Abstract: Breathing signals are extracted from reflected wireless signals that are reflected from objects and/or people in a room. Feature maps are formed for the breathing signals at respective time intervals, each feature map representing a correlation of the breathing signals at each voxel in a plurality of voxels that represent three-dimensional locations within the room. The identity of the source of the breathing signals can be determined by statistically comparing the features maps with one or more location templates. Alternatively, at least some of the feature maps can be grouped into clusters. The distance between a respective cluster and a predetermined sleep area of at least one person can be used to identify the source of the breathing signals associated with the respective cluster.

Abstract: A wireless method for predicting an inflammation state of a person under observation, comprising: (a) transmitting frequency-modulated continuous-wave (FMCW) wireless signals from one or more transmitting antennas; (b) receiving reflected FMCW wireless signals with one or more receiving antennas, at least some of the reflected FMCW wireless signals being reflected from the person; (c) repeating steps (a) and (b) continuously while the person is under observation; (d) producing reflected FMCW wireless data based on the reflected FMCW wireless signals; (e) providing the reflected FMCW wireless data as an input to a trained machine-learning (ML) model, the trained ML model having been trained with ground-truth data that represents ground-truth inflammation states and ground-truth reflected FMCW wireless data of one or more subjects with respect to time; and (f) predicting, with the trained ML model, whether the person under observation is in an inflamed state or a non-inflamed state.

Abstract: A method for tracking a sleep stage of a subject takes as input a sequence of observations sensed over an observation time period. The sequence of observation values is processed to yield a corresponding sequence of encoded observations using a first artificial neural network (ANN) and the sequence of encoded observation values is processed to yield a sequence of sleep stage indicators using a second artificial network. Each observation may correspond to an interval of the observation period (e.g., at least 30 seconds). The first ANN may be configured to reduce information representing a source of the sequence of observations in the encoded observations.