Preprint Article Version 1 NOT YET PEER-REVIEWED

Human Activity Recognition Based on Quantization on Feature’s Classification Capability

Cheng Xu 1,2,* , Xiaotong Zhang 1,2 , Jie He 1,2,*
  1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
  2. Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China
Version 1 : Received: 29 September 2016 / Approved: 29 September 2016 / Online: 29 September 2016 (12:57:00 CEST)

How to cite: Xu, C.; Zhang, X.; He, J. Human Activity Recognition Based on Quantization on Feature’s Classification Capability. Preprints 2016, 2016090121 (doi: 10.20944/preprints201609.0121.v1). Xu, C.; Zhang, X.; He, J. Human Activity Recognition Based on Quantization on Feature’s Classification Capability. Preprints 2016, 2016090121 (doi: 10.20944/preprints201609.0121.v1).

Abstract

Motion related human activity recognition using wearable sensors can potentially enable various useful daily applications. So far, most studies view it as a stand-alone mathematical classification problem without considering the physical nature of human motions. Consequently, they suffer from data dependencies and encounter the dimension disaster problem and the over-fitting issue, and their models are never human-readable. In this study, we start from a deep analysis on natural physical properties of human motions, and then propose a useful feature selection method to quantify each feature's classification contribution capability. On one hand, the "dimension disaster" problem can be avoid to some extent, due to the affined dimension of key features; On the other hand, over-fitting issue can be depressed since the knowledge implied in human motions are nearly invariant, which compensates the possible data inadequacy. The experiment results indicate that the proposed method performs superior to those adopted in related works, such as decision tree, k-NN, SVM, neural networks.

Subject Areas

activity recognition; physical attributes; classification capability

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