Abstract: Disclosed herein is a method and system a system that enables users to simply tap their smartphone or other electronic device to an object to discover and rapidly utilize contextual functionality. As described herein, the system and method provide for recognition of physical contact with uninstrumented objects, and summons object-specific interfaces.

Abstract: Embodiments disclosed herein describe a method of fabricating soft, flexible fibers using a 3D printer having an extrusion head. Embodiments of the method further include termination techniques to allow a series of fibers to be fabricated on the same object. Aspects of the certain embodiments offer a range of design parameters for controlling the properties of single strands and also of bundles of fibers. The method extends the capabilities of 3D printing without requiring any new hardware.

Abstract: Systems and processes for user identification using headphones associated with a first device are provided. For example, first movement information corresponding to movement of a second electronic device is detected. Second movement information corresponding to movement of a third electronic device is detected. A similarity score is determined based on the first movement information and the second movement information. In accordance with a determination that the similarity score is above a threshold similarity score, a user is identified as an authorized user of the first electronic device and the second electronic device. Based on the identification, an output is provided to the second electronic device.

Abstract: Embodiments are provided to recognize features and activities from an audio signal. In one embodiment, a model is generated from sound effect data, which is augmented and projected into an audio domain to form a training dataset efficiently. Sound effect data is data that has been artificially created or from enhanced sounds or sound processes to provide a more accurate baseline of sound data than traditional training data. The sound effect data is augmented to create multiple variants to broaden the sound effect data. The augmented sound effects are projected into various audio domains, such as indoor, outdoor, urban, based on mixing background sounds consistent with these audio domains. The model is installed on any computing device, such as a laptop, smartphone, or other device. Features and activities from an audio signal are then recognized by the computing device based on the model without the need for in-situ training.

Abstract: Embodiments are provided to recognize features and activities from an audio signal. In one embodiment, a model is generated from sound effect data, which is augmented and projected into an audio domain to form a training dataset efficiently. Sound effect data is data that has been artificially created or from enhanced sounds or sound processes to provide a more accurate baseline of sound data than traditional training data. The sound effect data is augmented to create multiple variants to broaden the sound effect data. The augmented sound effects are projected into various audio domains, such as indoor, outdoor, urban, based on mixing background sounds consistent with these audio domains. The model is installed on any computing device, such as a laptop, smartphone, or other device. Features and activities from an audio signal are then recognized by the computing device based on the model without the need for in-situ training.

Abstract: A method for a touch sensing system includes generating, by a first pair of electrodes at a first location in a conductive material, an electric field in the conductive material; generating measurement data by measuring, by one or more second pairs of electrodes, the electric field in the conductive material at one or more second locations in the conductive material, with each of the one or more second locations differing from the first location; generating, based on the measurement data, an approximation of the electric field in the conductive material; and classifying, based on the approximation, one or more regions of the interface into a given state.

Abstract: Individual health related events (e.g., handwashing events) can be detected based on multiple sensors including motion and audio sensors. Detecting a qualifying handwashing event can include detecting a qualifying scrubbing event based on motion data (e.g., accelerometer data) and a qualifying rinsing event based on audio data. In some examples, power consumption can be reduced by implementing one or more power saving mitigations.

Abstract: Internet of Things (“IoT”) appliances are gaining consumer traction, from smart thermostats to smart speakers. These devices generally have limited user interfaces, most often small buttons and touchscreens, or rely on voice control. Further, these devices know little about their surroundings—unaware of objects, people and activities around them. Consequently, interactions with these “smart” devices can be cumbersome and limited. The present invention presents an approach that enriches IoT experiences with rich touch and object sensing, offering a complementary input channel and increased contextual awareness. The present invention incorporates a range sensing technology into the computing devices, providing an expansive ad hoc plane of sensing just above the surface with which a device is associated. Additionally, the present invention can recognize and track a wide array of objects, including finger touches and hand gestures.

Abstract: The disclosure describes a sensor system that provides end users with intelligent sensing capabilities, and embodies both crowd sourcing and machine learning together. Further, a sporadic crowd assessment is used to ensure continued sensor accuracy when the system is relying on machine learning analysis. This sensor approach requires minimal and non-permanent sensor installation by utilizing any device with a camera as a sensor host, and provides human-centered and actionable sensor output.

Abstract: Systems and techniques for facilitating hand activity sensing are presented. In one example, a system obtains, from a wrist-worn computational device, hand activity data associated with a sustained series of hand motor actions in performance of a human task. The system also employs a machine learning technique to determine classification data indicative of a classification for the human task.

