System and method for determining user inputs on a wearable computing device

Abstract

A computer-implemented method for determining user inputs on a wearable computing device includes receiving proximity data generated by proximity sensors of the wearable computing device, the proximity data being indicative of a distance of a bottom side of a housing of the wearable computing device from an object. The computer-implemented method further includes determining when a tap occurs on the wearable computing device based at least in part on the proximity data. Additionally, the computer-implemented method includes determining a location of the tap on the wearable computing device based at least in part on the proximity data.

Description

SYSTEM AND METHOD FOR DETERMINING USER INPUTS ON A WEARABLE COMPUTING DEVICEFIELD OF THE INVENTION

[0001] The present disclosure relates generally to wearable computing devices, and more particularly, to systems and methods for determining user inputs on a wearable computing device.BACKGROUND

[0002] Recent advances in technology, including those available through consumer devices, have provided for corresponding advances in health detection and monitoring. For example, wearable computing devices such as fitness trackers and smart watches are able to determine information relating to the pulse or motion of a person wearing the device. For instance, certain wearable computing devices include a variety of sensors for measuring multiple biological parameters and, optionally, for enabling other features, such as GPS guidance, music, communications, payments, and / or the like that can be beneficial to a user of the device. Suitable sensors include a heart rate sensor, multi-purpose electrical sensors compatible with electrocardiogram (ECG) and electrodermal activity (EDA) applications, infrared sensors, a gyroscope, an altimeter, an accelerometer, a temperature sensor, an ambient light sensor, Wi-Fi, GPS, a vibration sensor, a speaker, and a microphone, among others. Conventional wearable computing devices have a limited area to add physical interface buttons. Some wearable computing devices may include a touch screen for more interface interactions. However, such touch screens often rely on capacitive sensing to detect touches, which may be difficult to use with gloves in cold weather and / or may be inaccurate with sweat. Moreover, the size of such touch screens may be small, which can limit the number of different input patterns (e.g., swipes, taps, and / or the like) using such screens. These limitations make it difficult for a user to interact with wearable computing devices, especially while performing exercise.

[0003] Accordingly, systems and methods for determining user inputs on a wearable computing device, particularly for determining user inputs on a wearable computing device having proximity sensors, would be welcomed in the art.SUMMARY OF THE INVENTION

[0004] Aspects and advantages of embodiments of the present disclosure will be set forth inpart in the following description, or can be learned from the description, or can be learned through practice of the embodiments.

[0005] In one aspect, a computer-implemented method for determining user inputs on a wearable computing device is provided. The computer-implemented method may include receiving, by one or more processors of the w earable computing device, proximity data generated by proximi ly sensors of the wearable computing device, where the proximity data may be indicative of a distance of a bottom side of a housing of the wearable computing device from an obj ect. The computer-implemented method may further include determining, by the one or more processors, when a tap occurs on the wearable computing device based at least in part on the proximity data. Additionally, the computer-implemented method may include determining, by the one or more processors, a location of the tap on the wearable computing device based at least in part on the proximity data.

[0006] In another aspect, a wearable computing device is provided. The wearable computing device may include a housing having a bottom side configured to be closest to a user when worn. The wearable computing device may further include proximity sensors configured to generate proximity data indicative of a distance of the bottom side of the housing to an object. Additionally, the wearable computing device may include one or more processors configured to receive the proximity data generated by the proximity sensors, determine when a tap occurs on the wearable computing device based at least in part on the proximity data, and determine a location of the tap on the wearable computing device based at least in part on the proximity data.

[0007] These and other features, aspects, and advantages of various embodiments of the present disclosure will become 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 related principles.BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Detailed discussion of embodiments directed to one of ordinary skill in the art is set forth in the specification, which makes reference to the appended figures, in which:

[0009] FIG. 1 A provides a front perspective view of a wearable computing device on a wrist of a user according to one embodiment of the present disclosure;

[0010] FIG. IB provides a rear view of the wearable computing device of FIG. 1 A according to one embodiment of the present disclosure;

[0011] FIG. 1C provides a side view of the wearable computing device of FIGS. 1A and IB according to one embodiment of the present disclosure;

[0012] FIG. ID illustrates various controller components of an example system that can be utilized with the wearable computing device of FIGS. 1A-1C according to one embodiment of the present disclosure;

[0013] FIG. IE provides a schematic diagram of an example set of devices, including the wearable computing device of FIGS. 1A-1C, that are able to communicate according to one embodiment of the present disclosure;

[0014] FIG. IF provides an example set of user inputs to the wearable computing device of FIGS. IA-IC, such as taps on the wearable computing device, detectable according to one embodiment of the present disclosure;

[0015] FIG. 2 provides an example graph of sensor data indicative of the user inputs to the wearable computing device from FIG. IF according to one embodiment of the present disclosure;

[0016] FIG. 3 illustrates a flow diagram of an embodiment of a method for detecting user inputs to the wearable computing device, such as taps on the wearable computing device, according to one embodiment of the present disclosure; and

[0017] FIG. 4 provides a method flow diagram of a method for detecting user inputs to the wearable computing device, such as taps on the wearable computing device, according to one embodiment of the present disclosure.DETAILED DESCRIPTION

[0018] Reference now will be made in detail to embodiments of the invention, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation of the invention, not limitation of the invention. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the scope or spirit of the invention. For instance, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that the present invention covers such modifications and variations as come within the scope of the appended claims and their equivalents.

[0019] Conventional wearable computing devices have a limited area to add physical interface buttons. Some wearable computing devices may include a touch screen for more interface interactions. However, such touch screens often rely on capacitive sensing to detecttouches, which may be difficult to use with gloves in cold weather and / or may be inaccurate with sweat. Moreover, the size of such touch screens may be small, which can limit the number of different input patterns (e.g., swipes) using such screens. These limitations make it difficult for a user to interact with wearable computing devices, especially while performing exercise.

[0020] As such, the present disclosure is related to a wearable computing device, particularly a wearable computing device having a plurality of proximity sensors, and a computer- implemented method for determining user inputs on such a wearable computing device that overcomes such difficulties. For instance, the proximity sensors may be positioned on a side of a housing of the wearable computing device configured to be closest to a user when worn, such as on a wrist-facing side of a smartwatch. The proximity sensors may be any suitable proximity sensors, such as optical sensors including photodiodes that measure light. The proximity sensors may be spaced apart on the housing of the wearable computing device such that, when a user taps on the device (e g., with their finger), each of the proximity' sensors experiences a respective response, where the combination of the responses of the proximitysensors can be used to determine the location of the tap. For example, the combined set of proximity data from the proximity sensors may be different for each location on the wearable computing device. As such, the combination of responses of the proximity sensors may be used to indicate the location of the tap and increase confidence that a tap has occurred over another movement of the wearable computing device, such as movements of the wearable computing device during running, and / or the like. In some instances, confidence in detecting taps may be further increased by using other existing sensors within the wearable computing device, such as accelerometers and / or the like. In general, different locations on the wearable computing device may be mapped to different actions to be taken with the wearable computing device. By knowing the locations of the taps on the wearable computing device, requests for the different actions to be taken with the wearable computing device may be received without adding more physical buttons or sensors to the wearable computing device.

