Systems and methods for monitoring food intake include an air pressure sensor for detecting ear canal deformation, according to some implementations. For example, the air pressure sensor detects a change in air pressure in the ear canal resulting from mandible movement. Other implementations include systems and methods for monitoring food intake that include a temporalis muscle activity sensor for detecting temporalis muscle activity, wherein at least a portion of the temporalis muscle activity sensor is coupled adjacent a temple portion of eyeglasses and disposed between the temple tip and the frame end piece. The temporalis muscle activity sensor may include an accelerometer, for example, for detecting movement of the temple portion due to mandibular movement from chewing.

A pleural effusion pre-screening system may be used to administer a percussive treatment to a patient's chest and/or back, sense the respiratory sounds from the percussive treatment, and analyze those respiratory sounds. The pleural effusion pre-screening system may have a high frequency chest wall oscillation (HFCWO) vest which includes at least one adjustable strap.

A method includes receiving multimodal data collected using at least one wearable device during an assessment window. The method also includes extracting biomarker features from the multimodal data, based on changes in the extracted biomarker features. The method also includes detecting that a stress event occurred during the assessment window. The method also includes accessing a plurality of templates of patterns in biomarker features, wherein a first subset of the templates is associated with unhealthy response to stress and a second subset of the templates is associated with healthy response to stress. The method also includes determining whether the stress event corresponds to a healthy response or an unhealthy response based on similarities between a pattern in the extracted biomarker features and the plurality of templates. The method also includes responsive to the stress event corresponding to an unhealthy response, providing a stress management recommendation.

A dynamic anatomic atlas is disclosed, comprising static atlas data describing atlas segments and dynamic atlas data comprising information on a dynamic property which information is respectively linked to the atlas segments.

Training a machine learning neural network (MLNN) in radiowave based monitoring of fall characteristics in diagnosing injury. The method comprises receiving, in a first set of input layers of the MLNN, from a millimeter wave (mmWave) radar sensing device, a set of mmWave radar point cloud data representing fall attributes associated with a subject, each of the first set associated with a respective fall attribute; receiving, at a second set of input layers of the MLNN, a set of personal attributes of the subject, training a MLNN classifier based on supervised training that establishes a correlation between an injury condition of the subject as generated at the output layer, the mmWave point cloud data, and personal attributes; and adjusting an initial matrix of weights by backpropagation to increase correlation between the injury condition, the mmWave point cloud data, and the personal attributes.

An intraoral scanner comprises a light source, a moveable opto-mechanical module, an axial actuator, and an image sensor. The light source is configured to generate light that is to be output onto an object external to the intraoral scanner. The moveable opto-mechanical module comprises integrated projection/imaging optics and an exit pupil, the projection/imaging optics having an optical axis, wherein the projection/imaging optics are entirely integrated into the moveable opto-mechanical module. The axial actuator is coupled to the projection/imaging optics and configured to move the moveable opto-mechanical module comprising an entirety of the projection/imaging optics in the optical axis to achieve a plurality of focus settings. The image sensor is configured to receive reflected light that has been reflected off of the object external to the intraoral scanner for the plurality of focus settings.

Introduced here are health management platforms able to monitor changes in the health state of a subject based on the context of digital activities performed by, or involving, the subject. Initially, a health management platform can identify a physiological response by examining physiological data associated with a subject. Then, the health management platform can identify a stimulus presented by an electronic device that provoked the physiological response by examining contextual data associated with the subject. The contextual data may be in the form of a screenshot of a computer program in use by the subject during the physiological response. In some embodiments, the health management platform prompts the subject to specify whether the physiological response is a positive physiological response that resulted in an upward shift in health or a negative physiological response that resulted in a downward shift in health.

In one embodiment, a health-monitoring system may access a waist-hip measurement of a user. The system may determine one or more stress-related parameters of the user using one or more computing devices. The system may determine one or more correlations between the waist-hip measurement and the one or more stress-related parameters of the user. The system may provide feedback to the user based on one or more of the one or more stress-related parameters or the determined correlations between the waist-hip measurement and the one or more stress-related parameters.

Various examples describe a livestock monitoring system and method. A sensor signal may comprise a rotational component describing a rotation of the sensor within an animal and a linear component describing a linear movement of the sensor within the animal. The rotational component may be used to identify an animal respiration signal. The animal respiration signal and the linear component may be used to generate a respiration-corrected linear component. An animal heart signal may be detected from the respiration-corrected linear component.

A feeding analysis system reviews collected feeding data and provides analysis of data regarding nutritive sucking, feeding, and related activity.