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

Sensors

Juri Taborri²  Emilia Scalona²  Eduardo Palermo²  Stefano Rossi¹  Paolo Cappa² 

[1] Department of Economics and Management, Industrial Engineering (DEIM), University of Tuscia, Via del Paradiso 47, I-01100 Viterbo, Italy; E-Mail:;Department of Mechanical and Aerospace Engineering, Sapienza University of Roma, Via Eudossiana 18, I-00184 Roma, Italy; E-Mails:

关键词: Hidden Markov Model;    inter-subject training;    gait phase partitioning;    Cerebral Palsy;    Inertial Measurement Units;    Wearable Sensor System;    pediatric subjects;   

DOI  :  10.3390/s150924514

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.