Real-time gait detection based on Hidden Markov Model: Is it possible to avoid training procedure?

Juri Taborri, Emilia Scalona, Stefano Rossi, Eduardo Palermo, Fabrizio Patane, Paolo Cappa

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

Abstract

In this paper we present and validate a methodology to avoid the training procedure of a classifier based on an Hidden Markov Model (HMM) for a real-time gait recognition of two or four phases, implemented to control pediatric active orthoses of lower limb. The new methodology consists in the identification of a set of standardized parameters, obtained by a data set of angular velocities of healthy subjects age-matched. Sagittal angular velocities of lower limbs of ten typically developed children (TD) and ten children with hemiplegia (HC) were acquired by means of the tri-axial gyroscope embedded into Magnetic Inertial Measurement Units (MIMU). The actual sequence of gait phases was captured through a set of four foot switches. The experimental protocol consists in two walking tasks on a treadmill set at 1.0 and 1.5 km/h. We used the Goodness (G) as parameter, computed from Receiver Operating Characteristic (ROC) space, to compare the results obtained by the new methodology with the ones obtained by the subject-specific training of HMM via the Baum-Welch Algorithm. Paired-sample t-tests have shown no significant statistically differences between the two procedures when the gait phase detection was performed with the gyroscopes placed on the foot. Conversely, significant differences were found in data gathered by means of gyroscopes placed on shank. Actually, data relative to both groups presented G values in the range of good/optimum classifier (i.e. G ≤ 0.3), with better performance for the two-phase classifier model. In conclusion, the novel methodology here proposed guarantees the possibility to omit the off-line subject-specific training procedure for gait phase detection and it can be easily implemented in the control algorithm of active orthoses.

Original languageEnglish
Title of host publication2015 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages141-145
Number of pages5
ISBN (Print)9781479964765
DOIs
Publication statusPublished - Jun 30 2015
Event2015 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2015 - Torino, Italy
Duration: May 7 2015May 9 2015

Other

Other2015 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2015
CountryItaly
CityTorino
Period5/7/155/9/15

    Fingerprint

Keywords

  • active orthoses
  • Hidden Markov Model
  • IMUs system
  • real-time gait detection
  • training procedure

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Science Applications

Cite this

Taborri, J., Scalona, E., Rossi, S., Palermo, E., Patane, F., & Cappa, P. (2015). Real-time gait detection based on Hidden Markov Model: Is it possible to avoid training procedure? In 2015 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2015 - Proceedings (pp. 141-145). [7145188] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MeMeA.2015.7145188