Article

This paper presents and tests a framework for encoding joint dynamics into energy
states using kinematic and kinetic knee joint sensor data and demonstrates how to
use this information to predict the future energy state (torque and velocity
requirements) of the joint without a priori knowledge of the activity sequence.
The intended application is for enhancing micro-controlled prosthetics by making
use of the embedded sensory potential of artificial limbs and classical
mechanical principles of a prosthetic joint to report instantaneous energy state
and most probable next energy state. When applied to the knee during preferred
and fast speed walking in 8 human subjects (66 preferred-speed trials and 50
fast-speed trials), it was found that joint energy states could be consistently
sequenced (75% consensus) according to mechanical energy transference conditions
and subsequences appeared to reflect the stability and energy dissipation
requirements of the knee during gait. When simple constraints were applied to the
energy transfer input conditions (their signs), simulations indicated that it was
possible to predict the future energy state with an accuracy of >80% when 2%
cycle in advance ( approximately 20 ms) of the switch and >60% for 4% (
approximately 40 ms) in advance. This study justifies future research to explore
whether this encoding algorithm can be used to identify submodes of other human
activity that are relevant to TFP control, such as chair and stair activities and
their transitions from walking, as well as unexpected perturbations.

Langue : ANGLAIS

Tiré à part : OUI