AI in communications for remote healthcare

Researcher: Julián Jerónimo Bañuelos

Recent technological advances in telecommunications networks, such as the development and deployment of the fifth-generation technology standard for broadband cellular networks (5G), have enabled a wide array of new functionalities for mobile networks, also including, for the first time, reduced capability Internet of Things (IoT) devices. 5G networks are designed to support integration with external applications that can manipulate the services the network provides to devices.

Provisioning devices with 5G network slicing capabilities is of great interest to manufacturers, carriers, and developers, particularly in IoT-type communication, which may feature unique bandwidth and resource requirements. External applications running either on the device or third-party applications can interact with the 5G network to request additional information associated with the device, such as positioning or augmenting the services assigned to the device by requesting new data flows with higher Quality of Service (QoS).

The implementation of technological support consisting on the use of tools and technological devices such as sensors, smartphones, and IoT devices, among others, as well as their interaction with modern network infrastructures, to gather insightful information from the current state of the user and its environment could have a relevant impact on how the monitoring of activity tracking is performed remotely and in a regular basis, regardless of the kind of setting involved. The automation of this regular activity tracking could enhance the quality of life of the user by enabling access to selectively processed knowledge that otherwise would have to be manually collected and analysed. It will also lead to an enhanced user experience in smart IoT environments.

Furthermore, the constant generation of data from the remote activity tracking tasks enables the possibility to explore additional challenges that can be tackled with the use of efficient algorithms that implement techniques of the field of Machine Learning. As most of the activity tracking tasks nowadays rely on the use of different types of sensors with different configurations, and since the brands and specific models of such sensors in the market have increase in recent years, it is necessary to define a strategy for dealing with sensor data that may come from different sources but that is to some extent correlated due to the nature of the activity tracking task. One sample scenario that will be studied is the generation of synthetic accelerometer data of a specific body position for a given human activity task due to the possibility of missing, corrupted, or incomplete data, from additional accelerometer data obtained from alternative body locations for the same human activity task.

This research aims to combine the technical implementation of an application programming interface for enabling 5G network slicing capabilities in Android devices while addressing different particular scenarios of remote activity tracking through machine learning including the generation of synthetic accelerometer data that is correlated to a dataset of collected accelerometer data from a set of tracked human activities.

Reference

Aalto-DC1

Research Areas

Research Host

Aalto University

PhD awarding institution/s

Aalto University & RMIT University

Location

Espoo (Finland)

Publications

RMIT and many of the REDI partners are HSR4R certified
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101034328.

Results reflect the author’s view only. The European Commission is not responsible for any use that may be made of the information it contains

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