Regressing grasping using force myography: an exploratory study

Biomed Eng Online. 2018 Oct 23;17(1):159. doi: 10.1186/s12938-018-0593-2.

Abstract

Background: Partial hand amputation forms more than 90% of all upper limb amputations. This amputation has a notable effect on the amputee's life. To improve the quality of life for partial hand amputees different prosthesis options, including externally-powered prosthesis, have been investigated. The focus of this work is to explore force myography (FMG) as a technique for regressing grasping movement accompanied by wrist position variations. This study can lay the groundwork for a future investigation of FMG as a technique for controlling externally-powered prostheses continuously.

Methods: Ten able-bodied participants performed three hand movements while their wrist was fixed in one of six predefined positions. The angle between Thumb and Index finger ([Formula: see text]), and Thumb and Middle finger ([Formula: see text]) were calculated as measures of grasping movements. Two approaches were examined for estimating each angle: (i) one regression model, trained on data from all wrist positions and hand movements; (ii) a classifier that identified the wrist position followed by a separate regression model for each wrist position. The possibility of training the system using a limited number of wrist positions and testing it on all positions was also investigated.

Results: The first approach had a correlation of determination ([Formula: see text]) of 0.871 for [Formula: see text] and [Formula: see text]. Using the second approach [Formula: see text] and [Formula: see text] were obtained. The first approach is over two times faster than the second approach while having similar performance; thus the first approach was selected to investigate the effect of the wrist position variations. Training with 6 or 5 wrist positions yielded results which were not statistically significant. A statistically significant decrease in performance resulted when less than five wrist positions were used for training.

Conclusions: The results indicate the potential of FMG to regress grasping movement, accompanied by wrist position variations, with a regression model for each angle. Also, it is necessary to include more than one wrist position in the training phase.

Keywords: Continuous grasping predication; Finger movement prediction; Force myography; Partial hand prosthesis; Random forest.

MeSH terms

  • Adult
  • Algorithms
  • Amputation, Surgical
  • Amputees
  • Artificial Limbs
  • Biomechanical Phenomena
  • Electromyography
  • Equipment Design
  • Female
  • Fingers
  • Hand / physiology*
  • Hand Strength*
  • Humans
  • Male
  • Movement
  • Myography / methods*
  • Regression Analysis
  • Signal Processing, Computer-Assisted
  • Thumb
  • Wrist / physiology*
  • Wrist Joint
  • Young Adult