A HMM distributed classifier to control robotic knee module of an active orthosis

The aim of this work is the evaluation of Distributed Classifier for the detection of gait phases that can be implemented in an active knee orthosis for the recovery of locomotion of pediatric subjects with neurological diseases, such as Cerebral Palsy (CP). The classifier is based on a Hierarchical Weighted Decision applied to the outputs of two or more scalar Hidden Markov Models (HMMs) trained by linear accelerations and angular velocities measured at shank and thigh. The kinematics of the dominant lower limb of ten healthy subjects were acquired by means of linear accelerometers and gyroscopes embedded in two inertial sensors. The actual sequence of gait phases was captured by means of foot switches. The experimental procedure consisted in one walking task, repeated for three times, on a treadmill at the preferred velocity of each subject. We compared the performance, in terms of sensitivity and specificity, of both Scalar Classifiers (SCs) and Distributed Classifiers (DCs) based on all the combinations of sagittal acceleration and sagittal angular velocity of the two body segments. The DC based on the angular velocities showed the highest values of sensitivity and specificity. The SC based on the angular velocity of shank was the better among others SCs, but the values of sensitivity and specificity are lower than 0.95. When we use only one sensor, placed on shank or thigh, the DC based on kinematic variables of shank showed better results, but not higher than 0.95. Consequently, the additional information provided by linear acceleration did not improve the performance and then, the gait-phase detection algorithm, which can be implemented in an active knee orthosis, has to be based on the output of two gyroscopes placed on shank and thigh.