to the on-wrist contact state 406. In addition to this, 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 and the device 100 remains in the off-wrist impedance sensor active state 404. In some embodiments, the shape of the impedance data may be additionally or alternatively monitored over time to determine whether the impedance sensor is contacting the user. For example, after the user begins to wear the device, moisture (e.g., grease, sweat, etc.) slowly begins to accumulate on the device, which causes the admittance value to also slowly increase over time. In some cases, the increase looks like a logarithmic function in shape. Therefore, the shape of the impedance data generated by the impedance sensor in the off-wrist impedance sensor active state 404 can be compared with the expected shape to determine whether the user has begun to wear the device 100.

[0066] In some cases, certain features of the wearable computing device 100 may still be disabled or not allowed to be enabled, such as exercise mode, sleep mode, payment mode, etc., when the device 100 is in the off-wrist impedance sensor active state 404. If the proximity detected by the PPG sensor in the off-wrist impedance sensor active state 404 indicates that the wearable computing device 100 is no longer close to the object (e.g., the PPG data is less than the PPG threshold or the proximity determined based on the PPG data is greater than the proximity threshold), it is determined that the device 100 is no longer likely to be worn by the user, and the device 100 is transitioned back to the off-wrist impedance sensor off state 402, and the impedance sensor is turned off or deactivated to save battery, prevent erroneous measurements, prevent unwanted LED flashing, etc. Additionally or alternatively, as described above, when the device 100 is in the off-wrist impedance sensor active state 404, it may be desirable for the dimming element 107 to be in a fully opaque or translucent state.

[0067] When the device 100 is in the Wrist In Contact state 406, both the PPG sensor and the impedance sensor are still turned on. The data generated by the impedance sensor in the Wrist In Contact state 406 continues to be compared with the impedance range (e.g., the skin contact range) to determine whether the impedance sensor is still contacting 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 Wrist In Contact state 406 remains within the impedance range, the impedance sensor is still contacting the user, and the device 100 is determined to be still being worn by the user, so the device 100 remains in the Wrist In Contact state 406. When the device 100 is in the Wrist In Contact state 406, one or more control actions may be performed, such as adjusting the proximity sensor (e.g., adjusting the LED light intensity of the PPG sensor based on the characteristics of the user's skin, adjusting the receiver sensitivity, etc., which may be different from previous wearing due to activity, sunburn, hygiene, etc.). Similarly, in some cases, when the device 100 is in the wrist contact state 406, certain features of the wearable computing device 100 may be enabled or allowed to be enabled, such as an exercise mode, a sleep mode, a payment mode, etc. Moreover, in some cases, when the device 100 is in the wrist contact state 406, the 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, changes in the measured contact impedance or admittance (e.g., changes in amplitude and / or angle) are correlated to changes in applied pressure, which can be used to improve estimated heart rate and / or other. Similarly, a combination of proximity data (e.g., from a PPG sensor) and impedance data may be used to improve biometric readings from other sensors. For example, PPG data may be presented as a decrease in a pulsating component (AC or DC) of reflectivity or impedance data plotted against wavelength, where a sudden change in reflectivity and / or a change in the pulsating component would indicate a pressure change. In some cases, the machine learning model can collect data from the device 100 when it is in the wrist contact state 406 to 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, when the device 100 is in the wrist contact state 406, it may be desirable for the dimming element 107 to be in a fully transparent or semi-transparent state.

[0068] In addition, if the data generated by the impedance sensor in the Wrist in Contact 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 the device 100 is no longer likely to be worn by the user, so the device 100 is transitioned to the Wrist in Contact state 408. If the proximity detected by the PPG sensor in the Wrist in Contact state 406 indicates that the wearable computing device 100 is no longer close to the object (e.g., the PPG data is less than the PPG threshold or the proximity determined based on the PPG data is greater than the proximity threshold), it is determined that the device 100 is no longer likely to be worn by the user, and the device 100 is transitioned back to the Wrist in Contact state 402 and the impedance sensor is turned off.