Abstract: Embodiments are provided to recognize features and activities from an audio signal. In one embodiment, a model is generated from sound effect data, which is augmented and projected into an audio domain to form a training dataset efficiently. Sound effect data is data that has been artificially created or from enhanced sounds or sound processes to provide a more accurate baseline of sound data than traditional training data. The sound effect data is augmented to create multiple variants to broaden the sound effect data. The augmented sound effects are projected into various audio domains, such as indoor, outdoor, urban, based on mixing background sounds consistent with these audio domains. The model is installed on any computing device, such as a laptop, smartphone, or other device. Features and activities from an audio signal are then recognized by the computing device based on the model without the need for in-situ training.

Abstract: A sensing system includes a sensor assembly and a back end server system. The sensor assembly includes a collection of sensors in communication with a control circuit. The sensors are each configured to sense one or more physical phenomena in an environment of the sensor assembly. The control circuit of the sensor assembly is configured to identify one or more selected sensors of the collection of sensors whose data corresponds to an event occurring in the environment of the sensor assembly and transmit data to the back end server system. The back end server system is configured to generate a first order virtual sensor by training a machine learning model to detect the event based on the data from at least one of the selected sensors and detect the event using the trained first order virtual sensor and data from the selected sensors.

Abstract: A sensing system includes a sensor assembly that is communicably connected to a computer system, such as a server or a cloud computing system. The sensor assembly includes a plurality of sensors that sense a variety of different physical phenomena. The sensor assembly featurizes the raw sensor data and transmits the featurized data to the computer system. Through machine learning, the computer system then trains a classifier to serve as a virtual sensor for an event that is correlated to the data from one or more sensor streams within the featurized sensor data. The virtual sensor can then subscribe to the relevant sensor feeds from the sensor assembly and monitor for subsequent occurrences of the event. Higher order virtual sensors can receive the outputs from lower order virtual sensors to infer nonbinary details about the environment in which the sensor assemblies are located.

Abstract: A method for a touch sensing system includes generating, by a first pair of electrodes at a first location in a conductive material, an electric field in the conductive material; generating measurement data by measuring, by one or more second pairs of electrodes, the electric field in the conductive material at one or more second locations in the conductive material, with each of the one or more second locations differing from the first location; generating, based on the measurement data, an approximation of the electric field in the conductive material; and classifying, based on the approximation, one or more regions of the interface into a given state.

Abstract: Disclosed herein is a method of interacting with a wearable electronic device. The wearable electronic device, comprising a vibration sensor, captures vibrations transmitted through a body part on which the electronic device is worn. The vibration can emanate from an object in contact with the user's body or by the motions of the body itself. Once received by the wearable electronic device, the vibrations are analyzed and identified as a specific object, data message, or movement.

Abstract: Disclosed herein is a method and system a system that enables users to simply tap their smartphone or other electronic device to an object to discover and rapidly utilize contextual functionality. As described herein, the system and method provide for recognition of physical contact with uninstrumented objects, and summons object-specific interfaces.

Abstract: A sensing system includes a sensor assembly that is communicably connected to a computer system, such as a server or a cloud computing system. The sensor assembly includes a plurality of sensors that sense a variety of different physical phenomena. The sensor assembly featurizes the raw sensor data and transmits the featurized data to the computer system. Through machine learning, the computer system then trains a classifier to serve as a virtual sensor for an event that is correlated to the data from one or more sensor streams within the featurized sensor data. The virtual sensor can then subscribe to the relevant sensor feeds from the sensor assembly and monitor for subsequent occurrences of the event. Higher order virtual sensors can receive the outputs from lower order virtual sensors to infer nonbinary details about the environment in which the sensor assemblies are located.

Abstract: Embodiments disclosed herein describe a method of fabricating soft, flexible fibers using a 3D printer having an extrusion head. Embodiments of the method further include termination techniques to allow a series of fibers to be fabricated on the same object. Aspects of the certain embodiments offer a range of design parameters for controlling the properties of single strands and also of bundles of fibers. The method extends the capabilities of 3D printing without requiring any new hardware.

Abstract: The disclosure describes a sensor system that provides end users with intelligent sensing capabilities, and embodies both crowd sourcing and machine learning together. Further, a sporadic crowd assessment is used to ensure continued sensor accuracy when the system is relying on machine learning analysis. This sensor approach requires minimal and non-permanent sensor installation by utilizing any device with a camera as a sensor host, and provides human-centered and actionable sensor output.

Abstract: An object recognition device that senses electrical signals conducted by the body of a human user (e.g., as a result of direct contact with or close proximity to a device emitting or conducting electromagnetic noise), compares the sensed electrical signals to a plurality of signatures of electrical signals produced by a corresponding plurality of types of electrical and electromechanical devices to determine the type of electrical or electromechanical device that generated the electrical signals sensed by the sensor, and communicates information to the human user related to or triggered by the electrical or electromechanical device.