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: 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: A system for monitoring health parameters of a user includes a housing including: a plurality of sensors disposed on an outer surface of the housing or within the housing for measuring a plurality of parameters of interest; a processor disposed in the housing and communicatively coupled to the plurality of sensors; a coupling element on the housing for coupling the housing to an accessory; and an accessory identifier positioned on or within the housing and communicatively coupled to the processor. In some embodiments, the housing is reversibly transitionable between an uncoupled state and a coupled state with the accessory. In the coupled state, the accessory identifier senses a type of accessory and the processor activates a subset of the plurality of sensors to measure a subset of the plurality of parameters of interest. In some embodiments, the user is a pregnant female and a fetus developing in the pregnant female.

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: 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 system for monitoring health parameters of a user includes a housing including: a plurality of sensors disposed on an outer surface of the housing or within the housing for measuring a plurality of parameters of interest; a processor disposed in the housing and communicatively coupled to the plurality of sensors; a coupling element on the housing for coupling the housing to an accessory; and an accessory identifier positioned on or within the housing and communicatively coupled to the processor. In some embodiments, the housing is reversibly transitionable between an uncoupled state and a coupled state with the accessory. In the coupled state, the accessory identifier senses a type of accessory and the processor activates a subset of the plurality of sensors to measure a subset of the plurality of parameters of interest. In some embodiments, the user is a pregnant female and a fetus developing in the pregnant female.

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.