Cluster Computing

, Volume 22, Supplement 4, pp 8141–8154 | Cite as

Adaptive multiple classifiers fusion for inertial sensor based human activity recognition

  • Yiming Tian
  • Xitai Wang
  • Wei ChenEmail author
  • Zuojun Liu
  • Lifeng Li


Aiming at the poor accuracy of single classifier in recognizing daily activities based on single accelerometer, this paper presents a method of daily activity recognition based on ensemble learning and full information matrix based fusion weight. Firstly, features from three attributes are extracted from the acceleration signals respectively. The three kinds of features can well describe the information of the activity, and they are relatively independent, which can reduce the interference caused by information redundancy in the process of fusion. Then three base classifiers of support vector machines are constructed based on three kinds of features respectively. Secondly, Euclidean distance between the test sample and every training sample for each type of feature vector is calculated to find out the k nearest neighbors of the test sample from the training set by the K-nearest neighbour method. The cluster analysis is used to compute the similarity between every neighbor and the test sample. Then, a proper threshold is utilized to remove the invalid neighbor whose similarity is less than the threshold. According to the effective neighbor, the full information matrix is constructed to calculate the accuracy. The weight of every single classifier is set dynamically according to the accuracy. Experiments showed that our proposed method get the best average recognition accuracy of 94.79% among several other weight functions when using majority voting method, besides, the time cost is also appealing.


Human activity recognition Multiple classifiers fusion Ensemble learning system Full information matrix Wearable triaxial accelerometer Adaptive weighted 



This work was supported by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China No. 2015BAI06B03.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Yiming Tian
    • 1
    • 2
  • Xitai Wang
    • 1
    • 2
  • Wei Chen
    • 1
    Email author
  • Zuojun Liu
    • 2
  • Lifeng Li
    • 1
  1. 1.Key Laboratory of Rehabilitation Aids Technology and System of the Ministry of Civil Affairs, Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age DisabilityNational Research Center for Rehabilitation Technical AidsBeijingChina
  2. 2.Department of Automation Engineering, School of Control Science and EngineeringHebei University of TechnologyTianjinChina

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