Abstract: The accuracy of physiological data measured through contact with skin can be validated by characterizing the forces at the surfaces where data is measured. Conventional devices do not monitor the fit of skin-based sensors, making the accuracy and confidence in physiological data dependent on the user ensuring that the device is fitted properly. Over time, the seating of a device will vary due to changes in user activity and the need to periodically remove a device. Inevitably, instances will arise where the device is not fitted correctly, which may result in skewed physiological metrics. By monitoring the forces acting on the housing of a device, the interface of skin sensors can be characterized allowing for confidence metrics in the corresponding physiological data to be determined. In some cases, a user can be notified when a device is not seated properly, and in some cases, data may even be calibrated based on the fit of a device.

Abstract: Method and system embodiments for discovering or tracking a device implantable in a subject using ultrasonic waves are described. The method for tracking the implantable device can include establishing a synchronization state with the implantable device, estimating a location of the implantable device, and determining whether to maintain or to adjust were an ultrasonic beam is being focused based on ultrasonic signal strength. The method for discovering an implantable device powered using ultrasonic waves can include emitting an ultrasonic beam to successively focus on a plurality of focal points, receiving ultrasonic backscatter corresponding to the ultrasonic beam focused on the focal point, and comparing the received ultrasonic backscatter with a predetermined pattern associated with the implantable device to be discovered to generate a score indicating how likely the ultrasonic backscatter comprises the predetermined pattern; and determining a location of the implantable device from based on the scores.

Abstract: The accuracy of physiological data measured through contact with skin can be validated by characterizing the forces at the surfaces where data is measured. Conventional devices do not monitor the fit of skin-based sensors, making the accuracy and confidence in physiological data dependent on the user ensuring that the device is fitted properly. Over time, the seating of a device will vary due to changes in user activity and the need to periodically remove a device. Inevitably, instances will arise where the device is not fitted correctly, which may result in skewed physiological metrics. By monitoring the forces acting on the housing of a device, the interface of skin sensors can be characterized allowing for confidence metrics in the corresponding physiological data to be determined. In some cases, a user can be notified when a device is not seated properly, and in some cases, data may even be calibrated based on the fit of a device.

Abstract: Approaches to determining a sleep fitness score for a user are provided, such as may be based upon monitored breathing disturbances of a user. The system receives user state data generated over a time period by a combination of sensors provided via a wearable tracker associated with the user. A system can use this information to calculate a sleep fitness score, breathing disturbance score, or other such value. The system can classify every minute within the time period as either normal or atypical, for example, and may provide such information for presentation to the user.

Abstract: The accuracy of physiological data measured through contact with skin can be validated by characterizing the forces at the surfaces where data is measured. Conventional devices do not monitor the fit of skin-based sensors, making the accuracy and confidence in physiological data dependent on the user ensuring that the device is fitted properly. Over time, the seating of a device will vary due to changes in user activity and the need to periodically remove a device. Inevitably, instances will arise where the device is not fitted correctly, which may result in skewed physiological metrics. By monitoring the forces acting on the housing of a device, the interface of skin sensors can be characterized allowing for confidence metrics in the corresponding physiological data to be determined. In some cases, a user can be notified when a device is not seated properly, and in some cases, data may even be calibrated based on the fit of a device.

Abstract: Approaches to determining a sleep fitness score for a user are provided, such as may be based upon monitored breathing disturbances of a user. The system receives user state data generated over a time period by a combination of sensors provided via a wearable tracker associated with the user. A system can use this information to calculate a sleep fitness score, breathing disturbance score, or other such value. The system can classify every minute within the time period as either normal or atypical, for example, and may provide such information for presentation to the user.

Abstract: Approaches to determining a sleep fitness score for a user are provided, such as may be based upon monitored breathing disturbances of a user. The system receives user state data generated over a time period by a combination of sensors provided via a wearable tracker associated with the user. A system can use this information to calculate a sleep fitness score, breathing disturbance score, or other such value. The system can classify every minute within the time period as either normal or atypical, for example, and may provide such information for presentation to the user.

Abstract: Approaches to determining a sleep fitness score for a user are provided, such as may be based upon monitored breathing disturbances of a user. The system receives user state data generated over a time period by a combination of sensors provided via a wearable tracker associated with the user. A system can use this information to calculate a sleep fitness score, breathing disturbance score, or other such value. The system can classify every minute within the time period as either normal or atypical, for example, and may provide such information for presentation to the user.

Abstract: Approaches to determining a sleep fitness score for a user are provided, such as may be based upon monitored breathing disturbances of a user. The system receives user state data generated over a time period by a combination of sensors provided via a wearable tracker associated with the user. A system can use this information to calculate a sleep fitness score, breathing disturbance score, or other such value. The system can classify every minute within the time period as either normal or atypical, for example, and may provide such information for presentation to the user.

Abstract: Approaches to determining a sleep fitness score for a user are provided, such as may be based upon monitored breathing disturbances of a user. The system receives user state data generated over a time period by a combination of sensors provided via a wearable tracker associated with the user. A system can use this information to calculate a sleep fitness score, breathing disturbance score, or other such value. The system can classify every minute within the time period as either normal or atypical, for example, and may provide such information for presentation to the user.