A Real-Time Patient Monitoring Framework for Fall Detection

الملخص EN

Fall detection is a major problem in the healthcare department.

Elderly people are more prone to fall than others.

There are more than 50% of injury-related hospitalizations in people aged over 65.

Commercial fall detection devices are expensive and charge a monthly fee for their services.

A more affordable and adaptable system is necessary for retirement homes and clinics to build a smart city powered by IoT and artificial intelligence.

An effective fall detection system would detect a fall and send an alarm to the appropriate authorities.

We propose a framework that uses edge computing where instead of sending data to the cloud, wearable devices send data to a nearby edge device like a laptop or mobile device for real-time analysis.

We use cheap wearable sensor devices from MbientLab, an open source streaming engine called Apache Flink for streaming data analytics, and a long short-term memory (LSTM) network model for fall classification.

The model is trained using a published dataset called “MobiAct.” Using the trained model, we analyse optimal sampling rates, sensor placement, and multistream data correction.

Our edge computing framework can perform real-time streaming data analytics to detect falls with an accuracy of 95.8%.