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Correction: Supuk, T.G., et al. Design, Development and Testing of a Low-Cost sEMG System and Its Use in Recording Muscle Activity in Human Gait. Sensors 2014, 14, 8235–8258
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Sensors 2014, 14(9), 16212-16234; doi:10.3390/s140916212

A Novel HMM Distributed Classifier for the Detection of Gait Phases by Means of a Wearable Inertial Sensor Network

1
Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, 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
School of Mechanical Engineering, Niccolò Cusano University, Via Don Carlo Gnocchi 3, I-00166 Roma, Italy
4
MARLab, Movement Analysis and Robotics Laboratory, Neurorehabilitation Division, IRCCS Children's Hospital "Bambino Gesù", Via Torre di Palidoro, I-00050 Fiumicino (RM), Italy
*
Author to whom correspondence should be addressed.
Received: 13 June 2014 / Revised: 5 August 2014 / Accepted: 26 August 2014 / Published: 2 September 2014
(This article belongs to the Special Issue Biomedical Sensors and Systems)
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Abstract

In this work, we decided to apply a hierarchical weighted decision, proposed and used in other research fields, for the recognition of gait phases. The developed and validated novel distributed classifier is based on hierarchical weighted decision from outputs of scalar Hidden Markov Models (HMM) applied to angular velocities of foot, shank, and thigh. The angular velocities of ten healthy subjects were acquired via three uni-axial gyroscopes embedded in inertial measurement units (IMUs) during one walking task, repeated three times, on a treadmill. After validating the novel distributed classifier and scalar and vectorial classifiers-already proposed in the literature, with a cross-validation, classifiers were compared for sensitivity, specificity, and computational load for all combinations of the three targeted anatomical segments. Moreover, the performance of the novel distributed classifier in the estimation of gait variability in terms of mean time and coefficient of variation was evaluated. The highest values of specificity and sensitivity (>0.98) for the three classifiers examined here were obtained when the angular velocity of the foot was processed. Distributed and vectorial classifiers reached acceptable values (>0.95) when the angular velocity of shank and thigh were analyzed. Distributed and scalar classifiers showed values of computational load about 100 times lower than the one obtained with the vectorial classifier. In addition, distributed classifiers showed an excellent reliability for the evaluation of mean time and a good/excellent reliability for the coefficient of variation. In conclusion, due to the better performance and the small value of computational load, the here proposed novel distributed classifier can be implemented in the real-time application of gait phases recognition, such as to evaluate gait variability in patients or to control active orthoses for the recovery of mobility of lower limb joints. View Full-Text
Keywords: gait detection; Hidden Markov Models; hierarchical decision; distributed classifier; wearable sensor network; gyroscopes gait detection; Hidden Markov Models; hierarchical decision; distributed classifier; wearable sensor network; gyroscopes
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Taborri, J.; Rossi, S.; Palermo, E.; Patanè, F.; Cappa, P. A Novel HMM Distributed Classifier for the Detection of Gait Phases by Means of a Wearable Inertial Sensor Network. Sensors 2014, 14, 16212-16234.

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