Remote healthcare monitoring has exponentially
grown over the past decade together with the increasing penetration
of Internet of Things (IoT) platforms. IoT-based health
systems help to improve the quality of healthcare services
through real-time data acquisition and processing. However,
traditional IoT architectures have some limitations. For instance,
they cannot properly function in areas with poor or unstable
Internet. Low power wide area network (LPWAN) technologies,
including long-range communication protocols such as LoRa,
are a potential candidate to overcome the lacking network
infrastructure. Nevertheless, LPWANs have limited transmission
bandwidth not suitable for high data rate applications such as fall
detection systems or electrocardiography monitoring. Therefore,
data processing and compression are required at the edge of
the network. We propose a system architecture with integrated
artificial intelligence that combines Edge and Fog computing,
LPWAN technology, IoT and deep learning algorithms to perform
health monitoring tasks. In particular, we demonstrate the feasibility
and effectiveness of this architecture via a use case of fall
detection using recurrent neural networks. We have implemented
a fall detection system from the sensor node and Edge gateway to
cloud services and end-user applications. The system uses inertial
data as input and achieves an average precision of over 90% and
an average recall over 95% in fall detection.
Index Terms—IoT; Edge Computing; Healthcare Monitoring;
LoRa; LPWAN; RNN; LSTM; Fall Detection