Article

A framework for activity classification using inertial sensors mounted on ankle
foot orthoses (AFOs) is presented. A decision tree-nearest neighbor algorithm
classifies activities using subject-specific training. Eight volunteer subjects
wore modified bilateral AFOs with shank and foot mounted triaxial accelerometers
and gyroscopes. The AFOs were fitted with hardware to induce different gait
perturbations: free rotation of the ankle, plantarflexion or "equinus" gait, and
locked ankle joint. For each condition, the subject performed eight gait
activities at varied slopes and standing, sitting, and lying postures. Using
video for ground truth, the algorithm had an overall mean sensitivity of 95%
using 50% of the data ( approximately 140 s) for training and demonstrated
upwards of 90% sensitivity with 25% of the data ( approximately 70 s) for
training. High sensitivities (>/= 87%) and PPV (>/= 90%) were achieved for all
annotated gait patterns for all perturbations, excluding stair climbing (63%,
77%) and descending (80%, 78%). Postures were classified with less sensitivity
and PPV than gait activities: lying (98%, 93%), standing (80%, 84%) and sitting
(64%, 75%). Non-annotated walking (68%) and standing (73%) were classified with
less sensitivity than were corresponding annotated events. Our results indicate
that AFOs are a suitable sensor platform for future research in activity
classification and gait monitoring in AFO users with perturbed gait using limited
training data.
CI - Copyright (c) 2013 Elsevier B.V. All rights reserved.

Langue : ANGLAIS