Abstract: A method for uterine activity monitoring may include: acquiring a plurality of signals from a plurality of sensors during uterine activity; processing the plurality of signals to extract a plurality of uterine electrical activity characteristics; analyzing the plurality of uterine electrical activity characteristics; and classifying the uterine activity as one of: a preterm labor contraction, a labor contraction, a Braxton-Hicks contraction, and a state of no contraction. A method of assessing over time a pre-term birth risk of a pregnant female may include: calculating a baseline pre-term birth risk score based on a user input; acquiring, over time, a signal from a sensor; analyzing the signal to extract a parameter of interest, such that the parameter of interest comprises a physiological parameter; and calculating an instant pre-term birth risk score based, at least in part, on the parameter of interest and the user input.

Abstract: Described herein are methods for identifying a labor state in a pregnant female, including: receiving an input indicating a gestational age; acquiring a physiological signal; processing the physiological signal to extract a parameter of interest; and feeding the parameter of interest into a machine learning model. The machine learning model is configured to: determine a first labor probability based on the parameter of interest, determine a second labor probability based on the parameter of interest or a second parameter of interest and the gestational age, and classify the labor state of the pregnant female based on the first and second labor probability.

Abstract: A system for monitoring fetal wellbeing over time during pregnancy includes a sensor coupled to a pregnant woman; a processor communicatively coupled to the sensor; and a computer-readable medium having non-transitory, processor-executable instructions stored thereon. Execution of the instructions causes the processor to perform a method including: acquiring a signal from a sensor; processing the signal to identify and extract a parameter of interest from the signal; and analyzing the parameter of interest to determine a degree of fetal wellbeing. The parameter of interest may include one or more of: an average fetal heart rate, an average fetal heart rate variability, a fetal kick or movement count, an average placental oxygenation level, an average placental temperature, an average placental pH, an average amount of amniotic fluid, a fetal heart rate profile, a fetal heart rate variability profile, and a fetal movement profile.

Abstract: Methods, systems, and devices for sleep staging algorithms are described. A system may receive physiological data associated with a user from a wearable ring device, where the physiological data is collected via the wearable ring device throughout a time interval. The system may input the physiological data into a machine learning classifier, and classify the physiological data, using the machine learning classifier, into at least one sleep stage of a set of sleep stages for at least a portion of the time interval. The system may subsequently cause a graphical user interface (GUI) of a user device to display an indication of the at least one sleep stage of the set of sleep stages based on classifying the physiological data.

Abstract: Methods, systems, and devices for illness detection are described. A method may include receiving heart rate variability (HRV) data associated with a user from a wearable device, the HRV data collected via the wearable device throughout a first time interval and a second time interval subsequent to the first time interval. The method may include inputting the HRV data into a classifier, and identifying a satisfaction of deviation criteria between a first subset of the HRV data collected throughout the first time interval and a second subset of the HRV data collected throughout the second time interval. The method may include causing a graphical user interface (GUI) of a user device to display an illness risk metric for the user based on the satisfaction of the deviation criteria, the illness risk metric associated with a relative probability that the user will transition from a healthy state to an unhealthy state.

Abstract: A method for uterine activity monitoring may include: acquiring a plurality of signals from a plurality of sensors during uterine activity; processing the plurality of signals to extract a plurality of uterine electrical activity characteristics; analyzing the plurality of uterine electrical activity characteristics; and classifying the uterine activity as one of: a preterm labor contraction, a labor contraction, a Braxton-Hicks contraction, and a state of no contraction. A method of assessing over time a pre-term birth risk of a pregnant female may include: calculating a baseline pre-term birth risk score based on a user input; acquiring, over time, a signal from a sensor; analyzing the signal to extract a parameter of interest, such that the parameter of interest comprises a physiological parameter; and calculating an instant pre-term birth risk score based, at least in part, on the parameter of interest and the user input.

Abstract: A system for monitoring fetal wellbeing over time during pregnancy includes a sensor coupled to a pregnant woman; a processor communicatively coupled to the sensor; and a computer-readable medium having non-transitory, processor-executable instructions stored thereon. Execution of the instructions causes the processor to perform a method including: acquiring a signal from a sensor; processing the signal to identify and extract a parameter of interest from the signal; and analyzing the parameter of interest to determine a degree of fetal wellbeing. The parameter of interest may include one or more of: an average fetal heart rate, an average fetal heart rate variability, a fetal kick or movement count, an average placental oxygenation level, an average placental temperature, an average placental pH, an average amount of amniotic fluid, a fetal heart rate profile, a fetal heart rate variability profile, and a fetal movement profile.

