Abstract

The advent of wearable sensors like accelerometers has opened a plethora of opportunities to recognize human activities from other low resolution sensory streams. In this paper we formulate recognizing activities from accelerometer data as a classification problem. In addition to the statistical and spectral features extracted from the acceleration data, we propose to extract features that characterize the variations in the first order derivative of the acceleration signal. We evaluate the performance of different state of the art discriminative classifiers like, boosted decision stumps (AdaBoost), support vector machines (SVM) and Regularized Logistic Regression(RLogReg) under three different evaluation scenarios(namely Subject Independent, Subject Adaptive and Subject Dependent). We propose a novel computationally inexpensive methodology for incorporating smoothing classification temporally, that can be coupled with any classifier with minimal training for classifying continuous sequences. While a 3% increase in the classification accuracy was observed on adding the new features, the proposed technique for continuous recognition showed a 2.5 - 3% improvement in the performance.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages3337-3340
Number of pages4
DOIs
StatePublished - Sep 16 2008
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: Mar 31 2008Apr 4 2008

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
CountryUnited States
CityLas Vegas, NV
Period3/31/084/4/08

Keywords

  • Accelerometers
  • AdaBoost
  • Human activity recognition
  • SVM

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Krishnan, N. C., & Panchanathan, S. (2008). Analysis of low resolution accelerometer data for continuous human activty recognition. In 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP (pp. 3337-3340). [4518365] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2008.4518365