Methods and systems are provided herein for analyzing, monitoring, and/or influencing a user's behavioral gesture in real-time. A gesture recognition method may be provided. The method may comprise: obtaining sensor data collected using at least one sensor located on a wearable device, wherein said wearable device is configured to be worn by a user; and analyzing the sensor data to determine a probability of the user performing a predefined gesture, wherein the probability is determined based in part on a magnitude of a motion vector in the sensor data, and without comparing the motion vector to one or more physical motion profiles.

A computer-implemented process for electromagnetic imaging, the process including the steps of: accessing scattering data representing at least a two-dimensional array of measurements of electromagnetic wave scattering by internal features of an object, wherein the object is generally symmetrical with respect to a plane of symmetry through the object, and each said measurement represents scattering of electromagnetic waves emitted by a corresponding antenna of an array of antennas disposed about the object as measured by a corresponding antenna of the array of antennas; and processing the scattering data to generate image data representing a spatial distribution of at least one internal feature of the object, wherein the generation of the image data does not involve tomographic reconstruction but is in accordance with statistical metrics of similarity between pairs of corresponding regions within the object on either side of the plane of symmetry.

Methods and apparatus monitor health by detection of sleep stage. For example, a sleep stage monitor (100) may access sensor data signals related to bodily movement and/or respiration movements. At least a portion of the detected signals may be analyzed to calculate respiration variability. The respiration variability may include one or more of variability of respiration rate and variability of respiration amplitude. A processor may then determine a sleep stage based on one or more of respiration variability and bodily movement, such as with a combination of both. The determination of sleep stages may distinguish between deep sleep and other stages of sleep, or may differentiate between deep sleep, light sleep and REM sleep. The bodily movement and respiration movement signals may be derived from one or more sensors, such as non-invasive sensor (e.g., a non-contact radio-frequency motion sensor or a pressure sensitive mattress).

A method for detecting vital information and humidity by using a frequency-modulated continuous wave (FMCW) radar device is provided, including: transmitting an incident radar signal to a field, detects a reflected radar signal reflected by a to-be-measured object and a reflected radar signal reflected by a humidity sensor tag, so as to obtain vital information of the to-be-measured object and humidity information detected by the humidity sensor tag.

In an example, the technique also detects and measures vital signs of each human target by continuous, non-intrusive method. In an example, the vital signs of interest include a heart rate and a respiratory rate, which can provide valuable information about the human's wellness. Additionally, the heart rate and respiratory rate can also be used to identify a particular person, if more than two target humans living in a home. Of course, there can be other variations, modifications, and alternatives.

A method for training a model for use in monitoring a health parameter in a person is disclosed. The method involves receiving control data that corresponds to a control element, wherein the control data corresponds to a health parameter of a person, receiving stepped frequency scanning data that corresponds to radio waves that have reflected from the control element, wherein the stepped frequency scanning data includes frequency and corresponding amplitude and phase data over a range of frequencies, generating training data by combining the control data with the stepped frequency scanning data in a time synchronous manner, and training a model using the training data to produce a trained model, wherein the trained model correlates stepped frequency scanning data to values that are indicative of a health parameter of a person.

Methods, devices, and systems for contactless cough detection and attribution are presented herein. Audio data may be received using a microphone. A cough may be identified as having occurred based on the received audio data. Radar data may be received indicative of reflected radio waves from a radar sensor. A state analysis process may be performed using the received radar data. The detected cough may be attributed to a particular user based at least in part on the state analysis process performed using the radar data.

A novel medical imaging system that is based on radio-wave signals at microwave frequencies and has unique properties. The system can be used for various diagnostic applications such as breast cancer detection, brain stroke detection, and assessment of internal bleeding (trauma emergencies).

A medical imaging system including a microwave antenna array having a transmitting antenna and a plurality of receiving antennae, wherein the transmitting antenna is configured to transmit microwave signals so as to illuminate a body part of a patient and the receiving antennae are configured to receive the microwave signals following scattering within the body part; a processor configured to process the scattered microwave signals and generate an output indicative of the internal structure of the body part to identify a region of interest within the body part; and a biopsy device comprising a biopsy needle movable relative to the microwave antenna array; wherein the receiving antennae are further configured to receive microwave signals scattered or emitted by the biopsy needle and the processor is further configured to monitor a position of the biopsy needle and to guide the biopsy needle to the identified region of interest within the body part.

A detection device for detecting and characterizing biological energy fields emitted by biological specimens is configured to collect and analyze an electromagnetic signal that includes millimeter-length waves generated by the interaction of atmospheric plasma with torsion waves of the biological energy field. The device performs spectral analysis on the millimeter waves to determine characteristics of the corresponding torsion waves that generated them. An array of several hundred non-thermal plasma plumes are placed directly in front of a circular horn. A switchable circular polarizer is used to select left hand circular, linear or right hand circular polarization. A low noise frequency converter allows a noise temperature of less than 1150 K. A frequency scan and averaging algorithm is developed to characterize noise temperature versus frequency, comparing signal and noise levels between plasma on and plasma off, and switching polarization sense.