Modeling the human knee for assistive technologies.
In this paper, we use motion capture technology together with an EMG-driven musculoskeletal model of the knee joint to predict muscle behavior during human dynamic movements. We propose a muscle model based on infinitely stiff tendons and show this allows speeding up 250 times the computation of muscle force and the resulting joint moment calculation with no loss of accuracy with respect to the previously developed elastic-tendon model. We then integrate our previously developed method for the estimation of 3-D musculotendon kinematics in the proposed EMG-driven model. This new code enabled the creation of a standalone EMG-driven model that was implemented and run on an embedded system for applications in assistive technologies such as myoelectrically controlled prostheses and orthoses.
Institute of Biomedical Engineering, National Research Council, 35127 Padova, Italy. massimo.srt@gmail.com
Massimo
M
Sartori
Monica
M
Reggiani
Enrico
E
Pagello
David G
DG
Lloyd
D000328:Adult; D001696:Biomechanical Phenomena / Q000502:physiology; D004576:Electromyography / Q000379:methods*; D006801:Humans; D007719:Knee Joint / Q000502:physiology*; D008297:Male; D008954:Models, Biological; D018482:Muscle, Skeletal / Q000502:physiology; D015203:Reproducibility of Results; D012656:Self-Help Devices; D013710:Tendons / Q000502:physiology