Abstract: An intraoral scanning system comprises and intraoral scanner and a processor. The intraoral scanner comprises one or more cameras and one or more structured light projectors, the intraoral scanner to generate a series of images using the one or more cameras, each image including at least a portion of a pattern projected by the one or more structured light projectors onto an intraoral three-dimensional surface. The processor runs a correspondence algorithm to compute respective three-dimensional positions of a plurality of features of the pattern on the intraoral three-dimensional surface, as captured in the series of images. The processor identifies the computed three-dimensional position of a detected feature of the pattern as corresponding to a particular projector ray r, in at least a subset of the series of images. The processor tracks the particular projector ray r across one or more additional images of the series of images.

Abstract: A method of distinguishing sleep period states that a person experiences during a sleep period, the method comprising: using a non-contact microphone to acquire a sleep sound signal representing sounds made by a person during sleep; segmenting the sleep sound signals into epochs; generating a sleep sound feature vector for each epoch; providing a first model that gives a probability that a given sleep period state experienced by the person in a given epoch exhibits a given sleep sound feature vector; providing a second model that gives a probability that a first sleep period state associated with a first epoch transitions to a second sleep period state associated with a subsequent second epoch; and processing the feature vectors using the first and second models to determine a sleep period state of the person from a plurality of possible sleep period states for each of the epochs.

Abstract: The present invention relates to a system and method for determining sleep quality parameters according to audio analyses, comprising: obtaining an audio recorded signal comprising sleep sounds of a subject; segmenting the signal into epochs; generating a feature vector for each epoch, wherein each of said feature vectors comprises one or more feature parameters that are associated with a particular characteristic of the signal and that are calculated according to the epoch signal or according to a signal generated from the epoch signal; inputting the generated feature vectors into a machine learning classifier and applying a preformed classifying model on the feature vectors that outputs a probabilities vector for each epoch, wherein each of the probabilities vectors comprises the probabilities of the epoch being each of the sleep quality parameters; inputting the probabilities vectors for each epoch into a machine learning time series model and applying a preformed sleep quality time series pattern function

Abstract: Provided herein is a method and system for the estimation of apnea-hypopnea index (AHI), as an indicator for Obstructive sleep apnea (OSA) severity, by combining speech descriptors from three separate and distinct speech signal domains. These domains include the acoustic short-term features (STF) of continuous speech, the long-term features (LTF) of continuous speech, and features of sustained vowels (SVF). Combining these speech descriptors may provide the ability to estimate the severity of OSA using statistical learning and speech analysis approaches.

Abstract: An intraoral scanning system comprises and intraoral scanner and a processor. The intraoral scanner comprises one or more cameras and one or more structured light projectors, the intraoral scanner to generate a series of images using the one or more cameras, each image including at least a portion of a pattern projected by the one or more structured light projectors onto an intraoral three-dimensional surface. The processor runs a correspondence algorithm to compute respective three-dimensional positions of a plurality of features of the pattern on the intraoral three-dimensional surface, as captured in the series of images. The processor identifies the computed three-dimensional position of a detected feature of the pattern as corresponding to a particular projector ray r, in at least a subset of the series of images. The processor tracks the particular projector ray r across one or more additional images of the series of images.

Abstract: A method of distinguishing sleep period states that a person experiences during a sleep period, the method comprising: using a non-contact microphone to acquire a sleep sound signal representing sounds made by a person during sleep; segmenting the sleep sound signals into epochs; generating a sleep sound feature vector for each epoch; providing a first model that gives a probability that a given sleep period state experienced by the person in a given epoch exhibits a given sleep sound feature vector; providing a second model that gives a probability that a first sleep period state associated with a first epoch transitions to a second sleep period state associated with a subsequent second epoch; and processing the feature vectors using the first and second models to determine a sleep period state of the person from a plurality of possible sleep period states for each of the epochs.

Abstract: A method for generating a 3D image includes driving structured light projector(s) to project a pattern of light on an intraoral 3D surface, and driving camera(s) to capture images, each image including at least a portion of the projected pattern, each one of the camera(s) comprising an array of pixels. A processor compares a series of images captured by each camera and determines which of the portions of the projected pattern can be tracked across the images. The processor constructs a three-dimensional model of the intraoral three-dimensional surface based at least in part on the comparison of the series of images. Other embodiments are also described.

Abstract: Apparatuses (e.g., systems, devices, etc.) and method for scanning both a subject's intraoral cavity as well as detecting, using the same scanner, e.g., wand, finger gestures and executing command controls based on these detected finger gestures.

Abstract: A method for generating a 3D image includes driving structured light projector(s) to project a pattern of light on an intraoral 3D surface, and driving camera(s) to capture images, each image including at least a portion of the projected pattern, each one of the camera(s) comprising an array of pixels. A processor compares a series of images captured by each camera and determines which of the portions of the projected pattern can be tracked across the images. The processor constructs a three-dimensional model of the intraoral three-dimensional surface based at least in part on the comparison of the series of images. Other embodiments are also described.

Abstract: The present invention relates to a system and method for determining sleep quality parameters according to audio analyses, comprising: obtaining an audio recorded signal comprising sleep sounds of a subject; segmenting the signal into epochs; generating a feature vector for each epoch, wherein each of said feature vectors comprises one or more feature parameters that are associated with a particular characteristic of the signal and that are calculated according to the epoch signal or according to a signal generated from the epoch signal; inputting the generated feature vectors into a machine learning classifier and applying a preformed classifying model on the feature vectors that outputs a probabilities vector for each epoch, wherein each of the probabilities vectors comprises the probabilities of the epoch being each of the sleep quality parameters; inputting the probabilities vectors for each epoch into a machine learning time series model and applying a preformed sleep quality time series pattern function

Abstract: Provided herein is a method and system for the estimation of apnea-hypopnea index (AHI), as an indicator for Obstructive sleep apnea (OSA) severity, by combining speech descriptors from three separate and distinct speech signal domains. These domains include the acoustic short-term features (STF) of continuous speech, the long-term features (LTF) of continuous speech, and features of sustained vowels (SVF). Combining these speech descriptors may provide the ability to estimate the severity of OSA using statistical learning and speech analysis approaches.

Abstract: A method of distinguishing sleep period states that a person experiences during a sleep period, the method comprising: using a non-contact microphone to acquire a sleep sound signal representing sounds made by a person during sleep; segmenting the sleep sound signals into epochs; generating a sleep sound feature vector for each epoch; providing a first model that gives a probability that a given sleep period state experienced by the person in a given epoch exhibits a given sleep sound feature vector; providing a second model that gives a probability that a first sleep period state associated with a first epoch transitions to a second sleep period state associated with a subsequent second epoch; and processing the feature vectors using the first and second models to determine a sleep period state of the person from a plurality of possible sleep period states for each of the epochs.