Biological constraints simplify the recognition of hand shapes

Abstract

This study sought to identify constraints that might lead to a concise system of recognizing fingerspelling hand shapes. Previous studies of grasping suggested that hand shape is controlled using combinations of a small number of neuromuscular synergies, but fingerspelling shapes appear to be more highly individuated and, therefore, might require a larger number of degrees of freedom. Static hand postures of the American Sign Language manual alphabet were recorded by measuring 17 joint angles. Principal components (PCs) analysis was compared to the use of subsets of individual variables (i.e., joint angles) for reduction in degrees of freedom. The first four PCs were similar across subjects. Classification using weightings from these four components was 86.6% accurate, while classification using four individual variables was 88.5% accurate (thumb abduction, as well as flexion at the index and middle finger proximal interphalangeal joints and the ring finger metacarpalphalangeal joint). When chosen for each subject, particular four-variable subsets yielded correct rates above 95%. This superior performance of variable subsets over PC weighting vectors suggests that the reduction in degrees of freedom is due to biomechanical and neuromuscular constraints rather than synergistic control. Thus, in future application to dynamic fingerspelling, reasonable recognition accuracy might be achieved with a significant reduction in both computational and measured degrees of freedom.

Design and Validation of a Morphing Myoelectric Hand Posture Controller Based on Principal Component Analysis of Human Grasping

“…The study of the principal components [7], [25], [26] of grasping has provided another method for the control of a myoelectric hand. Santello et al.…”

Limb versus Speech Motor Control: A Conceptual Review

“…This so-called dissimilation may support visual discrimination of words and recognition by the observer. Consequently, dissimilation was predominant for the index and middle finger joints which are known to be fundamental for letter recognition (Jerde et al, 2003b).…”

Application of Linear and Nonlinear Dimensionality Reduction Methods

“…Based on the principal component analysis, Jerde et al ( Jerde et al (2003)) found support for the existence of postural synergies of angular configuration. The shape of human hand can be predicted using a reduced set of variables and postural synergies.…”

“…(i) Postural synergies: In Jerde et al (2003); Mason et al (2001); Santello et al (1998;; Thakur et al (2008); Todorov & Ghahramani (2004), researchers found that the entire act of grasp can be described by a small number of dominant postures, which were defined as postural synergies.…”