Smart Healthcare Uses Stats to Spot a Stumble
From: KAUST Discovery - 04/16/2017

Researchers at the King Abdullah University of Science and Technology (KAUST)
in Saudi Arabia have developed a statistical scheme that detects when senior
citizens or others need help after falling. The system relies on combining
data from both wearable sensors and video surveillance. Although
accelerometers in most wearable sensors use manually-set thresholds to
trigger an alert signal, the KAUST researchers used exponentially weighted
moving average (EWMA) charts to dynamically monitor acceleration data over
time, enabling any unusual changes to a person's movements to be identified
as sharp deviations from the typical dataset. The team then integrated EWMA
chart-monitoring into a model "smart home" environment containing multiple
surveillance cameras to better recognize significant fall events. They note
their method uses computer-vision algorithms to subtract backgrounds and
imaging artifacts from the video data to focus only on human shapes.

Read the entire article at:
https://discovery.kaust.edu.sa/en/article/330/smart-healthcare-uses-stats-to-spot-a-stumble