Validation of Inter-Subject Training for Hidden Markov Models Applied to Gait Phase Detection in Children with Cerebral Palsy.

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

doi:10.3390/s150924514

Abstract

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.

Highlights

IntroductionCorrect discrimination of gait phases during human walking represents a crucial issue in different research fields, such as in clinical assessment between normal and pathological gait [1,2], athlete coaching [3,4], monitoring health status through classification of daily life activities [5,6], human-machine interaction [7,8], and robotic rehabilitation [9,10]
Correct discrimination of gait phases during human walking represents a crucial issue in different research fields, such as in clinical assessment between normal and pathological gait [1,2], athlete coaching [3,4], monitoring health status through classification of daily life activities [5,6], human-machine interaction [7,8], and robotic rehabilitation [9,10].The decision concerning the number of phases in the adopted partition model for the human gait varies according to the requirement of the specific research field
The procedure, here validated on typically developed children and children with hemiplegia, consists in the identification of a standardized-parameter set obtained from angular velocity data

Summary

Introduction

Correct discrimination of gait phases during human walking represents a crucial issue in different research fields, such as in clinical assessment between normal and pathological gait [1,2], athlete coaching [3,4], monitoring health status through classification of daily life activities [5,6], human-machine interaction [7,8], and robotic rehabilitation [9,10]. The decision concerning the number of phases in the adopted partition model for the human gait varies according to the requirement of the specific research field. Even though the most widespread approach is based on the four-phase model [1,10,11,12], models with a different number of gait phases are used [13,14,15]. Among the second group of algorithms, Mannini et al reported that the ones based on Hidden Markov Models (HMM) demonstrated the best performance [11]

Objectives

Methods

Results

Discussion

Conclusion