The described systems and methods determine a driver's fitness to safely operate a moving vehicle based at least in part upon observed UVB exposure patterns, where the driver's UVB exposure levels may serve as a proxy for vitamin D levels in that driver's body. A smart ring, wearable on a user's finger, continuously monitors user's exposure to UVB light. This UVB exposure data, representing UVB exposure patterns, can be utilized, in combination with driving data, to train a machine learning model, which will predict the user's level of risk exposure based at least in part upon observed UVB exposure patterns. The user can be warned of this risk to prevent them from driving or to encourage them to get more sunlight exposure before driving. In some instances, the disclosed smart ring system may interact with the user's vehicle to prevent it from starting while exposed to high risk due to deteriorated psychological or physiological conditions stemming from insufficient UVB exposure.

The present invention provides a method of conducting a sleep analysis by collecting physiologic and kinetic data from a subject, preferably via a wireless in-home data acquisition system, while the subject attempts to sleep at home. The sleep analysis, including clinical and research sleep studies and cardiorespiratory studies, can be used in the diagnosis of sleeping disorders and other diseases or conditions with sleep signatures, such as Parkinson's, epilepsy, chronic heart failure, chronic obstructive pulmonary disorder, or other neurological, cardiac, pulmonary, or muscular disorders. The method of the present invention can also be used to determine if environmental factors at the subject's home are preventing restorative sleep.

A method and system for artificial intelligence-based radiofrequency ablation parameter optimization and information synthesis are provided. The method is applied to a radiofrequency ablation controller including a processor and an artificial intelligence module. The processor of the radiofrequency ablation controller preprocesses sample data and sends the preprocessed sample data to the artificial intelligence module. The artificial intelligence module establishes an artificial neural network model according to the preprocessed sample data and a radiofrequency ablation control parameter for the sample data. The processor preprocesses signals collected by sensors on a plasma wand. The artificial intelligence module imports preprocessed sensor data into the artificial neural network model for analysis and fusion, to obtain the radiofrequency ablation control parameter.

A method according to one embodiment includes receiving baseline health data of a user, receiving activity-based health data of the user collected by a sensor device worn by the user while participating in physical activity, and receiving environmental-based data of the user. A baseline core body temperature of the user is calculated. It is determined whether a core body temperature of the user has increased a predetermined amount from the baseline core body temperature of the user for a predetermined amount of time during each of a predetermined number of time intervals. In response to a determination that the core body temperature has increased the predetermined amount from the baseline core body temperature of the user for the predetermined amount of time during each of the predetermined number of time intervals, a personalized schedule is generated for the user to follow while participating in the physical activity.

A method according to one embodiment includes determining whether a user has successfully heat acclimatized to a predetermined environment. Subsequent to a determination that the user has successfully heat acclimatized, it is determined whether the user has heat de-acclimatized to the predetermined environment. In response to a determination that the user has heat de-acclimatized, a predetermined process is performed. The predetermined process includes determining whether the user has successfully heat re-acclimatized to the predetermined environment. The predetermined process further includes updating a personalized schedule and outputting the updated personalized schedule to a user device of the user in response to a determination that the user has successfully heat re-acclimatized to the predetermined environment, and outputting an alert to the user device of the user and/or to a user device of a second user in response to a determination that the user has not successfully heat re-acclimatized to the predetermined environment.

A method according to one embodiment includes receiving baseline health data of a user, receiving activity-based health data of the user collected by a sensor device worn by the user while participating in physical activity and receiving environmental-based data of the user collected by the sensor device worn by the user. An alert is output for display on a user device in response to a determination that a core body temperature of the user is greater than a predetermined threshold temperature. A personalized schedule is generated for the user to follow while participating in the physical activity. The personalized schedule includes at least one instruction of when to start participating in the physical activity and at least one instruction of when to stop participating in the physical activity. The method further includes outputting the personalized schedule for display on the user device.

The described systems and methods determine a driver's fitness to safely operate a moving vehicle based at least in part upon observed impairment patterns. A smart ring, wearable on a user's finger, continuously monitors impairment levels. This impairment data, representing impairment patterns, can be utilized, in combination with driving data, to train a machine learning model, which will predict the user's level of risk exposure based at least in part upon observed impairment patterns. The user can be warned of this risk to prevent them from driving or to encourage them to delay driving. In some instances, the disclosed smart ring system may interact with the user's vehicle to prevent it from starting while the user is in a state of impairment induced by substance intoxication.

Various embodiments concern video patient monitoring with detection zones. Various embodiments can comprise a camera, a user interface, and a computing system. The computing system can be configured to perform various steps based on reception of a frame from the camera, including: calculate a background luminance of the frame; monitor for a luminance change of a zone as compared to one or more previous frames, the luminance change indicative of patient motion in the zone; and compare the background luminance to an aggregate background luminance, the aggregate background luminance based on the plurality of frames. If the background luminance changed by more than a predetermined amount, then the aggregate background luminance can be set to the background luminance, luminance information of the previous frames can be disregarded, and motion detection can be disregarded.

A wearable device is provided. The wearable device includes a housing including a transparent part, a circuit board disposed in the housing and including a first surface facing the transparent part and a second surface opposite to the first surface, a first integrated circuit (IC) layer disposed adjacent to the circuit board, a first sensor module, at least a part of which is disposed in the first IC layer, a second sensor module disposed adjacent to the first sensor module, and a second IC layer electrically connected to the first IC layer and the circuit board, and including a processor configured to process data acquired by the first sensor module and the second sensor module, wherein the circuit board, the first IC layer, and the second IC layer are stacked and disposed in a direction perpendicular to the first surface or the second surface of the circuit board.

Circadian health recommendations and/or automation instructions and spectral data on which they are based can be provided to a user through a mobile device that lacks a spectral sensor. A user mobile device lacking a spectral data uploads place and time data to a cloud-based circadian health system. A light-exposure model of the circadian health system estimates spectral data values based on the place and time data. The light-exposure model’s estimation can be based on data received by the circadian health system based on user devices equipped with spectral sensors and from other sources. A relatively small number of mobile user devices (with spectral sensors) can thus provide for spectral value estimates for a relatively large population of mobile user devices that lack spectral sensors—greatly expanding the range and number of people that benefit from improved circadian health.