Abstract: A technique for detecting an environmental change to a delivery zone via an unmanned aerial vehicle includes obtaining an anchor image and an evaluation image, each representative of the delivery zone, providing the anchor image and the evaluation image to a machine learning model to determine an embedding score associated with a distance between representations of the anchor image and the evaluation image within an embedding space, and determining an occurrence of the environmental change to the delivery zone when the embedding score is greater than a threshold value.

Abstract: The present invention is directed to a wearable fitness and/or heart rate monitoring device which includes motion detecting sensor(s), a galvanic skin response (GSR) sensor, a user input interface, or any combination thereof. The device accounts for signal noise, produced when an individual passes thru varying exercise states, by filtering out the motion of the individual using a 3-axis accelerometer. The invention also provides for systems and methods of operating the monitor which extracts and/or stores true or correct heart rate values from the heartbeat waveform signal observed from a PPG.

Abstract: A technique for avoiding obstacles by an unmanned aerial vehicle (UAV) includes: acquiring an aerial image of a ground area below the UAV; analyzing the aerial image to identify a shadow in the aerial image cast by an object rising from the ground area; determining a pixel length of the shadow in the aerial image; calculating an estimated height of the object based at least on the pixel length of the shadow and an angle of the sun when the aerial image is acquired; and generating a clearance zone around the object having at least one dimension determined based on the estimated height, wherein the clearance zone represents a region in space to avoid when navigating the UAV.

Abstract: In some embodiments, an unmanned aerial vehicle (UAV) is provided. The UAV comprises one or more processors; a camera; one or more propulsion devices; and a computer-readable medium having instructions stored thereon that, in response to execution by the one or more processors, cause the UAV to perform actions comprising: receiving at least one image captured by the camera; generating labels for pixels of the at least one image by providing the at least one image as input to a machine learning model; identifying one or more landing spaces in the at least one image based on the labels; determining a relative position of the UAV with respect to the one or more landing spaces; and transmitting signals to the one or more propulsion devices based on the relative position of the UAV with respect to the one or more landing spaces.

Abstract: Individual-specific changes in health conditions are detected using a latent space mapping generated from baseline physiological data collected in a longitudinal study of the individual. Historical baseline data in an n-dimensional input space is modeled into a k-dimensional latent space, where k<n. The mapping for the model is then used to convert new physiological data into a latent space point, which can be used to determine if the associated newly collected physiological data is anomalous and thus indicative of a change in health condition. Anomalies can be inferred from poor fit between the point and baseline data within the latent space, or form large error between data reconstructed into the input space from the point and the original new physiological data.

Abstract: A technique for validating a presence of a package carried by an unmanned aerial vehicle (UAV) includes: capturing an image of a scene below the UAV with a camera mounted to the UAV and oriented to face down from the UAV; analyzing the image to identify whether the package is present in the image; and determining whether the package is attached to the UAV, via a tether extending from an underside of the UAV, based at least on the analyzing of the image.

Abstract: A method includes receiving a two-dimensional (2D) image captured by a camera on a unmanned aerial vehicle (UAV) and representative of an environment of the UAV. The method further includes applying a trained machine learning model to the 2D image to produce a semantic image of the environment and a depth image of the environment, where the semantic image comprises one or more semantic labels. The method additionally includes retrieving reference depth data representative of the environment, wherein the reference depth data includes reference semantic labels. The method also includes aligning the depth image of the environment with the reference depth data representative of the environment to determine a location of the UAV in the environment, where the aligning associates the one or more semantic labels from the semantic image with the reference semantic labels from the reference depth data.

Abstract: The present disclosure relates to activity monitoring systems and methods for gating whether or not steps should be counted in an observation window based on whether a decision tree concludes there are consecutive step activities (versus no activity or other activities) in the observation window. Particularly, certain aspects are directed to a method that includes obtaining acceleration data for an observation window of an accelerometer, inputting two or more characteristics of the acceleration data into a decision tree to determine activity occurring within the observation window, assigning a first class to the observation window when the determined activity is associated with consecutive steps, assigning a second class to the observation window when the determined activity is not associated with consecutive steps, and when the first class is assigned to the observation window, determining a step count for the observation window using frequency analysis.

Abstract: A technique for detecting an environmental change to a delivery zone via an unmanned aerial vehicle includes obtaining an anchor image and an evaluation image, each representative of the delivery zone, providing the anchor image and the evaluation image to a machine learning model to determine an embedding score associated with a distance between representations of the anchor image and the evaluation image within an embedding space, and determining an occurrence of the environmental change to the delivery zone when the embedding score is greater than a threshold value.

Abstract: A method includes capturing, by a sensor on an unmanned aerial vehicle (UAV), an image of a delivery location. The method further includes determining, based on the image of the delivery location, a segmentation image. The segmentation image segments the delivery location into a plurality of pixel areas with corresponding semantic classifications. The method also includes determining, based on the segmentation image, a distance-to-obstacle image of a delivery zone at the delivery location. The distance-to-obstacle image comprises a plurality of pixels, each pixel representing a distance in the segmentation image from a nearest pixel area with a semantic classification indicative of an obstacle in the delivery location. Additionally, the method includes selecting, based on the distance-to-obstacle image, a delivery point in the delivery zone. The method also includes positioning the UAV above the delivery point in the delivery zone for delivery of a payload.

