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Summary: As the wearable heater is increasingly popular due to its versatile applications, there is a growing need to improve the tensile stability of the wearable heater. However, maintaining the stability and precise control of heating in resistive heaters for wearable electronics remains challenging due to multiaxial dynamic deformation with human motion. Here, we propose a pattern study for a circuit control system without complex structure or deep learning of the liquid metal (LM)-based wearable heater. The LM direct ink writing (DIW) method was used to fabricate the wearable heaters in various designs. Through the study about the pattern, the significance of input power per unit area for steady average temperature with tension was proven, and the directionality of the pattern was shown to be a factor that makes feedback control difficult due to the difference in resistance change according to strain direction. For this issue, a wearable heater with the same minimal resistance change regardless of the tension direction was developed using Peano curves and sinuous pattern structure. Lastly, by attaching to a human body model, the wearable heater with the circuit control system shows stable heating (52.64°C, with a standard deviation of 0.91°C) in actual motion.

Abstract Purpose The purpose of this study is to classify the discoid lateral meniscus (DLM) according to the signal and shape in magnetic resonance imaging (MRI), and to provide information not only in diagnosis but also in treatment. Materials and Methods We reviewed 162 cases who diagnosed with DLM by MRI and underwent arthroscopic procedures from April 2010 to March 2018. Three observers reviewed MRI findings of all cases and predicted arthroscopic tear using three MRI criteria (criterion 1,2 and 3). Among three criteria, the criterion that most accurately predicts arthroscopic tear was selected. Using this criterion, the cases of predicted tear were named group 1. In addition, group 1 was divided into three subgroups (group 1a, 1b and 1c) by deformation or displacement on MRI and arthroscopic type of tear and procedures were analyzed according to these subgroups. Results The intra-meniscal signal change itself (criterion 3) on MRI showed the highest agreement with the arthroscopic tear. No meniscal deformation and displacement on MRI (group 1a) showed no specific type of tear and more cases of meniscal saucerization. The meniscal deformation on MRI (group 1b) showed more simple horizontal tears and more cases of meniscal saucerization. The meniscal displacement on MRI (group 1c) showed more peripheral tears and more cases of meniscal repair and subtotal meniscectomy. Comparing arthroscopic type of tear and type of arthroscopic procedure between three subgroups, there were significant differences in three groups (P

Summary: Soft wearable electronics are rapidly developing through exploration of new materials, fabrication approaches, and design concepts. Although there have been many efforts for decades, a resurgence of interest in liquid metals (LMs) for sensing and wiring functional properties of materials in soft wearable electronics has brought great advances in wearable electronics and materials. Various forms of LMs enable many routes to fabricate flexible and stretchable sensors, circuits, and functional wearables with many desirable properties. This review article presents a systematic overview of recent progresses in LM-enabled wearable electronics that have been achieved through material innovations and the discovery of new fabrication approaches and design architectures. We also present applications of wearable LM technologies for physiological sensing, activity tracking, and energy harvesting. Finally, we discuss a perspective on future opportunities and challenges for wearable LM electronics as this field continues to grow.

Many gaze data visualization techniques intuitively show eye movement together with visual stimuli. The eye tracker records a large number of eye movements within a short period. Therefore, visualizing raw gaze data with the visual stimulus appears complicated and obscured, making it difficult to gain insight through visualization. To avoid the complication, we often employ fixation identification algorithms for more abstract visualizations. In the past, many scientists have focused on gaze data abstraction with the attention map and analyzed detail gaze movement patterns with the scanpath visualization. Abstract eye movement patterns change dramatically depending on fixation identification algorithms in the preprocessing. However, it is difficult to find out how fixation identification algorithms affect gaze movement pattern visualizations. Additionally, scientists often spend much time on adjusting parameters manually in the fixation identification algorithms. In this paper, we propose a gaze behavior-based data processing method for abstract gaze data visualization. The proposed method classifies raw gaze data using machine learning models for image classification, such as CNN, AlexNet, and LeNet. Additionally, we compare the velocity-based identification (I-VT), dispersion-based identification (I-DT), density-based fixation identification, velocity and dispersion-based (I-VDT), and machine learning based and behavior-based modelson various visualizations at each abstraction level, such as attention map, scanpath, and abstract gaze movement visualization.

Gaze movement and visual stimuli have been utilized to analyze human visual attention intuitively. Gaze behavior studies mainly show statistical analyses of eye movements and human visual attention. During these analyses, eye movement data and the saliency map are presented to the analysts as separate views or merged views. However, the analysts become frustrated when they need to memorize all of the separate views or when the eye movements obscure the saliency map in the merged views. Therefore, it is not easy to analyze how visual stimuli affect gaze movements since existing techniques focus excessively on the eye movement data. In this paper, we propose a novel visualization technique for analyzing gaze behavior using saliency features as visual clues to express the visual attention of an observer. The visual clues that represent visual attention are analyzed to reveal which saliency features are prominent for the visual stimulus analysis. We visualize the gaze data with the saliency features to interpret the visual attention. We analyze the gaze behavior with the proposed visualization to evaluate that our approach to embedding saliency features within the visualization supports us to understand the visual attention of an observer.