Abstract
In Internet of things (IoT) dedicated for healthcare, heterogeneous data can be gathered from different body sensors, environmental sensors and other data sources such as cameras, audio recorders, etc. The aggregation, synchronization, processing and fusion of these heterogeneous data are critical tasks to accurately provide real-time healthcare services. This paper provides a survey on different multi-sensor data fusion techniques in IoT for healthcare. Through focusing on decision-level fusion, the paper explains different advanced techniques such as machine learning that are needed for the integration of multiple healthcare data sources. Detailed comparisons of sensors used, healthcare applications, types of environment, accuracy metrics and results are discussed. In addition, we present observations and recommendations for researches who wish to work in sensor fusion for healthcare.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call