中枢神経系疾患の治療は正常細胞（ニューロン）の機能維持を目的とするが，脳血管障害のように機能障害の原因が細胞の死滅に基づくことは多い．一方，脳腫瘍の治療においては薬物療法や放射線療法といった腫瘍細胞の死滅を目標とするものが大きな位置を占める．いずれの場合にも，細胞死の機序を理解することは各種病態や治療法の理解のうえで重要である．現在のところ最も研究の進んでいる細胞死の型はアポトーシスである．そのなかで重要な位置を占めるミトコンドリアにおける反応および抗アポトーシス因子について概要を紹介する．

Neuroscience 細胞死：最近の知見

This chapter introduces the finite element method (FEM) as a tool for solution of classical electromagnetic problems. Although we discuss the main points in the application of the finite element method to electromagnetic design, including formulation and implementation, those who seek deeper understanding of the finite element method should consult some of the works listed in the bibliography section.

Introduction to the Finite Element Method

Lectures on Cauchy’s Problem in Linear Partial Differential Equations. By J. Hadamard. Pp. viii+316. 15s.net. 1923. (Per Oxford University Press.)

Patterns of Attachment

The skin is the largest organ of the human body and can sense pressure, temperature, and other complex environmental stimuli or conditions. The mimicry of human skin's sensory ability via electronics is a topic of innovative research that could find broad applications in robotics, artificial intelligence, and human-machine interfaces, all of which promote the development of electronic skin (e-skin). To imitate tactile sensing via e-skins, flexible and stretchable pressure sensor arrays are constructed based on different transduction mechanisms and structural designs. These arrays can map pressure with high resolution and rapid response beyond that of human perception. Multi-modal force sensing, temperature, and humidity detection, as well as self-healing abilities are also exploited for multi-functional e-skins. Other recent progress in this field includes the integration with high-density flexible circuits for signal processing, the combination with wireless technology for convenient sensing and energy/data transfer, and the development of self-powered e-skins. Future opportunities lie in the fabrication of highly intelligent e-skins that can sense and respond to variations in the external environment. The rapidly increasing innovations in this area will be important to the scientific community and to the future of human life.

/pdf/recent-progress-in-electronic-skin-2wjoiargll.pdf

Recent Progress in Electronic Skin.

Artificial skin and tactile sensing for socially interactive robots

Electrical impedance tomography (EIT) is a nondestructive imaging technique used to estimate the internal conductivity distribution of a conductive domain by taking potential measurements only at the domain boundaries. If a thin electrically conductive material that responds to pressure with local changes in conductivity is used as a conductive domain, then EIT can be used to create a large-scale pressure-sensitive artificial skin for robotics applications. This paper presents a review of EIT and its application as a robotics sensitive skin, including EIT excitation and image reconstruction techniques, materials, and skin fabrication techniques. Touch interpretation via EIT-based artificial skins is also reviewed.

/pdf/electrical-impedance-tomography-for-artificial-sensitive-53wbx0r325.pdf

Electrical Impedance Tomography for Artificial Sensitive Robotic Skin: A Review

This paper investigates the possibility of recognising individual persons from their walking gait using three-dimensional ‘skeleton’ data from an inexpensive consumer-level sensor, the Microsoft ‘Kinect’. In an experimental pilot study it is shown that the K-means algorithm — as a candidate unsupervised clustering algorithm — is able to cluster gait samples from four persons with a nett accuracy of 43.6%.

Unsupervised clustering of people from 'skeleton' data

During social interaction humans extract important information from tactile stimuli that can improve their understanding of the interaction. The development of a similar capability in a robot will contribute to the future success of intuitive human-robot interaction. This paper presents a thin, flexible and stretchable artificial skin for robotics based on the principle of electrical impedance tomography. This skin, which can be used to extract information such as location, duration and intensity of touch, was used to cover the forearm and upper arm of a full-size mannequin. A classifier based on the 'LogitBoost' algorithm was used to classify the modality of eight different types of touch applied by humans to the mannequin arm. Experiments showed that the modality of touch was correctly classified in approximately 71% of the trials. This was shown to be comparable to the accuracy of humans when identifying touch. The classification accuracies obtained represent significant improvements over previous classification algorithms applied to artificial sensitive skins. It is shown that features based on touch duration and intensity are sufficient to provide a good classification of touch modality. Gender and cultural background were examined and found to have no statistically significant effect on the classification results.

Interpretation of the modality of touch on an artificial arm covered with an EIT-based sensitive skin

Electrical impedance tomography (EIT) is a technique used to estimate the internal conductivity of an electrically conductive body by using measurements made only at its boundary. If this body is made of a thin, flexible, and stretchable material that responds to touch with local changes in conductivity, it can be used to create an artificial sensitive skin. Mathematically, the EIT reconstruction problem is an ill-posed nonlinear inverse problem in which it is commonly assumed that electrodes are located only on the surface of the body. In a thin sensitive skin, however, electrodes can readily be located within the 2-D conducting domain. This paper compares existing electrode-drive patterns with new patterns in which a number of reference electrodes are located inside of the sensitive skin. Simulation results and experimental data show improvements in both resolution and robustness to noise of the reconstructed image. These improvements are shown to be consistent for several commonly used regularization methods.

Mari Velonaki

Papers

Artificial skin and tactile sensing for socially interactive robots

Electrical Impedance Tomography for Artificial Sensitive Robotic Skin: A Review

Unsupervised clustering of people from 'skeleton' data

Interpretation of the modality of touch on an artificial arm covered with an EIT-based sensitive skin

Improved Image Reconstruction for an EIT-Based Sensitive Skin With Multiple Internal Electrodes