[0021] Accordingly, the disclosed methods allow for more user inputs without requiring additional hardware, which reduces costs, saves space for other hardware components within the housing of the wearable computing device, and eliminates mechanical points of failure, while improving usability of the wearable computing device, particularly during exercise.

[0022] With reference now- to the Figures, example embodiments of the present disclosure will be discussed in further detail.

[0023] Referring now to the drawings, FIGS. 1 A-1E illustrate perspective views of awearable computing device 100 according to the present disclosure and various controller components of an example system and various devices that can be utilized with such wearable computing device 100. In particular, as shown in FIG. 1A, the wearable computing device 100 may be worn on a user’s forearm 102 like a wristwatch. Thus, as shown, the wearable computing device 100 may include a wristband having one or more parts, such as the two wristband straps 130, 132, for securing the wearable computing device 100 to the user’s forearm 102. However, it should be appreciated that the wearable computing device 100 may be worn at any other suitable location by a user, such as, for example, on an ankle.

[0024] In addition, as shown in FIGS. 1A-1C and IE, the wearable computing device 100 has a housing 104 that contains the electronics associated with the wearable computing device 100. The housing 104 has an upper side 104U including an electronic display screen 106 and an outer covering 108. For example, in an embodiment, the outer covering 108 may be constructed of glass, polycarbonate, acrylic, or similar. The electronic display screen 106 may be arranged within the housing 104 and viewable through the outer covering 108. Moreover, in an embodiment, the electronic display 106 may cover an electronics package (not shown), which may also be housed within the housing 104. Additionally, the wearable computing device 100 may also include one or more buttons 118 (FIG. 1 C), such as on a perimeter 104P of the housing 104, that may be implemented to provide a mechanism to activate various sensors of the wearable computing device 100 to collect certain health data of the user and / or for a user to otherwise interact with the wearable computing device 100.

[0025] Referring particularly to FIG. IB, the housing 104 of the wearable computing device 100 further includes a dorsal wrist-side face 104L (alternatively referred to herein as “bottom side 104L’") configured to be closest to a user when worn. For instance, the bottom side 104L of the housing 104 may sit against a dorsal wrist of a user when being worn by the user. A plurality of sensor electrodes 112 may be positioned on the bottom side 104L of the housing 104 so as to maintain skin contact with the user w hen being worn by the user. Thus, in such embodiments, each of the sensor electrodes 112 may be configurable to measure, at least, electrical impedance of the user at a location of the skin contact on the dorsal wrist.Accordingly, in one or more embodiments, one or more (or all) of the plurality of sensor electrodes 112 may be impedance sensor electrodes.

[0026] Further, the sensor electrodes 112 described herein may be constructed of any suitable material. For example, in an embodiment, the sensor electrodes 112 described herein may be constructed of stainless steel, graphene, or any other material having a suitable conductivity and / or corrosion resistance and may have an optional PVD coating, that may be 1 -micrometerthick titanium nitride. In such embodiments, the PVD coating may provide a desired color to the sensor electrodes 112, thereby preventing oxidation beyond what the stainless steel already provides, and also increases durability. In additional embodiments, PVD and surface finish can be used to increase / decrease moisture retention, which affects the impedance signal and user comfort. In particular embodiments, the sensor electrodes 112 may be formed of an alloy of tin and nickel (TiN) with a shiny or mirror surface finish. Moreover, in an embodiment, the sensor electrodes 112 may be constructed of a hydrophobic material or a transparent material.

[0027] In some embodiments, the wearable computing device 100 may also include at least one additional biometric sensor electrode in addition to the impedance sensor electrodes 112. In such embodiments, the additional biometric sensor electrode may include one or more temperature sensors (such as an ambient temperature sensor or a skin temperature sensor), a humidity sensor, a pressure sensor, a microphone, an optical sensor (e.g., a lights sensor such as a photoplethysmography (PPG) sensor), and / or the like. For instance, the wearable computing device 100 is illustrated as including multiple detectors 114 for detecting light. Moreover, the wearable computing device 100 is illustrated as including multiple light sources or emitters 116 (e.g., light-emitting diodes (LEDs)) for emitting light. The detectors 114 may be used alone and be used to detect ambient light (light not emitted from the wearable computing device 100) or may be used in combination with the emitters 116 such that the detectors 114 detect both ambient light and light from the emitters 116. Use of emitters 1 16 may particularly be useful in dark environments, where there is little ambient light.

[0028] For instance, the detectors 114 and emitters 116 may be capable of being used, in one example, for obtaining optical PPG measurements. Some PPG technologies rely on detecting light at a single spatial location, adding signals taken from two or more spatial locations, or an algorithmic combination thereof. Both of these approaches result in a single spatial measurement from which the heart rate (HR) estimate (or other physiological metrics) can be determined. In some embodiments, a PPG device employs a single light source (e.g., emitter 116) coupled to a single detector (i.e.. a single light path to a single detector 114). Alternatively, a PPG device may employ multiple light sources (e.g., multiple emitters 1 16) coupled to a single detector (i.e., a single light to a single detector 114) or multiple detectors (i.e., two or more light paths to different detectors 114). In other embodiments, a PPG device employs multiple detectors coupled to a single light source or multiple light sources (i.e., two or more light paths). In some cases, the light source(s) may be configured to emit one ormore of green, red, infrared (IR) light, as well as any other suitable wavelengths in the spectrum (such as long IR for metabolic monitoring). For example, a PPG device may employ a single light source and two or more light detectors each configured to detect a specific wavelength or wavelength range. In some cases, each detector is configured to detect a different wavelength or wavelength range from one another. 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 configured to detect a specific wavelength or wavelength range different from one or more other detectors). In embodiments employing multiple light paths, the PPG device may evaluate the signals resulting from the multiple light paths to determine a HR estimate or other physiological metrics.

