Systems and methods for gesture control are disclosed. In some embodiments, a system may include a plurality of electrode pairs, a motion sensor, a controller, and a classifier. The system may be configured to: enter a monitoring state in which the system is configured to receive data; receive a first set of data; determine that the first set of data does not satisfy one or more action criteria; return to the monitoring state without transmitting the first set of data to the classifier; receive a second set of data; determine that the second set of data satisfies the one or more action criteria; transmit the second set of data to the classifier; using the classifier, analyze the second set of data to generate an interpreted output indicating a gesture performed by the person; and based on the interpreted output, generate a machine instruction.

One or more techniques and/or systems are disclosed for providing for improved monitoring of an individual, wherein imaging data is received from a plurality of imaging sensors. The received imaging data is aggregated and patterns at targeted data segments of the aggregated imaging data are analyzed using one or more algorithms to determine one or more values. The one or more values determined from the analyzed patterns are classified to represent at least one of a physiological state determination or a positioning of the individual. Data corresponding to the classified one or more values is then output and displayed.

A system may obtain a set of features characterizing a segment of inertial measurement unit (IMU) data generated by an IMU of an ear-wearable device. The system may apply a machine learning model (MLM) that takes the features characterizing the segment of the IMU data as input. The system may determine, based on output values produced by the MLM, whether a user of the ear-wearable device has potentially been subject to physical abuse. The system may then perform an action in response to determining that the user of the ear-wearable device has potentially been subject to physical abuse.

A heart monitoring system includes at least two sensors embedded in a seat, such as a driver's seat in a vehicle. One of the sensors obtains a phonocardiogram (PCG) of the driver's heart in addition to noise. Another sensor is a reference sensor that obtains a noise signal, but does not include the PCG signal. Processing circuitry receives the heart signal with the noise and the reference noise signal, and performs adaptive filtering to remove the noise from the heart signal. Further analysis detects a heart rate or other heart measurements in the heart signal, and may output an alert if a heart condition is detected.

A distributed patient monitoring system comprises at least one standalone portable ultrasound imaging unit configured to be fixed to a stable position against the skin on a patient's body and capable of prolonged ultrasound data acquisition, including an ultrasound imaging array, transmit-receive circuitry, a beamformer, backend signal and image processing subsystem, power and communication subsystems, and a monitoring workstation connected to each standalone portable ultrasound imaging unit configured to request and receive ultrasound imaging information from each standalone portable ultrasound imaging unit, and configured to analyze and display acquired ultrasound information.

The present technology relates to interatrial shunting systems and methods. In some embodiments, the present technology includes interatrial shunting systems that include a shunting element having a lumen extending therethrough that is configured to fluidly couple the left atrium and the right atrium when the shunting element is implanted in a patient. The system can also include an energy receiving component for receiving energy from an energy source positioned external to the body, an energy storage component for storing the received energy, and/or a flow control mechanism for adjusting a geometry of the lumen.

The body-worn wireless two-way communication system comprises a non-invasive and non-implanted system which allows for clear wireless two-way communications. This system is generally comprised of a mouthpiece component, relay component, infrastructure communication device, an optional earpiece component, and an optional system control which may interface with the relay component.

A maternal and fetal monitoring device using multiple sensing units is disclosed. The maternal and fetal monitoring device comprises: a processor module and a plurality of sensor modules, wherein each said sensor module comprises: an inertial sensor, a temperature sensor and a first acoustic sensor. After being attached onto a maternal body, each said sensor module collects a body temperature signal, an inertia signal and a sound signal. Subsequently, the processor module determines a maternal body posture by analyzing the inertia signal, determines a maternal physical condition and a fetal physical condition by analyzing the inertial signal, the plurality of first sound signals and the second sound signal, and estimates physiological parameters of the maternal body and a fetus by analyzing the body temperature sensing signal, the plurality of first sound signals, and the second sound signals.

This relates to the use of sensor evaluation in a multi-sensor environment. In a first aspect, this specification describes apparatus comprising: at least one processor; and at least one memory including computer program code. The at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to perform: receive sensor data from a plurality of sensors collected during a first time period; process the received sensor data through a plurality of layers of a neural network to generate an output indicative of the sensing quality of each of the plurality of sensors for a task; and cause a subset of the plurality of sensors to collect data during a second time period based on the output indicative of the suitability of each of the plurality of sensors for the task.

To identify physiological states that are predictive of a person's performance, a system provides physiological and behavioral interfaces and a data processing pipeline. Physiological sensors generate physiological data about the person while performing a task. The behavioral interface generates performance data about the person while performing the task. The pipeline collects the physiological and performance data along with reference data from a population of people performing the same or similar tasks. In various implementations, the physiological states are brain states. In one implementation, the pipeline computes bandpower ratios. In another implementation, the pipeline decomposes the physiological data into frequency-banded components, identifies brain states derived from the decomposed data—for example, clusters of correlations of decomposed data envelopes—grades the performance data, compares the graded performance data to the brain states, and identifies statistical relationships between the brain states and levels of performance.