What works for bridges may work for fall detection

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Researchers at University of South Carolina‘s College of Engineering and Computing, plus faculty at the University of South Carolina School of Medicine and the College of Social Work, are taking a different approach to fall detection in the home. Using information from standard matchbox-sized sensors that monitor bridge safety via vibrations, they have developed an algorithm that tracks vibrations associated with an individual’s movement. The article emphasizes the fall detection aspects (a person hitting the floor is different than a ball), but what about anticipating the changes in gait and movement that could help predict a propensity for falls? Point taken about resistance to devices, thus an ‘invisible system’, but can the algorithms model to help predict as well? Suggestion: go two states north and talk with their colleagues at University of Virginia, who are testing AFrame Digital’s ‘wristwatch’ for same with an NIH grant [TA 2 Sep]. Hat tip to reader Shana Duthie of Nurture Connect via Twitter. Fall monitoring device could end standoffs, keep seniors safer (Medical Express)