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: The present disclosure generally relates to navigating user interfaces using hand gestures.

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 providing predicted RGB images includes at least one processor configured to obtain an infrared image of a subject, and to obtain a reference RGB image of the subject. The at least one processor is further configured to provide the infrared image and the reference RGB image to a machine learning model, the machine learning model having been trained to output predicted RGB images of subjects based on infrared images and reference RGB images of the subjects. The at least one processor is further configured to provide a predicted RGB image of the subject based on output by the machine learning model.

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: The present disclosure generally relates to navigating user interfaces using hand gestures.

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: 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: 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: A device implementing a system for providing predicted RGB images includes at least one processor configured to obtain an infrared image of a subject, and to obtain a reference RGB image of the subject. The at least one processor is further configured to provide the infrared image and the reference RGB image to a machine learning model, the machine learning model having been trained to output predicted RGB images of subjects based on infrared images and reference RGB images of the subjects. The at least one processor is further configured to provide a predicted RGB image of the subject based on output by the machine learning model.

Abstract: The present disclosure generally relates to navigating user interfaces using hand gestures.

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: A device implementing a system for providing predicted RGB images includes at least one processor configured to obtain an infrared image of a subject, and to obtain a reference RGB image of the subject. The at least one processor is further configured to provide the infrared image and the reference RGB image to a machine learning model, the machine learning model having been trained to output predicted RGB images of subjects based on infrared images and reference RGB images of the subjects. The at least one processor is further configured to provide a predicted RGB image of the subject based on output by the machine learning model.

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: 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: A device implementing a system for providing predicted RGB images includes at least one processor configured to obtain an infrared image of a subject, and to obtain a reference RGB image of the subject. The at least one processor is further configured to provide the infrared image and the reference RGB image to a machine learning model, the machine learning model having been trained to output predicted RGB images of subjects based on infrared images and reference RGB images of the subjects. The at least one processor is further configured to provide a predicted RGB image of the subject based on output by the machine learning model.

Abstract: A solar concentrator module (80) employs a luminescent concentrator material (82) between photovoltaic cells (86) having their charge-carrier separation junctions (90) parallel to front surfaces (88) of photovoltaic material 84 of the photovoltaic cells (86). Intercell areas (78) covered by the luminescent concentrator material (82) occupy from 2 to 50% of the total surface area of the solar concentrator modules (80). The luminescent concentrator material (82) preferably employs quantum dot heterostructures, and the photovoltaic cells (86) preferably employ low-cost high-efficiency photovoltaic materials (84), such as silicon-based photovoltaic materials.

Abstract: A solar concentrator module (80) employs a luminescent concentrator material (82) between photovoltaic cells (86) having their charge-carrier separation junctions (90) parallel to front surfaces (88) of photovoltaic material 84 of the photovoltaic cells (86). Intercell areas (78) covered by the luminescent concentrator material (82) occupy from 2 to 50% of the total surface area of the solar concentrator modules (80). The luminescent concentrator material (82) preferably employs quantum dot heterostructures, and the photovoltaic cells (86) preferably employ low-cost high-efficiency photovoltaic materials (84), such as silicon-based photovoltaic materials.