[0029] Moreover, as will be described in greater detail below, the data generated by the detectors 1 14 may additionally, or alternatively, be used as proximity data indicative of a distance of different locations of the bottom side 104L of the wearable computing device 100 from an object, such as the user’s forearm 102 (FIG. 1A). As will be described below in greater detail, the proximity data may. in turn, be used to determine user inputs, such as taps, swipes, and / or the like on the wearable computing device 100, indicative of requests to perform actions with the wearable computing device 100. However, it should be appreciated that any other suitable sensors configured to generate data indicative of proximity of the bottom side 104L of the wearable computing device 100 may be used instead of, or in addition to. the detectors 114 and emitters 116 described herein. In some embodiments, some of the detectors 114 are arranged in pairs, each pair being positioned substantially opposite each other about a center point (e.g., about the center emitter 116, about a center of the housing 104, and / or the like). For instance, in one embodiment, the detectors 114 include at least one pair of detectors 114 positioned substantially opposite from each other about a center of the bottom side 104L of the housing 104 of the wearable computing device 100. In some instances, the detectors 114 are evenly spaced apart about a center of the bottom side 104L of the housing 104. In one instance, such as shown in FIG. IB, four detectors 114 are provided on the bottom side 104L. However, it should be noted that any other suitable number of detectors 114 may instead be used that enables the method described herein, such as two, three, five, six, or more detectors 114.

[0030] The detectors 114 and emitters 116 may be arranged within the housing 104 and at least partially exposed through the bottom side 104L of the housing 104. The sensor electrodes 112 may be positioned around the detectors 114 and emitters 116 on the bottom side 104L of the housing 104. In alternative embodiments, the detectors 114 and emitters116 may be positioned around the sensor electrodes 112 and / or in another other suitable configuration such as adjacent to, interspersed with, surrounded by. or below the sensor electrodes 112. For example, in embodiments where the sensor electrodes 112 are transparent, the sensor electrodes 112 may be arranged atop the detectors 114 and emitters 116.

[0031] As particularly shown in FIG. 1C, the perimeter 104P of the housing 104 of the wearable computing device 100 generally extends between the upper side 104U and the bottom side 104L of the housing 104. In some instances, the wristband straps 130, 132 may be attached to the housing 104 on the perimeter 104P.

[0032] Referring now to FIG. ID, components of an example system 150 that can be utilized with the wearable computing device 100 in accordance with various embodiments are illustrated. In particular, as shown, the system 150 may also include at least one controller 152. In an embodiment, the controller(s) 152 may be a central processing unit (CPU) or graphics processing unit (GPU) for executing instructions that can be stored in a memory device 154, such as flash memory or DRAM, among other such options. For example, in an embodiment, the memory device 154 may include RAM. ROM, FLASH memory, or other non-transitory digital data storage, and may include a control program comprising sequences of instructions which, when loaded from the memory device 154 and executed using the controller(s) 152, cause the controller(s) 152 to perform the functions that are described herein. As would be apparent to one of ordinary skill in the art. the system 150 can include many types of memory, data storage, or computer-readable media, such as data storage for program instructions for execution by the controller or any suitable processor. The same or separate storage can be used for images or data, a removable memory can be available for sharing information with other devices, and any number of communication approaches can be available for sharing with other devices.

[0033] The system 150 also includes one or more power components 156, such as may include a battery operable to be recharged through conventional plug-in approaches, or through other approaches such as capacitive charging through proximity with a power mat or other such device.

[0034] In addition, as shown, the system 150 includes any suitable user interface elements in communication with the controller 152, such as the display 106 of the wearable computing device 100. The display 106 may be any suitable display type, such as a touch screen, organic light emitting diode (OLED), liquid crystal display (LCD), and / or the like. In further embodiments, the system 150 can also include at least one additional I / O device 158configured to allow the controller 152 to receive conventional inputs from a user. These conventional inputs can include, for example, a push button (e.g.. button 118 in FIG. 1C), touch pad, touch screen, wheel, joystick, keyboard, mouse, keypad, and / or any other such device or element whereby a user can input a command to the system 150. In another embodiment, the I / O device(s) 158 may be connected by a wireless infrared or Bluetooth or other link as well in some embodiments. In some embodiments, the I / O device(s) 158 may additionally, or alternatively, include a microphone or other audio capture element that accepts voice or other audio commands. For example, in particular embodiments, the system 150 may not include any buttons, but might be controlled only through a combination of visual and audio commands, such that a user can control the wearable computing device 100 without having to be in contact therewith. In certain embodiments, the I / O elements 158 may also include one or more of the sensor electrodes 112 described herein, optical sensors, barometric sensors (e.g., altimeter, etc.), and the like.

[0035] The system 150 may also include one or more wireless components 160 operable to allow the controller 152 to communicate with one or more electronic devices within a communication range of the particular wireless channel. The wireless channel can be any appropriate channel used to enable devices to communicate wirelessly, such as Bluetooth, cellular, NFC, Ultra-Wideband (UWB), or Wi-Fi channels. It should be understood that the system 150 can have one or more conventional wired communications connections as known in the art.

[0036] Still referring to FIG. ID, the system 150 may also include an optics package 1 13, where the optics package 113 at least includes a plurality of the detectors 114 and, optionally includes one or more emitters 116. In one embodiment, the detectors 114 and optional emitters 116 may be coupled to the controller 152 directly or indirectly using driver circuitry 162 by which the controller 152 may drive the emitters 116 and obtain signals from the detectors 114.

[0037] Moreover, the system 150 may include one or more internal motion sensors 164 (e.g., accelerometers, gyroscopes, and / or the like) inside the housing 104 and configured to generate motion data indicative of movement of the wearable computing device 100. with the motion sensors 164 being in communication with the controller 152.

[0038] A host computer 168 can communicate with the wireless networking components 160 via one or more netw orks 166, w hich may include one or more local area networks, wide area networks, UWB. and / or internetworks using any of terrestrial or satellite links. In some embodiments, the host computer 168 executes control programs and / or application programsthat are configured to perform some of the functions described herein.

[0039] In some embodiments, the system 150 may include at least one imaging element, such as one or more cameras that are able to capture images of the surrounding environment and that are able to image a user, people, or objects in the vicinity of the device. The imaging element can include any appropriate technology , such as a CCD image capture element having a sufficient resolution, focal range, and viewable area to capture an image of the user when the user is operating the device. Further image capture elements may also include depth sensors. Methods for capturing images using a camera element with a computing device are well known in the art and will not be discussed herein in detail. It should be understood that image capture can be performed using a single image, multiple images, periodic imaging, continuous image capturing, image streaming, etc. Further, the system 150 can include the ability to start and / or stop image capture, such as when receiving a command from a user, application, or other device.

