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Elbow angle generation during activities of daily living using a submovement prediction model

Abstract

The present study aimed to develop a realistic model for the generation of human activities of daily living (ADL) movements. The angular profiles of the elbow joint during functional ADL tasks such as eating and drinking were generated by a submovement-based closed-loop model. First, the ADL movements recorded from three human participants were broken down into logical phases, and each phase was decomposed into submovement components. Three separate artificial neural networks were trained to learn the submovement parameters and were then incorporated into a closed-loop model with error correction ability. The model was able to predict angular trajectories of human ADL movements with target access rate = 100%, VAF = 98.9%, and NRMSE = 4.7% relative to the actual trajectories. In addition, the model can be used to provide the desired target for practical trajectory planning in rehabilitation systems such as functional electrical stimulation, robot therapy, brain-computer interface, and prosthetic devices.

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Abbreviations

ADL:

Activities of daily living

ANN:

Artificial neural network

BCI:

Brain–computer interface

CC:

Correlation coefficient

FES:

Functional electrical stimulation

NRMSE:

Normalized RMSE

RMSE:

Root mean square error

SD:

Standard deviation

tansig:

Tangent-sigmoid

VAF:

Variance accounted for

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Correspondence to Ali Fallah.

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Naghibi, S.S., Fallah, A., Maleki, A. et al. Elbow angle generation during activities of daily living using a submovement prediction model. Biol Cybern 114, 389–402 (2020). https://doi.org/10.1007/s00422-020-00834-w

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Keywords

  • Submovement
  • Activities of daily living
  • Elbow angle
  • Closed-loop model
  • Artificial neural network
  • Movement generation