Pneumatic actuator and flexible piezoelectric sensor for soft virtual reality glove system

The desire to directly touch and experience virtual objects led to the development of a tactile feedback device. In this paper, a novel soft pneumatic actuator for providing tactile feedback is proposed and demonstrated. The suggested pneumatic actuator does not use an external air compressor but it is operated by internal air pressure generated by an electrostatic force. By using the actuator, we designed a glove to interact with virtual reality. The finger motions are detected by attached flexible piezoelectric sensors and transmitted to a virtual space through Bluetooth for interconnecting with a virtual hand. When the virtual finger touches the virtual object, the actuators are activated and give the tactile feedback to the real fingertip. The glove is made of silicone rubber material and integrated with the sensors and actuators such that users can wear them conveniently with light weight. This device was tested in a virtual chess board program, wherein the user picked up virtual chess pieces successfully.

Neural Network Training Techniques for Enhancing the Performance of a Distributive Tactile Sensor

This work describes neural network training techniques for enhancing the performance of a one-dimensional beam for determining load positions. The system was a distributive system which relied on the detection of changes in the surface properties that can be seen across the surface. The demonstrated distributive system was 400 mm in length. The applied load positions within the range 60–300 mm could be determined with an average percentage error of 0.2% of the beam length which corresponded to a position error of 0.8 mm using a network trained with 10 training positions. It was found that the errors were higher for the load applied near the edges of the beam, leading to an average percentage error of 3.6% for the whole length of the beam. The normalization of the network output can be employed to reduce the average percentage error by approximately 1% for a given number of training positions. The performance was improved by introducing more training positions in the less sensitive area. The described training technique not only reduced the prediction error but also enlarged the areas where the prediction errors were satisfactorily small. Using the described technique, the overall prediction error could be decreased by 0.13–0.24% of the beam length for 30–10 training positions.

The area where the prediction errors were within 2 mm was increased by 14.5–7.3% of the beam length for a network trained with 4 training positions in the middle portion using 10–30 training positions.

Review on aspects of artificial tactile feedback in laparoscopic surgery

Since direct manual tissue palpation is not possible in laparoscopic surgery, feedback information on tactile tissue properties is considerably diminished. Restoring part of the surgeon's sense of touch through devices capable of providing artificial tactile feedback (ATF) is an active field of applied research and development. Despite more than two decades of research, technical development of such devices is still basic, and pre-clinical as well as clinical experience is limited. This article provides an overview of the technological aspects of ATF in laparoscopic surgery, gives background information on principles of human perception of related feedback information, and reviews current research attempts in the field of ATF systems in laparoscopic surgery, broken down into three main system components: tactile sensor, display, and data processing. Tactile sensors have been developed to measure tissue compliance, reveal hidden structures or foreign bodies in tissue through measurement of pressure distribution, and to identify and locate arteries by detecting their pulsation. Furthermore, different solutions for presenting tactile data to the surgeon have been developed. Visual and auditory displays are easy to implement into the operating room equipment, while tactile displays still suffer from difficulties concerning their performance and requirements for clinical usability. The role of the data processing system as the linking component in an artificial tactile feedback system has been identified as crucial for effectiveness of the system and easy reception of tactile data by the surgeon. The investigations on theoretical and technological foundations of ATF have led to an extensive database in recent years. An application-driven development approach will likely be a driving factor in the future evolution of this field.

Ultrasonic six-axis deformation sensing

In this paper, we describe a newly developed deformation sensing scheme in a soft medium, which is based on precise encoding and decoding of deformation components into ultrasound wavefronts. It can detect three translational components and three rotational components of displacement around a transmitter position nearly simultaneously. We assume a cell structure that consists of a 2 x 2 ultrasonic transmitter matrix and a 2 x 2 ultrasonic receiver matrix, which are placed face to face at a distance of a few tens of wavelengths. All of the transmitter elements are driven sinusoidally and simultaneously, but they are switched into the same, reversed, or quadrature phases to generate a particular shape of wavefront on the receiver matrix. The receiver elements are connected in such a way to obtain amplitude and spatial gradients of the wavefront at a center of the receiver matrix. First, we describe the transduction theory for the six dimensions and show the orthogonality, locality, and simultaneity of this sensing scheme. Then, we describe the fabrication and experimental evaluation of the cell. We also describe a prototype tactile sensor in which a single cell is embedded in a flexible hemispherical fingertip-like body.