Validation of inter-subject training for hidden markov models applied to gait phase detection in children with Cerebral Palsy

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

Research output: Contribution to journalArticlepeer-review


Gait-phase recognition is a necessary functionality to drive robotic rehabilitation devices for lower limbs. Hidden Markov Models (HMMs) represent a viable solution, but they need subject-specific training, making data processing very time-consuming. Here, we validated an inter-subject procedure to avoid the intra-subject one in two, four and six gait-phase models in pediatric subjects. The inter-subject procedure consists in the identification of a standardized parameter set to adapt the model to measurements. We tested the inter-subject procedure both on scalar and distributed classifiers. Ten healthy children and ten hemiplegic children, each equipped with two Inertial Measurement Units placed on shank and foot, were recruited. The sagittal component of angular velocity was recorded by gyroscopes while subjects performed four walking trials on a treadmill. The goodness of classifiers was evaluated with the Receiver Operating Characteristic. The results provided a goodness from good to optimum for all examined classifiers (0 <G <0.6), with the best performance for the distributed classifier in two-phase recognition (G = 0.02). Differences were found among gait partitioning models, while no differences were found between training procedures with the exception of the shank classifier. Our results raise the possibility of avoiding subject-specific training in HMM for gait-phase recognition and its implementation to control exoskeletons for the pediatric population.

Original languageEnglish
Pages (from-to)24514-24529
Number of pages16
JournalSensors (Switzerland)
Issue number9
Publication statusPublished - Sep 23 2015


  • Cerebral palsy
  • Gait phase partitioning
  • Hidden Markov model
  • Inertial measurement units
  • Inter-subject training
  • Pediatric subjects
  • Wearable sensor system

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics
  • Analytical Chemistry
  • Biochemistry


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