  Human activity recognition serves an important part in building continuous
behavioral monitoring systems, which are deployable for visual surveillance,
patient rehabilitation, gaming, and even personally inclined smart homes. This
paper demonstrates our efforts to develop a collaborative decision fusion
mechanism for integrating the predicted scores from multiple learning
algorithms trained on smartphone sensor based human activity data. We present
an approach for fusing convolutional neural network, recurrent convolutional
network, and support vector machine by computing and fusing the relative
weighted scores from each classifier based on Tsallis entropy to improve human
activity recognition performance. To assess the suitability of this approach,
experiments are conducted on two benchmark datasets, UCI-HAR and WISDM. The
recognition results attained using the proposed approach are comparable to
existing methods.
