title: Vision-based segmentation of continuous mechanomyographic grasping sequences for training multifunction prostheses.
abstract: In designing mechanomyographic (MMG) signal classifiers for prosthetic control, the acquisition of long, continuous streams of MMG signals is typically preferred over the painstaking collection of individual, isolated contractions. However, a major challenge with continuous collection is the subsequent separation of the MMG data stream into segments representing individual contractions. This paper proposes an automatic, vision-based segmentation method for continuously recorded MMG data streams. MMG data acquisition was synchronized with transverse plane video acquisition of functional grip sequences. The automatic segmentation system can track a hand, recognize grips and detect grip transition times as well as extraneous hand movements. The system recognizes two grips with an average accuracy of 97.8 +/- 4%, and seven grips with an accuracy of 73 +/- 20%. The contraction initiation and termination times agree closely (within 1.3 +/- 1 frames) with values obtained manually.
authors.affiliation: Univ. of Toronto, Ont. natasha.alves@utoronto.ca
authors.forename: Natasha
authors.initials: N
authors.lastname: Alves
authors.forename: Tom
authors.initials: T
authors.lastname: Chau
mesh_terms: D001132:Arm; D018737:Hand Strength; D006801:Humans; D018482:Muscle, Skeletal / Q000502:physiology*; D009213:Myography; D011474:Prosthesis Design