[0069] When the device is transitioned to the wrist contactless state 408, both the PPG sensor and the impedance sensor are still turned on. The data generated by the impedance sensor in the wrist contactless state 408 continues to be compared with the impedance range (e.g., skin contact range) to determine whether the impedance sensor is contacting 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 wrist contactless state 408 returns to the impedance range, the impedance sensor is contacting the user again, and therefore the device 100 is confirmed to be worn by the user again, so the device 100 is transitioned back to the wrist contact state 406. In addition, if the data generated by the impedance sensor in the wrist contactless state 408 remains outside the impedance range, the impedance sensor is still not contacting the user, but may still be worn by the user, so the device 100 remains in the wrist contactless state 408. In some cases, as will be described below, control actions may occur when the device 100 is in the wrist contactless state 408. For example, in one case, the device 100 may prompt the user to adjust the device 100 (e.g., tighten the wristband 103 of the wearable device), and optionally, if certain modes (e.g., exercise, sleep, etc.) are active, or enabled from the previous wrist contact state 406, or are intended to be activated, the device 100 may prompt the user to perform an action to check the fit after adjusting the device 100 (e.g., flick the wrist to check whether the device 100 remains in contact during exercise). In one embodiment, other data generated by the device 100 in the wrist contactless state 408 may be adjusted to address the problem that the device 100 may not move synchronously with the user due to, for example, being too loose. If the proximity detected by the PPG sensor in the wrist contactless state 408 indicates that the wearable computing device 100 is no longer close to the object (e.g., the PPG data is less than the PPG threshold or the proximity determined based on the PPG data is greater than the proximity threshold), it is determined that the device 100 is no longer likely to be worn by the user, and the device 100 is transitioned back to the off-wrist impedance sensor off state 402 and the impedance sensor is turned off.

[0070] Thus, it should be appreciated that in the illustrated embodiment, the device 100 is initially confirmed as being worn by the user (i.e., in the On-Wrist Contact State 406) only when the PPG data is above the PPG threshold and the impedance data is within the impedance range (e.g., above the minimum threshold admittance value and below the maximum threshold admittance value), and continues to be confirmed until the PPG data is below the PPG threshold. It should also be appreciated that the use of the combination of impedance and PPG data as described prevents the device 100 from unilaterally predicting that the device 100 is in the On-Wrist State based on the PPG sensor being close to a reflective surface. Similarly, it should be appreciated that in some embodiments, the use of the combination of impedance data and PPG data can prevent erroneous off-wrist determinations. For example, if the PPG data indicates that the proximity is not close enough to be worn by the user, but the impedance data simultaneously indicates contact with the user's skin, the device 100 may remain in the On-Wrist State 406, 408. Additionally or alternatively, as described above, when the device 100 is in the On-Wrist Contactless State 408, it may be desirable for the dimming element 107 to be in a fully transparent or translucent state.

[0071] Furthermore, it should be understood that although the device 100 is shown as being in the wrist non-contact state 408 only after being in the wrist contact state 406, in other embodiments, the device 100 may be in the wrist non-contact state 408 without first being in the wrist contact state 406. For example, in some embodiments, when the admittance value is non-zero but otherwise outside the impedance range, the device 100 may be in the wrist non-contact state 408 without first being in the wrist contact state 406. Furthermore, it should be understood that in some embodiments, the device 100 may have any other suitable number or combination of states. For example, in one embodiment, the device 100 has only three states, such as the off-wrist impedance sensor off state 402, the wrist contact state 406, and the wrist non-contact state 408, wherein after determining that the device 100 is within the proximity threshold and turning on the impedance sensor based on the PPG data, when the impedance data is within the impedance range, the wrist contact state 406 is determined, or when the impedance data is outside the impedance range, the wrist non-contact state 408 is determined. In addition, it should be understood that the combination of PPG data and impedance data can be used to determine the skin color of the user, which in turn can be used to improve the determination of the wear state. For example, different skin colors can each be associated with a different expected reflectivity detected by PPG data for a certain contact level detected by an impedance sensor (or any other pressure indicating sensor). Therefore, once the skin color of the user is determined, the interpretation of PPG data and other data can be calibrated for such skin color.

[0072] Reference now Fig. 8A and Figure 8B, a flowchart of one embodiment of a method 450 for detecting a wearing state of a wearable computing device is provided. In an embodiment, for example, the wearable computing device may be any suitable wearable computing device, such as the one described herein with reference to Figures 1 to 6 Therefore, in general, the present invention refers to the wearable computing device 100 described herein. Figures 1 to 6 The wearable computing device 100 and Figure 7 The method 450 is described with reference to the wear states 402, 404, 406, and 408 described in the embodiment of the present invention. However, it should be understood that the disclosed method 450 can be implemented using any other suitable wearable computing device having any other suitable configuration and any other suitable wear state. In addition, although for ease of illustration and discussion, Fig. 8A and Figure 8B The steps performed in a particular order are depicted, but the methods disclosed herein are not limited to any particular order or arrangement. Using the disclosure provided herein, those skilled in the art will appreciate that the 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.

