Abstract: A system including a radar unit, a processor unit and a communication module, the radar unit including a transmitter antenna connected to an oscillator and transmitting electromagnetic waves into a monitored region, and a receiver antenna receiving electromagnetic waves reflected by objects within the monitored region and generating raw data, the communication module communicating alerts to third parties, the processor unit including a moving body isolation processor, a tracker module receiving and processing data from the radar unit to identify and track location of moving targets over time thereby generating target data, an alert-threshold generator generating an alert-threshold, a neural network receiving from the tracker module target data inputs selected from height profiles, signal-to-noise ratio and radial distance to object and generating a fall likelihood score, and a fall identification module receiving the fall likelihood score and detecting a fall event if the likelihood score is above an alert-th

Abstract: Using transmitters and receivers for detecting occupants in a vehicle, and classifying those occupants according to the geometry of the vehicle, where sets of complex values associated with voxels in a predetermined region of the vehicle are converted into 3D complex images, where clusters of voxels in the 3D complex images are analyzed to determine presence of occupants, and where positions of seats may determine which seats in the vehicle are occupied.

Abstract: Target monitoring and alert systems for identifying and tracking targets within radar data. Raw frame data is filtered to remove data relating to reflections from static objects. Moving targets are identified in the filtered data and their location is tracked over time generating target data. Neural networks may process the target data, calculate a fall likelihood score and generate a fall-alert if this is above an alert-threshold. A person identification module may extract the position and motion characteristics of each target from the data and generate a probabilistic identification of the target with a person.

Abstract: Systems and methods for monitoring health and wellbeing in an ongoing manner use radar chips to scan monitored regions such that data obtained by the scanning radar chip are processed to identify targets within the monitored region. Health indication and activity parameters are gathered via a wellbeing prediction engine assesses the targets using a machine learning model.

Abstract: Vehicle cabin monitoring using a radar unit centrally positioned within the cabin to obtain image data of the vehicle cabin and a processor to generate detect occupancy of seats within the vehicle cabin, categorize occupants, detect posture, determine seatbelt status and monitor life signs of the occupants. An output unit may execute responses appropriate to the status of occupants of the vehicle cabin.

Abstract: Fall detection systems and methods use radar chips to scan monitored regions such that data obtained by the scanning radar chip are processed to identify targets within the monitored region. Targets are tracked and profiled indicating their posture and fall detection rules are applied. Standard energy profiles and time dependent energy profiles are generated for various segments of the monitored region and compared to the current energy profile for each target segment of the monitored region. Anomalies are detected, false fall alerts filtered out and verified fall alerts are generated.

Abstract: Fall detection systems and methods use radar chips to scan monitored regions such that data obtained by the scanning radar chip are processed to identify targets within the monitored region. Targets are tracked and profiled indicating their posture and fall detection rules are applied. Standard energy profiles and time dependent energy profiles are generated for various segments of the monitored region and compared to the current energy profile for each target segment of the monitored region. Anomalies are detected, false fall alerts filtered out and verified fall alerts are generated.

Abstract: Using transmitters and receivers for detecting occupants in a vehicle, and classifying those occupants according to the geometry of the vehicle, where sets of complex values associated with voxels in a predetermined region of the vehicle are converted into 3D complex images, where clusters of voxels in the 3D complex images are analyzed to determine presence of occupants, and where positions of seats may determine which seats in the vehicle are occupied.