Design of a lightweight, cost effective thimble-like sensor for haptic applications based on contact force sensors.

This paper describes the design and calibration of a thimble that measures the forces applied by a user during manipulation of virtual and real objects. Haptic devices benefit from force measurement capabilities at their end-point. However, the heavy weight and cost of force sensors prevent their widespread incorporation in these applications. The design of a lightweight, user-adaptable, and cost-effective thimble with four contact force sensors is described in this paper. The sensors are calibrated before being placed in the thimble to provide normal and tangential forces. Normal forces are exerted directly by the fingertip and thus can be properly measured. Tangential forces are estimated by sensors strategically placed in the thimble sides. Two applications are provided in order to facilitate an evaluation of sensorized thimble performance. These applications focus on: (i) force signal edge detection, which determines task segmentation of virtual object manipulation, and (ii) the development of complex object manipulation models, wherein the mechanical features of a real object are obtained and these features are then reproduced for training by means of virtual object manipulation.

Human hand descriptions and gesture recognition for object manipulation.

This work focuses on obtaining realistic human hand models that are suitable for manipulation tasks. A 24 degrees of freedom (DoF) kinematic model of the human hand is defined. The model reasonably satisfies realism requirements in simulation and movement. To achieve realism, intra- and inter-finger constraints are obtained. The design of the hand model with 24 DoF is based upon a morphological, physiological and anatomical study of the human hand. The model is used to develop a gesture recognition procedure that uses principal components analysis (PCA) and discriminant functions. Two simplified hand descriptions (nine and six DoF) have been developed in accordance with the constraints obtained previously. The accuracy of the simplified models is almost 5% for the nine DoF hand description and 10% for the six DoF hand description. Finally, some criteria are defined by which to select the hand description best suited to the features of the manipulation task.