[0073] exist Fig. 8A Beginning at step (452) of method 450, it is assumed that the wearable computing device 100 is off-wrist or otherwise not being worn by the user. Thus, at step (454) of method 450, the proximity sensor (e.g., PPG sensor) is turned on, and the impedance sensor (e.g., EDA sensor) is turned off (or turned off if not already turned off), which corresponds to the above reference to Figure 7 The off-wrist impedance sensor off state 402 is described. Thereafter, at step (456) of method 450, the data collected by the PPG sensor is compared with the PPG threshold to determine whether the proximity of the wearable computing device 100 is within a certain proximity of an object (e.g., a user) associated with the wearable computing device 100 being possibly worn by the user. When the PPG data generated by the PPG sensor is less than the PPG threshold (the proximity determined based on the PPG data is greater than or exceeds the proximity threshold), it is determined that the wearable computing device 100 is not close to the object and the wearable computing device 100 is probably not being worn by the user, and thus the off-wrist impedance sensor off state is confirmed at step (458) of method 450 and the impedance sensor remains in the off state. However, when the PPG data generated by the PPG sensor is greater than the PPG threshold (the proximity determined based on the PPG data is less than the proximity threshold), it is determined that the wearable computing device 100 is probably being worn by the user, and the impedance sensor is turned on at step (460) of method 450. After turning on the impedance sensor, the device 100 is transitioned to the off-wrist impedance sensor active state 404 at step ( 462 ) of method 450 .

[0074] In the off-wrist impedance sensor active state 404, Figure 8B At step (464) of method 450 in the embodiment, 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 the proximity associated with the wearable computing device 100 being likely to be worn by the user. If the PPG data generated by the PPG sensor is less than the PPG threshold (the proximity determined based on the PPG data is greater than the proximity threshold), it is determined that the wearable computing device 100 is no longer proximate to the object and the wearable computing device 100 is no longer likely to be worn by the user, so the device 100 is transitioned back to the off-wrist impedance sensor off state at step (458) of method 450, and the impedance sensor is turned off at step 454 ( Fig. 8A ) returns to the off state at step (464). Otherwise, if the PPG data generated by the PPG sensor is greater than the PPG threshold (the proximity determined based on the PPG data is less than the proximity threshold), it is determined that the wearable computing device 100 is still proximate to the object and the wearable computing device 100 is still likely to be worn by the user. As long as the wearable computing device 100 is still likely to be worn by the user based on the PPG data at step (464), the impedance data can be evaluated at step (466) of method 450.

[0075] For example, at step (466), the impedance data (e.g., the admittance value) is compared to an impedance range (e.g., the 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 and the device 100 remains in the off-wrist impedance sensor active state 404, and the method returns to step (462) ( Fig. 8A ). In addition, if the data generated by the impedance sensor is determined to be within the impedance-related range, the device 100 is determined or confirmed to be worn by the user, and the device 100 is transitioned to the on-wrist contact state 406 at step (468) of method 450. Therefore, when the PPG sensor is detecting reflections from a reflective surface other than the skin, the impedance data can be used to avoid making an erroneous on-wrist determination based on the PPG data.

[0076] It should be understood that in one embodiment, steps (464), (466) may alternatively be performed in reverse order, such that for the same time instance, if the impedance data is within the impedance-related range and the PPG data indicates that the proximity is not less than the proximity threshold (the device 100 is not close enough to the user), the impedance data may overwrite the proximity determined from the PPG data, such that it may be determined that the proximity is still less than the proximity threshold (the device 100 is close enough to the user), and for that time instance, one of the wrist states 406, 408 is determined instead of the off-wrist impedance sensor off state 402. In addition, if the impedance data is outside the impedance range and the PPG data indicates that the proximity is not less than the proximity threshold (the device 100 is not close enough to the user), the impedance data confirms that the proximity determined from the PPG data at that time instance is not less than the proximity threshold, and returns to the off-wrist impedance sensor off state 402 at step (458). Thus, when the PPG sensor reading is affected by moisture (e.g., sweat, rain, etc.), the impedance data may be used to avoid making an erroneous off-wrist determination based on the PPG data.

[0077] Returning to the illustrated method 450, at 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 the proximity associated with the wearable computing device 100 being likely to be worn by the user. If the PPG data generated by the PPG sensor is less than the PPG threshold (the proximity determined based on the PPG data is greater than the proximity threshold), it is determined that the wearable computing device 100 is no longer proximate to the object and the wearable computing device 100 is no longer likely to be worn by the user, so the device 100 is transitioned back to the off-wrist impedance sensor off state at step (458) of method 450, and the impedance sensor is turned on at step 454 ( Fig. 8A ) returns to the off state at step (464). Otherwise, if the PPG data generated by the PPG sensor is greater than the PPG threshold (the proximity determined based on the PPG data is less than the proximity threshold), it is determined that the wearable computing device 100 is still proximate to the object and the wearable computing device 100 is still likely to be worn by the user. As long as the wearable computing device 100 is still likely to be worn by the user based on the PPG data at step (464), the impedance data can be evaluated at step (472) of method 450.

