Abstract: An electronic watch includes a housing defining a side surface of the electronic watch, a transparent cover coupled to the housing and defining a front surface of the electronic watch, an image-sensing element, and a crown extending from the side of the housing and defining an imaging surface. The crown may include a light-directing feature configured to direct, onto the image-sensing element, an image of an object in contact with the imaging surface.

Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, sensor data for a first window of time and additional sensor data for a second window of time overlapping the first window of time. The sensor data and the additional sensor data are provided as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture, predicted gesture start time, and predicted gesture end time based on the sensor data. A predicted gesture is determined based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.

Abstract: A watch can include a watch body and a band for securing the watch to the user. The watch body can detect an identification of the band or combination of band portions, which can serve as an input to initiate actions performed by the watch body. For example, a type, model, color, size, or other characteristic of a band can be determined and used to select a corresponding action performed by the watch body. Identification of the band can be performed by components of the watch body that also serve other purposes. The watch body can respond to the identification of a particular band by performing particular functions, such as changing an aspect of a user interface or altering settings of the watch body.

Abstract: An electronic device includes a display and a first input region that is separate from the display. In response to detecting a substantially stationary input on the first input region, the electronic device displays selectable options on the display. While displaying the selectable options on the display, the device detects a movement input on the first input region. In response to detecting the movement input on the first input region, the device selects different selectable options from the displayed selectable options based on the characteristic values of the movement input on the first input region.

Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.

Abstract: An electronic device with a first off-display input region detects a first portion of an input on the input region. In response to the first portion of the input: when the first portion of the input meets first criteria without a characteristic intensity of the first input increasing above a first intensity threshold, the electronic device provides a first output that indicates a current state of a first attribute of the electronic device without altering the first attribute. The electronic device detects a second portion of the input. In response to the second portion of the input: if the second portion of the first input meets second criteria that require the characteristic intensity of the input increases above the first intensity threshold, the electronic device performs a first operation that alters the first attribute of the electronic device; and otherwise, the electronic device forgoes performing the first operation.

Abstract: An electronic watch includes a housing defining a side surface of the electronic watch, a transparent cover coupled to the housing and defining a front surface of the electronic watch, an image-sensing element, and a crown extending from the side of the housing and defining an imaging surface. The crown may include a light-directing feature configured to direct, onto the image-sensing element, an image of an object in contact with the imaging surface.

Abstract: Contact or movement gestures between a first body part and a second body part can be detected. Sense circuitry can be configured to sense a signal at the sense electrode (e.g., configured to contact the second body part) in response to a drive signal applied to the drive electrode (e.g., configured to contact the first body part). Processing circuitry can be configured to detect contact in accordance with a determination that one or more criteria are met (e.g., an amplitude criterion and a non-distortion criterion). Additionally or alternatively, processing circuitry can be configured to detect a movement gesture in accordance with a determination that one or more criteria are met (e.g., a contact criterion and a movement criterion).

Abstract: An electronic watch includes a housing defining a side surface of the electronic watch, a transparent cover coupled to the housing and defining a front surface of the electronic watch, an image-sensing element, and a crown extending from the side of the housing and defining an imaging surface. The crown may include a light-directing feature configured to direct, onto the image-sensing element, an image of an object in contact with the imaging surface.

Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.

Abstract: Contact or movement gestures between a first body part and a second body part can be detected. Sense circuitry can be configured to sense a signal at the sense electrode (e.g., configured to contact the second body part) in response to a drive signal applied to the drive electrode (e.g., configured to contact the first body part). Processing circuitry can be configured to detect contact in accordance with a determination that one or more criteria are met (e.g., an amplitude criterion and a non-distortion criterion). Additionally or alternatively, processing circuitry can be configured to detect a movement gesture in accordance with a determination that one or more criteria are met (e.g., a contact criterion and a movement criterion).

Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.

Abstract: Contact or movement gestures between a first body part and a second body part can be detected. Sense circuitry can be configured to sense a signal at the sense electrode (e.g., configured to contact the second body part) in response to a drive signal applied to the drive electrode (e.g., configured to contact the first body part). Processing circuitry can be configured to detect contact in accordance with a determination that one or more criteria are met (e.g., an amplitude criterion and a non-distortion criterion). Additionally or alternatively, processing circuitry can be configured to detect a movement gesture in accordance with a determination that one or more criteria are met (e.g., a contact criterion and a movement criterion).

Abstract: An electronic watch includes a housing defining a side surface of the electronic watch, a transparent cover coupled to the housing and defining a front surface of the electronic watch, an image-sensing element, and a crown extending from the side of the housing and defining an imaging surface. The crown may include a light-directing feature configured to direct, onto the image-sensing element, an image of an object in contact with the imaging surface.

Abstract: An electronic device with a first off-display input region detects a first portion of an input on the input region. In response to the first portion of the input: when the first portion of the input meets first criteria without a characteristic intensity of the first input increasing above a first intensity threshold, the electronic device provides a first output that indicates a current state of a first attribute of the electronic device without altering the first attribute. The electronic device detects a second portion of the input. In response to the second portion of the input: if the second portion of the first input meets second criteria that require the characteristic intensity of the input increases above the first intensity threshold, the electronic device performs a first operation that alters the first attribute of the electronic device; and otherwise, the electronic device forgoes performing the first operation.

Abstract: In accordance with some embodiments, a method is performed at an electronic device with a display, an input region that is distinct from the display, and one or more tactile output generators for generating tactile outputs. The method includes: detecting a press input on the input region with a finger; and in response to detecting the press input on the input region: in accordance with a determination that the input region is not separated from the finger by an interstitial material, generating a first tactile output; and in accordance with a determination that the input region is separated from the finger by a first interstitial material, generating a second tactile output that is different from the first tactile output.

Abstract: Contact or movement gestures between a first body part and a second body part can be detected. Sense circuitry can be configured to sense a signal at the sense electrode (e.g., configured to contact the second body part) in response to a drive signal applied to the drive electrode (e.g., configured to contact the first body part). Processing circuitry can be configured to detect contact in accordance with a determination that one or more criteria are met (e.g., an amplitude criterion and a non-distortion criterion). Additionally or alternatively, processing circuitry can be configured to detect a movement gesture in accordance with a determination that one or more criteria are met (e.g., a contact criterion and a movement criterion).

Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.

Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.

Abstract: Contact or movement gestures between a first body part and a second body part can be detected. Sense circuitry can be configured to sense a signal at the sense electrode (e.g., configured to contact the second body part) in response to a drive signal applied to the drive electrode (e.g., configured to contact the first body part). Processing circuitry can be configured to detect contact in accordance with a determination that one or more criteria are met (e.g., an amplitude criterion and a non-distortion criterion). Additionally or alternatively, processing circuitry can be configured to detect a movement gesture in accordance with a determination that one or more criteria are met (e.g., a contact criterion and a movement criterion).