Fall Detection With UWB Radars and CNN-LSTM Architecture

• Published in: IEEE journal of biomedical and health informatics Vol. 25; no. 4; pp. 1273 - 1283
• Main Authors: Maitre, Julien, Bouchard, Kevin, Gaboury, Sebastien
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Apartments; Artificial neural networks; Brain injury; Burns; Cameras; Classification; CNN-LSTM; Detection; End of life; Fall; Fall detection; Falls; Feature extraction; Hemorrhage; Informatics; Injuries; Leave-one-subject-out; Long short-term memory; Model testing; Neural networks; Pain; Short term; Three-dimensional displays; Trauma; Ultra wideband radar; Ultrawideband

Fall detection is a major challenge for researchers. Indeed, a fall can cause injuries such as femoral neck fracture, brain hemorrhage, or skin burns, leading to significant pain. However, in some cases, trauma caused by an undetected fall can get worse with the time and conducts to painful end of l...