Modeling the human knee for assistive technologies

Massimo Sartori, Monica Reggiani, Enrico Pagello, David G Lloyd

Research output: Contribution to journalArticleAcademicpeer-review

39 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)2642-2649
Number of pages8
JournalIEEE transactions on biomedical engineering
Volume59
Issue number9
DOIs
Publication statusPublished - 2012
Externally publishedYes

Keywords

  • Assistive technologies
  • Electromyography (EMG)
  • Knee joint
  • Mmusculoskeletal modeling

Fingerprint Dive into the research topics of 'Modeling the human knee for assistive technologies'. Together they form a unique fingerprint.

  • Cite this