A transfer fusion framework for body sensor networks (BSNs): Dynamic domain adaptation from distribution evaluation to domain evaluation

• Domain adaptation was embedded in BSN fusion framework to overcome multidomain deviation. • Domain adaptation contains evaluation efforts from distribution to domain. • A solution for quantitatively evaluating the domain weights was proposed. • Transfer fusion framework has promising effects and universality for cross-domain fusion in BSNs. Information fusion is a scenario-driven, long-term, challenging task in body sensor networks (BSNs). Recent strategies, such as supervised or semi-supervised learning, rely heavily on fusion-decision models that are supported by prior knowledge or experience. Such techniques typically need a large amount of labeled physiological data and require the target data to have the same distribution as the prior data, which is challenging to achieve in practice. Domain adaptation (DA) is considered a viable solution for the cross-domain problem in BSNs. However, the correlation between the fusion rules and DA was rarely considered in existing studies. The independence of the domain deviation for each sensing source was ignored. This study aims to provide a universal solution for cross-domain information fusion scenarios in BSNs. Firstly, a transfer fusion framework (TF) was proposed to simultaneously solve the problems of the information fusion and multidomain deviation in BSNs by adjusting the correlation between the DA and fusion. Secondly, a DA algorithm based on dynamic domain evaluation (DDE) was proposed for the DA stage of the TF framework. In addition, we provided a feasible solution for quantitatively evaluating the domain weights. Extensive experiments on emotion recognition, fall detection, daily activity recognition, and hand movement recognition have verified the adaptability and promising performance of TF and DDE for cross-domain information fusion in BSNs. The average classification accuracy of the TF is 8.39% and 6.71% higher than that of the conventional fusion transfer framework and the baseline method, respectively. In all transfer tasks, DDE shows the best performance, which is 4.66% higher than the classification accuracy of the optimal comparison method. This study can provide technical support for BSN applications based on cross-domain information fusion.