Improving the control of prostheses in arm amputees with approaches based on motor coordination (bibtex)
by , , , , , , ,
Bibtex Entry:
  author    = {Merad, M and de Montalivet, E and Legrand, M and Touillet, A and Martinet N and Paysant, J and Roby-Brami, A and Jarrassé, N},
  title     = {Improving the control of prostheses in arm amputees with approaches based on motor coordination},
  booktitle = {Computer Methods in Biomechanics and Biomedical Engineering: Supplement for the International French Society of Biomechanics Conference},
  year      = {2019},
  pages     = {xx},
  abstract  = {Recent advances in mechatronics and robotics have led to the production of prostheses with a growing number of active degrees of freedom (DoFs). Unfortunately, the gap is growing between this improved hardware and their still counter-intuitive, sequential and limited myoelectric control. To overcome some of these control limitations, there is a renewal of interest in using movements rather than electrophysiological signals as control inputs. Among those movement-based control approaches, some aim to create a more intuitive and natural control by using the motion of the residual limb and body to predict and automatize partly the movement of the prosthesis. Such control rely on the use of models of natural interjoint coordination which naturally exists in healthy subjects.
We thus here propose to use Radial Basis Function Networks (RBFN) to estimate flexion/extension prosthetic elbow movements from the residual shoulder angular movements, measured with embedded Inertial Measurement Units (IMUs). The performance of this control model was assessed with an experimental evaluation with 3 transhumeral amputated participants who used this automatically driven elbow prosthesis (and a generic myoelectric control for comparison) to perform reaching tasks. The automatic control mode relied on a generic model of the shoulder-elbow coordination which was built from data obtained from a group of healthy subjects. Several metrics were thus used to quantify the task performance, along with the movement strategies exhibited by the amputated participants to illustrate the possibilities and present limitations of such control approaches.
  category  = {ACTN},
  crac      = {n},
  hal       = {n},
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