Physical Activity Recognition Using Multi-Sensor Fusion and Extreme Learning Machines

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Sensor based Physical Activity (PA) recognition is an imperative research topic due to its wide applications in many areas such as mobile healthcare, elderly care and personalized recommendation. However, PA recognition based on single sensor suffers from issues such as limited spatial coverage, imprecision and uncertainty. In contrast, fusion of heterogeneous sensor sources brings improved resolution, precision and robustness. In this paper, a multi-sensor fusion method based on variants of Extreme Learning Machine (ELM) is presented to improve the recognition performance in terms of speed and accuracy. The Kernel ELM, Weighted ELM, Regularized ELM are tasked directly to handle the multi-activity classification problem using the real-world test data with 46 test subjects. Their performance has been evaluated against previously published methods based on Support Vector Machines (SVM) and K-Nearest Neighbor (KNN) using a two-step subject-wise cross validation scheme. The multi-sensor fusion based Kernel ELM has shown favorable performance with 88.4% average testing accuracy, outperforming SVM by 1.1%. And for real-time PA recognition, Kernel ELM is 30% faster than SVM. The ELMs trained with the two-step, subject-wise cross validation approach, combined with feature level data fusion also led to a better cross-person generalization than SVM and KNN.