Transition-Aware Human Activity Recognition Using Smartphones

NEUROCOMPUTING

卷:171

Article

Reyes-Ortiz, Jorge-L.^1,2  Oneto, Luca³  Sama, Albert²  Parra, Xavier²  Anguita, Davide¹ 

[1] Univ Genoa, DIBRIS, I-16145 Genoa, Italy

[2] Univ Politecn Cataluna, CETpD, Vilanova I La Geltru 08800, Spain

[3] Univ Genoa, DITEN, I-16145 Genoa, Italy

关键词: Activity Recognition;    Smartphones;    Transitions;    Support Vector Machines;    Machine learning;   

DOI  :  10.1016/j.neucom.2015.07.085

来源: Elsevier

【 摘 要 】

This work presents the Transition-Aware Human Activity Recognition (TAHAR) system architecture for the recognition of physical activities using smartphones. It targets real-time classification with a collection of inertial sensors while addressing issues regarding the occurrence of transitions between activities and unknown activities to the learning algorithm. We propose two implementations of the architecture which differ in their prediction technique as they deal with transitions either by directly learning them or by considering them as unknown activities. This is accomplished by combining the probabilistic output of consecutive activity predictions of a Support Vector Machine (SVM) with a heuristic filtering approach. The architecture is validated over three case studies that involve data from people performing a broad spectrum of activities (up to 33), while carrying smartphones or wearable sensors. Results show that TAHAR outperforms state-of-the-art baseline works and reveal the main advantages of the architecture. (C) 2015 Elsevier B.V. All rights reserved.

【 授权许可】

Free