Learning an EMG Controlled Game: Task-Specific Adaptations and Transfer

PLoS One. 2016 Aug 24;11(8):e0160817. doi: 10.1371/journal.pone.0160817. eCollection 2016.

Abstract

Video games that aim to improve myoelectric control (myogames) are gaining popularity and are often part of the rehabilitation process following an upper limb amputation. However, direct evidence for their effect on prosthetic skill is limited. This study aimed to determine whether and how myogaming improves EMG control and whether performance improvements transfer to a prosthesis-simulator task. Able-bodied right-handed participants (N = 28) were randomly assigned to 1 of 2 groups. The intervention group was trained to control a video game (Breakout-EMG) using the myosignals of wrist flexors and extensors. Controls played a regular Mario computer game. Both groups trained 20 minutes a day for 4 consecutive days. Before and after training, two tests were conducted: one level of the Breakout-EMG game, and grasping objects with a prosthesis-simulator. Results showed a larger increase of in-game accuracy for the Breakout-EMG group than for controls. The Breakout-EMG group moreover showed increased adaptation of the EMG signal to the game. No differences were found in using a prosthesis-simulator. This study demonstrated that myogames lead to task-specific myocontrol skills. Transfer to a prosthesis task is therefore far from easy. We discuss several implications for future myogame designs.

MeSH terms

  • Adult
  • Analysis of Variance
  • Artificial Limbs
  • Female
  • Humans
  • Learning*
  • Male
  • Motor Skills*
  • Task Performance and Analysis
  • Upper Extremity
  • Video Games*
  • Young Adult

Grants and funding

This work was supported by the Northern Netherlands Provinces Alliance (SNN).