Hand Posture Identification Research Articles

Optical Fiber Force Myography Sensor for Identification of Hand Postures

A low-cost optical fiber force myography sensor for noninvasive hand posture identification is proposed. The transducers are comprised of 10 mm periodicity silica multimode fiber microbending devices mounted in PVC plates, providing 0.05 N−1 sensitivity over ~20 N range. Next, the transducers were attached to the user forearm by means of straps in order to monitor the posterior proximal radial, the anterior medial ulnar, and the posterior distal radial muscles, and the acquired FMG optical signals were correlated to the performed gestures using a 5 hidden layers, 20-neuron artificial neural network classifier with backpropagation architecture, followed by a competitive layer. The overall results for 9 postures and 6 subjects indicated a 98.4% sensitivity and 99.7% average accuracy, being comparable to the electromyographic approaches. Moreover, in contrast to the current setups, the proposed methodology allows the identification of poses characterized by different configurations of fingers and wrist joint displacements with the utilization of only 3 transducers and a simple interrogation scheme, being suitable to further applications in human-computer interfaces.

Adaptive multiple sets of CSS features for hand posture recognition

In this paper, an adaptive feature extraction approach based on curvature scale space (CSS) is presented for translation, scale, and rotation invariant recognition of hand postures. First, hands are segmented from hand posture images into binary silhouettes and then binary hand contours are computed. CSS images are then used to represent the contours of hand postures. In particular, adaptive multiple sets of CSS features are extracted to address the problem of deep concavities in the contours of hand postures. Finally, 1-nearest neighbor techniques are used to perform adaptive multiple sets of CSS feature matching for hand posture identification. Results indicate that the proposed approach performs well in the recognition of hand postures. And, the proposed approach is more accurate than previous methods which were based on conventional features. The proposed technique could be useful in improving the recognition of hand postures.

The model-based dynamic hand posture identification using genetic algorithm

This paper proposes a new hand posture identification system which applies genetic algorithm to develop an efficient 3D hand-model-fitting method. The 3D hand-model-fitting method consists of (1) finding the closed-form inverse kinematics solution, (2) defining the alignment measure function for the wrist-fitting process, and (3) applying genetic algorithm to develop the dynamic hand posture identification process. In contrast to the conventional computationally intensive hand-model-fitting methods, we develop an off-line training process to find the closed-form inverse kinematics solution functions, and a fast model-based hand posture identification process. In the experiments, we will illustrate that our hand posture identification system is very effective.