Abstract: A periodic physiological signal such as pulse data can be analyzed in the time domain to identify peaks and other features of interest, and to evaluate how well the signal corresponds to an expected shape. For example, state machines may be used to sequentially analyze samples of time domain data and perform peak detection, signal quality analysis, and so forth. This time domain analysis model permits adaptations to known variations in typical signals, and advantageously enables accurate physiological inferences over a greater range of user-specific contexts including different activity types, fitness levels, and medical conditions.

Abstract: A physiological monitor uses a light source and a number of detectors to determine whether a physiological monitor is positioned for acquisition of physiological data. More specifically, an intensity of the light source, as measured at two photodetectors at different distances from the light source, can be used to accurately detect whether the monitor is properly positioned for use. The disclosed methods may advantageously leverage existing physiological monitoring hardware (such as light emitting diodes and photodetectors), and may improve on the accuracy of prior art techniques using, e.g., capacitive sensors and/or other hardware to detect proper device positioning.

Abstract: Tightness of a wearable device can be evaluated through direct observations of how the device responds to a physical stimulus. For example, by applying a varying pattern of vibrations such as a CHIRP signal with a haptic output element or the like to a device strapped to a wrist or other body part, the mechanical and/or optical response of the device can be measured to infer the amount of tension that is retaining the device against the body, or more generally, to evaluate whether the device is properly fitted to a user. Results can then be presented to a user objectively using Newtons or some other metric, or subjectively by providing qualitative assessments of fit. Recommendations for adjustments may also or instead be provided to the user for optimal performance of the wearable device.

Abstract: A model of data quality is derived for physiological monitoring with a wearable device by comparing data from the wearable device to concurrent data acquisition from a ground truth device such as a chest strap or electrocardiography (EKG) heart rate monitor. With this comparative data, a machine learning model or the like may be derived to prospectively evaluate data quality based on the data acquisition context, as determined, for example, by other sensor data and signals from the wearable device.

Abstract: Variations in pulse shape over time can be used to draw inferences about activity, health, and age of an individual. For example, PPG pulses may be mapped to a latent space where variations in shape can be measured directly in terms of distance between pulses. In one aspect, pulse-to-pulse comparisons for an individual can be used to estimate strain, recovery, sleep, and so forth. Longer term measurements (e.g., over weeks, month, or years) can be used to detect changes in health and fitness for the individual. In another aspect, pulse-to-pulse comparisons among different individuals can be used to estimate relative cardiovascular health, age, and the like.

Abstract: A model of data quality is derived for physiological monitoring with a wearable device by comparing data from the wearable device to concurrent data acquisition from a ground truth device such as a chest strap or electrocardiography (EKG) heart rate monitor. With this comparative data, a machine learning model or the like may be derived to prospectively evaluate data quality based on the data acquisition context, as determined, for example, by other sensor data and signals from the wearable device.

Abstract: A physiological monitoring device controls an optical signal acquisition system within a number of discrete operating states, each providing values for controllable parameters such as illumination intensity for a light source and the gain for an optical detector. Using this technique, a small number of operating states may be defined, such as operating states that are known to work well within expected use scenarios. This approach advantageously facilitates optimal or near optimal operation across a range of most likely use cases while avoiding complex or continuous optimization problems. The list of operating states may further be prioritized so that a best operating state can be selected based on, e.g., signal quality or environmental conditions.

Abstract: A model of data quality is derived for physiological monitoring with a wearable device by comparing data from the wearable device to concurrent data acquisition from a ground truth device such as a chest strap or electrocardiography (EKG) heart rate monitor. With this comparative data, a machine learning model or the like may be derived to prospectively evaluate data quality based on the data acquisition context, as determined, for example, by other sensor data and signals from the wearable device.

Abstract: A physiological monitoring device controls an optical signal acquisition system within a number of discrete operating states, each providing values for controllable parameters such as illumination intensity for a light source and the gain for an optical detector. Using this technique, a small number of operating states may be defined, such as operating states that are known to work well within expected use scenarios. This approach advantageously facilitates optimal or near optimal operation across a range of most likely use cases while avoiding complex or continuous optimization problems. The list of operating states may further be prioritized so that a best operating state can be selected based on, e.g., signal quality or environmental conditions.

Abstract: A physiological signal such as a heart rate acquired from a monitoring device is processed to reduce interference, ambiguity, or artifacts arising during various activities. For example, the system can process a physiological signal to account for motion artifacts in the physiological signal and, thus, reduce the impact of movement on the physiological signal. Additionally, or alternatively, the system can process a physiological signal based on one or more measurement contexts associated with a wearable device. In general, the physiological signal processed as described herein can be useful as a reliable, continuous indication of a physiological parameter and, thus, can serve as the basis for other physiological assessments (e.g., heart rate variability) derived from the physiological parameter.

Abstract: A physiological signal such as a heart rate acquired from a monitoring device is processed to reduce interference, ambiguity, or artifacts arising during various activities. For example, the system can process a physiological signal to account for motion artifacts in the physiological signal and, thus, reduce the impact of movement on the physiological signal. Additionally, or alternatively, the system can process a physiological signal based on one or more measurement contexts associated with a wearable device. In general, the physiological signal processed as described herein can be useful as a reliable, continuous indication of a physiological parameter and, thus, can serve as the basis for other physiological assessments (e.g., heart rate variability) derived from the physiological parameter.

Abstract: A physiological signal such as a heart rate acquired from a monitoring device is processed to reduce interference, ambiguity, or artifacts arising during various activities. For example, the system can process a physiological signal to account for motion artifacts in the physiological signal and, thus, reduce the impact of movement on the physiological signal. Additionally, or alternatively, the system can process a physiological signal based on one or more measurement contexts associated with a wearable device. In general, the physiological signal processed as described herein can be useful as a reliable, continuous indication of a physiological parameter and, thus, can serve as the basis for other physiological assessments (e.g., heart rate variability) derived from the physiological parameter.

Abstract: A physiological signal such as a heart rate acquired from a monitoring device is processed to reduce interference, ambiguity, or artifacts arising during various activities. For example, the system can process a physiological signal to account for motion artifacts in the physiological signal and, thus, reduce the impact of movement on the physiological signal. Additionally, or alternatively, the system can process a physiological signal based on one or more measurement contexts associated with a wearable device. In general, the physiological signal processed as described herein can be useful as a reliable, continuous indication of a physiological parameter and, thus, can serve as the basis for other physiological assessments (e.g., heart rate variability) derived from the physiological parameter.

Abstract: A compact lens system for imaging a sample comprising a substrate having a well formed therein. Index matching material is disposed in the well and a lens member is further disposed in the well in optical contact with the index matching material disposed in the well. A spacer member extends from at least one of the substrate and the lower transparent member to define a spacing from a focal point of the lens member, wherein the lens member and index matching material cooperate to image a sample disposed below the lower transparent member.

Abstract: A compact lens system for imaging a sample comprising a substrate having a well formed therein. Index matching material is disposed in the well and a lens member is further disposed in the well in optical contact with the index matching material disposed in the well. A spacer member extends from at least one of the substrate and the lower transparent member to define a spacing from a focal point of the lens member, wherein the lens member and index matching material cooperate to image a sample disposed below the lower transparent member.