Ishizuka Hiroki

Measurement of pressure distribution applied to a fingertip is crucial for the teleoperation of robots and human computer interface. Previous studies have acquired pressure distribution by affixing a sensor array to the fingertip or by optically recording the deformation of an object. However, these existing methods inhibit the fingertip from directly contacting the texture, and the pressure applied to the fingertip is measured indirectly. In this study, we propose a method to measure pressure distribution by directly touching a transparent object, focusing on the change in skin color induced by the applied pressure, caused by blood flow. We evaluated the relationship between pressure and skin color change when local pressure is applied, and found a correlation between the pressure and the color change. However, the contact area and the color change area did not align perfectly. We further explored the factor causing the spatial non-uniformity of the color change, by accounting for the stress distribution using finite element analysis. These results suggest that the proposed measurement method can be utilized to measure the internal stress distribution, and it is anticipated to serve as a simple sensor in the field of human computer interface.

In this study, we propose a method to reconstruct photoplethysmogram (PPG) waveforms from other stealthily recorded physiological signals. The proposed method focuses on the frequency characteristics between two physiological signals and reconstructs the target PPG waveform using a regression model. We investigate the feasibility of the proposed method to reconstruct target PPG signals from respiratory (RSP) and PPG signals recorded at non-genuine measurement sites using the two datasets of physiological signals. The results indicate that the proposed method achieves similarities between the target PPG and reconstructed PPG signals with correlation coefficients more than 0.860.

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.

To develop a photoplethysmogram (PPG)-based authentication system with countermeasures, we investigate a "presentation attack" against the authentication. The attack uses the PPG for performing measurements on various sites on each subject's body. It records PPG on a nongenuine measurement site stealthily, generates a spoofing signal based on the recorded PPG, and transmits the signal to the authentication device. To investigate the feasibility of the attack, we developed a PPG-based authentication system. We recorded the PPGs of the subjects' bodies using the developed system and investigated the feasibility of attack in the experiment. The results indicated that an attack can occur with a probability of more than 80 % under ideal conditions.