Wearable sensor based multimodal human activity recognition exploiting the diversity of classifier ensemble
Effectively utilizing multimodal information (e.g., heart rate and acceleration) is a promising way to achieve wearable sensor based human activity recognition (HAR). In this paper, an activity recognition approach MARCEL (<u>M</u>ultimodal <u>A</u>ctivity <u>R</u>ecognition with <u>C</u>lassifier <u>E</u>nsemble) is proposed, which exploits the diversity of base classifiers to construct a good ensemble for multimodal HAR, and the diversity measure is obtained from both labeled and unlabeled data. MARCEL uses neural network (NN) as base classifiers to construct the HAR model, and the diversity of classifier ensemble is embedded in the error function of the model. In each iteration, the error of the model is decomposed and back-propagated to base classifiers. To ensure the overall accuracy of the model, the weights of base classifiers are learnt in the classifier fusion process with sparse group lasso. Extensive experiments show that MARCEL is able to yield a competitive HAR performance, and has its superiority on exploiting multimodal signals.