Abstract: The disclosure describes a system comprising: a memory; and one or more processors in communication with the memory. The one or more processors are configured to: receive, from a set of sensors, biometric data indicative of a muscle contraction of a patient over a period of time; and determine, based on the biometric data, a muscle contraction vector indicating a direction of the muscle contraction over the period of time. Additionally, the one or more processors are configured to determine, based on the biometric data, a likelihood that the muscle contraction comprises a true labor uterine contraction; and output, for display by a user device, the muscle contraction vector indicating the direction of the muscle contraction and the likelihood that the muscle contraction comprises a true labor uterine contraction.

Abstract: A system includes a wearable device configured to be worn by a pregnant patient and including a plurality of electrodes, the wearable device configured to sense a first patient data associated with the patient and her fetus, a second device configured to sense a second patient data associated with the patient and her fetus, and a computing device configured to obtain the first and second patient data. The computing device is also configured to identify first features of a maternal or a fetal heart rate signal determined from the first patient data and to identify second features of a maternal or a fetal biometric signal determined from the second patient data, and to determine, based on applying the first and second features as inputs to a machine learning model, predicted outcomes associated with the patient, and to generate reports including an indication of the predicted outcomes for display.

Abstract: In some examples, a system includes a memory; and one or more processors in communication with the memory. The one or more processors are configured to: receive, from a user device, a user input including a request to initiate a physiological data collection procedure; cause the user device to display a first interactive page including one or more instructions for preparing for the physiological data collection procedure; and receive, from the user device, a user input including a request to start the physiological data collection procedure. Additionally, the one or more processors are configured to cause the user device to display a second interactive page including a set of icons, wherein each icon of the set of icons corresponds to a sensor of a set of sensors on a wearable device of the patient.

Abstract: A system for non-invasively acquiring maternal and/or fetal biopotential signals includes a wearable device configured to be worn by a pregnant patient comprising a plurality of electrodes configured to detect maternal and/or fetal biopotential signals associated with the patient and her fetus. The system further includes a computing device configured to receive the maternal and/or fetal biopotential signals from each of a plurality of electrode pairs and select, based on processing the maternal and/or fetal biopotential signals, one of the plurality of electrodes to use as a reference electrode during a monitoring session for the patient.

Abstract: The disclosure describes techniques for predicting maternal and/or fetal health outcomes based on maternal and/or fetal patient data. The patient data may include, for example, data regarding sensed biopotential signals such as maternal and/or fetal electrocardiography (ECG) signals, maternal and/or fetal electromyography (EMG) signals, and/or other biopotential signals. The patient data may further include maternal and/or fetal biometric data and/or health assessment data. The system determines, based on processing the patient data using a machine learning model trained with historical patient data for a plurality of patients, one or more predicted outcomes associated with the patient.