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.
From KAUST Discovery
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