[0078] If the data generated by the impedance sensor is still determined to be within the impedance range, the device 100 is determined or confirmed to be worn by the user, and the device 100 remains in the wrist contact state 406, and the method returns to step (468). In addition, if the data generated by the impedance sensor in the off-wrist impedance sensor active state 404 is no longer still within the impedance range, the device 100 is transitioned to the wrist non-contact state 408 (at step (474) of the method 450). Figure 7 ).

[0079] After determining at step (474) that the wrist is in contact-free state 408, at step (476) of method 450, a notification is provided prompting the user to adjust the wearable device (e.g., tighten the wristband 103 of the wearable device). Fig.12 As shown, an example of a notification is shown, in which a user interface (e.g., display 206) provides a visual notification indicating to the user that the wearable device 100 needs to be adjusted (e.g., "the band is too loose"). It should be understood that the notification can indicate any other suitable message (e.g., "check the fit of the band", "the band may be too loose or too tight", etc.). Moreover, it should be understood that the notification can be provided in any other suitable manner or combination of manners, such as by tactile feedback, by audio feedback, and / or other means. In some embodiments, a notification is provided only when a certain event threshold occurs. For example, if it is determined that the wrist contactless state 408 lasts for a specific duration or a specific number of instances within a time period, a notification can be provided. In one embodiment, the positivity of the notification can be set at least in part based on the activity mode. For example, if a specific activity mode is activated, the device can prompt a notification when it is determined that the duration or number of instances in the wrist contactless state 408 is shorter or less than when the specific activity mode is not activated.

[0080] Furthermore, in some cases, a notification may also request confirmation that the notification has been received. Fig.13 , an example of a notification is shown, wherein a user interface (e.g., display 206) provides a text-based notification that requests confirmation of receipt of the notification by interacting with the user interface (e.g., by pressing “got it”). In one case, Fig.14 As shown, the notification may alternatively or additionally include an image-based notification requesting adjustment and / or confirmation. Additionally, in some embodiments, the notification may request confirmation that the adjustment has been performed. For example, also as shown Fig.14 As shown, the notification may prompt the user to perform a certain action or predefined gesture after adjusting the 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 may be requested, such as tapping the device 100.

[0081] When the notification includes a prompt to confirm whether the adjustment has been performed, at step (478) of method 450, it is re-determined whether a confirmation indicating that the adjustment (e.g., tightening) has been performed has been received. For example, motion data indicating that the user has performed 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 check whether the tightening is sufficient. Otherwise, if no confirmation is received, the method returns to step (476) and continues to wait.

[0082] If after adjusting device 100, the process returns to step (470) to re-determine the wear state of the wearable computing device, and at step (474) it is again determined that the wear state is in the wrist-free state, then in some cases, the next notification at step (476) may indicate to the user that device 100 is still not properly fitted (e.g., if Fig.14 In addition, if the process returns to step (464) after adjusting the device 100, it is determined at step (468) that the wrist is in contact, then a notification may be provided indicating to the user that the adjustment of the device 100 was successful (e.g., Fig.14 "Great! Your belt is properly adjusted and will give you the best possible sensing.").

[0083] Reference now Fig. 9 , a flowchart of one embodiment of a method 500 for detecting a wearing state of a wearable computing device is provided. In an embodiment, for example, the wearable computing device may be any suitable wearable computing device, such as the one described herein with reference to Figures 1 to 6 Therefore, in general, the present invention refers to the wearable computing device 100 described herein. Figures 1 to 6 The wearable computing device 100 and Figure 7 The method 500 is described with reference to the wear states 402, 404, 406, and 408 described in the embodiment of the present invention. However, it should be understood that the disclosed method 500 can be implemented using any other suitable wearable computing device having any other suitable configuration and any other suitable wear state. In addition, although for ease of illustration and discussion, Fig. 9 The steps performed in a particular order are depicted, but the methods discussed herein are not limited to any particular order or arrangement. Using the disclosure provided herein, those skilled in the art will appreciate that the 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.

