Actigraphy, or the non-invasive study of human activity-rest cycles, is a field of study of growing importance as ambulatory and at-home monitoring of patients becomes more popular.
Researchers at Stanford have developed a cloud-based behind-the-meter (BTM) system that can cut energy costs and reduce reliance on the grid close to 93% respectively.
Machine learning models currently require extensive computational resources and this demand is growing rapidly with new models and applications being introduced.