Next Article in Journal
Research on the Sensing Performance of the Tuning Fork-Probe as a Micro Interaction Sensor
Next Article in Special Issue
Multi-Sensor Calibration of Low-Cost Magnetic, Angular Rate and Gravity Systems
Previous Article in Journal
Sea-Based Infrared Scene Interpretation by Background Type Classification and Coastal Region Detection for Small Target Detection
Previous Article in Special Issue
A Non-Contact Measurement System for the Range of Motion of the Hand
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(9), 24514-24529; doi:10.3390/s150924514

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

1
Department of Mechanical and Aerospace Engineering, Sapienza University of Roma, Via Eudossiana 18, I-00184 Roma, Italy
2
Department of Economics and Management, Industrial Engineering (DEIM), University of Tuscia, Via del Paradiso 47, I-01100 Viterbo, Italy
3
MAR Lab, Movement Analysis and Robotics Laboratory, Neurorehabilitation Division, IRCCS Children’s Hospital “Bambino Gesù”, Via Torre di Palidoro snc, I-00050 Fiumicino (RM), Italy
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Oliver Amft
Received: 15 July 2015 / Accepted: 18 September 2015 / Published: 23 September 2015
(This article belongs to the Special Issue Sensor Systems for Motion Capture and Interpretation)
View Full-Text   |   Download PDF [720 KB, uploaded 23 September 2015]   |  

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. View Full-Text
Keywords: Hidden Markov Model; inter-subject training; gait phase partitioning; Cerebral Palsy; Inertial Measurement Units; Wearable Sensor System; pediatric subjects Hidden Markov Model; inter-subject training; gait phase partitioning; Cerebral Palsy; Inertial Measurement Units; Wearable Sensor System; pediatric subjects
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Taborri, J.; Scalona, E.; Palermo, E.; Rossi, S.; Cappa, P. Validation of Inter-Subject Training for Hidden Markov Models Applied to Gait Phase Detection in Children with Cerebral Palsy. Sensors 2015, 15, 24514-24529.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top