When older adults fall at home, every second counts -- especially when they are alone. New research aims to cut reaction times with a human action recognition (HAR) algorithm that uses local computing power to analyze sensor data and detect abnormal movements without transmitting to a processing center offsite.
When older adults fall at home, every second counts -- especially when they are alone. New research aims to cut reaction times with a human action recognition algorithm that uses local computing power to analyze sensor data and detect abnormal movements without transmitting to a processing center offsite.
Chen said the research is important for an underserved population:"When many people talk about high tech, they are discussing something cutting edge, like a fancier algorithm, a more powerful assistant to do jobs faster or having more entertainment available. We observed a group of people -- senior citizens -- who need more help but normally do not have sufficient resources or the opportunity to tell high-tech developers what they need.
"The most dangerous place for falls is the bathroom, but nobody wants to set up a camera there," Chen said."People would hate it." Another idea, which Chen is exploring with Associate Professor Shiqi Zhang from the Department of Computer Science, is for the system to include a robot dog or similar"pet" that would keep a closer watch as someone did their daily tasks. Last fall, Zhang demonstrated how a robot dog might guide someone with visual impairment through tugs on a leash.
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