[0084] Method 500 is similar to that described above. Fig. 8A and Figure 8BMethod 450 described in , except that device 100 can be in wrist non-contact state 408 without first being in wrist contact state 406. For example, method 500 has the same steps (452) to (460). However, after step (460), method 500 does not have step (462). Alternatively, after step (460), method 500 moves directly to step (464') (the same as step (464) in method 450), and then moves to step (466'). As with step (466) in method 450, in step (466') of method 500, impedance data (e.g., an admittance value included in the impedance data) is compared with an impedance range (e.g., an admittance value associated with skin contact of an 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, the device 100 is determined or confirmed to be worn by the user, and the device 100 is transitioned to the contact at wrist state 406 at step (468). However, unlike step (466) in method 450, in step (466') of method 500, 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, and the device 100 is transitioned to the contact at wrist state 404 at step (474'). Thereafter, steps (476') and (478') in method 500 are the same as steps (476) and (478) in method 450, except that after confirming that the adjustment (e.g., tightening) is performed at step (478') in method 500, the method returns to step 464'.

[0085] Reference now Fig.10 , a flowchart of one embodiment of a method 600 for detecting a wearing state of a wearable computing device is provided. In an embodiment, for example, the wearable computing device may be any suitable wearable computing device, such as the one described herein with reference to Figures 1 to 6 Therefore, in general, the present invention refers to the wearable computing device 100 described herein. Figures 1 to 6 The wearable computing device 100 and optionally Figure 7 The method 600 is described with reference to the wear states 402, 404, 406, and 408 described in the embodiment of the present invention. However, it should be understood that the disclosed method 600 can be implemented using any other suitable wearable computing device having any other suitable configuration and having any other suitable wear state. In addition, although for ease of illustration and discussion, Fig.10The steps performed in a particular order are depicted, but the methods discussed herein are not limited to any particular order or arrangement. Using the disclosure provided herein, those skilled in the art will appreciate that the 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.

[0086] At step (602), the method 600 may include obtaining impedance data over a time period via an impedance sensor of the wearable computing device. For example, impedance data generated by an impedance sensor (e.g., an EDA sensor) of the wearable computing device 100 over a time period may be obtained. The time period may be any suitable time period, such as, for example, approximately one minute.

[0087] Further, at step (604), method 600 may include calculating an admittance value of the impedance sensor during the time period based at least in part on the impedance data. Generally speaking, admittance is the reciprocal of impedance. Thus, in one case, the reciprocal of the impedance data provides the admittance value. However, in other embodiments, the impedance data may have been configured to include the admittance value. The admittance value of the impedance sensor during the time period may, for example, be an average value of the admittance values ​​determined based on the impedance data.

[0088] Furthermore, at step (606), the method 600 may include determining, based at least in part on the calculated admittance value, that a wear state of the wearable computing device during the time period corresponds to an on-wrist state in which the wearable computing device is being worn by the user or an off-wrist state in which the wearable computing device is not being worn by the user. For example, the admittance value may be compared to an impedance range to determine the wear state of the wearable computing device 100. If the admittance value is within the impedance range (e.g., equal to or above a minimum threshold admittance value and equal to or below a maximum threshold admittance value), the wear state corresponds to an on-wrist state in which the wearable computing device 100 is being worn by the user. Additionally, if the admittance value is outside the impedance range, the wear state corresponds to an off-wrist state in which the wearable computing device 100 is not being worn by the user. In some cases, a 2D region in a complex admittance space (e.g., based on a phasor representation of admittance, where the imaginary part of the admittance (representing reactance) is plotted relative to the real part of the admittance (representing resistance)) may be additionally or alternatively defined that corresponds to human skin contact. When the admittance (e.g., reactance and resistance) from the impedance data falls within the 2D region, the wear state corresponds to the on-wrist state in which the wearable computing device 100 is being worn by the user. Otherwise, if the admittance from the impedance data is outside the 2D region, the wear state corresponds to the off-wrist state in which the wearable computing device 100 is not being worn by the user. By using such a 2D admittance region, materials with similar overall admittance magnitudes (combinations of reactance and resistance) can be prevented from being erroneously detected as skin, because at least one of the reactance or resistance of the admittance may be different from the reactance or resistance of the skin. In some cases, 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 within a given time period is greater than a threshold time period, it can be determined that the device 100 is in the on-wrist state. Conversely, if the total time that the admittance value is within the impedance range within a given time period is less than a threshold time period, it can be determined that the device 100 is in the off-wrist state.

[0089] Additionally, at step (608), method 600 may include performing a control action based at least in part on the wear state of the computing device. For example, the control action may include generating a notification indicating the wear state of the wearable computing device during the time period. In some cases, 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 data from a sensor when the wear state intermittently corresponds to the off-wrist state, and / or disabling one or more features of the wearable computing device 100 (e.g., payment mode) when the wear state corresponds to the off-wrist state for a sustained period of time. In one case, when the wear state corresponds to the on-wrist state for a sustained period of time, the control action may include enabling certain features of the wearable computing device 100 or allowing certain features of the wearable computing device to be used, such as an exercise mode, a sleep mode, a payment mode, etc.

