Human activity recognition for an intelligent knee orthosis

Diliana Rebelo, Christoph Amma, Hugo Gamboa, Tanja Schultz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

This paper investigates the possibility to classify isolated human activities from biosignal sensors integrated into a knee orthosis. An intelligent orthosis that is capable to recognize its wearers activity would be able to adapt itself to the users situation for enhanced comfort. We use a setup with three modalities: accelerometry, electromyography and goniometry to measure leg motion and muscle activity of the wearer. We segment signals in motion primitives and apply Hidden Markov Models to classify these isolated motion primitives. We discriminate between seven activities like for example walking stairs and ascend or descend a hill. In a small user study we reach an average person-dependent accuracy of 98% and a person-independent accuracy of 79%.

Original languageEnglish
Title of host publicationBIOSIGNALS 2013 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing
Pages368-371
Number of pages4
Publication statusPublished - 2013
EventInternational Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2013 - Barcelona, Spain
Duration: 11 Feb 201314 Feb 2013

Conference

ConferenceInternational Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2013
CountrySpain
CityBarcelona
Period11/02/1314/02/13

Keywords

  • Biosignals
  • Hidden Markov models
  • Human activity recognition
  • Signal-processing

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