Braille Display for Portable Device Using Flip-Latch Structured Electromagnetic Actuator.

We propose an electromagnetic-based braille display that can represent two-dimensional information. The key principle is a flip-latch structure, which allows satisfying requirements of both protrusion force for braille recognition and low power consumption. A magnet-inserted flip-latch has an eccentric shape, and is driven by and flips over the protruded voice coil and pushing the braille pin. Then it acts as a latch to lock and maintain the pin protrusion without additional energy consumption. We manufacture braille display modules and arrange them into a braille display with a total of 192 pins (16 columns and 12 rows). The pin-to-pin spacing is 2.5 mm, and the thickness of the display is about 5.5 mm. Each pin can switch states in 5 ms of operating time with 1W of power. In this paper, we describe the design and operating mechanism of the proposed actuator and perform operation tests to obtain stable driving conditions for the display. Finally, applications and limitations of the proposed braille display are analyzed.

Context-Aware Winter Sports Based on Multivariate Sequence Learning.

In this paper, we present an intelligent system that is capable of estimating the status of a player engaging in winter activities based on the sequence analysis of multivariate time-series sensor signals. Among the winter activities, this paper mainly focuses on downhill winter sports such as alpine skiing and snowboarding. Assuming that the mechanical vibrations generated by physical interaction between the ground surface and ski/snowboard in motion can describe the ground conditions and playing contexts, we utilize inertial and vibration signals to categorize the motion context. For example, the proposed system estimates whether the player is sitting on a ski lift or standing on the escalator, or skiing on wet or snowy ground, etc. To measure the movement of a player during a game or on the move, we develop a custom embedded system comprising a motion sensor and piezo transducer. The captured multivariate sequence signals are then trained in a supervised fashion. We adopt artificial neural network approaches (e.g., 1D convolutional neural network, and gated recurrent neural networks, such as long short-term memory and gated recurrent units). The experimental results validate the feasibility of the proposed approach.

Haptic Rendering of 3D Geometry on 2D Touch Surface Based on Mechanical Rotation.

In this paper, we present a robotic surface display that physically imitates the orientation of virtual 3D geometry touched through a 2D flat screen. The proposed approach renders the surface orientation of 3D geometry such that users can tactually obtain relative geometric information, which plays a significant role in the process of real-world haptic object perception. Taking advantage of the planar aspect of touch surfaces, the system constructs a rotation matrix to control the pose of a surface with minimal mechanical movements with given partial geometric information (i.e., normal vector at the point of touch). To evaluate the proposed rendering scheme, we conducted a geometric task (two alternative forced choices) with a set of hand-sized cylindrically curved geometries in which participants were asked to identify which of the two surfaces they perceived as being more curved. Curvatures with the same polarities (i.e., convex-convex and concave-concave) were employed in the study and psychometric curves estimated to obtain the threshold of the curvature difference and to validate the proposed rendering scheme. Possible applications of the proposed system and its limitations are also presented.