Majority-consensus fusion approach for elderly IoT-based healthcare applications

Abstract

Nowadays, tremendous growth of Internet of Things (IoT) applications is seen in smart environments such as medical remote care applications which are crucial due to the general aging of the population. With the recent advancements in IoT-based healthcare technologies, activity recognition can be used as the key part of the intelligent healthcare systems to monitor elderly people to live independently at homes and promote a better care. Recently, the evidence theory and its derivates approaches began to take place in the fields of activity recognition in these smart systems. However, these approaches are generally inconsistent with the probability calculus due to the lower and upper probability bounds considering the combined evidences. To overcome these challenges and to get more precisely the reconcilement between the evidence theory with the frequentist approach of probability calculus, this work proposes a new methodology for combining beliefs, addressing some of the disadvantages exhibited by the evidence theory and its derivatives. This methodology merges the non-normalized conjunctive and the majority rules. The proposed rule is evaluated in numerical simulation case studies involving the activity recognition in a smart home environment. The results show that this strategy produces intuitive results in favor of the more committed hypothesis.