title: A novel HMM distributed classifier for the detection of gait phases by means of a wearable inertial sensor network.
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.
authors.affiliation: Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana 18, I-00184 Roma, Italy. j.taborri@gmail.com.
authors.forename: Juri
authors.initials: J
authors.lastname: Taborri
authors.affiliation: Department of Economics and Management, Industrial Engineering (DEIM), University of Tuscia, Via del Paradiso 47, I-01100 Viterbo, Italy. stefano.rossi@unitus.it.
authors.forename: Stefano
authors.initials: S
authors.lastname: Rossi
authors.affiliation: Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana 18, I-00184 Roma, Italy. eduardo.palermo@uniroma1.it.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Palermo
authors.affiliation: School of Mechanical Engineering, Niccolò Cusano University, Via Don Carlo Gnocchi 3, I-00166 Roma, Italy. fabrizio.patane@unicusano.it.
authors.forename: Fabrizio
authors.initials: F
authors.lastname: Patanè
authors.affiliation: Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana 18, I-00184 Roma, Italy. paolo.cappa@uniroma1.it.
authors.forename: Paolo
authors.initials: P
authors.lastname: Cappa
mesh_terms: D061725:Accelerometry / Q000295:instrumentation*; D001185:Artificial Intelligence; D003195:Computer Communication Networks / Q000295:instrumentation*; D003198:Computer Simulation; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D010363:Pattern Recognition, Automated / Q000379:methods*; D014159:Transducers; D055815:Young Adult