The goal of this work is the identification of humans based on motion data in the form of natural hand gestures. In this paper, the identification problem is formulated as classification with classes corresponding to persons' identities, based on recorded signals of performed gestures. The identification performance is examined with a database of twenty-two natural hand gestures recorded with two types of hardware and three state-of-art classifiers: Linear Discrimination Analysis (LDA), Support Vector machines (SVM) and k-Nearest Neighbour (k-NN). Results show that natural hand gestures allow for an effective human classification.
Hand data gathering techniques can be divided into: device-based, where mechanical or optical sensors are attached to a glove, allowing for measurement of finger flex, hand position and acceleration, e.g. @cite_23 , and vision-based, when hands are tracked based on the data from optical sensors e.g. @cite_10 . A survey of glove-based systems for motion data gathering, as well as their applications can be found in @cite_6 , while @cite_11 provides a comprehensive analysis of the integration of various sensors into gesture recognition systems.
@cite_10 @cite_6 @cite_23 @cite_11
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This paper reports on the development of a hand to machine interface device that provides real-time gesture, position and orientation information. The key element is a glove and the device as a whole incorporates a collection of technologies. Analog flex sensors on the glove measure finger bending. Hand position and orientation are measured either by ultrasonics, providing five degrees of freedom, or magnetic flux sensors, which provide six degrees of freedom. Piezoceramic benders provide the wearer of the glove with tactile feedback. These sensors are mounted on the light-weight glove and connected to the driving hardware via a small cable. Applications of the glove and its component technologies include its use in conjunction with a host computer which drives a real-time 3-dimensional model of the hand allowing the glove wearer to manipulate computer-generated objects as if they were real, interpretation of finger-spelling, evaluation of hand impairment in addition to providing an interface to a visual programming language. Hand movement data acquisition is used in many engineering applications ranging from the analysis of gestures to the biomedical sciences. Glove-based systems represent one of the most important efforts aimed at acquiring hand movement data. While they have been around for over three decades, they keep attracting the interest of researchers from increasingly diverse fields. This paper surveys such glove systems and their applications. It also analyzes the characteristics of the devices, provides a road map of the evolution of the technology, and discusses limitations of current technology and trends at the frontiers of research. A foremost goal of this paper is to provide readers who are new to the area with a basis for understanding glove systems technology and how it can be applied, while offering specialists an updated picture of the breadth of applications in several engineering and biomedical sciences areas. Abstract This paper describes the development of a remote handling control of an anthropomorphic robotic hand, the SCHUNK S5FH, using the human hand as master by measuring its motion with OptiTrack Technology. The goal of this work is to enhance manipulation studies on the human hand and to instantly transfer those studies on robotic hands. A preliminary study on methods and devices used for fingers tracking led to the choice of a simplified kinematic model of the human hand on the basis of the available motion tracking system. Using the same criteria, the analysis of protocols for markers allocation led to define the number and a method for their arrangement on the fingers and palm. In order to overcome the limitation of the Motion Capture System, a method for identification and labeling has been developed according to their anatomical arrangement. Afterwards, the tracking is performed using the constraints between marker positions on the kinematic chain of the hand and a dynamic labeling algorithm robust with respect to noise, outliers and loss of markers. The validation is performed using the right hand of different subjects and considering different tasks involving flexion extension and abduction adduction of fingers and thumb opposition. For testing and validation, preliminary studies on synergies for manipulation tasks such as screwing a cup, has been conducted on the human hand and transferred on the robotic hand. An experiment was conducted to investigate gesture recognition with a human hand manipulating the DataGlove, an electronically instrumented glove which provides information about finger and hand position. A total of 22 gestures in three classes were investigated. The first class contained gestures which only involved finger flexure. The second class contained gestures which required both finger flexure and hand orientation. The third class of gestures required finger motion in addition to flexure and orientation. Only four sensors were necessary to positively identify specific gestures from groups of up to 15 gestures. The results show the specific number of sensors required to positively identify a gesture from a group. This depends on the number of gestures in a group, as well as the class of gestures. >