Abstract

This paper proposes a method for human activity classification in home based monitoring. The proposed approach is based on minimum jerk (MinJerk), a primary model for smooth path planning employed by human motor control in upper-extremity motion. Based on new evidences that show common control strategies in lower and upper extremity, MinJerk is adapted in our study to estimate the foot motion with fifth order polynomial functions. Experimental data are recorded during walking and going up and down the stairs using a single inertial measurement unit. Features of interest in this study are the optimized curve fitting coefficients. Using a structured support vector machine with radial basis function, an overall accuracy of 98.6% is achieved for activity classification. The proposed method is also capable of detecting the transitions between the movements with accuracy of 99.96%.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE/RAS-EMBS International Conference on Rehabilitation Robotics
Subtitle of host publicationEnabling Technology Festival, ICORR 2015
EditorsDavid Braun, Haoyong Yu, Domenico Campolo
PublisherIEEE Computer Society
Pages909-912
Number of pages4
ISBN (Electronic)9781479918072
DOIs
StatePublished - Sep 28 2015
Event14th IEEE/RAS-EMBS International Conference on Rehabilitation Robotics, ICORR 2015 - Singapore, Singapore
Duration: Aug 11 2015Aug 14 2015

Publication series

NameIEEE International Conference on Rehabilitation Robotics
Volume2015-September
ISSN (Print)1945-7898
ISSN (Electronic)1945-7901

Other

Other14th IEEE/RAS-EMBS International Conference on Rehabilitation Robotics, ICORR 2015
CountrySingapore
CitySingapore
Period8/11/158/14/15

Keywords

  • Home-Based Monitoring
  • Human Activity Classification
  • Minimum Jerk
  • Motor Control Model

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Rehabilitation
  • Electrical and Electronic Engineering

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