[0040] Referring now to FIG. IE, a schematic diagram of an environment 170 in which aspects of various embodiments can be implemented is illustrated. In particular, as shown, a user might have a number of different devices that are able to communicate using at least one wireless communication protocol. For example, as shown, the user might have a wearable computing device, such as a smartwatch or fitness tracker (e.g., the wearable computing device 100), which the user would like to be able to communicate with other devices, such as a smartphone 172, a tablet computer 174, and / or the like. Applications may allow communication between multiple devices and a wearable computing device to enable a user to obtain information from the w earable computing device. For example, data captured using a sensor of the wearable computing device 100 may be communicated to the smartphone 172 and / or the tablet computer 174 using an application installed on the smartphone 172 and / or the tablet computer 174. The user may also w ant the wearable computing device 100 to be able to communicate with a sendee provider, such as w ith the host computer 168 of a service provider, or other such entity, that is able to obtain and process data from the w earable computing device 100 and provide functionality that may not otherwise be available on the wearable computing device 100 or applications installed on the other devices. In addition, as shown, the wearable computing device 100 may be able to communicate with the service provider (e.g., the host computer 168 of the service provider) through at least one network (e.g., the network 166), such as the Internet or a cellular network, or may communicate over a wireless connection such as Bluetooth® to the other device(s) (e.g.. the smartphone 172 and / or the tablet computer 174), where the other device(s) then communicate with the serviceprovider over the at least one network. There may be a number of other types of, or reasons for. communications in various embodiments.

[0041] In addition to being able to communicate, a user may also want the devices to be able to communicate in a number of ways or with certain aspects. For example, the user may want communications between the devices to be secure, particularly where the data may include personal health data or other such communications. The device or application providers may also be required to secure this information in at least some situations. The user may want the devices to be able to communicate with each other concurrently, rather than sequentially. This may be particularly true where pairing may be required, as the user may prefer that each device be paired at most once, such that no manual pairing is required. The user may also desire the communications to be as standards-based as possible, not only so that little manual intervention is required on the part of the user but also so that the devices can communicate with as many other types of devices as possible, which is often not the case for various proprietary formats. A user may thus desire to be able to walk in a room with one device and have such device automatically communicate with another target device with little to no effort on the part of the user. In various conventional approaches, a device will utilize a communication technology such as Wi-Fi to communicate with other devices using wireless local area networking (WLAN). Smaller or lower capacity devices, such as many Internet of Things (loT) devices, instead utilize a communication technology such as Bluetooth®, and in particular Bluetooth Low Energy (BLE) which has very low power consumption.

[0042] In further embodiments, the environment 170 illustrated in FIG. IE enables data to be captured, processed, and displayed in a number of different ways. For example, data may be captured using sensors on the wearable computing device 100, but due to limited resources on the wearable computing device 100, the data may be transferred to the smartphone 172. the tablet computer 174, and / or the service provider 168 (or a cloud resource) for processing, and results of that processing may then be presented back to that user on the wearable computing device 100, smartphone 172, the tablet computer 174 and / or another such device associated with that user. In at least some embodiments, a user may also be able to provide input such as health data using an interface on any of these devices, which can then be considered when making that determination.

[0043] As indicated above, conventional wearable computing devices have a limited area to add physical interface buttons. Some wearable computing devices may include a touch screen for more interface interactions, however, such touch screens often rely on capacitive sensing to detect touches and the screen size may limit the number of different inputs (e.g..taps, swipes, etc.). These limitations make it difficult for a user to interact with wearable computing devices, especially while performing exercise.

[0044] Thus, as will be described below in greater detail, existing sensors of the wearable computing device 100 may be used to detect user inputs on the wearable computing device 100. For instance, referring now to FIG. IF, an example set of user inputs to the wearable computing device 100 shown in FIGS. 1A-1C are illustrated, such as taps on the wearable computing device, detected according to one embodiment of the present disclosure. For example, the wearable computing device 100 may display biometric readings, such as heartbeats per minute (bpm) 180A (e.g., 110 bpm), steps 180B (e.g., 4,678 steps), and / or any other suitable biometric readings on the display screen 106, such as cardio fitness score, active zone minutes, resting heart rate, heart rhythm, heart rate variability, breathing rate, skin temperature, blood oxygen level, sleep tracking (sleep score, sleep stages, etc.), stress level readings, and / or the like. However, it should be appreciated that the display screen 106 may be used to display any other suitable information or combinations of information such as location, health tracking, paired phone notifications, digital assistant, time, battery life, and / or the like.

[0045] Moreover, the display screen 106 may additionally, or alternatively, display different actions that may be taken with the wearable computing device 100 when interacted with by a user. For instance, in the illustrated embodiment, four different actions are spaced apart at different locations on the display screen, where the different actions are associated with interacting with the display screen 106 of the wearable computing device in different ways. For example, a first action 182A corresponds to playing a previous audio track (e.g., previous song, previous podcast, previous workout, and / or the like), a second action 182B corresponds to skipping to a next audio track (e.g., next song, next podcast, next workout, and / or the like), a third action 182C corresponds to pausing audio (e.g., pausing a song, a podcast, a workout, and / or the like), and a fourth action 182D corresponds to activating a digital assistant (and any other suitable equipment, such as a microphone, a camera, and / or the like). However, it should be appreciated that any other suitable number of actions and / or any other suitable actions or combinations of actions may be displayed on the display screen 106. Moreover, the actions may be positioned at any other suitable location on the wearable computing device and / or in any other suitable positioning relative to each other. For instance, the actions may be provided outside of the display screen 106, such as embossed, engraved, molded, printed, and / or the like around a perimeter of the display screen 106, displayed on a secondary display screen, and / or the like. Similarly, one of the actions may be positioned atthe center of the display screen 106.

[0046] The actions may be mapped with respective regions of the device 100 and stored within and accessible from the memory 154 (FIG. ID) of the controller 152 (FIG. ID) and / or stored at and accessible from any other suitable location, such as on the host computer 168 (FIG. ID). In some instances, the display screen 106 may be used to access different screen pages associated with different purposes (e.g.. main screen page displaying time, second screen page for providing biometric data, a third screen page for displaying exercise plans, a fourth screen page for providing music application controls, and / or the like), where one or more of the screen pages may have a different action or combination of actions from other screen pages, where the mapping for the different screen pages may similarly be stored within and accessible from any suitable location. In one or more instances, the actions for one or more of the display screens may be mapped based on user preferences or selection, such as in settings associated with the wearable computing device 100.

