A recent study by CDC found out that among older adults, falls are the leading cause of both fatal and non-fatal injuries. As mobile and personal health devices gain in popularity, increasing amounts of data are collected via embedded sensors, such as heart-rate monitors and accelerometers. Hear how AI (artificial intelligence) and machine learning can be used to detect falls, by analyzing real data obtained from digital wireless wristbands used at health-care facilities. In addition to detecting falls, the same information can be used to recognize different forms of human motion to ultimately create a better predictor of fall possibility. Results show a significant improvement in motion-recognition rate, while overall accuracy involving seven selected activity classes is greater than 90 percent, compared to the most recent literature at 54 percent.
Speaker: Ismail Uysal, Director of RFID Lab for Applied Research and Assistant Professor, University of South Florida