Abstract: A computer-implemented method comprises receiving an image captured by a camera on an unmanned aerial vehicle (UAV). The image depicts an environment below the UAV. A feature mask associated with the image is generated via a machine learning model that is trained to identify and semantically label pixels representing the environment depicted in the image. One or more reference tiles associated with the environment are retrieved. The reference tiles are associated with particular geographic locations and specify semantically labeled pixels representing the geographic locations. The semantically labeled pixels of the feature mask are correlated with the semantically labeled pixels of at least one of the one or more reference tiles to determine the geographic location of the UAV in the environment.

Abstract: A method includes navigating, by an unmanned aerial vehicle (UAV), to a first altitude above a first delivery point at a delivery location. The method further includes determining, by the UAV, a second delivery point at the delivery location. The method includes navigating, by the UAV, through a descending trajectory to move the UAV from the first altitude above the first delivery point to a second altitude above the second delivery point at the delivery location. The second altitude is lower than the first altitude. The method additionally includes delivering, by the UAV, a payload to the second delivery point at the delivery location. The method includes after delivering the payload, navigating, by the UAV, through an ascending trajectory to move the UAV from a third altitude above the second delivery point to a fourth altitude above the first delivery point. The fourth altitude is higher than the third altitude.

Abstract: A computer-implemented method comprises receiving, by an image processing system, a depth image captured by a stereo camera on an unmanned aerial vehicle (UAV). One or more pixels of the depth image are associated with corresponding depth values indicative of distances of one or more objects to the stereo camera. The image processing system determines that one or more pixels of the depth image are associated with invalid depth values. The image processing system infers, based on a distribution of the one or more pixels of the depth image that are associated with invalid depth values, a presence of a potential obstacle in an environment of the UAV. The UAV is controlled based on the inferred presence of the potential obstacle.

Abstract: A method includes capturing, by a sensor on an unmanned aerial vehicle (UAV), an image of a delivery location. The method also includes determining, based on the image of the delivery location, a segmentation image. The segmentation image segments the delivery location into a plurality of pixel areas with corresponding semantic classifications. The method additionally includes determining, based on the segmentation image, a percentage of obstacle pixels within a surrounding area of a delivery point at the delivery location, wherein each obstacle pixel has a semantic classification indicative of an obstacle in the delivery location. The method further includes based on the percentage of obstacle pixels being above a threshold percentage, aborting a delivery process of the UAV.

Abstract: A technique for detecting an environmental change to a delivery zone via an unmanned aerial vehicle includes obtaining an anchor image and an evaluation image, each representative of the delivery zone, providing the anchor image and the evaluation image to a machine learning model to determine an embedding score associated with a distance between representations of the anchor image and the evaluation image within an embedding space, and determining an occurrence of the environmental change to the delivery zone when the embedding score is greater than a threshold value.

Abstract: The present invention is directed to a wearable fitness and/or heart rate monitoring device which includes motion detecting sensor(s), a galvanic skin response (GSR) sensor, a user input interface, or any combination thereof. The device accounts for signal noise, produced when an individual passes thru varying exercise states, by filtering out the motion of the individual using a 3-axis accelerometer. The invention also provides for systems and methods of operating the monitor which extracts and/or stores true or correct heart rate values from the heartbeat waveform signal observed from a PPG.

Abstract: The present invention is directed to a wearable fitness and/or heart rate monitoring device which includes motion detecting sensor(s), a galvanic skin response (GSR) sensor, a user input interface, or any combination thereof. The device accounts for signal noise, produced when an individual passes thru varying exercise states, by filtering out the motion of the individual using a 3-axis accelerometer. The invention also provides for systems and methods of operating the monitor which extracts and/or stores true or correct heart rate values from the heartbeat waveform signal observed from a PPG.

Abstract: A technique for detecting an environmental change to a delivery zone via an unmanned aerial vehicle includes obtaining an anchor image and an evaluation image, each representative of the delivery zone, providing the anchor image and the evaluation image to a machine learning model to determine an embedding score associated with a distance between representations of the anchor image and the evaluation image within an embedding space, and determining an occurrence of the environmental change to the delivery zone when the embedding score is greater than a threshold value.

Abstract: Methods, systems, and devices for identifying abnormal sleep conditions are disclosed. During each of a plurality of non-zero time periods, a wearable device configured to be worn on a body of a user may capture (i) a physiological parameter measurement and (ii) a non-physiological parameter measurement, thereby providing a plurality of physiological parameter measurements and a plurality of non-physiological parameter measurements, respectively. Based at least in part on the plurality of physiological parameter measurements and the plurality of non-physiological parameter measurements, the wearable device may identify an abnormal sleep condition. Responsive to identifying the abnormal sleep condition, the wearable device may cause an output device to provide a notification, with the output device being a part of or connected to the wearable device.

Abstract: The present disclosure relates to systems and methods for probabilistically estimating an individual's sleep stage based on spectral analyses of pulse rate and motion data. In one implementation, the method may include receiving signals from sensors worn by the individual, the signals including a photoplethysmographic (PPG) signal and an accelerometer signal; dividing the PPG signal into segments; determining a beat interval associated with each segment; resampling the set of beat intervals to generate an interval signal; and generating signal features based on the interval signal and the accelerometer signal, including a spectrogram of the interval signal. The method may further include determining a sleep stage for the individual by comparing the signal features to a sleep stage classifier included in a learning library. The sleep stage classifier may include one or more functions defining a likelihood that the individual is in the sleep stage based on the signal features.