Identification of Gait Patterns Related to Health Problems of Elderly

  • Bogdan Pogorelc
  • Matjaž Gams
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6406)


A system for automatic identification of gait patterns related to health problems of elderly for the purpose of supporting their independent living is proposed in this paper. The gait of the user is captured with the motion capture system, which consists of tags attached to the body and sensors situated in the apartment. Position of the tags is acquired by the sensors and the resulting time series of position coordinates are analyzed with machine learning algorithms in order to identify the specific health problem. We propose novel features for training a machine learning classifier that classifies the user’s gait into: i) normal, ii) with hemiplegia, iii) with Parkinson’s disease, iv) with pain in the back and v) with pain in the leg. Results show that naive Bayes needs more tags and less noise to reach classification accuracy of 98 % than random forest for 99 %.


Health problems detection human motion analysis gait analysis machine learning data mining human locomotion ambient intelligence 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Bogdan Pogorelc
    • 1
    • 2
  • Matjaž Gams
    • 1
    • 2
  1. 1.Department of Intelligent SystemsJožef Stefan InstituteLjubljanaSlovenia
  2. 2.Špica International d. o. o.LjubljanaSlovenia

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