RFID Journal LIVE! 2017SessionsImproving Patient Safety With Wearable Sensors

Improving Patient Safety With Wearable Sensors

Breakout Session
The population of the United States is aging. Based on the latest predictions by the Administration of Aging (AoA), by 2020 there will be approximately 55 million people in the country aged 65 or older, which is almost double its value in 1990. 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 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

  • Dr. Ismail Uysal
    Director of RFID Lab for Applied Research and Assistant Professor
    University of South Florida