Deep-learning-based fall detection based on human keypoints

• Main Authors: Zheng, Jiannan, Chan, Chi Chung, Au, Andrew Tsun-Hong, Zhang, Dong
• Format: Patent
• Language: English
• Published: 05.03.2024
• Subjects: ALARM SYSTEMS; CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; DIAGNOSIS; HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA; HUMAN NECESSITIES; HYGIENE; IDENTIFICATION; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; MEDICAL OR VETERINARY SCIENCE; ORDER TELEGRAPHS; PHYSICS; SIGNALLING; SIGNALLING OR CALLING SYSTEMS; SURGERY

Various embodiments of a fall-detection system for detecting personal fall while preserving the privacy of a detected person are disclosed. This disclosed fall-detection system can begin by receiving a sequence of video images comprising a person being monitored. The disclosed fall-detection system then processes each video image in the sequence of video images by: detecting the person in the video image; and extracting a skeletal figure of the detected person by identifying a set of human keypoints from the detected person. Next, the disclosed fall-detection system processes the sequence of skeletal figures corresponding to the sequence of video images by labeling each skeletal figure in the sequence of skeletal figures with an action among a set of predetermined actions. The disclosed fall-detection system subsequently generates a fall/non-fall decision for the detected person based on the set of action labels corresponding to the sequence of video images.