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Improving Human Motion Identification Using Motion Dependent Classification

  • Evangelia PippaEmail author
  • Iosif Mporas
  • Vasileios Megalooikonomou
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 736)

Abstract

In this article, we present a new methodology for human motion identification based on motion dependent binary classifiers that afterwards fuse their decisions to identify an Activity of Daily Living (ADL). Temporal and spectral features extracted from the sensor signals (accelerometer and gyroscope) and concatenated to a single feature vector are used to train motion dependent binary classification models. Each individual model is capable to recognize one motion versus all the others. Afterwards the decisions are combined by a fusion function using as weights the sensitivity values derived from the evaluation of each motion dependent classifier on the provided training set. The proposed methodology was evaluated using SVMs for the motion dependent classifiers and is compared against the common multiclass classification approach optimized using either feature selection or subject dependent classification. The classification accuracy of the proposed methodology reaches 99% offering competitive performance comparing to the other approaches.

Keywords

Human motion identification ADLs Classification Fusion Feature extraction Accelerometers Gyroscopes 

Notes

Acknowledgements

The research reported in the present paper was partially supported by the FrailSafe Project (H2020- PHC-21-2015-690140) “Sensing and predictive treatment of frailty and associated co-morbidities using advanced personalized models and advanced interventions”, co-funded by the European Commission under the Horizon 2020 research and innovation program.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Evangelia Pippa
    • 1
    Email author
  • Iosif Mporas
    • 2
  • Vasileios Megalooikonomou
    • 1
  1. 1.Multidimentional Data Analysis and Knowledge Discovery Laboratory, Department of Computer Engineering and InformaticsUniversity of PatrasRion-PatrasGreece
  2. 2.School of Engineering and TechnologyUniversity of HertfordshireHatfieldUK

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