[0047] As will be described below' in greater detail, the responses from a plurality7of the detectors 114 may be monitored to detect taps on the wearable computing device 100 and determine the location of such taps, where the respective locations of the taps may be correlated to requests for different actions. Generally, each action 182A, 182B, 182C, 182D must be sufficiently spaced apart from the other actions such that taps at the different actions may be differentiated from each other. For instance, a first tap input T1 on the wearable computing device 100 at a location closest to the upper left detector 114 in FIG. IF (e.g.. on the perimeter 104P of the wearable computing device 100 closest to the upper left detector 114 in FIG. IF than the other detectors 114 in FIG. IF) may correspond to an action in the upper left portion of the wearable computing device 100 (e.g., the first action 182A). Similarly, a second tap input T2 on the wearable computing device 100 at a location closest to the upper right detector 114 in FIG. IF (e.g., on the perimeter 104P of the wearable computing device 100 closest to the upper right detector 114 in FIG. IF than the other detectors 114 in FIG. IF) may correspond to an action in the upper right portion of the wearable computing device 100 (e.g., the second action 182B). Moreover, a third tap input T3 on the wearable computing device 100 at a location closest to the lower right detector 114 in FIG. IF (e.g., on the perimeter 104P of the wearable computing device 100 closest to the lower right detector 114 in FIG. IF than the other detectors 114 in FIG. IF) may correspond to an action in the lower right portion of the wearable computing device 100 (e.g., the third action 182C). Additionally, a fourth tap input T4 on the wearable computing device 100 at a location closest to the lower left detector 114 in FIG. IF (e.g., on the perimeter 104P of thewearable computing device 100 closest to the lower left detector 114 in FIG. IF than the other detectors 114 in FIG. IF) may correspond to an action in the lower left portion of the wearable computing device 100 (e.g., the fourth action 182D).

[0048] It should be appreciated that, while the tap inputs Tl, T2, T3, T4 are shown and described as being on the perimeter 104P of the housing 104 of the wearable computing device 100, to the sides of where the bands 130. 132 connect to the housing 104, tap inputs at other locations on the wearable computing device 100, such as on the upper surface 104U. may be similarly identifiable and distinguishable. As will be described below, the presence and / or location of taps on the perimeter 104P may be more easily identifiable than the presence and / or location of taps at other areas on the wearable computing device 100, such as on the upper surface 104U, as taps on the perimeter 104P may generally cause a greater change in light detected by the proximity sensors (e.g., the detectors 114) than taps on the upper surface 104U. For example, when the wearable computing device 100 is configured as a smartwatch, depending on how tight the user wears the straps 130, 132, there may be greater movement of the housing 104 during taps on the perimeter 104P than taps on the upper surface 104U. that may create a larger, more identifiable response with the proximity sensors. Moreover, in addition to taps, swipes on the device may be detected in a substantially similar manner described herein.

[0049] Referring now to FIG. 2, an example graph 200 of sensor data indicative of the user inputs to the wearable computing device from FIG. IF according to one embodiment of the present disclosure is illustrated. Particularly, the data from a plurality of the detectors 1 14 indicates an amount of detected light monitored over a period of time, where the monitored data is used to determine when and where user inputs, such as taps, occur on the wearable computing device 100. For instance, a first plot SI in the graph 200 corresponds to data from a first one of the detectors 114 in FIG. IF, a second plot S2 in the graph 200 corresponds to data from a second one of the detectors 114 in FIG. IF, a third plot S3 in the graph 200 corresponds to data from a third one of the detectors 114 in FIG. IF, and a fourth plot S4 in the graph 200 corresponds to data from a fourth one of the detectors 114 in FIG. IF.

[0050] In general, when a tap input occurs, such as any of the tap inputs Tl, T2, T3. T4 in FIG. IF, the plot SI, S2, S3, S4 for each of the detectors 114 may experience a wave-like response. Depending on the location of the tap input relative to a particular detector 114, the magnitude and pattern of the wave-like response may be different for each tap input. For instance, tap inputs on the perimeter 104P of the wearable computing device 100, such as the tap inputs Tl, T2, T3, T4, may cause the region of the housing 104 of the wearablecomputing device 100 where the tap input occurs to initially lift then lower, whereas an opposite region (e.g., comer) of the housing may be caused to initially lower then lift. As such, the light detected at the detector closest to the location of the tap to increase then decrease, whereas the light detected at the detector at the location opposite the tap may decrease then increase. However, depending on how a user wears the wearable computing device 100, such as how tightly and where a user places the straps 130, 132 (e.g.. closer or further from a styloid process), the magnitudes and patterns of the wave-like responses in response to taps at the different locations may vary for each user. Moreover, it may be difficult to determine from a response of a single proximity sensor whether a tap or another movement of the wearable computing device 100 has occurred.

[0051] As such, in some instances, expected responses may be tailored to individual users. For instance, expected responses may be recorded when a user performs a set-up procedure for the wearable computing device 100 during a first use and / or during a calibration procedure sometime after the first use. As an example, a user may be guided through tap inputs at different locations on the wearable computing device 100. where the data from a plurality of the proximity sensors during known taps at each location may be recorded and stored as an expected response for the respective location. As different users may wear the wearable computing device 100 differently, recording and storing expected responses for an individual wearable computing device 100 may provide increased accuracy over standardized expected responses. However, it should be appreciated that standardized expected responses for different known tap locations may instead, or additionally, be used for multiple wearable computing devices 100. Moreover, monitoring combinations of the response patterns of different detectors 114 may increase confidence when detecting a tap input over another movement of the wearable computing device 100.

[0052] The plots SI, S2, S3, S4 include example responses recorded by the proximity sensors (e.g., detectors 114) during the different, known tap inputs Tl, T2, T3, T4, where the wavelike responses for the different tap inputs may be used as expected responses when monitoring for tap inputs during subsequent monitoring.

[0053] For example, when a user provides the first tap input Tl during a first tap instance it between a first time tl and a second time t2, each of the plots S 1, S2, S3, S4 has a corresponding wave-like response. For instance, during the first tap instance il, the wavelike response of the plot SI includes an initial, slight decrease followed by a subsequent, larger increase, then a small decrease in magnitude of detected light. The wave-like response of the plot S2 similarly has an initial, slight decrease when the plot SI has the similar initialslight decrease, followed by a subsequent, larger increase when the plot SI has the similar larger increase, then a small decrease when the plot SI has the similar small decrease in magnitude of detected light during the first tap instance il. The wave-like response of plot S4 experiences no initial decrease where the plots SI, S2 experience their initial decreases, but has a substantial increase when the substantial increases occur in the plots SI, S2, followed by a subsequent, smaller decrease when the smaller decreases occur with the plots51. S2. However, the wave-hke response of plot S3 has a different pattern during the first tap instance il than the plots SI, S2, S4. For instance, the plot S3 has a substantially opposite response from the plots SI, S2, S4 during the first tap instance il. Particularly, during the first tap instance il, the plot S3 has an initial slight increase when the plots SI, S2 experience the initial slight decrease, followed by a subsequent, larger decrease when the plots SI, S2, S4 experience the larger increase, before experiencing a slight increase where the plots SI,52, S4 experience the slight decrease in magnitude of detected light. As such, when later responses from the proximity' sensors (e g., detectors 114) substantially match the expected responses at the first instance il. it can be determined that the first tap input T1 has occurred.

