Machine learning for wearable IoT-based applications: A survey

This paper gives an overview about applying machine learning (ML) in wearable Wireless Body Area Network (WBAN). It highlights the main challenges and open issues for deploying ML models in such sensitive networks. The WBAN is an emerging technology in the last few years, which attracts lots of interest from the academic and industrial communities. It enables a wide range of IoT-based applications in medical, lifestyle, sport, and entertainment. WBAN are constrained in many aspects such as those related to power resources, communication capabilities, and computation power. Moreover, these networks generate ample amount of sensory data. To overcome the limitations of these networks and make use of the big data generated, artificial intelligence is the best way to deal with gigantic data and help in automating several aspects of the network and its applications. This survey paper aims at reporting numerous ways ML is used to benefit these networks, the design factors that are considered when implementing the ML algorithms, and the communication technologies used in connecting wearable WBAN in the IoT era. The reported studies are based on real overviewed experiment and extensive simulation results.

Automated summary

This paper provides an overview of the application of machine learning in wearable Wireless Body Area Networks (WBAN), highlighting the challenges faced and the potential benefits of using artificial intelligence to overcome limitations and automate processes.