[0090] Reference now Fig.11 , a flowchart of one embodiment of a method 700 for detecting a wearing state of a wearable computing device is provided. In an embodiment, for example, the wearable computing device may be any suitable wearable computing device, such as the one described herein with reference to Figures 1 to 6 Therefore, in general, the present invention refers to the wearable computing device 100 described herein. Figures 1 to 6 The wearable computing device 100 and optionally Figure 7 The method 700 is described with reference to the wear states 402, 404, 406, and 408 described in the embodiment of the present invention. However, it should be understood that the disclosed method 700 can be implemented using any other suitable wearable computing device having any other suitable configuration and having any other suitable wear state. In addition, although for ease of illustration and discussion, Fig.11 The steps performed in a particular order are depicted, but the methods discussed herein are not limited to any particular order or arrangement. Using the disclosure provided herein, those skilled in the art will appreciate that the 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.

[0091] At step (702), method 700 may include obtaining impedance data via an impedance sensor (e.g., an EDA sensor) of the wearable computing device during a time period when the user is wearing the wearable computing device. For example, impedance data generated by an impedance sensor of the wearable computing device 100 during a time period may be obtained. Generally speaking, the impedance data includes a plurality of measurements indicating that electrodes of the impedance sensor contacted the user's skin during the time period.

[0092] Further, at step (704), method 700 may include determining, based at least in part on the impedance data, that the electrodes of the impedance sensor are not in contact with the user's skin for a threshold amount of the time period. For example, the impedance data may have data points only when the impedance electrodes are in contact with the skin. Thus, the total number of multiple measurements obtained 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., at least 100 measurements should be taken in 1 minute, which is associated with contact between the electrode and the skin lasting at least 50% (30 seconds) of the minute). If the total number of multiple measurements obtained 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 are not in contact with the user's skin for a threshold amount of the time period, and the wrist-free state 408 is determined. If the total number of multiple measurements obtained during the time period is less than the threshold number of measurements, particularly if the total number of multiple measurements obtained during the 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 the time period, and the wrist-off state is determined. Additionally, if the total number of measurements obtained during the time period is greater than the threshold number of measurements, it is determined that the electrodes of the impedance sensor were in contact with the user's skin for a threshold amount of the time period, and a wrist contact state 406 is determined.

[0093] Additionally, at step (706), method 700 may include performing a control action in response to determining that the electrodes of the impedance sensor are not in contact with the user's skin for a threshold amount of the time period. For example, when the total number of multiple measurements obtained during the time period is less than the threshold number of measurements, the wearable computing device 100 may determine that the device 100 is not properly fitted to the user, and then perform a control action. For example, as described above, the device 100 may generate a notification to prompt the user to tighten the band that secures the wearable computing device to the user, and the notification may be used to prompt the user to adjust the wearable computing device 100 (e.g., tighten the band 103, rotate the band 103, etc.). Similarly, as described above, the device 100 may adjust the collected data to address the problem of improper fit of the device 100.

[0094] 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 the PPG data from the PPG sensor. However, in some cases, methods 600, 700 are performed only when it is determined that the proximity of device 100 is below a proximity threshold based on the PPG data from the PPG sensor, such as described with reference to steps (452) to (460) of methods 450, 500.

[0095] In addition, it should be understood that methods 450, 500, 600, 700 may be performed when certain activity modes of the wearable computing device 100 are initiated. For example, certain applications (e.g., exercise tracking, sleep tracking, meditation, etc.) require more accurate data to generate reports, or may have a higher subjective value to the user than data during other activities. When the wearable computing device 100 is not properly fitted (e.g., loose), the data generated by the biometric sensors of the wearable computing device 100 may be insufficient (e.g., sporadic or inaccurate) to generate reports. Therefore, when one of these applications on the wearable computing device 100 may be launched (such as based on the user's habits, pre-programmed launch time, biometric readings, and / or other) or explicitly launched by the user, methods 450, 500, 600, 700 may be used to ensure that the wearable computing device 100 is worn on the user and / or the device 100 is properly fitted to the user. Further, by prompting the user to correct the fit of the wearable computing device 100, situations where an application (e.g., sleep tracking) is unable to generate a report (e.g., sleep score, snoring report, etc.) due to insufficient biometric data, which is at least partially caused by the wearable computing device 100 not being properly worn on the user's wrist (e.g., loose) can be avoided. Data generated by the wearable computing device 100 can also be edited or highlighted to indicate the confidence of the data according to the wearing state during the active mode. For example, when the wearing state is in the wrist contactless state 408, a low confidence value for the data can be suggested and / or the data can be not displayed, so the data quality is low and / or the reliability is low, while when the wearing state is in the wrist contact state 406, a higher confidence value for the data can be suggested. In addition, some applications (such as payment applications) may require a higher level of certainty that the wearable computing device 100 is being worn by the user to enable. Therefore, it may be beneficial to further require the user to perform an authorization action using the wearable computing device 100 to make a payment (e.g., using PIN entry, motion mode, etc.) if the worn state is one of the on-wrist states 406, 408, and to cancel the payment authorization for the wearable computing device 100 if the worn state is the off-wrist state 402, 404, etc.