[0054] When a user provides the second tap input T2 during a second tap instance i2, between a third time t3 and a subsequent fourth time t4, each of the plots S 1, S2, S3, S4 again has a corresponding wave-like response. For instance, the wave-like response of the plot SI includes an initial, slight decrease followed by a subsequent increase, then a decrease in magnitude of detected light. The wave-like response of the plot S2 during the second tap instance i2 similarly has an initial, slight decrease when the plot SI has the similar initial slight decrease, followed by a subsequent, substantial increase yvhen the plot SI has the smaller increase, then a small decrease when the plot SI has the subsequent decrease in magnitude of detected light. The wave-like response of plot S4 experiences a slight decrease where the plots S 1, S2 experience their initial decreases, but has a large increase when the increases occur in the plots SI, S2 that is smaller than the substantial increase in the second plot S2, followed by a subsequent decrease when the smaller decreases occur in the plots SI, S2. During the second tap instance i2, the plot S3 has almost no change relative to the plots S I. S2. S4. For instance, the plot S3 an initial slight increase when the plots SI, S2, S4 experience the initial slight decrease, followed by a subsequent, slight decrease when the plots SI, S2, S4 experience the larger increase, before experiencing a slight increase where the plots SI, S2, S4 experience the decrease in magnitude of detected light. As such, when later responses from the proximity sensors (e.g.. detectors 114) substantially match theexpected responses at the second instance i2, it can be determined that the second tap input T2 has occurred.

[0055] When a user provides the third tap input T3 during a third tap instance i3, between a fifth time t5 and a subsequent sixth time t6, each of the plots SI, S2, S3, S4 again has a corresponding wave-like response. For instance, the wave-like response of the plot SI includes an initial, slight decrease followed by a subsequent, substantial increase, then a substantial decrease in magnitude of detected light. The wave-like response of the plot S2 during the third tap instance i3 has an initial, slight decrease when the plot SI has the similar initial slight decrease, followed by a subsequent, slight increase when the plot SI has the substantial increase, then a substantial decrease when the plot SI has the similar substantial decrease in magnitude of detected light. The wave-like response of plot S4 experiences an initial decrease where the plots SI, S2 experience their initial decreases, but has a further decrease when the increases occur in the plots SI, S2, followed by a subsequent, further decrease when the smaller decreases occur in the plots SI, S2. During the third tap instance i3, the plot S3 has an initial slight decrease when the plots SI, S2, S4 experience the initial slight decrease, followed by a subsequent, increase when the plots SI, S2 experience the increases, before experiencing a slight decrease where the plots SI, S2, S4 experience the larger decreases in magnitude of detected light. As such, when later responses from the proximity sensors (e.g., detectors 114) substantially match the expected responses at the third instance i3. it can be determined that the third tap input T3 has occurred.

[0056] When a user provides the fourth tap input T4 during a fourth tap instance i4, between a seventh time t7 and a subsequent eighth time t8, each of the plots SI, S2, S3, S4 again has a corresponding wave-like response. For instance, the wave-like response of the plot SI includes an initial, slight decrease followed by a subsequent, substantial increase, then a substantial decrease in magnitude of detected light. The wave-like response of the plot S2 during the fourth tap instance i4 has an initial, slight increase when the plot S 1 has the initial slight decrease, followed by a subsequent increase when the plot SI has the substantial increase, then a substantial decrease when the plot SI has the similar larger decrease in magnitude of detected light. The wave-like response of plot S4 experiences an initial decrease where the plots S2 experiences the initial decrease, then an increase when the increases occur in the plots SI, S2, followed by a subsequent decrease when the larger decreases occur in the plots SI, S2. During the fourth tap instance i4, the plot S3 has an initial slight decrease when the plots S2. S4 experience the initial slight decrease, followed by a subsequent, slight increase when the plots SI, S2, S4 experience the increases, beforeexperiencing a slight decrease where the plots SI, S2, S4 experience the larger decreases in magnitude of detected light. As such, when later responses from the proximity sensors (e.g.. detectors 1 14) substantially match the expected responses at the fourth instance i4, it can be determined that the fourth tap input T4 has occurred.

[0057] It should be appreciated that, “substantially match” with regards to the different plots SI, S2, S3, S4 at the example instances il, i2, i3, i4 is intended to mean that at least two of the responses for a given instance are identifiable as being one type of tap input compared to other tap inputs. For example, the example responses provided by the second and fourth plots S2, S4 each have different magnitudes and / or patterns at each of the instances il, i2, i3, i4, whereas the first and third plots SI, S3 each have fairly similar magnitudes and patterns at both the third and fourth instances i3, i4. As such, the proximity data from the sensors corresponding to the first and third plots SI, S3 can be evaluated with the data from the sensors corresponding to at least one of the second and fourth plots S2, S4 to confirm a third tap input T3 over a fourth tap input T4. For instance, if the data from the sensor corresponding to first plot SI and / or the data from the sensor corresponding to the third plots S3 is close to both the expected response from the third instance i3 or the fourth instance 14 (e g., may correspond to either the third or fourth tap input T3, T4), then the data from the sensor corresponding to the second plot S2 and / or the data from the sensor corresponding to the fourth plot S4 may also be compared to the expected responses for the sensors to confirm whether the expected responses aligns with the third instance i3 or the fourth instance i4 (and thus, whether the tap input corresponds to the third or fourth tap input T3, T4).

[0058] In some instances, at least three of the responses for a given instance must correspond to the respective expected responses to confirm a tap location. In one or more instances, all of the responses for a given instance must correspond to the respective expected responses to confirm a tap location. If a given number of the responses at a given instance do not match the expected responses (e.g., two or more of the responses at the instance do not match any of the expected responses), then the tap instance may be ignored and / or a request for the user to confirm the tap may be issued (e.g., “Please tap again” may be displayed on the screen 106).

[0059] Referring now to FIG. 3. a flow diagram of one embodiment of a method 300 for detecting user inputs to the wearable computing device, such as taps on the wearable computing device, is provided. In an embodiment, for example, the wearable computing device may be any suitable wearable computing device, such as the wearable computing device 100 described herein with reference to FIGS. 1A-1F. Thus, in general, the method 300 is described herein with reference to the wearable computing device 100 of FIGS. 1A-1Fand the proximity data described in FIG. 2. However, it should be appreciated that the disclosed method 300 may be implemented with any other suitable wearable computing device having any other suitable configurations and with any other suitable proximity data. In addition, although FIG. 3 depict steps performed in a particular order for purposes of illustration and discussion, the methods discussed herein are not limited to any particular order or arrangement. One skilled in the art, using the disclosures provided herein, will appreciate that various steps of the methods disclosed herein can be omitted, rearranged, combined, added, and / or adapted in various ways without deviating from the scope of the present disclosure.

