Machine-Learning-Based Fine Tuning of Input Signals for Mechano-Tactile Display.

Deducing the input signal for a tactile display to present the target surface (i.e., solving the inverse problem for tactile displays) is challenging. We proposed the encoding and presentation (EP) method in our prior work, where we encoded the target surface by scanning it using an array of piezoelectric devices (encoding) and then drove the piezoelectric devices using the obtained signals to display the surface (presentation). The EP method reproduced the target texture with an accuracy of over 80% for the five samples tested, which we refer to as replicability. Machine learning is a promising method for solving inverse problems. In this study, we designed a neural network to connect the subjective evaluation of tactile sensation and the input signals to a display; these signals are described as time-domain waveforms. First, participants were asked to touch the surface presented by the mechano-tactile display based on the encoded data from the EP method. Then, the participants recorded the similarity of the surface compared to five material samples, which were used as the input. The encoded data for the material samples were used as the output to create a dataset of 500 vectors. By training a multilayer perceptron with the dataset, we deduced new inputs for the display. The results indicate that using machine learning for fine tuning leads to significantly better accuracy in deducing the input compared to that achieved using the EP method alone. The proposed method is therefore considered a good solution for the inverse problem for tactile displays.

Reduction of Parasitic Capacitance of A PDMS Capacitive Force Sensor.

Polymer-based flexible micro electro mechanical systems (MEMS) tactile sensors have been widely studied for a variety of applications, such as medical and robot fields. The small size and flexibility are of great advantage in terms of accurate measurement and safety. Polydimethylsiloxane (PDMS) is often used as the flexible structural material. However, the sensors are likely subject to large parasitic capacitance noise. The smaller dielectric constant leads to smaller influences of parasitic capacitance and a larger signal-to-noise ratio. In this study, the sensor underwent ultraviolet (UV) exposure, which changes Si⁻CH3 bonds in PDMS to Si⁻O, makes PDMS nanoporous, and leads to a low dielectric constant. In addition, we achieved further reduction of the dielectric constant of PDMS by washing it with an ethanol⁻toluene buffer solution after UV exposure. This simple but effective method can be readily applicable to improve the signal-to-noise ratio of PDMS-based flexible capacitive sensors. In this study, we propose reduction techniques for the dielectric constant of PDMS and applications for flexible capacitive force sensors.

Three-dimensional Electrodes formation using liquid metal in micro channels for 3-axis Capacitive Force Sensor.

In this study, we propose three-dimensional formation of electrodes made of liquid metal. First, three-dimensional micro channels are formed by stacking PDMS layers. Second, liquid metal, galinstan, is introduced into the channels to form electrodes and wirings. This process is crucial to develop miniaturized 3-axis capacitive force sensor. In our previous work, we proposed a ball-point-pen like capacitive sensor, however, it was difficult to be miniaturized to be mounted onto an endoscope retaining the function. We successfully manufactured 3-axis cylindrical capacitive force sensor, 5 mm in diameter and small enough to be mounted onto an endoscope. We experimentally characterized the sensor. The proposed and demonstrated sensor can be readily applicable for endoscopic palpation.