Abstract: A non-wearable Personal Emergency Response System (PERS) architecture is provided, implementing RF interferometry using synthetic aperture antenna arrays to derive ultra-wideband echo signals which are analyzed and then processed by a two-stage human state classifier and abnormal states pattern recognition. Systems and methods transmit ultra-wide band radio frequency signals at, and receive echo signals from, the environment, process the received echo signals to yield a range-bin-based slow signal that is spatially characterized over a plurality of spatial range bins, and estimate respiration parameter(s) of the human(s) by analyzing the slow signal. The antennas may be arranged in several linear baselines, implement virtual displacements, and may be set into multiple communicating sub-arrays. A classifier uses respiration and other derived features to classify the state of the human(s).

Abstract: A method for detecting an indication of likelihood of malignancy, comprising: receiving a sequence of anatomical images of a breast of a target individual acquired over a time interval during which contrast is administered, analyzing the sequence of anatomical images to identify: a baseline pre-contrast image denoting lack of contrast, a peak contrast image denoting a peak contrast enhancement, an initial uptake image denoting initial contrast enhancement, and a delayed response image denoting final contrast enhancement, creating a multi-channel image representation comprising: intensity channel including the peak contrast enhanced image, contrast-update channel including the computed difference between the peak contrast enhanced image and the pre-contrast image, and contrast-washout channel including the computed difference between the initial uptake image and the delayed response image, and computing by a trained deep convolutional neural network, a classification category indicative of likelihood of malign

Abstract: A method for detecting an indication of likelihood of malignancy, comprising: receiving a sequence of anatomical images of a breast of a target individual acquired over a time interval during which contrast is administered, analyzing the sequence of anatomical images to identify: a baseline pre-contrast image denoting lack of contrast, a peak contrast image denoting a peak contrast enhancement, an initial uptake image denoting initial contrast enhancement, and a delayed response image denoting final contrast enhancement, creating a multi-channel image representation comprising: intensity channel including the peak contrast enhanced image, contrast-update channel including the computed difference between the peak contrast enhanced image and the pre-contrast image, and contrast-washout channel including the computed difference between the initial uptake image and the delayed response image, and computing by a trained deep convolutional neural network, a classification category indicative of likelihood of malign

Abstract: A non-wearable Personal Emergency Response System (PERS) architecture is provided, implementing RF interferometry using synthetic aperture antenna arrays to derive ultra-wideband echo signals which are analyzed and then processed by a two-stage human state classifier and abnormal states pattern recognition. Systems and methods transmit ultra-wide band radio frequency signals at, and receive echo signals from, the environment, process the received echo signals to yield a range-bin-based slow signal that is spatially characterized over a plurality of spatial range bins, and estimate respiration parameter(s) of the human(s) by analyzing the slow signal. The antennas may be arranged in several linear baselines, implement virtual displacements, and may be set into multiple communicating sub-arrays. A classifier uses respiration and other derived features to classify the state of the human(s).

Abstract: A non-wearable Personal Emergency Response System (PERS) architecture is provided, implementing RF interferometry using synthetic aperture antenna arrays to derive ultra-wideband echo signals which are analyzed and then processed by a two-stage human state classifier and abnormal states pattern recognition. Systems and methods transmit ultra-wide band radio frequency signals at, and receive echo signals from, the environment, process the received echo signals to yield a range-bin-based slow signal that is spatially characterized over a plurality of spatial range bins, and estimate respiration parameter(s) of the human(s) by analyzing the slow signal. The antennas may be arranged in several linear baselines, implement virtual displacements, and may be set into multiple communicating sub-arrays. A classifier uses respiration and other derived features to classify the state of the human(s).

Abstract: A non-wearable Personal Emergency Response System (PERS) architecture is provided, having a synthetic aperture antenna based RF interferometer followed by two-stage human state classifier and abnormal states pattern recognition. Systems and methods transmit ultra-wide band radio frequency signals at, and receive echo signals from, the environment, process the received echo signals to yield a range-bin-based slow signal that is spatio-temporally characterized over multiple spatial range bins and multiple temporal sub-frames, respectively, and derive from the slow signal a Doppler signature and a range-time energy signature as motion characteristics of human(s) in the environment and optionally also derive location data as movement characteristics thereof. The decision process is carried out based on the instantaneous human state (local decision) followed by abnormal states patterns recognition (global decision).

Abstract: A non-wearable Personal Emergency Response System (PERS) architecture is provided, having a synthetic aperture antenna based RF interferometer followed by two-stage human state classifier and abnormal states pattern recognition. Systems and methods transmit ultra-wide band radio frequency signals at, and receive echo signals from, the environment, process the received echo signals to yield a range-bin-based slow signal that is spatio-temporally characterized over multiple spatial range bins and multiple temporal sub-frames, respectively, and derive from the slow signal a Doppler signature and a range-time energy signature as motion characteristics of human(s) in the environment and optionally also derive location data as movement characteristics thereof. The decision process is carried out based on the instantaneous human state (local decision) followed by abnormal states patterns recognition (global decision).

Abstract: A non-wearable Personal Emergency Response System (PERS) architecture is provided, implementing RF interferometry using synthetic aperture antenna arrays to derive ultra-wideband echo signals which are analyzed and then processed by a two-stage human state classifier and abnormal states pattern recognition. Systems and methods transmit ultra-wide band radio frequency signals at, and receive echo signals from, the environment, process the received echo signals to yield a range-bin-based slow signal that is spatially characterized over a plurality of spatial range bins, and estimate respiration parameter(s) of the human(s) by analyzing the slow signal. The antennas may be arranged in several linear baselines, implement virtual displacements, and may be set into multiple communicating sub-arrays. A classifier uses respiration and other derived features to classify the state of the human(s).