[0060] Starting at step (302) of the method 300, proximity data generated by at least two proximity sensors is received. For instance, proximity data generated by the detectors 114 is received by one or more processors (e.g., one or more processors of the controller 152, the host computer 168, and / or the like). Thereafter, at step (304), the proximity data is analyzed to determine whether the proximity' data is indicative of a tap. For instance, the proximity data from a plurality of the proximity sensors may be compared to expected responses associated with taps, such as the responses of plots SI, S2, S3, S4 at the instances il. 12, i3, i4 from FIG. 2, to determine whether the proximity data matches one of the expected responses associated with different taps. When the proximity' data matches an expected response (e.g., matches the response at one of the instances il, i2, i3, i4). it may be determined that the proximity data is indicative of a tap and the method 300 may proceed to step (306). However, if the proximity data does not match an expected response (e.g., does not substantially match the response at one of the instances il, i2, i3, i4), it may be determined that the proximity' data is not indicative of a tap and the method 300 may return to step (302).

[0061] At step (306), a location of the tap identified at step (304) may be determined. For instance, each of the expected responses may be associated with a respective tap location on the w earable computing device 100. For example, as described above, the response at the first tap instance il in FIG. 2 w as associated with the first tap input T1 at the upper left region of the wearable computing device 100 in FIG. IF. the response at the second tap instance i2 in FIG. 2 w as associated with the second tap input T2 at the upper right region of the wearable computing device 100 in FIG. IF, the response at the third tap instance i3 in FIG. 2 w as associated with the third tap input T3 at the low er right region of the wearable computing device 100 in FIG. IF, and the response at the fourth tap instance i4 in FIG. 2 was associated with the fourth tap input T4 at the lower left region of the wearable computing device 100 in FIG. IF.

[0062] After the location of the tap is identified at step (306), the method 300 proceeds to step (308) where the action associated with the identified location of the tap is determined. For instance, as described above the tap input T1 at the upper left region of the w earable computing device 100 in FIG. IF is associated with the first action 182A, the tap input T2 at the upper right region of the wearable computing device 100 in FIG. IF is associated with the second action 182B, the tap input T4 at the lower right region of the wearable computing device 100 in FIG. IF is associated with the third action 182C, and the tap input T4 at the lower left region of the wearable computing device 100 in FIG. IF is associated with the fourth action 182D. As indicated above, the actions may be mapped with respective regions of the device 100 and stored within and accessible from the memory 154 of the controller 152 and / or stored at and accessible from any other suitable location, such as on the host computer 168. In some instances, the display screen 106 may be used to access different screen pages associated with different purposes, where one or more of the screen pages may have a different action or combination of actions from other screen pages, where the mapping for the different screen pages may similarly be stored within and accessible from any suitable location. In such instance, the screen page which the display screen is currently displaying must be identified by the one or more processors before determining the action associated with the location of the tap identified in step (306).

[0063] Once the action associated with the location of the tap is determined in step (308), the method 300 may proceed to perform the requested action associated with the location of the tap at step (310). For instance, the wearable computing device may perform the first action 182A (e.g., play a previous audio track) associated with the first tap input Tl, the second action 182B (e g., skip to a next audio track) associated with the second tap input T2, the third action 182C (e.g., pause audio) associated with the third tap input T3. or the fourth action 182D (e.g., activate a digital assistant) associated with the fourth tap input T4.

[0064] In some instances, it may first be determined if the proximity data is indicative of the wearable computing device 100 being worn by a user (e.g., is on-wrist) before proceeding to step (302) and / or step (304). For instance, if a proximity of the wearable computing device 100 is below (closer than) a proximity threshold based on proximity data from one or more of the proximity sensors (e.g., from the detector(s) 114), impedance data from the sensor electrode(s) 112, and / or the like, the wearable computing device 100 is determined to be w orn. How ever, if the proximity of the wearable computing device 100 is determined to be above (further than) a proximity threshold, the wearable computing device 100 is determined as not being worn, and the proximity continues to be monitored until the proximity is belowthe proximity threshold.

[0065] In some embodiments, the method 300 may confirm that the wearable computing device 100 is in a state where a user is likely trying to provide an input to the wearable computing device 100 before proceeding to step (302) and / or step (304). For instance, the method 300 may further include receiving motion data at an optional step (312). The motion data may be generated by the motion sensors 164 (e.g., accelerometers, gyroscopes, and / or the like) inside the housing 104 and indicative of movement of the wearable computing device 100. Thereafter, at step (314), it may be determined whether the motion data is indicative of a request to wake the device. For instance, the motion data may be compared to different motion patterns associated with different movements of the wearable computing device 100 to wake the display screen 106 of the wearable computing device 100, such as a motion pattern associated with a user tapping the wearable computing device 100, with a user shaking the wearable computing device 100, a user moving the wearable computing device 100 to look at the screen 106 of the wearable computing device 100, and / or any other suitable motion pattern. As with the expected responses, the motion patterns may include motion data recorded and stored based on guidance during a set-up process and / or a subsequent calibration procedure for the wearable computing device 100. However, in some instances, the motion patterns may additionally, or alternatively, be standardized patterns provided with the wearable computing device 100. If the motion data is indicative of (e.g., matches) a motion pattern to wake the device, the method 300 may proceed to waking the device at step (316) before proceeding with step (302), where proximity data is subsequently monitored for tap inputs.

[0066] In some embodiments, the motion data may additionally, or alternatively, be used to confirm whether an instance is indicative of a tap. For instance, during a tap instance, the motion data may be different than for other movements of the wearable computing device 100, such as motion data when a user is walking, running, looking at the device, and / or the like. For example, the motion data may have a larger change in magnitude over time during a tap than during walking, running, and / or the like. As such, if the motion data is over a particular magnitude threshold associated with a tap, in addition to the proximity data matching an expected response pattern, then the tap instance may be confirmed.

[0067] Referring now to FIG. 4, a flow diagram of one embodiment of a method 400 for detecting user inputs to the wearable computing device, such as taps on the wearable computing device, is provided. In an embodiment, for example, the wearable computing device may be any suitable wearable computing device, such as the wearable computingdevice 100 described herein with reference to FIGS. 1A-1F. Thus, in general, the method 400 is described herein with reference to the wearable computing device 100 of FIGS. 1A-1F, the graph 200 of proximity data described with reference to FIG. 2, and, optionally, the method 300 described in FIG. 3. However, it should be appreciated that the disclosed method 400 may be implemented with any other suitable wearable computing device having any other suitable configurations and / or with any other suitable proximity data. In addition, although FIG. 4 depicts steps performed in a particular order for purposes of illustration and discussion, the methods discussed herein are not limited to any particular order or arrangement. One skilled in the art, using the disclosures provided herein, will appreciate that various steps of the methods disclosed herein can be omitted, rearranged, combined, added, and / or adapted in various ways without deviating from the scope of the present disclosure.

