No abstract

The human body has unique electrical characteristics. These characteristics have been investigated in various studies in human-computer interaction (HCI) and related research fields. Such studies include applications for using the body as a conductive lead for transmission or electric field sensing and activating human muscles or organs. However, electricity is not completely safe for the human body; therefore, to avoid harming users, careful consideration is essential when developing such devices. The knowledge required for such consideration is spread throughout a large number research fields, and it can be difficult for researchers in the HCI field to comprehend all of them. The purpose of this article is to support researchers in developing systems that apply electricity to the human body and to serve as a basis for further research. This article reviews previous research pertaining to HCI in which users come into contact with electricity. In addition, considerations of how and where this type of research can be expanded, along with guidelines grounded in other fields for designing systems safely and addressing ethical concerns, are presented. An understanding of the field and of the related safety issues will enhance the understanding of limitations and potential and can clarify the design space.

Purpose: Cystic adventitial artery disease is an uncommon non-atherosclerotic peripheral vessel disease. Furthermore cystic adventitial disease of the common femoral artery is an extremely rare entity. We report the case of a 54 year-old man complaining of intermittent claudication who was referred to our vascular service. Methods and Results: Doppler ultrasound and multidetector-row computed tomography (CT) with 3-dimensional volume rendering revealed severe stenosis with cystic an adventitial cyst in the common femoral artery. Intra-operative Doppler ultrasound showed the cyst to be multilocular type. Reversed great saphenous vein interposition was successfully placed. Conclusion: Removal of cyst together with artery and interposition using reversed great saphenous vein is the optimal treatment procedure to prevent recurrence.

No abstract

Figure 1: Applying data augmentation approach for the estimation/generation phase in deep pose estimation can improve the quality of extreme/wild motion pose estimation. Our approach can be used with pre-trained models, and without new training with self-collected dataset. This figure shows results compared with using raw OpenPose [1]. ABSTRACTContributions of recent deep-neural-network (DNN) based techniques have been playing a significant role in human-computer interaction (HCI) and user interface (UI) domains. One of the commonly used DNNs is human pose estimation. This kind of technique is widely used for motion capturing of humans, and to generate or modify virtual avatars. However, in order to gain accuracy and to use such systems, large and precise datasets are required for the machine learning (ML) procedure. This can be especially difficult for extreme/wild motions such as acrobatic movements or motions in specific sports, which are difficult to estimate in typically provided training models. In addition, training may take a long duration, and will require a high-grade GPU for sufficient speed. To address these issues, we propose a method to improve the pose estimation accuracy for extreme/wild motions by using pre-trained models, i.e., without performing the training procedure by yourselves. We assume our method to encourage usage of these DNN techniques for users in application areas that are out of the ML field, and to help users without high-end computers to apply them for personal and end use cases.