How AI Overcomes the Challenges of Indoor Asset Tracking in Hospitals
by Adrian Jennings, Cognosos Chief Product Officer
In IoT applications, AI is most often employed at the “top end” of the data stack – operating on large datasets, often from multiple sources. In a hospital setting, for example, AI and RTLS might be used for predictive analytics: can you predict the rate of ER admissions based on the weather? Can you better estimate when equipment requires maintenance based on usage?
At the “bottom end” of every IoT stack, however, AI is beginning to be applied to the sensors themselves with a very important effect: AI enables low-quality sensors to achieve very high-quality performance, delivering a return on investment that’s been absent in many IoT solutions until now.
Read the full article on IoT For All.