[0068] At step (402), the method 400 may include receiving proximity data generated by proximity sensors of a wearable computing device and indicative of a distance of a bottom side of a housing of the wearable computing device from an object. For instance, as provided above, the proximity data generated by the proximity sensors (e.g.. the detectors 114) of the wearable computing device 100 may be indicative of a distance of the bottom side 104L of the housing 104 of the wearable computing device 100 from an object, such as a user. The proximity data may be received by one or more processors of the system 150 and / or environment 170.

[0069] Further, at step (404), the method 400 may include determining when a tap occurs on the wearable computing device based at least in part on the proximity' data. For example, as discussed above, the proximity data from the different proximity sensors may be compared to expected responses for taps at different locations on the wearable computing device 100. such as to the expected responses for the tap inputs Tl, T2, T3, T4 determined from the instances il, i2, i3, i4 across the plots SI, S2, S3, S4. If the proximity data from the proximity sensors substantially matches one of the expected responses, then a tap is determined to have occurred.

[0070] Additionally, at step (406). the method 400 may include determining a location of the tap on the wearable computing device based at least in part on the proximity data. For instance, as described above, each expected response may be associated with a tap at a particular location on the w earable computing device 100. As such, when the proximity data matches a particular expected response, the location on the wearable computing device 100 mapped to such expected response is determined as the location of the tap. Further, asdescribed above, the location of the tap may be mapped to a particular action, where the particular action may then be performed by the wearable computing device 100.

[0071] The technology discussed herein makes reference to servers, databases, software applications, and other computer-based systems, as well as actions taken and information sent to and from such systems. The inherent flexibility of computer-based systems allows for a great variety of possible configurations, combinations, and divisions of tasks and functionality between and among components. For instance, 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.

[0072] Although the above-described methods 300, 400 for detecting user inputs to a wearable computing device, such as taps on a wearable computing device, have been discussed in the context of using multiple proximity sensors, where the proximity sensors have been described in examples as PPG sensors, it should be understood that the methods are not intended to be limited to detecting user inputs to a wearable computing device, such as taps on a wearable computing device based on PPG data. It should be appreciated that the methods 300, 400 can be used with any suitable proximity sensor to detect user inputs to a wearable computing device, such as taps on a wearable computing device.

[0073] While the present subject matter has been described in detail with respect to various specific example embodiments thereof, each example is provided by way of explanation, not limitation of the disclosure. Those skilled in the art, upon attaining an understanding of the foregoing, can readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, the subject disclosure does not preclude inclusion of such modifications, variations and / or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art. For instance, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that the present disclosure covers such alterations, variations, and equivalents.

Claims

WHAT IS CLAIMED IS:

1. A computer-implemented method for determining user inputs on a wearable computing device, comprising: receiving, by one or more processors of the wearable computing device, proximity data generated by proximity sensors of the wearable computing device, the proximity data being indicative of a distance of a bottom side of a housing of the wearable computing device from an object: determining, by the one or more processors, when a tap occurs on the wearable computing device based at least in part on the proximity data; and determining, by the one or more processors, a location of the tap on the wearable computing device based at least in part on the proximity’ data.

2. The computer-implemented method of claim 1 , wherein determining when the tap occurs comprises determining when the tap occurs on a perimeter of the wearable computing device.

3. The computer-implemented method of claim 1, wherein determining when the tap occurs comprises: comparing the proximity data to respective expected responses of the proximity sensors for known taps at different locations on the wearable computing device; and determining that the tap has occurred when the proximity data substantially matches the expected responses for one of the known taps.

4. The computer-implemented method of claim 3, wherein determining the location of the tap comprises determining a location associated with the one of the known taps.

5. The computer-implemented method of claim 3, wherein determining when the tap occurs further comprises determining that the proximity data from each of the proximity sensors includes a w ave-like response during the tap.

6. The computer-implemented method of claim 1, further comprising: receiving, by the one or more processors, motion data generated by one or more internal motion sensors of the wearable computing device, wherein determining w hen the tap occurs on the w earable computing device is further based at least in part on the motion data.

7. The computer-implemented method of claim 6, wherein determining when the tap occurs on the wearable computing device based at least in part on the motion datacomprises determining that the motion data is indicative of a request to wake the wearable computing device.

8. The computer-implemented method of claim 7, wherein determining when the tap occurs on the wearable computing device comprises determining that the motion data is indicative of the request to wake the wearable computing device before determining when the tap occurs on the wearable computing device based at least in part on the proximity data.

9. The computer-implemented method of claim 7, wherein determining when the tap occurs on the wearable computing device comprises determining that a magnitude of the motion data is above a threshold indicative of taps.

10. The computer-implemented method of claim 1, further comprising: determining, by the one or more processors, a requested action associated with the location of the tap; and performing, by the one or more processors, the requested action.

11. The computer-implemented method of claim 10, wherein the requested action includes interacting with a display screen of the wearable computing device.

12. A wearable computing device, comprising: a housing having a bottom side configured to be closest to a user when worn; proximity sensors configured to generate proximity data indicative of a distance of the bottom side of the housing to an object; and one or more processors configured to: receive the proximity data generated by the proximity sensors; determine when a tap occurs on the wearable computing device based at least in part on the proximity' data; and determine a location of the tap on the wearable computing device based at least in part on the proximity data.

13. The wearable computing device of claim 12, wherein the one or more processors are configured to determine when the tap occurs by: comparing the proximity data to respective expected responses of the proximity sensors for known taps at different locations on the wearable computing device; and determining that the tap has occurred when the proximity data substantially matches the expected responses for one of the know n taps.

14. The w earable computing device of claim 13, w herein the one or more processors are configured to determine the location of the tap by determining a location associated with the one of the known taps.

15. The wearable computing device of claim 13, wherein the one or more processors are further configured to determine when the tap occurs by determining that the proximity data from each of the proximity sensors includes a wave-like response during the tap.

16. The wearable computing device of claim 12, further comprising internal motion sensors configured to generate motion data indicative of motion of the housing, wherein the one or more processors are further configured to receive the motion data, and wherein the one or more processors are configured to determine when the tap occurs on the wearable computing device further based at least in part on the motion data.

17. The wearable computing device of claim 16. wherein the one or more processors are configured to determine when the tap occurs on the wearable computing device based at least in part on the motion data by determining that the motion data is indicative of a request to wake the wearable computing device.

18. The wearable computing device of claim 17. wherein the one or more processors are configured to determine that the motion data is indicative of the request to wake the wearable computing device before determining when the tap occurs on the wearable computing device based at least in part on the proximity data.

19. The wearable computing device of claim 12. wherein the one or more processors are further configured to: determine a requested action associated with the location of the tap; and perform the requested action.

20. The wearable computing device of claim 12, wherein the proximity sensors are photodiodes configured to detect light, the proximity data indicating an amount of light detected, the amount of light detected being indicative of the distance.

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