[0096] The technology discussed herein relates to servers, databases, software applications, and other computer-based systems, as well as the actions taken and the information sent to and from such systems. The inherent flexibility of computer-based systems allows for multiple possible configurations, combinations, and partitioning of tasks and functions between and among components. For example, the processes discussed herein can be implemented using a single device or component or multiple devices or components working in combination. Databases and applications can be implemented on a single system, or distributed across multiple systems. Distributed components can operate sequentially or in parallel.

[0097] Furthermore, although the above methods for determining the wear state of the wearable computing device have been discussed in the context of using a PPG sensor, it should be understood that these methods are not intended to be limited to determining the wear 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, and / or others configured to determine the proximity of the wearable computing device to the user. In such embodiments, the wear state of the wearable computing device can 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, and / or others.

[0098] Although the subject matter has been described in detail with respect to various specific example embodiments of the subject matter, each example is provided by way of explanation rather than limitation of the present disclosure. Those skilled in the art, after understanding the foregoing, may easily produce changes, variations, and equivalents of such embodiments. Therefore, the present disclosure does not exclude such modifications, variations, and / or additions to the subject matter that would be readily apparent to those of ordinary skill in the art. For example, a feature shown or described as part of one embodiment may be used together with another embodiment to produce yet another embodiment. Therefore, the present disclosure is intended to cover such changes, variations, and equivalents.

Claims

1. A computer-implemented method for determining a wear state of a wearable computing device, the method comprising: obtaining, 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; obtaining, 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; as well as Determining, via the one or more processors based at least in part on the impedance data and the second proximity data, that the worn state of the wearable computing device corresponds to one of an off-wrist state, an on-wrist contact state, or an on-wrist no-contact state.

2. The computer-implemented method of claim 1, wherein: Activating the impedance sensor based on the first proximity data includes: determining, via the one or more processors, whether the wearable computing device is proximate to a user's skin based at least in part on the first proximity data; and The impedance sensor is activated, via the one or more processors, in response to determining that the wearable computing device is proximate to the skin of the user based at least in part on the first proximity data.

3. The computer-implemented method of claim 2, wherein: Determining that the wearing state of the wearable computing device corresponds to the wrist contact state includes: determining, via the one or more processors, based at least in part on the second proximity data, that the wearable computing device remains proximate to the skin of the user; determining, via the one or more processors, based at least in part on the impedance data, that the impedance sensor is contacting the skin of the user; and In response to determining that the wearable computing device remains proximate to the skin of the user and the impedance sensor is contacting the skin of the user, determining, via the one or more processors, that the worn state of the wearable computing device corresponds to the contact-at-wrist state.

4. The computer-implemented method of claim 3, wherein: Determining that the impedance sensor is contacting 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. The computer-implemented method of claim 2, wherein: Determining that the wear state of the wearable computing device corresponds to the no-contact-on-wrist state includes: determining, via the one or more processors, based at least in part on the second proximity data, that the wearable computing device remains proximate to the skin of the user; determining, via the one or more processors, based at least in part on the impedance data, that the impedance sensor is not contacting the skin of the user; and In response to determining that the wearable computing device remains proximate to the skin of the user and the impedance sensor is not contacting the skin of the user, determining, via the one or more processors, that the worn state of the wearable computing device corresponds to the no-contact-on-wrist state.

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

7. The computer-implemented method of claim 5, further comprising: After determining that the worn state of the wearable computing device corresponds to the no-contact-at-wrist state, generating, via the one or more processors, a notification to prompt the user to tighten a band of the wearable computing device.

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

9. The computer-implemented method of claim 8, further comprising: obtaining, via the one or more processors, motion data generated by one or more motion sensors of the wearable computing device, the motion data indicating that the user performed the predefined gesture; as well as obtaining, via the one or more processors, second impedance data in response to obtaining the motion data indicating that the user performed the predefined gesture, the second impedance data being generated by the impedance sensor; obtaining, via the one or more processors, third proximity data in response to obtaining the motion data indicating that the user performed the predefined gesture, the third proximity data being generated by the proximity sensor; as well as The worn state of the wearable computing device is re-determined, via the one or more processors, based at least in part on the second impedance data and the third proximity data.

