A novel HMM distributed classifier for the detection of gait phases by means of a wearable inertial sensor network.

Juri Taborri,Fabrizio Patanè,Paolo Cappa,Stefano Rossi,Eduardo Palermo

doi:10.3390/s140916212

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.

Highlights

IntroductionThe identification of events and phases in the human gait is an essential starting point for:
The identification of events and phases in the human gait is an essential starting point for:(i) assessing the degree of recovery in walking ability in patients after interventions or rehabilitation treatments [1,2]; (ii) classifying the activity of daily living, including the overall health status of individuals [3,4]; and (iii) controlling synchronously active orthoses and exoskeletons for the recovery of lower limb mobility [5].Several approaches and technologies have been developed in order to detect gait phases
Taking into account the Distributed Classifiers, TPRDC st and TNRDC st were statistically lower than the same parameters evaluated with the other DC

Summary

Introduction

The identification of events and phases in the human gait is an essential starting point for:. Several approaches and technologies have been developed in order to detect gait phases. Motion capture systems based on marker tracking and six-component force platforms still represent the gold standard for extracting gait patterns [6,7,8,9,10]. The detection of human motion can be evaluated by means of self-contained wearable systems, which do not rely on camera-based systems and can be used outdoors for continuous data logging. The cited references represent a few of the numerous works available in the literature

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Methods

Results

Conclusion