A system for assessing and monitoring a fetal electrocardiogram (ECG) and heart rate in a pregnant mother comprises wearable mechanical-electronic sensors, e.g., embedded in a wrist or arm band, which can measure mechanical pulse signals from the mother, and an abdomen patch which can measure the combined ECG signals of the fetus and mother. In another embodiment, the sensors in the wrist or arm band measure the combined fetal/maternal ECG signals, and the mother's mechanical pulse signals. By signal processing and gating out the maternal ECG signals as correlated with the mechanical maternal pulse signals, the fetal ECG and heart rate can be measured and monitored. These measurements may be displayed on the wrist or arm band device, or wirelessly through a remote device, mobile phone or computer. Sensors in the abdominal patch may also measure uterine electromyogram, uterine contractions, and fetal movements, to be correlated with the fetal ECG.

A computer-implemented method includes: monitoring, by a computing device, exposed stimuli based on sensor data; monitoring, by the computing device, a response to the stimuli based on the sensor data; detecting, by the computing device, a deviation between the response to the stimuli and an expected response to the stimuli, wherein the expected response to the stimuli is determined based on information stored by a knowledge corpus; and executing, by the computing device, a deviation instruction based on the detecting the deviation.

A fetal movement measuring device includes a detection unit and a smart device. The detection unit is selected from elastic materials and is attachable to an abdomen of a pregnant woman. The detection unit includes multiple branch sections extending outward therefrom. Each of the branch sections includes a fetal movement sensor and an electrode patch. The fetal movement sensor and the electrode patch are connected to a main circuit board to transmit an abdominal dynamic physiological signal detected by the fetal movement sensor and a fetal electrocardiographic signal detected by the electrode patch to the main circuit board. The smart device is operable to receive the abdominal dynamic physiological signal and the fetal electrocardiographic signal from the main circuit board and includes a fetal movement algorithm program that calculates and generates fetal movement data and the fetal heart rate.

Devices, systems and methods are described herein that enable and make assessments related to pregnancy, including assessments related to a labor-related status such as the onset of true labor. In some embodiments, a pregnancy monitoring system comprises a portable, noninvasive data acquisition device adhered to a patient's skin, comprising at least two electrodes to measure uterine electrical activity, signal processing means, data storage means, and a battery. Systems for notification of the labor-related status are described.

Utilizing a computing device to generate a personalized maternal nutrition plan for a pregnant mother. The computing device receives real-time maternal physiological data, fetal physiological data, and real-time environmental data associated with the pregnant mother. A real-time relationship is established. The real-time relationship is compared with historically established relationships between pregnant mothers and fetuses. The maternal physiological data is compared with pre-pregnancy data of the pregnant mother. The computing device detects one or more abnormalities in the real-time established relationship between the pregnant mother and the fetus based upon the comparison of the real-time established relationship with historically established relationships and the comparison of the maternal physiological data with pre-pregnancy data of the pregnant mother. The computing device generates a maternal nutrition plan. A notification is generated by the computing device and outputted to one or more electronic devices associated with the pregnant mother and/or one or more physicians.

A computing device arrangement (20) for receiving fetal monitoring data from a fetal monitoring system is disclosed. The computing device arrangement comprises a display device (30) arranged to display the fetal monitoring data, a touchscreen device (32) and a processor arrangement (24) communicatively coupled to the display device and the touchscreen device. The processor arrangement is arranged to receive the fetal monitoring data from the fetal monitoring system, control the display device to display the fetal monitoring data, receive a freeform input (36) in a region of the touchscreen device from the touchscreen device; modify the fetal monitoring data by annotating a displayed region (34) of the fetal monitoring data with the freeform input, said displayed region corresponding to the region of the touchscreen device; and generate an output of the modified fetal monitoring data to for storage in a data storage device (28). Also disclosed are a fetal monitoring system including such a computing device arrangement and a method of generating freeform annotations with such a computing device arrangement.

A micro impulse radar (MIR) system includes a first sensor, a second sensor, and a control circuit. The first sensor includes a micro impulse radar (MIR) sensor configured to receive a plurality of radar returns corresponding to an MIR radar signal transmitted towards a subject. The second sensor is configured to detect sensor data regarding the subject. The control circuit is configured to calculate a physiological parameter of the subject based on the plurality of radar returns and the sensor data.

A stethoscope system includes a microphone device configured to receive a plurality of sound waves from the subject and output an audio signal corresponding to the plurality of sound waves; and a control circuit configured to receive the audio signal from the microphone device and calculate a physiological parameter based on the audio signal.

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.

A system for the assessment and monitoring of the fetal electrocardiogram (ECG) and heart rate in a pregnant mother comprises wearable mechanical-electronic sensors, e.g., embedded in a wrist or arm band, which can measure the mechanical pulse signals from the mother, and an abdomen patch which can measure the combined ECG signals of the fetus and mother. The wrist or arm unit and the abdominal patch unit communicate wirelessly. By signal processing and gating out the maternal ECG signals as correlated with the mechanical maternal pulse signals, the fetal ECG and heart rate can be measured and monitored. These measurements may be displayed on the wrist or arm band device, or wirelessly through a remote device, mobile phone or computer. Electronic-mechanical sensors can also be embedded in the abdominal patch to measure uterine electromyogram (EMG), uterine contractions and fetal movements, to be correlated with the fetal ECG.