10. The computer-implemented method of claim 5, after determining that the wear state of the wearable computing device corresponds to the no-contact-at-wrist state, the method further comprising at least one of: adjusting data generated by the wearable computing device acquired during the wrist-free contact state; or A confidence value of the data generated by the wearable computing device acquired during the wrist-free contact state is adjusted.

11. The computer-implemented method of claim 2, wherein: Determining that the worn 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 proximate 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 contacting the skin of the user; and In response to determining that the wearable computing device is still not proximate to the skin of the user and the impedance sensor is not contacting the skin of the user, determining, via the one or more processors, that the worn state of the wearable computing device corresponds to an off-wrist state.

12. The computer-implemented method of claim 11, further comprising: In response to determining that the worn state of the wearable computing device corresponds to the off-wrist state and is no longer proximate to the skin of the user, deactivating, via the one or more processors, the impedance sensor.

13. A wearable computing device comprising: Proximity sensor; Impedance sensor; as well as One or more processors configured to: obtaining 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; obtaining impedance data generated by the impedance sensor during a second time period occurring after the first time period; obtaining second proximity data generated by the proximity sensor during the second time period; and A determination is made based at least in part on the impedance data and the second proximity data that a worn state of the wearable computing device corresponds to one of an off-wrist state, an on-wrist contact state, or an on-wrist no-contact state.

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. The wearable computing device of claim 13, further comprising: build; as well as a dimming element located between the cover and at least the proximity sensor, The one or more processors are further configured to control the dimming element to change between a transparent state and an opaque state based at least in part on the wearing state.

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

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

19. The wearable computing device of claim 17, wherein: The first electrode and the second electrode are made of a transparent material, and the first electrode and the second electrode are positioned above the proximity sensor.

20. A computer-implemented method for determining a wear state of a wearable computing device, the method comprising: obtaining, 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; calculating, via the one or more processors, an admittance value of the impedance sensor over the time period based at least in part on the impedance data; determining, via the one or more processors, based at least in part on the calculated admittance value, that the wear state of the wearable computing device during the time period corresponds to an on-wrist state in which the wearable computing device is being worn by a user or an off-wrist state in which the wearable computing device is not being worn by the user; as well as A control action is performed, via the one or more processors, based at least in part on the worn state of the wearable computing device.

21. The computer-implemented method of claim 20, wherein: Determining that the wear state of the wearable computing device during the time period corresponds to the on-wrist state includes: determining, via the one or more processors, that a calculated admittance value of the impedance sensor during the time period is within a range of values ​​indicating that an electrode of the impedance sensor is in contact with skin; and In response to determining that the calculated admittance value is within the range of values, determining, via the one or more processors, that the worn state of the wearable computing device corresponds to the on-wrist state.

22. The computer-implemented method of claim 20, wherein: Determining that the wearing state of the wearable computing device within the time period corresponds to the off-wrist state includes: determining, via the one or more processors, that a calculated admittance value of the impedance sensor is not within a range of values ​​indicating that an electrode of the impedance sensor is in contact with skin during the time period; and Determining, via the one or more processors, that the worn state of the wearable computing device corresponds to the off-wrist state.

23. The computer-implemented method of claim 20, wherein: Performing the control action includes generating, via the one or more processors, a notification indicating the wearing state of the wearable computing device during the time period.

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 determined wear state of the wearable computing device within the time period.

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

26. A computer-implemented method for determining a wear state of a wearable computing device, the method comprising: obtaining, 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 during which a user was wearing the wearable computing device; determining, via the one or more processors, that an electrode of the impedance sensor is not in contact with the user's skin for the threshold amount of time period based at least in part on the impedance data; as well as In response to determining that the electrode of the impedance sensor has not been in contact with the skin of the user for the threshold amount of time, a control action is performed via the one or more processors.

27. The computer-implemented method of claim 26, wherein: the impedance data comprising a plurality of measurements indicative of contact of the electrode with the skin of the user during the time period; and Determining that the electrode of the impedance sensor has not been in contact with the skin for the threshold amount of time period includes: determining, by the one or more processors, that a total number of the plurality of measurements obtained during the time period is less than a threshold number of measurements; as well as In response to determining that the total number of the measurements obtained during the time period is less than the threshold number of measurements, determining, via the one or more processors, that the electrode was not in contact with the skin of the user for the threshold amount of the time period.

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