Abstract: Described herein are methods for identifying a labor state in a pregnant female, including: receiving an input indicating a gestational age; acquiring a physiological signal; processing the physiological signal to extract a parameter of interest; and feeding the parameter of interest into a machine learning model. The machine learning model is configured to: determine a first labor probability based on the parameter of interest, determine a second labor probability based on the parameter of interest or a second parameter of interest and the gestational age, and classify the labor state of the pregnant female based on the first and second labor probability.

Abstract: Described herein are systems and methods for contraction monitoring. For example, a system for contraction monitoring includes an electrode patch including at least two electrodes, and a sensor module configured to be connected to the electrode patch. In some embodiments, the sensor module includes a signal acquisition module, a signal processing module, a power management module, a sensor control module, and a memory module and/or a data transmission module. In some embodiments, a method for contraction monitoring includes measuring, using the signal acquisition module, bio-potential signals by providing at least two electrodes on the abdomen of a pregnant woman.

Abstract: A device for monitoring the physical activity of a living being is disclosed. In one aspect, there is a data input module configured to receive information about the living being's heart beat rate value, motion intensity and anthropometric characteristics. Further, there is an activity recognition and storage module configured to detect, from information received about the living being's motion intensity, the living being's activity and to store information about the living being's heart beat rate value and the motion intensity associated with that detected activity. Further, there is a heart beat rate analysis module configured to determine, from a plurality of heart beat rate values associated with each detected activity, statistics of the distribution of heart beat rate values for each activity or a subset of activities.

Abstract: Described herein are systems and methods for contraction monitoring. For example, a system for contraction monitoring includes an electrode patch including at least two electrodes, and a sensor module configured to be connected to the electrode patch. In some embodiments, the sensor module includes a signal acquisition module, a signal processing module, a power management module, a sensor control module, and a memory module and/or a data transmission module. In some embodiments, a method for contraction monitoring includes measuring, using the signal acquisition module, bio-potential signals by providing at least two electrodes on the abdomen of a pregnant woman.

Abstract: An example device includes: a data input module configured to receive information about a living being's physiological signals, coordinates, and motion intensity; an activity recognition module configured to calculate, from information received about the living being's motion intensity, a living being's activity; a location recognition module, configured to calculate, from information received about the living being's coordinates, a living being's location; a memory storage configured to store information about the living being's physiological signals and activity in association with the location; a normalization parameters estimator module configured to use a mathematical model to calculate a plurality of normalization parameters for a plurality of detected activities and locations; and a model selector module configured to determine, based on the plurality of normalization parameters and the living being's location, a set of location-specific normalization parameters used to further calculate normalized phy

Abstract: Systems and methods for monitoring the onset or occurrence of labor contractions and detecting or estimating labor in a pregnant female are provided.

Abstract: A system for detecting and quantifying deviations from physiological signals normality and methods for making and using same. Each subject physiology follows unique patterns. The physiological signals can be affected by one or more factors such as circadian rhythm, disease and/or external stressors. Deviations of physiological signals from the normality of a subject can be indicative of external events that might require proper lifestyle management or just in time interventions, such as being exposed to high stress or the progress/onset of specific disease conditions. The disclosed system advantageously can quantify such deviations.

Abstract: A system suitable for generating messages based on biometric and contextual data and methods for making and using the same. One or more sensors can be used to measure biometric and/or contextual data. The measured data are analyzed using behavior analytics to capture behavior of a selected user. The behavior is analyzed with messaging analytics to generate personalized messages related to the behavior. The message preferably is personalized at at least one level, such as time, place and format of message delivery, content of the message, and tone of the message. The message personalization can further adapt to changes in the user's behavior and/or preferences. Thereby, the method and system advantageously can provide biometric and context based messaging for motivating healthy behavior.

Abstract: An example device includes: a data input module configured to receive information about a living being's physiological signals, coordinates, and motion intensity; an activity recognition module configured to calculate, from information received about the living being's motion intensity, a living being's activity; a location recognition module, configured to calculate, from information received about the living being's coordinates, a living being's location; a memory storage configured to store information about the living being's physiological signals and activity in association with the location; a normalization parameters estimator module configured to use a mathematical model to calculate a plurality of normalization parameters for a plurality of detected activities and locations; and a model selector module configured to determine, based on the plurality of normalization parameters and the living being's location, a set of location-specific normalization parameters used to further calculate normalized phy

Abstract: A device for monitoring the physical activity of a living being is disclosed. In one aspect, there is a data input module configured to receive information about the living being's heart beat rate value, motion intensity and anthropometric characteristics. Further, there is an activity recognition and storage module configured to detect, from information received about the living being's motion intensity, the living being's activity and to store information about the living being's heart beat rate value and the motion intensity associated with that detected activity. Further, there is a heart beat rate analysis module configured to determine, from a plurality of heart beat rate values associated with each detected activity, statistics of the distribution of heart beat rate values for each activity or a subset of activities.