Synergy-Based Hand Pose Sensing: Optimal Glove Design

TL;DR

This paper presents a method for designing optimal hand pose sensing gloves by leveraging human hand usage patterns, aiming to maximize information capture and minimize reconstruction error across various sensing configurations.

Contribution

It introduces a novel framework for glove sensor placement using geometrical synergy and gradient flow techniques, considering continuous, discrete, and hybrid sensing cases.

Findings

Optimal sensor placement reduces reconstruction error.

Simulations confirm improved hand pose estimation performance.

Framework applicable to various sensing configurations.

Abstract

In this paper we study the problem of improving human hand pose sensing device performance by exploiting the knowledge on how humans most frequently use their hands in grasping tasks. In a companion paper we studied the problem of maximizing the reconstruction accuracy of the hand pose from partial and noisy data provided by any given pose sensing device (a sensorized "glove") taking into account statistical a priori information. In this paper we consider the dual problem of how to design pose sensing devices, i.e. how and where to place sensors on a glove, to get maximum information about the actual hand posture. We study the continuous case, whereas individual sensing elements in the glove measure a linear combination of joint angles, the discrete case, whereas each measure corresponds to a single joint angle, and the most general hybrid case, whereas both continuous and discrete sensing elements are available. The objective is to provide, for given a priori information and fixed number of measurements, the optimal design minimizing in average the reconstruction error. Solutions relying on the geometrical synergy definition as well as gradient flow-based techniques are provided. Simulations of reconstruction performance show the effectiveness of the proposed optimal design.