In this paper, we report on the optimization of the shape of dry microneedle electrodes for electroencephalography (EEG) on hairy locations and compare the electrodes we developed with conventional wet electrodes. We propose the use of SU-8-based candle-shaped microneedle electrodes (CMEs), which have pillars of 1.0 mm height and 0.4 mm diameter with a gap of 0.43 mm between pillars. Microneedles are formed on the top of the pillars. The shape was determined by how well the pillars can avoid hairs and support the microneedles to penetrate through the stratum corneum. The skin–electrode contact impedances of the fabricated CMEs were found to be higher and less stable than those of conventional wet electrodes. However, the CMEs successfully acquired signals with qualities as good as those of conventional wet electrodes. Given the usability of the CMEs, which do not require skin preparation or gel, they are promising alternatives to conventional wet electrodes.

The task of three-dimensional (3D) human pose estimation from a single image can be divided into two parts: (1) Two-dimensional (2D) human joint detection from the image and (2) estimating a 3D pose from the 2D joints. Herein, we focus on the second part, i.e., a 3D pose estimation from 2D joint locations. The problem with existing methods is that they require either (1) a 3D pose dataset or (2) 2D joint locations in consecutive frames taken from a video sequence. We aim to solve these problems. For the first time, we propose a method that learns a 3D human pose without any 3D datasets. Our method can predict a 3D pose from 2D joint locations in a single image. Our system is based on the generative adversarial networks, and the networks are trained in an unsupervised manner. Our primary idea is that, if the network can predict a 3D human pose correctly, the 3D pose that is projected onto a 2D plane should not collapse even if it is rotated perpendicularly. We evaluated the performance of our method using Human3.6M and the MPII dataset and showed that our network can predict a 3D pose well even if the 3D dataset is not available during training.

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