Gait segmentation using bipedal foot pressure patterns

S. M M De Rossi, S. Crea, M. Donati, P. Rebersek, D. Novak, N. Vitiello, T. Lenzi, J. Podobnik, M. Munih, M. C. Carrozza

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

Abstract

We present an automated gait segmentation method based on the analysis of foot plantar pressure patterns elaborated from two wireless pressure-sensitive insoles. The 64 pressure signals recorded by each device are elaborated to extract 10 feature variables which are used to segment the gait cycle into 6 sub-phases following a simplified version of Perry's gait model. The method is based on a Hidden Markov Model with a minimum phase length constraint and a univariate Gaussian emission model, which is decoded using a classic Viterbi algorithm. The method is tested on a pool of 5 healthy young subjects walking at two different speeds, through a leave-one-out cross-subject validation. The results show that the method is highly effective, yielding to an average performance of about 95% of correct phase classification, and 85 to 90% of phase transitions detected inside an acceptance window of 50ms.

Original languageEnglish
Title of host publicationProceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
Pages361-366
Number of pages6
DOIs
Publication statusPublished - 2012
Event2012 4th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2012 - Rome, Italy
Duration: Jun 24 2012Jun 27 2012

Other

Other2012 4th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2012
Country/TerritoryItaly
CityRome
Period6/24/126/27/12

ASJC Scopus subject areas

  • Artificial Intelligence
  • Biomedical Engineering
  • Mechanical Engineering

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